阅读说明：本技术 一种综采工作面设备上窜下滑偏移测量方法及系统 (Deviation measurement method and system for equipment leap-up and slip-down of fully mechanized coal mining face ) 是由 臧万顺 冯龙 张强 苏金鹏 田莹 沈伟挺 刘文卓 赵进 姜玉燕 王博申 于 2021-08-19 设计创作，主要内容包括：本申请实施例公开了一种综采工作面设备上窜下滑偏移测量方法,方法包括：接收第一激光雷达采集的第一巷道壁对应的第一点云数据；以及,接收第二激光雷达采集的第二巷道壁对应的第二点云数据；分别对第一点云数据与第二点云数据进行滤波处理以及曲面拟合处理,得到第一基准平面与第二基准平面；确定第一激光雷达与第一基准平面之间的第一距离,以及第二激光雷达与第二基准平面之间的第二距离；基于第一距离与预存的第一标准距离,确定第一偏移量,以及基于第二距离与预存的第二标准距离,确定第二偏移量；根据第一偏移量以及第二偏移量,确定综采设备发生的偏移类型。用以解决现有的综采设备偏移量测量方法的测量精度低的技术问题。(The embodiment of the application discloses a method for measuring the shifting-up and sliding-down offset of fully mechanized coal mining face equipment, which comprises the following steps: receiving first point cloud data corresponding to a first roadway wall acquired by a first laser radar; receiving second point cloud data corresponding to a second roadway wall acquired by a second laser radar; respectively carrying out filtering processing and surface fitting processing on the first point cloud data and the second point cloud data to obtain a first reference plane and a second reference plane; determining a first distance between the first laser radar and the first reference plane and a second distance between the second laser radar and the second reference plane; determining a first offset based on the first distance and a pre-stored first standard distance, and determining a second offset based on the second distance and a pre-stored second standard distance; and determining the offset type of the fully mechanized mining equipment according to the first offset and the second offset. The method is used for solving the technical problem of low measurement precision of the existing fully mechanized mining equipment offset measurement method.)

1. A fully mechanized coal mining face equipment fleeing and sliding deviation measuring method is characterized by comprising the following steps:

receiving first point cloud data corresponding to a first roadway wall acquired by a first laser radar; receiving second point cloud data corresponding to a second roadway wall acquired by a second laser radar;

respectively carrying out filtering processing and surface fitting processing on the first point cloud data and the second point cloud data to obtain a first reference plane and a second reference plane;

determining a first distance between the first lidar and the first reference plane and a second distance between the second lidar and the second reference plane;

determining a first offset based on the first distance and a pre-stored first standard distance, and determining a second offset based on the second distance and a pre-stored second standard distance;

and determining the offset type of the fully mechanized mining equipment according to the first offset and the second offset.

2. The method for measuring the shift of the fully mechanized coal mining face equipment up and down according to claim 1, wherein the filtering and surface fitting are respectively performed on the first point cloud data and the second point cloud data to obtain a first reference plane and a second reference plane, and specifically comprises:

performing Gaussian filtering on the first point cloud data to remove isolated points in the first point cloud data; and carrying out Gaussian filtering on the second point cloud data to remove isolated points in the second point cloud data;

performing surface fitting on the first point cloud data subjected to Gaussian filtering through a preset algorithm to obtain a first reference plane; and performing surface fitting on the second point cloud data subjected to Gaussian filtering to obtain a second reference plane.

3. The fully mechanized coal mining face equipment fleeing and sliding deviation measurement method according to claim 2, characterized in that the first point cloud data is subjected to gaussian filtering to remove isolated points in the first point cloud data; and performing Gaussian filtering on the second point cloud data to remove isolated points in the second point cloud data, specifically comprising:

according toObtaining a two-dimensional Gaussian filter weight function h (x, y); wherein the content of the first and second substances,x represents the x coordinate of the point cloud data deviating from the center of the two-dimensional Gaussian filter weight function, y represents the y coordinate of the point cloud data deviating from the center of the two-dimensional Gaussian filter weight function, and lambda_xcAnd λ_ycRepresents the cut-off wavelength of a low-pass gaussian filter;

obtaining a two-dimensional Gaussian filter function w (x, y) according to w (x, y) ═ z (x-xi, y- η) h (xi, η) d xi d η; wherein xi and eta are differential variables required by convolution integral, and z (x-xi, y-eta) is original point cloud data;

discretizing the two-dimensional Gaussian filter function w (x, y) to obtain a two-dimensional discrete Gaussian filter process formula:wherein g and k are discrete calculation coefficients required for calculating a Gaussian evaluation reference plane w, and the range of g is g₁～g₂K ranges from k₁～k₂(ii) a Wherein i ═ g₁,...,L_x-g₂，j＝k₁,...,L_y-k₂，L_xAnd L_yAre sampled data points; Δ x, Δ y are sampling intervals;

by said two-dimensional discrete Gaussian filterWave process formula w (x)_i,y_i) And performing two-dimensional Gaussian filtering on the first point cloud data and the second point cloud data.

4. The method for measuring the equipment up-shift and down-shift deviation of the fully mechanized mining face according to claim 2, wherein the first point cloud data subjected to Gaussian filtering is subjected to surface fitting through a preset algorithm to obtain the first reference plane; and performing surface fitting on the second point cloud data subjected to the gaussian filtering to obtain the second reference plane, specifically comprising:

for the point cloud data point column Q after filtering_j,k(j 1, 2.. n; k 1, 2.. e), constructing a singly-added parameter sequence { m ] according to the sequence of subscripts j and k respectively_jAnd { p }_k}；

According to the parameter sequence m_jAnd { p }_kConstructing a B spline basis function { A }_j,c(m)}、{A_k,x(p)}；

According to a unitary function fitting formula for n rows of point cloud dataObtaining r space point rows of point cloud data of each line

According to the space point column of r columnsFormula of a univariate function fittingObtaining an intermediate parameter l_zkFor the intermediate parameter l_zkCarrying out summation calculation to obtain a control vertex l_jk；

According toAnd respectively carrying out surface fitting on the first point cloud data and the second point cloud data to obtain the first datum plane and the second datum plane.

5. The method for measuring the downshifting offset of the fully mechanized mining face equipment according to claim 1, wherein determining a first distance between the first lidar and the first reference plane and a second distance between the second lidar and the second reference plane specifically comprises:

establishing a first three-dimensional coordinate system by taking the first laser radar as an origin;

determining distances between the first laser radar and all point cloud data on the first reference plane based on the origin coordinates of the first three-dimensional coordinate system and the three-dimensional coordinates of all point cloud data on the first reference plane in the first three-dimensional coordinate system, and determining the minimum value of the obtained distances as a first distance between the first laser radar and the first reference plane;

establishing a second three-dimensional coordinate system by taking the second laser radar as an origin;

and determining the distance between the second laser radar and all the point cloud data on the second reference plane based on the origin coordinates of the second three-dimensional coordinate system and the three-dimensional coordinates of all the point cloud data on the second reference plane in the second three-dimensional coordinate system, and determining the minimum value in the obtained distances as the second distance between the second laser radar and the second reference plane.

6. The method for measuring the equipment leap-down deviation of the fully mechanized mining face according to claim 1, wherein determining a first deviation amount based on the first distance and a pre-stored first standard distance, and determining a second deviation amount based on the second distance and a pre-stored second standard distance specifically comprises:

before the fully mechanized mining equipment runs, determining a first standard distance between the first laser radar and the first reference plane, and determining a second standard distance between the second laser radar and the second reference plane;

storing the first standard distance and the second standard distance in a memory;

in the operation process of the fully mechanized mining equipment, comparing the first distance with the first standard distance, solving a first difference value, and determining the absolute value of the first difference value as the first offset;

and comparing the second distance with the second standard distance, solving a second difference value, and determining the absolute value of the second difference value as the second offset.

7. The method for measuring the drifting-up and sliding-down deviation of the fully mechanized mining face equipment according to claim 6, wherein the determining the type of deviation of the fully mechanized mining face equipment based on the first deviation and the second deviation specifically comprises:

determining the offset type of the fully mechanized mining equipment as a glide offset under the condition that the first distance is greater than a first standard distance and the second distance is less than a second standard distance;

determining the offset type of the fully mechanized mining equipment as a fleeing offset under the condition that the first distance is smaller than a first standard distance and the second distance is larger than a second standard distance;

and determining that the fully mechanized mining equipment is not offset under the condition that the first distance is equal to a first standard distance and the second distance is equal to a second standard distance.

8. The method for measuring the equipment leap-down deviation of the fully mechanized mining face according to claim 1, wherein after determining the type of deviation of the fully mechanized mining equipment according to the first deviation and the second deviation, the method further comprises:

under the condition that the fully mechanized mining equipment deviates, sending the first deviation, the second deviation and the deviation type to a control center of the fully mechanized mining equipment through a wireless communication module;

under the condition that the offset type of the fully-mechanized mining equipment is gliding offset, controlling the fully-mechanized mining equipment to move the first offset to the first roadway wall through the control center;

under the condition that the offset type of the fully-mechanized mining equipment is a channeling offset, controlling the fully-mechanized mining equipment to move the second offset to the second roadway wall through the control center;

and sending the first offset, the second offset, the offset type and the result of the adjustment of the control center on the fully mechanized mining equipment to display equipment.

9. A fully mechanized coal mining face equipment leap-up and slip-down offset measurement system, the system comprising:

the laser radar comprises a first laser radar and a second laser radar which are respectively used for acquiring first point cloud data of the first roadway wall and second point cloud data of the second roadway wall in real time;

the processor is used for respectively carrying out filtering processing and surface fitting processing on the first point cloud data and the second point cloud data to obtain a first reference plane and a second reference plane; determining a first distance between the first laser radar and the first reference plane and a second distance between the second laser radar and the second reference plane; the processor is further configured to determine a first offset based on the first distance and a pre-stored first standard distance; determining a second offset based on the second distance and a pre-stored second standard distance; and determining the type of the deviation of the fully mechanized mining equipment based on the first deviation and the second deviation;

and the fully mechanized mining equipment control center is used for adjusting the fully mechanized mining equipment according to the first offset, the second offset and the offset type.

10. The fully mechanized coal mining face equipment downswing offset measurement system of claim 9,

the first laser radar is mounted on a first protection plate of the fully mechanized mining equipment, and the second laser radar is mounted on a second protection plate of the fully mechanized mining equipment;

the first protection plate and the second protection plate are respectively installed at two ends, close to the roadway wall, of the fully mechanized mining equipment and used for protecting the fully mechanized mining equipment.

Technical Field

The application relates to the field of offset measurement, in particular to a method and a system for measuring the up-shifting and down-shifting offset of fully mechanized coal mining face equipment.

Background

The coal seam of the fully mechanized coal mining face of the coal mine is naturally formed, the thickness of the coal seam is not uniformly distributed, and the coal seam has a certain gradient. According to the difference of the gradient, the coal bed can be divided into a near-horizontal layer, a slowly inclined layer, an inclined layer and a steeply inclined layer. In the process of collecting coal on different working surfaces, fully mechanized mining equipment such as a scraper conveyor and the like can generate upward or downward component force in the inclined direction of the coal mining working surface under the action of factors such as gravity, thrust and the like, so that the head of the scraper conveyor is overlapped and dislocated to become a blasting fuse which causes accidents. The danger degree is directly proportional to the amount of shift of the upward-shifting and downward-shifting of the scraper conveyor, and when the scraper conveyor continuously advances, the upward-shifting and downward-shifting shift of the scraper conveyor is aggravated due to the accumulation of the gap shift of the hydraulic support and the connecting pin lug of the scraper conveyor and sensor errors. If the end head driving part of the scraper conveyor cannot be checked and adjusted in time, the problems of damage of the end head driving part of the scraper conveyor, the extrusion or the inversion of a hydraulic support, damage of a push rod, damage of a cable, production interruption and the like can be caused, and then production stop is caused. In the process of advancing the fully mechanized mining equipment, the upward movement or downward movement offset is at any time, and the offset changes in real time, so that the upward movement and downward movement offset of the equipment needs to be tested in real time so as to be adjusted in real time.

At present, the offset measuring method of the fully-mechanized mining equipment directly measures the real-time distance between the fully-mechanized mining equipment and a roadway mainly through equipment such as laser ranging and ultrasonic ranging sensors, and on one hand, the measuring efficiency is low because most of the ranging methods transmit acquired information to a computer for processing. On the other hand, combine to adopt the working face and can appear the dust when having equipment work, general light principle distancer can scan the dust and enter, has certain probability to regard the dust that scans as the lateral wall in tunnel, leads to measurement accuracy greatly reduced, and the lateral wall in colliery tunnel is unevenness, if only measure the tunnel lateral wall on the distance of a point can not represent the true distance of fully mechanized equipment and tunnel, also can lead to the measurement of offset to appear the deviation.

Disclosure of Invention

The embodiment of the application provides a method and a system for measuring the deviation of the equipment channeling downwards on a fully mechanized coal mining face, which are used for solving the following technical problems: the existing method for measuring the offset of the fully mechanized mining equipment has low measurement precision.

The embodiment of the application adopts the following technical scheme:

the embodiment of the application provides a method for measuring the shifting-up and sliding-down deviation of fully-mechanized coal mining face equipment, which comprises the following steps: receiving first point cloud data corresponding to a first roadway wall acquired by a first laser radar; receiving second point cloud data corresponding to a second roadway wall acquired by a second laser radar; respectively carrying out filtering processing and surface fitting processing on the first point cloud data and the second point cloud data to obtain a first reference plane and a second reference plane; determining a first distance between the first lidar and the first reference plane and a second distance between the second lidar and the second reference plane; determining a first offset based on the first distance and a pre-stored first standard distance, and determining a second offset based on the second distance and a pre-stored second standard distance; and determining the offset type of the fully mechanized mining equipment according to the first offset and the second offset.

In a feasible implementation manner, the filtering and surface fitting processing are respectively performed on the first point cloud data and the second point cloud data to obtain a first reference plane and a second reference plane, and the method specifically includes: performing Gaussian filtering on the first point cloud data to remove isolated points in the first point cloud data; and carrying out Gaussian filtering on the second point cloud data to remove isolated points in the second point cloud data; performing surface fitting on the first point cloud data subjected to Gaussian filtering through a preset algorithm to obtain a first reference plane; and performing surface fitting on the second point cloud data subjected to Gaussian filtering to obtain a second reference plane.

In a possible implementation manner, gaussian filtering is performed on the first point cloud data to remove isolated points in the first point cloud data; and performing Gaussian filtering on the second point cloud data to remove isolated points in the second point cloud data, specifically comprising: according toObtaining a two-dimensional Gaussian filter weight function h (x, y); wherein the content of the first and second substances,x represents the x coordinate of the point cloud data deviating from the center of the two-dimensional Gaussian filter weight function, y represents the y coordinate of the point cloud data deviating from the center of the two-dimensional Gaussian filter weight function, and lambda_xcAnd λ_ycRepresents the cut-off wavelength of a low-pass gaussian filter; obtaining a two-dimensional Gaussian filter function w (x, y) according to w (x, y) ═ z (x-xi, y- η) h (xi, η) d xi d η; wherein xi and eta are differential variables required by convolution integral, and z (x-xi, y-eta) is original point cloud data; discretizing the two-dimensional Gaussian filter function w (x, y) to obtain a two-dimensional discrete Gaussian filter process formula:wherein g and k are discrete calculation coefficients required for calculating a Gaussian evaluation reference plane w, and the range of g is g₁～g₂K ranges from k₁～k₂(ii) a Wherein i ═ g₁,...,L_x-g₂，j＝k₁,...,L_y-k₂，L_xAnd L_yAre sampled data points; Δ x, Δ y are sampling intervals; by said two-dimensional discrete Gaussian filter process formula w (x)_i,y_i) And performing two-dimensional Gaussian filtering on the first point cloud data and the second point cloud data.

In a feasible implementation manner, performing surface fitting on the first point cloud data subjected to gaussian filtering through a preset algorithm to obtain the first reference plane; and performing surface fitting on the second point cloud data subjected to the gaussian filtering to obtain the second reference plane, specifically comprising: for the point cloud data point column Q after filtering_j,k(j 1,2, n, k 1,2, e) in the order of the indices j and k, respectively, to construct a singly increasing sequence of parametersAnd { p_k}; according to the parameter sequenceAndconstructing B-spline basis functionsAccording to a unitary function fitting formula for n rows of point cloud dataObtaining r space point rows of point cloud data of each lineAccording to the space point column of r columnsFormula of a univariate function fittingObtaining an intermediate parameter l_zkFor the intermediate parameter l_zkCarrying out summation calculation to obtain a control vertex l_jk(ii) a According toAnd respectively carrying out surface fitting on the first point cloud data and the second point cloud data to obtain the first datum plane and the second datum plane.

In a possible embodiment, determining a first distance between the first lidar and the first reference plane and a second distance between the second lidar and the second reference plane includes: establishing a first three-dimensional coordinate system by taking the first laser radar as an origin; determining distances between the first laser radar and all point cloud data on the first reference plane based on the origin coordinates of the first three-dimensional coordinate system and the three-dimensional coordinates of all point cloud data on the first reference plane in the first three-dimensional coordinate system, and determining the minimum value of the obtained distances as a first distance between the first laser radar and the first reference plane; establishing a second three-dimensional coordinate system by taking the second laser radar as an origin; and determining the distance between the second laser radar and all the point cloud data on the second reference plane based on the origin coordinates of the second three-dimensional coordinate system and the three-dimensional coordinates of all the point cloud data on the second reference plane in the second three-dimensional coordinate system, and determining the minimum value in the obtained distances as the second distance between the second laser radar and the second reference plane.

In a possible implementation manner, determining a first offset based on the first distance and a pre-stored first standard distance, and determining a second offset based on the second distance and a pre-stored second standard distance specifically includes: before the fully mechanized mining equipment runs, determining a first standard distance between the first laser radar and the first reference plane, and determining a second standard distance between the second laser radar and the second reference plane; storing the first standard distance and the second standard distance in a memory; in the operation process of the fully mechanized mining equipment, comparing the first distance with the first standard distance, solving a first difference value, and determining the absolute value of the first difference value as the first offset; and comparing the second distance with the second standard distance, solving a second difference value, and determining the absolute value of the second difference value as the second offset.

In a possible implementation manner, determining a type of offset occurring in the fully mechanized mining equipment based on the first offset and the second offset specifically includes: determining the offset type of the fully mechanized mining equipment as a glide offset under the condition that the first distance is greater than a first standard distance and the second distance is less than a second standard distance; determining the offset type of the fully mechanized mining equipment as a fleeing offset under the condition that the first distance is smaller than a first standard distance and the second distance is larger than a second standard distance; and determining that the fully mechanized mining equipment is not offset under the condition that the first distance is equal to a first standard distance and the second distance is equal to a second standard distance.

In a possible embodiment, after determining the type of offset occurring in the fully mechanized mining device according to the first offset and the second offset, the method further includes: under the condition that the fully mechanized mining equipment deviates, sending the first deviation, the second deviation and the deviation type to a control center of the fully mechanized mining equipment through a wireless communication module; under the condition that the offset type of the fully-mechanized mining equipment is gliding offset, controlling the fully-mechanized mining equipment to move the first offset to the first roadway wall through the control center; under the condition that the offset type of the fully-mechanized mining equipment is a channeling offset, controlling the fully-mechanized mining equipment to move the second offset to the second roadway wall through the control center; and sending the first offset, the second offset, the offset type and the result of the adjustment of the control center on the fully mechanized mining equipment to display equipment.

The embodiment of the application also provides a fully mechanized coal mining face equipment leap-up gliding offset measurement system, and the system includes: the laser radar comprises a first laser radar and a second laser radar, and is used for acquiring first point cloud data of a first roadway wall of the fully-mechanized mining equipment and second point cloud data of a second roadway wall of the fully-mechanized mining equipment in real time; the processor is used for respectively carrying out filtering processing and surface fitting processing on the first point cloud data and the second point cloud data to obtain a first reference plane and a second reference plane; determining a first distance between the first laser radar and the first reference plane and a second distance between the second laser radar and the second reference plane; the processor is further configured to determine a first offset based on the first distance and a pre-stored first standard distance; determining a second offset based on the second distance and a pre-stored second standard distance; and determining the type of the deviation of the fully mechanized mining equipment based on the first deviation and the second deviation; and the fully mechanized mining equipment control center is used for adjusting the fully mechanized mining equipment according to the first offset, the second offset and the offset type.

In a possible embodiment, the first lidar is mounted on a first guard plate of the fully mechanized mining device, and the second lidar is mounted on a second guard plate of the fully mechanized mining device; the first protection plate and the second protection plate are respectively installed at two ends, close to the roadway wall, of the fully mechanized mining equipment and used for protecting the fully mechanized mining equipment.

The embodiment of the application adopts at least one technical scheme which can achieve the following beneficial effects:

1. three-dimensional point cloud data of roadway walls on two sides of fully mechanized mining equipment are collected through the laser radar and transmitted to the ARM processor to be processed, the distance between the fully mechanized mining equipment and the roadway walls on the two sides is obtained, the precision of the combination of the laser radar and the ARM processor for measuring the offset of the fully mechanized mining equipment is higher, data do not need to be transmitted to a computer for processing, the data processing speed is higher, the measuring efficiency is higher, and the intelligent working requirement of a fully mechanized mining working face can be met.

And 2, the ARM processor performs Gaussian filtering on the point cloud data acquired by the laser radar, and filters dust generated by the fully mechanized mining face, so that errors caused by the dust are reduced. And then fitting the rugged coal wall into a smooth reference plane through curve fitting, and then calculating the distance between the laser radar and the reference plane through three-dimensional coordinates. Interference factors such as dust and coal wall unevenness are eliminated, and the measurement accuracy of the distance between the laser radar and the coal wall is higher.

Drawings

In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings needed to be used in the description of the embodiments or the prior art will be briefly introduced below, it is obvious that the drawings in the following description are only some embodiments described in the present application, and for those skilled in the art, other drawings can be obtained according to the drawings without any creative effort. In the drawings:

fig. 1 is a flowchart of a method for measuring the deviation of the equipment slipping down from the top of the fully mechanized mining face according to an embodiment of the present application;

fig. 2 is a schematic structural diagram of a fully mechanized mining face device provided by an embodiment of the present application;

1. a roadway wall; 2.a first laser radar; 3 a first guard plate; 4 a first hydraulic support; 5. a second hydraulic mount; 6. a third hydraulic mount; 7. a second guard plate; 8. a second laser radar; 9. a scraper conveyor; 10. a bottom adjusting hydraulic cylinder;

fig. 3 is a schematic structural diagram of a fully mechanized mining face equipment up-shifting and down-shifting deviation measurement system according to an embodiment of the present application;

fig. 4 is a schematic structural diagram of a device for measuring shifting-up and sliding-down deviation of fully mechanized coal mining face equipment according to an embodiment of the present application;

Detailed Description

In order to make those skilled in the art better understand the technical solutions in the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all of the embodiments. All other embodiments, which can be obtained by a person skilled in the art without making any inventive step based on the embodiments of the present disclosure, shall fall within the scope of protection of the present application.

Fig. 1 is a flowchart of a method for measuring a shifting-up and sliding-down offset of fully mechanized mining face equipment according to an embodiment of the present application, and as shown in fig. 1, the method specifically includes steps S101 to S106:

s101, specifically, the processor adopts an ARM processor. The ARM processor collects first point cloud data corresponding to a first roadway wall of the fully-mechanized mining equipment in real time through a first laser radar installed on a first protection plate of the fully-mechanized mining equipment, and collects second point cloud data corresponding to a second roadway wall of the fully-mechanized mining equipment in real time through a second laser radar installed on a second protection plate of the fully-mechanized mining equipment.

In particular, the fully mechanized mining equipment is used for coal collection on a coal face. And the left side and the right side of the fully mechanized mining equipment are respectively provided with a laser radar for measuring the distance between the two sides of the fully mechanized mining equipment and the walls of the left roadway and the right roadway. And the ARM processor receives point cloud data transmitted back by the two laser radars in real time.

As a possible embodiment, as shown in fig. 2, the fully mechanized mining equipment mainly comprises a scraper conveyor 9, a bottom adjusting hydraulic cylinder 10, a first hydraulic support 4, a second hydraulic support 5 and a third hydraulic support 6. A first protection plate 3 and a second protection plate 7 are respectively arranged on two sides of the fully mechanized mining equipment, a first laser radar 2 is arranged on the first protection plate 3, and a second laser radar 8 is arranged on the second protection plate 7. The first laser radar 2 collects the three-dimensional point cloud data of the first roadway wall in real time, and the second laser radar collects the three-dimensional point cloud data of the second roadway wall in real time. It should be noted that a side roadway wall closer to the first laser radar is a first roadway wall, and a side roadway wall closer to the second laser radar is a second roadway wall. The left-right relationship in fig. 2 does not represent a real left-right relationship, and is used only to indicate a positional relationship of each member.

In one embodiment, 1 is a first roadway wall if 2 is the first lidar and 1 is a second roadway wall if 2 is the second lidar. Protection plates are installed at two ends of the fully mechanized mining equipment, and the laser radar is installed on the protection plates, so that the distance between the fully mechanized mining equipment and the roadway wall can be conveniently determined while the fully mechanized mining equipment is protected.

S102, the ARM processor carries out filtering processing and surface fitting processing on the first point cloud data and the second point cloud data respectively to obtain a first reference plane and a second reference plane.

Specifically, after receiving point cloud data of two lane walls collected by two laser radars, the ARM processor performs gaussian filtering on the point cloud data of the two lane walls respectively to remove isolated points in the point cloud data. And performing surface fitting on the point cloud data subjected to Gaussian filtering through a preset surface fitting algorithm to obtain a corresponding reference plane.

As a possible embodiment, can be based onObtaining a two-dimensional Gaussian filter weight function h (x, y); wherein the content of the first and second substances,x represents the x coordinate of the point cloud data deviating from the center of the two-dimensional Gaussian filter weight function, y represents the y coordinate of the point cloud data deviating from the center of the two-dimensional Gaussian filter weight function, and lambda_xcAnd λ_ycRepresents the cut-off wavelength of a low-pass gaussian filter;

further, a two-dimensional gaussian filter function w (x, y) is obtained according to w (x, y) ═ z (x- ξ, y- η) h (ξ, η) d ξ d η; wherein xi and eta are differential variables required by convolution integral, and z (x-xi, y-eta) is original point cloud data;

further, since the acquired point cloud data is discrete points, the two-dimensional gaussian filter function w (x, y) is discretized to obtain a two-dimensional discrete gaussian filter process formula:

wherein g and k are discrete calculation coefficients required for calculating a Gaussian evaluation reference plane w, and the range of g is g₁～g₂K ranges from k₁～k₂；i＝g₁,...,L_x-g₂，j＝k₁,...,L_y-k₂，L_xAnd L_yAre sampled data points; Δ x, Δ y are sampling intervals; by the formula w (x)_i,y_i) And performing two-dimensional Gaussian filtering on the point cloud data of the roadway walls on the two sides, retaining effective three-dimensional point cloud data, and filtering the collected point cloud data of the dust.

As a feasible implementation mode, the point array Q of the point cloud data after Gaussian filtering_j,k(j 1,2, n, k 1,2, e) in the order of the indices j and k, respectively, to construct a singly increasing sequence of parametersAnd { p_k}; then according to the parameter sequenceAnd { p_kConstructing B spline basis function{A_k,x(p)}；

Further, firstly according toRespectively fitting the n-line point cloud data by a unitary function, and substituting { A ] into the formula_z,x(p_k) And Q_j,kObtaining r space point rows of point cloud data of each line

Further in accordance withFor r rows of space point rowsFitting a unitary function, substituting in the above formulaAnd { A_z,c(m_j) Get the intermediate parameter l_zkThen to the intermediate parameter l_zkCarrying out summation calculation to obtain a control vertex l of the point cloud data_jk；

Finally, according toAnd respectively carrying out surface fitting on the point cloud data of the roadway walls on the two sides to obtain a first reference plane and a second reference plane.

S103, the ARM processor determines a first distance between the first lidar 2 and the first reference plane and a second distance between the second lidar 8 and the second reference plane.

Specifically, a first three-dimensional coordinate system is established with the first laser radar 2 as an origin, and a second three-dimensional coordinate system is established with the second laser radar 8 as an origin, respectively. And determining the three-dimensional coordinates of all the fitted point cloud data on the first reference plane in the first three-dimensional coordinate system, and determining the three-dimensional coordinates of all the fitted point cloud data on the second reference plane in the second three-dimensional coordinate system.

In an embodiment, the coordinate axes of the first three-dimensional coordinate system and the second three-dimensional coordinate system may be the same as the coordinate axes of the geodetic coordinate system, or may point to any three directions perpendicular to each other, and may be set according to actual requirements, which is not limited in this embodiment of the application.

Further, based on the origin coordinates (0,0,0) of the first three-dimensional coordinate system and the three-dimensional coordinates of all the point cloud data on the first reference plane in the first three-dimensional coordinate system, the coordinate distances between the first laser radar 2 and all the point cloud data on the first reference plane are calculated, and the minimum value of all the obtained coordinate distances is the first distance between the first laser radar 2 and the first reference plane. And calculating the coordinate distances between the second laser radar 8 and all the point cloud data on the second reference plane based on the origin coordinates (0,0,0) of the second three-dimensional coordinate system and the three-dimensional coordinates of all the point cloud data on the second reference plane in the second three-dimensional coordinate system, wherein the minimum value of all the obtained coordinate distances is the second distance between the second laser radar 8 and the second reference plane. If the actual distance between the laser radar and the reference plane needs to be obtained, the calculated coordinate distance is multiplied by the unit length of the three-dimensional coordinate system.

S104, the ARM processor determines a first offset based on the first distance and a pre-stored first standard distance, and determines a second offset based on the second distance and a pre-stored second standard distance.

Specifically, before the fully mechanized mining equipment starts to operate, the ARM processor determines a first standard distance between the first laser radar 2 and the first reference plane and a second standard distance between the second laser radar 8 and the second reference plane according to the processes shown in S101 to S103, and stores the first standard distance and the second standard distance in a memory of the ARM processor.

Further, in the operation process of the fully mechanized mining equipment, the ARM processor compares the calculated first distance with a first standard distance in the memory in real time and calculates a first difference value between the first distance and the first standard distance, and an absolute value of the first difference value is a first offset. Meanwhile, the ARM processor compares the calculated second distance with the second standard distance in real time and calculates a second difference value of the calculated second distance and the second standard distance, and the absolute value of the second difference value is a second offset.

In one embodiment, if the calculated first distance is 12cm and the first standard distance is 10cm, the first offset amount is 2 cm.

And S105, the ARM processor determines the offset type of the fully mechanized mining equipment according to the first offset and the second offset.

Specifically, under the condition that the first distance is greater than the first standard distance and the second distance is less than the second standard distance, the offset type of the fully mechanized mining equipment is glide offset; determining the offset type of the fully mechanized mining equipment as a fleeing offset under the condition that the first distance is smaller than the first standard distance and the second distance is larger than the second standard distance; and determining that the fully mechanized mining equipment is not offset under the condition that the first distance is equal to the first standard distance and the second distance is equal to the second standard distance.

In one embodiment, as shown in fig. 2, if the calculated first distance is a and the second distance is B, the pre-stored first standard distance is a and the second standard distance is B. If a is greater than A and B is less than B, judging that the fully mechanized mining equipment has gliding deviation. If a < A and B > B, the equipment is judged to have the upward shift. Similarly, since the first distance may be b and the second distance may be a, the jump offset and the slide offset are specifically offset in which direction, depending on which direction lidar the worker has set as the first lidar.

S106, the ARM processor sends the first offset, the second offset and the offset type to a control center of the fully mechanized mining equipment under the condition that the fully mechanized mining equipment is offset, so that the control center can adjust the fully mechanized mining equipment in real time.

Specifically, when the fully mechanized mining equipment is deviated, the first deviation amount, the second deviation amount and the deviation type are sent to a control center of the fully mechanized mining equipment through a wireless communication module. And under the condition that the offset type of the fully-mechanized mining equipment is gliding offset, the control center controls the fully-mechanized mining equipment to move towards the first roadway wall by a first offset amount. And under the condition that the offset type of the fully-mechanized mining equipment is the upward-channeling offset, the control center controls the fully-mechanized mining equipment to move a second offset to the wall of the second roadway.

In one embodiment, if the information received by the control center generates a downslide offset for the fully mechanized mining device and the first offset is 3cm, the fully mechanized mining device is controlled to move 3cm towards the first roadway wall. And if the information received by the control center is that the fully mechanized mining equipment generates upward movement offset and the second offset is 3cm, controlling the fully mechanized mining equipment to move 3cm to the second roadway wall.

In general, when the fully mechanized mining equipment is shifted, the first shift amount and the second shift amount are the same, for example, the left side of the fully mechanized mining equipment is shifted by 3cm to the left, and the right side of the fully mechanized mining equipment is also shifted by 3cm to the left. The method and the device have the advantages that for avoiding special situations, when the fully mechanized mining equipment is adjusted, the downslide offset is adjusted based on the first offset, and the upswing offset is adjusted based on the second offset.

And further, the ARM processor sends the first offset, the second offset, the offset type and a result of the adjustment of the fully mechanized mining equipment by the control center, which are calculated in real time, to the display equipment, and displays the result in the display equipment so that a worker can check the state of the fully mechanized mining equipment.

Fig. 3 is a schematic structural diagram of a shifting-up and sliding-down deviation measuring system for fully mechanized coal mining face equipment provided by an embodiment of the present application, and as shown in fig. 3, the system includes:

the first laser radar 310 and the second laser radar 320 are used for acquiring first point cloud data of a first roadway wall of the fully-mechanized mining equipment and second point cloud data of a second roadway wall of the fully-mechanized mining equipment in real time.

As a possible embodiment, the first lidar 310 is mounted on a first fender of the fully mechanized mining device, and the second lidar 320 is mounted on a second fender of the fully mechanized mining device. The first protection plate and the second protection plate are respectively installed at two ends, close to the roadway wall, of the fully-mechanized mining equipment and used for protecting the fully-mechanized mining equipment from being abraded by the roadway wall.

The ARM processor 330 is configured to perform filtering processing and surface fitting processing on the first point cloud data and the second point cloud data respectively to obtain a first reference plane and a second reference plane; determining a first distance between the first laser radar and the first reference plane and a second distance between the second laser radar and the second reference plane; the ARM processor is further used for determining a first offset based on the first distance and a pre-stored first standard distance; determining a second offset based on the second distance and a pre-stored second standard distance; and determining the type of the deviation of the fully mechanized mining equipment based on the first deviation and the second deviation.

The method comprises the following steps of respectively carrying out filtering processing and surface fitting processing on first point cloud data and second point cloud data to obtain a first reference plane and a second reference plane, and specifically comprises the following steps: performing Gaussian filtering on the first point cloud data to remove isolated points in the first point cloud data; and performing Gaussian filtering on the second point cloud data to remove isolated points in the second point cloud data; performing surface fitting on the first point cloud data subjected to Gaussian filtering through a preset algorithm to obtain a first reference plane; and performing surface fitting on the second point cloud data subjected to Gaussian filtering to obtain a second reference plane.

Performing Gaussian filtering on the first point cloud data to remove isolated points in the first point cloud data; and performing Gaussian filtering on the second point cloud data to remove isolated points in the second point cloud data, and specifically comprising the following steps: according toObtaining a two-dimensional Gaussian filter weight function h (x, y); wherein the content of the first and second substances,x represents the x coordinate of the point cloud data deviating from the center of the two-dimensional Gaussian filter weight function, y represents the y coordinate of the point cloud data deviating from the center of the two-dimensional Gaussian filter weight function, and lambda_xcAnd λ_ycRepresents the cut-off wavelength of a low-pass gaussian filter; obtaining a two-dimensional Gaussian filter function w (x, y) according to w (x, y) ═ z (x-xi, y- η) h (xi, η) d xi d η; wherein xi and eta are differential variables required by convolution integral, and z (x-xi, y-eta) is original point cloud data; discretizing the two-dimensional Gaussian filter function w (x, y) to obtain a two-dimensional discrete Gaussian filter process formula:wherein g and k are discrete calculation coefficients required for calculating a Gaussian evaluation reference plane w, and the range of g is g₁～g₂K ranges from k₁～k₂(ii) a Wherein i ═ g₁,...,L_x-g₂，j＝k₁,...,L_y-k₂，L_xAnd L_yAre sampled data points; Δ x, Δ y are sampling intervals; formula w (x) by a two-dimensional discrete Gaussian filtering process_i,y_i) And performing two-dimensional Gaussian filtering on the first point cloud data and the second point cloud data.

Performing surface fitting on the first point cloud data subjected to Gaussian filtering through a preset algorithm to obtain a first point cloud dataA reference plane; and performing surface fitting on the second point cloud data subjected to the gaussian filtering to obtain a second reference plane, wherein the method specifically comprises the following steps: for the point cloud data point column Q after filtering_j,k(j 1,2, n, k 1,2, e) in the order of the indices j and k, respectively, to construct a singly increasing sequence of parametersAnd { p_k}; according to a parameter sequenceAnd { p_kConstructing B spline basis functionAccording to a unitary function fitting formula for n rows of point cloud dataObtaining r space point rows of point cloud data of each lineAccording to the space point column of r columnsFormula of a univariate function fittingObtaining an intermediate parameter l_zkThen to the intermediate parameter l_zkCarrying out summation calculation to obtain a control vertex l_jk(ii) a According toAnd respectively carrying out surface fitting on the first point cloud data and the second point cloud data to obtain a first reference plane and a second reference plane.

Wherein, confirm the first distance between first laser radar and the first reference plane to and the second distance between second laser radar and the second reference plane, specifically include: establishing a first three-dimensional coordinate system by taking the first laser radar as an origin; determining the distance between the first laser radar and all point cloud data on the first reference plane based on the origin coordinates of the first three-dimensional coordinate system and the three-dimensional coordinates of all the point cloud data on the first reference plane in the first three-dimensional coordinate system, and determining the minimum value in the obtained distances as the first distance between the first laser radar and the first reference plane; establishing a second three-dimensional coordinate system by taking the second laser radar as an origin; and determining the distance between the second laser radar and all the point cloud data on the second reference plane based on the origin coordinates of the second three-dimensional coordinate system and the three-dimensional coordinates of all the point cloud data on the second reference plane in the second three-dimensional coordinate system, and determining the minimum value in the obtained distance as the second distance between the second laser radar and the second reference plane.

Wherein, based on first distance and the first standard distance of prestoring, confirm first offset to and based on second distance and the second standard distance of prestoring, confirm the second offset, specifically include: before the fully mechanized mining equipment runs, determining a first standard distance between a first laser radar and a first reference plane, and determining a second standard distance between a second laser radar and a second reference plane; storing the first standard distance and the second standard distance in a memory; in the operation process of the fully mechanized mining equipment, comparing the first distance with the first standard distance, solving a first difference value, and determining the absolute value of the first difference value as a first offset; and comparing the second distance with the second standard distance, solving a second difference value, and determining the absolute value of the second difference value as a second offset.

The method for determining the offset type of the fully mechanized mining equipment based on the first offset and the second offset specifically comprises the following steps: determining the offset type of the fully mechanized mining equipment as a downslide offset under the condition that the first distance is greater than the first standard distance and the second distance is less than the second standard distance; determining the offset type of the fully mechanized mining equipment as a fleeing offset under the condition that the first distance is smaller than the first standard distance and the second distance is larger than the second standard distance; and determining that the fully mechanized mining equipment is not offset under the condition that the first distance is equal to the first standard distance and the second distance is equal to the second standard distance.

As a feasible implementation manner, the ARM processor 330 includes a data processing module and a communication module, and the laser radar performs data communication with the ARM processor 330 by using the corresponding communication module, so as to enhance the anti-interference capability in data transmission.

And the comprehensive mining equipment control center 340 is used for adjusting the comprehensive mining equipment according to the first offset, the second offset and the offset type.

Fig. 4 is a schematic structural view of a device for measuring shifting-up and sliding-down deviation of fully mechanized coal mining face equipment provided by an embodiment of the present application, and as shown in fig. 4, the device includes:

at least one processor; and a memory communicatively coupled to the at least one processor; wherein the memory stores instructions executable by the at least one processor to enable the at least one processor to perform any of steps S101-S106.

The embodiments in the present application are described in a progressive manner, and the same and similar parts among the embodiments can be referred to each other, and each embodiment focuses on the differences from the other embodiments. In particular, for the embodiments of the apparatus, the device, and the nonvolatile computer storage medium, since they are substantially similar to the embodiments of the method, the description is simple, and for the relevant points, reference may be made to the partial description of the embodiments of the method.

The foregoing description of specific embodiments of the present application has been presented. Other embodiments are within the scope of the following claims. In some cases, the actions or steps recited in the claims may be performed in a different order than in the embodiments and still achieve desirable results. In addition, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing may also be possible or may be advantageous.

The above description is only an example of the present application and is not intended to limit the present application. Various modifications and changes may occur to those skilled in the art to which the embodiments of the present application pertain. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the embodiments of the present application should be included in the scope of the claims of the present application.