CN115127510A - Triphibian three-dimensional unmanned multi-platform linkage landslide intelligent patrol system - Google Patents

Triphibian three-dimensional unmanned multi-platform linkage landslide intelligent patrol system Download PDF

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CN115127510A
CN115127510A CN202210730795.6A CN202210730795A CN115127510A CN 115127510 A CN115127510 A CN 115127510A CN 202210730795 A CN202210730795 A CN 202210730795A CN 115127510 A CN115127510 A CN 115127510A
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landslide
area
monitoring
module
early warning
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唐亮
李博宇
丛晟亦
凌贤长
陈宏伟
唐文冲
田爽
毛小刚
张熙阳
荣仲笛
张钟远
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Harbin Institute of Technology
China Railway 17th Bureau Group Co Ltd
Chongqing Research Institute of Harbin Institute of Technology
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Harbin Institute of Technology
China Railway 17th Bureau Group Co Ltd
Chongqing Research Institute of Harbin Institute of Technology
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01B—MEASURING LENGTH, THICKNESS OR SIMILAR LINEAR DIMENSIONS; MEASURING ANGLES; MEASURING AREAS; MEASURING IRREGULARITIES OF SURFACES OR CONTOURS
    • G01B21/00—Measuring arrangements or details thereof, where the measuring technique is not covered by the other groups of this subclass, unspecified or not relevant
    • G01B21/32—Measuring arrangements or details thereof, where the measuring technique is not covered by the other groups of this subclass, unspecified or not relevant for measuring the deformation in a solid
    • 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
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
    • G01C7/00—Tracing profiles
    • 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
    • G01S17/89—Lidar systems specially adapted for specific applications for mapping or imaging
    • G—PHYSICS
    • G08—SIGNALLING
    • G08B—SIGNALLING OR CALLING SYSTEMS; ORDER TELEGRAPHS; ALARM SYSTEMS
    • G08B21/00—Alarms responsive to a single specified undesired or abnormal condition and not otherwise provided for
    • G08B21/02—Alarms for ensuring the safety of persons
    • G08B21/10—Alarms for ensuring the safety of persons responsive to calamitous events, e.g. tornados or earthquakes
    • 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
    • Y02T10/60—Other road transportation technologies with climate change mitigation effect
    • Y02T10/7072—Electromobility specific charging systems or methods for batteries, ultracapacitors, supercapacitors or double-layer capacitors

Abstract

The invention discloses a triphibian unmanned multi-platform linkage landslide intelligent patrol system, which comprises an information acquisition system, an intelligent dock system and a monitoring and early warning system, wherein the information acquisition system comprises an unmanned aerial vehicle, a patrol robot dog and an unmanned boat; the intelligent dock system comprises a self-induction cabin door for the access of information acquisition system equipment, a wireless transmission module and a wireless charging platform; the monitoring and early warning system comprises a data processing module, a three-dimensional modeling module, a danger prediction and early warning module, an unmanned ship, a patrol machine dog and an unmanned aerial vehicle acquire all data of a monitored side slope and send the data to the monitoring and early warning system through a wireless transmission module, the three-dimensional modeling module establishes a side slope three-dimensional model according to acquired information and predicts the landslide condition of the side slope, the danger prediction and early warning module executes early warning when the probability value of landslide occurrence reaches the set limit value, multi-dimensional evaluation is carried out on the side slope, and the precision of the accurate early warning of the side slope landslide is improved.

Description

Triphibian three-dimensional unmanned multi-platform linkage landslide intelligent patrol system
Technical Field
The invention belongs to the field of geological disaster monitoring, and relates to a triphibian unmanned multi-platform linkage landslide intelligent patrol system.
Background
In particular to large and medium landslides, which has the characteristics of difficult prevention, difficult rescue, large harm, large treatment difficulty and the like.
The landslide monitoring traditional method is mainly a geodetic method, manual monitoring is taken as a main means, deformation monitoring is carried out through a total station lead and an electromagnetic wave distance measurement triangle elevation method, although the geodetic method accumulates a large amount of experience, the monitoring frequency is low, the monitoring neutral period is long, personnel are required to arrive at a site for observation, the workload is large, and the safety of measuring personnel is difficult to guarantee in a complex environment.
In recent years, the rapid development of unmanned aerial vehicle technology, the more mature monitoring methods such as laser scanning and digital photogrammetry, and the unmanned aerial vehicle remote sensing is beginning to be widely applied to the field of natural disaster monitoring. In particular, high-resolution optical or radar remote sensing is widely applied to landslide monitoring and analysis, but inspection robot dogs and unmanned boats are rarely applied to landslide monitoring.
The existing landslide monitoring system takes an unmanned aerial vehicle as a monitoring platform, does not adopt a land-water-air triphibian stereo technology, cannot effectively monitor landslides in a monitoring area, has multiple and wide geological disaster points in China, is mostly complex in environment and covered by vegetation, and limits the flight of the unmanned aerial vehicle; landslide often occurs under extreme weather conditions, and when the ambient wind exceeds the bearable capacity of the unmanned aerial vehicle, the aircraft can drift and crash; and the remote sensing measurement technology of the unmanned aerial vehicle cannot be used for landslide of a deteriorated zone in a reservoir water seasonal lifting easily-induced hydro-fluctuation area.
The geological environment and weather conditions of landslide occurrence are fully considered, landslide monitoring in some areas by the traditional landslide monitoring means and the traditional unmanned monitoring system is very difficult, so that an economic, practical, efficient and intelligent landslide monitoring system is constructed, full-range normalized monitoring and surface deformation analysis of landslide disasters under extreme conditions are realized, and the system has important practical significance for disaster prevention and reduction.
Disclosure of Invention
The invention provides an intelligent triphibian unmanned multi-platform linked landslide patrol system, which aims at solving the problems that landslide and collapse are easily caused under extreme natural conditions such as heavy rainfall and landslide is easily caused, and reservoir water seasonal lifting is easily caused to landslide in a degraded zone of a hydro-fluctuation area. The system applies a new water-land-air trinity mapping technology, namely, an intelligent unmanned ship carrying a multi-beam depth sounder is adopted to acquire monitoring data of a reservoir water seasonal lifting falling area; carrying a laser radar by using an unmanned aerial vehicle and an inspection robot dog to obtain a true three-dimensional model of a monitored area, and further obtaining accurate land monitoring data; a circular mark is arranged in a potential landslide geological disaster hidden danger area, and a binocular stereoscopic vision measurement means is combined for supplementary measurement of landslide deformation monitoring. The measures enable land area monitoring data and water area monitoring data to be in seamless connection, greatly improve landslide monitoring efficiency, avoid personnel risk to the maximum extent, and provide a brand-new solution for landslide monitoring.
The purpose of the invention is realized by the following technical scheme:
the utility model provides a three-dimensional unmanned many platforms of land, water and air linkage landslide intelligence system of patrolling and defending, includes information acquisition system, intelligent storehouse depressed place system and monitoring early warning system, wherein:
the information acquisition system is a water, land and air triphibian platform and comprises an unmanned aerial vehicle (an aerial platform), an inspection robot dog (a ground platform) and an unmanned ship (an underwater platform);
the unmanned aerial vehicle is provided with an airspace information acquisition module and an airspace attitude determination positioning module;
the inspection robot dog is carried with a land area information acquisition module and a land area attitude determination positioning module;
the unmanned ship is carried with a water area information acquisition module and a water area attitude determination positioning module;
the water area information acquisition module is a multi-beam depth sounder and a storage, the multi-beam depth sounder transmits sound waves covered by a wide sector to an underwater area, a receiving transducer array is used for receiving narrow beams of the sound waves, an irradiation footprint of underwater topography is formed through orthogonality of pointing directions of transmitting and receiving sectors, the irradiation footprint is processed, and point cloud data of the underwater topography are obtained and stored in the storage;
the land area information acquisition module and the airspace information acquisition module are a three-dimensional laser scanning radar, a binocular stereo vision camera and a memory, the three-dimensional laser scanning radar acquires original point cloud data of a monitored area and stores the original point cloud data in the memory, and the binocular stereo vision camera acquires an identification point image of a potential landslide geological disaster hidden danger area and stores the identification point image in the memory;
the air space attitude determination positioning module is used for acquiring information such as coordinates, postures and speeds of the unmanned aerial vehicle, the land area attitude determination positioning module is used for acquiring information such as coordinates, postures and speeds of the inspection robot dog, and the water area attitude determination positioning module is used for acquiring information such as coordinates, postures and speeds of the unmanned ship;
the intelligent dock system comprises a self-induction cabin door for the access of information acquisition system equipment, a wireless transmission module and a wireless charging platform;
a solar cell panel is further arranged on one side of the wireless charging platform and is connected with the storage battery;
the monitoring and early warning system comprises a data processing module, a three-dimensional modeling module and a danger prediction and early warning module;
the data processing module is used for denoising, checking and filtering point cloud information transmitted by the intelligent dock system and transmitting the processed information to the three-dimensional modeling module and the danger prediction and early warning module, the data processing module is used for performing rain and fog removal processing on image information transmitted by the intelligent dock system to obtain a clear mark point image, and then performing edge detection and ellipse fitting on the mark point image to obtain a three-dimensional coordinate of the mark point in a world coordinate system;
the three-dimensional modeling module generates a land DEM model and a water area DEM model according to the acquired information, and the water area DEM models and the land DEM models are spliced to obtain an underwater and overwater integrated DEM model;
and the danger prediction and early warning module establishes a danger prediction model according to the acquired information, calculates the probability value of landslide occurrence of the monitored area through the danger prediction model, and executes early warning when the probability value of landslide occurrence reaches a set limit value.
A use method of the triphibian unmanned multi-platform linkage landslide intelligent patrol system comprises the following steps:
step 1, early preparation
Dividing the slope into a plurality of monitoring areas according to the activity range of the information acquisition system, building an intelligent dock system of a storage device and identification points for the binocular stereoscopic vision lens to identify in the central area of the monitoring areas, and setting target points for coordinate alignment in an amphibious area;
step 2, monitoring operation
Step 2.1, remotely sensing and monitoring by adopting an unmanned aerial vehicle, inspecting whether a landslide with larger deformation occurs or not, remotely sensing and monitoring by adopting a low altitude of the unmanned aerial vehicle for monitoring the deformation of the surface layer of a small-range slope body, and recording three-dimensional point cloud data; meanwhile, a binocular stereoscopic vision camera carried by the unmanned aerial vehicle shoots the mark points of the potential landslide geological disaster hidden danger areas, acquires image information of the mark points, and stores the three-dimensional point cloud data and the image information in a storage;
2.2, if an area which the unmanned aerial vehicle cannot enter exists in the monitoring area or the weather condition is not suitable for the unmanned aerial vehicle to fly, monitoring the area by an inspection robot dog from an intelligent dock system, carrying a three-dimensional laser scanning radar by the inspection robot dog, and recording three-dimensional point cloud data; meanwhile, the binocular stereoscopic vision camera for the inspection robot dog shoots the mark points of the potential landslide geological disaster hidden danger areas, acquires the image information of the mark points and stores the three-dimensional point cloud data and the image information in the memory;
step 2.3, if a slope soaked for a long time or a short time exists in the monitored area, the intelligent dock system close to the water area sends a signal, the unmanned ship starts from the intelligent dock system to monitor the area, the unmanned ship carries a multi-beam depth sounder, sound waves covered by a wide sector are emitted to the water bottom by using a transmitting transducer array, narrow-beam reception is carried out on the sound waves by using a receiving transducer array, irradiation footprints of a slope body at the water bottom are formed by orthogonality of the directions of transmitting and receiving sectors, the footprints are processed, water depth values of hundreds or even more measured points at the water bottom in a vertical plane perpendicular to the course can be given by one-time detection, and the water depth values of the measured points are stored in a memory;
step 2.4, after the inspection of the monitoring area is finished, the unmanned aerial vehicle, the inspection robot dog and the unmanned ship return to the intelligent dock system, and monitoring information is transmitted to the monitoring and early warning system through a wireless transmission module of the intelligent dock system;
step 3, image data processing
3.1, processing the image acquired by the binocular stereoscopic vision camera by an image processing module of the monitoring and early warning system through a dark channel prior defogging method and a morphological component analysis based image decomposition rain removing method to obtain a clear mark point image;
3.2, filtering, denoising and binaryzation are carried out on the mark point images collected by the binocular stereo vision camera to obtain circular mark point target parameters, then edge point coarse positioning is carried out through a Canny algorithm, and edge sub-pixel positioning is carried out through a Zernike moment algorithm to improve positioning accuracy;
3.3, fitting an elliptic equation by using a least square method, and performing elliptic fitting according to the obtained image sub-pixel edge so as to position a circle center coordinate;
step 4, point cloud data processing
The monitoring and early warning system carries out data denoising, data inspection and data filtering processing on the original point cloud data;
step 5, three-dimensional modeling
The three-dimensional modeling module is used for directly observing and analyzing elevation information of a monitored area by constructing a digital elevation model, generating a land area DEM model and a water area DEM model by using ground point cloud data, and splicing the DEM models of the water area and the land area by coordinate registration to obtain an overwater and underwater integrated DEM model;
step 6, danger early warning analysis
6.1, on the basis of the mark point coordinates, combining an overwater and underwater integrated DEM model, carrying out three-dimensional modeling on the landslide, comparing point cloud data of the landslide body obtained by monitoring at each stage, and extracting deformation sliding data of feature points, feature lines and feature surfaces on the landslide body;
and 6.2, the danger prediction and early warning module establishes a danger prediction model according to the three-dimensional coordinate information of the mark points and the water and underwater integrated DEM model, calculates the probability value of landslide of the monitored area through the danger prediction model, and executes early warning when the probability value of landslide reaches a set limit value.
Through adopting above-mentioned technical scheme, the information acquisition system passes through unmanned ship, it acquires each item data of monitoring side slope to patrol and examine machine dog and unmanned aerial vehicle, including three-dimensional point cloud data and mark point photo, and send to in the monitoring early warning system through wireless transmission module, the three-dimensional model of side slope is established according to the information of acquireing to the three-dimensional modeling module and is predicated the landslide condition of side slope, dangerous prediction and early warning module carry out the early warning when probability value that the landslide takes place reaches the limit value that sets up according to the landslide, thereby carry out the multidimension degree evaluation to the side slope, improve the precision of the accurate early warning of side slope landslide.
Compared with the prior art, the invention has the following advantages:
1. the invention constructs a triphibian three-dimensional unmanned multi-platform linked landslide intelligent patrol system based on a water-land-air trinity surveying and mapping technology, adopts an unmanned aerial vehicle, a patrol robot dog and an unmanned boat to jointly acquire information in a landslide area, and carries a laser radar and a binocular stereoscopic vision camera on the unmanned aerial vehicle and the patrol robot dog, and carries a multi-beam depth finder on the unmanned boat, so that the remote full-range real-time monitoring on a mountain landslide monitoring area can be realized without personnel involvement.
2. The method overcomes the defects that the traditional monitoring can not dynamically and real-timely identify and track the landslide hidden danger points and the burst points in extreme weather, the manual troubleshooting efficiency is low, the difficulty is high, the danger is high, and the potential hidden danger is difficult to actively prevent and control, and can realize the ' intelligent bin dock ' extremely-fast response, ' autonomous planning ' patrol route, ' identifying and tracking ' disaster dangerous case, ' three-dimensional platform ' intelligent linkage ' and ' data analysis ' forecast and early warning in extreme natural conditions.
Drawings
FIG. 1 is a block diagram of a triphibian unmanned multi-platform linked landslide intelligent patrol system;
FIG. 2 is a flow chart of landslide monitoring;
FIG. 3 is a flow chart of multivariate data processing.
Detailed Description
The technical solution of the present invention is further described below with reference to the accompanying drawings, but not limited thereto, and any modification or equivalent replacement of the technical solution of the present invention without departing from the spirit and scope of the technical solution of the present invention shall be covered by the protection scope of the present invention.
The invention provides a water, land and air triphibian unmanned multi-platform linkage landslide intelligent patrol system, which comprises an information acquisition system, an intelligent dock system and a monitoring and early warning system, wherein the information acquisition system comprises a first platform, a second platform and a third platform, and the monitoring and early warning system comprises a first platform, a second platform and a third platform, wherein the first platform comprises a first platform, a second platform and a third platform, the second platform comprises a third platform, and the third platform comprises a third platform, a fourth platform and a fourth platform, the third platform comprises a third platform, a fourth platform and a fourth platform, the fourth platform comprises a fourth platform, and the fourth platform comprises a fourth platform, a fourth platform and a fourth platform, the fifth platform comprises a fourth platform, a fifth platform and a fourth platform, the fifth platform is a third platform, the fifth platform comprises a fourth platform, a fourth platform and a fourth platform, the fifth platform comprises a fourth platform and a fourth platform, a third platform and a fourth platform, a fourth platform and a fourth platform, a third system, a fourth platform, a third system, a fourth system, a third system, a fourth system, a third system, a fourth system, a third system, a:
the information acquisition system is divided into three parts, namely land, water and air: unmanned ship, patrol and examine machine dog and unmanned aerial vehicle, each part still is furnished with information acquisition module and decides appearance orientation module.
The information acquisition system is a land, water and air triphibian platform, and is divided into three parts: unmanned ships and boats, inspection robot dogs and unmanned planes;
the unmanned aerial vehicle is provided with an airspace information acquisition module and an airspace attitude determination positioning module, and is used for carrying out large-range inspection on a monitored area;
the inspection robot dog is provided with a land area information acquisition module and a land area attitude and positioning module, and is monitored from the ground under the extreme weather condition that the unmanned aerial vehicle is limited to fly or in the geological environment which is not beneficial to the unmanned aerial vehicle to fly, so that the monitoring blind area of the unmanned aerial vehicle is compensated;
the unmanned ship is carried with a water area information acquisition module and a water area attitude determination positioning module, and monitors the seasonal lifting and falling area of reservoir water in a monitoring area, so that the overwater and underwater data are integrated, and the data are seamlessly connected;
the water area information acquisition module is a multi-beam depth sounder and a storage, the multi-beam depth sounder transmits sound waves covered by a wide sector to an underwater area, a receiving transducer array is used for receiving narrow beams of the sound waves, irradiation footprints for underwater topography are formed through orthogonality of the directions of the transmitting and receiving sectors, the footprints are properly processed, and water depth values of hundreds or even more underwater measured points in a vertical plane perpendicular to the course can be given out through one-time detection, so that point cloud data of the underwater topography are obtained and stored in the storage;
the land area information acquisition module and the airspace information acquisition module are a three-dimensional laser scanning radar, a binocular stereoscopic vision camera and a memory, the three-dimensional laser scanning radar is used for acquiring original point cloud data of a monitored area and storing the original point cloud data in the memory, and the binocular stereoscopic vision camera is used for acquiring an identification point image of a potential landslide geological disaster hidden danger area and storing the identification point image in the memory;
the air space attitude determination positioning module is used for acquiring information such as coordinates, postures and speeds of the unmanned aerial vehicle, the land area attitude determination positioning module is used for acquiring information such as coordinates, postures and speeds of the inspection robot dog, and the water area attitude determination positioning module is used for acquiring information such as coordinates, postures and speeds of the unmanned ship;
the intelligent dock system comprises a self-induction cabin door for the access of information acquisition system equipment, a wireless transmission module and a wireless charging platform;
a solar cell panel is further arranged on one side of the wireless charging platform and is connected with the storage battery;
the monitoring and early warning system comprises a data processing module, a three-dimensional modeling module and a danger prediction and early warning module;
the data processing module can perform denoising, inspection and filtering on the point cloud information transmitted by the intelligent dock system, denoising processing can remove noise point information in the data to enable the point cloud data to have higher accuracy, data inspection is performed on the denoised information to ensure that the point cloud density can guarantee subsequent monitoring requirements, data filtering can realize classification of the point cloud data, construction of a subsequent model is facilitated, and the processed information is transmitted to the three-dimensional modeling module and the danger prediction and early warning module;
the data processing module can perform rain and fog removal processing on image information transmitted by the intelligent dock system to obtain a clear mark point image, and then perform edge detection and ellipse fitting on the mark point image to obtain a three-dimensional coordinate of the mark point in a world coordinate system;
the three-dimensional modeling module generates a land DEM model and a water area DEM model according to the acquired information, and the water area DEM models and the land area DEM models are spliced to obtain an overwater and underwater integrated DEM model;
and the danger prediction and early warning module establishes a danger prediction model according to the acquired information, calculates the probability value of landslide occurrence of the monitored area through the danger prediction model, and executes early warning when the probability value of landslide occurrence reaches a set limit value.
The three-dimensional laser scanning radar is a core component of the system, and the working principle of the system is that a laser generator emits a beam of narrow pulse, a receiver receives a reflected wave signal after the narrow pulse is contacted with a target, and the distance between a laser and a measured object is obtained by calculating the time interval from pulse emission to reflected wave signal reception. The principle of calculating the space coordinate of the measured object is to obtain the space coordinate (x) of the laser scanner by the airborne differential GPS locator 0 ,y 0 ,z 0 ) Then, the laser scanner acquires that the distance between the laser scanner and the measured object is D, and thereby the spatial coordinates (x, y, z) of the measured object on the ground at this moment are calculated, as shown in formula (1):
Figure BDA0003713319020000101
where κ is the heading angle, ω is the roll angle, and ψ is the pitch angle.
In the invention, the binocular stereoscopic vision camera comprises a left CCD camera and a right CCD camera, and is used for acquiring the image of the specific identification point and storing the image in the storage. Due to the matching relation of corresponding points on the left image and the right image, the three-dimensional coordinates of the measuring points in the world coordinate system can be obtained according to the geometric constraints of the two CCD cameras, and the equation is as follows:
Figure BDA0003713319020000111
in the formula (f) l And f r Effective focal lengths of the left and right CCD cameras, respectively, (x) l ，y l ) As the coordinates of the measuring point of the coordinate system of the image plane of the left CCD camera, (x) r ，y r ) Is the right sideThe coordinates of the CCD camera image plane coordinate system measuring point, R and T are respectively a rotation matrix and a translation vector from a world coordinate system to a camera coordinate system:
Figure BDA0003713319020000112
Figure BDA0003713319020000113
in the formula (r) 1 、r 2 、r 3 )、(r 4 、r 5 、r 6 ) And (r) 7 、r 8 、r 9 ) Is a representation of the base vector of the camera coordinate system in the world coordinate system; t is t x 、t y 、t z Numerically equal to the coordinates of the world coordinate system origin in the camera coordinate system.
In the invention, the wireless transmission module can adopt the modes of Bluetooth, WIFI, 5G, ZIGBE, GPRS and the like.
A use method of the triphibian unmanned multi-platform linkage landslide intelligent patrol system comprises the following steps:
step 1, early preparation
According to the range of motion of information acquisition system (unmanned aerial vehicle, patrol and examine machine dog and unmanned ship), divide the side slope into polylith monitoring area, set up the intelligent storehouse depressed place system of storage device and the identification point that supplies two mesh stereo vision camera lenses discernments in monitoring area central zone, set up the target point that supplies the coordinate registration to use in the region that meets land and water. The method comprises the following steps:
1) the monitored area completely covers the mountain area to be detected;
2) the coincidence rate of the monitoring areas is more than 30 percent and less than 50 percent;
3) the mark points are composed of a reflecting plate and a reflecting film, and the mark point supports are made of steel structures and erected at the measuring points according to the field conditions to support the mark points;
4) unmanned aerial vehicle, patrol and examine machine dog and unmanned ships and light boats and light.
Step 2, monitoring operation
After the preparation work is finished, starting monitoring work, and as shown in fig. 2, firstly, remotely sensing and monitoring by using an unmanned aerial vehicle, inspecting whether a landslide with large deformation occurs or not, remotely sensing and monitoring by using the unmanned aerial vehicle at low altitude for monitoring the deformation of the surface layer of a small-range slope body, and recording three-dimensional point cloud data; meanwhile, a binocular stereoscopic vision camera carried by the unmanned aerial vehicle shoots the mark points of the potential landslide geological disaster hidden danger areas, acquires image information of the mark points, and stores the three-dimensional point cloud data and the image information in a storage.
If an area which the unmanned aerial vehicle cannot enter exists in the monitoring area or the weather condition is not suitable for the unmanned aerial vehicle to fly, the inspection robot dog starts from the intelligent dock system to monitor the area, carries a three-dimensional laser scanning radar and records three-dimensional point cloud data; meanwhile, the binocular stereoscopic vision camera for the inspection robot dog shoots the mark points of the potential landslide geological disaster hidden danger areas, acquires the image information of the mark points, and stores the three-dimensional point cloud data and the image information in the memory.
If a long-term or short-term (seasonal) submerged slope exists in the monitored area, the intelligent dock system close to the water area sends out signals, the unmanned ship starts from the intelligent dock system to monitor the area, the unmanned ship carries a multi-beam depth finder, sound waves covered by a wide sector are emitted to the water bottom by using a transmitting transducer array, narrow-beam receiving is carried out on the sound waves by using a receiving transducer array, irradiation footprints of a water bottom slope body are formed through orthogonality of the directions of the transmitting and receiving sectors, proper treatment is carried out on the footprints, hundreds or even more water depth values of the measured points at the water bottom in a vertical plane perpendicular to the course can be given out through one-time detection, and the water depth values of the measured points are stored in a memory.
After the inspection of the monitoring area is finished, the unmanned aerial vehicle, the inspection robot dog and the unmanned ship return to the intelligent cabin docking system, and monitoring information is transmitted to the monitoring and early warning system through a wireless transmission module of the intelligent cabin docking system.
Step 3, image data processing
3.1 image rain and fog removal
Interference factors such as rain marks and rain fog may exist in an image acquired by the binocular stereoscopic vision camera, the image processing module of the monitoring and early warning system performs certain processing on the image through a dark channel prior defogging method and a Morphological Component Analysis (MCA) image decomposition defogging method, image details are highlighted, contrast is improved, the image looks clearer, and a clear mark point image is obtained.
3.2 edge detection
And filtering, denoising and binaryzation are carried out on the collected mark point images to obtain circular identification point target parameters, then edge point rough positioning is carried out through a Canny algorithm, and edge sub-pixel positioning is carried out through a Zernike moment algorithm to improve positioning accuracy.
3.3 ellipse fitting
The circular mark presents an ellipse on the camera imaging plane, and the invention selects an ellipse fitting method for positioning. And fitting an elliptic equation by using a least square method, and performing elliptic fitting according to the obtained image sub-pixel edge so as to position the coordinates of the circle center.
The ellipse equation is:
ax 2 +bxy+cy 2 +dx+ey+f＝0 (5)
in the formula, a, b, c, d, e and f are parameters for fitting an ellipse.
The general form of the ellipse is converted to a matrix form:
f(u,v)＝u*v＝1 (6)
wherein u ═ a b c d e 1]，v＝[x 2 xy y 2 x y 1]T, introducing the constraint condition | | | u | | | ═ 1 in the above problem, the objective function can be established:
Figure BDA0003713319020000141
wherein M is a penalty factor.
The central coordinate (x) of the ellipse can be obtained by carrying out optimization solution c ,y c ) Then the mark point can be obtainedAnd recording the three-dimensional coordinates of each mark point in different time periods to realize the measurement of the deformation of the landslide body.
Figure BDA0003713319020000142
Step 4, point cloud data processing
The monitoring and early warning system carries out data denoising, data inspection and data filtering processing on the original point cloud data.
4.1 data De-noising
The denoising of the point cloud data is one of the necessary steps before the point cloud data is used, and the denoising of the noise point information in the data can enable the point cloud data to have higher accuracy. Noise is typically small amplitude noise and discrete points due to imperfections in the instrument itself or interference from external factors. Discrete points usually have no obvious rule and are represented in a disordered state, and a spatial topological relation among data can be established to carry out denoising by using a K-nearest neighbor method. The method selects a Gaussian function based on the kernel density to calculate the relation between points and adjacent points, and screens point cloud data with lower density and more dispersion as data noise points. The influence of each point in the neighborhood data is expressed mathematically, and the function is an influence function, as shown in equation (9). Setting an observation point P i Can be defined as the sum of the influence functions of all K-nearest points of the point, and the point P is calculated by selecting the Gaussian influence function i The influence of the surrounding approach point.
Figure BDA0003713319020000151
In the formula, (x, y) is a point cloud coordinate, and σ is a density parameter.
Kernel density function D P (P i ) As shown in equation (10):
Figure BDA0003713319020000152
in the formula, k is P i The number of all K-neighbor data points.
When the density value of the undetermined point is smaller than a preset threshold value, the undetermined point is judged as noise.
4.2 data verification
The data inspection is to check the density of the point cloud data, and the greater the density of the point cloud data, the better the expression capability for the micro terrain. The point cloud data is mainly used for assisting in DEM production in topographic analysis, theoretically, the higher the density of the point cloud data is, the higher the DEM product precision can be generated, the better the quality can be, and otherwise, the quality of the DEM data produced by the point cloud data with low density is relatively poorer. For areas with larger topographic relief and more complex ground objects, the higher the required density of the point cloud data, different density limit values are set according to different monitoring areas so as to ensure that the point cloud data can meet the subsequent monitoring requirements.
4.3 data Filtering
The data filtering can realize the classification of the point cloud data, the point cloud data is divided into two types of ground points and non-ground points, and then an interpolation method is used for generating DEM products required by monitoring on the classified ground point data. At present, a plurality of point cloud filtering algorithms are researched, and different filtering algorithms are selected according to different monitoring environments to optimize the monitoring effect, so that accurate data support is provided for subsequent production requirements.
Step 5, three-dimensional modeling
The three-dimensional modeling module directly observes and analyzes the elevation information of the monitored area by constructing a digital elevation model. And generating a land DEM model and a water area DEM model by using the discrete ground point cloud data, splicing the DEM models of the water area and the land area through coordinate registration, setting a target point in a water and land adjacent area by the coordinate registration, enabling adjacent scanning points to have more than 3 same name target points, and unifying adjacent scanning data together through forced attachment of the target points to obtain the water and underwater integrated DEM model.
Step 6, danger early warning analysis
On the basis of the coordinates of the mark points, a water and water integrated DEM model is combined to perform three-dimensional modeling of the landslide, the point cloud data of the landslide body obtained by monitoring at each stage is compared, deformation sliding data of characteristic points, characteristic lines and characteristic surfaces on the landslide body are extracted, and the multi-source data processing flow is shown in figure 3.
The danger prediction and early warning module establishes a danger prediction model according to the three-dimensional coordinate information of the mark points and the water and underwater integrated DEM model, calculates the probability value of landslide of the monitored area through the danger prediction model, and executes early warning when the probability value of landslide reaches a set limit value.

Claims (9)

1. The utility model provides a three-dimensional unmanned many platforms of land, water and air linkage landslide intelligence system of patrolling and defending, its characterized in that the system of patrolling and defending includes information acquisition system, intelligent storehouse depressed place system and monitoring early warning system, wherein:
the information acquisition system is a water, land and air triphibian platform and comprises an unmanned aerial vehicle, an inspection robot dog and an unmanned ship;
the unmanned aerial vehicle is provided with an airspace information acquisition module and an airspace attitude determination positioning module;
the inspection robot dog is carried with a land area information acquisition module and a land area attitude determination positioning module;
a water area information acquisition module and a water area attitude determination positioning module are carried on the unmanned ship;
the water area information acquisition module is a multi-beam depth sounder and a storage, the multi-beam depth sounder transmits sound waves covered by a wide sector to an underwater area, a receiving transducer array is used for receiving narrow beams of the sound waves, an irradiation footprint of underwater topography is formed through orthogonality of pointing directions of transmitting and receiving sectors, the irradiation footprint is processed, and point cloud data of the underwater topography are obtained and stored in the storage;
the land area information acquisition module and the airspace information acquisition module are a three-dimensional laser scanning radar, a binocular stereoscopic vision camera and a memory, the three-dimensional laser scanning radar acquires original point cloud data of a monitored area and stores the original point cloud data in the memory, and the binocular stereoscopic vision camera acquires an identification point image of a potential landslide geological disaster hidden danger area and stores the identification point image in the memory;
the air space attitude determination positioning module is used for acquiring the coordinate, attitude and speed information of the unmanned aerial vehicle, the land area attitude determination positioning module is used for acquiring the coordinate, attitude and speed information of the inspection robot dog, and the water area attitude determination positioning module is used for acquiring the coordinate, attitude and speed information of the unmanned ship;
the intelligent dock system comprises a self-induction cabin door for the access of information acquisition system equipment, a wireless transmission module and a wireless charging platform;
the monitoring and early warning system comprises a data processing module, a three-dimensional modeling module and a danger prediction and early warning module;
the data processing module is used for denoising, checking and filtering point cloud information transmitted by the intelligent dock system and transmitting the processed information to the three-dimensional modeling module and the danger prediction and early warning module, the data processing module is used for performing rain and fog removal processing on image information transmitted by the intelligent dock system to obtain a clear mark point image, and then performing edge detection and ellipse fitting on the mark point image to obtain a three-dimensional coordinate of the mark point in a world coordinate system;
the three-dimensional modeling module generates a land DEM model and a water area DEM model according to the acquired information, and the water area DEM models and the land DEM models are spliced to obtain an underwater and overwater integrated DEM model;
and the danger prediction and early warning module establishes a danger prediction model according to the acquired information, calculates the probability value of landslide occurrence of the monitored area through the danger prediction model, and executes early warning when the probability value of landslide occurrence reaches a set limit value.
2. The amphibious three-dimensional unmanned multi-platform linked landslide intelligent patrol system as claimed in claim 1, wherein the binocular stereoscopic vision cameras comprise a left CCD camera and a right CCD camera for acquiring images of specific identification points and storing the images in the storage.
3. The land, water and air triphibian unmanned multi-platform linkage landslide intelligent patrol system according to claim 1, wherein the wireless transmission module is in a Bluetooth, WIFI, 5G, ZIGBE or GPRS mode.
4. The land, water and air triphibian three-dimensional unmanned multi-platform linked landslide intelligent patrol system according to claim 1, wherein a solar panel is further arranged on one side of the wireless charging platform and is connected with a storage battery.
5. A use method of the water, land and air triphibian unmanned multi-platform linkage landslide intelligent patrol system as claimed in any one of claims 1-4, wherein the method comprises the following steps:
step 1, early preparation
Dividing the slope into a plurality of monitoring areas according to the activity range of the information acquisition system, building an intelligent dock system of a storage device and identification points for the binocular stereoscopic vision lens to identify in the central area of the monitoring areas, and setting target points for coordinate alignment in an amphibious area;
step 2, monitoring operation
Step 2.1, remotely sensing and monitoring by adopting an unmanned aerial vehicle, inspecting whether a landslide with larger deformation occurs or not, remotely sensing and monitoring by adopting a low altitude of the unmanned aerial vehicle for monitoring the deformation of the surface layer of a small-range slope body, and recording three-dimensional point cloud data; meanwhile, a binocular stereoscopic vision camera carried by the unmanned aerial vehicle shoots the mark points of the potential landslide geological disaster hidden danger areas, acquires image information of the mark points, and stores the three-dimensional point cloud data and the image information in a storage;
2.2, if an area which the unmanned aerial vehicle cannot enter exists in the monitoring area or the weather condition is not suitable for the unmanned aerial vehicle to fly, monitoring the area by an inspection robot dog from an intelligent dock system, carrying a three-dimensional laser scanning radar by the inspection robot dog, and recording three-dimensional point cloud data; meanwhile, the binocular stereoscopic vision camera for the inspection robot dog shoots the mark points of the potential landslide geological disaster hidden danger areas, acquires the image information of the mark points and stores the three-dimensional point cloud data and the image information in the memory;
step 2.3, if a slope soaked for a long time or a short time exists in the monitored area, the intelligent dock system close to the water area sends a signal, the unmanned ship starts from the intelligent dock system to monitor the area, the unmanned ship carries a multi-beam depth sounder, sound waves covered by a wide sector are emitted to the water bottom by using a transmitting transducer array, narrow-beam reception is carried out on the sound waves by using a receiving transducer array, irradiation footprints of a slope body at the water bottom are formed by orthogonality of the directions of transmitting and receiving sectors, the footprints are processed, water depth values of hundreds or even more measured points at the water bottom in a vertical plane perpendicular to the course can be given by one-time detection, and the water depth values of the measured points are stored in a memory;
step 2.4, after the inspection of the monitoring area is finished, the unmanned aerial vehicle, the inspection robot dog and the unmanned ship return to the intelligent storage dock system, and monitoring information is transmitted to the monitoring and early warning system through a wireless transmission module of the intelligent storage dock system;
step 3, image data processing
3.1, processing the image acquired by the binocular stereoscopic vision camera by an image processing module of the monitoring and early warning system through a dark channel prior defogging method and a morphological component analysis based image decomposition rain removing method to obtain a clear mark point image;
3.2, filtering, denoising and binaryzation are carried out on the mark point image acquired by the binocular stereoscopic vision camera to obtain a circular mark point target parameter, then edge point coarse positioning is carried out through a Canny algorithm, and edge sub-pixel positioning is carried out through a Zernike moment algorithm to improve positioning accuracy;
3.3, fitting an ellipse equation by using a least square method, and performing ellipse fitting according to the obtained image sub-pixel edge so as to position a circle center coordinate;
step 4, point cloud data processing
The monitoring and early warning system carries out data denoising, data inspection and data filtering processing on the original point cloud data;
step 5, three-dimensional modeling
The three-dimensional modeling module is used for directly observing and analyzing elevation information of a monitored area by constructing a digital elevation model, generating a land area DEM model and a water area DEM model by using ground point cloud data, and splicing the DEM models of the water area and the land area by coordinate registration to obtain an overwater and underwater integrated DEM model;
step 6, danger early warning analysis
6.1, on the basis of the mark point coordinates, combining an overwater and underwater integrated DEM model, carrying out three-dimensional modeling on the landslide, comparing point cloud data of the landslide body obtained by monitoring at each stage, and extracting deformation sliding data of feature points, feature lines and feature surfaces on the landslide body;
and 6.2, the danger prediction and early warning module establishes a danger prediction model according to the three-dimensional coordinate information of the mark points and the water and underwater integrated DEM model, calculates the probability value of landslide of the monitored area through the danger prediction model, and executes early warning when the probability value of landslide reaches a set limit value.
6. The use method of the land, water and air triphibian unmanned multi-platform linkage landslide intelligent patrol system according to claim 5, wherein the monitoring area is to completely cover a mountain area to be detected.
7. The use method of the land, water and air triphibian unmanned multi-platform linkage landslide intelligent patrol system according to claim 5 or 6, wherein the coincidence rate of the monitoring areas is more than 30% and less than 50%.
8. The use method of the land, water and air triphibian unmanned multi-platform linkage landslide intelligent patrol system according to claim 5, wherein the mark points are composed of a reflective plate and a reflective film, and the mark point supports are made of steel structures and erected at measuring points according to field conditions to support the mark points.
9. The use method of the land, water and air triphibian unmanned multi-platform linkage landslide intelligent patrol system according to claim 5, wherein the unmanned aerial vehicle, the patrol robot dog and the unmanned ship can share one intelligent dock system according to specific conditions of a monitored area, and the intelligent dock systems can be arranged separately.
CN202210730795.6A 2022-06-24 2022-06-24 Triphibian three-dimensional unmanned multi-platform linkage landslide intelligent patrol system Pending CN115127510A (en)

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* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115574785A (en) * 2022-12-12 2023-01-06 河海大学 Hydraulic engineering safety monitoring method and platform based on data processing
CN115793093A (en) * 2023-02-02 2023-03-14 水利部交通运输部国家能源局南京水利科学研究院 Empty ground integrated equipment for diagnosing hidden danger of dam
CN116311047A (en) * 2023-03-01 2023-06-23 四川省公路规划勘察设计研究院有限公司 Landslide monitoring method, device, medium and server for air-space-ground multisource fusion
CN116642536A (en) * 2023-05-31 2023-08-25 交通运输部天津水运工程科学研究所 Breakwater structure safety monitoring and early warning system based on multi-source data

Cited By (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115574785A (en) * 2022-12-12 2023-01-06 河海大学 Hydraulic engineering safety monitoring method and platform based on data processing
CN115574785B (en) * 2022-12-12 2023-02-28 河海大学 Hydraulic engineering safety monitoring method and platform based on data processing
CN115793093A (en) * 2023-02-02 2023-03-14 水利部交通运输部国家能源局南京水利科学研究院 Empty ground integrated equipment for diagnosing hidden danger of dam
CN116311047A (en) * 2023-03-01 2023-06-23 四川省公路规划勘察设计研究院有限公司 Landslide monitoring method, device, medium and server for air-space-ground multisource fusion
CN116311047B (en) * 2023-03-01 2023-09-05 四川省公路规划勘察设计研究院有限公司 Landslide monitoring method, device, medium and server for air-space-ground multisource fusion
CN116642536A (en) * 2023-05-31 2023-08-25 交通运输部天津水运工程科学研究所 Breakwater structure safety monitoring and early warning system based on multi-source data

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