Visual interactive display method and system for high-precision surveying and mapping data

Abstract

The invention discloses a visual interactive display method and system for high-precision surveying and mapping data, relates to the technical field of data visualization, and improves the display interaction efficiency of the surveying and mapping data. The surveying and mapping points are arranged in the target scene, the confidence radiuses are set for the surveying and mapping points, the surveying and mapping cells and the visual dynamic scene are generated based on the confidence radiuses, and according to the data collected by the surveying and mapping points, the data particles are generated by the surveying and mapping cells and released in the visual dynamic scene. The surveying and mapping cell is divided into a plurality of data display areas, a data permeable membrane is arranged for each data display area, then data particles are filtered through the data permeable membranes, data fusion of the data display areas is completed according to a filtering result, and user interaction operation is obtained. And generating an influence field according to the user interaction operation, inputting the influence field into the visual dynamic scene, screening each data display area according to the influence field, and generating an interaction result based on a screening result.

Description

Technical Field

[0001] This invention relates to the field of data visualization technology, specifically to a method and system for the interactive visualization of high-precision surveying and mapping data. Background Technology

[0002] In today's surveying and mapping field, the effective display and interaction of high-precision surveying and mapping data is a crucial task. With the continuous development of technology, surveying and mapping techniques have become increasingly advanced, and the amount and accuracy of acquired data have significantly improved. However, how to present this massive and complex high-precision surveying and mapping data to users in an intuitive and easy-to-understand way, and achieve efficient interactive operation, has become an urgent problem to be solved.

[0003] Traditional methods of displaying surveying and mapping data often simply present it in the form of static charts or two-dimensional maps. This approach fails to fully reflect the three-dimensional spatial characteristics and dynamic changes of the data, resulting in a lack of in-depth and comprehensive understanding for users. Moreover, traditional methods lack effective interactive mechanisms, making it difficult for users to flexibly filter and analyze data according to their own needs, and hindering the timely acquisition of data information of interest, thus significantly reducing the utilization efficiency of surveying and mapping data.

[0004] Furthermore, when processing large-scale surveying and mapping data, traditional methods are prone to data redundancy and information overload, causing critical information to be buried and affecting the accuracy of the data and the scientific nature of decision-making. At the same time, due to the lack of effective control and screening of data quality, some inaccurate or low-quality data may be presented to users, further affecting the reliability and usability of surveying and mapping data. Therefore, this paper provides a method and system for the visualization and interactive display of high-precision surveying and mapping data. Summary of the Invention

[0005] The purpose of this invention is to provide a method and system for visualizing and interactively displaying high-precision surveying and mapping data, so as to solve the problems in the background technology.

[0006] To achieve the above objectives, the present invention provides the following technical solution: A method for visualizing and interactively displaying high-precision surveying and mapping data includes the following steps: Step S1: Set up survey points in the target scene, set confidence radii for the survey points, and generate survey cells and a visual dynamic scene based on the confidence radii. Based on the data collected by the survey points, the survey cells generate data particles and release them into the visual dynamic scene. Step S2: Divide the survey cell into several data display areas, set a data penetration membrane for each data display area, and then filter the data particles through the data penetration membrane. Based on the filtering results, complete the data fusion of the data display areas. Step S3: Obtain user interaction operations, generate an influence field based on user interaction operations and input it into the visualized dynamic scene, filter each data display area based on the influence field, and generate interaction results based on the filtering results.

[0007] Furthermore, the process of setting up survey points within the target scene includes: Ground mapping points and spatial mapping points are deployed within the target scene. The ground mapping points are equipped with integrated mapping terminals, which include environmental sensors, total stations and GNSS receivers. The aerial mapping points adopt a two-layer architecture of "low-altitude UAV swarm + satellite remote sensing". The low-altitude UAV swarm covers the low-altitude area below 500m, and each UAV is equipped with lidar and hyperspectral camera. Satellite remote sensing is responsible for the high-altitude area above 500m. An alternating acquisition frequency is set for ground and aerial survey points. That is, when the ground survey points are acquiring data of the target scene within the alternating acquisition frequency, the aerial survey points are in a dormant state, and vice versa.

[0008] Furthermore, the process of setting confidence radii for survey points and establishing visualized dynamic scenes includes: The confidence radius R is a core indicator used to measure the reliability of survey point data. The larger the value, the higher the reliability of the data. A survey cell is formed by the confidence radius of each survey point, and the intersection area of ​​adjacent survey cells is called the shared zone, thus obtaining a visualized dynamic scene; Whenever a ground survey point or an aerial survey point is in a dormant state, the ground survey point or aerial survey point divides the survey data collected during the previous alternating acquisition frequency into several survey datasets of equal volume according to the spatiotemporal location distribution, and disperses them in the visualized dynamic scene in the form of data particles. The data particles are data packets containing various information factors of the mapping dataset, including geometric factors, attribute factors, and temporal factors.

[0009] Furthermore, the process of dividing the data display area includes: The division dimensions are set, including data dimensions and spatial dimensions. Based on the division dimensions, several data display areas are divided into survey cells within the dynamic visualization area. Each area is divided using a combination of "rectangle + irregular boundary".

[0010] Furthermore, the process of setting up the data penetration membrane includes: The data penetration membrane is used as a "smart gate" in the data display area. Its core function is to filter data particles according to preset penetration rules, allowing only qualified data particles to enter the data display area and participate in fusion. The data penetration membrane adopts a two-layer architecture of "hardware acceleration + software rules": the bottom layer architecture is implemented through FPGA chip, which is responsible for real-time processing of geometric factors in data particles, and the upper layer architecture is a rule engine based on deep learning, which is responsible for analyzing attribute factors and time series factors. Each data penetration membrane is configured with an independent "rule memory" to store the filtering rule set of the corresponding data display area. The filtering rule set includes geometric information filtering, attribute information filtering, and time series information filtering.

[0011] Furthermore, the data particle fusion process includes: Whenever a ground survey point or an aerial survey point generates data particles in a survey cell, each data display area retrieves the data particles through a data penetration membrane and performs a fusion operation on each data particle. The credibility weight of each data particle is obtained, and then the fusion ratio weight of the data particles is obtained. Based on the fusion ratio weight of each data particle, the fusion of spatial factors and attribute factors in each data particle is completed. When the dormant state of ground or aerial survey points ends, each survey point stops generating data particles. At the same time, based on the data contained in the fused data particles, data rendering is performed on each data display area, and data is filled into the visualized dynamic scene according to the completion of the rendering results.

[0012] Furthermore, the generation process of the influence field includes: Acquire user interaction operations, which include basic operations, measurement operations (and analysis operations); Data demand directions are generated based on user interaction operations. These data demand directions include data-associated three-dimensional coordinates, data labels, data ranges, and time ranges. The center position of the selected location of the user interaction operation is recorded as the interaction source point, and then the influence range S of the interaction source point is obtained according to the content of the user interaction operation. An influence field is established with the influence range S as the radius and the interaction source point as the center or boundary. Various data filtering labels are generated based on the data demand direction. The influence field is in the form of a three-dimensional circle or a three-dimensional polygon.

[0013] Furthermore, the process of generating the interaction results of user interaction operations includes: Based on the three-dimensional coordinates corresponding to the interaction source point, the influence field is mapped onto the visualized dynamic scene. Then, the data filtering labels are matched with the data penetration membrane of the data display area covered by the influence field. If the filtering rules of the data penetration membrane match any data filtering label, the corresponding data display area is recorded as the rendering display area; otherwise, the corresponding data display area is hidden. Once all data filtering tags or data display areas have completed the matching operation, real-time rendering is performed based on the data particles contained in the rendering display area, thereby generating the corresponding interactive results for the user's interactive operations.

[0014] A high-precision surveying and mapping data visualization and interactive display system includes a surveying and mapping data acquisition module, a visualization dynamic scene module, and a user operation analysis module; The mapping data acquisition module is used to set ground mapping points and aerial mapping points for the target scene, and to set an alternating acquisition frequency to collect data from the target scene. The visualization dynamic scene module is used to set the confidence radius for each survey point, and generate survey cells and visualization dynamic scenes based on the confidence radius. According to the data collected by the survey points, the survey cells generate data particles and release them into the visualization dynamic scene. The survey cells are divided into several data display areas. A data penetration membrane is set for the data display areas, and the data particles are filtered through the data penetration membrane. The data fusion of the data display areas is completed based on the filtering results. The user operation analysis module is used to generate an influence field based on user interaction operations, filter the data display area through the influence field, and generate interaction results based on the filtering results.

[0015] The technical effects and advantages provided by the present invention in the above technical solution are as follows: 1. In terms of visualization, this invention sets survey points within the target scene, generates survey cells and visualizes dynamic scenes by setting confidence radii, and releases data particles within them. This allows for the dynamic and three-dimensional display of survey data, vividly presenting the spatial distribution and changes of the data. This enables users to understand the characteristics and patterns of survey data more intuitively and comprehensively, to a certain extent making up for the shortcomings of traditional static display methods and greatly improving users' cognition and understanding of the data.

[0016] 2. In terms of data processing, this invention divides the surveyed cell into several data display areas and sets up a data permeation membrane to filter and fuse data particles. This effectively removes redundant information and low-quality data, improving the accuracy and reliability of the data. Simultaneously, data fusion integrates scattered data into valuable information, uncovering underlying patterns and providing users with more accurate and useful decision-making support.

[0017] 3. In terms of interactivity, this invention acquires user interaction operations and generates an influence field input into a visualized dynamic scene. It can filter various data display areas according to user needs and quickly generate interactive results. This allows users to independently explore and analyze surveying data according to their own interests and concerns, obtain the information they need in a timely manner, and greatly improve the efficiency of data utilization and the scientific nature of decision-making. Compared with traditional display methods that lack interactivity, it can better meet the personalized needs of users and enhance the user experience. Attached Figure Description

[0018] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this invention. For those skilled in the art, other drawings can be obtained based on these drawings.

[0019] Figure 1 This is a flowchart of a method for visualizing and interactively displaying high-precision surveying data according to the present invention.

[0020] Figure 2 This is a system structure diagram of a high-precision surveying and mapping data visualization and interactive display system according to the present invention. Detailed Implementation

[0021] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0022] Please see Figure 1 As shown, a method for visualizing and interactively displaying high-precision surveying and mapping data includes the following steps: Step S1: Set up survey points in the target scene, set confidence radii for the survey points, and generate survey cells and a visual dynamic scene based on the confidence radii. Based on the data collected by the survey points, the survey cells generate data particles and release them into the visual dynamic scene. Step S2: Divide the survey cell into several data display areas, set a data penetration membrane for each data display area, and then filter the data particles through the data penetration membrane. Based on the filtering results, complete the data fusion of the data display areas. Step S3: Obtain user interaction operations, generate an influence field based on user interaction operations and input it into the visualized dynamic scene, filter each data display area based on the influence field, and generate interaction results based on the filtering results.

[0023] Step S1 is achieved through the following process: Step S101, the arrangement of ground survey points and aerial survey points, specifically includes: Deploy ground mapping points and spatial mapping points within the target scene (such as urban building complexes, mountain forests, mining operation faces, etc.); The ground mapping points are deployed in a graded manner according to the terrain complexity of the target scene. For example, for flat areas (such as city squares), a grid density of 5m×5m is used, and for complex terrain (such as mountain gullies), the density is increased to 2m×2m. Each ground survey point is equipped with an integrated survey terminal, which has built-in environmental sensors (such as temperature and humidity sensors, barometric pressure sensors, etc.), a total station, and a GNSS receiver. The data acquisition diameter of each integrated survey terminal is 2-5 times the grid density. The aerial mapping points adopt a two-layer architecture of "low-altitude UAV swarm + satellite remote sensing". The low-altitude UAV swarm covers the low-altitude area below 500m, and each UAV is equipped with lidar and hyperspectral camera. Satellite remote sensing is responsible for the high-altitude area above 500m. In this way, data collection of each aerial mapping point is completed through the low-altitude UAV swarm and satellite remote sensing. It should be noted that the spatial distribution of aerial survey points follows the principle of "covering the blind spots of ground survey points." For example, in areas that are difficult to cover by ground survey points, such as the tops of high-rise buildings and the shadow areas of valleys, the density of aerial survey points needs to be increased to 1.5 times that of ground survey points. An alternating acquisition frequency is set for ground and aerial survey points. That is, while the ground survey points are acquiring data of the target scene within the alternating acquisition frequency, the aerial survey points are in a dormant state, and vice versa. The duration of the alternating acquisition frequency is generally set to 5 to 10 minutes.

[0024] Step S102, setting the confidence radius, specifically includes the following process: The confidence radius R is a core indicator used to measure the reliability of survey point data. The larger the value, the higher the data reliability. The calculation formula is as follows: ;in For scene coefficients, To ensure the accuracy of surveying equipment, , , These represent environmental error, occlusion error, and time decay error, respectively. , , These are the weight parameters.

[0025] Step S103: Generate data particles and establish a visualized dynamic scene. The specific process includes: A survey cell is formed by the confidence radius of each survey point, and the intersection area of ​​adjacent survey cells is called the shared zone, thus obtaining a visualized dynamic scene; Whenever a ground survey point or an aerial survey point is in a dormant state, the ground survey point or aerial survey point divides the survey data collected during the previous alternating acquisition frequency into several survey datasets of equal volume according to the spatiotemporal location distribution, and disperses them in the visualized dynamic scene in the form of data particles. The data particles are data packets containing various information factors of the mapping dataset, wherein the information factors include: Geometric factors: include the three-dimensional coordinates, curvature (plane / surface identifier), and slope (terrain tilt angle, range 0-90°) of the location corresponding to the mapping dataset. Attribute factors: include material labels (e.g., 12 categories of labels such as concrete, soil, vegetation, etc.) of spatial objects at the corresponding locations in the mapping dataset, image data, and environmental data (temperature, humidity, air pressure, light intensity, etc.). Time series factor: collection timestamp; Data particles roam freely within their assigned survey cells. The roaming speed is related to the time series factor; for example, the speed is 0.5 m / s within 10 minutes of acquisition, 0.3 m / s from 10 to 30 minutes, and 0.1 m / s after 30 minutes. After entering the shared zone, they can migrate to adjacent cells, but the migration probability is inversely proportional to the confidence radius of the cell (the larger the radius, the more reliable the data, and the stronger the migration attraction). It should be noted that the data particles are set with a particle lifecycle to avoid congestion of data particles within the mapping cell. The particle lifecycle includes: setting a standard confidence radius range; for data particles in mapping cells with a confidence radius smaller than the standard confidence radius range, the particle lifecycle is set to 30 to 60 minutes; for data particles in mapping cells with a confidence radius within the standard confidence radius range, the particle lifecycle is set to 60 to 120 minutes; otherwise, the particle lifecycle is set to 120 minutes or more.

[0026] Step S2 is achieved through the following process: Step S201: Divide the data display area. The specific process includes: The division dimensions are defined, including data dimensions and spatial dimensions. The data dimensions include a geometric region (emphasizing geometric information such as three-dimensional coordinates and slope), a spectral region (emphasizing spectral information such as reflectance and band analysis), and a temporal region (emphasizing temporal information such as data change rate and historical trend). Spatial dimensions include “micro-areas” (such as details of building components), “meso-areas” (such as street blocks), and “macro-areas” (such as the entire city). Based on the division dimensions, the survey cells within the dynamic visualization area are divided into several data display areas. Each area is divided using a combination of "rectangles + irregular boundaries". Rectangular boundaries ensure basic coverage, while irregular boundaries (based on the scene terrain outline) avoid cross-area data interference. For example, the data display area for urban bridges uses the outline of the bridge as an irregular boundary, while expanding outward to form a rectangular buffer zone. Only related data related to the bridge is displayed within the buffer zone. It should be noted that the data display areas corresponding to the various division dimensions in the surveying data block are in an overlapping or spliced ​​state.

[0027] Step S202: Set up the data permeation membrane. The specific process includes: The data penetration membrane is used as a "smart gate" in the data display area. Its core function is to filter data particles according to preset penetration rules, allowing only qualified data particles to enter the data display area and participate in fusion. The data penetration membrane adopts a two-layer architecture of "hardware acceleration + software rules": the bottom layer architecture is implemented by FPGA chip, which is responsible for real-time processing of geometric factors in data particles (such as whether the three-dimensional coordinates correspond to which data display area), and the upper layer architecture is a rule engine based on deep learning, which is responsible for analyzing attribute factors and time series factors. Each data penetration membrane is configured with an independent "rule memory" to store the filtering rule set of the corresponding data display area. The filtering rule set can be customized and modified by users through API interface, and the modification takes effect within 5 seconds. The filtering rule set includes: Geometric information filtering: This is achieved through "coordinate range + curvature matching". The x and y coordinates in the three-dimensional coordinate system must fall within the boundary of the data display area, the z coordinate must meet the regional elevation range (e.g., the Z coordinate in the bridge area must be between 5-30m), and the deviation of the curvature value from the dominant curvature of the region (e.g., the curvature of the bridge deck is flat, ≤0.01) must be ≤5%. Attribute information filtering: A "label whitelist" mechanism is adopted. For example, only particles labeled "concrete" and "steel structure" are allowed to enter the building area. Data integrity must be ≥90%. Otherwise, they will be marked as "incomplete particles" and data particles will not be allowed to pass through the data penetration membrane. Time-series information filtering: The collected timestamps must be within the time window of the region of interest (e.g., for construction monitoring areas, only data from the last 24 hours is considered).

[0028] Step S203, fusing data particles, specifically includes: Whenever a ground survey point or an aerial survey point generates data particles in a survey cell, each data display area retrieves the data particles through a data penetration membrane and performs a fusion operation on each data particle to form a continuous regional data result. Calculate the confidence weight w=β*R for each data particle, where β is a dynamically adjusted parameter ranging from 0.5 to 1.5, and then obtain the fusion ratio weight of the data particles: ,in , The fusion ratio weight and credibility weight of the j-th data particle are represented, and N represents the total number of data particles to be fused. Then, based on the fusion ratio weight of each data particle, the fusion of spatial factors and attribute factors in each data particle is completed; For example, if the x-coordinates of the three-dimensional coordinates recorded by the two spatial factors are 100.2m and 100.4m, and their corresponding fusion weights are 0.4 and 0.6, then the fused x-coordinate is 100.38m. When the dormancy state of ground or aerial survey points ends, each survey point stops generating data particles. At the same time, based on the data contained in the fused data particles, data rendering is performed on each data display area (for example, generating a 3D image model of the corresponding spatial location based on spectral data, and generating a thermal pattern distribution image of the corresponding spatial location based on temperature data). Data is then used to fill in the visualized dynamic scene based on the completion of the rendering results.

[0029] Step S3 is achieved through the following process: Step S301: Generate an influence field. The specific process includes: Acquire user interaction operations, which include basic operations (such as mouse clicks, scroll wheel operations, etc.), measurement operations (such as clicking two or more points in the scene using a measurement tool, and the system calculating geometric parameters in real time), and analysis operations (such as time series comparison: selecting two time points to view data changes, profile cutting: generating a terrain profile map along a specified line, etc.). Data demand directions are generated based on user interaction operations. These data demand directions include data-associated three-dimensional coordinates, data labels, data ranges, and time ranges. The center position of the location selected by the user interaction is recorded as the interaction source point, and then the influence range S of the interaction source point is obtained based on the content of the user interaction: ;in Indicates the basic radius of operation. These are the operation intensity coefficient and the operation duration, respectively. It should be noted that for measurement operations, a spherical space circumscribed by the polygonal space matrix formed by clicking the selected position is automatically generated, and the radius of the spherical space is the basic radius of the operation. An influence field is established with the influence range S as the radius and the interaction source point as the center or boundary. Various data filtering labels are generated based on the data demand direction. The influence field is in the form of a three-dimensional circle or a three-dimensional polygon.

[0030] Step S302, Real-time scene rendering, the specific process includes: Based on the three-dimensional coordinates corresponding to the interaction source point, the influence field is mapped onto the visualized dynamic scene. Then, the data filtering labels are matched with the data penetration membrane of the data display area covered by the influence field. If the filtering rules of the data penetration membrane match any data filtering label, the corresponding data display area is recorded as the rendering display area; otherwise, the corresponding data display area is hidden. Once all data filtering tags or data display areas have completed the matching operation, real-time rendering is performed based on the data particles contained in the rendering display area, thereby generating the corresponding interactive results for the user's interactive operations.

[0031] Please see Figure 2 As shown, the present invention also discloses a high-precision surveying and mapping data visualization and interactive display system, including a surveying and mapping data acquisition module, a visualization dynamic scene module, and a user operation analysis module; The mapping data acquisition module is used to set ground mapping points and aerial mapping points for the target scene, and to set an alternating acquisition frequency to collect data from the target scene. The visualization dynamic scene module is used to set the confidence radius for each survey point, and generate survey cells and visualization dynamic scenes based on the confidence radius. According to the data collected by the survey points, the survey cells generate data particles and release them into the visualization dynamic scene. The survey cells are divided into several data display areas. A data penetration membrane is set for the data display areas, and the data particles are filtered through the data penetration membrane. The data fusion of the data display areas is completed based on the filtering results. The user operation analysis module is used to generate an influence field based on user interaction operations, filter the data display area through the influence field, and generate interaction results based on the filtering results.

[0032] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A method for visual interactive display of high-precision mapping data, characterized in that, The method comprises the following steps: Step S1, setting surveying points in a target scene, setting a confidence radius for the surveying points, and generating surveying cells and a visual dynamic scene based on the confidence radius, and according to the data collected by the surveying points, the surveying cells generate data particles which are released in the visual dynamic scene; Step S2, dividing the surveying cells into a plurality of data display areas, setting a data permeation membrane for each data display area, filtering the data particles through the data permeation membrane, and completing data fusion of the data display areas according to the filtering results; Step S3, obtaining a user interactive operation, generating an influence field into the visual dynamic scene according to the user interactive operation, screening the data display areas according to the influence field, and generating an interactive result based on the screening result.

2. The method of claim 1, wherein, The process of setting surveying points in the target scene comprises: Ground surveying points and space surveying points are arranged in the target scene, and the ground surveying points are provided with integrated surveying terminals, wherein the integrated surveying terminals are internally provided with environment sensors, total stations and GNSS receivers; The space surveying points adopt a double-layer structure of "low-altitude unmanned aerial vehicle group + satellite remote sensing", wherein the low-altitude unmanned aerial vehicle group conforms to a low-altitude area below 500 m, each unmanned aerial vehicle is provided with a laser radar and a hyperspectral camera, and the satellite remote sensing is responsible for a high-altitude area above 500 m; The ground surveying points and the space surveying points are set with an alternate collection frequency, that is, during the process that the ground surveying points collect the target scene in the alternate collection frequency, the space surveying points are in a dormant state, and vice versa.

3. The method of claim 2, wherein, The process of setting a confidence radius for the surveying points and establishing a visual dynamic scene comprises: The confidence radius R is a core index for measuring the reliability of surveying point data, and the greater the value, the higher the data reliability; A surveying cell is formed by the confidence radius of each surveying point, the intersection area of adjacent surveying cells is recorded as a shared band, and then a visual dynamic scene is obtained; When the ground surveying points or the space surveying points are in a dormant state, the surveying data collected by the ground surveying points or the space surveying points during the last alternate collection frequency is divided into a plurality of surveying data sets with equal volume according to the spatial position distribution, and is scattered in the visual dynamic scene in the form of data particles; The data particle is a data packet containing a plurality of information factors of the surveying data set, wherein the information factors include geometric factors, attribute factors and time sequence factors.

4. The method of claim 3, wherein, The division process of the data display area comprises: A division dimension is set, the division dimension comprises a data dimension and a spatial dimension, a plurality of data display areas are divided in the surveying cells in the dynamic visual area according to the division dimension, and each area is divided by a "rectangular + irregular boundary" mixed division.

5. The method of claim 4, wherein, The setting process of the data permeation membrane comprises: The data permeation membrane is used as an "intelligent gate" for the data display area, and its core function is to filter the data particles according to a preset permeation rule, and only allow the data particles meeting the conditions to enter the data display area and participate in fusion; The data penetration membrane adopts a double-layer structure: the bottom layer is responsible for processing geometric factors in real time, and the upper layer is responsible for analyzing attribute factors and time sequence factors. Each data penetration membrane is configured with an independent "rule memory" to store the filtering rule set of the corresponding data display area. The filtering rule set includes geometric information filtering, attribute information filtering, and time sequence information filtering.

6. The method of claim 5, wherein, The fusion process of the data particles includes: When the ground survey point or the aerial survey point generates a data particle in the survey cell, each data display area retrieves the data particle through the data penetration membrane and performs fusion operation on each data particle; The fusion proportion weight of each data particle is obtained, and the fusion of the spatial factors and the attribute factors in each data particle is completed according to the fusion proportion weight of each data particle; When the dormancy state of the ground survey point or the aerial survey point ends, the data display area is rendered according to the data contained in the fused data particle, and the visual dynamic scene is filled with data according to the rendering result.

7. The method of claim 6, wherein, The generation process of the influence field includes: Obtain user interaction operation, generate data demand direction based on user interaction operation, the data demand direction includes data correlation three-dimensional coordinate, data label, data range and setting time range; The center position of the selected position of the user interaction operation is recorded as the interaction source point, and then the influence range S of the interaction source point is obtained according to the user interaction operation content; An influence field is established with the influence range S as the radius and the interaction source point as the center or the boundary, and multiple data filtering labels are generated based on the data demand direction. The influence field is in the form of a three-dimensional circle or a three-dimensional polygon.

8. The method of claim 7, wherein, The generation process of the interaction result of the user interaction operation includes: Map the influence field to the visual dynamic scene according to the interaction source point, match the data filtering label with the data penetration membrane of the data display area covered by the influence field, if the filtering rule of the data penetration membrane matches any data filtering label, record the corresponding data display area as the rendering display area, otherwise hide the corresponding data display area; After all data filtering labels or data display areas complete the mutual matching operation, real-time rendering is performed based on the data particles contained in the rendering display area, and then the interaction result corresponding to the user interaction operation is generated.

9. A visual interactive display system of high-precision surveying data, applied to the visual interactive display method of high-precision surveying data according to any one of claims 1 to 8, characterized in that, It includes a survey data acquisition module, a visual dynamic scene module, and a user operation analysis module. The survey data acquisition module is used to set ground survey points and aerial survey points for the target scene, and set an alternate acquisition frequency to collect the target scene; The visual dynamic scene module is used to set a confidence radius for each survey point, generate survey cells and a visual dynamic scene based on the confidence radius, generate data particles in the visual dynamic scene based on the data collected by the survey points, divide the survey cells into data display areas, set data penetration membranes for the data display areas, and then filter the data particles through the data penetration membranes to complete data fusion of the data display areas; The user operation analysis module is used to generate an influence field based on user interaction operation, filter the data display areas through the influence field, and generate an interaction result based on the filtering result.

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