CN114897040B

CN114897040B - Coal face straightening method and device and fully-mechanized coal face system

Info

Publication number: CN114897040B
Application number: CN202210258931.6A
Authority: CN
Inventors: 李铮; 戴卫东; 李函阳
Original assignee: Ningxia Guangtianxia Technology Co ltd
Current assignee: Ningxia Guangtianxia Technology Co ltd
Priority date: 2022-03-16
Filing date: 2022-03-16
Publication date: 2023-06-16
Anticipated expiration: 2042-03-16
Also published as: CN114897040A

Abstract

The invention discloses a coal face straightening method, a coal face straightening device and a fully-mechanized coal face system. The method comprises the following steps: acquiring point cloud data acquired by radar equipment arranged on a baffle of a scraper; screening out point clouds with coordinates within a preset range based on the point cloud data; clustering point clouds with coordinates within a preset range by adopting a density-based clustering algorithm to obtain at least one point cloud cluster, and identifying a target point cloud cluster corresponding to the coal face from the at least one point cloud cluster; judging whether the coal face is straight or not according to the distance between the sampling point in the target point cloud cluster and the scraper baffle; if not, generating excavation parameters according to the distance between the sampling points in the target point cloud cluster and the scraper baffle, so that the controller can control the coal mining machine to excavate based on the excavation parameters. By adopting the scheme, the straightening recognition precision and recognition efficiency of the coal face can be improved, and the straightening precision and straightening efficiency of the coal face can be improved.

Description

Coal face straightening method and device and fully-mechanized coal face system

Technical Field

The invention relates to the technical field of exploration, in particular to a coal face straightening method and device and a fully-mechanized coal face system.

Background

Along with the continuous development of science and technology and society, the intelligent degree of coal mine exploration is higher and higher. In the field of intelligent coal mine exploration, the straightness of a coal face has an important influence on the coal mining efficiency and the coal mining safety. When the coal face is bent (namely, the radial depth of excavation is inconsistent), the defects of low coal mining efficiency and low coal mining safety can occur, and therefore, the straightening of the coal face has very important significance in the field of coal mine exploration.

Currently, the commonly adopted coal face straightening method is a manual straightening method, namely whether the coal face is straight or not is judged manually, and under the condition that the coal face is determined to be uneven, the coal face is straightened by manually adjusting parameters. However, by adopting the method, the straightening efficiency of the coal face is low, and the straightening precision is low.

Disclosure of Invention

The present invention has been made in view of the above problems, and it is an object of the present invention to provide a coal face straightening method, apparatus and fully mechanized coal face system that overcomes or at least partially solves the above problems.

According to a first aspect of the present invention there is provided a coal face straightening method comprising:

acquiring point cloud data acquired by radar equipment arranged on a baffle of a scraper;

Screening out point clouds with coordinates within a preset range based on the point cloud data;

clustering the point clouds with the coordinates in a preset range by adopting a density-based clustering algorithm to obtain at least one point cloud cluster, and identifying a target point cloud cluster corresponding to the coal face from the at least one point cloud cluster;

judging whether the coal face is straight or not according to the distance between the sampling point in the target point cloud cluster and the scraper baffle;

if not, generating excavation parameters according to the distance between the sampling points in the target point cloud cluster and the scraper baffle, so that the controller can control the coal mining machine to excavate based on the excavation parameters.

In an alternative embodiment, the preset range is determined by:

acquiring a target point cloud cluster corresponding to the identified coal face in a historical time window;

and determining the preset range according to the distribution range of each target point cloud cluster in the historical time window.

In an optional implementation manner, the determining the preset range according to the distribution range of each target point cloud cluster in the historical time window further includes:

superposing all target point cloud clusters in the historical time window to obtain superposed point cloud clusters;

Respectively determining the maximum boundary value and the minimum boundary value of the superimposed point cloud cluster in the x, y and z directions;

for any direction, taking the sum of the maximum boundary value in the direction and a preset redundancy value as the maximum boundary value in the direction in the preset range; and taking the difference between the minimum boundary value in the direction and the preset redundancy value as the minimum boundary value in the direction in the preset range.

for any target point cloud cluster in a historical time window, respectively determining the maximum boundary value and the minimum boundary value of the target point cloud cluster in the x, y and z directions;

for any direction, counting the average value and standard deviation of the maximum boundary value of the target point cloud cluster corresponding to the direction; counting the average value and standard deviation of the minimum boundary value of the target point cloud cluster corresponding to the direction;

according to the average value and standard deviation of the maximum boundary value of the target point cloud cluster corresponding to each direction, the average value and standard deviation of the minimum boundary value of the target point cloud cluster corresponding to each direction and the adjustment coefficient, the preset range is determined by adopting the following formula:

wherein ,ithe values are respectivelyx，y，z；i _max In a preset rangeiMaximum boundary value of direction;i _{avg_max} is thatiAverage value of maximum boundary values of target point cloud clusters corresponding to the directions;i _{std_max} is thatiStandard of maximum boundary value of target point cloud cluster corresponding to directionDifference;i _min in a preset rangeiA minimum boundary value for the direction;i _{avg_min} is thatiAverage value of minimum boundary values of target point cloud clusters corresponding to the directions;i _{std_min} is thatiStandard deviation of minimum boundary value of target point cloud cluster corresponding to direction;kto adjust the coefficients.

In an alternative embodiment, the method further comprises:

acquiring the uneven straight frequency of the coal face in a historical time window;

determining the adjustment coefficient according to the uneven frequency of the coal face; wherein the adjustment coefficient is positively related to the coal face unevenness frequency.

In an optional embodiment, the identifying the target point cloud cluster corresponding to the coal face from the at least one point cloud cluster further includes:

and taking the point cloud cluster with the largest number of the contained sampling points as the target point cloud cluster corresponding to the coal face.

In an optional embodiment, the clustering the point cloud with the coordinates within a preset range to obtain at least one point cloud cluster further includes:

Taking the three-dimensional coordinates of any sampling point in the point cloud with the coordinates in a preset range as the clustering characteristic of the sampling point, and clustering based on the clustering characteristic of the sampling point to obtain at least one point cloud cluster; or alternatively

And generating a corrected three-dimensional coordinate of the sampling point according to the three-dimensional coordinate of the sampling point and the weight of the corresponding dimension direction aiming at any sampling point in the point cloud with the coordinate in a preset range, taking the corrected three-dimensional coordinate as the clustering characteristic of the sampling point, and clustering based on the clustering characteristic of the sampling point to obtain at least one point cloud cluster.

In an optional embodiment, the acquiring the point cloud data acquired by the radar device disposed on the scraper blade further includes: acquiring point cloud data acquired by the radar equipment at different positions of the scraper baffle;

the screening the point cloud with coordinates within a preset range based on the point cloud data further comprises: screening out point clouds with coordinates corresponding to any position within a preset range according to the point cloud data acquired from any position;

the method further comprises: splicing point clouds with coordinates corresponding to different positions within a preset range to obtain spliced point clouds;

The clustering the point cloud with the coordinates within the preset range further includes: clustering is conducted on the splicing point cloud.

In an alternative embodiment, after the obtaining the splice point cloud, the method further comprises: performing voxel grid downsampling on the splicing point cloud to obtain downsampled point cloud;

the clustering the point cloud with the coordinates within the preset range further includes: and clustering the down-sampled point clouds.

In an optional embodiment, the generating the mining parameter according to the distance between the sampling point and the scraper baffle in the target point cloud cluster further includes:

determining an overdriving area and an overdriving depth and/or determining an undersampling area and an undersampling depth according to the distance between the sampling point in the target point cloud cluster and the scraper baffle; and generating excavation parameters according to the super-mining area and the super-mining depth and/or the undersampling area and the undersampling depth.

According to a second aspect of the present invention there is provided a coal face straightening apparatus comprising:

the acquisition module is used for acquiring point cloud data acquired by radar equipment arranged on a baffle of the scraper machine;

the first screening module is used for screening out point clouds with coordinates within a preset range based on the point cloud data;

The second screening module is used for clustering the point clouds with coordinates within a preset range by adopting a density-based clustering algorithm to obtain at least one point cloud cluster, and identifying a target point cloud cluster corresponding to the coal face from the at least one point cloud cluster;

the judging module is used for judging whether the coal face is straight or not according to the distance between the sampling point in the target point cloud cluster and the scraper baffle;

and the parameter generation module is used for generating excavation parameters according to the distance between the sampling points in the target point cloud cluster and the scraper baffle if the judgment result of the judgment module is negative, so that the controller can control the coal mining machine to excavate based on the excavation parameters.

In an alternative embodiment, the first screening module is further configured to:

wherein ,ithe values are respectivelyx，y，z；i _max In a preset rangeiMaximum boundary value of direction;i _{avg_max} is thatiAverage value of maximum boundary values of target point cloud clusters corresponding to the directions; i _{std_max} Is thatiStandard deviation of maximum boundary value of target point cloud cluster corresponding to direction;i _min in a preset rangeiA minimum boundary value for the direction;i _{avg_min} is thatiAverage value of minimum boundary values of target point cloud clusters corresponding to the directions;i _{std_min} is thatiStandard deviation of minimum boundary value of target point cloud cluster corresponding to direction;kto adjust the coefficients.

In an alternative embodiment, the second screening module is further configured to:

In an alternative embodiment, the acquisition module is further configured to: acquiring point cloud data acquired by the radar equipment at different positions of the scraper baffle;

the first screening module is further configured to: screening out point clouds with coordinates corresponding to any position within a preset range according to the point cloud data acquired from any position;

the device comprises a splicing module, a splicing module and a processing module, wherein the splicing module is used for splicing point clouds with coordinates corresponding to different positions within a preset range so as to obtain spliced point clouds;

the second screening module is further configured to: clustering is conducted on the splicing point cloud.

In an alternative embodiment, the apparatus further comprises: the downsampling module is used for downsampling the splicing point cloud in a voxel grid mode to obtain a downsampled point cloud;

the second screening module is further configured to: and clustering the down-sampled point clouds.

In an alternative embodiment, the parameter generation module is further configured to: determining an overdriving area and an overdriving depth and/or determining an undersampling area and an undersampling depth according to the distance between the sampling point in the target point cloud cluster and the scraper baffle; and generating excavation parameters according to the super-mining area and the super-mining depth and/or the undersampling area and the undersampling depth.

According to a third aspect of the present invention there is provided a fully mechanized face system comprising:

the coal mining machine is used for mining coal resources;

the scraper machine is used for transporting the coal resources excavated by the coal mining machine;

the hydraulic support is used for providing support and pushing the scraper machine;

the radar equipment is arranged on the baffle plate in the scraper machine and used for collecting point cloud data;

the coal face straightener of claim 11; and

and the controller is used for receiving the excavation parameters sent by the coal mining straightening device and controlling the coal mining machine to excavate according to the excavation parameters.

According to a fourth aspect of the present invention there is provided a computing device comprising: the device comprises a processor, a memory, a communication interface and a communication bus, wherein the processor, the memory and the communication interface complete communication with each other through the communication bus;

the memory is used for storing at least one executable instruction, and the executable instruction enables the processor to execute the operation corresponding to the coal face straightening method.

According to a fifth aspect of the present invention, there is provided a computer storage medium having stored therein at least one executable instruction for causing a processor to perform operations corresponding to the above-described coal face straightening method.

The scheme provided by the invention utilizes the point cloud data acquired by the radar equipment to realize the straight recognition and the straightening of the coal face, thereby improving the accuracy and the efficiency of the straight recognition of the coal face and the straightening accuracy and the efficiency of the coal face. In addition, after the point cloud data acquired by the radar equipment are obtained, the point cloud corresponding to the coal face is accurately identified from the acquired point cloud data in a multi-stage screening mode of preset range screening and density cluster screening, so that the flatness identification and straightening accuracy of the coal face is further improved.

The foregoing description is only an overview of the present invention, and is intended to be implemented in accordance with the teachings of the present invention in order that the same may be more clearly understood and to make the same and other objects, features and advantages of the present invention more readily apparent.

Drawings

Various other advantages and benefits will become apparent to those of ordinary skill in the art upon reading the following detailed description of the preferred embodiments. The drawings are only for purposes of illustrating the preferred embodiments and are not to be construed as limiting the invention. Also, like reference numerals are used to designate like parts throughout the figures. In the drawings:

FIG. 1 is a schematic flow chart of a coal face straightening method according to an embodiment of the present invention;

fig. 2 shows a schematic view of a point cloud according to a first embodiment of the present invention;

FIG. 3 is a schematic view of another point cloud according to the first embodiment of the present invention;

fig. 4 is a schematic flow chart of a coal face straightening method according to a second embodiment of the present invention;

fig. 5 shows a schematic view of a point cloud according to a second embodiment of the present invention;

fig. 6 shows another point cloud schematic provided in the second embodiment of the present invention;

fig. 7 shows a schematic view of still another point cloud according to the second embodiment of the present invention;

FIG. 8 is a schematic view of a coal face straightener according to a third embodiment of the present invention;

fig. 9 is a schematic structural diagram of a fully-mechanized coal mining face system according to a fourth embodiment of the present invention;

fig. 10 is a schematic diagram showing a placement position of a radar apparatus according to a fourth embodiment of the present invention;

fig. 11 is a schematic structural diagram of a computing device according to a sixth embodiment of the present invention.

Detailed Description

Exemplary embodiments of the present invention will be described in more detail below with reference to the accompanying drawings. While exemplary embodiments of the present invention are shown in the drawings, it should be understood that the present invention may be embodied in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the invention to those skilled in the art.

Example 1

Fig. 1 shows a schematic flow chart of a coal face straightening method according to an embodiment of the invention. The coal face straightening method provided by the embodiment of the invention can be applied to an intelligent fully-mechanized coal face, so that the straightening accuracy and the straightening efficiency of the coal face in the fully-mechanized coal face can be conveniently improved.

As shown in fig. 1, the method comprises the steps of:

step S110, acquiring point cloud data acquired by radar equipment arranged on a baffle of the scraper.

In the embodiment of the invention, the radar equipment is utilized to scan the coal face to obtain point cloud data, and the automatic identification of the coal face flatness is carried out based on the obtained point cloud data, so that the detection precision and the detection efficiency of the coal face flatness are improved.

Specifically, the radar apparatus is provided in the scraper blade guard. By the arrangement mode, on one hand, radar equipment is conveniently placed, and the assembly process is simplified; on the other hand, when detecting whether the coal face is straight, whether the coal face is parallel to the plane where the scraper baffle is located is detected, so that the point cloud data coordinate system conversion process can be simplified by adopting the setting mode, and the overall execution efficiency of the method is improved. In addition, the embodiment of the invention does not limit the specific position of the radar equipment in the baffle plate of the scraper machine, the specific type of the radar equipment and the like. For example, the radar device may be fixedly disposed in the scraper blade or may be movable in the scraper blade; still alternatively, the radar device may be a laser device or a millimeter wave device, or the like. In addition, as the application scene of the radar equipment in the embodiment of the invention is a coal mine exploration scene, more dust exists in the use environment of the radar equipment, the dust generally reduces the data acquisition precision of the laser radar equipment, and the millimeter wave radar equipment has stronger anti-interference capability on the dust, so that the embodiment of the invention can preferably select the millimeter wave radar equipment, such as 4D millimeter wave radar equipment and the like, to improve the acquisition precision of point cloud data.

Further, after the radar device collects the point cloud data, the execution body of the embodiment of the invention acquires the point cloud data. The point cloud data specifically comprises three-dimensional coordinates of each sampling point.

Furthermore, in an alternative embodiment, the step S110 and the subsequent steps may be performed once after every n times of excavation, that is, the flatness detection and straightening may be performed on the coal face after every n times of excavation are performed; or, acquiring the frequency determined as the coal face unevenness frequency in the preset history time window, and making the frequency of executing the step S110 and the subsequent steps in the future time window positively correlated to the coal face unevenness frequency, namely, the higher the frequency of executing the step S110 and the subsequent steps in the future time window is, the higher the frequency of executing the coal face unevenness frequency in the history time window is; otherwise, if the frequency of the uneven coal face is higher and lower, the frequency of executing the step S110 and the subsequent steps in the next preset time period is lower.

Because the radar equipment is closer to the coal face and the coal mining exploration scene is a semi-closed or approximately-closed scene, radar multipath effect usually occurs, and therefore the acquired point cloud data comprise sampling points of the non-coal face. As shown in fig. 2, each scatter point in fig. 2 is a sampling point acquired by the radar device. As can be seen from fig. 2, the sampling points acquired by the radar apparatus include a plurality of sampling points other than the coal face 21. Based on this, the embodiment of the present invention further performs multi-level screening through step S120 and step S130, so as to screen the point cloud data corresponding to the coal face from the obtained point cloud data.

Step S120, screening out point clouds with coordinates in a preset range based on the point cloud data.

The first screen in the multi-level screen is a screen based on a preset range. The point cloud which obviously does not belong to the coal face can be removed through the screening of the preset range, so that the interference of the point cloud which obviously does not belong to the coal face on the subsequent clustering process is avoided, the recognition accuracy of the point cloud of the coal face is improved, the data volume of the subsequent clustering process is reduced, and the overall execution efficiency is improved.

When screening based on a preset range is performed, the preset range needs to be generated in advance. The preset range is specifically a value range in the x, y and z directions, and the preset range may be expressed as: x is x _min ＜x＜x _max And y is _min ＜y＜y _max And z _min ＜z＜z _max. wherein ,x_min Is the minimum boundary value in the x direction, x _max Is the maximum boundary value in the x direction, y _min Is the minimum boundary value in the y direction, y _max Is the maximum boundary value in the y direction, z _min Is the minimum boundary value in the z direction, z _max Is the maximum boundary value in the z direction.

The preset range may be a fixed parameter configured according to an empirical value, a dynamic parameter generated based on historical data, or a combination of both. For example, when the method is adopted to straighten the coal face for the first time, a preset range can be configured according to an empirical value; and dynamically adjusting the preset range according to the historical data when a certain amount of historical data is obtained.

In an alternative embodiment, an embodiment of generating a preset range based on historical data includes the steps of: acquiring a target point cloud cluster corresponding to the identified coal face in a historical time window; and determining a preset range according to the distribution range of each target point cloud cluster in the historical time window. Wherein the historical time window is specifically a recent historical time, such as a recent hour, and the like. In this embodiment, the preset range is dynamically determined according to the distribution range of the target point cloud cluster corresponding to the coal face which has been identified through history, so that the adaptation degree of the preset range and the current actual excavation condition can be improved, and the determination accuracy of the preset range can be improved.

Further optionally, the determining the preset range according to the distribution range of each target point cloud cluster in the historical time window includes, but is not limited to, the following determining modes:

determining a first mode: superposing all target point cloud clusters in the historical time window to obtain superposed point cloud clusters; respectively determining the maximum boundary value and the minimum boundary value of the superimposed point cloud cluster in the x, y and z directions; for any direction, taking the sum of the maximum boundary value in the direction and a preset redundancy value as the maximum boundary value in the direction in a preset range; and taking the difference between the minimum boundary value in the direction and the preset redundancy value as the minimum boundary value in the direction in the preset range. Specifically, the preset range is determined by the following formula 1:

(equation 1)

In the formula 1 of the present invention,irespectively taking valuesx，y，z；i _max In a preset rangeiMaximum boundary value of direction;i _{i_max} is formed by overlapping point cloud clustersiMaximum boundary value in direction; i _min in a preset rangeiA minimum boundary value for the direction;i _{i_min} is formed by overlapping point cloud clustersiMinimum boundary value in direction;pis a preset redundancy value andpis a positive number.

In the determining mode, the point clouds of the plurality of historical target point cloud clusters are overlapped, so that the overall distribution condition of the point clouds corresponding to the coal face in the historical time window can be obtained, the preset range is determined based on the distribution range of the overlapped point cloud clusters, and the determining precision of the preset range is further improved.

And a second determination mode: for any target point cloud cluster in a historical time window, respectively determining the maximum boundary value and the minimum boundary value of the target point cloud cluster in the x, y and z directions; for any direction, counting the average value and standard deviation of the maximum boundary value of the target point cloud cluster corresponding to the direction; counting the average value and standard deviation of the minimum boundary value of the target point cloud cluster corresponding to the direction; according to the average value and standard deviation of the maximum boundary values of the target point cloud clusters corresponding to all directions, the average value and standard deviation of the minimum boundary values of the target point cloud clusters corresponding to all directions and the adjustment coefficient, the preset range is determined by adopting the following formula 2:

(equation 2)

In the formula 2 of the present invention,irespectively taking valuesx，y，z；i _max In a preset rangeiMaximum boundary value of direction;i _{avg_max} is thatiAverage value of maximum boundary values of target point cloud clusters corresponding to the directions;i _{std_max} is thatiStandard deviation of maximum boundary value of target point cloud cluster corresponding to direction;i _min in a preset rangeiA minimum boundary value for the direction;i _{avg_min} is thatiAverage value of minimum boundary values of target point cloud clusters corresponding to the directions;i _{std_min} is thatiStandard deviation of minimum boundary value of target point cloud cluster corresponding to direction;kto adjust the coefficients.

In the determining mode, the maximum boundary value and the minimum boundary value of each historical target point cloud cluster in each direction are firstly determined, then the average value of the maximum boundary values and the standard deviation of the maximum boundary values corresponding to each direction are counted, the average value of the minimum boundary values and the standard deviation of the minimum boundary values corresponding to each direction are correspondingly counted, finally, the preset range is obtained by combining the adjustment coefficients, and the determining precision of the preset range is further improved.

Optionally, the uneven frequency of the coal face in the historical time window can be obtained; determining an adjustment coefficient according to the uneven frequency of the coal face; the adjustment coefficient is positively related to the uneven frequency of the coal face, namely, the higher the uneven frequency of the coal face detected in a historical time window is, the larger the adjustment coefficient is, so that the larger the maximum boundary value in each direction in a preset range is, the smaller the minimum boundary value is, the range length of the preset range is enlarged, the true sampling points in the coal face are prevented from being removed, and the identification precision of point cloud data corresponding to the whole coal face is improved.

After determining the preset range, the point cloud data acquired in the step S110 is compared with the preset range, so as to screen out the point cloud with coordinates within the preset range. As shown in fig. 3, fig. 3 is a schematic diagram of the point cloud after the screening in this step, and it can be seen from the figure that the interference point cloud can be greatly reduced by the screening in this step, so that the implementation of the subsequent step is facilitated.

Step S130, clustering point clouds with coordinates within a preset range by adopting a density-based clustering algorithm to obtain at least one point cloud cluster, and identifying a target point cloud cluster corresponding to the coal face from the at least one point cloud cluster.

The point cloud screened in step S120 still contains many other sampling points outside the coal face, so that the second stage screening is performed in this step to accurately reject the other sampling points outside the coal face. Specifically, in view of the fact that sampling points in the coal face are denser, the step adopts a density-based clustering algorithm to cluster the point clouds with coordinates within a preset range obtained in the step S120 so as to obtain at least one point cloud cluster; and taking the point cloud cluster with the largest number of the contained sampling points as the target point cloud cluster corresponding to the coal face.

Specifically, the clustering process can be performed by adopting a DBSCAN algorithm, and the clustering process comprises the following steps: the search radius and the minimum number of samples in the domain are preconfigured. Further, randomly selecting one sampling point from the sampling points which are not accessed, acquiring the adjacent points with the distance from the sampling point being smaller than or equal to the searching radius, and determining the sampling point as noise if the number of the adjacent points of the sampling point is smaller than the minimum sample number; if the number of the neighboring points of the sampling point is greater than or equal to the minimum sample number, the sampling point is determined to be a core point, and a new cluster label is allocated to the sampling point. Distributing the new cluster label to the adjacent points which are not distributed with the cluster label and correspond to the sampling point; and taking the cluster where the adjacent point of the distributed cluster label corresponding to the sampling point is located as the extension of the new cluster. And repeating the operations of arbitrarily selecting one sampling point from the sampling points which are not accessed and the follow-up operation until all the sampling points are determined to be noise or intra-cluster points, and ending the clustering.

The distance between sampling points is specifically the distance between clustering features of the sampling points, and clustering is specifically performed based on the clustering features of the sampling points so as to obtain at least one point cloud cluster. Wherein, the clustering characteristic of the sampling points includes, but is not limited to, calculation by the following way:

the first calculation mode is as follows: and taking the three-dimensional coordinates of the sampling points as the clustering characteristics of the sampling points. For example, the three-dimensional coordinates of the sampling points P are x ₁ 、y ₁ 、z ₁ The cluster characteristic of the sampling point P is (x ₁ 、y ₁ 、z ₁ ). By adopting the method, the clustering process can be simplified, and the overall execution efficiency is improved.

And a second calculation mode: and generating corrected three-dimensional coordinates of the sampling points according to the three-dimensional coordinates of the sampling points and the weights of the corresponding dimension directions, and taking the corrected three-dimensional coordinates as clustering features of the sampling points. When the overexcavation or underexcavation degree is serious, the above-mentioned calculation mode is adopted to easily identify the overexcavation or underexcavation part in the coal face as noise, and based on the noise, corresponding weights are configured for each dimension direction in the calculation mode, and the weight of the dimension direction corresponding to the normal direction (namely, the excavation depth direction) of the scraper baffle is smaller than the weight of the other dimension directions. For example, the three-dimensional coordinates of the sampling points P are x ₁ 、y ₁ 、z ₁ The weights in the x, y and z directions are s respectively ₁ ，s ₂ ，s ₃ (s if the x direction is the normal direction of the scraper baffle ₁ ＜s ₂ And s ₁ ＜s ₃ ) The cluster characteristic of the sampling point P is (s _1* x ₁ 、s _2* y ₁ 、s _3* z ₁ ). By adopting the calculation mode, the clustering precision can be improved, and the misjudgment rate can be reduced.

Step S140, judging whether the coal face is straight or not according to the distance between the sampling point in the target point cloud cluster and the scraper baffle; if not, step S150 is performed.

The point cloud cluster corresponding to the coal face is the target point cloud cluster, if the distances between sampling points in the target point cloud cluster and the scraper baffle are the same or the difference of the distances is within a preset error range, the coal face is determined to be parallel to the scraper baffle, namely the coal face is straight, and then follow-up excavation is continued according to standard excavation parameters; otherwise, if it is determined that the coal face is not straight, a subsequent step S150 is performed to determine new excavation parameters for straightening.

And step S150, generating excavation parameters according to the distance between the sampling points in the target point cloud cluster and the scraper baffle, so that the controller can control the coal mining machine to excavate based on the excavation parameters.

If the coal face is not straight, determining the next excavation parameter according to the distance between the sampling point in the target point cloud cluster and the scraper baffle. The method comprises the steps of determining an overdrawing area and an overdrawing depth and/or determining an undersampling area and an undersrawing depth according to the distance between a sampling point in a target point cloud cluster and a scraper baffle; and then generating mining parameters according to the super-mining area and the super-mining depth and/or the undersampling area and the undersampling depth. Optionally, the mining parameters may also be determined in combination with the position of the shearer and/or a preset mining schedule, etc.

Therefore, the embodiment of the invention utilizes the point cloud data acquired by the radar equipment to realize the straight recognition and the straightening of the coal face, improves the straight recognition precision and recognition efficiency of the coal face, and improves the straightening precision and the straightening efficiency of the coal face. In addition, after the point cloud data acquired by the radar equipment are obtained, the point cloud corresponding to the coal face is accurately identified from the acquired point cloud data in a multi-stage screening mode of preset range screening and density cluster-based screening, so that the accuracy of straight identification and straightening of the coal face is further improved.

Example two

Fig. 4 is a schematic flow chart of a coal face straightening method according to a second embodiment of the present invention. The coal face straightening method provided by the embodiment of the invention can be further optimized.

As shown in fig. 4, the method comprises the steps of:

step S410, acquiring point cloud data acquired by the radar device at different positions of the scraper baffle.

Because the radar equipment collects less point cloud data at one position, the characteristics of the coal face cannot be accurately and comprehensively reflected when the point cloud data are less, and therefore the defect that the accuracy of the coal face flatness detection result and the straightening result obtained based on the point cloud data is not high is caused.

Based on the point cloud data, the radar equipment can acquire the point cloud data at different positions of the baffle of the scraper. In order to facilitate standardized processing of point cloud data acquired at different positions, in the embodiment of the invention, the distances between the different positions and a standard coal face are the same, wherein the standard coal face refers to a coal wall plane when the coal face is not bent under ideal conditions. In an alternative embodiment, corresponding radar devices can be arranged at different positions of the scraper baffle, for example, one radar device can be arranged in the scraper baffle every n meters along the width direction of the coal face, and the straight lines where the radar devices are positioned are parallel to the width direction of the coal wall, so that the radar devices can acquire point cloud data at a plurality of positions; in yet another alternative embodiment, only one radar apparatus may be provided, which may be moved on the scraper bar by a preset moving member, which may be a walking robot, a preset conveyor, or the like, and whose moving track is parallel to the width direction of the coal wall, thereby saving costs.

Step S420, for the point cloud data acquired at any position, screening out the point cloud with the coordinates corresponding to the position in a preset range.

And (3) screening out the point cloud with coordinates within a preset range corresponding to each position by adopting a corresponding method in the step (120) aiming at the point cloud data acquired by each position.

And step S430, splicing the point clouds with the coordinates in a preset range corresponding to different positions to obtain spliced point clouds.

Specifically, because the coordinate systems corresponding to the point clouds collected at different positions are different, the point clouds corresponding to the different positions need to be standardized before being spliced, that is, the coordinate systems corresponding to the point clouds at different positions are unified. For example, the coordinate system corresponding to the first position may be used as the world coordinate system, and then coordinate system conversion processing may be performed on the point clouds corresponding to the other positions according to the offset of the other positions relative to the first position.

Taking a radar device to collect point cloud data corresponding to different positions through movement as an example, taking a coordinate system corresponding to the position where the radar device collects the point cloud for the first time as a world coordinate system, the coordinates of the point clouds corresponding to other positions can be converted by the following formula 3:

(equation 3)

In the formula 3 of the present invention,p _i+1 represents any point cloud corresponding to the (i+1) th position,xthe x-direction value representing the i-th position, v represents the moving speed of the radar device, △tThe acquisition time difference between the (i+1) th position and the (i) th position is represented, y represents the value in the y direction, and z represents the value in the z direction. Wherein the direction of movement of the radar device is in the x direction.

Step S440, performing voxel grid downsampling on the spliced point cloud to obtain a downsampled point cloud.

The step is an optional step in the embodiment of the present invention, that is, after the point clouds corresponding to different positions and having coordinates within a preset range are spliced to obtain the spliced point clouds, the spliced point clouds may be further clustered. However, as shown in fig. 5, the spliced point cloud obtained after the splicing process includes a larger number of sampling points and includes a larger number of repeated sampling points. Therefore, in order to facilitate the subsequent clustering process, the embodiment of the present invention further downsamples the splice point cloud through step S440, so as to reduce the number of sampling points in the point cloud by a proper amount.

Specifically, the embodiment of the invention performs downsampling through a voxel grid filtering algorithm. The implementation process comprises the following steps: and creating a three-dimensional voxel grid based on the splice point cloud, wherein each voxel is a three-dimensional cube with consistent size, and each voxel comprises at least one sampling point. The center of gravity of each voxel is further used to approximate the sampling point within that voxel, wherein if the quality of each sampling point is the same, the center of gravity of the point in each voxel should be the average of the coordinates of each sampling point. By adopting the downsampling mode, the curved surface can be accurately sampled, so that the whole implementation precision of the method is improved.

As shown in fig. 6, the number of point clouds can be reduced by implementing the step, so that the processing amount of subsequent clustering is reduced, and the overall implementation efficiency of the method is improved.

Step S450, clustering the down-sampled point clouds by adopting a density-based clustering algorithm to obtain at least one point cloud cluster, and identifying a target point cloud cluster corresponding to the coal face from the at least one point cloud cluster.

As shown in fig. 7, the clustering process in this step can extract the point cloud corresponding to the coal face.

Step S460, judging whether the coal face is straight or not according to the distance between the sampling point in the target point cloud cluster and the scraper baffle; if not, generating excavation parameters according to the distance between the sampling points in the target point cloud cluster and the scraper baffle, so that the controller can control the coal mining machine to excavate based on the excavation parameters.

In addition, the embodiment of the invention acquires the point cloud data acquired by the radar equipment at different positions of the baffle of the scraper, and screens out the point cloud with the coordinates corresponding to any position within a preset range according to the point cloud data acquired at any position; splicing point clouds with coordinates corresponding to different positions within a preset range to obtain spliced point clouds for subsequent clustering based on the spliced point clouds, so that bending recognition precision and straightening precision of the coal face are improved; and the voxel grid downsampling is carried out after the splice point cloud is obtained, so that the clustering throughput is reduced, and the bending recognition efficiency and the straightening efficiency of the coal face are improved.

Example III

Fig. 8 shows a schematic structural diagram of a coal face straightening apparatus according to a third embodiment of the present invention. As shown in fig. 8, the coal face straightener 800 includes: the system comprises an acquisition module 810, a first screening module 820, a second screening module 830, a judging module 840 and a parameter generating module 850.

The acquiring module 810 is configured to acquire point cloud data acquired by a radar device disposed on a baffle of the scraper;

a first screening module 820, configured to screen out a point cloud with coordinates within a preset range based on the point cloud data;

The second screening module 830 is configured to cluster the point clouds with coordinates within a preset range by using a density-based clustering algorithm, so as to obtain at least one point cloud cluster, and identify a target point cloud cluster corresponding to the coal face from the at least one point cloud cluster;

the judging module 840 is configured to judge whether the coal face is straight according to the distance between the sampling point in the target point cloud cluster and the scraper baffle;

and the parameter generating module 850 is configured to generate an excavation parameter according to a distance between the sampling point in the target point cloud cluster and the scraper baffle if the judgment result of the judging module is negative, so that the controller controls the coal mining machine to excavate based on the excavation parameter.

for any direction, counting the average value and standard deviation of the maximum boundary value of the target point cloud cluster corresponding to the direction; counting the average value and standard deviation of the minimum boundary values of the cloud clusters of each target point corresponding to the direction;

wherein ,ithe values are respectivelyx，y，z；i _max In a preset rangeiMaximum boundary value of direction;i _{avg_max} is thatiAverage value of maximum boundary values of target point cloud clusters corresponding to the directions; i _{std_max} Is thatiStandard deviation of maximum boundary value of target point cloud cluster corresponding to direction;i _min in a preset rangeiA minimum boundary value for the direction;i _{avg_min} is thatiAverage value of minimum boundary values of target point cloud clusters corresponding to the directions;i _{std_min} is thatiMinimum target point cloud cluster corresponding to directionStandard deviation of boundary values;kto adjust the coefficients.

determining the adjustment coefficient according to the frequency; wherein the adjustment coefficient is positively correlated to the frequency.

The specific implementation process of each module of the device may refer to the description in the method embodiment, and will not be repeated herein.

Example IV

Fig. 9 shows a schematic structural diagram of a fully-mechanized coal mining face according to a fourth embodiment of the present invention. As shown in fig. 9, the fully mechanized face system 900 includes: a shearer 910, a scraper 920, a hydraulic support 930, a radar apparatus 940, a straightener controller 800 and a controller 950.

A shearer 910 for excavating coal resources;

a scraper 920 for transporting the coal resources excavated by the coal mining machine;

the hydraulic support 930 is used for providing support and pushing the scraper machine;

Radar equipment 940, set up in the baffle in the scraper for gathering point cloud data. Alternatively, as shown in fig. 10, the scraper blade 921 is opposite to the coal face 960, a track 922 is provided on the scraper blade 921, and the radar device 940 may move in the track 922 and collect point cloud data.

Straightener controller 800, the specific function of which can be described in relation to embodiment three;

and the controller 950 is used for receiving the excavation parameters sent by the coal mining straightening device and controlling the coal mining machine to excavate according to the excavation parameters.

Example five

A fifth embodiment of the present invention provides a non-volatile computer storage medium, where at least one executable instruction is stored, where the computer executable instruction may perform the coal face straightening method in any of the foregoing method embodiments.

Example six

Fig. 11 is a schematic structural diagram of a computing device according to a sixth embodiment of the present invention. The specific embodiments of the present invention are not limited to a particular implementation of a computing device.

As shown in fig. 11, the computing device may include: a processor 1102, a communication interface (Communications Interface), a memory 1006, and a communication bus 1008.

Wherein: processor 1102, communication interface 1104, and memory 1106 communicate with each other via a communication bus 1108. A communication interface 1104 for communicating with network elements of other devices, such as clients or other servers. Processor 1102 is configured to execute program 1110, and may specifically perform the relevant steps in the above-described embodiments of the coal face straightening method.

In particular, program 1110 may include program code including computer-operating instructions.

The processor 1102 may be a central processing unit CPU, or a specific integrated circuit ASIC (Application Specific Integrated Circuit), or one or more integrated circuits configured to implement embodiments of the present invention. The one or more processors included by the computing device may be the same type of processor, such as one or more CPUs; but may also be different types of processors such as one or more CPUs and one or more ASICs.

Memory 1106 for storing program 1110. The memory 1106 may include high-speed RAM memory or may also include non-volatile memory (non-volatile memory), such as at least one disk memory. The program 1110 may be specifically adapted to cause the processor 1102 to perform the steps of the above-described embodiments of the coal face straightening method.

The algorithms or displays presented herein are not inherently related to any particular computer, virtual system, or other apparatus. Various general-purpose systems may also be used with the teachings herein. The required structure for a construction of such a system is apparent from the description above. In addition, embodiments of the present invention are not directed to any particular programming language. It will be appreciated that the teachings of the present invention described herein may be implemented in a variety of programming languages, and the above description of specific languages is provided for disclosure of enablement and best mode of the present invention.

In the description provided herein, numerous specific details are set forth. However, it is understood that embodiments of the invention may be practiced without these specific details. In some instances, well-known methods, structures and techniques have not been shown in detail in order not to obscure an understanding of this description.

Similarly, it should be appreciated that in the above description of exemplary embodiments of the invention, various features of the embodiments of the invention are sometimes grouped together in a single embodiment, figure, or description thereof for the purpose of streamlining the disclosure and aiding in the understanding of one or more of the various inventive aspects. However, the disclosed method should not be construed as reflecting the intention that: i.e., the claimed invention requires more features than are expressly recited in each claim. Rather, as the following claims reflect, inventive aspects lie in less than all features of a single foregoing disclosed embodiment. Thus, the claims following the detailed description are hereby expressly incorporated into this detailed description, with each claim standing on its own as a separate embodiment of this invention.

Those skilled in the art will appreciate that the modules in the apparatus of the embodiments may be adaptively changed and disposed in one or more apparatuses different from the embodiments. The modules or units or components of the embodiments may be combined into one module or unit or component and, furthermore, they may be divided into a plurality of sub-modules or sub-units or sub-components. Any combination of all features disclosed in this specification (including any accompanying claims, abstract and drawings), and all of the processes or units of any method or apparatus so disclosed, may be used in combination, except insofar as at least some of such features and/or processes or units are mutually exclusive. Each feature disclosed in this specification (including any accompanying claims, abstract and drawings), may be replaced by alternative features serving the same, equivalent or similar purpose, unless expressly stated otherwise.

Furthermore, those skilled in the art will appreciate that while some embodiments herein include some features but not others included in other embodiments, combinations of features of different embodiments are meant to be within the scope of the invention and form different embodiments. For example, in the following claims, any of the claimed embodiments can be used in any combination.

Various component embodiments of the invention may be implemented in hardware, or in software modules running on one or more processors, or in a combination thereof. Those skilled in the art will appreciate that some or all of the functionality of some or all of the components according to embodiments of the present invention may be implemented in practice using a microprocessor or Digital Signal Processor (DSP). The present invention can also be implemented as an apparatus or device program (e.g., a computer program and a computer program product) for performing a portion or all of the methods described herein. Such a program embodying the present invention may be stored on a computer readable medium, or may have the form of one or more signals. Such signals may be downloaded from an internet website, provided on a carrier signal, or provided in any other form.

It should be noted that the above-mentioned embodiments illustrate rather than limit the invention, and that those skilled in the art will be able to design alternative embodiments without departing from the scope of the appended claims. In the claims, any reference signs placed between parentheses shall not be construed as limiting the claim. The word "comprising" does not exclude the presence of elements or steps not listed in a claim. The word "a" or "an" preceding an element does not exclude the presence of a plurality of such elements. The invention may be implemented by means of hardware comprising several distinct elements, and by means of a suitably programmed computer. In the unit claims enumerating several means, several of these means may be embodied by one and the same item of hardware. The use of the words first, second, third, etc. do not denote any order. These words may be interpreted as names. The steps in the above embodiments should not be construed as limiting the order of execution unless specifically stated.

Claims

1. A method of coal face straightening, comprising:

2. The method according to claim 1, wherein the preset range is determined by:

3. The method of claim 2, wherein determining the preset range according to the distribution range of each target point cloud cluster in the historical time window further comprises:

4. The method of claim 2, wherein determining the preset range according to the distribution range of each target point cloud cluster in the historical time window further comprises:

wherein ,ithe values are respectivelyx，y，z；i _max In a preset rangeiMaximum boundary value of direction;i _{avg_max} is thatiAverage value of maximum boundary values of target point cloud clusters corresponding to the directions;i _{std_max} is thatiStandard deviation of maximum boundary value of target point cloud cluster corresponding to direction;i _min in a preset rangeiA minimum boundary value for the direction;i _{avg_min} is thatiAverage value of minimum boundary values of target point cloud clusters corresponding to the directions;i _{std_min} is thatiStandard deviation of minimum boundary value of target point cloud cluster corresponding to direction;kfor the adjustment factor.

5. The method according to claim 4, wherein the method further comprises:

6. The method of claim 1, wherein identifying a target point cloud cluster corresponding to a coal face from the at least one point cloud cluster further comprises:

7. The method of claim 6, wherein clustering the point clouds with coordinates within a predetermined range to obtain at least one point cloud cluster further comprises:

8. The method according to any one of claims 1 to 7, wherein the acquiring the point cloud data acquired by the radar device provided to the scraper blade further comprises: acquiring point cloud data acquired by the radar equipment at different positions of the scraper baffle;

9. The method of claim 8, wherein after the obtaining the splice point cloud, the method further comprises: performing voxel grid downsampling on the splicing point cloud to obtain downsampled point cloud;

10. The method of any one of claims 1-7, wherein generating the mining parameter from the distance between the sampling point and the scraper baffle in the target point cloud cluster further comprises:

determining an overdriving area and an overdriving depth and/or determining an undersampling area and an undersampling depth according to the distance between the sampling point in the target point cloud cluster and the scraper baffle;

and generating excavation parameters according to the super-mining area and the super-mining depth and/or the undersampling area and the undersampling depth.

11. A coal face straightener, comprising:

12. A fully mechanized coal mining face system, comprising:

the coal mining machine is used for mining coal resources;

the radar equipment is arranged in the baffle plate of the scraper machine and used for collecting point cloud data;

the coal face straightener of claim 11;

13. A computing device, comprising: the device comprises a processor, a memory, a communication interface and a communication bus, wherein the processor, the memory and the communication interface complete communication with each other through the communication bus;

the memory is configured to store at least one executable instruction that causes the processor to perform operations corresponding to the coal face straightening method according to any one of claims 1 to 10.

14. A computer storage medium having stored therein at least one executable instruction for causing a processor to perform operations corresponding to the coal face straightening method according to any one of claims 1 to 10.