A holographic battlefield situation awareness system and method fusing multi-source data

Abstract

The application relates to the technical field of holographic perception, and discloses a holographic battlefield situation awareness system and method fusing multi-source data. The method is as follows: taking unmanned aerial vehicle oblique photography data, three-dimensional laser scanning point cloud data and artificial modeling data as multi-source battlefield UD data sets; importing the multi-source battlefield UD data sets into a holographic operation system to render a battlefield holographic scene; creating a battlefield holographic roaming state of a situation awareness personnel entering the battlefield holographic scene; under the battlefield holographic roaming state, superimposing a plotting result, calculation data and a terrain heat map to the battlefield holographic scene and executing scene locking to obtain a battlefield holographic situation awareness graph. The application solves the fusion rendering distortion problem caused by the non-uniformity of multi-source heterogeneous battlefield data formats and the non-uniformity of point density, realizes accurate superposition and locking archiving of multi-dimensional situation information in the same holographic scene, and provides a battlefield holographic situation awareness graph with quantitative spatial calculation capability for command decision.

Description

Technical Field

[0001] This invention relates to the field of holographic sensing technology, and in particular to a holographic battlefield situational awareness system and method that integrates multi-source data. Background Technology

[0002] Modern battlefield situational awareness demands increasingly higher real-time and complete three-dimensional spatial information. However, traditional situational awareness systems, which use two-dimensional electronic maps or traditional three-dimensional GIS platforms, can only present planar battlefield information. Commanders cannot enter the three-dimensional battlefield space for full-view analysis, and the spatial cognitive dimension is severely limited, making it difficult to meet the command and decision-making needs of modern high-intensity warfare.

[0003] Meanwhile, battlefield 3D data comes from diverse sources. Data from UAV oblique photography, 3D laser scanning, and manual modeling varies in format and coordinate system. Existing technologies lack the capability for unified format conversion and real-time fusion rendering on a single platform. These fragmented data sources prevent the formation of a complete and coherent 3D battlefield scene, hindering the comprehensive presentation of battlefield situational information. Furthermore, traditional 3D rendering engines, when processing petabyte-scale battlefield point-in-the-moment data, are limited by video memory capacity and rendering pipeline throughput, resulting in prolonged loading times and image quality degradation and distortion as the data volume increases. This severely impacts command and decision-making efficiency. Summary of the Invention

[0004] The main objective of this invention is to provide a holographic battlefield situational awareness system and method that integrates multi-source data. This invention solves the problem of rendering distortion caused by inconsistent formats and uneven point density of multi-source heterogeneous battlefield data. It realizes the accurate superposition and locking of multi-dimensional situational information in the same holographic scene, and provides a battlefield holographic situational awareness map with quantitative spatial calculation capabilities for command and decision-making.

[0005] To achieve the above objectives, this invention provides a holographic battlefield situational awareness method that integrates multi-source data, comprising the following steps: The UAV oblique photography data, 3D laser scanning point cloud data and artificial modeling data are converted to UD format and interpolated to obtain a multi-source battlefield UD dataset. The multi-source battlefield UD dataset is imported into the holographic computing system, and the battlefield real scene base layer and the troop and equipment model overlay layer are fused and rendered to obtain a battlefield holographic scene. The 3D tracking glasses and 3D tracking handles worn by the situational awareness personnel entering the holographic battlefield scene are sampled frame by frame and the field of view range is updated to obtain a viewpoint-synchronized holographic battlefield roaming state. In the holographic battlefield roaming state, the plotting results, measurement data, and terrain heat map are overlaid onto the holographic battlefield scene and scene locking is performed to obtain a holographic battlefield situational awareness map.

[0006] Optionally, in a first implementation of the first aspect of the present invention, the UAV oblique photography data, 3D laser scanning point cloud data, and manually modeled data are converted to UD format and interpolated to obtain a multi-source battlefield UD dataset, including: The geometric coordinates and color attribute information in UAV oblique photography data, 3D laser scanning point cloud data and artificial modeling data are uniformly converted into UD format to obtain UD format battlefield data. The UD format battlefield data is input into the Solidscan module, and point density compensation interpolation is performed on the regions with uneven point density caused by the scanning interval in the UD format battlefield data to obtain a multi-source battlefield UD dataset.

[0007] Optionally, in a second implementation of the first aspect of the present invention, the multi-source battlefield UD dataset is imported into a holographic computing system, and the battlefield real-scene background layer and the troop and equipment model overlay layer are fused and rendered to obtain a battlefield holographic scene, including: Import the multi-source battlefield UD dataset into the holographic computing system; In the holographic computing system, the battlefield real-world point cloud UD data in the multi-source battlefield UD dataset is used as the base map layer and the troop and equipment model UD data is used as an independent overlay layer. In the detail processing engine, spatial alignment registration is performed on the base map layer and the independent overlay layer to obtain multi-layer fused battlefield scene data. The system acquires the three-dimensional coordinates and line of sight of the current viewpoint of the situational awareness personnel, and performs view frustum range recognition and fusion rendering on the multi-layer fused battlefield scene data to obtain a holographic battlefield scene.

[0008] Optionally, in a third implementation of the first aspect of the present invention, the three-dimensional coordinates and line-of-sight orientation of the current viewpoint of the situational awareness personnel are obtained, and the view frustum range is identified and fused to render the multi-layered fused battlefield scene data to obtain a holographic battlefield scene, including: The first viewpoint 3D coordinates and first line of sight orientation of the situational awareness personnel are obtained, and based on the first viewpoint 3D coordinates and first line of sight orientation, the rendering data blocks of the corresponding resolution level within the field of view frustum are extracted from the spatial resolution level index of the multi-layer fused battlefield scene data. The rendering data block is pushed to the rendering pipeline, and the battlefield real scene background layer and the troop and equipment model overlay layer are rendered in the same frame to obtain a battlefield holographic scene.

[0009] Optionally, in a fourth implementation of the first aspect of the present invention, frame-by-frame pose sampling and frustum range updating are performed on the 3D tracking glasses and 3D tracking handles worn by the situational awareness personnel entering the holographic battlefield scene to obtain a viewpoint-synchronized holographic battlefield roaming state, including: Frame-by-frame pose sampling is performed on the 3D tracking glasses and 3D tracking handles worn by the situational awareness personnel entering the holographic battlefield scene to obtain the head three-dimensional coordinates, yaw angle, pitch angle, roll angle and handle three-dimensional coordinates and orientation angle data for each frame; The head's three-dimensional coordinates, yaw angle, pitch angle, and roll angle data are input into the detail processing engine of the holographic computing system to perform a view frustum update on the battlefield holographic scene, thereby obtaining a viewpoint-synchronized battlefield holographic roaming state.

[0010] Optionally, in the fifth implementation of the first aspect of the present invention, the head's three-dimensional coordinates, yaw angle, pitch angle, and roll angle data are input into the detail processing engine of the holographic computing system to perform a view frustum update on the battlefield holographic scene, obtaining a viewpoint-synchronized battlefield holographic roaming state, including: The head 3D coordinates are used as the second viewpoint 3D coordinates, and the yaw angle, pitch angle and roll angle data are used as the second line of sight orientation. The details processing engine is then used to calculate the current frame view frustum space parameters. Based on the current frame view frustum space parameters, the corresponding data blocks are extracted from the multi-layer fused battlefield scene data of the battlefield holographic scene, and the detail processing engine is driven to perform frame-by-frame view synchronous rendering of the battlefield holographic scene to obtain a viewpoint-synchronized battlefield holographic roaming state.

[0011] Optionally, in a sixth implementation of the first aspect of the present invention, the head three-dimensional coordinates are used as the second viewpoint three-dimensional coordinates, and the yaw angle, pitch angle, and roll angle data are used as the second line-of-sight orientation. These are then input into the detail processing engine to calculate the current frame's frustum space parameters, including: Perform Euler angle rotation calculation on the yaw angle, pitch angle and roll angle data to obtain a line-of-sight direction vector describing the second line-of-sight orientation; Using the head's three-dimensional coordinates as the second viewpoint's three-dimensional coordinates and the gaze direction vector as the view frustum axis direction, the view frustum spatial parameters of the current frame are calculated through the view frustum near clipping plane and far clipping plane boundary of the detail processing engine.

[0012] Optionally, in the seventh implementation of the first aspect of the present invention, in the holographic battlefield roaming state, the plotting results, measurement data, and terrain heat map are superimposed onto the holographic battlefield scene and scene locking is performed to obtain a holographic battlefield situational awareness map, including: In the holographic battlefield roaming state, calculate the troop deployment plotting data of the troop model in the holographic battlefield scene, and calculate the spatial measurement data in the holographic battlefield scene; Color mapping is performed based on the minimum and maximum elevation coordinates of each point in the multi-source battlefield UD dataset to obtain a terrain heat map. The terrain heat map, the troop deployment plotting data, and the spatial measurement data are then superimposed onto the battlefield holographic scene, and scene locking is performed to obtain a battlefield holographic situational awareness map.

[0013] Optionally, in the eighth implementation of the first aspect of the present invention, in the holographic battlefield roaming state, calculating the troop deployment plotting data of the troop model in the holographic battlefield scene, and calculating spatial measurement data in the holographic battlefield scene, includes: Based on the real-time three-dimensional coordinates and orientation angle data of the 3D tracking handle, position mapping update, orientation angle synchronous rotation, unit number color assignment, and three-dimensional coordinate anchoring operation of the troop model in the battlefield holographic scene are performed to obtain troop deployment plotting data. The first target point and the second target point are selected sequentially in the holographic battlefield scene using the 3D tracking handle, and the straight-line distance between the first target point and the second target point in the coordinate system of the holographic battlefield scene is calculated; the first line segment direction vector and the second line segment direction vector are calculated sequentially using the 3D tracking handle, and the angle between the line segments is calculated based on the first line segment direction vector and the second line segment direction vector; The straight-line distance and the angle between the line segments together constitute the spatial measurement data.

[0014] This invention also provides a holographic battlefield situational awareness system that integrates multi-source data, comprising: The data conversion module is used to convert UAV oblique photography data, 3D laser scanning point cloud data and artificial modeling data into UD format and perform point cloud interpolation to obtain a multi-source battlefield UD dataset. The fusion rendering module is used to import the multi-source battlefield UD dataset into the holographic computing system, and to perform fusion rendering on the battlefield real scene base layer and the troop and equipment model overlay layer to obtain a battlefield holographic scene. The 3D tracking module is used to perform frame-by-frame pose sampling and view frustum update on the 3D tracking glasses and 3D tracking handle worn by situational awareness personnel entering the holographic battlefield scene, so as to obtain a viewpoint-synchronized holographic battlefield roaming state. The scene locking module is used to overlay the plotting results, measurement data and terrain heat map onto the holographic battlefield scene and perform scene locking in the holographic battlefield roaming state to obtain a holographic battlefield situational awareness map.

[0015] In summary, this invention solves the problem of rendering distortion caused by inconsistent formats and uneven point densities in multi-source heterogeneous battlefield data by uniformly converting UAV oblique photography data, 3D laser scanning point cloud data, and manually modeled data into UD format and performing point density compensation interpolation processing through the Solidscan module. This allows multi-source battlefield data to complete spatial alignment registration and achieve multi-layer overlay and fusion display within the same coordinate system. Based on the dynamic streaming frustum retrieval mechanism of the detail processing engine, data blocks of the corresponding resolution level are dynamically extracted and pushed to the rendering pipeline only according to the frustum range and observation distance of the current viewpoint. This overcomes the technical bottleneck of traditional rendering engines being unable to process PB-level battlefield data in real time due to limitations in video memory capacity, achieving second-level loading and distortion-free rendering output of ultra-large-scale 3D battlefield data. Furthermore, the invention utilizes a spatial dynamic position capture system to perform 3D tracking... The tracking glasses and 3D tracking handle perform frame-by-frame pose sampling synchronized with the projection refresh rate. The head's three-dimensional coordinates and yaw, pitch, and roll angle data drive the detail processing engine to perform frame-by-frame viewpoint synchronous rendering in real time. This enables situational awareness personnel to achieve immersive roaming and judgment in a holographic battlefield scene with strictly synchronized viewpoints, eliminating the spatial misalignment problem caused by viewpoint delay in traditional display methods. In the holographic battlefield roaming state, the 3D tracking handle performs dynamic plotting and text label anchoring on the force model, calculates Euclidean distance between any two points, and calculates vector angles between line segments. Based on the linear equidistant hierarchical color mapping of battlefield point cloud elevation data, a terrain heat map is generated. This achieves precise superposition and locking of multi-dimensional situational information in the same holographic scene, providing a battlefield holographic situational awareness map with quantitative spatial calculation capabilities for command and decision-making. Attached Figure Description

[0016] Figure 1 This is a schematic diagram of the steps of a holographic battlefield situational awareness method that integrates multi-source data in one embodiment of the present invention; Figure 2 This is a schematic diagram illustrating the steps of fusing and rendering the battlefield real-scene background layer and the troop and equipment model overlay layer in an embodiment of the present invention; Figure 3 This is a schematic diagram illustrating the steps of calculating troop deployment plotting data of a troop model in a battlefield holographic scene according to an embodiment of the present invention; Figure 4 This is a block diagram of the holographic battlefield situational awareness system that integrates multi-source data in an embodiment of the present invention.

[0017] The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0018] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0019] Reference Figure 1 This embodiment provides a holographic battlefield situational awareness method that integrates multi-source data, including the following steps: S1. Convert UAV oblique photography data, 3D laser scanning point cloud data and artificial modeling data into UD format and perform point cloud interpolation to obtain a multi-source battlefield UD dataset. S2. Import the multi-source battlefield UD dataset into the holographic computing system, and perform fusion rendering on the battlefield real scene base layer and the troop and equipment model overlay layer to obtain the battlefield holographic scene. S3 performs frame-by-frame pose sampling and view frustum range update on the 3D tracking glasses and 3D tracking handles worn by situational awareness personnel entering the holographic battlefield scene to obtain a viewpoint-synchronized holographic battlefield roaming state. S4, in the holographic battlefield roaming state, overlays the plotting results, measurement data and terrain heat map onto the holographic battlefield scene and performs scene locking to obtain the holographic battlefield situational awareness map.

[0020] In one example, UAV oblique photogrammetry data, 3D laser scan point cloud data, and manually modeled data are converted to UD format and interpolated to obtain a multi-source battlefield UD dataset, including: The geometric coordinates and color attribute information in UAV oblique photography data, 3D laser scanning point cloud data and artificial modeling data are uniformly converted into UD format to obtain UD format battlefield data. Input the UD format battlefield data into the Solidscan module, and perform point density compensation interpolation on the areas of uneven point density caused by the scan spacing in the UD format battlefield data to obtain a multi-source battlefield UD dataset.

[0021] In this example, all acquired raw 3D data is imported into a unified data preprocessing environment. This environment reads the point cloud model generated from UAV oblique photogrammetry, the high-density point cloud data output from 3D laser scanning equipment, and the manually modeled equipment and force unit models. It also parses the geometric coordinate and color attribute information contained in each data file. The geometric coordinate information is represented as 3D coordinate vectors, and the color attribute information is recorded as red, green, and blue channel color values. The data is then subjected to coordinate transformation according to a unified coordinate reference, aligning the spatial positions of data from different sources within the same spatial coordinate system. Finally, based on this unified coordinate system, all data undergoes UD format conversion. During the conversion process, the three-dimensional spatial coordinates and corresponding color attribute information of each point element are reorganized using spatial index encoding. The geometric coordinate information (X1, Y1, Z1) and color attribute information (R1, G1, B1) in the original data are re-encoded into UD data structure units, forming a data organization structure based on spatial resolution levels. In this process, a spatial hierarchical index relationship is established, enabling point cloud data of different precision levels to be dynamically called according to the observation distance. After the unified conversion of UD format is completed, UD format battlefield data is obtained, in which each UD data unit contains two data fields: three-dimensional spatial coordinates and color attributes. The hierarchical storage of data blocks is realized through the spatial hierarchical index structure. UD format battlefield data is input into the Solidscan module for point cloud density compensation processing. Within the Solidscan module, local spatial neighborhood analysis is performed on the UD format point cloud data. By statistically analyzing the number and spatial distribution of points in the neighborhood surrounding each point cloud element, regions with uneven point density caused by scanning intervals are identified, and the point density value of these regions is calculated. The point density calculation follows the formula ρ=N / V, where ρ represents the point density value of the local spatial region in points per cubic meter, N represents the actual number of points contained within the local spatial region, and V represents the spatial volume of the local spatial neighborhood, calculated using the cubic spatial volume formula V=L. 3 The calculation is performed, where L represents the side length of the local spatial neighborhood. The preset neighborhood side length L is 0.5 meters. After calculating the point density value of each spatial region, the point density value of each region is compared with a preset standard point density threshold. When the point density of a local region is lower than the preset threshold, the region is determined to be a region with insufficient point density. The preset standard point density threshold is set to 800 points per cubic meter. After identifying the region with insufficient point density, the Solidscan module calls the point density compensation interpolation algorithm. Based on the spatial distribution relationship of the existing point cloud in the neighborhood, spatial interpolation is performed on the missing region to generate compensation points. The coordinates of the interpolated points are obtained by averaging the coordinates of the neighboring points. The calculation formula is as follows: , Among them, (X) i ,Yi Z i ) represents the spatial coordinates of the newly generated compensation point, X k Y k Z k The coordinates of the k-th neighboring point involved in the interpolation calculation are represented by m, which represents the number of neighboring points involved in the interpolation calculation and is preset to be 8. The new points generated by the interpolation calculation inherit the comprehensive color attributes of the neighboring points, so that the compensated point cloud is consistent in spatial continuity and visual effect. After the point density compensation interpolation processing is completed, all the original point cloud data and the newly generated compensation points are combined to form a new UD format point cloud data set, so that the battlefield 3D data achieves a uniform density in spatial distribution and eliminates the point cloud hole phenomenon caused by the difference in scanning spacing, thus obtaining a multi-source battlefield UD dataset.

[0022] like Figure 2 In one example, a multi-source battlefield UD dataset is imported into a holographic computing system, and the battlefield real-scene background layer and the troop and equipment model overlay layer are fused and rendered to obtain a battlefield holographic scene, including: 201. Import the multi-source battlefield UD dataset into the holographic computing system; 202. In the holographic computing system, the battlefield real-world point cloud UD data in the multi-source battlefield UD dataset is used as the base map layer and the troop and equipment model UD data is used as an independent overlay layer. In the detail processing engine, spatial alignment registration is performed on the base map layer and the independent overlay layer to obtain multi-layer fused battlefield scene data. 203. Obtain the three-dimensional coordinates and line of sight of the current viewpoint of the situational awareness personnel, and perform view frustum range recognition and fusion rendering on the multi-layer fused battlefield scene data to obtain a holographic battlefield scene.

[0023] In this example, a multi-source battlefield UD dataset is imported into a holographic computing system. The data scheduling module in the holographic computing system performs parsing and loading of the UD data packets, enabling the system to read the 3D geometric coordinates and color attribute information recorded in the UD dataset. Simultaneously, a unified battlefield spatial coordinate benchmark is established within the system. The imported multi-source battlefield UD dataset undergoes data structure recognition within the holographic computing system. The battlefield real-world point cloud UD data recording the battlefield terrain and features is set as the base map layer, while the manually modeled UD data representing troop units and combat equipment is set as an independent overlay layer. The detail processing engine reads the 3D spatial coordinates of each data element in the base map layer and the independent overlay layer. Spatial alignment registration is performed on the two types of layers using a unified coordinate benchmark, ensuring that the troop and equipment models are spatially consistent with the battlefield real-world point cloud data. During spatial alignment, the spatial offset between the model coordinates and the base map coordinates needs to be calculated, and spatial correction is completed according to the 3D coordinate translation formula. The calculation relationship is expressed as (X2,Y2,Z2)=(X1+ΔX, Y1+ΔY, Z1+ΔZ), where (X2,Y2,Z2) represent the corrected model coordinates, (X1,Y1,Z1) represent the original model coordinates, and ΔX, ΔY, and ΔZ represent the spatial translations along the X, Y, and Z axes, respectively. The preset spatial correction accuracy is 0.02 meters. Through spatial alignment registration processing, the terrain and feature point cloud data in the base map layer and the troop and equipment models in the independent overlay layers form spatially consistent multi-layered fused battlefield scene data. After completing the construction of the multi-layered fused battlefield scene data, the holographic computing system acquires the three-dimensional coordinates and gaze orientation of the situational awareness personnel's current viewpoint. The three-dimensional coordinates are acquired by the spatial dynamic position capture system and represent the position of the user's head in the holographic space (X1+ΔZ). v ,Y v Z v The gaze direction is calculated based on the user's head posture and used to determine the current viewpoint's observation direction. After obtaining the viewpoint information, a spatial view cone structure is constructed based on the viewpoint coordinates and the gaze direction. View cone range recognition processing is then performed on the multi-layered fused battlefield scene data. During this process, it is necessary to determine whether each data point is within the view cone range. Visible data points are determined by calculating the spatial distance between the data point and the viewpoint, as well as the angle between the data point and the gaze direction. The distance between the data point and the viewpoint is calculated using the spatial distance formula: Perform the calculation, where D represents the spatial distance between the data point and the current viewpoint, (X p ,Y p Z p (X) represents the spatial coordinates of a point in cloud data or a model node in a battlefield scene. v ,Y v Z vThe coordinates represent the spatial coordinates of the situation awareness personnel's current viewpoint. After completing the identification of the viewpoint range, the holographic computing system only performs fusion rendering processing on the data within the viewpoint range. Through the rendering pipeline, the battlefield real-world point cloud data of the base map layer and the troop and equipment models of the independent overlay layer are superimposed and displayed in real time according to the spatial hierarchy. The system restores the real visual effect based on the color attribute information recorded by each data point. At the same time, the detail processing engine automatically selects data blocks of different spatial resolution levels to participate in rendering based on the observation distance. While ensuring rendering efficiency, the system maintains the detail accuracy of the battlefield scene. Finally, the holographic display system generates a 3D visualization of the battlefield that is updated synchronously with the user's viewpoint, resulting in a holographic battlefield scene with a real spatial hierarchy and troop deployment information.

[0024] After obtaining the holographic battlefield scene, the process also includes: calculating the geographic coverage scale of the battlefield target area based on the maximum and minimum boundary values ​​of the three-dimensional coordinates of all points in the multi-source battlefield UD dataset to obtain the battlefield geographic coverage scale parameter; performing linear scaling conversion based on the battlefield geographic coverage scale parameter and the actual size of the effective display area of ​​the holographic space to obtain the battlefield scene display scale parameter; inputting the battlefield scene display scale parameter into the holographic computing system, performing scaling restoration processing on the straight-line distance measurement result between any two points in the battlefield holographic scene to obtain the spatial distance measurement result under the corresponding real geographic coordinate system of the battlefield.

[0025] In one example, the 3D coordinates and line-of-sight orientation of the situational awareness personnel's current viewpoint are obtained, and the view frustum range is identified and fused from the multi-layered fused battlefield scene data to obtain a holographic battlefield scene, including: The system acquires the first-viewpoint 3D coordinates and first line of sight orientation of the situational awareness personnel, and extracts rendering data blocks of the corresponding resolution level within the field of view from the spatial resolution level index of the multi-layer fused battlefield scene data based on the first-viewpoint 3D coordinates and first line of sight orientation. The rendering data blocks are pushed to the rendering pipeline, and the battlefield real scene base layer and the troop and equipment model overlay layer are rendered in the same frame to obtain the battlefield holographic scene.

[0026] In this example, the first viewpoint 3D coordinates and first gaze orientation of a situational awareness person entering the holographic space are acquired through a spatial dynamic position capture system. The first viewpoint 3D coordinates represent the real-time spatial position (X1, Y1, Z1) of the user's head in the holographic space, and the first gaze orientation is obtained by parsing the user's head posture data; its direction is represented by the gaze direction vector (V). x V y V zThe spatial dynamic position capture system performs frame-by-frame pose sampling on the 3D tracking glasses at a sampling frequency synchronized with the holographic display refresh rate. It transmits the 3D coordinates and gaze direction data obtained from each frame to the holographic computing system in real time. After receiving the 3D coordinates and gaze direction of the first viewpoint, the holographic computing system constructs a spatial frustum structure for the current observation frame, using the first viewpoint's 3D coordinates as vertices and the gaze direction as the central axis. Based on this spatial frustum structure, it performs visible data block retrieval processing in the spatial resolution level index of the multi-layered fused battlefield scene data. During the retrieval process, it reads the spatial level index information recorded in the UD data structure and determines the appropriate resolution level to be invoked by calculating the spatial distance between the scene data points and the first viewpoint. When the calculated distance is within a preset distance range, it extracts the corresponding resolution level rendering data block from the spatial resolution level index based on the correspondence between distance and spatial resolution level. The preset near-distance rendering threshold is 30 meters, and the mid-distance rendering threshold is 120 meters. When D is less than 30 meters, a high-resolution rendering block is invoked. The resolution-level data is used in a hierarchical manner. When D is greater than or equal to 30 meters and less than 120 meters, medium-resolution data is called. When D is greater than or equal to 120 meters, low-resolution data is called. This reduces the amount of data loading in distant areas and ensures the detail display accuracy in close areas. After the rendering data blocks within the field of view are extracted, the holographic computing system pushes the extracted rendering data blocks to the rendering pipeline in the order of data hierarchy. In the rendering pipeline, point cloud data rendering processing is performed on the battlefield real scene base layer. The terrain and feature point cloud data are pixel-mapped according to their spatial coordinates and color attributes to generate the base map scene. Then, in the same rendering frame, model rendering processing is performed on the troop and equipment model overlay layer. By reading the model node coordinates and material color information, the troop and equipment model is overlaid on the generated base map image. The occlusion relationship is calculated according to the depth buffer order, so that the base map base layer and the troop and equipment model overlay layer are merged and displayed in the same frame. The output is a three-dimensional battlefield visualization that is updated synchronously with the viewpoint of the situation awareness personnel, resulting in a battlefield holographic scene with spatial depth relationship and troop deployment information.

[0027] In one example, frame-by-frame pose sampling and frustum update are performed on the 3D tracking glasses and 3D tracking handles worn by situational awareness personnel entering a holographic battlefield scene to obtain a viewpoint-synchronized holographic battlefield roaming state, including: Frame-by-frame pose sampling is performed on the 3D tracking glasses and 3D tracking handles worn by situational awareness personnel entering the holographic battlefield scene to obtain the head 3D coordinates, yaw angle, pitch angle, roll angle and handle 3D coordinates and orientation angle data for each frame; The head's three-dimensional coordinates, yaw angle, pitch angle, and roll angle data are input into the detail processing engine of the holographic computing system to perform a view frustum update on the battlefield holographic scene, resulting in a viewpoint-synchronized battlefield holographic roaming state.

[0028] In this example, after the battlefield holographic scene is constructed and put into operation, the spatial dynamic position capture system performs continuous pose sampling on the 3D tracking glasses and 3D tracking handles worn by the situational awareness personnel entering the holographic space. The 3D tracking glasses are equipped with active tracking markers. The spatial dynamic position capture system uses multiple infrared cameras to identify the spatial position of the active tracking markers in real time and performs frame-by-frame sampling of the user's head movement at a sampling frequency synchronized with the holographic display refresh rate. Within each frame sampling period, the system obtains the head's three-dimensional spatial position and posture data. The head's spatial position is represented in three-dimensional coordinates as (X1, Y1, Z1), and the posture information is represented by three rotation angles. The data includes yaw, pitch, and roll angles. Simultaneously, the 3D tracking handle is sampled as a secondary tracking target in the spatial dynamic position capture system. Its spatial position and attitude orientation are identified through tracking markers on the handle. In each frame sampling period, the handle's three-dimensional coordinates (X2, Y2, Z2) and orientation angle data are obtained. All sampled data is transmitted in real-time to the holographic computing system in frame-by-frame data stream format. In the holographic computing system, the head's three-dimensional coordinates and attitude angle data obtained from each frame are used to calculate the attitude matrix. The yaw, pitch, and roll angles are converted into a line-of-sight vector to determine the user's current viewing direction. The three components of the line-of-sight vector are calculated using rotation angles, and their calculation relationship is expressed as follows: , , V x V y V z This represents the components of the line-of-sight vector along the three coordinate axes. Indicates the yaw angle. The pitch angle, the gaze direction vector, and the head's 3D coordinates together constitute the spatial description parameters of the user's viewpoint in the current frame. After obtaining the viewpoint parameters, the head's 3D coordinates, yaw angle, pitch angle, and roll angle data are input into the detail processing engine of the holographic computing system. The detail processing engine uses the user's viewpoint coordinates as the vertex of the view cone and determines the direction of the view cone's central axis based on the gaze direction vector, constructing the spatial view cone structure of the current observation frame. After the view cone structure is constructed, the visibility range of the spatial data in the battlefield holographic scene is determined. By calculating the spatial distance between each data point in the scene and the current viewpoint, it is determined whether it is within the visible area. After completing the distance determination and view cone range recognition, the detail processing engine performs view cone range update processing on the battlefield holographic scene, that is, only the data within the view cone range is retained for the current frame rendering, while the user's viewpoint position changes. The data call hierarchy is dynamically adjusted so that high-resolution data is loaded for near-field areas and low-resolution data is loaded for far-field areas, thus ensuring a balance between rendering efficiency and display accuracy. After the view frustum update is completed, the holographic computing system regenerates the 3D scene of the current frame based on the updated view frustum range and outputs the image to the holographic display system in real time. This keeps the display viewpoint of the battlefield scene synchronized with the user's head movement direction. When the situation awareness personnel move or turn their heads in the holographic space, the system updates the viewpoint parameters and view frustum range frame by frame, so that the 3D battlefield scene changes in real time with the user's viewpoint, forming a 3D browsing effect consistent with the user's real-world spatial movement. This results in a battlefield holographic roaming state with synchronized viewpoint updates, allowing situation awareness personnel to freely roam and observe and assess the situation in the 3D battlefield space in an immersive manner.

[0029] In one example, the head's 3D coordinates, yaw angle, pitch angle, and roll angle data are input into the detail processing engine of the holographic computing system. This performs a view frustum update on the battlefield holographic scene, resulting in a viewpoint-synchronized battlefield holographic roaming state, including: The head 3D coordinates are used as the second viewpoint 3D coordinates, and the yaw angle, pitch angle and roll angle data are used as the second line of sight orientation. These are then input into the detail processing engine to calculate the current frame's view frustum space parameters. Based on the current frame's view frustum space parameters, the corresponding data blocks are extracted from the multi-layer fused battlefield scene data of the battlefield holographic scene. This drives the detail processing engine to perform frame-by-frame viewpoint synchronous rendering of the battlefield holographic scene, resulting in a viewpoint-synchronized battlefield holographic roaming state.

[0030] In this example, the head 3D coordinates sampled in each frame are directly used as the second viewpoint 3D coordinates and input into the detail processing engine. The second viewpoint 3D coordinates are represented as (X2, Y2, Z2). At the same time, the yaw angle, pitch angle, and roll angle data sampled within the same frame are used as the second line of sight orientation and input into the detail processing engine. This allows the detail processing engine to determine the position of the view frustum apex based on the second viewpoint 3D coordinates, and to determine the direction of the view frustum central axis based on the yaw angle and pitch angle. At the same time, the rotation state of the view frustum local coordinate system is corrected by combining the roll angle. Thus, the view frustum spatial parameters of the current frame are calculated, and a view frustum description result is formed for data scheduling and rendering. After calculating the view frustum spatial parameters for the current frame, the detail processing engine performs view frustum range identification on the multi-layered fused battlefield scene data of the holographic battlefield scene based on the view frustum vertex position and the view frustum central axis direction. It then extracts corresponding data blocks within the view frustum range from the spatial resolution layer index of the multi-layered fused battlefield scene data, ensuring that only data located within the currently visible area is selected for the rendering process. Simultaneously, the detail processing engine determines the resolution layer of the data blocks to be called based on the spatial distance between the second viewpoint's 3D coordinates and the extracted data blocks. This prioritizes high-resolution data blocks in areas close to the second viewpoint to ensure detail display capabilities, while lower-resolution data blocks are called in areas far from the second viewpoint to reduce rendering load. The extracted data blocks are then pushed to the appropriate layers according to the base layer and overlay layer hierarchy. The rendering pipeline drives the detail processing engine to perform fusion rendering on the battlefield real-world base layer and the overlay layer of troop and equipment models within the same frame. This ensures that the point cloud terrain texture of the base layer and the troop and equipment models of the overlay layer are displayed under the condition that the depth relationship is consistent. When the situation awareness personnel move or turn their heads in the holographic space, the spatial dynamic position capture system continuously outputs new second viewpoint 3D coordinates and second line of sight orientation. Based on this, the detail processing engine updates the view frustum space parameters in each frame and re-executes data block extraction and fusion rendering. This ensures that the display perspective of the battlefield holographic scene and the actual viewpoint of the situation awareness personnel are updated synchronously frame by frame, resulting in a viewpoint-synchronized battlefield holographic roaming state. This allows the situation awareness personnel to continuously roam and observe and assess the situation in an immersive manner within the holographic space.

[0031] In the viewpoint-synchronized battlefield holographic roaming state, the process also includes: using the current head 3D coordinates and yaw, pitch, and roll angle data as spatial view position parameters for key battlefield nodes, performing bookmark recording and storage processing to obtain a key battlefield node bookmark dataset; reading the spatial view position parameters of a specified bookmark from the key battlefield node bookmark dataset, inputting the spatial view position parameters into the detail processing engine to perform current frame frustum range coverage replacement processing to obtain the frustum spatial parameters for the viewpoint to jump to the specified bookmark position; and extracting data blocks of the corresponding resolution level from the multi-layer fused battlefield scene data based on the frustum spatial parameters and performing re-rendering processing to obtain a battlefield holographic situation view at the specified bookmark position.

[0032] In one example, the head's 3D coordinates are used as the second viewpoint's 3D coordinates, and the yaw, pitch, and roll angles are used as the second line-of-sight orientation. These are input into the detail processing engine to calculate the current frame's frustum space parameters, including: Perform Euler angle rotation calculations on the yaw angle, pitch angle, and roll angle data to obtain the line-of-sight direction vector describing the second line-of-sight orientation; Using the head's 3D coordinates as the second viewpoint's 3D coordinates and the gaze direction vector as the view frustum axis direction, the view frustum spatial parameters of the current frame are calculated through the near and far clipping planes of the view frustum from the detail processing engine.

[0033] In this example, yaw, pitch, and roll angle data obtained from frame-by-frame sampling are read, and Euler angle rotation numerical calculations are performed on the attitude angles in the holographic computing system to obtain the line-of-sight direction vector describing the second line-of-sight orientation. During the calculation, a rotation matrix is ​​constructed based on the yaw, pitch, and roll angles, where the yaw angle represents the rotation angle around the Z-axis, the pitch angle represents the rotation angle around the Y-axis, and the roll angle represents the rotation angle around the X-axis. The three-axis rotation calculations are performed sequentially according to the Euler angle order. The rotation matrix calculation relationship is expressed as follows: , where R represents the global rotation matrix; Indicates the yaw angle; Indicates the pitch angle; This represents the roll angle. The three basic rotation matrices are represented as follows: ; ; ; The rotational relationship of the head posture in the battlefield space coordinate system is obtained by calculating the above rotation matrix, and then the unit line-of-sight vector is... As the initial viewing direction, the viewing direction vector of the current frame is obtained by matrix multiplication using the rotation matrix and the initial viewing vector. The calculation relationship is expressed as follows: Where V represents the line-of-sight vector describing the second line of sight, V0 represents the initial observation direction unit vector, and R represents the rotation matrix calculated from the yaw angle, pitch angle, and roll angle. After obtaining the line-of-sight vector, the three-dimensional coordinates of the head acquired by the spatial dynamic position capture system are used as the three-dimensional coordinates of the second viewpoint and input into the detail processing engine. The three-dimensional coordinates of the second viewpoint are represented as (X2, Y2, Z2). The line-of-sight vector is used as the central axis of the view cone to establish the view cone space structure of the current frame. The detail processing engine calculates the view cone space parameters of the current frame based on the preset field of view angle, near clipping distance, and far clipping distance. The preset field of view angle is 90°, the near clipping distance is set to 0.5 meters, and the far clipping distance is set to 800 meters. The depth range and left, right, upper, and lower boundaries of the view cone space are determined by the above parameters. Based on this, the spatial distance between each data point in the battlefield scene and the second viewpoint is calculated, and this distance is uniformly defined as L1. The calculation relationship is expressed as follows: Where L1 represents the spatial distance between a data point in the battlefield scene and the second viewpoint, (X p ,Y p Z p (X2, Y2, Z2) represents the three-dimensional coordinates of the battlefield scene data point, and (X2, Y2, Z2) represents the three-dimensional coordinates of the second viewpoint. When the calculated distance satisfies 0.5 ≤ L1 ≤ 800, the data point is determined to be within the current frame's view frustum space range and participates in the rendering process. When the distance exceeds this range, it does not participate in the current frame's rendering. Thus, the viewing direction vector calculated by the Euler angle rotation matrix and the view frustum space boundary conditions obtained based on the near clipping plane and the far clipping plane boundary can form the complete set of view frustum space parameters for the current frame.

[0034] In one example, during a holographic battlefield roaming session, the plotting results, measurement data, and terrain heatmap are overlaid onto the holographic battlefield scene, and scene locking is performed to obtain a holographic battlefield situational awareness map, including: In the holographic battlefield roaming state, calculate the troop deployment plotting data of the troop model in the holographic battlefield scene, and calculate the spatial measurement data in the holographic battlefield scene; Color mapping is performed based on the minimum and maximum elevation coordinates of each point in the multi-source battlefield UD dataset to obtain a terrain heat map. The terrain heat map, troop deployment plotting data, and spatial measurement data are then overlaid onto the battlefield holographic scene, and scene locking is performed to obtain a battlefield holographic situational awareness map.

[0035] In this example, during the continuous operation of the viewpoint-synchronized battlefield holographic roaming mode, the holographic computing system performs troop deployment plotting data calculations on the overlay of troop and equipment models within the battlefield holographic scene. This ensures that the troop deployment plotting data maintains the same spatial coordinate reference as the actual battlefield background layer. This allows each troop model's deployment position, orientation, and associated annotation information within the battlefield holographic scene to form a recordable and reproducible plotting data structure. The troop deployment plotting data includes troop model identifiers, the 3D coordinates of the troop models in the UD coordinate system, troop model attitude parameters, and deployment timestamps. Furthermore, the troop deployment plotting data is displayed as 3D text within the battlefield holographic scene. The tagging method and spatial positioning of the force model enable situational awareness personnel to read the deployment elements expressed by the force deployment plotting data from any observation angle. Simultaneously, the holographic computing system calls the measurement function to perform spatial measurement data calculations on key targets in the battlefield holographic scene. This spatial measurement data includes spatial distance and spatial angle calculation results. Specifically, when situational awareness personnel select a starting point target and an ending point target in the battlefield holographic scene, the measurement function reads the three-dimensional coordinates (X1, Y1, Z1) of the starting point target and the three-dimensional coordinates (X2, Y2, Z2) of the ending point target and calculates the spatial distance D between the two points. The calculation of the spatial distance D satisfies the formula: , Where D represents the spatial distance between the starting target and the ending target in meters, X1 represents the coordinate of the starting target along the X-axis in meters, Y1 represents the coordinate of the starting target along the Y-axis in meters, Z1 represents the coordinate of the starting target along the Z-axis in meters, X2 represents the coordinate of the ending target along the X-axis in meters, Y2 represents the coordinate of the ending target along the Y-axis in meters, and Z2 represents the coordinate of the ending target along the Z-axis in meters. The spatial distance D is displayed numerically within the holographic battlefield scene and anchored to the corresponding measurement line segment. When situational awareness personnel perform angle calculations on two intersecting situational line segments, the measurement function reads the direction vector components (A, B, C, D) of the two line segments in the three-dimensional coordinate system. x A y A z ) and (B x B y B z And calculate the included angle θ, which satisfies the formula: ;

[0036] Where θ represents the angle between the two situation line segments in degrees, A x A y A z B represents the components of the direction vector of the first situational line segment along the X, Y, and Z axes, respectively. x By B z These represent the components of the direction vector of the second situational line segment along the X, Y, and Z axes, respectively, with the included angle θ archived together as spatial angle measurement data and corresponding measurement primitives. After completing the calculation and visualization of troop deployment plotting data and spatial measurement data, the holographic computing system generates a terrain heat map based on the elevation coordinates recorded at each point in the multi-source battlefield UD dataset. The terrain heat map generation process first traverses all point cloud data in the battlefield real-world point cloud UD data package and extracts the elevation coordinate H of each point cloud data, while simultaneously calculating the minimum elevation coordinate H. min With the maximum elevation coordinate H max Where H represents the terrain elevation corresponding to the point cloud data in meters. min H represents the minimum elevation of the point cloud data of the battlefield target area, in meters. max This represents the maximum elevation of the point cloud data for the battlefield target area, expressed in meters. For each point cloud data point, calculate the elevation normalized color level value C, satisfying the formula: ;

[0037] Where C represents the elevation normalized color level value and its value ranges from 0 to 1, H min With H max All elevation statistics are directly derived from multi-source battlefield UD datasets without introducing external thresholds. Linear color mapping is performed based on the color level value C, and the battlefield real-world point cloud UD data package is colored and rendered point by point to obtain a terrain heat map layer covering the entire battlefield. The holographic computing system overlays the terrain heat map layer, troop deployment plotting data, and spatial measurement data in the same battlefield holographic scene, so that the terrain and feature information of the battlefield real-world base layer, the deployment information of the troop equipment model overlay layer, the numerical annotation information of the spatial measurement data, and the elevation situation information of the terrain heat map layer form a unified fused situational expression. After the overlay is completed, scene locking processing is performed. Scene locking processing writes the spatial position and display attributes of the troop deployment plotting data, spatial measurement data, and terrain heat map layer into the locking record and sets the locking flag to the locked state. The battlefield holographic scene in the locked state can only be read-only and cannot be modified, resulting in a battlefield holographic situational awareness map that can be used for command and analysis. It also supports multiple people to enter the same locked situation map in a shared field of view for immersive joint situational analysis.

[0038] After obtaining the viewpoint-synchronized battlefield holographic roaming state, the process includes: determining the target browsing mode from among the following modes—regular walking mode, aviation flight mode, fixed-altitude horizontal sweep mode, and eye orientation control mode—based on situational assessment and task type; configuring the target browsing mode by applying the viewpoint motion control parameters of the detail processing engine to obtain the target browsing mode configuration parameters; calculating the viewpoint displacement by applying the real-time 3D coordinates and orientation angle data of the 3D tracking handle and the 3D coordinates of the head according to the motion constraint rules of the target browsing mode to obtain the viewpoint displacement data of the current browsing mode; and inputting the viewpoint displacement data into the detail processing engine to perform battlefield holographic scene perspective update processing to obtain the battlefield holographic situational browsing view corresponding to the target browsing mode.

[0039] like Figure 3 In one example, under battlefield holographic roaming mode, the troop deployment plotting data of the troop model in the battlefield holographic scene is calculated, and spatial measurement data in the battlefield holographic scene is calculated, including: 301. Based on the real-time three-dimensional coordinates and orientation angle data of the 3D tracking handle, perform position mapping update, orientation angle synchronous rotation, unit number color assignment and text label three-dimensional coordinate anchoring operations on the troop model in the battlefield holographic scene to obtain troop deployment plotting data. 302. Using the 3D tracking handle, sequentially select the first target point and the second target point in the battlefield holographic scene, and calculate the straight-line distance between the first target point and the second target point in the battlefield holographic scene coordinate system; use the 3D tracking handle to sequentially calculate the first line segment direction vector and the second line segment direction vector, and calculate the angle between the line segments based on the first line segment direction vector and the second line segment direction vector; 303. Combine the straight-line distance and the angle between line segments to form spatial measurement data.

[0040] In this example, the interaction processing module performs position mapping updates on the troop model based on the real-time 3D coordinates of the 3D tracking handle, ensuring that the troop model's position in the battlefield holographic scene coordinate system corresponds to the spatial position indicated by the 3D tracking handle. Simultaneously, the interaction processing module performs synchronous rotation of the troop model's orientation angle based on the orientation angle data of the 3D tracking handle, ensuring that the troop model's posture is consistent with the pointing direction of the 3D tracking handle. During the troop model's posture update process, the posture parameters of the troop model are written into the troop deployment plotting record. After completing the position mapping update and orientation angle synchronization rotation, the interaction processing module performs color assignment processing based on the unit number of the force model, so that different unit numbers correspond to different color identifiers, and stores the color identifiers as display attribute fields of the force deployment plotting data. At the same time, the interaction processing module generates three-dimensional text labels for the force model in the battlefield holographic scene and performs three-dimensional coordinate anchoring operation for the text labels, so that the anchoring coordinates of the text labels are consistent with the spatial coordinates of the force model, forming force deployment plotting data that includes force model identifiers, force model three-dimensional coordinates, force model attitude angle parameters, unit number color parameters, and text label anchoring coordinates. The interactive processing module uses a 3D tracking handle to sequentially select a first target point and a second target point in the holographic battlefield scene to perform spatial distance calculations. The module reads the three-dimensional coordinates (X1, Y1, Z1) of the first target point and the three-dimensional coordinates (X2, Y2, Z2) of the second target point in the same coordinate system. Based on these coordinates, it calculates the straight-line distance L1 between the two points, where L1 represents the distance in meters. X1, Y1, and Z1 represent the three-dimensional coordinates of the first target point in the holographic battlefield scene coordinate system, and X2, Y2, and Z2 represent the three-dimensional coordinates of the second target point in the same coordinate system. The straight-line distance L1 is then written into the spatial measurement data as the distance calculation result. Simultaneously, the interactive processing module uses the 3D tracking handle to sequentially select the endpoints of two intersecting line segments and calculates the direction vectors of the first and second line segments. The direction vector of the first line segment is represented as (A...). x A y A z The direction vector of the second line segment is represented as (B). x B y B z The angle θ between the line segments is calculated based on the dot product of the two directional vectors and their modulus.

[0041] Reference Figure 4 This embodiment provides a holographic battlefield situational awareness system that integrates multi-source data, including: Data conversion module 1 is used to convert UAV oblique photography data, 3D laser scanning point cloud data and artificial modeling data into UD format and perform point cloud interpolation to obtain a multi-source battlefield UD dataset. Fusion rendering module 2 is used to import multi-source battlefield UD datasets into the holographic computing system, and to perform fusion rendering on the battlefield real scene base layer and the troop and equipment model overlay layer to obtain a battlefield holographic scene. 3D tracking module 3 is used to perform frame-by-frame pose sampling and view frustum range update on the 3D tracking glasses and 3D tracking handle worn by situational awareness personnel entering the holographic battlefield scene, so as to obtain a viewpoint-synchronized holographic battlefield roaming state. Scene locking module 4 is used to overlay the plotting results, measurement data and terrain heat map onto the battlefield holographic scene and perform scene locking in the battlefield holographic roaming state to obtain a battlefield holographic situational awareness map.

[0042] In this embodiment, the specific implementation of each unit in the above system embodiment is described in the above method embodiment, and will not be repeated here.

[0043] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, system, article, or method that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, system, article, or method. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, system, article, or method that includes that element.

[0044] The above description is only a preferred embodiment of the present invention and does not limit the patent scope of the present invention. Any equivalent structural or procedural transformations made based on the content of the present invention specification and drawings, or direct or indirect applications in other related technical fields, are similarly included within the patent protection scope of the present invention.

Claims

1. A holographic battlefield situational awareness method that integrates multi-source data, characterized in that, include: The UAV oblique photography data, 3D laser scanning point cloud data and artificial modeling data are converted to UD format and interpolated to obtain a multi-source battlefield UD dataset. The multi-source battlefield UD dataset is imported into the holographic computing system, and the battlefield real scene base layer and the troop and equipment model overlay layer are fused and rendered to obtain a battlefield holographic scene. The 3D tracking glasses and 3D tracking handles worn by the situational awareness personnel entering the holographic battlefield scene are sampled frame by frame and the field of view range is updated to obtain a viewpoint-synchronized holographic battlefield roaming state. In the holographic battlefield roaming state, the plotting results, measurement data, and terrain heat map are overlaid onto the holographic battlefield scene and scene locking is performed to obtain a holographic battlefield situational awareness map.

2. The holographic battlefield situational awareness method that integrates multi-source data according to claim 1, characterized in that, The UAV oblique photography data, 3D laser scanning point cloud data, and manually modeled data were converted to UD format and interpolated to obtain a multi-source battlefield UD dataset, including: The geometric coordinates and color attribute information in UAV oblique photography data, 3D laser scanning point cloud data and artificial modeling data are uniformly converted into UD format to obtain UD format battlefield data. The UD format battlefield data is input into the Solidscan module, and point density compensation interpolation is performed on the regions with uneven point density caused by the scanning interval in the UD format battlefield data to obtain a multi-source battlefield UD dataset.

3. The holographic battlefield situational awareness method that integrates multi-source data according to claim 1, characterized in that, The multi-source battlefield UD dataset is imported into a holographic computing system, and the battlefield real-scene background layer and the troop and equipment model overlay layer are fused and rendered to obtain a battlefield holographic scene, including: Import the multi-source battlefield UD dataset into the holographic computing system; In the holographic computing system, the battlefield real-world point cloud UD data in the multi-source battlefield UD dataset is used as the base map layer and the troop and equipment model UD data is used as an independent overlay layer. In the detail processing engine, spatial alignment registration is performed on the base map layer and the independent overlay layer to obtain multi-layer fused battlefield scene data. The system acquires the three-dimensional coordinates and line of sight of the current viewpoint of the situational awareness personnel, and performs view frustum range recognition and fusion rendering on the multi-layer fused battlefield scene data to obtain a holographic battlefield scene.

4. The holographic battlefield situational awareness method that integrates multi-source data according to claim 3, characterized in that, The system acquires the 3D coordinates and line-of-sight orientation of the current viewpoint of the situational awareness personnel, and performs view frustum range identification and fusion rendering on the multi-layered fused battlefield scene data to obtain a holographic battlefield scene, including: The first viewpoint 3D coordinates and first line of sight orientation of the situational awareness personnel are obtained, and based on the first viewpoint 3D coordinates and first line of sight orientation, the rendering data blocks of the corresponding resolution level within the field of view frustum are extracted from the spatial resolution level index of the multi-layer fused battlefield scene data. The rendering data block is pushed to the rendering pipeline, and the battlefield real scene background layer and the troop and equipment model overlay layer are rendered in the same frame to obtain a battlefield holographic scene.

5. The holographic battlefield situational awareness method that integrates multi-source data according to claim 1, characterized in that, Frame-by-frame pose sampling and view frustum update are performed on the 3D tracking glasses and 3D tracking handles worn by situational awareness personnel entering the holographic battlefield scene to obtain a viewpoint-synchronized holographic battlefield roaming state, including: Frame-by-frame pose sampling is performed on the 3D tracking glasses and 3D tracking handles worn by the situational awareness personnel entering the holographic battlefield scene to obtain the head three-dimensional coordinates, yaw angle, pitch angle, roll angle and handle three-dimensional coordinates and orientation angle data for each frame; The head's three-dimensional coordinates, yaw angle, pitch angle, and roll angle data are input into the detail processing engine of the holographic computing system to perform a view frustum update on the battlefield holographic scene, thereby obtaining a viewpoint-synchronized battlefield holographic roaming state.

6. The holographic battlefield situational awareness method that integrates multi-source data according to claim 5, characterized in that, The head's three-dimensional coordinates, yaw angle, pitch angle, and roll angle data are input into the detail processing engine of the holographic computing system to perform a view frustum update on the battlefield holographic scene, resulting in a viewpoint-synchronized battlefield holographic roaming state, including: The head 3D coordinates are used as the second viewpoint 3D coordinates, and the yaw angle, pitch angle and roll angle data are used as the second line of sight orientation. The details processing engine is then used to calculate the current frame view frustum space parameters. Based on the current frame view frustum space parameters, the corresponding data blocks are extracted from the multi-layer fused battlefield scene data of the battlefield holographic scene, and the detail processing engine is driven to perform frame-by-frame view synchronous rendering of the battlefield holographic scene to obtain a viewpoint-synchronized battlefield holographic roaming state.

7. The holographic battlefield situational awareness method that integrates multi-source data according to claim 6, characterized in that, Using the head's three-dimensional coordinates as the second viewpoint's three-dimensional coordinates, and the yaw, pitch, and roll angle data as the second line-of-sight orientation, the details processing engine is input to calculate the current frame's frustum space parameters, including: Perform Euler angle rotation calculation on the yaw angle, pitch angle and roll angle data to obtain a line-of-sight direction vector describing the second line-of-sight orientation; Using the head's three-dimensional coordinates as the second viewpoint's three-dimensional coordinates and the gaze direction vector as the view frustum axis direction, the view frustum spatial parameters of the current frame are calculated through the view frustum near clipping plane and far clipping plane boundary of the detail processing engine.

8. The holographic battlefield situational awareness method that integrates multi-source data according to claim 1, characterized in that, In the holographic battlefield roaming state, the plotting results, measurement data, and terrain heatmap are overlaid onto the holographic battlefield scene and scene locking is performed to obtain a holographic battlefield situational awareness map, including: In the holographic battlefield roaming state, calculate the troop deployment plotting data of the troop model in the holographic battlefield scene, and calculate the spatial measurement data in the holographic battlefield scene; Color mapping is performed based on the minimum and maximum elevation coordinates of each point in the multi-source battlefield UD dataset to obtain a terrain heat map. The terrain heat map, the troop deployment plotting data, and the spatial measurement data are then superimposed onto the battlefield holographic scene, and scene locking is performed to obtain a battlefield holographic situational awareness map.

9. The holographic battlefield situational awareness method that integrates multi-source data according to claim 8, characterized in that, In the holographic battlefield roaming state, the troop deployment plotting data of the troop model in the holographic battlefield scene is calculated, and spatial measurement data is calculated in the holographic battlefield scene, including: Based on the real-time three-dimensional coordinates and orientation angle data of the 3D tracking handle, position mapping update, orientation angle synchronous rotation, unit number color assignment, and three-dimensional coordinate anchoring operation of the troop model in the battlefield holographic scene are performed to obtain troop deployment plotting data. The first target point and the second target point are selected sequentially in the holographic battlefield scene using the 3D tracking handle, and the straight-line distance between the first target point and the second target point in the coordinate system of the holographic battlefield scene is calculated; the first line segment direction vector and the second line segment direction vector are calculated sequentially using the 3D tracking handle, and the angle between the line segments is calculated based on the first line segment direction vector and the second line segment direction vector; The straight-line distance and the angle between the line segments together constitute the spatial measurement data.

10. A holographic battlefield situational awareness system integrating multi-source data, characterized in that, The steps for implementing the holographic battlefield situational awareness method that fuses multi-source data as described in any one of claims 1 to 9 include: The data conversion module is used to convert UAV oblique photography data, 3D laser scanning point cloud data and artificial modeling data into UD format and perform point cloud interpolation to obtain a multi-source battlefield UD dataset. The fusion rendering module is used to import the multi-source battlefield UD dataset into the holographic computing system, and to perform fusion rendering on the battlefield real scene base layer and the troop and equipment model overlay layer to obtain a battlefield holographic scene. The 3D tracking module is used to perform frame-by-frame pose sampling and view frustum update on the 3D tracking glasses and 3D tracking handle worn by situational awareness personnel entering the holographic battlefield scene, so as to obtain a viewpoint-synchronized holographic battlefield roaming state. The scene locking module is used to overlay the plotting results, measurement data and terrain heat map onto the holographic battlefield scene and perform scene locking in the holographic battlefield roaming state to obtain a holographic battlefield situational awareness map.

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