CN116448080A - Unmanned aerial vehicle-based oblique photography-assisted earth excavation construction method - Google Patents

Unmanned aerial vehicle-based oblique photography-assisted earth excavation construction method Download PDF

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CN116448080A
CN116448080A CN202310717761.8A CN202310717761A CN116448080A CN 116448080 A CN116448080 A CN 116448080A CN 202310717761 A CN202310717761 A CN 202310717761A CN 116448080 A CN116448080 A CN 116448080A
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model
unmanned aerial
aerial vehicle
data
oblique photography
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CN116448080B (en
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刘妹
徐岩
孙跃文
董帅
李伟
秦永彪
霍晓龙
李萌
张鑫
王韵迪
赵芳
刘红伍
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Nanjing Xinbo Technology Co ltd
Xi'an Jiu'an Technology Co ltd
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Nanjing Xinbo Technology Co ltd
Xi'an Jiu'an Technology Co ltd
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
    • G01C11/00—Photogrammetry or videogrammetry, e.g. stereogrammetry; Photographic surveying
    • G01C11/04—Interpretation of pictures
    • G01C11/30—Interpretation of pictures by triangulation
    • G01C11/34—Aerial triangulation
    • E—FIXED CONSTRUCTIONS
    • E02—HYDRAULIC ENGINEERING; FOUNDATIONS; SOIL SHIFTING
    • E02D—FOUNDATIONS; EXCAVATIONS; EMBANKMENTS; UNDERGROUND OR UNDERWATER STRUCTURES
    • E02D1/00—Investigation of foundation soil in situ
    • E02D1/02—Investigation of foundation soil in situ before construction work
    • E—FIXED CONSTRUCTIONS
    • E02—HYDRAULIC ENGINEERING; FOUNDATIONS; SOIL SHIFTING
    • E02D—FOUNDATIONS; EXCAVATIONS; EMBANKMENTS; UNDERGROUND OR UNDERWATER STRUCTURES
    • E02D17/00—Excavations; Bordering of excavations; Making embankments
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
    • G01C11/00—Photogrammetry or videogrammetry, e.g. stereogrammetry; Photographic surveying
    • G01C11/04—Interpretation of pictures
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
    • G01C7/00—Tracing profiles
    • G01C7/02—Tracing profiles of land surfaces
    • G01C7/04—Tracing profiles of land surfaces involving a vehicle which moves along the profile to be traced
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S17/00—Systems using the reflection or reradiation of electromagnetic waves other than radio waves, e.g. lidar systems
    • G01S17/86—Combinations of lidar systems with systems other than lidar, radar or sonar, e.g. with direction finders
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S17/00—Systems using the reflection or reradiation of electromagnetic waves other than radio waves, e.g. lidar systems
    • G01S17/88—Lidar systems specially adapted for specific applications
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S7/00—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00
    • G01S7/48—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S17/00
    • G01S7/4802—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S17/00 using analysis of echo signal for target characterisation; Target signature; Target cross-section
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/10—Complex mathematical operations
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
    • G06Q50/08—Construction
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T17/00—Three dimensional [3D] modelling, e.g. data description of 3D objects
    • G06T17/05—Geographic models
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V20/00—Scenes; Scene-specific elements
    • G06V20/10—Terrestrial scenes
    • G06V20/17—Terrestrial scenes taken from planes or by drones
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N23/00—Cameras or camera modules comprising electronic image sensors; Control thereof
    • H04N23/60—Control of cameras or camera modules
    • H04N23/695—Control of camera direction for changing a field of view, e.g. pan, tilt or based on tracking of objects
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B64—AIRCRAFT; AVIATION; COSMONAUTICS
    • B64U—UNMANNED AERIAL VEHICLES [UAV]; EQUIPMENT THEREFOR
    • B64U2101/00—UAVs specially adapted for particular uses or applications
    • B64U2101/30—UAVs specially adapted for particular uses or applications for imaging, photography or videography
    • B64U2101/31—UAVs specially adapted for particular uses or applications for imaging, photography or videography for surveillance
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B64—AIRCRAFT; AVIATION; COSMONAUTICS
    • B64U—UNMANNED AERIAL VEHICLES [UAV]; EQUIPMENT THEREFOR
    • B64U2201/00—UAVs characterised by their flight controls
    • B64U2201/10—UAVs characterised by their flight controls autonomous, i.e. by navigating independently from ground or air stations, e.g. by using inertial navigation systems [INS]
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
    • Y02T10/00—Road transport of goods or passengers
    • Y02T10/10—Internal combustion engine [ICE] based vehicles
    • Y02T10/40—Engine management systems

Abstract

The invention discloses a method for assisting earth excavation construction based on unmanned aerial vehicle oblique photography, which comprises the following steps: s1, planning and distributing a preemptive unmanned aerial vehicle according to an earthwork project; s2, dispatching a pioneer unmanned aerial vehicle to perform full-area oblique photography and laser radar positioning; s3, performing three-dimensional modeling by using oblique photography data and radar positioning data, performing recognition analysis on the whole-area scene and constructing a digital twin model; s4, performing construction simulation by using a digital twin model; s5, distributing engineering construction tasks according to an actual construction scheme; s6, setting a patrol period, and dispatching a patrol unmanned aerial vehicle to periodically monitor an actual construction site; s7, acquiring shooting monitoring data acquired by the inspection unmanned aerial vehicle in real time. According to the invention, by setting the whole-area cruising task for the earthwork project and combining oblique photography with laser radar positioning, high-precision three-dimensional scene data acquisition and real-time visual presentation of the earthwork site are realized.

Description

Unmanned aerial vehicle-based oblique photography-assisted earth excavation construction method
Technical Field
The invention relates to the technical field of unmanned aerial vehicle oblique photography, in particular to a method for assisting earth excavation construction based on unmanned aerial vehicle oblique photography.
Background
Earth excavation refers to the process of excavating, exploiting or excavating materials such as soil, rock, etc. from the ground or underground by mechanical equipment and tools. It is generally used in the fields of construction, engineering, road construction, mine, etc. The main equipment for earth excavation comprises an excavator, a bulldozer, a loader, a forklift and the like. Prior to earth excavation, a series of preparation work is required, including investigation of the site, planning of the excavation plan, determining the depth of excavation, preparing materials and equipment, etc. Meanwhile, the safety problem is considered, and necessary safety measures are adopted to ensure the life safety of workers.
The traditional earth excavation method mainly relies on a measurer to conduct manual measurement, the operation difficulty is high, the efficiency is low, and the accuracy of a measurement result is difficult to guarantee. Meanwhile, because manual measurement is difficult to meet the requirements of complete coverage and delicateness, a large number of unknown areas exist in earthwork, hidden danger is possibly caused, and engineering construction risks are increased. Therefore, it is necessary to accurately measure and model earthworks by means of modern technology to improve work efficiency and accuracy.
Oblique photography refers to the effect of tilting a camera at an angle during photography, thereby tilting the subject or background in the photograph. Such photographic skills are often used to create a strong visual impact and can be impressive. The unmanned aerial vehicle oblique photography is oblique photography performed by applying unmanned aerial vehicle technology, and an oblique photography image is generated by taking a ground image in the flight process by using an oblique photography instrument carried on the unmanned aerial vehicle. Compared with the traditional oblique photography, the unmanned aerial vehicle oblique photography has higher flexibility and precision, and can better shoot areas with complex or difficult-to-reach terrains, such as mountain areas, valleys, building groups and the like. Meanwhile, as the unmanned aerial vehicle can fly stably, the oblique photographic instrument can accurately position and control the oblique angle, and the generated image is more accurate and clear.
The unmanned aerial vehicle oblique photography technology can acquire a large amount of high-precision data in a short time, and is widely applied to earthworks. The existing unmanned aerial vehicle oblique photography technology can meet the requirements of earthworks, but has some improvement defects in practical application.
For example, 1. Positioning accuracy is not high: the camera pose in oblique photography needs to be measured by sensors such as GPS/INS, and the sensors have positioning errors, so that the precision of the camera pose is not high, and the model precision is affected. 2. Insufficient data density: the density of point cloud data acquired by oblique photography is low, so that the morphology and detail of objects on the ground cannot be completely reflected, and the subsequent modeling and analysis are affected. 3. The ground obstacle cannot be effectively identified: oblique photography can only acquire image information on the ground, and effective identification and measurement cannot be performed on obstacles (such as stones, pipes, etc.) on the ground.
For the problems in the related art, no effective solution has been proposed at present.
Disclosure of Invention
Aiming at the defects of the prior art, the invention provides a method for assisting earth excavation construction based on unmanned aerial vehicle oblique photography, which has the advantages of realizing efficient and high-precision earth engineering site information acquisition, construction simulation and inspection monitoring by applying unmanned aerial vehicle oblique photography, laser radar positioning, digital twin model and other technologies in the earth engineering field, improving the construction efficiency and quality of earth engineering, reducing the safety risk and cost expense of the earth engineering, and further solving the problems of insufficient precision and unclear obstacle identification in the construction field of the existing oblique photography technology.
In order to realize the advantages of high-efficiency and high-precision information acquisition, construction simulation and inspection monitoring of the earthwork site, improvement of the construction efficiency and quality of the earthwork and reduction of the safety risk and cost expenditure of the earthwork in the earthwork field by using unmanned aerial vehicle oblique photography, laser radar positioning, digital twin model and other technologies, the invention adopts the following specific technical scheme:
the method for assisting earth excavation construction based on unmanned aerial vehicle oblique photography comprises the following steps:
s1, planning and distributing a pre-dispatching unmanned aerial vehicle according to an earthwork project, and setting a whole-area cruising task;
s2, dispatching a pioneer unmanned aerial vehicle to perform full-area oblique photography and laser radar positioning;
s3, performing three-dimensional modeling by using oblique photography data and radar positioning data, performing recognition analysis on a full-area scene, constructing a digital twin model, and performing model accuracy verification and reconstruction optimization;
s4, performing construction simulation by using the digital twin model to generate an actual construction scheme;
s5, distributing engineering construction tasks according to an actual construction scheme, and carrying out earth excavation construction;
s6, setting a patrol period, and dispatching a patrol unmanned aerial vehicle to periodically monitor an actual construction site;
s7, acquiring shooting monitoring data acquired by the inspection unmanned aerial vehicle in real time, synchronizing the shooting monitoring data to a digital twin model, and analyzing and evaluating the excavation progress and the construction quality.
Further, the dispatching of the pioneer unmanned aerial vehicle for full-area oblique photography and laser radar positioning comprises the following steps:
s21, selecting any area in the whole area of the earthwork as a correction area, and setting eight model correction points forming a cube structure in the correction area;
s22, setting the lowest standard of the ground resolution obtained by measurement, calculating the flying height of the preemptive unmanned aerial vehicle according to the focal length of a camera lens carried by the preemptive unmanned aerial vehicle, and planning a whole-area cruising route;
and S23, dispatching the pre-dispatching unmanned aerial vehicle to perform triangulation according to the whole-area cruising route, and realizing whole-area oblique photography and laser radar positioning.
Further, the method for performing three-dimensional modeling by using oblique photography data and radar positioning data, performing recognition analysis on a full-area scene, constructing a digital twin model, and performing model accuracy verification and reconstruction optimization comprises the following steps:
s31, denoising preprocessing is carried out on oblique photographic data and radar positioning data respectively;
s32, classifying original point cloud data in the radar positioning data by using a point cloud segmentation algorithm;
s33, constructing a three-dimensional earth model by utilizing the ground point cloud data classified by the original point cloud data and combining the dense point cloud data generated by the oblique photography data;
s34, identifying and marking obstacles appearing in oblique photographic data by utilizing artificial intelligence, acquiring physical attribute parameters of different types of obstacles, and positioning in a three-dimensional earthwork model;
s35, constructing a three-dimensional scene model by using obstacle point cloud data classified by original point cloud data, modifying by using oblique photography data, and integrating the three-dimensional scene model with a three-dimensional earth model to obtain a digital twin model;
s36, performing accuracy verification on the digital twin model by using the model correction points, and performing reconstruction optimization on the digital twin model according to the verification result.
Further, the original point cloud data comprise ground point cloud data, obstacle point cloud data and environment point cloud data, and the obstacles comprise buildings, vegetation, roads, rocks and water bodies;
the eight model correction points respectively form an upper horizontal plane and a lower horizontal plane, the two model correction points form a group, and the two model correction points in the same group keep the same horizontal coordinates and different heights.
Further, constructing a three-dimensional earth model by using the ground point cloud data classified by the original point cloud data and the dense point cloud data generated by combining the oblique photography data comprises the following steps:
s331, filtering original point cloud data, removing data except ground point cloud data, and filling a data hole in a data interpolation mode;
s332, carrying out homonymous point automatic matching and free net beam adjustment on oblique photographic data by adopting a pyramid matching strategy to obtain high-resolution dense point cloud data;
s333, carrying out block calculation on the dense point cloud data, converting the dense point cloud data into an irregular triangular net, and constructing an irregular triangular net model by utilizing the irregular triangular net;
s334, performing texture mapping on the irregular triangular mesh model by using oblique photographic data;
and S335, fusing the filled ground point cloud data with the irregular triangular net model to obtain a complete three-dimensional earthwork model for earthwork.
Further, constructing a three-dimensional scene model by using obstacle point cloud data classified by original point cloud data, modifying by using oblique photographing data, and integrating the obstacle point cloud data with a three-dimensional earth model to obtain a digital twin model, wherein the method comprises the following steps of:
s351, positioning various different types of barriers in a three-dimensional earthwork model;
s352, extracting key feature points of each obstacle, calculating n nearest neighbor points of each key feature point, fitting a local paraboloid, calculating fitting coefficients of each paraboloid by using a least square method, and calculating local curvature of the corresponding key feature point by using the fitting coefficients;
s353, automatically capturing key feature points with local curvature larger than a preset curvature threshold value by using a computer, and determining the positions of the key feature points;
s354, traversing a model database according to the type of each obstacle and the corresponding key characteristic points of the obstacle, constructing a three-dimensional scene model of each obstacle in a template matching mode, and matching corresponding physical attribute parameters according to the type of the obstacle;
s355, integrating all barriers and physical attribute parameters thereof in the three-dimensional earthwork model, automatically mapping textures of the three-dimensional scene model by using oblique photographic data, and finally constructing a digital twin model of the whole region of the internal earthwork.
Further, the accuracy of the digital twin model is verified by using the model correction points, and the reconstruction optimization of the digital twin model according to the verification result comprises the following steps:
s361, determining a virtual correction point corresponding to each model correction point in the digital twin model, determining the position of the virtual correction point and calculating the digital physical quantity;
s362, obtaining actual measurement physical quantity of a cube structure formed by model correction points;
s363, calculating the error quantity between the digital physical quantity and the actually measured physical quantity by using an error analysis mathematical model to realize the precision verification of the digital twin model;
s364, selecting a virtual correction point and calculating the offset between the model direction vector and the actual measurement direction vector by the corresponding model correction point;
s365, correcting, adjusting and reconstructing and optimizing the digital twin model according to the accuracy verification result and the offset result.
Further, calculating the error amount between the digital physical quantity and the actually measured physical quantity by using the error analysis mathematical model, and realizing the precision verification of the digital twin model comprises the following steps:
s3631, calculating the volume of a cube structure formed by the model correction points and the virtual correction points, and the height difference of the two model correction points in the same group;
s3632, calculating the error amount between the model correction point and the virtual correction point by using an error analysis mathematical model, wherein the calculation formula of the error analysis mathematical model is as follows:
；
in the method, in the process of the invention,Qrepresents the amount of error that occurs,T R representing the volume of a cube structure formed by eight model correction points in the measured physical quantity,T S representing the volume of a cube structure of eight virtual correction points in the digital physical quantity,a 1 、a 2 、a 3 respectively, the weight values are represented by the weight values,nrepresenting the number of groups of eight virtual correction points or eight model correction points divisions,n=4，irepresenting the number of groups in which each two virtual correction points or model correction points are located,R i represent the firstiThe height difference between the two model correction points in the set,S i represent the firstiThe difference in height between the two virtual correction points in the group,R 0 representing the average height difference between the four model correction points.
Further, selecting the virtual correction point and calculating the offset between the model direction vector and the actual measurement direction vector by the corresponding model correction point comprises the following steps:
s3641, randomly selecting a virtual correction point and three adjacent virtual correction points thereof, and determining a corresponding model correction point and three adjacent model correction points thereof;
s3642, calculating vectors of the selected virtual correction point and the model correction point in three directions, and calculating offset values of the vectors of the corresponding directions between the two directions respectively, wherein an offset value calculation formula is as follows:
；
in the method, in the process of the invention,a vector offset value between the direction vector representing the virtual correction point and the direction vector of the model correction point,H j a direction vector representing the virtual correction point,H l direction vector representing model correction pointx j ，y j ) Vector value representing virtual correction point #x l ，y l ) Vector value representing model correction point, +.>Representing the vector difference.
S3643, respectively calculating vector offset values of three direction vectors, and taking the calculation result as three offset degrees of a digital twin model, wherein the calculation formula is as follows:
；
where U represents the degree of offset.
Further, setting a patrol period, and dispatching a patrol unmanned aerial vehicle to periodically monitor an actual construction site, wherein the method comprises the following steps:
s61, setting a patrol period of the patrol unmanned aerial vehicle according to the construction period, and setting a patrol route and a patrol range of the patrol unmanned aerial vehicle according to real-time construction progress;
s62, dispatching a patrol unmanned aerial vehicle to carry out patrol oblique photography on the construction site;
s63, capturing and positioning vehicle equipment and constructor positions in the construction site by using the inspection unmanned aerial vehicle, and measuring a foundation pit excavated in the construction site.
Compared with the prior art, the invention provides a method for assisting earth excavation construction based on unmanned aerial vehicle oblique photography, which has the following beneficial effects:
(1) By setting a whole-area cruising task for an earthwork project, combining oblique photography with laser radar positioning, high-precision three-dimensional scene data acquisition can be realized, high-precision acquisition is carried out on multi-type obstacle information such as terrains, landforms, buildings, vegetation and the like in the earthwork area, and then a digital twin model is quickly constructed by utilizing high-precision three-dimensional data, so that real-time visual presentation of an earthwork site is realized, and the construction efficiency and construction quality of earthwork are effectively improved.
(2) By automatically identifying multiple types of obstacles, constructing a three-dimensional earth model and a three-dimensional scene model formed by the obstacles, and finally integrating to form a digital twin model, the multiple types of obstacles can be rapidly and accurately positioned and identified in the digital twin model, so that the safety and efficiency of earth works are improved; the comprehensive construction simulation can be realized, and the construction scheme is optimized and adjusted through the digital twin model, so that the risk and cost of the earthwork are reduced to the greatest extent; in addition, the real-time monitoring and the accurate control of the progress and quality of the earthwork are realized through the digital twin model, so that the problems are found and solved in time, and the smooth proceeding of the earthwork is ensured.
(3) The precision verification of the digital twin model is realized by calculating the error amount between the digital twin model and the actually measured physical quantity by utilizing the mathematical model, the precision and the reliability of the digital twin model can be effectively improved by reconstruction optimization, and the construction scheme can be optimized and improved according to the precision verification result of the digital twin model so as to ensure the construction precision and quality.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings that are needed in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a flowchart of a method of assisting earth excavation construction based on unmanned aerial vehicle oblique photography according to an embodiment of the present invention.
Detailed Description
According to the embodiment of the invention, a method for assisting earth excavation construction based on unmanned aerial vehicle oblique photography is provided.
The invention will be further described with reference to the accompanying drawings and detailed description, as shown in fig. 1, a method for assisting earth excavation construction based on unmanned aerial vehicle oblique photography according to an embodiment of the invention, the method comprising the following steps:
s1, planning and distributing a pre-dispatching unmanned aerial vehicle according to an earthwork project, and setting a whole-area cruising task.
In the earthwork project, the unmanned aerial vehicle can be sent to set the whole-area cruising task, so that comprehensive and omnibearing monitoring and investigation can be carried out on the whole engineering project.
According to the planning of the earthwork project, the unmanned aerial vehicle is allocated and sent firstly, namely before the earthwork project starts, the unmanned aerial vehicle is required to be deployed and allocated to a corresponding area for cruising tasks. The first step of implementation of the earthwork project is to carry out full-area cruising on a construction site through the unmanned aerial vehicle, obtain basic data of the site and provide a basis for subsequent construction and data analysis.
When the pre-dispatching unmanned aerial vehicle is distributed, the model numbers and the number of the unmanned aerial vehicles are determined by considering the topography of projects, the size of areas and the requirements of construction tasks, and a reasonable cruising plan is formulated so as to complete the cruising tasks of the whole area in the shortest time. Meanwhile, corresponding safety measures are required to be formulated, so that the unmanned aerial vehicle can safely carry out the cruising task, and influence on surrounding environment and personnel is reduced.
S2, dispatching a pre-sent unmanned aerial vehicle to perform full-area oblique photography and laser radar positioning, wherein the method comprises the following steps of:
s21, selecting any area in the whole earthwork area as a correction area, and setting eight model correction points forming a cube structure in the correction area.
The eight model correction points respectively form an upper horizontal plane and a lower horizontal plane, the two model correction points form a group, and the two model correction points in the same group keep the same horizontal coordinates and different heights.
S22, setting the lowest standard of the ground resolution obtained by measurement, calculating the flying height of the preemptive unmanned aerial vehicle according to the focal length of the camera lens carried by the preemptive unmanned aerial vehicle, and planning a whole-area cruising route.
The minimum standard of the ground resolution can be determined according to engineering requirements and measurement accuracy requirements, and in general, the higher the minimum standard of the ground resolution should be, the better. Next, the flying height of the preemptive unmanned aerial vehicle is calculated by using the focal length of the camera lens carried by the preemptive unmanned aerial vehicle, so as to ensure that the set ground resolution standard can be met.
In practical application, the horizontal view angle and the vertical view angle of the camera can be calculated according to parameters such as the pixel size, the sensor size, the focal length and the like of the photographing lens carried by the pre-sent unmanned aerial vehicle. Then, the flying height of the pre-sent unmanned aerial vehicle can be calculated according to the triangulation principle by combining the set ground resolution standard. According to the flying height of the pre-sent unmanned aerial vehicle, the full-area cruising route can be further planned, so that the remote sensing measurement and data acquisition of the full-area unmanned aerial vehicle are realized.
And S23, dispatching the pre-dispatching unmanned aerial vehicle to perform triangulation according to the whole-area cruising route, and realizing whole-area oblique photography and laser radar positioning.
In unmanned aerial vehicle tilt photography and lidar positioning, triangulation is one of the commonly used measurement methods. By establishing triangular grids in the three-dimensional space and measuring the side lengths and angles of the triangles, the information such as the distance, the height and the angle between the unmanned aerial vehicle and the target site can be calculated, and therefore accurate measurement of oblique photography and laser radar positioning is achieved.
And S3, performing three-dimensional modeling by using oblique photography data and radar positioning data, performing recognition analysis on the full-area scene, constructing a digital twin model, and performing model accuracy verification and reconstruction optimization.
By means of three-dimensional modeling through oblique photography data and laser radar positioning data, a high-precision digital model can be constructed, wherein the digital model comprises landforms, buildings, roads, vegetation and the like, and obstacles and the like possibly occurring in engineering construction. By identifying and analyzing various elements in the digital model, the geographic environment and the structural characteristics of the engineering area can be better known, the construction scheme can be better planned, and the possible problems in the construction process can be predicted.
The digital twin model is a bidirectional corresponding relation between an actual physical environment and a digital environment, can simulate the behavior and response of the actual environment in the digital environment, simulate the engineering construction scheme, and forecast possible problems and difficulties in the construction process. Meanwhile, the digital twin model can also be monitored in real time in the construction process, and construction quality and progress are estimated and optimized.
The method comprises the following steps of performing three-dimensional modeling by using oblique photography data and radar positioning data, performing recognition analysis on a full-area scene, constructing a digital twin model, and performing model accuracy verification and reconstruction optimization:
s31, denoising preprocessing is carried out on the oblique photographing data and the radar positioning data respectively.
Specifically, before three-dimensional modeling is performed, denoising pretreatment is performed on oblique photographic data and laser radar positioning data, so that data quality can be improved, and interference of noise on modeling results is avoided.
The specific method of denoising preprocessing can be selected according to the characteristics of the data, for example, for oblique photographic data, image processing software can be used for performing noise filtering, sharpening, denoising and other processes; for laser radar positioning data, a filtering algorithm such as Gaussian filtering, median filtering and the like can be used for denoising, and meanwhile, parameter adjustment is carried out according to data conditions so as to achieve an optimal effect.
S32, classifying original point cloud data in the radar positioning data by using a point cloud segmentation algorithm.
The original point cloud data comprises ground point cloud data, obstacle point cloud data and environment point cloud data, and the obstacle comprises buildings, vegetation, roads, rocks and water bodies.
S33, constructing a three-dimensional earthwork model by utilizing ground point cloud data classified by original point cloud data and combining dense point cloud data generated by oblique photography data, wherein the three-dimensional earthwork model comprises the following steps of:
s331, filtering the original point cloud data, removing data except the ground point cloud data, and filling the data hole by utilizing a data interpolation mode.
And S332, carrying out homonymous point automatic matching and free net beam adjustment on the oblique photographic data by adopting a pyramid matching strategy to obtain high-resolution dense point cloud data.
S333, carrying out block calculation on the dense point cloud data, converting the dense point cloud data into an irregular triangular net, and constructing an irregular triangular net model by utilizing the irregular triangular net.
S334, performing texture mapping on the irregular triangular mesh model by using oblique photographic data.
The irregular triangular mesh model is subjected to texture mapping by using oblique photographic data, and real color and texture information shot in the oblique photographic data can be mapped to the surface of the three-dimensional model, so that the three-dimensional model is more real and lifelike.
Color and texture information in oblique photography data is mapped to the surface of the triangle mesh model. This process is generally divided into two steps:
a. generating a texture image: using the photograph and camera parameters in the oblique photography data, a color value for each pixel point may be generated. These color values are integrated into a texture image as input to the texture map.
b. Texture mapping is performed: and mapping the color value of the corresponding position in the texture image to the surface of the triangle according to the texture coordinate of each triangle, and generating a three-dimensional model with real textures.
And S335, fusing the filled ground point cloud data with the irregular triangular net model to obtain a complete three-dimensional earthwork model for earthwork.
S34, identifying and marking obstacles appearing in the oblique photographic data by utilizing artificial intelligence, acquiring physical attribute parameters of different types of obstacles, and positioning in the three-dimensional earthwork model.
The physical attribute parameters of the obstacles need to be collected in the construction of the digital twin model, and for the common obstacles such as buildings, vegetation, roads, rocks, water bodies and the like, the following attribute data generally need to be collected:
building: building height, facade material, roof type, building area, building volume, etc.
Vegetation: vegetation type, height, coverage area, leaf area index, tree species information, etc.
And (3) road: road width, road surface material, road surface gradient, road surface quality, road length, etc.
Rock: the type, size, morphology, color, texture, distribution density, etc. of the rock.
Water body: the type, depth, quality, area, level change, etc. of the water body.
The method for acquiring the physical attribute parameters comprises remote sensing image interpretation, site survey and other geographic information acquisition technologies. By collecting and analyzing the attribute data, a digital twin model can be constructed more accurately, and finer support is provided for engineering planning, design, construction and management.
S35, constructing a three-dimensional scene model by using obstacle point cloud data classified by original point cloud data, modifying by using oblique photographic data, and integrating the three-dimensional scene model with a three-dimensional earth model to obtain a digital twin model, wherein the method comprises the following steps of:
s351, positioning the different types of obstacles in the three-dimensional earthwork model.
S352, extracting key feature points of each obstacle, calculating n nearest neighbor points of each key feature point, fitting a local paraboloid, calculating fitting coefficients of each paraboloid by using a least square method, and calculating local curvature of the corresponding key feature point by using the fitting coefficients.
；
；
In the middle of (a)x，y) Represents the horizontal coordinates of the key feature points,P（x，y) An expression representing a partial paraboloid is presented,A、B、C、D、E、Feach representing a respective fitting coefficient of a local paraboloid,Krepresenting the local curvature of the key feature points.
S353, automatically capturing key feature points with local curvature larger than a preset curvature threshold by using a computer, and determining the positions of the key feature points.
S354, traversing a model database according to the type of each obstacle and the corresponding key characteristic points, constructing a three-dimensional scene model of each obstacle in a template matching mode, and matching corresponding physical attribute parameters according to the type of the obstacle.
S355, integrating all barriers and physical attribute parameters thereof in the three-dimensional earthwork model, automatically mapping textures of the three-dimensional scene model by using oblique photographic data, and finally constructing a digital twin model of the whole region of the internal earthwork.
S36, performing accuracy verification on the digital twin model by using a model correction point, and performing reconstruction optimization on the digital twin model according to a verification result, wherein the method comprises the following steps of:
s361, determining a virtual correction point corresponding to each model correction point in the digital twin model, determining the position of the virtual correction point and calculating the digital physical quantity.
S362, obtaining the actual measurement physical quantity of the cube structure formed by the model correction points.
S363, calculating the error quantity between the digital physical quantity and the actually measured physical quantity by using an error analysis mathematical model to realize the precision verification of the digital twin model, and comprises the following steps:
s3631, calculating the volume of the cube structure formed by the model correction points and the virtual correction points, and the height difference of the two model correction points in the same group.
S3632, calculating the error amount between the model correction point and the virtual correction point by using an error analysis mathematical model, wherein the calculation formula of the error analysis mathematical model is as follows:
；
in the method, in the process of the invention,Qrepresents the amount of error that occurs,T R representing the volume of a cube structure formed by eight model correction points in the measured physical quantity,T S representing the volume of a cube structure of eight virtual correction points in the digital physical quantity,a 1 、a 2 、a 3 respectively, the weight values are represented by the weight values,nrepresenting the number of groups of eight virtual correction points or eight model correction points divisions,n=4，irepresenting the number of groups in which each two virtual correction points or model correction points are located,R i represent the firstiThe height difference between the two model correction points in the set,S i representation ofFirst, theiThe difference in height between the two virtual correction points in the group,R 0 representing the average height difference between the four model correction points.
S364, selecting a virtual correction point and a corresponding model correction point to calculate the offset between the model direction vector and the actual measurement direction vector, comprising the following steps:
s3641, randomly selecting one virtual correction point and three adjacent virtual correction points, and determining a corresponding model correction point and three adjacent model correction points.
S3642, calculating vectors of the selected virtual correction point and the model correction point in three directions, and calculating offset values of the vectors of the corresponding directions between the two directions respectively, wherein an offset value calculation formula is as follows:
；
in the method, in the process of the invention,a vector offset value between the direction vector representing the virtual correction point and the direction vector of the model correction point,H j a direction vector representing the virtual correction point,H l direction vector representing model correction pointx j ，y j ) Vector value representing virtual correction point #x l ，y l ) Vector value representing model correction point, +.>Representing the vector difference.
S3643, respectively calculating vector offset values of three direction vectors, and taking the calculation result as three offset degrees of a digital twin model, wherein the calculation formula is as follows:
；
where U represents the degree of offset.
S365, correcting, adjusting and reconstructing and optimizing the digital twin model according to the accuracy verification result and the offset result.
And according to the accuracy verification result and the offset result, the errors and the offsets existing in the digital twin model can be determined, and the accuracy and the precision of the digital twin model are improved through correction adjustment and reconstruction optimization. Specifically, the following measures can be taken:
and correcting errors in the digital twin model according to the accuracy verification result, for example, adjusting parameters such as height, volume and the like in the three-dimensional earth model, and re-labeling the obstacle model.
The offset present in the digital twin model is adjusted, for example, the coordinate system in the model is adjusted, or the spatial position information in the model is corrected, based on the offset results.
And the accuracy and the precision of the data in the digital twin model are improved by reconstruction and optimization. For example, the data in the digital twin model may be cryptographically compressed, or a new data processing algorithm may be utilized to improve the resolution and accuracy of the model.
S4, performing construction simulation by using the digital twin model to generate an actual construction scheme.
The digital twin model can simulate a plurality of aspects such as construction progress simulation, process simulation, safety risk assessment and the like, and helps to realize optimization and control of a construction process.
In the construction simulation, risk factors in the construction process, such as movement of mechanical equipment, material handling, personnel activities and the like, can be evaluated through a digital twin model, so that possible problems are recognized in advance, and a targeted solution is made.
Based on the digital twin model, simulation of different earthwork excavation schemes can be performed, influence of the digital twin model on the field environment and the structure is evaluated, and an optimal excavation scheme is determined, so that influence on the field environment and the structure is reduced to the greatest extent, and construction efficiency and quality are improved. The digital twin model is utilized to carry out construction simulation, so that earthwork excavation processes under different conditions can be simulated, construction risks are estimated, a construction team is helped to predict possible problems, a scheme is timely adjusted, the construction risks are reduced, and construction safety is guaranteed.
And S5, distributing engineering construction tasks according to an actual construction scheme, and carrying out earth excavation construction.
S6, setting a patrol period, and dispatching a patrol unmanned aerial vehicle to periodically monitor the actual construction site.
Setting a patrol period, and dispatching a patrol unmanned aerial vehicle to periodically monitor an actual construction site, wherein the method comprises the following steps of:
s61, setting a patrol period of the patrol unmanned aerial vehicle according to the construction period, and setting a patrol route and a patrol range of the patrol unmanned aerial vehicle according to the real-time construction progress.
S62, dispatching a patrol unmanned aerial vehicle to carry out patrol oblique photography on the construction site.
S63, capturing and positioning vehicle equipment and constructor positions in the construction site by using the inspection unmanned aerial vehicle, and measuring a foundation pit excavated in the construction site.
S7, acquiring shooting monitoring data acquired by the inspection unmanned aerial vehicle in real time, synchronizing the shooting monitoring data to a digital twin model, and analyzing and evaluating the excavation progress and the construction quality.
Acquiring photographic monitoring data acquired by the inspection unmanned aerial vehicle in real time, and processing and analyzing the data through an image processing technology comprises the following steps: and extracting an earthwork area in the digital twin model by using an image segmentation technology, and comparing pixel values of the earthwork area in actual photographic data so as to evaluate the earthwork excavation progress.
And detecting the defect conditions of cracks, collapse and the like in an earthwork area in the digital twin model by utilizing a computer vision technology, and comparing the defect conditions in actual photographic data so as to evaluate the construction quality.
By comparing and analyzing the digital twin model and the actual monitoring data, the problems in the earth excavation construction can be found in time, and the problems can be regulated and optimized, so that the construction efficiency and quality are improved.
In summary, by means of the technical scheme, through setting the whole-area cruising task for the earthwork project, the combination of oblique photography and laser radar positioning can realize high-precision three-dimensional scene data acquisition, high-precision acquisition of various obstacle information such as terrains, landforms, buildings, vegetation and the like in the earthwork area, and then a digital twin model is quickly constructed by utilizing the high-precision three-dimensional data, so that real-time visual presentation of an earthwork site is realized, and the construction efficiency and the construction quality of earthwork are effectively improved. By automatically identifying multiple types of obstacles, constructing a three-dimensional earth model and a three-dimensional scene model formed by the obstacles, and finally integrating to form a digital twin model, the multiple types of obstacles can be rapidly and accurately positioned and identified in the digital twin model, so that the safety and efficiency of earth works are improved; the comprehensive construction simulation can be realized, and the construction scheme is optimized and adjusted through the digital twin model, so that the risk and cost of the earthwork are reduced to the greatest extent; in addition, the real-time monitoring and the accurate control of the progress and quality of the earthwork are realized through the digital twin model, so that the problems are found and solved in time, and the smooth proceeding of the earthwork is ensured. The precision verification of the digital twin model is realized by calculating the error amount between the digital twin model and the actually measured physical quantity by utilizing the mathematical model, the precision and the reliability of the digital twin model can be effectively improved by reconstruction optimization, and the construction scheme can be optimized and improved according to the precision verification result of the digital twin model so as to ensure the construction precision and quality.
The foregoing description of the preferred embodiments of the invention is not intended to be limiting, but rather is intended to cover all modifications, equivalents, alternatives, and improvements that fall within the spirit and scope of the invention.

Claims (10)

1. The method for assisting earth excavation construction based on unmanned aerial vehicle oblique photography is characterized by comprising the following steps of:
s1, planning and distributing a pre-dispatching unmanned aerial vehicle according to an earthwork project, and setting a whole-area cruising task;
s2, dispatching the pre-sent unmanned aerial vehicle to perform full-area oblique photography and laser radar positioning;
s3, performing three-dimensional modeling by using oblique photography data and radar positioning data, performing recognition analysis on a full-area scene, constructing a digital twin model, and performing model accuracy verification and reconstruction optimization;
s4, performing construction simulation by using the digital twin model to generate an actual construction scheme;
s5, distributing engineering construction tasks according to the actual construction scheme, and carrying out earth excavation construction;
s6, setting a patrol period, and dispatching a patrol unmanned aerial vehicle to periodically monitor an actual construction site;
s7, acquiring photographing monitoring data acquired by the inspection unmanned aerial vehicle in real time, synchronizing to the digital twin model, and analyzing and evaluating the excavation progress and the construction quality.
2. The method for assisting earth excavation construction based on unmanned aerial vehicle oblique photography according to claim 1, wherein the dispatching the unmanned aerial vehicle for full area oblique photography and laser radar positioning comprises the following steps:
s21, selecting any area in the whole area of the earthwork as a correction area, and setting eight model correction points forming a cube structure in the correction area;
s22, setting the lowest standard of the ground resolution obtained by measurement, calculating the flying height of the preemptive unmanned aerial vehicle according to the focal length of a camera lens carried by the preemptive unmanned aerial vehicle, and planning a whole-area cruising route;
and S23, dispatching the pre-sending unmanned aerial vehicle to perform triangulation according to the whole-area cruising route, and realizing whole-area oblique photography and laser radar positioning.
3. The method for assisting earth excavation construction based on unmanned aerial vehicle oblique photography according to claim 2, wherein the three-dimensional modeling is performed by utilizing oblique photography data and radar positioning data, the recognition analysis is performed on a full-area scene, a digital twin model is constructed, and the model accuracy verification and reconstruction optimization comprises the following steps:
s31, denoising preprocessing is carried out on the oblique photography data and the radar positioning data respectively;
s32, classifying original point cloud data in the radar positioning data by using a point cloud segmentation algorithm;
s33, constructing a three-dimensional earth model by utilizing the ground point cloud data classified by the original point cloud data and combining the dense point cloud data generated by the oblique photographic data;
s34, identifying and marking obstacles appearing in the oblique photographic data by utilizing artificial intelligence, acquiring physical attribute parameters of different types of obstacles, and positioning in the three-dimensional earthwork model;
s35, constructing a three-dimensional scene model by using obstacle point cloud data classified by original point cloud data, modifying by using the oblique photography data, and integrating the oblique photography data with the three-dimensional earth model to obtain a digital twin model;
s36, performing accuracy verification on the digital twin model by using a model correction point, and performing reconstruction optimization on the digital twin model according to a verification result.
4. A method of assisting earth excavation construction based on unmanned aerial vehicle oblique photography as claimed in claim 3, wherein the raw point cloud data comprises ground point cloud data, obstacle point cloud data and environmental point cloud data, and the obstacle comprises a building, vegetation, a road, rock and a water body;
the eight model correction points respectively form an upper horizontal plane and a lower horizontal plane, the model correction points are in a group, and the two model correction points in the same group keep the same horizontal coordinates and different heights.
5. The method for assisting earth excavation construction based on unmanned aerial vehicle oblique photography according to claim 4, wherein the constructing a three-dimensional earth model by utilizing the classified ground point cloud data of the original point cloud data and combining the dense point cloud data generated by the oblique photography data comprises the following steps:
s331, filtering the original point cloud data, removing data except the ground point cloud data, and filling a data hole in a data interpolation mode;
s332, carrying out homonymy point automatic matching and free net beam adjustment on the oblique photographic data by adopting a pyramid matching strategy to obtain high-resolution dense point cloud data;
s333, carrying out block calculation on the dense point cloud data, converting the dense point cloud data into an irregular triangular net, and constructing an irregular triangular net model by utilizing the irregular triangular net;
s334, performing texture mapping on the irregular triangular mesh model by utilizing the oblique photographic data;
and S335, fusing the filled ground point cloud data with the irregular triangular network model to obtain a complete three-dimensional earthwork model for earthwork.
6. The method for assisting earth excavation construction based on unmanned aerial vehicle oblique photography according to claim 5, wherein the method for constructing a three-dimensional scene model by using obstacle point cloud data classified by original point cloud data, modifying by using the oblique photography data, and integrating the three-dimensional scene model to obtain a digital twin model comprises the following steps:
s351, positioning the barriers of different types in the three-dimensional earth model;
s352, extracting key feature points of each obstacle, calculating n nearest neighbor points of each key feature point, fitting a local paraboloid, calculating fitting coefficients of each paraboloid by using a least square method, and calculating local curvature of the corresponding key feature point by using the fitting coefficients;
s353, automatically capturing the key feature points with the local curvature larger than a preset curvature threshold by using a computer, and determining the positions of the key feature points;
s354, traversing a model database according to the type of each obstacle and the corresponding key characteristic points, constructing a three-dimensional scene model of each obstacle in a template matching mode, and matching corresponding physical attribute parameters according to the type of the obstacle;
s355, integrating all barriers and the physical attribute parameters of the barriers in the three-dimensional earthwork model, automatically mapping textures of the three-dimensional scene model by utilizing the oblique photographing data, and finally constructing a digital twin model of the whole region of the internal earthwork.
7. The method for assisting earth excavation construction based on unmanned aerial vehicle oblique photography according to claim 6, wherein the digital twin model is subjected to accuracy verification by using a model correction point, and reconstruction optimization is performed on the digital twin model according to a verification result, comprising the following steps:
s361, determining a virtual correction point corresponding to each model correction point in the digital twin model, determining the position of the virtual correction point and calculating a digital physical quantity;
s362, obtaining the actual measurement physical quantity of the cube structure formed by the model correction points;
s363, calculating the error quantity between the digital physical quantity and the actually measured physical quantity by using an error analysis mathematical model to realize the precision verification of the digital twin model;
s364, selecting the virtual correction points and the corresponding model correction points to calculate the offset between the model direction vector and the actual measurement direction vector;
s365, correcting, adjusting and reconstructing and optimizing the digital twin model according to the accuracy verification result and the offset result.
8. The method for assisting earth excavation construction based on unmanned aerial vehicle oblique photography according to claim 7, wherein the calculating the error amount between the digital physical quantity and the actually measured physical quantity by using an error analysis mathematical model, and realizing the accuracy check of the digital twin model comprises the following steps:
s3631, respectively calculating the volume of the cube structure formed by the model correction points and the virtual correction points, the height difference of the two model correction points in the same group and the height difference of the two virtual correction points in the same group;
s3632, calculating the error amount between the model correction point and the virtual correction point by using the error analysis mathematical model, wherein the calculation formula of the error analysis mathematical model is as follows:
；
in the method, in the process of the invention,Qrepresenting the error amount;
T R representing the volume of a cube structure formed by eight model correction points in the measured physical quantity;
T S representing the volume of a cube structure formed by eight virtual correction points in the digital physical quantity;
a 1 、a 2 、a 3 respectively representing weight values;
nrepresenting the number of groups of eight virtual correction points or eight model correction points divisions,n=4；
irepresenting the group number of each two virtual correction points or model correction points;
R i represent the firstiHeight difference between two model correction points in the group;
S i represent the firstiHeight difference between two virtual correction points in the group;
R 0 representing the average height difference between the four model correction points.
9. The method for assisting earth excavation construction based on unmanned aerial vehicle oblique photography according to claim 7, wherein the selecting the virtual correction point and the corresponding model correction point to calculate the offset between the model direction vector and the actual measurement direction vector comprises the following steps:
s3641, randomly selecting one virtual correction point and three adjacent virtual correction points, and determining the corresponding model correction point and three adjacent model correction points;
s3642, calculating vectors of the selected virtual correction point and the model correction point in three directions, and calculating offset values of the vectors in the corresponding directions between the two directions respectively, wherein the offset value calculation formula is as follows:
；
in the method, in the process of the invention,a vector offset value between the direction vector representing the virtual correction point and the direction vector of the model correction point;
H j a direction vector representing the virtual correction point;
H l a direction vector representing a model correction point;
（x j ，y j ) Vector values representing virtual correction points;
（x l ，y l ) Vector values representing model correction points;
representing the vector difference;
s3643, respectively calculating the vector offset values of the three direction vectors, and taking the calculation result as three offset degrees of the digital twin model, wherein the calculation formula is as follows:
；
where U represents the degree of offset.
10. The method for assisting earth excavation construction based on unmanned aerial vehicle oblique photography according to claim 1, wherein the setting of the inspection cycle, dispatching the inspection unmanned aerial vehicle to periodically monitor the actual construction site comprises the following steps:
s61, setting a patrol period of the patrol unmanned aerial vehicle according to the construction period, and setting a patrol route and a patrol range of the patrol unmanned aerial vehicle according to real-time construction progress;
s62, dispatching the inspection unmanned aerial vehicle to carry out inspection oblique photography on the construction site;
s63, capturing and positioning vehicle equipment and constructor positions in the construction site by using the inspection unmanned aerial vehicle, and measuring the excavated foundation pit in the construction site.
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