A digital asset management method based on drone point cloud model

Abstract

The present invention discloses a digital asset management method based on a drone point cloud model, which relates to the field of road section data assets. The method includes setting a drone collection route, using a drone equipped with a laser radar and a high-definition camera to obtain radar point cloud data and oblique image data of a target road section; combining the drone position and attitude data to construct a road section three-dimensional point cloud model library; by fuzzy identifying and automatically marking road assets in the road section three-dimensional point cloud model, extracting asset-related information and data, and generating a road section asset library; configuring a security strategy for the road section three-dimensional point cloud model in the road section asset library according to the road section confidentiality level; importing the constructed three-dimensional point cloud model into a digital twin platform for real-time dynamic display, and predicting the life of road section components to generate a maintenance strategy. The present invention uses a drone equipped with a radar to collect data to comprehensively collect road sections and directly identify road section assets, which is more efficient without manual work.

Description

Technical Field

[0001] The present invention relates to the field of road section data assets, and in particular to a digital asset management method based on an unmanned aerial vehicle point cloud model. Background Art

[0002] With the continuous advancement of drone technology and the popularization of laser scanning technology, it has become easier and more economical to obtain high-precision, high-resolution 3D point cloud data. The high-definition camera or laser radar (LiDAR) carried by the drone can obtain high-resolution image data and accurate 3D point cloud data. These data can record the surface features, topography and surrounding environment of the road section in detail, providing rich information for subsequent analysis and modeling.

[0003] Traditional digital asset collection relies on manual handheld scanners to perform modular scanning of scenes. This method not only has a long operation cycle, but is also difficult to apply to larger buildings or complex terrains.

[0004] In order to solve these problems, a digital asset management method based on UAV point cloud model is urgently needed. Summary of the invention

[0005] To solve the above problems, this application proposes a digital asset management method based on a drone point cloud model, comprising the following steps:

[0006] S1. Set up the drone collection route, use the drone equipped with laser radar and high-definition camera to obtain radar point cloud data and oblique image data of the target road section;

[0007] S2, based on radar point cloud data and oblique image data, combined with drone position and attitude data, build a three-dimensional point cloud model library of the road section;

[0008] S3, based on the established model library combined with the road design drawings and maintenance records, fuzzy recognition and automatic marking of road assets in the three-dimensional point cloud model of the road section, and extraction of asset-related information and data to generate a road section asset library;

[0009] S4. Obtaining relevant information about the road section, evaluating the confidentiality of the road section according to the relevant information about the road section to obtain a confidentiality level of the road section, and configuring a security policy for the three-dimensional point cloud model of the road section in the road section asset library according to the confidentiality level of the road section;

[0010] S5. Import the constructed 3D point cloud model into the digital twin platform, collect real-time road section information for real-time dynamic display, and predict the life of road section components to generate maintenance strategies.

[0011] Preferably, the specific contents of setting the drone collection route in S1 include:

[0012] Obtain regional geographic information, and analyze the regional geographic information to obtain road section information and intersection information;

[0013] Construct a regional route virtual network based on the road section and intersection information, and mark the intersections on the regional route virtual network to obtain intersection nodes;

[0014] The number of intersection nodes of the road section is counted and sorted in descending order to obtain the road section order, and the road section with the first road section order is the main road section;

[0015] Conduct flight simulation with one end of the main route as the starting point and the other end as the focus, and record the flight time, simulated route coverage length and number of simulated nodes of several flight simulation routes;

[0016] According to the length of the simulated routes, the routes of the flight simulations are sorted in descending order to obtain the order of the flight simulation routes;

[0017] Filtering the flight simulation route ranked first in the flight simulation route sequence to obtain an initial simulation route group;

[0018] The first weight coefficient and the second weight coefficient are respectively configured for the time of the route and the number of nodes passed by the simulation to obtain the route evaluation value of the initial simulation route group;

[0019] The route evaluation values ​​are sorted in descending order, and the initial simulation route with the highest route evaluation value is selected as the drone collection route.

[0020] Preferably, the expression of the route evaluation value of the initial simulation route group is:

[0021] ;

[0022] in, is the route evaluation value of the initial simulation route group, is the first weight coefficient, is the second weight coefficient, is the time of the route, is the number of nodes passed by the simulation.

[0023] Preferably, the specific contents of constructing the road section 3D point cloud model library based on radar point cloud data and oblique image data in combination with the drone position and attitude data include:

[0024] Preprocess the radar point cloud data and oblique image data respectively;

[0025] Taking time as the reference axis, divide time into several equal time periods, and match the geographical information of the corresponding time periods to generate the corresponding geographical coordinate system;

[0026] Register radar point cloud data, vector data and oblique image data into the geographic coordinate system to generate a geographic information data set;

[0027] Extracting feature data of geographic information data groups of different time periods to obtain road section features, and dividing time periods into different feature time period feature groups according to the road section features;

[0028] Combined with the collection time, weather influence and road section characteristics, the weight allocation strategy is configured for the radar point cloud data, vector data and oblique image data of different time period feature groups;

[0029] According to the weight distribution strategy, radar point cloud data, vector data and oblique image data are fused to obtain fused data;

[0030] The position and attitude data of the UAV are used to spatially register the fused data to build a 3D point cloud model library.

[0031] Preferably, the specific contents of constructing the road section three-dimensional point cloud model library based on radar point cloud data and oblique image data in combination with the position and attitude data of the drone also include:

[0032] Real-time monitoring of intersection information while using drones equipped with laser radar and high-definition cameras to simultaneously obtain radar point cloud data, vector data, and oblique image data of the target road section;

[0033] A threshold for newly generated intersections is set according to the update requirements of the 3D point cloud model library;

[0034] The number of newly generated intersections is counted. If the number of newly generated intersections exceeds the threshold of newly generated intersections, the intersection nodes are updated to generate an updated drone collection route.

[0035] Preferably, the road section related information is obtained, the road section confidentiality level is evaluated according to the road section related information to obtain the road section confidentiality level, and the specific content of configuring the security strategy for the road section three-dimensional point cloud model in the road section asset library according to the road section confidentiality level is:

[0036] Analyze the relevant information of the road section to obtain the geographical location of the road section, the frequency of use of the road section, and the surrounding environment of the road section;

[0037] A road section importance evaluation table and a road section sensitivity evaluation table are preset, and the road section is evaluated according to the road section importance evaluation table and the road section sensitivity value evaluation table to obtain an importance evaluation value and a road section sensitivity value;

[0038] The confidentiality weight coefficient is configured for the importance evaluation value and the road section sensitivity value to obtain the road section confidentiality value;

[0039] If the road section confidentiality value is lower than the road section confidentiality value threshold, the road section is defined as a public road section;

[0040] A matching table of road section confidentiality value and key digits is preset;

[0041] If the road section confidentiality value is higher than the road section confidentiality value threshold, the number of secret key bits is configured according to the road section confidentiality value;

[0042] The secret key is scrambled and reorganized according to the number of secret key bits to obtain a security key and then generate a security policy.

[0043] Preferably, the constructed three-dimensional point cloud model is imported into the digital twin platform, real-time road section information is collected for real-time dynamic display, and the life of road section components is predicted to generate maintenance strategies. The specific contents are as follows:

[0044] Import the constructed 3D point cloud model into the digital twin platform to ensure that the model’s geometry, texture, and attribute information are complete and correct;

[0045] Associating 3D point cloud models with related design drawings and maintenance record static data;

[0046] The associated real-time monitoring data is dynamically displayed in real time on the digital twin platform;

[0047] The damage of road components in future time periods is predicted based on the monitoring data, and the maintenance time and frequency are set according to the prediction results to generate a maintenance strategy.

[0048] To sum up, compared with traditional technologies, the digital asset management method based on drone point cloud model of the present invention adopts the method of drone equipped with radar to collect data for comprehensive collection of road sections and directly identify road section assets. The drone is combined with automatic route planning solutions to easily obtain high-precision and high-resolution real-life three-dimensional models, effectively reducing the time and financial costs of scene scanning. The drone collection route is set through a virtual model, which does not require manual work and is more efficient. The three-dimensional point cloud model is imported into the digital twin platform for real-time display with realistic three-dimensional scenes to realize road section visualization.

[0049] The technical method of the present invention is further described in detail below through the drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] Figure 1 This is a step diagram of a digital asset management method based on a drone point cloud model of the present invention. DETAILED DESCRIPTION

[0051] The technical method of the present invention is further described below by means of the accompanying drawings and embodiments. It should be noted that unless otherwise specifically stated, the relative arrangement of the components and steps, the numerical expressions and the numerical values ​​described in these embodiments do not limit the scope of the present application.

[0052] The following description of at least one exemplary embodiment is merely illustrative in nature and is in no way intended to limit the present application, its application, or uses.

[0053] Technologies, systems, and devices known to ordinary technicians in the relevant art may not be discussed in detail, but where appropriate, the technologies, systems, and devices should be considered part of the specification.

[0054] In all examples shown and discussed herein, any specific values ​​should be interpreted as merely exemplary and not limiting. Therefore, other examples of the exemplary embodiments may have different values.

[0055] Unless otherwise defined, technical or scientific terms used in the present invention shall have the common meanings understood by one having ordinary skills in the field to which the present invention belongs.

[0056] The present invention provides a digital asset management method based on a drone point cloud model, comprising the following steps:

[0057] S1. Set up the drone collection route, use the drone equipped with laser radar and high-definition camera to obtain radar point cloud data and oblique image data of the target road section;

[0058] Furthermore, the specific contents of setting the drone collection route in S1 include:

[0059] Obtain regional geographic information, and analyze the regional geographic information to obtain road section information and intersection information;

[0060] Construct a regional route virtual network based on the road section and intersection information, and mark the intersections on the regional route virtual network to obtain intersection nodes;

[0061] The number of intersection nodes of the road section is counted and sorted in descending order to obtain the road section order, and the road section with the first road section order is the main road section;

[0062] Conduct flight simulation with one end of the main route as the starting point and the other end as the focus, and record the flight time, simulated route coverage length and number of simulated nodes of several flight simulation routes;

[0063] According to the length of the simulated routes, the routes of the flight simulations are sorted in descending order to obtain the order of the flight simulation routes;

[0064] Filtering the flight simulation route ranked first in the flight simulation route sequence to obtain an initial simulation route group;

[0065] The first weight coefficient and the second weight coefficient are respectively configured for the time of the route and the number of nodes passed by the simulation to obtain the route evaluation value of the initial simulation route group;

[0066] The route evaluation values ​​are sorted in descending order, and the initial simulation route with the highest route evaluation value is selected as the drone collection route.

[0067] Furthermore, the expression of the route evaluation value of the initial simulation route group is:

[0068] ;

[0069] in, is the route evaluation value of the initial simulation route group, is the first weight coefficient, is the second weight coefficient, is the time of the route, is the number of nodes passed by the simulation.

[0070] in, , based on the maximum allowed inspection time of the current staff Define the first weight coefficient , .

[0071] S2, based on radar point cloud data and oblique image data, combined with drone position and attitude data, build a three-dimensional point cloud model library of the road section;

[0072] Furthermore, based on radar point cloud data and oblique image data, combined with drone position and attitude data, the specific contents of the road section 3D point cloud model library include:

[0073] Preprocess the radar point cloud data and oblique image data respectively;

[0074] Point cloud data processing: De-noising, filtering, down-sampling and other processing are performed on the radar point cloud data to reduce the data volume and improve the data quality. By removing some noise points and rasterizing, the three-dimensional vectorization of road feature targets is realized to vector data.

[0075] Vector data processing: ensure the integrity and accuracy of vector data, remove redundant and erroneous data, and convert vector data into a format suitable for subsequent processing.

[0076] Oblique image data processing: Oblique images are corrected, stitched, and textured to obtain clear and coherent images of the road and surrounding environment.

[0077] UAV data processing: Time synchronization and spatial registration of the UAV’s position and attitude data to ensure data accuracy and consistency.

[0078] Taking time as the reference axis, divide time into several equal time periods, and match the geographical information of the corresponding time periods to generate the corresponding geographical coordinate system;

[0079] Register radar point cloud data, vector data and oblique image data into the geographic coordinate system to generate a geographic information data set;

[0080] Extracting feature data of geographic information data groups of different time periods to obtain road section features, and dividing time periods into different feature time period feature groups according to the road section features;

[0081] Combined with the collection time, weather influence and road section characteristics, the weight allocation strategy is configured for the radar point cloud data, vector data and oblique image data of different time period feature groups;

[0082] According to the weight distribution strategy, radar point cloud data, vector data and oblique image data are fused to obtain fused data;

[0083] The position and attitude data of the UAV are used to spatially register the fused data to build a 3D point cloud model library.

[0084] During the construction process, it is necessary to consider the geometric characteristics of the road surface such as flatness, slope, curvature, as well as the location and attribute information of traffic facilities such as road markings and traffic signs.

[0085] The constructed 3D point cloud model of the road section is optimized, including removing redundant data, filling in void areas, smoothing the surface, etc., to improve the accuracy and visualization of the model.

[0086] At the same time, the model can be simplified as needed to reduce the amount of data and improve real-time performance.

[0087] Furthermore, based on radar point cloud data and oblique image data, combined with drone position and attitude data, the specific contents of constructing a road section 3D point cloud model library also include:

[0088] Real-time monitoring of intersection information while using drones equipped with laser radar and high-definition cameras to simultaneously obtain radar point cloud data, vector data, and oblique image data of the target road section;

[0089] A threshold for newly generated intersections is set according to the update requirements of the 3D point cloud model library;

[0090] The number of newly generated intersections is counted. If the number of newly generated intersections exceeds the threshold of newly generated intersections, the intersection nodes are updated to generate an updated drone collection route.

[0091] S3, based on the established model library combined with the road design drawings and maintenance records, fuzzy recognition and automatic marking of road assets in the three-dimensional point cloud model of the road section, and extraction of asset-related information and data to generate a road section asset library;

[0092] The road segment asset library includes some data related to the road segment;

[0093] For roads: road length, road width, number of lanes, number and location of bridges and tunnels, number and location of infrastructure, location and angle of curves, etc.;

[0094] Bridges: location, bridge type, length, width, height, number of lanes, number of piers, spacing, diameter, bridge facilities, etc.;

[0095] Tunnels: location, length, width, height, number of lanes, and facilities in the tunnel;

[0096] Infrastructure (gantry, high pole, display screen): quantity, type, location, size, etc. (each gantry: location, number of cameras and antennas, gantry cabinet location, size; high pole: location, diameter, height, equipment type;

[0097] Display screen: location, rod diameter, height, size, area), etc.

[0098] S4. Obtaining relevant information about the road section, evaluating the confidentiality of the road section according to the relevant information about the road section to obtain a confidentiality level of the road section, and configuring a security policy for the three-dimensional point cloud model of the road section in the road section asset library according to the confidentiality level of the road section;

[0099] Further, the road section related information is obtained, and the confidentiality level of the road section is evaluated according to the road section related information to obtain the road section confidentiality level. The specific content of the security strategy configured for the road section three-dimensional point cloud model in the road section asset library according to the road section confidentiality level is as follows:

[0100] Analyze the relevant information of the road section to obtain the geographical location of the road section, the frequency of use of the road section, and the surrounding environment of the road section;

[0101] A road section importance evaluation table and a road section sensitivity evaluation table are preset according to the geographical location of the road section, the frequency of use of the road section, and the surrounding environment of the road section. The road section is evaluated according to the road section importance evaluation table and the road section sensitivity value evaluation table to obtain an importance evaluation value and a road section sensitivity value;

[0102] The road section importance assessment table and the road section sensitivity assessment table are tools used to evaluate the relative importance of each road section in the road network and its sensitivity to environmental or socio-economic impacts. They are usually used in the fields of transportation planning, infrastructure construction, environmental protection, etc. to help decision makers identify key sections and formulate corresponding management strategies.

[0103] The confidentiality weight coefficient is configured for the importance evaluation value and the road section sensitivity value to obtain the road section confidentiality value;

[0104] Furthermore, the expression of the road section confidentiality value is:

[0105] ;

[0106] in, Confidential value for the road segment, is the confidentiality weight coefficient, is the importance assessment value, It is the sensitive value of the road section.

[0107] Among them, the confidentiality weight coefficient , according to the different requirements of the confidentiality value of different road sections on the importance evaluation value and the sensitivity value of the road section, the selection The fixed value.

[0108] Calculate the confidentiality average value of the obtained road section confidentiality value, define the confidentiality average value as the initial threshold, and the confidentiality average value is updated in real time according to the collected and calculated data until the road section confidentiality value threshold is obtained;

[0109] If the road section confidentiality value is lower than the road section confidentiality value threshold, the road section is defined as a public road section;

[0110] A matching table of road section confidentiality value and key digits is preset;

[0111] If the road section confidentiality value is higher than the road section confidentiality value threshold, the number of secret key bits is configured according to the road section confidentiality value;

[0112] The secret key is scrambled and reorganized according to the number of secret key bits to obtain a security key and then generate a security policy.

[0113] In addition, a strict identity authentication mechanism can be established to ensure that only authenticated users can access the system. According to the user's position and needs, the corresponding data access rights are assigned to them to prevent irrelevant personnel from accessing sensitive information. All user operation logs are recorded, including login time, accessed data, and operations performed, etc., for subsequent review and tracking.

[0114] S5. Import the constructed 3D point cloud model into the digital twin platform, collect real-time road section information for real-time dynamic display, and predict the life of road section components to generate maintenance strategies.

[0115] Furthermore, the constructed 3D point cloud model is imported into the digital twin platform, real-time road section information is collected for real-time dynamic display, and the life of road section components is predicted to generate maintenance strategies. The specific contents are as follows:

[0116] Import the constructed 3D point cloud model into the digital twin platform to ensure that the model’s geometry, texture, and attribute information are complete and correct;

[0117] Associating 3D point cloud models with related design drawings and maintenance record static data;

[0118] The associated real-time monitoring data is dynamically displayed in real time on the digital twin platform;

[0119] The damage of road components in future time periods is predicted based on the monitoring data, and the maintenance time and frequency are set according to the prediction results to generate a maintenance strategy.

[0120] Design an intuitive and easy-to-use user interface to display the 3D point cloud model and its corresponding real-time data, such as vehicle movement trajectory, traffic congestion, etc.

[0121] Provide users with functions for interacting with digital twin models, such as zooming and rotating views, and querying information about specific locations.

[0122] The drone modeling and inspection system integrates multiple data sources such as lidar point cloud, vector model, oblique image model, etc. It collects data of components along the road, builds a three-dimensional radar point cloud model, performs denoising and post-processing, analyzes the application of the model in the digital twin, and extracts the asset data in the model to achieve prediction, maintenance and inspection arrangements.

[0123] Finally, it should be noted that the above embodiments are only used to illustrate the technical method of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that they can still modify or replace the technical method of the present invention with equivalents, and these modifications or equivalent replacements cannot cause the modified technical method to deviate from the spirit and scope of the technical method of the present invention.

Claims

1. A digital asset management method based on drone point cloud model, characterized in that: The following steps are involved: S1. Set up the drone collection route, use the drone equipped with laser radar and high-definition camera to obtain radar point cloud data and oblique image data of the target road section; S2, based on radar point cloud data and oblique image data, combined with drone position and attitude data, build a three-dimensional point cloud model library of the road section; S3, fuzzy recognition and automatic marking of road assets in the three-dimensional point cloud model of the road section are performed according to the established model library combined with the road design drawings and maintenance records, and asset-related information and data are extracted to generate a road section asset library; S4. Obtaining relevant information about the road section, evaluating the confidentiality of the road section according to the relevant information about the road section to obtain a confidentiality level of the road section, and configuring a security policy for the three-dimensional point cloud model of the road section in the road section asset library according to the confidentiality level of the road section; S5. Import the constructed 3D point cloud model into the digital twin platform, collect real-time road section information for real-time dynamic display, and predict the life of road section components to generate maintenance strategies; The specific contents of setting the drone collection route in S1 include: Obtain regional geographic information, and analyze the regional geographic information to obtain road section information and intersection information; Construct a regional route virtual network based on the road section and intersection information, and mark the intersections on the regional route virtual network to obtain intersection nodes; The number of intersection nodes of the road section is counted and sorted in descending order to obtain the road section order, and the road section with the first road section order is the main road section; Conduct flight simulation with one end of the main route as the starting point and the other end as the end point, and record the flight time, simulated route coverage length, and number of simulated nodes of several flight simulation routes; According to the length of the simulated routes, the routes of the flight simulations are sorted in descending order to obtain the order of the flight simulation routes; Filtering the flight simulation route ranked first in the flight simulation route sequence to obtain an initial simulation route group; The first weight coefficient and the second weight coefficient are respectively configured for the time of the route and the number of nodes passed by the simulation to obtain the route evaluation value of the initial simulation route group; The route evaluation values ​​are sorted in descending order, and the initial simulation route with the highest route evaluation value is selected as the drone collection route; Based on radar point cloud data and oblique image data, the specific contents of the road section 3D point cloud model library built in combination with drone position and attitude data also include: Real-time monitoring of intersection information while using drones equipped with laser radar and high-definition cameras to obtain radar point cloud data and oblique image data of target sections; A threshold for newly generated intersections is set according to the update requirements of the 3D point cloud model library; The number of newly generated intersections is counted. If the number of newly generated intersections exceeds the threshold of newly generated intersections, the intersection nodes are updated to generate an updated drone collection route. Obtain relevant information about the road section, evaluate the confidentiality of the road section based on the relevant information to obtain the confidentiality level of the road section, and configure the security strategy for the three-dimensional point cloud model of the road section in the road section asset library according to the confidentiality level of the road section. The specific contents are as follows: Analyze the relevant information of the road section to obtain the geographical location of the road section, the frequency of use of the road section, and the surrounding environment of the road section; A road section importance evaluation table and a road section sensitivity evaluation table are preset, and the road section is evaluated according to the road section importance evaluation table and the road section sensitivity value evaluation table to obtain an importance evaluation value and a road section sensitivity value; The confidentiality weight coefficient is configured for the importance evaluation value and the road section sensitivity value to obtain the road section confidentiality value; If the road section confidentiality value is lower than the road section confidentiality value threshold, the road section is defined as a public road section; A matching table of road section confidentiality value and key digits is preset; If the road section confidentiality value is higher than the road section confidentiality value threshold, the number of secret key bits is configured according to the road section confidentiality value; The secret key is scrambled and reorganized according to the number of secret key bits to obtain a security key and then generate a security policy.

2. The digital asset management method based on drone point cloud model according to claim 1 is characterized in that: The expression of the route evaluation value of the initial simulation route group is: ； in, is the route evaluation value of the initial simulation route group, is the first weight coefficient, is the second weight coefficient, is the time of the route, is the number of nodes passed by the simulation.

3. The digital asset management method based on drone point cloud model according to claim 1 is characterized in that: Based on radar point cloud data and oblique image data, combined with drone position and attitude data, the specific contents of constructing a three-dimensional point cloud model of a road section include: Preprocess the radar point cloud data and oblique image data respectively; Taking time as the reference axis, divide time into several equal time periods, and match the geographical information of the corresponding time periods to generate the corresponding geographical coordinate system; Register the radar point cloud data and oblique image data into the geographic coordinate system to generate a geographic information data set; Extracting feature data of geographic information data groups of different time periods to obtain road section features, and dividing time periods into feature groups of different time periods according to the road section features; Combined with the acquisition time, weather influence and road section characteristics, the weight allocation strategy is configured for the radar point cloud data, vector data and oblique image data of feature groups in different time periods; The radar point cloud data and the oblique image data are fused according to the weight distribution strategy to obtain fused data; The position and attitude data of the UAV are used to spatially register the fused data and construct a three-dimensional point cloud model.

4. The digital asset management method based on drone point cloud model according to claim 3 is characterized in that: Import the constructed 3D point cloud model into the digital twin platform, collect real-time road section information for real-time dynamic display, and predict the life of road section components to generate maintenance strategies. The specific contents are as follows: Import the constructed 3D point cloud model into the digital twin platform to ensure that the model’s geometry, texture, and attribute information are complete and correct; Associating 3D point cloud models with related design drawings and maintenance record static data; The associated real-time monitoring data is dynamically displayed in real time on the digital twin platform; The damage of road components in future time periods is predicted based on the monitoring data, and the maintenance time and frequency are set according to the prediction results to generate a maintenance strategy.

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