WO2020228189A1 - 一种综采工作面高精度三维导航地图的生成系统及方法 - Google Patents

一种综采工作面高精度三维导航地图的生成系统及方法 Download PDF

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WO2020228189A1
WO2020228189A1 PCT/CN2019/104100 CN2019104100W WO2020228189A1 WO 2020228189 A1 WO2020228189 A1 WO 2020228189A1 CN 2019104100 W CN2019104100 W CN 2019104100W WO 2020228189 A1 WO2020228189 A1 WO 2020228189A1
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data
precision
mechanized mining
roadway
fully mechanized
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PCT/CN2019/104100
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English (en)
French (fr)
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刘万里
葛世荣
王世博
伊世学
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中国矿业大学
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Priority to AU2019446069A priority Critical patent/AU2019446069B2/en
Priority to US17/607,864 priority patent/US11971258B2/en
Publication of WO2020228189A1 publication Critical patent/WO2020228189A1/zh

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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
    • G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
    • G01C21/005—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 with correlation of navigation data from several sources, e.g. map or contour matching
    • 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
    • E—FIXED CONSTRUCTIONS
    • E21—EARTH OR ROCK DRILLING; MINING
    • E21C—MINING OR QUARRYING
    • E21C35/00—Details of, or accessories for, machines for slitting or completely freeing the mineral from the seam, not provided for in groups E21C25/00 - E21C33/00, E21C37/00 or E21C39/00
    • E21C35/24—Remote control specially adapted for machines for slitting or completely freeing the mineral
    • E—FIXED CONSTRUCTIONS
    • E21—EARTH OR ROCK DRILLING; MINING
    • E21C—MINING OR QUARRYING
    • E21C39/00—Devices for testing in situ the hardness or other properties of minerals, e.g. for giving information as to the selection of suitable mining tools
    • E—FIXED CONSTRUCTIONS
    • E21—EARTH OR ROCK DRILLING; MINING
    • E21C—MINING OR QUARRYING
    • E21C41/00—Methods of underground or surface mining; Layouts therefor
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
    • G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
    • G01C21/10—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration
    • G01C21/12—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration executed aboard the object being navigated; Dead reckoning
    • G01C21/16—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration executed aboard the object being navigated; Dead reckoning by integrating acceleration or speed, i.e. inertial navigation
    • G01C21/165—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration executed aboard the object being navigated; Dead reckoning by integrating acceleration or speed, i.e. inertial navigation combined with non-inertial navigation instruments
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
    • G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
    • G01C21/10—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration
    • G01C21/12—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration executed aboard the object being navigated; Dead reckoning
    • G01C21/16—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration executed aboard the object being navigated; Dead reckoning by integrating acceleration or speed, i.e. inertial navigation
    • G01C21/165—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration executed aboard the object being navigated; Dead reckoning by integrating acceleration or speed, i.e. inertial navigation combined with non-inertial navigation instruments
    • G01C21/1652—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration executed aboard the object being navigated; Dead reckoning by integrating acceleration or speed, i.e. inertial navigation combined with non-inertial navigation instruments with ranging devices, e.g. LIDAR or RADAR
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
    • G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
    • G01C21/38—Electronic maps specially adapted for navigation; Updating thereof
    • G01C21/3804—Creation or updating of map data
    • G01C21/3807—Creation or updating of map data characterised by the type of data
    • G01C21/3826—Terrain data
    • 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
    • G01S13/00—Systems using the reflection or reradiation of radio waves, e.g. radar systems; Analogous systems using reflection or reradiation of waves whose nature or wavelength is irrelevant or unspecified
    • G01S13/86—Combinations of radar systems with non-radar systems, e.g. sonar, direction finder
    • G01S13/865—Combination of radar systems with lidar systems
    • 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
    • G01S13/00—Systems using the reflection or reradiation of radio waves, e.g. radar systems; Analogous systems using reflection or reradiation of waves whose nature or wavelength is irrelevant or unspecified
    • G01S13/88—Radar or analogous systems specially adapted for specific applications
    • G01S13/885—Radar or analogous systems specially adapted for specific applications for ground probing
    • 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
    • G01S13/00—Systems using the reflection or reradiation of radio waves, e.g. radar systems; Analogous systems using reflection or reradiation of waves whose nature or wavelength is irrelevant or unspecified
    • G01S13/88—Radar or analogous systems specially adapted for specific applications
    • G01S13/93—Radar or analogous systems specially adapted for specific applications for anti-collision purposes
    • G01S13/931—Radar or analogous systems specially adapted for specific applications for anti-collision purposes of land vehicles
    • 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
    • G01S17/02—Systems using the reflection of electromagnetic waves other than radio waves
    • G01S17/06—Systems determining position data of a target
    • G01S17/08—Systems determining position data of a target for measuring distance only
    • 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
    • G01S17/87—Combinations of systems using electromagnetic waves other than radio waves
    • 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
    • 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
    • G01S7/4808—Evaluating distance, position or velocity data
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS; TAGS
    • G01V1/00—Seismology; Seismic or acoustic prospecting or detecting
    • G01V1/28—Processing seismic data, e.g. for interpretation or for event detection
    • G01V1/30—Analysis
    • 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
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T17/00—Three dimensional [3D] modelling, e.g. data description of 3D objects
    • G06T17/20—Finite element generation, e.g. wire-frame surface description, tesselation

Definitions

  • the invention relates to a generation system and method, and is particularly suitable for a system and method for generating a high-precision three-dimensional navigation map of a fully mechanized mining face used in the field of unmanned mining technology.
  • Unmanned coal mining is a cutting-edge technology commonly pursued in the field of international coal mining. It is an important means to reduce casualties and ensure safe production. It is also an effective way to achieve safe, efficient and green mining in coal mines in my country.
  • unmanned mining can be realized by a combination of memory cutting and manual remote intervention.
  • the geological conditions and coal seam structures of most working faces in my country are very complex (such as coal seams). Large undulations and dangerous geological structures such as faults and folds exist), resulting in unmanned mining that cannot be achieved with existing technologies.
  • the system for generating high-precision three-dimensional navigation maps for fully mechanized mining face of the present invention includes a vehicle-mounted mobile measurement platform and channel wave seismometers arranged on both sides of the coal seam to be mined.
  • the transmitter in the tunnel and the receiver arranged in the lower tunnel at intervals, the receivers are connected by a network cable and connected with a wireless transmitter;
  • the vehicle-mounted mobile measurement platform is equipped with a data receiving processor, lidar, inertial navigation device and ground penetrating radar.
  • the data receiving processor and ground penetrating radar are fixed above the vehicle-mounted mobile measuring platform, and the lidar is fixed above the data receiving processor.
  • the inertial navigation device is fixed at the center of the vehicle-mounted mobile measurement platform.
  • the lidar, inertial navigation device and ground penetrating radar are used for data transmission through the network cable and the data receiving processor.
  • the receiver of the trough wave seismograph is performed through the wireless transmitter and the data receiving processor. data transmission.
  • the transmitter is a trough wave seismic signal transmitter
  • the receiver is a trough wave seismic signal receiver
  • the trough wave seismic signal receiver transmits data to the data receiving processor through a wireless transmitter
  • the seismic signal receiver is set correspondingly to form a trough wave seismic signal detection sequence.
  • the method for generating high-precision three-dimensional navigation map of fully mechanized mining face is as follows:
  • the vehicle-mounted mobile measurement platform moves at a uniform speed of about 40km/h in the upper roadway, left middle cut roadway, lower roadway and right middle cut roadway around the coal seam to be mined:
  • the data receiving processor moves with the vehicle-mounted mobile measurement platform to the vicinity of the wireless transmitter and acquires the geological data of the coal seam to be mined collected by the channel wave seismometer through wireless signals: including the seam thickness information, the change inclination angle of the coal seam and the dangerous geological structure Spatial location;
  • the lidar continuously emits laser beams to the surface of the roadway roof, and calculates the distance between the roadway surface and the lidar according to the return time difference of the reflected laser beams, and forms the three-dimensional point cloud data of the roadway; Send the three-dimensional point cloud data to the data receiving processor;
  • the inertial navigation device calculates the posture and motion trajectory information of the vehicle-mounted mobile measurement platform through the rotation angular velocity and acceleration vector of the vehicle-mounted mobile measurement platform relative to the inertial system, and then transmits it to the data receiving processor through the network cable connection;
  • the ground penetrating radar transmits electromagnetic wave signals to the coal seam of the roadway roof through the transmitting unit, and the receiving unit detects the propagation time of the electromagnetic wave signal in the coal seam, and accurately calculates the remaining coal thickness data of the upper and lower roadway roofs on the fully mechanized mining face, and connects them through the network cable. Transmit to the data receiving processor;
  • step d Use the Delaunay triangulation of coal seams, faults/folds and roadways generated in step d to draw a high-precision profile of the Delaunay triangulation, calculate the topological relationship of the profile, and generate the topological data structure of the profile;
  • Information automatic query database including the query of coal seam thickness, coal seam inclination, fault/fold space location and roadway space location, constructs a high-precision three-dimensional navigation map of fully mechanized mining face.
  • the methods of coordinate conversion, feature fusion and consistency processing of the data collected by the vehicle-mounted mobile measurement platform and the trough wave seismograph are as follows: First, the collected data elements are classified and classified and the feature attribute mapping is based on the unchanged features of the fully mechanized mining face. Identify the same or similar attribute characteristics, organize and express them through the least squares method, establish an attribute characteristic conversion table, eliminate the difference in attribute characteristics caused by different classification and grading standards, and achieve the consistency of the characteristic expression of the collected data; then, use entities based on the same name The matching method realizes the combination of attributes and characteristics of the collected data;
  • the specific coordinate conversion adopts the seven-parameter coordinate conversion method:
  • (X,Y,Z) T is the three-dimensional coordinate of the coal seam to be mined
  • (X',Y',Z') T is the three-dimensional coordinate of the laser scanning roadway
  • ⁇ X, ⁇ Y, ⁇ Z are the two coordinate origins
  • the translation parameters of ⁇ X , ⁇ Y , ⁇ Z are the three coordinate axis rotation parameters, and m is the scale parameter;
  • the feature fusion method of the vehicle-mounted mobile measurement platform (1) and the groove wave seismograph uses at least one of parameter template method, feature compression and clustering algorithm, K-order nearest neighbor method, artificial neural network and fuzzy integral method Fusion.
  • the steps to generate the Delaunay triangulation of the coal seams, faults/folds and roadways of the fully mechanized mining face to be mined include:
  • d3 takes out the constrained edges from the line link list as the base edges, and applies the maximum included angle criterion to generate left and right triangles as the initial triangulation. If the constrained edges are boundaries, a triangle is generated and the triangles are stored in the initial triangulation;
  • step d4 uses the three sides of the newly generated triangle in step d3 as the base side, and generates a new triangulation according to the one-step growth method: Take out the base side, and according to the constraint circle criterion of the Delaunay triangulation, find out the third triangle that forms the triangle with the baseline Point, the two end points of the baseline are connected with the third point to form a new triangle, and the new triangle is stored in the triangulation network at the same time, until the extended side of the new triangle is a boundary side or has been used twice;
  • step d5 Repeat step d4 until the last layer of triangles cannot be expanded, and use the LOP (Local Optimization Procedure) algorithm to optimize all other triangulations except the initial triangulation.
  • LOP Local Optimization Procedure
  • the steps of generating the profile topological data structure include:
  • e1 generates a line linked list database from the collection of all straight line segments in the triangulation generated in step d5, and initializes the left and right area codes of all straight line segments to the effective information -1;
  • e2 Choose i straight line segments from the line link list to form a polygon, search for the boundary of the polygon, if the left code and right code of the current straight line segment are not -1, select the next straight line segment and continue this step; if all straight line segments have left code , The right code is not -1, indicating that all the left and right sides of the straight line segment have been searched, end this step and continue to the next step; if the left code or right code of the current straight line segment is not -1, you can set the end point Or the starting point is the current node.
  • the present invention can provide high-precision navigation information for fully mechanized mining equipment, and is the prerequisite and basis for realizing underground unmanned mining.
  • the main advantages are:
  • the present invention can provide accurate coal seam thickness information, coal seam change inclination and spatial location of dangerous geological structures for fully mechanized mining equipment, and has functions of high-precision positioning, information perception, path planning, etc., and provides benchmark decision-making for unmanned mining in accordance with;
  • the present invention can quickly integrate the data collected by trough wave seismograph, lidar, integrated navigation device and ground penetrating radar, and automatically generate high-precision three-dimensional navigation map of fully mechanized mining face.
  • the method for acquiring the collected data of the present invention is simple, easy to operate, fast, and the acquired measured data is accurate.
  • Figure 1 is a layout diagram of the system for generating a high-precision three-dimensional navigation map of a fully mechanized mining face of the present invention
  • FIG. 2 is a layout diagram of the vehicle-mounted mobile measurement system of the present invention
  • Figure 3 is a flow chart of the method for generating a high-precision three-dimensional navigation map of a fully mechanized mining face of the present invention
  • 1-Vehicle-mounted mobile measurement platform 2-Data receiving processor; 3-Lidar; 4-Inertial navigation device; 5-Landmine detection.
  • the system for generating a high-precision three-dimensional navigation map of a fully mechanized mining face of the present invention includes a vehicle-mounted mobile measurement platform 1 and channel wave seismometers arranged on both sides of the coal seam to be mined.
  • the transmitter in the upper tunnel and the receiver arranged in the lower tunnel at intervals, the receivers are connected by a network cable and connected with a wireless transmitter; the transmitter is a trough wave seismic signal transmitter, and the receiver is a trough wave seismic signal receiver
  • the groove wave seismic signal receiver transmits the data to the data receiving processor 2 through the wireless transmitter.
  • the groove wave seismic signal transmitter and the groove wave seismic signal receiver are arranged correspondingly to form a groove wave seismic signal detection sequence;
  • the vehicle-mounted mobile measurement platform 1 is equipped with a data receiving processor 2, a lidar 3, an inertial navigation device 4, and a ground penetrating radar 5.
  • the data receiving processor 2 and the ground penetrating radar 5 are fixed on the vehicle for mobile measurement Above the platform 1, the lidar 3 is fixed above the data receiving processor 2, and the inertial navigation device 4 is fixed at the center of the vehicle-mounted mobile measurement platform 1.
  • the lidar, 3 inertial navigation device 4 and ground penetrating radar 5 are connected to the data receiving processor through the network cable 2
  • the receiver of the groove wave seismograph performs data transmission with the data receiving processor 2 through the wireless transmitter.
  • a method for generating a high-precision three-dimensional navigation map of a fully mechanized mining face is as follows:
  • the vehicle-mounted mobile measurement platform 1 moves at a uniform speed of about 40km/h in the upper roadway, left middle cut roadway, lower roadway and right middle cut roadway around the coal seam to be mined:
  • the data receiving processor 2 moves with the vehicle-mounted mobile measurement platform 1 to the vicinity of the wireless transmitter and acquires the geological data of the coal seam to be mined collected by the channel wave seismometer through wireless signals: including the seam thickness information, the change inclination of the coal seam and the danger Spatial location of geological structure;
  • the lidar 3 continuously emits laser beams to the surface of the roadway roof, and calculates the distance between the roadway surface and the lidar according to the return time difference of the reflected laser beams, and forms the three-dimensional point cloud data of the roadway; the lidar 3 passes The network cable sends the 3D point cloud data to the data receiving processor 2;
  • the inertial navigation device 4 calculates the posture and motion trajectory information of the vehicle-mounted mobile measurement platform 1 through the rotation angular velocity and acceleration vector of the vehicle-mounted mobile measurement platform 1 relative to the inertial system, and then transmits it to the data receiving processor 2 through a network cable connection;
  • the ground penetrating radar 5 transmits electromagnetic wave signals to the coal seam of the roadway roof through the transmitting unit, and the receiving unit detects the propagation time of the electromagnetic wave signal in the coal seam, and accurately calculates the remaining coal thickness data of the upper and lower roadway roofs on the fully mechanized mining face, and passes the network cable Connect and transmit to the data receiving processor 2;
  • the specific coordinate conversion adopts the seven-parameter coordinate conversion method:
  • (X,Y,Z) T is the three-dimensional coordinate of the coal seam to be mined
  • (X',Y',Z') T is the three-dimensional coordinate of the laser scanning roadway
  • ⁇ X, ⁇ Y, ⁇ Z are the two coordinate origins
  • the translation parameters of ⁇ X , ⁇ Y , ⁇ Z are the three coordinate axis rotation parameters, and m is the scale parameter;
  • the feature fusion method of the vehicle-mounted mobile measurement platform 1 and the groove wave seismograph acquisition data adopts at least one of parameter template method, feature compression and clustering algorithm, K-order nearest neighbor method, artificial neural network and fuzzy integral method. ;
  • the data receiving processor 2 Through the data receiving processor 2, the data collected by the vehicle-mounted mobile measurement platform 1 and the channel wave seismometer are processed, and the Delaunay triangulations of the coal seams, faults/folds and roadways of the fully mechanized mining face to be mined are generated;
  • the Delaunay triangulation steps of the coal seams, faults/folds, and roadways of the mining face include:
  • d3 takes out the constrained edges from the line link list as the base edges, and applies the maximum included angle criterion to generate left and right triangles as the initial triangulation. If the constrained edges are boundaries, a triangle is generated and the triangles are stored in the initial triangulation;
  • step d4 uses the three sides of the newly generated triangle in step d3 as the base side, and generates a new triangulation according to the one-step growth method: Take out the base side, and according to the constraint circle criterion of the Delaunay triangulation, find out the third triangle that forms the triangle with the baseline Point, the two end points of the baseline are connected with the third point to form a new triangle, and the new triangle is stored in the triangulation network at the same time, until the extended side of the new triangle is a boundary side or has been used twice;
  • step d5 Repeat step d4 until the last layer of triangles cannot be expanded, and use the LOP (Local Optimization Procedure) algorithm to optimize all triangulations except the initial triangulation;
  • LOP Local Optimization Procedure
  • step d Use the Delaunay triangulation of coal seams, faults/folds and roadways generated in step d to draw a high-precision profile of the Delaunay triangulation, calculate the topological relationship of the profile, and generate the topological data structure of the profile; generate the topological data structure of the profile include:
  • step e1 Collect all the straight line segments in the triangulation generated in step d5 to generate a line linked list database, and initialize the left and right area codes of all straight line segments to -1 of valid information;
  • e2 Choose i straight line segments from the line link list to form a polygon, search for the boundary of the polygon, if the left code and right code of the current straight line segment are not -1, select the next straight line segment and continue this step; if all straight line segments have left code , The right code is not -1, indicating that all the left and right sides of the straight line segment have been searched, end this step and continue to the next step; if the left code (right code) of the current straight line segment is not -1, you can change it The end point or start point is the current node.
  • the coal seam, faults/folds, roadway, and high-precision profile establish the fully mechanized face to be mined
  • the navigation information automatically queries the database, including the query of coal seam thickness, coal seam inclination, fault/fold spatial location and roadway spatial location, and constructs a high-precision three-dimensional navigation map of fully mechanized mining face.

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  • Engineering & Computer Science (AREA)
  • Remote Sensing (AREA)
  • Radar, Positioning & Navigation (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
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  • Acoustics & Sound (AREA)
  • Environmental & Geological Engineering (AREA)
  • Geophysics (AREA)
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  • Control Of Position, Course, Altitude, Or Attitude Of Moving Bodies (AREA)
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Abstract

一种综采工作面高精度三维导航地图的生成系统及方法，适用于无人化开采技术领域使用。生成系统包括槽波地震仪、激光雷达、组合导航装置、探地雷达和数据处理单元：数据处理单元获取各个传感器采集的数据；对采集数据进行坐标转换、特征融合和一致性处理，生成煤层、断层/褶皱、巷道的Delaunay三角网；绘制三角网的高精度剖面图，计算剖面图拓扑关系，生成剖面图拓扑数据结构，建立基于高精度剖面图的导航信息自动查询数据库平台，构建出综采工作面高精度三维导航地图。本发明生成的高精度三维导航地图可为综采装备提供准确的煤层厚度信息、煤层变化倾角和危险地质构造空间位置，起到高精度定位、信息感知、路径规划等功能。

Description

一种综采工作面高精度三维导航地图的生成系统及方法 技术领域
本发明涉及一种生成系统及方法，尤其适用于无人化开采技术领域使用的一种综采工作面高精度三维导航地图的生成系统及方法。
背景技术
煤矿无人化开采是国际煤炭开采领域共同追求的前沿技术，是减少人员伤亡、保障安全生产的重要手段也是我国煤矿实现安全、高效、绿色开采的有效途径。目前，在地质条件和煤层结构相对简单的工作面可以采用记忆截割和人工远程干预相结合方式来实现无人化开采，但我国大部分工作面的地质条件和煤层结构都非常复杂(如煤层起伏变化大，存在断层、褶皱等危险地质构造)，导致现有技术无法实现无人化开采。
发明内容
技术问题：针对上述技术的不足之处，提供一种步骤简单，适用方便，全自动化且精度高的综采工作面高精度三维导航地图的生成系统及方法。
为实现上述技术目的，本发明的综采工作面高精度三维导航地图的生成系统，包括车载移动测量平台和设置在待开采煤层两侧的槽波地震仪，槽波地震仪包括间隔设置在上巷道中的发射器和间隔设置在下巷道中的接收器，接收器之间通过网线连接并连接有无线发射器；
车载移动测量平台上分别设有数据接收处理器、激光雷达、惯性导航装置和探地雷达，数据接收处理器和探地雷达固定在车载移动测量平台上方，激光雷达固定在数据接收处理器上方，惯性导航装置固定在车载移动测量平台中心位置，激光雷达、惯性导航装置和探地雷达通过网线与数据接收处理器进行数据传输，槽波地震仪的接收器通过无线发射器与数据接收处理器进行数据传输。
所述发射器为槽波地震信号发射器，接收器为槽波地震信号接受器，槽波地震信号接受器通过无线发射器将数据传送给数据接收处理器，槽波地震信号发射器与槽波地震信号接受器对应设置，形成槽波地震信号检波序列。
综采工作面高精度三维导航地图的生成方法，其步骤如下：
a.车载移动测量平台在待开采煤层周围的上巷道、左中切巷、下巷道和右中切巷中以40km/h左右的速度匀速运动：
b.数据接收处理器随着车载移动测量平台移动到无线发射器附近通过无线信号获取槽波地震仪采集的待开采煤层的地质数据：包括待开采煤层煤层厚度信息、煤层变化倾角和危险地质构造空间位置；
激光雷达不间断的向巷道顶板表面发射激光光束，并根据接收到反射回来的激光光束 的返回时间差计算出巷道表面与激光雷达之间的距离，形成巷道的三维点云数据；激光雷达通过网线将三维点云数据发送给数据接收处理器；
惯性导航装置通过车载移动测量平台相对于惯性系的旋转角速度和加速度矢量，计算得到车载移动测量平台的位姿和运动轨迹信息，然后通过网线连接传送给数据接收处理器；
探地雷达通过发射单元向巷道顶板煤层中发射电磁波信号，接受单元通过检测电磁波信号在煤层中的传播时间，精确的计算出综采工作面上下巷道顶板的剩留煤厚数据，并通过网线连接传送给数据接收处理器；
c.对车载移动测量平台和槽波地震仪采集的数据进行坐标转换、特征融合和一致性处理；
d.通过数据接收处理器对车载移动测量平台和槽波地震仪采集的数据进行处理，分别生成待开采综采工作面的煤层、断层/褶皱和巷道的Delaunay三角网；
e.利用步骤d中生成的煤层、断层/褶皱和巷道的Delaunay三角网，绘制Delaunay三角网的高精度剖面图，计算剖面图拓扑关系，生成剖面图拓扑数据结构；
f.根据车载移动测量平台和槽波地震仪采集的数据，以及构建的综采工作面的煤层、断层/褶皱、巷道的Delaunay三角网和高精度剖面图，建立待开采综采工作面的导航信息自动查询数据库，包括煤层厚度、煤层倾角、断层/褶皱空间位置和巷道空间位置的查询，构建出综采工作面高精度三维导航地图。
车载移动测量平台和槽波地震仪采集的数据进行坐标转换、特征融合和一致性处理方法为：首先，通过综采工作面特征不变的特性对采集的数据要素分类分级和特征属性映射，列出相同或相似的属性特征，通过最小二乘法进行整理表达，建立属性特征转换表，消除不同分类分级标准导致的属性特征差异，实现采集数据在特征表达上的一致性；然后，采用基于同名实体匹配方法实现采集数据属性特征合并；
具体坐标转换采用七参数坐标转换法：
Figure PCTCN2019104100-appb-000001
式中，(X,Y,Z) T是待开采煤层的三维坐标，(X',Y',Z') T是激光扫描巷道的三维坐标；ΔX，ΔY，ΔZ是两个坐标原点之间的平移参数，ε X，ε Y，ε Z是3个坐标轴旋转参数，m是尺度参数；
其中，车载移动测量平台(1)和槽波地震仪采集数据的特征融合方法采用参数模板法、特征压缩和聚类算法、K阶最近邻近法、人工神经网络和模糊积分法中的至少一种进行融合。
生成待开采综采工作面的煤层、断层/褶皱、巷道的Delaunay三角网步骤包括：
d1先将所有参与构网的数据包括煤层、断层/褶皱、巷道激光扫描和探地雷达探测顶板数据，由小到大按照先坐标X后Y再Z的顺序进行排序，存入到一个点数据链表中；
d2根据点数据链表创建点数据格网索引，将点数据分块管理，并将约束边存入线链表中；
d3从线链表中依次取出约束边作为基边，应用夹角最大准则生成左右三角形作为初始三角网，若约束边为边界，则生成一个三角形，将三角形存入初始三角网中；
d4以步骤d3新生成的一层三角形的三条边为基边，按照一步生长法生成新的三角网：取出基边，按约束Delaunay三角网的约束圆准则，找出与基线构成三角形的第三点，基线的两个端点与第三点相连，构成新的三角形，同时将该新三角形存入三角网，直到该新的三角形扩展边为边界边或已使用两次；
d5重复步骤d4，直到最后一层三角形不能扩展，并用LOP(Local Optimization Procedure)算法对除初始三角网以外的其它所有三角网进行优化。
生成剖面图拓扑数据结构步骤包括：
e1将步骤d5生成的三角网中所有直线段集合生成线链表数据库，并将所有直线段的左右区域码初始化为有效信息的-1；
e2从线链表中任选i个直线段组成多边形，搜索该多边形边界，如果当前直线段的左码和右码都不是-1，选取下一条直线段，继续本步骤；如果所有直线段左码、右码都不是-1，表明所有直线段的左右两侧都已经被搜索，结束本步骤并继续下一步骤；如当前直线段的左码或右码不为-1，则可以将其终点或起点作为当前的结点，从线链表，按逆时针方向寻找下一直线段，把下一直线段的另一个端点设为当前结点，重复寻找，直到返回到起始直线段，形成Delaunay三角网的高精度剖面图的拓扑数据结构。
有益效果：本发明能为综采装备提供高精度导航信息，是实现地下无人开采的前提和基础，具有的主要优点有：
1)本发明可为综采装备提供准确的煤层厚度信息、煤层变化倾角和危险地质构造空间位置，起到高精度定位、信息感知、路径规划等功能，为无人化开采提供基准化的决策依据；
2)本发明可快速融合槽波地震仪、激光雷达、组合导航装置和探地雷达采集的数据，自动生成综采工作面高精度三维导航地图‘’
3)本发明采集数据的获取方法简单，操作方便、速度快，且所获取的实测数据准确。
附图说明
图1是本发明的综采工作面高精度三维导航地图的生成系统布置图
图2是本发明的车载移动测量系统布置图
图3是本发明的综采工作面高精度三维导航地图的生成方法流程图
图中：1-车载移动测量平台；2-数据接受处理器；3-激光雷达；4-惯性导航装置；5-探地雷 达。
具体实施方式
下面结合附图中的实施例对本发明作进一步的描述：
如图1所示，本发明的综采工作面高精度三维导航地图的生成系统，包括车载移动测量平台1和设置在待开采煤层两侧的槽波地震仪，槽波地震仪包括间隔设置在上巷道中的发射器和间隔设置在下巷道中的接收器，接收器之间通过网线连接并连接有无线发射器；所述发射器为槽波地震信号发射器，接收器为槽波地震信号接受器，槽波地震信号接受器通过无线发射器将数据传送给数据接收处理器2，槽波地震信号发射器与槽波地震信号接受器对应设置，形成槽波地震信号检波序列；
如图2所示，车载移动测量平台1上分别设有数据接收处理器2、激光雷达3、惯性导航装置4和探地雷达5，数据接收处理器2和探地雷达5固定在车载移动测量平台1上方，激光雷达3固定在数据接收处理器2上方，惯性导航装置4固定在车载移动测量平台1中心位置，激光雷达、3惯性导航装置4和探地雷达5通过网线与数据接收处理器2进行数据传输，槽波地震仪的接收器通过无线发射器与数据接收处理器2进行数据传输。
如图3所示，一种综采工作面高精度三维导航地图的生成方法，其步骤如下：
a.车载移动测量平台1在待开采煤层周围的上巷道、左中切巷、下巷道和右中切巷中以40km/h左右的速度匀速运动：
b.数据接收处理器2随着车载移动测量平台1移动到无线发射器附近通过无线信号获取槽波地震仪采集的待开采煤层的地质数据：包括待开采煤层煤层厚度信息、煤层变化倾角和危险地质构造空间位置；
激光雷达3不间断的向巷道顶板表面发射激光光束，并根据接收到反射回来的激光光束的返回时间差计算出巷道表面与激光雷达之间的距离，形成巷道的三维点云数据；激光雷达3通过网线将三维点云数据发送给数据接收处理器2；
惯性导航装置4通过车载移动测量平台1相对于惯性系的旋转角速度和加速度矢量，计算得到车载移动测量平台1的位姿和运动轨迹信息，然后通过网线连接传送给数据接收处理器2；
探地雷达5通过发射单元向巷道顶板煤层中发射电磁波信号，接受单元通过检测电磁波信号在煤层中的传播时间，精确的计算出综采工作面上下巷道顶板的剩留煤厚数据，并通过网线连接传送给数据接收处理器2；
c.对车载移动测量平台1和槽波地震仪采集的数据进行坐标转换、特征融合和一致性处理；车载移动测量平台1和槽波地震仪采集的数据进行坐标转换、特征融合和一致性处 理方法为：首先，通过综采工作面特征不变的特性对采集的数据要素分类分级和特征属性映射，列出相同或相似的属性特征，通过最小二乘法进行整理表达，建立属性特征转换表，消除不同分类分级标准导致的属性特征差异，实现采集数据在特征表达上的一致性；然后，采用基于同名实体匹配方法实现采集数据属性特征合并；
具体坐标转换采用七参数坐标转换法：
Figure PCTCN2019104100-appb-000002
式中，(X,Y,Z) T是待开采煤层的三维坐标，(X',Y',Z') T是激光扫描巷道的三维坐标；ΔX，ΔY，ΔZ是两个坐标原点之间的平移参数，ε X，ε Y，ε Z是3个坐标轴旋转参数，m是尺度参数；
其中，车载移动测量平台1和槽波地震仪采集数据的特征融合方法采用参数模板法、特征压缩和聚类算法、K阶最近邻近法、人工神经网络和模糊积分法中的至少一种进行融合；
d.通过数据接收处理器2对车载移动测量平台1和槽波地震仪采集的数据进行处理，分别生成待开采综采工作面的煤层、断层/褶皱和巷道的Delaunay三角网；生成待开采综采工作面的煤层、断层/褶皱、巷道的Delaunay三角网步骤包括：
d1先将所有参与构网的数据包括煤层、断层/褶皱、巷道激光扫描和探地雷达探测顶板数据，由小到大按照先坐标X后Y再Z的顺序进行排序，存入到一个点数据链表中；
d2根据点数据链表创建点数据格网索引，将点数据分块管理，并将约束边存入线链表中；
d3从线链表中依次取出约束边作为基边，应用夹角最大准则生成左右三角形作为初始三角网，若约束边为边界，则生成一个三角形，将三角形存入初始三角网中；
d4以步骤d3新生成的一层三角形的三条边为基边，按照一步生长法生成新的三角网：取出基边，按约束Delaunay三角网的约束圆准则，找出与基线构成三角形的第三点，基线的两个端点与第三点相连，构成新的三角形，同时将该新三角形存入三角网，直到该新的三角形扩展边为边界边或已使用两次；
d5重复步骤d4，直到最后一层三角形不能扩展，并用LOP(Local Optimization Procedure)算法对除初始三角网以外的其它所有三角网进行优化；
e.利用步骤d中生成的煤层、断层/褶皱和巷道的Delaunay三角网，绘制Delaunay三角网的高精度剖面图，计算剖面图拓扑关系，生成剖面图拓扑数据结构；生成剖面图拓扑数据结构步骤包括：
e1将步骤d5生成的三角网中所有直线段集合生成线链表数据库，并将所有直线段的 左右区域码初始化为有效信息的-1；
e2从线链表中任选i个直线段组成多边形，搜索该多边形边界，如果当前直线段的左码和右码都不是-1，选取下一条直线段，继续本步骤；如果所有直线段左码、右码都不是-1，表明所有直线段的左右两侧都已经被搜索，结束本步骤并继续下一步骤；如当前直线段的左码(右码)不为-1，则可以将其终点或起点作为当前的结点，从线链表，按逆时针方向寻找下一直线段，把下一直线段的另一个端点设为当前结点，重复寻找，直到返回到起始直线段，形成Delaunay三角网的高精度剖面图的拓扑数据结构；
f.根据车载移动测量平台1和槽波地震仪采集的数据，以及构建的综采工作面的煤层、断层/褶皱、巷道的Delaunay三角网和高精度剖面图，建立待开采综采工作面的导航信息自动查询数据库，包括煤层厚度、煤层倾角、断层/褶皱空间位置和巷道空间位置的查询，构建出综采工作面高精度三维导航地图。

Claims (6)

  1. 一种综采工作面高精度三维导航地图的生成系统，其特征在于：包括车载移动测量平台(1)和设置在待开采煤层两侧的槽波地震仪，槽波地震仪包括间隔设置在上巷道中的发射器和间隔设置在下巷道中的接收器，接收器之间通过网线连接并连接有无线发射器；
    车载移动测量平台(1)上分别设有数据接收处理器(2)、激光雷达(3)、惯性导航装置(4)和探地雷达(5)，数据接收处理器(2)和探地雷达(5)固定在车载移动测量平台(1)上方，激光雷达(3)固定在数据接收处理器(2)上方，惯性导航装置(4)固定在车载移动测量平台(1)中心位置，激光雷达、(3)惯性导航装置(4)和探地雷达(5)通过网线与数据接收处理器(2)进行数据传输，槽波地震仪的接收器通过无线发射器与数据接收处理器(2)进行数据传输。
  2. 根据权利要求1所述的综采工作面高精度三维导航地图的生成系统，其特征在于：所述发射器为槽波地震信号发射器，接收器为槽波地震信号接受器，槽波地震信号接受器通过无线发射器将数据传送给数据接收处理器(2)，槽波地震信号发射器与槽波地震信号接受器对应设置，形成槽波地震信号检波序列。
  3. 一种使用权利要求1所述综采工作面高精度三维导航地图的生成系统的综采工作面高精度三维导航地图的生成方法，其特征在于步骤如下：
    a.车载移动测量平台(1)在待开采煤层周围的上巷道、左中切巷、下巷道和右中切巷中以40km/h左右的速度匀速运动：
    b.数据接收处理器(2)随着车载移动测量平台(1)移动到无线发射器附近通过无线信号获取槽波地震仪采集的待开采煤层的地质数据：包括待开采煤层煤层厚度信息、煤层变化倾角和危险地质构造空间位置；
    激光雷达(3)不间断的向巷道顶板表面发射激光光束，并根据接收到反射回来的激光光束的返回时间差计算出巷道表面与激光雷达之间的距离，形成巷道的三维点云数据；激光雷达(3)通过网线将三维点云数据发送给数据接收处理器(2)；
    惯性导航装置(4)通过车载移动测量平台(1)相对于惯性系的旋转角速度和加速度矢量，计算得到车载移动测量平台(1)的位姿和运动轨迹信息，然后通过网线连接传送给数据接收处理器(2)；
    探地雷达(5)通过发射单元向巷道顶板煤层中发射电磁波信号，接受单元通过检测电磁波信号在煤层中的传播时间，精确的计算出综采工作面上下巷道顶板的剩留煤厚数据，并通过网线连接传送给数据接收处理器(2)；
    c.对车载移动测量平台(1)和槽波地震仪采集的数据进行坐标转换、特征融合和一 致性处理；
    d.通过数据接收处理器(2)对车载移动测量平台(1)和槽波地震仪采集的数据进行处理，分别生成待开采综采工作面的煤层、断层/褶皱和巷道的Delaunay三角网；
    e.利用步骤d中生成的煤层、断层/褶皱和巷道的Delaunay三角网，绘制Delaunay三角网的高精度剖面图，计算剖面图拓扑关系，生成剖面图拓扑数据结构；
    f.根据车载移动测量平台(1)和槽波地震仪采集的数据，以及构建的综采工作面的煤层、断层/褶皱、巷道的Delaunay三角网和高精度剖面图，建立待开采综采工作面的导航信息自动查询数据库，包括煤层厚度、煤层倾角、断层/褶皱空间位置和巷道空间位置的查询，构建出综采工作面高精度三维导航地图。
  4. 根据权利要求3所述的综采工作面高精度三维导航地图的生成方法，其特征在于车载移动测量平台(1)和槽波地震仪采集的数据进行坐标转换、特征融合和一致性处理方法为：首先，通过综采工作面特征不变的特性对采集的数据要素分类分级和特征属性映射，列出相同或相似的属性特征，通过最小二乘法进行整理表达，建立属性特征转换表，消除不同分类分级标准导致的属性特征差异，实现采集数据在特征表达上的一致性；然后，采用基于同名实体匹配方法实现采集数据属性特征合并；
    具体坐标转换采用七参数坐标转换法：
    Figure PCTCN2019104100-appb-100001
    式中，(X,Y,Z) T是待开采煤层的三维坐标，(X',Y',Z') T是激光扫描巷道的三维坐标；ΔX，ΔY，ΔZ是两个坐标原点之间的平移参数，ε X，ε Y，ε Z是3个坐标轴旋转参数，m是尺度参数；
    其中，车载移动测量平台(1)和槽波地震仪采集数据的特征融合方法采用参数模板法、特征压缩和聚类算法、K阶最近邻近法、人工神经网络和模糊积分法中的至少一种进行融合。
  5. 根据权利要求3所述的综采工作面高精度三维导航地图的生成方法，其特征在于生成待开采综采工作面的煤层、断层/褶皱、巷道的Delaunay三角网步骤包括：
    d1先将所有参与构网的数据包括煤层、断层/褶皱、巷道激光扫描和探地雷达探测顶板数据，由小到大按照先坐标X后Y再Z的顺序进行排序，存入到一个点数据链表中；
    d2根据点数据链表创建点数据格网索引，将点数据分块管理，并将约束边存入线链表中；
    d3从线链表中依次取出约束边作为基边，应用夹角最大准则生成左右三角形作为初始三角网，若约束边为边界，则生成一个三角形，将三角形存入初始三角网中；
    d4以步骤d3新生成的一层三角形的三条边为基边，按照一步生长法生成新的三角网：取出基边，按约束Delaunay三角网的约束圆准则，找出与基线构成三角形的第三点，基线的两个端点与第三点相连，构成新的三角形，同时将该新三角形存入三角网，直到该新的三角形扩展边为边界边或已使用两次；
    d5重复步骤d4，直到最后一层三角形不能扩展，并用LOP(Local Optimization Procedure)算法对除初始三角网以外的其它所有三角网进行优化。
  6. 根据权利要求3和所述的综采工作面高精度三维导航地图的生成方法，其特征在于生成剖面图拓扑数据结构步骤包括：
    e1将步骤d5生成的三角网中所有直线段集合生成线链表数据库，并将所有直线段的左右区域码初始化为有效信息的-1；
    e2从线链表中任选i个直线段组成多边形，搜索该多边形边界，如果当前直线段的左码和右码都不是-1，选取下一条直线段，继续本步骤；如果所有直线段左码、右码都不是-1，表明所有直线段的左右两侧都已经被搜索，结束本步骤并继续下一步骤；如当前直线段的左码(右码)不为-1，则可以将其终点或起点作为当前的结点，从线链表，按逆时针方向寻找下一直线段，把下一直线段的另一个端点设为当前结点，重复寻找，直到返回到起始直线段，形成Delaunay三角网的高精度剖面图的拓扑数据结构。
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