CN114882339B - Coal mine roadway eyelet autonomous identification method based on real-time dense point cloud map - Google Patents

Coal mine roadway eyelet autonomous identification method based on real-time dense point cloud map Download PDF

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CN114882339B
CN114882339B CN202210288413.9A CN202210288413A CN114882339B CN 114882339 B CN114882339 B CN 114882339B CN 202210288413 A CN202210288413 A CN 202210288413A CN 114882339 B CN114882339 B CN 114882339B
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CN114882339A (en
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王宏伟
刘宇
武仲斌
耿毅德
付翔
张纯旺
王浩然
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Taiyuan University of Technology
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    • G06COMPUTING; CALCULATING OR COUNTING
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    • G06V20/00Scenes; Scene-specific elements
    • GPHYSICS
    • G01MEASURING; TESTING
    • G01SRADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S17/00Systems using the reflection or reradiation of electromagnetic waves other than radio waves, e.g. lidar systems
    • G01S17/86Combinations of lidar systems with systems other than lidar, radar or sonar, e.g. with direction finders
    • GPHYSICS
    • G01MEASURING; TESTING
    • G01SRADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S17/00Systems using the reflection or reradiation of electromagnetic waves other than radio waves, e.g. lidar systems
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    • G01S17/89Lidar systems specially adapted for specific applications for mapping or imaging
    • GPHYSICS
    • G06COMPUTING; CALCULATING OR COUNTING
    • G06VIMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00Arrangements for image or video recognition or understanding
    • G06V10/20Image preprocessing
    • G06V10/25Determination of region of interest [ROI] or a volume of interest [VOI]
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    • G06COMPUTING; CALCULATING OR COUNTING
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Abstract

The invention provides an autonomous recognition method for coal mine roadway holes based on a real-time dense point cloud map, which is applicable to the technical field of intelligent support of underground coal mine anchor cables. According to the invention, a centimeter-level point cloud map is constructed by an LIO-SAM algorithm and a normal distribution transformation registration method, key points are searched by utilizing angle characteristics of point clouds in key frames according to fixed geometric forms of ladder beams and holes, an interested region is obtained, and micro target detection is realized in a complex anchor network of a top plate and a side wall by enhancing the resolution of the point clouds of the interested region, so that autonomous planning and decision-making of a drill boom are facilitated. The method has good instantaneity and can improve the working efficiency of the anchor rod drill carriage.

Description

Coal mine roadway eyelet autonomous identification method based on real-time dense point cloud map
Technical Field
The invention relates to the field of coal mine self-supporting, in particular to an autonomous recognition method for coal mine roadway holes based on dense laser point clouds, and belongs to the technical fields of multi-sensor fusion and laser point cloud feature extraction.
Background
The underground support operation efficiency is a core problem of efficient mining of the underground coal mine. The intelligent roadway driving working face support can effectively reduce the potential safety hazard problem caused by the difference of the empty top distances, lighten the working intensity of personnel and improve the support efficiency. The anchor rod drill carriage is the main mechanical equipment for supporting operation. Currently, the whole drill carriage design (authorized publication number: CN110374498B; CN109723380B; CN109098741B; CN 107191137B), the drill boom structure design and driving (authorized publication number: CN107654190B; CN103485717B; CN102434098B; CN 102418540B), automatic drilling, rod loading, anchor rod fastening (authorized publication number: CN109098741B; CN 110454204B), automatic positioning of the vehicle body (application publication number: CN113685139A; CN 112947314A) and the like are studied intensively. However, in the intelligent coal mining process, the relations of the anchor rod drill carriage and the tunneling machine are becoming more and more compact, and new requirements and new challenges are provided for the anchor rod drill carriage supporting method and technology. The anchor rod (anchor cable) hole identification is a problem to be solved urgently in support intelligence.
The aspect of underground target detection has research foundation, such as underground target detection and tracking of coal mine based on machine vision and convolutional neural network Faster-RCNN (authorized publication number: CN 108182413B). Meanwhile, the non-mining small target detection technology is stably developed, such as small target detection is realized based on the technologies of deep bidirectional feature pyramid enhancement network (authorized publication number: CN 109472298B), weighted sparse residual error model establishment (authorized publication number: CN 108062523B), foreground pixel separation (authorized publication number: CN 108648211B) and the like. However, in a complex underground coal mine environment, the capability of the prior art for detecting tiny targets such as anchor rod (anchor cable) holes is insufficient, and accurate detection of the anchor holes cannot be realized.
Disclosure of Invention
The invention overcomes the defects existing in the prior art, and solves the technical problems that: the coal mine roadway eyelet autonomous identification method based on the dense laser point cloud is provided for realizing detection of tiny targets in complex anchor networks of roadway top plates and sides, providing basis for autonomous planning and decision making of a drill boom and improving working efficiency of an anchor rod drill carriage.
In order to solve the technical problems, the invention adopts the following technical scheme: 1. the autonomous recognition method for the coal mine roadway holes based on the real-time dense point cloud map is characterized by comprising the following steps of:
S1, fixedly arranging a first laser radar and an inertial element on an anchor rod drill carriage, and arranging two solid laser radars on the anchor rod drill carriage through a control turntable respectively;
S2, fusing the first laser radar and the inertial element data by adopting an LIO-SAM algorithm when the anchor-rod drill carriage runs, outputting a map, rotating a control turntable when the anchor-rod drill carriage is stationary, acquiring point cloud data by the two solid-state laser radars, and registering all frame point cloud data acquired by the two solid-state laser radars; registering and filling by taking a map output by the anchor rod drill carriage as a framework and taking point cloud data obtained by the anchor rod drill carriage when the anchor rod drill carriage is stationary as nodes, so as to construct a roadway point cloud map with centimeter-level resolution;
s3, separating a three-dimensional sub-map from the roadway point cloud map, setting a sliding window body, enabling the sliding window body to move on the three-dimensional sub-map, and searching key points in the sliding window body until all point clouds of the three-dimensional sub-map are traversed;
s4, constructing an interested region through key points, adjusting the postures of the two solid-state laser radars, scanning the interested region again, and enhancing the spatial resolution of the interested region;
S5, establishing an index for the point cloud of the region of interest, projecting the point cloud of the region of interest to a two-dimensional plane to obtain a projection region I ', and searching in the projection region I' to obtain the position of the hole.
In the step S5, after searching for an eyelet, all the eyelets in the interested area are found according to the established navigation system and the construction design requirement, and the eyelets are anchor rod holes or anchor cable holes.
In the step S5, the specific method for searching the hole is as follows:
Searching four point clouds in the projection area I', wherein the four point clouds meet the following conditions: and after finding out, mapping the four point clouds back to the point cloud corresponding to the region of interest according to the point cloud index, and thus obtaining the position of the hole.
In the step S2, a normal distribution transformation registration method is adopted to register all frame point cloud data acquired by the two solid-state laser radars.
In the step S3, the three-dimensional sub-map is an area 2m forward from the laser radar a.
In the step S3, the method for searching the key points is as follows:
S301, finding a point x 1 in the sliding window, calculating the Euclidean distance between the point x 1 and the rest points, finding all points with the Euclidean distance smaller than a first threshold value as adjacent points, taking the point x 1 as a starting point, taking each adjacent point of x 1 as an end point to construct vectors, calculating the included angle theta between each vector, and judging whether the included angle theta meets the following conditions: 89 DEG < theta < 93 DEG, if present, identifying the point as a key point;
s302, traversing all points in the sliding window, and repeating the step S301 until all key points in the sliding window are found;
S303, enabling the sliding window to move on the three-dimensional sub-map, repeating the steps S301-S302, and finding all key points in the three-dimensional sub-map.
The first threshold is 13mm.
In the step S4, when the region of interest is scanned again, the vertical resolution of the point cloud of the region is enhanced to reach 0.1 °.
In the step S4, the method for constructing the region of interest includes:
And calculating Euclidean distance between each key point and other key points, and finding out the key points with Euclidean distance smaller than a second threshold value, and drawing spheres by taking each key point as a center, wherein all spheres form an interested region.
In the step S4, when a sphere is drawn, the radius of the sphere is 18-22 mm, and the second threshold value is 35mm.
Compared with the prior art, the invention has the following beneficial effects:
The invention provides an autonomous recognition method of coal mine tunnel holes based on dense laser point clouds, which overcomes the severe conditions of uneven illumination, dust and water mist in tunnels, builds a centimeter-level point cloud map through a laser radar-inertial odometer, searches key points by utilizing angle characteristics of point clouds in key frames according to fixed geometric forms of ladder beams and holes, obtains a region of interest, realizes tiny target detection in complex anchor networks of a roof and a side wall by enhancing the point cloud resolution of the region of interest, and is beneficial to autonomous planning and decision-making of a drill boom. The method has good instantaneity and can improve the working efficiency of the anchor rod drill carriage.
Drawings
Fig. 1 is a schematic flow chart of a coal mine roadway eyelet autonomous identification method based on a real-time dense point cloud map according to an embodiment of the invention;
FIG. 2 is a schematic diagram of an embodiment of an anchor drilling machine;
FIG. 3 is a schematic illustration of the placement of anchor cable holes in a downhole anchor network;
FIG. 4 is a schematic diagram of the effect of the roadway roof point cloud region of interest before and after enhancement;
In the figure: 1 is an anchor rod drill carriage supporting platform, 2 is a first laser radar, 3 is an inertial element, 4 is a turntable, and 5 is a solid laser radar.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more clear, the technical solutions in the embodiments of the present invention will be clearly and completely described below, and it is apparent that the described embodiments are some embodiments of the present invention, but not all embodiments; all other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
As shown in fig. 1, the embodiment of the invention provides an autonomous recognition method for coal mine roadway holes based on a real-time dense point cloud map, which comprises the following steps:
S1, fixedly arranging a first laser radar and an inertial element on an anchor and drill carriage support platform, and arranging two solid-state laser radars on the anchor and drill carriage support platform through a control turntable respectively, wherein the control turntable is shown in fig. 2.
S2, constructing a dense point cloud map. The method comprises the steps that when an anchor rod drill carriage runs, a LIO-SAM algorithm is adopted to fuse first laser radar and inertial element data, a map is output, when the anchor rod drill carriage is stationary, a control turntable is rotated, point cloud data are acquired through two solid laser radars, and all frame point cloud data acquired by the two solid laser radars are registered; and registering and filling by taking a map output by the anchor rod drill carriage when the anchor rod drill carriage runs as a framework and taking point cloud data obtained by the anchor rod drill carriage when the anchor rod drill carriage is stationary as nodes, so as to construct a roadway point cloud map with centimeter-level resolution.
In the embodiment, a normal distribution transformation registration method is adopted to register all frame point cloud data acquired by the two solid-state laser radars.
In the embodiment, the LIO-SAM algorithm (Tightly-coupled LIDAR INERTIAL Odometry via Smoothing AND MAPPING) is adopted to fuse the first laser radar and the inertial element data, so that the positioning and the map building in the running process of the anchor and drill carriage can be realized. The first laser radar is a 128-line laser radar, and the inertial element is a 9-axis inertial element. Because LIO-SAM algorithm is in order to guarantee the real-time of location and drawing, the point cloud map only comprises key frames. In order to improve the consistency of the point cloud map, the working procedure and the operation specification of the anchor rod drill carriage are comprehensively considered, and when a drill carriage operation gap is static, the two solid-state laser radars and the turntable are combined for use, and a normal distribution transformation registration method (NDT) is adopted to register and combine all frame point clouds. Wherein the solid-state lidar is a variable-focus lidar. When the anchor rod drill carriage runs, LIO-SAM algorithm output is adopted as a skeleton map, and when the anchor rod drill carriage is stationary, a normal distribution transformation registration method (NDT) output result is used for registering and filling on the skeleton map. Finally, the initial pose of the LIO-SAM algorithm is used as a reference to establish a northeast navigation system, a roadway point cloud map with centimeter-level resolution is drawn, and the detailed description of the geometric forms of the roadway roof anchor net, the ladder beam and the lateral anchor net is realized.
And S3, detecting the key point cloud.
Separating a three-dimensional sub-map from the roadway point cloud map, setting a sliding window body, enabling the sliding window body to move on the three-dimensional sub-map, and searching key points in the sliding window body until all point clouds of the three-dimensional sub-map are traversed.
Specifically, in this embodiment, since the pitch between anchor rods is usually 800×800mm, the three-dimensional sub-map is an area with the laser radar a as a starting point and 2m forward.
In the step S3, the method for searching the key points is as follows:
s301, finding a point x 1 in the sliding window, calculating the Euclidean distance between the point x 1 and the rest points, finding all points with the Euclidean distance smaller than a first threshold value as adjacent points, taking the point x 1 as a starting point, taking each adjacent point of x 1 as an end point to construct vectors, calculating the included angle theta between each vector, and judging whether the included angle theta meets the following conditions: 89 DEG < θ < 93 DEG, if present, identifying the point as a keypoint.
In this embodiment, the first threshold is determined according to the diamond-shaped structures and the actual sizes of the roof anchor net and the side anchor net, and the first threshold is 13mm. Taking x i as an example of any point in a point cloud in a sliding window, assuming that a plurality of points x 1,x2,x3 and the distance between the points are smaller than a first threshold, taking x 1,x2,x3 as a starting point and x 1,x2,x3 as an ending point, constructing a vector , and calculating an included angle between two vectors, wherein if an included angle is approximately equal to 90 degrees, the point is a key point.
S302, traversing all points in the sliding window, and repeating the step S301 until all key points in the sliding window are found;
S303, moving the sliding window on the three-dimensional sub-map, repeating the steps S301 to S302, finding all key points in the three-dimensional sub-map, and enabling the moving steps to meet the following conditions:
m<A/2,n<B/2,p<C/2,
wherein m, n and p are the moving steps of the sliding window in the x, y and z axis directions of the navigation system respectively, and A, B and C are the dimensions of the sliding window in the x, y and z axis directions of the navigation system respectively.
Specifically, in this embodiment, the sliding window size is specifically 500×500×50mm, the initial position of the window is at the minimum value of the dense map x, y, z, and in order to prevent the window edge feature from being missing, the window center moves by 240×240×20mm on the navigation system x, y, z axis until all points in the map complete the angle feature calculation.
S4, enhancing the resolution of the region of interest: and constructing an interested region through the key points, adjusting the postures of the two solid-state laser radars, scanning the interested region again, and enhancing the spatial resolution of the interested region.
In the step S4, the method for constructing the region of interest includes:
And calculating Euclidean distance between each key point and other key points, and finding out the key points with Euclidean distance smaller than a second threshold value, and drawing spheres by taking each key point as a center, wherein all spheres form an interested region.
In the step S4, when a sphere is drawn, the radius of the sphere is 18-22 mm, and the second threshold value is 35mm.
Since the diameter of the anchor rod (anchor cable) hole is about 32mm, if there are several key points, the distance between the key points is smaller than the second threshold value, which means that the key points may be located on the same anchor rod (anchor cable) hole, a region of interest I can be found with the key points as the center. Similarly, multiple regions of interest may be found.
Because of the small size of the target, the resolution of the point cloud map of the region of interest needs to be enhanced. According to the position information of the key points, the rotary table is adjusted to change the postures of the two solid-state laser radars, the region of interest is scanned again, and the vertical resolution of the point cloud of the region is enhanced to reach 0.1 degrees.
Fig. 4 is a schematic diagram of cloud points before and after enhancement of a region of interest according to an embodiment of the present invention, where (a) is a dense point cloud map with a vertical resolution of 5 ° and (b) is a dense point cloud map with a vertical resolution of 0.5-0.1 °. The experimental environment is the environment with the net section of the tunnel of 4600 multiplied by 3900mm and the dust concentration of 600mg/cm 3. As can be seen from fig. 4, the characteristics of cloud points are more remarkable after the enhancement, so that the anchor hole recognition accuracy can be improved.
S5, detecting anchor rod (anchor rope) holes: and (3) establishing an index for the point cloud of the region of interest, projecting the point cloud of the region of interest to a two-dimensional plane to obtain a projection region I ', and searching in the projection region I' to obtain the positions of the holes (anchor rods and anchor rope holes).
And (3) carrying out point cloud voxelization (Voxel) on the region of interest I after resolution enhancement, establishing kd-tree to carry out point cloud index reassignment on each grid, projecting the point cloud to an XOY two-dimensional plane of the original established northeast navigation system, and representing the projection region by I'.
In the step S5, the specific method for searching the hole is as follows:
Searching four point clouds in the projection area I', wherein the four point clouds meet the following conditions: and the diameter of the circle is equal to 0.9-1.1 times of the diameter of the hole, and after the circle is found, the four point clouds are mapped back to the point clouds corresponding to the region of interest according to the point cloud index, so that the position of the hole is obtained.
Assuming that these four point clouds are F, G, H, J, let and/> included angles/> and/> included angles/> when α+β=180° or α=β=90°, it is illustrated that the point cloud F, G, H, J is on a circle. If the diameter of the circle is in the range of 0.9-1.1 times of the diameter of the hole, the four point clouds are mapped back to the point cloud corresponding to the region of interest according to the point cloud index and the pixel grid, namely the position of the anchor rod (anchor cable) hole is located.
Table 1 comparison of experimental results of various identification methods
Because the positions of the holes are in accordance with the law of construction design requirements, in this embodiment, after one hole is searched, all holes in the region of interest can be found according to the navigation system established by the LIO-SAM and the construction design requirements, and the holes are anchor rod holes or anchor cable holes.
In summary, the invention provides a coal mine roadway eyelet autonomous identification method based on dense laser point cloud, which overcomes the severe conditions of uneven illumination, dust and water mist in a roadway, constructs a centimeter-level point cloud map through a laser radar-inertial odometer, searches key points by utilizing angle characteristics of point cloud in key frames according to fixed geometric forms of ladder beams and eyelets and obtains a region of interest, realizes tiny target detection in a complex anchor network of a top plate and a side wall by enhancing the point cloud resolution of the region of interest, and improves the detection precision of anchor rod (anchor cable) holes so as to provide technical guidance and data support for unmanned operation of an anchor rod drill carriage, thereby being beneficial to autonomous planning and decision-making of a drill boom. The method has good instantaneity and can improve the working efficiency of the anchor rod drill carriage.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solution of the present invention, and not for limiting the same; although the invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some or all of the technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit of the invention.

Claims (10)

1. The autonomous recognition method for the coal mine roadway holes based on the real-time dense point cloud map is characterized by comprising the following steps of:
S1, fixedly arranging a first laser radar and an inertial element on an anchor rod drill carriage, and arranging two solid laser radars on the anchor rod drill carriage through a control turntable respectively;
S2, fusing the first laser radar and the inertial element data by adopting an LIO-SAM algorithm when the anchor-rod drill carriage runs, outputting a map, rotating a control turntable when the anchor-rod drill carriage is stationary, acquiring point cloud data by the two solid-state laser radars, and registering all frame point cloud data acquired by the two solid-state laser radars; registering and filling by taking a map output by the anchor rod drill carriage as a framework and taking point cloud data obtained by the anchor rod drill carriage when the anchor rod drill carriage is stationary as nodes, so as to construct a roadway point cloud map with centimeter-level resolution;
s3, separating a three-dimensional sub-map from the roadway point cloud map, setting a sliding window body, enabling the sliding window body to move on the three-dimensional sub-map, and searching key points in the sliding window body until all point clouds of the three-dimensional sub-map are traversed;
s4, constructing an interested region through key points, adjusting the postures of the two solid-state laser radars, scanning the interested region again, and enhancing the spatial resolution of the interested region;
S5, establishing an index for the point cloud of the region of interest, projecting the point cloud of the region of interest to a two-dimensional plane to obtain a projection region I ', and searching in the projection region I' to obtain the position of the hole.
2. The autonomous recognition method of coal mine tunnel holes based on the real-time dense point cloud map according to claim 1, wherein in the step S5, after searching a hole, all holes in the interested area are found according to the established navigation system and the construction design requirement, and the holes are anchor rod holes or anchor rope holes.
3. The autonomous recognition method of coal mine tunnel holes based on the real-time dense point cloud map according to claim 1, wherein in the step S5, the specific method for searching holes is as follows:
Searching four point clouds in the projection area I', wherein the four point clouds meet the following conditions: and after finding out, mapping the four point clouds back to the point cloud corresponding to the region of interest according to the point cloud index, and thus obtaining the position of the hole.
4. The autonomous recognition method of coal mine roadway holes based on the real-time dense point cloud map of claim 1, wherein in the step S2, a normal distribution transformation registration method is adopted to register all frame point cloud data acquired by two solid-state laser radars.
5. The autonomous recognition method of coal mine roadway holes based on the real-time dense point cloud map according to claim 1, wherein in the step S3, the three-dimensional sub-map is an area with a laser radar a as a starting point and 2m forward.
6. The autonomous recognition method of coal mine tunnel holes based on the real-time dense point cloud map of claim 1, wherein in the step S3, the method for searching the key points is as follows:
S301, finding a point x 1 in the sliding window, calculating the Euclidean distance between the point x 1 and the rest points, finding all points with the Euclidean distance smaller than a first threshold value as adjacent points, taking the point x 1 as a starting point, taking each adjacent point of x 1 as an end point to construct vectors, calculating the included angle theta between each vector, and judging whether the included angle theta meets the following conditions: 89 DEG < theta < 93 DEG, if present, identifying the point as a key point;
s302, traversing all points in the sliding window, and repeating the step S301 until all key points in the sliding window are found;
S303, enabling the sliding window to move on the three-dimensional sub-map, repeating the steps S301-S302, and finding all key points in the three-dimensional sub-map.
7. The autonomous recognition method of coal mine tunnel holes based on the real-time dense point cloud map of claim 6, wherein the first threshold is 13mm.
8. The autonomous recognition method of coal mine tunnel holes based on the real-time dense point cloud map according to claim 1, wherein in the step S4, when the region of interest is scanned again, the vertical resolution of the point cloud of the region is enhanced to 0.1 °.
9. The autonomous recognition method of coal mine roadway holes based on the real-time dense point cloud map according to claim 1, wherein in the step S4, the construction method of the region of interest is as follows:
And calculating Euclidean distance between each key point and other key points, and finding out the key points with Euclidean distance smaller than a second threshold value, and drawing spheres by taking each key point as a center, wherein all spheres form an interested region.
10. The autonomous recognition method of coal mine roadway holes based on the real-time dense point cloud map according to claim 9, wherein in the step S4, when a sphere is drawn, the radius of the sphere is 18-22 mm, and the second threshold is 35mm.
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基于点云中心的激光雷达与相机联合标定方法研究;康国华;张琪;张晗;徐伟证;张文豪;;仪器仪表学报;20191215(第12期);全文 *
融合激光与视觉点云信息的定位与建图方法;张伟伟;陈超;徐军;;计算机应用与软件;20200712(第07期);全文 *

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