WO2020173030A1 - 一种基于顶煤厚度变化量实时监测的智能化放煤方法 - Google Patents

一种基于顶煤厚度变化量实时监测的智能化放煤方法 Download PDF

Info

Publication number
WO2020173030A1
WO2020173030A1 PCT/CN2019/094518 CN2019094518W WO2020173030A1 WO 2020173030 A1 WO2020173030 A1 WO 2020173030A1 CN 2019094518 W CN2019094518 W CN 2019094518W WO 2020173030 A1 WO2020173030 A1 WO 2020173030A1
Authority
WO
WIPO (PCT)
Prior art keywords
coal
caving
thickness
time
coal caving
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Ceased
Application number
PCT/CN2019/094518
Other languages
English (en)
French (fr)
Inventor
刘万里
于斌
王世博
伊世学
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
China University of Mining and Technology CUMT
China University of Mining and Technology Beijing CUMTB
Original Assignee
China University of Mining and Technology CUMT
China University of Mining and Technology Beijing CUMTB
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by China University of Mining and Technology CUMT, China University of Mining and Technology Beijing CUMTB filed Critical China University of Mining and Technology CUMT
Publication of WO2020173030A1 publication Critical patent/WO2020173030A1/zh
Anticipated expiration legal-status Critical
Ceased legal-status Critical Current

Links

Images

Classifications

    • E—FIXED CONSTRUCTIONS
    • E21—EARTH OR ROCK DRILLING; MINING
    • E21C—MINING OR QUARRYING
    • E21C41/00—Methods of underground or surface mining; Layouts therefor
    • E21C41/16—Methods of underground mining; Layouts therefor
    • E21C41/18—Methods of underground mining; Layouts therefor for brown or hard coal
    • E—FIXED CONSTRUCTIONS
    • E21—EARTH OR ROCK DRILLING; MINING
    • E21D—SHAFTS; TUNNELS; GALLERIES; LARGE UNDERGROUND CHAMBERS
    • E21D23/00—Mine roof supports for step- by- step movement, e.g. in combination with provisions for shifting of conveyors, mining machines, or guides therefor
    • E21D23/12—Control, e.g. using remote control
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01F—MEASURING VOLUME, VOLUME FLOW, MASS FLOW OR LIQUID LEVEL; METERING BY VOLUME
    • G01F1/00—Measuring the volume flow or mass flow of fluid or fluent solid material wherein the fluid passes through a meter in a continuous flow
    • G01F1/66—Measuring the volume flow or mass flow of fluid or fluent solid material wherein the fluid passes through a meter in a continuous flow by measuring frequency, phase shift or propagation time of electromagnetic or other waves, e.g. using ultrasonic flowmeters
    • 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

Definitions

  • the invention relates to a coal caving method for a fully mechanized caving face, in particular to an intelligent coal caving method based on real-time monitoring of top coal thickness variation, and belongs to the technical field of fully mechanized caving automatic mining.
  • Coal is my country’s main energy source. Thick coal seams are rich in reserves and mainly adopt fully mechanized caving mining methods.
  • the national "13th Five-Year Plan” lists "accelerating the development and application of unmanned coal mining technology” as a key project in the energy field. State Administration of Work Safety The “mechanized replacement of personnel, automated reduction of personnel” scientific and technological strengthening security special action has pointed out the direction for scientific and technological innovation in the coal industry, and the realization of intelligent fully-mechanized caving mining in thick coal seams will become an important development trend.
  • the second is to apply the electro-hydraulic control system to realize the opening and closing of the coal caving opening.
  • the basic method is to embed the preset coal caving program in the electro-hydraulic control system, and realize the caving according to the preset coal caving action and coal caving time. Opening and closing of coal mouth.
  • the thickness of the coal seam in the working face varies greatly, whether it is between adjacent supports or in the strike length direction. Therefore, there is a big defect in completing the opening and closing of the caving mouth in advance according to the unified coal caving procedure of all the supports of the working face. Due to the change of the thickness of the top coal at any time, the opening and closing time of the caving mouth also changes accordingly.
  • the purpose of the present invention is to overcome the deficiencies in the prior art, and provide a real-time monitoring system based on the change of top coal thickness, which is convenient to use, can effectively improve the efficiency of top coal caving, and reduce the labor intensity of coal caving workers. Intelligent coal caving method.
  • the intelligent coal caving method based on real-time monitoring of top coal thickness variation of the present invention uses ultra-wideband radar device and laser three-dimensional scanning device to monitor top coal thickness information in real time, and automatically according to the top coal thickness variation
  • the specific steps include:
  • the ultra-wideband radar device includes a transmitter, a receiver and a control machine; the hydraulic support includes: a top beam, a pole, and a shield Beam, tail beam, base, hydraulic support controller and electro-hydraulic servo control system.
  • the transmitting device transmits the ultra-wideband radar signal into the top coal seam
  • the receiving device receives the echo signal after the ultra-wideband radar signal is reflected by the coal and rock interface and performs de-noising processing.
  • the control machine the radar signal is recorded from transmission to reception. Time to get the propagation time of the ultra-wideband radar signal in the coal seam;
  • L is the thickness of the top coal
  • C is the propagation speed of electromagnetic waves in vacuum
  • ⁇ T is the propagation time of the signal in the coal seam
  • ⁇ is the relative permittivity
  • the radial basis neural network is used to establish the corresponding relationship equation between the change of the top coal thickness and the coal caving time;
  • step 1 denoising processing is performed on the received ultra-wideband radar signal, specifically: wavelet denoising, deconvolution adaptive denoising and partial differential denoising.
  • step 3 described in 3 data processing is performed on the three-dimensional geometric features of the coal flow surface scanned by the laser, specifically a point cloud denoising method based on a multi-dimensional tree.
  • a radial basis neural network is used to establish a neural network model with top coal thickness, coal flow geometric characteristics, coal flow velocity, and coal caving volume as input and top coal flow time parameters as output.
  • the present invention Due to the adoption of the above technical solution, the present invention has the following advantages compared with the prior art:
  • the present invention automatically adjusts the opening and closing time of the caving opening according to the change of top coal thickness in real time to realize adaptive coal caving, which effectively eliminates the caving caused by factors such as the change of top coal thickness at any time, poor caving regularity, and complicated control variables. Over-discharge or under-discharge occurs in the coal process. Therefore, the present invention has strong adaptability and can effectively control the opening and closing time of the coal caving outlet;
  • the ultra-wideband radar technology can obtain top coal thickness information in real time, and the laser three-dimensional scanning technology can monitor the coal caving amount at the coal caving mouth in real time; and Based on this, a nonlinear function equation between the variation of the top coal thickness and the caving time is established, and the optimal opening and closing time of each caving opening in each coal caving stage is calculated. Therefore, the present invention can be more objective and accurate To determine the best opening and closing time of the coal caving port, the control of the entire coal caving process is relatively accurate and the effect is significant;
  • Fig. 1 is a flow chart of the opening and closing of caving openings based on real-time monitoring of the change in top coal thickness of the present invention.
  • Figure 2 is a diagram of the topological structure of the radial basis neural network of the present invention.
  • Fig. 3 is a schematic diagram of the arrangement of the ultra-wideband radar device and the laser three-dimensional scanning device of the present invention on the hydraulic support.
  • the intelligent coal caving method based on the real-time monitoring of the top coal thickness variation of the present invention uses the ultra-wideband radar device 4 and the laser three-dimensional scanning device 6 to monitor the top coal thickness information in real time, and automatically according to the top coal thickness variation
  • the specific steps include:
  • the ultra-wideband radar device 4 includes a transmitting device, a receiving device and a control machine connected in sequence; the hydraulic support includes a top beam 1.
  • Vertical pole 2, shield beam 3, tail beam 5, base 7, controller and electro-hydraulic servo control system top beam 1 is set on vertical pole 2, vertical pole 2 is set on base 7, and shield beam 3 is set on Between the top beam 1 and the base 7, the tail beam 5 is connected to the rear of the top beam 1.
  • the controller and electro-hydraulic servo control system have different installation positions according to different hydraulic support product models. Transmit the ultra-wideband radar signal greater than 0.25 into the top coal seam through the transmitting device.
  • the receiving device receives the echo signal reflected by the ultra-wideband radar signal through the coal-rock interface and performs de-noising processing. According to the control machine, the radar signal is recorded from transmission to The time used for receiving is the propagation time of the ultra-wideband radar signal in the coal seam; the denoising processing of the received ultra-wideband radar signal is specifically: wavelet denoising, deconvolution adaptive denoising and partial differential denoising .
  • L is the thickness of the top coal
  • C is the propagation velocity of electromagnetic waves in vacuum
  • ⁇ T is the propagation time of the signal in the coal seam
  • ⁇ is the relative permittivity
  • Data processing is performed on the 3D point cloud data of the coal flow surface scanned by laser, specifically the point cloud denoising method based on KD tree, which mainly uses statistical principles to calculate the mean and standard deviation of the distance between a point and each point in the neighborhood, and set The best threshold interval, if the distance of a point in the neighborhood is not in this interval, the point is considered a noise point.
  • the specific process is: establish a KD tree for the coal flow point cloud P of the coal caving mouth scanned by the laser, traverse the point cloud P, and find it through the KD tree
  • P is the laser scanning point cloud set
  • N is the KD tree point cloud model
  • k is the point cloud model size
  • ⁇ is the standard deviation coefficient
  • D is the distance between the point clouds
  • ⁇ is the mean
  • ⁇ is the standard deviation
  • the radial basis neural network is used to establish the correspondence between the change in the top coal thickness and the coal caving time Relation equation; as shown in Figure 2, establish a radial basis neural network model with top coal thickness, coal flow geometric characteristics, coal flow velocity, coal caving volume and other factors as input, and top coal flow time parameters as output , Its input and output relationship is:
  • t is the node center of the hidden layer
  • ⁇ is the standard deviation of the basis function in the hidden layer
  • the method for determining the standard deviation ⁇ of the basis function and the network weight ⁇ in the hidden layer of the radial basis function neural network is as follows:
  • d max is the maximum distance between the selected centers, n is the number of hidden nodes, G is the output of the hidden layer, and d is the expected output of the output layer;
  • x M represents the coal caving parameters of the M th group (top coal thickness, coal flow geometric characteristics, coal flow velocity, and caving coal amount)
  • y j represents the top coal flow time corresponding to the M th group at time j.
  • ⁇ is the network weight.
  • step 6 The optimal opening and closing time of the coal caving opening obtained in step 6 is transmitted to the hydraulic support controller, and the hydraulic support controller sends opening and closing instructions to the coal caving opening through the electro-hydraulic servo control system to realize the control of the fully mechanized caving face Intelligent coal caving.
  • Figure 3 is a schematic diagram of the arrangement of the ultra-wideband radar device 4 and the laser three-dimensional scanning device 6 on the hydraulic support;
  • the ultra-wideband radar device 4 is installed on the top beam of the hydraulic support near the middle and rear part, and transmits the ultra-wideband radar signal to the top coal seam through the transmitter
  • the receiving device receives the echo signal reflected by the ultra-wideband radar signal through the coal-rock interface;
  • the laser three-dimensional scanning device 6 is installed on the base of the hydraulic support, close to the tail beam and aligned with the coal caving opening of the hydraulic support, after the coal caving starts ,
  • the laser three-dimensional scanning device 6 continuously scans the coal flow information discharged from the coal caving port in real time.

Landscapes

  • Engineering & Computer Science (AREA)
  • Remote Sensing (AREA)
  • Mining & Mineral Resources (AREA)
  • Physics & Mathematics (AREA)
  • Radar, Positioning & Navigation (AREA)
  • Geology (AREA)
  • Geochemistry & Mineralogy (AREA)
  • General Life Sciences & Earth Sciences (AREA)
  • Electromagnetism (AREA)
  • General Physics & Mathematics (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Fluid Mechanics (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • Length Measuring Devices By Optical Means (AREA)
  • Optical Radar Systems And Details Thereof (AREA)

Abstract

一种基于顶煤厚度变化量实时监测的智能化放煤方法，利用超宽带雷达和激光三维扫描技术实时监测顶煤厚度信息，根据顶煤厚度变化量自动调整放煤口开闭，包括如下步骤：（一）超宽带雷达装置（4）根据雷达回波信号计算出顶煤厚度变化量；（二）激光三维扫描装置（6）根据扫描的放煤口煤流几何特征、煤流速度和放煤时间计算出放煤量；（三）建立顶煤厚度变化量与放煤时间之间的函数方程，计算出放煤口在不同放煤阶段中的最佳开闭时间；（四）通过放顶煤控制器向放煤口发出开启和关闭指令，实现综放工作面的智能化放煤。该方法使得整个放煤过程控制准确、适应性强，无需人工干预，有效提高放煤效率、降低劳动强度。

Description

一种基于顶煤厚度变化量实时监测的智能化放煤方法 技术领域
本发明涉及一种综放工作面的放煤方法，尤其是一种基于顶煤厚度变化量实时监测的智能化放煤方法，属于综放自动化开采技术领域。
背景技术
煤炭是我国的主体能源，厚煤层储量丰富且主要采用综放开采方法，国家“十三五”规划将“加快推进煤炭无人开采技术研发和应用”列入能源领域重点工程，国家安全监管总局开展的“机械化换人、自动化减人”科技强安专项行动为煤炭行业科技创新指明了方向，厚煤层实现智能化综放开采将成为重要的发展趋势。
在综放开采过程中，亟待解决的难题之一是如何正确判断放煤口最佳开闭时间，是目前急待解决的与智能化综放开采相配套的技术问题。目前，对于放煤口的开闭时间主要有两种方法，其一是由放煤工人通过视觉和听觉，以及经验积累来确定放煤口的开闭，这样就会带来过放或者欠放情况发生，造成大量丢煤或矸石混入现象发生使得资源浪费和增加后期洗选成本，同时放煤过程会产生大量的粉尘和瓦斯，对放煤操作工人的生命健康造成巨大威胁。二是应用电液控制系统来实现放煤口的开闭，基本方法是在电液控制系统中嵌入预先设定好的放煤程序,按照预先设定好的放煤动作及放煤时间实现放煤口的开闭。但在工作面煤层厚度变化较大,无论是在相邻支架之间还是在走向长度方向都存在较大差异。因此预先按照工作面全部支架设定统一的放煤程序完成放煤口的开闭存在较大的缺陷,由于顶煤厚度随时变化导致放煤口开闭时间也随之变化,如若放煤口开闭时间一成不变,就必然导致放煤过程中出现过放或者欠放情况,使煤质下降或者资源遭受损失。因此，根据顶煤厚度变化量实时自动调整放煤口开闭时间而实现自适应放煤,是实现真正意义上智能化放煤的关键技术。
发明内容
技术问题：本发明的目的是要克服现有技术中的不足之处，提供了一种使用方便、能有效提高放顶煤效率、降低放煤工人劳动强度的基于顶煤厚度变化量实时监测的智能化放煤方法。
技术方案：为了实现上述目的，本发明的基于顶煤厚度变化量实时监测的智能化放煤方法，利用超宽带雷达装置和激光三维扫描装置实时监测顶煤厚度信息，根据顶煤厚度变化量自动调整放煤口开闭时间，具体步骤包括：
(1)把超宽带雷达装置安装在液压支架的顶梁靠近中后部位置，所述超宽带雷达装置包括发射装置、接收装置和控制机；所述液压支架包括：顶梁、立杆、掩护梁、尾梁、底座、液压支架控制器和电液伺服控制系统。其中，发射装置把超宽带雷达信号发射到顶煤煤层中，接收装置接收超宽带雷达信号经由煤岩分界面反射后的回波信号并进行去噪处理，根据控制机记录雷达信号从发射到接收所用的时间得到超宽带雷达信号在煤层中的传播 时间；
(2)实时采集超宽带雷达信号在煤层中的传播时间，根据公式
Figure PCTCN2019094518-appb-000001
计算出顶煤厚度，并采用时域有限差分方法来提高顶煤厚度的计算精度，
式中：L为顶煤厚度，C为在真空中的电磁波的传播速度，ΔT为信号在煤层中的传播时间，ε为相对介电常数；
(3)把激光三维扫描装置安装在液压支架上靠近尾梁并对准液压支架的放煤口，放煤开始后，通过激光三维扫描装置实时连续扫描放煤口放出的煤流信息，得到煤流表面三维几何特征和煤流速度；
(4)利用激光三维扫描采集的煤流表面三维几何特征和煤流速度，结合放煤时间计算出放煤口实际放煤量；
(5)根据步骤2计算的顶煤厚度和步骤4计算的放煤口实际放煤量结果，采用径向基神经网络建立顶煤厚度的变化量与放煤时间之间的对应关系方程；
(6)结合建立的对应关系方程和的顶煤厚度的变化量，计算出放煤口在各个放煤阶段中最佳开闭时间；
(7)将获得的放煤口最佳开闭时间传输给液压支架控制器，液压支架控制器通过电液伺服控制系统向放煤口发出开启和关闭指令，实现综放工作面的智能化放煤。
所述的步骤1中，对接收到的超宽带雷达信号进行去噪处理，具体为：小波去噪、反卷积自适应去噪和偏微分去噪。
3所述的步骤3中，对激光扫描的煤流表面三维几何特征进行数据处理，具体为基于多维树的点云去噪方法。
所述的步骤5中，采用径向基神经网络建立以顶煤厚度、煤流几何特征、煤流速度、放煤口放煤量作为输入，以顶煤流动时间参数为输出的神经网络模型。
有益效果：由于采用了上述技术方案，本发明与现有技术相比具有如下优点：
(1)本发明根据顶煤厚度变化量实时自动调整放煤口开闭时间来实现自适应放煤,有效的消除了由于顶煤厚度随时变化、放出规律性差、控制变量复杂等因素造成的放煤过程中出现的过放或者欠放情况，因此，本发明适应性强，并能够有效控制放煤口开闭时间；
(2)本发明提供的基于顶煤厚度变化量实时监测的智能化放煤方法，超宽带雷达技术能实时获取顶煤厚度信息，激光三维扫描技术能实时监测放煤口的放煤量；并据此建立了顶煤厚度的变化量与放煤时间之间的非线性函数方程，计算出每个放煤口在各个放煤阶段中最佳开闭时间，因此，本发明能够较为客观、准确的确定放煤口最佳开闭时间，对整个放煤过程控制比较准确，效果显著；
(3)本发明提供的基于顶煤厚度变化量实时监测的智能化放煤方法，由于在获得放 煤口在各个放煤阶段中最佳开闭时间后经由放顶煤控制器向液压支架电液控制系统发出指令，由电液控制系统实现对放煤口的关闭和开启，因此，本发明能够实现在整个放煤开闭过程无需人工干预，有效提高了放煤效率、降低了放煤工人劳动强度。
附图说明
图1是本发明的基于顶煤厚度变化量实时监测的放煤口开闭流程图。
图2是本发明的径向基神经网络拓扑结构图。
图3是本发明的超宽带雷达装置和激光三维扫描装置在液压支架上布置示意图。
图中：1-顶梁；2-立杆；3-掩护梁；4-超宽带雷达装置；5-尾梁；6-激光三维扫描装置；7-底座。
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，本发明的基于顶煤厚度变化量实时监测的智能化放煤方法，利用超宽带雷达装置4和激光三维扫描装置6实时监测顶煤厚度信息，根据顶煤厚度变化量自动调整放煤口开闭时间，具体步骤包括：
(1)把超宽带雷达装置安装在液压支架的顶梁1靠近中后部位置，所述超宽带雷达装置4包括依次连接的发射装置、接收装置和控制机；所述的液压支架包括顶梁1、立杆2、掩护梁3、尾梁5、底座7、控制器和电液伺服控制系统，顶梁1设在立杆2上，立杆2设在底座7上，掩护梁3设在顶梁1与底座7之间，尾梁5连接在顶梁1后部，控制器和电液伺服控制系统根据液压支架产品型号的不同其安装位置也不同。通过发射装置将大于0.25的超宽带雷达信号发射到顶煤煤层中，接收装置接收超宽带雷达信号经由煤岩分界面反射后的回波信号并进行去噪处理，根据控制机记录雷达信号从发射到接收所用的时间得到超宽带雷达信号在煤层中的传播时间；所述对接收到的超宽带雷达信号进行去噪处理，具体为：小波去噪、反卷积自适应去噪和偏微分去噪。
(2)实时采集超宽带雷达信号在煤层中的传播时间，根据公式
Figure PCTCN2019094518-appb-000002
计算出顶煤厚度，并采用时域有限差分方法来提高顶煤厚度的计算精度，
公式中：L为顶煤厚度，C为在真空中的电磁波的传播速度，ΔT为信号在煤层中的传播时间，ε为相对介电常数；
(3)在液压支架靠近尾梁并对准液压支架的放煤口处安装激光三维扫描装置6，放煤开始后，通过激光三维扫描装置6实时连续扫描放煤口放出的煤流信息，得到煤流表面三维几何特征和煤流速度；对激光扫描的煤流表面三维几何特征所得到的点云数据进行数据处理，具体为基于多维(K-Demension，KD)树的点云去噪方法。
对激光扫描的煤流表面三维点云数据进行数据处理，具体为基于KD树的点云去噪方 法，其主要是利用统计原理，通过计算点与邻域各点距离的均值和标准差，设置最佳阈值区间，如果邻域中某点的距离不在此区间，则认为该点为噪声点。
具体的过程是：对激光三维扫描的放煤口煤流点云P建立KD树，遍历点云P，通过KD树查找其
Figure PCTCN2019094518-appb-000003
k值可根据点云模型的大小来决定；计算
Figure PCTCN2019094518-appb-000004
内每个点到当前点P的欧氏距离D＝{d 1,d 2,...,d k}；计算距离D的均值μ和标准差σ；最后判断如果某个点的距离d i不在区间μ±ασ中，则将其对应的点去除；α的取值根据邻域的大小来决定。其中P为激光扫描点云集，N为KD树点云模型，k为点云模型大小，α为标准差系数，D为点云之间的距离，μ是均值和σ是标准差；
(4)利用步骤3中激光三维扫描装置6采集的煤流表面三维几何特征和煤流速度，结合放煤时间计算出放煤口实际的放煤量；
(5)根据步骤2中计算出的顶煤厚度和步骤4中计算出放煤口实际的放煤量结果，采用径向基神经网络建立顶煤厚度的变化量与放煤时间之间的对应关系方程；如图2所示，建立以顶煤厚度、煤流几何特征、煤流速度、放煤口放煤量等因素作为输入，以顶煤流动时间参数为输出的径向基神经网络模型，其输入输出关系式为：
Figure PCTCN2019094518-appb-000005
Figure PCTCN2019094518-appb-000006
式中：t是隐含层的节点中心，σ是隐含层中基函数的标准差；
径向基神经网络中隐含层中基函数的标准差σ和网络权值ω的确定方法如下：
Figure PCTCN2019094518-appb-000007
式中：d max是所选取的中心之间的最大距离，n是隐含节点的个数，G是隐含层输出，d是输出层的期望输出；
采用径向基神经网络建立以顶煤厚度、煤流几何特征、煤流速度、放煤口放煤量作为输入，以顶煤流动时间参数为输出的神经网络模型；
x M代表第M组的放煤参数(顶煤厚度、煤流几何特征、煤流速度、放煤口放煤量)，y j表示j时刻第M组对应的顶煤流动时间。
Figure PCTCN2019094518-appb-000008
是隐含层基函数，ω是网络权值。
(6)结合步骤5中建立的非线性函数方程和步骤1中监测的顶煤厚度的变化量，准确计算出放煤口在矸石阶段、煤矸混放阶段和放煤三个阶段中最佳开闭时间；
(7)将步骤6中获得的放煤口最佳开闭时间传输给液压支架控制器，液压支架控制器通过电液伺服控制系统向放煤口发出开启和关闭指令，实现综放工作面的智能化放煤。
图3是超宽带雷达装置4和激光三维扫描装置6在液压支架上布置示意图；超宽带雷达装置4安装在液压支架顶梁上靠近中后部，通过发射装置把超宽带雷达信号发射到顶煤煤层中，接收装置接收超宽带雷达信号经由煤岩分界面反射后的回波信号；激光三维扫描装置6安装在液压支架底座上，靠近尾梁并对准液压支架的放煤口，放煤开始后，激光三维扫描装置6实时连续扫描放煤口放出的煤流信息。

Claims (4)

  1. 一种基于顶煤厚度变化量实时监测的智能化放煤方法，其特征在于，利用超宽带雷达装置和激光三维扫描装置实时监测顶煤厚度信息，根据顶煤厚度变化量自动调整放煤口开闭时间，具体步骤包括：
    (1)把超宽带雷达装置安装在液压支架的顶梁靠近中后部位置，所述的超宽带雷达装置包括发射装置、接收装置和控制机；所述液压支架包括：顶梁、立杆、掩护梁、尾梁、底座、液压支架控制器和电液伺服控制系统，其中，发射装置把超宽带雷达信号发射到顶煤煤层中，接收装置接收超宽带雷达信号经由煤岩分界面反射后的回波信号并进行去噪处理，根据控制机记录雷达信号从发射到接收所用的时间得到超宽带雷达信号在煤层中的传播时间；
    (2)实时采集超宽带雷达信号在煤层中的传播时间，根据公式
    Figure PCTCN2019094518-appb-100001
    计算出顶煤厚度，并采用时域有限差分方法来提高顶煤厚度的计算精度，
    式中：L为顶煤厚度，C为在真空中的电磁波的传播速度，ΔT为信号在煤层中的传播时间，ε为相对介电常数；
    (3)把激光三维扫描装置安装在液压支架上靠近尾梁并对准液压支架的放煤口，放煤开始后，通过激光三维扫描装置实时连续扫描放煤口放出的煤流信息，得到煤流表面三维几何特征和煤流速度；
    (4)利用激光三维扫描采集的煤流表面三维几何特征和煤流速度，结合放煤时间计算出放煤口实际放煤量；
    (5)根据步骤2计算的顶煤厚度和步骤4计算的放煤口实际放煤量结果，采用径向基神经网络建立顶煤厚度的变化量与放煤时间之间的对应关系方程；
    (6)结合建立的对应关系方程和的顶煤厚度的变化量，计算出放煤口在各个放煤阶段中最佳开闭时间；
    (7)将获得的放煤口最佳开闭时间传输给液压支架控制器，液压支架控制器通过电液伺服控制系统向放煤口发出开启和关闭指令，实现综放工作面的智能化放煤。
  2. 根据权利要求1中所述的基于顶煤厚度变化量实时监测的智能化放煤方法，其特征在于：所述的步骤1中，对接收到的超宽带雷达信号进行去噪处理，具体为：小波去噪、反卷积自适应去噪和偏微分去噪。
  3. 根据权利1中所述的基于顶煤厚度变化量实时监测的智能化放煤方法，其特征在于：所述的步骤3中，对激光扫描的煤流表面三维几何特征进行数据处理，具体为基于多维(K-Demension，KD)树的点云去噪方法。
  4. 根据权利1中所述的基于顶煤厚度变化量实时监测的智能化放煤方法，其特征在于：所述的步骤5中，采用径向基神经网络建立以顶煤厚度、煤流几何特征、煤流速度、放煤口放煤量作为输入，以顶煤流动时间参数为输出的神经网络模型。
PCT/CN2019/094518 2019-02-27 2019-07-03 一种基于顶煤厚度变化量实时监测的智能化放煤方法 Ceased WO2020173030A1 (zh)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
CN201910145595.2A CN109856631B (zh) 2019-02-27 2019-02-27 一种基于顶煤厚度变化量实时监测的智能化放煤方法
CN201910145595.2 2019-02-27

Publications (1)

Publication Number Publication Date
WO2020173030A1 true WO2020173030A1 (zh) 2020-09-03

Family

ID=66899198

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/CN2019/094518 Ceased WO2020173030A1 (zh) 2019-02-27 2019-07-03 一种基于顶煤厚度变化量实时监测的智能化放煤方法

Country Status (2)

Country Link
CN (1) CN109856631B (zh)
WO (1) WO2020173030A1 (zh)

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116986247A (zh) * 2023-09-26 2023-11-03 华夏天信智能物联股份有限公司 用于刮板机的智能控制方法及系统
CN118913276A (zh) * 2024-07-17 2024-11-08 山东神力起重机械有限公司 一种自动控制卸煤的控制系统

Families Citing this family (13)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN109856631B (zh) * 2019-02-27 2020-12-11 中国矿业大学 一种基于顶煤厚度变化量实时监测的智能化放煤方法
CN110145353B (zh) * 2019-06-14 2024-06-14 中国矿业大学(北京) 一种基于rfid的自动化放煤控制系统及方法
CN111252498B (zh) * 2020-03-16 2021-08-17 河南理工大学 一种放顶煤工作面后部刮板运输机煤量自动控制方法
CN111594167A (zh) * 2020-05-18 2020-08-28 国家能源集团宁夏煤业有限责任公司枣泉煤矿 用于自动化综放工作面的放煤方法
CN111779524B (zh) * 2020-06-30 2021-05-28 中国矿业大学 一种综放工作面液压支架智能放煤方法
CN111878170B (zh) * 2020-07-03 2022-02-11 太原理工大学 基于超声波强度测试的放顶煤放煤作业控制的装置与方法
CN111764902B (zh) * 2020-08-05 2021-09-14 天地科技股份有限公司 一种综采放顶煤工作面智能化放煤控制方法
CN111967745B (zh) * 2020-08-06 2023-10-24 中煤能源研究院有限责任公司 基于bp神经网络和概率统计的综采放煤时间智能确定方法
CN111912345B (zh) * 2020-09-16 2024-12-27 中国矿业大学 一种液压支架随动自补偿放煤量激光扫描装置及方法
CN112211657B (zh) * 2020-10-30 2022-08-30 西安科技大学 一种智能判定放顶煤液压支架放煤口关闭的方法
CN112558577B (zh) * 2021-02-19 2021-06-08 天津美腾科技股份有限公司 放顶煤控制方法、装置、电子设备和计算机可读存储介质
CN113137231B (zh) * 2021-05-24 2023-03-17 天津美腾科技股份有限公司 自动放煤的控制方法、系统、装置和电子设备
CN121382311A (zh) * 2025-10-22 2026-01-23 中国矿业大学(北京) 基于煤岩分界面随放动态追踪与视觉融合的智能放煤方法

Citations (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
DE4016929C1 (en) * 1990-05-25 1991-09-26 Hermann Hemscheidt Maschinenfabrik Gmbh & Co., 5600 Wuppertal, De Roof support for mine - has canopy with bar hinged to floor skids by extendable props
CN104314599A (zh) * 2014-09-29 2015-01-28 天地科技股份有限公司 一种基于神经网络的综采工作面自动化放煤系统和方法
CN106437713A (zh) * 2016-10-21 2017-02-22 山东科技大学 一种综放工作面自动化放煤装置及放煤过程识别方法
CN107676095A (zh) * 2017-11-01 2018-02-09 天地科技股份有限公司 厚煤层放顶煤开采装置及方法
CN107725089A (zh) * 2017-11-21 2018-02-23 天地科技股份有限公司 无人值守的自动化放煤系统和方法
CN109113741A (zh) * 2018-07-17 2019-01-01 北京天地玛珂电液控制系统有限公司 基于视频监视图像识别的自动化放煤控制系统
CN109856631A (zh) * 2019-02-27 2019-06-07 中国矿业大学 一种基于顶煤厚度变化量实时监测的智能化放煤方法

Family Cites Families (13)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN1052526C (zh) * 1996-10-03 2000-05-17 中国矿业大学北京研究生部 轻型低位放顶煤液压支架
CN2420422Y (zh) * 2000-02-20 2001-02-21 兖矿集团有限公司 自动控制的放顶煤支架
CN101463729A (zh) * 2009-01-15 2009-06-24 天地科技股份有限公司 大采高放顶煤液压支架
CN102493827B (zh) * 2011-11-30 2014-04-16 北京天地玛珂电液控制系统有限公司 一种放顶煤工作面自动放煤控制系统及其放煤方法
CN103207999B (zh) * 2012-11-07 2016-02-17 中国矿业大学(北京) 一种基于煤岩图像特征抽取以及分类识别的煤岩分界方法和系统
RU2513952C1 (ru) * 2012-12-26 2014-04-20 Федеральное государственное бюджетное учреждение науки Институт угля Сибирского отделения Российской академии наук (ИУ СО РАН) Секция механизированной крепи очистного забоя мощного пласта с принудительным выпуском угля
US20160090839A1 (en) * 2014-11-26 2016-03-31 Larry G. Stolarczyk Method of protecting the health and well-being of coal mine machine operators
CN105182820B (zh) * 2015-08-25 2017-12-05 太原理工大学 一种煤矿综采工作面大型装备集中控制平台的实现方法
CN105134274B (zh) * 2015-09-29 2017-09-22 天地科技股份有限公司 带三级顶煤回收装置的两柱大采高放顶煤液压支架
CN105973773B (zh) * 2016-07-22 2018-10-16 山东科技大学 用于煤矿综放工作面产尘规律测定的实验装置及实验方法
CN107101683A (zh) * 2017-06-15 2017-08-29 西安科技大学 一种基于激光雷达与速度信息的煤流量监测系统
CN107762546B (zh) * 2017-11-01 2018-10-23 天地科技股份有限公司 机器学习的自动化放煤控制系统和方法
CN208367232U (zh) * 2018-07-19 2019-01-11 成都理工大学 勘探顶煤煤厚的爬行探测装置

Patent Citations (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
DE4016929C1 (en) * 1990-05-25 1991-09-26 Hermann Hemscheidt Maschinenfabrik Gmbh & Co., 5600 Wuppertal, De Roof support for mine - has canopy with bar hinged to floor skids by extendable props
CN104314599A (zh) * 2014-09-29 2015-01-28 天地科技股份有限公司 一种基于神经网络的综采工作面自动化放煤系统和方法
CN106437713A (zh) * 2016-10-21 2017-02-22 山东科技大学 一种综放工作面自动化放煤装置及放煤过程识别方法
CN107676095A (zh) * 2017-11-01 2018-02-09 天地科技股份有限公司 厚煤层放顶煤开采装置及方法
CN107725089A (zh) * 2017-11-21 2018-02-23 天地科技股份有限公司 无人值守的自动化放煤系统和方法
CN109113741A (zh) * 2018-07-17 2019-01-01 北京天地玛珂电液控制系统有限公司 基于视频监视图像识别的自动化放煤控制系统
CN109856631A (zh) * 2019-02-27 2019-06-07 中国矿业大学 一种基于顶煤厚度变化量实时监测的智能化放煤方法

Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116986247A (zh) * 2023-09-26 2023-11-03 华夏天信智能物联股份有限公司 用于刮板机的智能控制方法及系统
CN116986247B (zh) * 2023-09-26 2023-12-26 华夏天信智能物联股份有限公司 用于刮板机的智能控制方法及系统
CN118913276A (zh) * 2024-07-17 2024-11-08 山东神力起重机械有限公司 一种自动控制卸煤的控制系统

Also Published As

Publication number Publication date
CN109856631A (zh) 2019-06-07
CN109856631B (zh) 2020-12-11

Similar Documents

Publication Publication Date Title
CN109856631B (zh) 一种基于顶煤厚度变化量实时监测的智能化放煤方法
CN107676095B (zh) 厚煤层放顶煤开采装置及方法
CN108983284A (zh) 一种适用于海上斜缆数据的f-p域鬼波压制方法
CN108415079B (zh) 基于凿岩冲击声识别的岩层分界面圈定方法
AU2019100966A4 (en) Automatic coal drawing control system and method
CN110658528A (zh) 基于激光雷达的综采工作面成套设备偏移监测方法
CN108519587A (zh) 一种实时的空中目标运动模式识别及参数估计方法
CN115584985B (zh) 一种基于刀盘动力响应的地层特征实时识别与掘进参数动态调整方法
CN107085001B (zh) 大型工艺管线缺陷探测方法
CN104373153A (zh) 一种煤矿井下综放工作面的煤岩性状识别方法与系统
CN115640512A (zh) 基于集合经验模态分解的煤岩识别方法
CN116066121A (zh) 一种tbm掘进隧道围岩实时分级方法、盾构机
CN107202797B (zh) 非接触式连续热镀锌生产线带钢焊缝检测系统及其方法
CN112558154B (zh) 一种正交各向异性介质速度建模方法及系统
CN111912345A (zh) 一种液压支架随动自补偿放煤量激光扫描装置及方法
CN113670413A (zh) 一种用于散料装车的激光雷达数据实时处理与通信方法
CN217595226U (zh) 一种电磁阀检测模块和物料分选系统
CN121091387B (zh) 一种基于分布式光纤传感的地下结构边界识别方法及系统
CN116542502A (zh) 基于卷积神经网络的建筑物施工周期优化系统及方法
CN115922066A (zh) 一种基于实时同轴视觉监测的焊接熔透控制方法及系统
CN119439309B (zh) 一种基于多种探测方法信息融合超前地质预测系统
CN114487344A (zh) 一种盾构机水平传送带上渣土改良状态的实时判定方法
CN111749723A (zh) 放顶煤控制方法及系统
CN117488634B (zh) 一种基于三维激光扫描的压实度无人自动检测系统及方法
CN112257735A (zh) 一种基于人工智能的内部缺陷雷达检测系统

Legal Events

Date Code Title Description
121 Ep: the epo has been informed by wipo that ep was designated in this application

Ref document number: 19916882

Country of ref document: EP

Kind code of ref document: A1

NENP Non-entry into the national phase

Ref country code: DE

122 Ep: pct application non-entry in european phase

Ref document number: 19916882

Country of ref document: EP

Kind code of ref document: A1