CN107885912B - Rapid collision detection method for mass underground pipelines - Google Patents
Rapid collision detection method for mass underground pipelines Download PDFInfo
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Abstract
The invention discloses a rapid collision detection method for mass underground pipelines, which relates to the field of underground three-dimensional space data processing and comprises the following steps: inputting pipeline data, and generating a body model for the pipeline data; dividing a pipeline space grid; constructing a collision detection task group; performing a collision detection task; and traversing the collision detection task groups, combining collision results of pipelines with the same ID in each collision detection task group, and acquiring collision detection results of all pipelines in the whole range. The invention has the advantages that: according to the information such as the pipe diameter of the pipeline, a cylinder model, a cuboid model and the like are established for the pipeline, the complexity and the data volume of establishing three-dimensional models such as a triangular mesh model and the like are reduced, and compared with a method based on analytic geometry, the method reduces the calculation complexity of collision detection under the condition of not reducing the accuracy of collision detection. The method for establishing the space grid is used for removing a large amount of pipeline collision detection which is far away and obviously has no collision, so that the detection efficiency is improved.
Description
Technical Field
The invention relates to the field of underground three-dimensional space data processing, in particular to a rapid collision detection method for massive underground pipelines, which creates cylinder, cuboid and other body models for pipelines according to the information of pipe diameters and the like of the pipelines, reduces the complexity and data quantity of three-dimensional models such as a triangular mesh model and the like on one hand, and reduces the calculation complexity of collision detection on the other hand compared with a method based on analytic geometry under the condition of not reducing the collision detection accuracy.
Background
The urban underground pipeline facilities bear important tasks of maintaining normal operation of cities, but the burying of various underground pipelines is complicated, so that the exploration of important information such as positions, attributes and the like of various underground pipelines becomes an important task related to various pipeline projects. When each type of professional pipeline is collected on a drawing, the problem of checking the collision logic of the comprehensive pipeline inevitably occurs. The method is used for discovering the problems of cross collision and the like of different structures in the design drawing in advance, so that engineering designers can modify the drawing before construction to avoid delaying the progress of the engineering. With the rapid development of economic society, the urbanization process development of China has entered a rapid growth period, and the urban pipe network management work has also made great progress, but because of various historical and practical reasons, the pipe network management still lags behind the urban construction development level, and the situation appears objectively:
(1) the number of various pipe network devices is increased, the types of the various pipe network devices are various, and the pipe network devices are criss-cross, so that daily management work is increasingly complicated, a lot of accident potential hazards and operation risks are increased, and a severe challenge is brought to safe operation of a pipe network.
(2) As various survey and survey data increase dramatically, the management and maintenance of various pipeline data becomes more and more
The more complicated the network, however, the traditional network management means has many problems, such as incomplete basic information, backward data storage mode, low storage precision, low data acquisition and updating efficiency, inconvenient information retrieval, untimely daily emergency handling, etc., which brings much inconvenience to managers and designers.
(3) In a pipe network system, underground pipelines occupy a large part, and the hidden and buried depth arrangement of the underground pipelines brings great difficulty to the planning, design and management of the whole system, so that the establishment of a comprehensive pipe network information system is increasingly urgent.
Disclosure of Invention
The embodiment of the invention provides a rapid collision detection method for massive underground pipelines, which solves the problems that a cylindrical model, a cuboid model and other body models are created for pipelines according to the information of the pipe diameters of the pipelines and the like, on one hand, the complexity and the data volume for creating three-dimensional models such as a triangular mesh model and the like are reduced, and on the other hand, compared with a method based on analytic geometry, the calculation complexity of collision detection is reduced under the condition of not reducing the collision detection accuracy. The method for establishing the space grid is used for removing a large amount of pipeline collision detection which is far away and obviously has no collision, so that the detection efficiency is improved. A method based on cooperative computing of a CPU and a GPU is used for accelerating a large number of pipeline collision detection tasks with the same operation process, so that the overall detection speed is improved.
The embodiment of the invention provides a rapid collision detection method for mass underground pipelines, which comprises the following steps:
inputting pipeline data, generating a body model for the pipeline data: acquiring pipeline information from a pipeline data file according to a set pipeline data format and a set region range; generating a corresponding body model for the pipeline according to the pipeline information, and calculating an enclosure for the body model; counting all the information of the pipeline bounding volumes;
dividing a pipeline space grid: calculating the scale information of the pipeline space grid, and filling the pipeline into the space grid by using the information of the pipeline body model bounding body; acquiring a pipeline grid set in a designated area range;
constructing a collision detection task group: constructing a collision detection task set for each pipeline grid in the formulated region range to obtain all detection task groups to be executed;
performing a collision detection task group: detecting the support condition of the running environment to the unified computing equipment architecture, and if the running environment supports the unified computing equipment architecture, executing a collision detection task in a way of cooperative processing of a central processing unit and a graphic processor; if the operating environment does not support the unified computing equipment architecture, accelerating the execution of the collision detection task in a central processing unit by using a parallel computing mode based on memory sharing;
merging collision detection results: and traversing the collision detection task groups, combining collision results of pipelines with the same ID in each collision detection task group, and acquiring collision detection results of all pipelines in the whole range.
A rapid collision detection method for massive underground pipelines is disclosed, wherein the input pipeline data is used for generating a body model for the pipeline data, and the method comprises the following steps:
acquiring pipeline information: acquiring pipeline information from a pipeline data file according to a set pipeline data format;
generating a pipeline body model: generating a corresponding body model for the pipeline according to the acquired pipeline starting point coordinate, the acquired pipeline ending point coordinate and the acquired pipeline diameter information: if the pipe diameter information is the radius, generating a cylinder model for the pipeline; if the pipe diameter information is the product of the side lengths, a cuboid model is generated for the pipeline;
generating a pipeline enclosure: generating an enclosure for the pipeline according to the acquired pipeline starting point coordinate, the acquired pipeline ending point coordinate and the acquired pipeline diameter information;
the calculation formula is as follows:
wherein:
x is the X-axis component of the pipeline coordinate; y is a pipeline coordinate Y-axis component; z is a Z-axis component of a pipeline coordinate; subscript start represents the pipeline start point coordinates; subscript end represents the pipeline termination point coordinates; MIN is a method for obtaining a smaller value; MAX is a method for obtaining a larger value; MAX { GJ } is a larger value in the obtained pipe diameter, if the pipe diameter is a radius, the larger value is the radius, and if the pipe diameter is a product of side lengths, the larger value is the larger value in the side lengths;
calculating a pipeline body model enclosure through a formula:
wherein: xmin is the minimum value of the bounding volume in the X-axis direction; xmax is the maximum value of the bounding body in the X-axis direction; ymin is the minimum value of the enclosing body in the Y-axis direction; ymax is the maximum value of the enclosing body in the Y-axis direction; zmin is the minimum value of the bounding volume in the Z-axis direction; zmax is the maximum value of the enclosing body in the Z-axis direction;
counting pipeline information: and counting the information of the surrounding body of the pipeline and acquiring the occupied space range of all the pipelines.
A rapid collision detection method for massive underground pipelines is disclosed, wherein the division of a pipeline space grid comprises the following steps:
calculating the space grid scale: calculating the space grid scale according to a formula by using pipeline information;
which has the formula of
Wherein:
li is the ith line length; GJi is the pipe diameter of the ith pipeline; n is the number of pipelines;
calculating by the formula:
lx is the size of the space grid in the X-axis direction; ly is the size of the space grid in the Y-axis direction; lz is the size of the space grid in the Z-axis direction;
calculating the initial grid index of the pipeline: calculating the initial index of the space grid occupied by the pipeline by utilizing the information of the pipeline bounding volume, wherein the formula is as follows:
wherein:
xmin is the minimum value of the pipeline model on the X axis; ymin is the minimum value of the pipeline model on the Y axis; zmin is the minimum value of the pipeline model on the Z axis; xmax is the maximum value of the pipeline model on the X-axis; ymax is the maximum value of the pipeline model on the Y axis; zmax is the maximum value of the pipeline model on the Z axis; xo is the X-axis coordinate of the starting point of the pipeline grid; yo is the Y-axis coordinate of the starting point of the pipeline grid; zo is the Z-axis coordinate of the starting point of the pipeline grid; lx is the size of the pipeline grid in the X-axis direction; ly is the size of the pipeline grid in the Y-axis direction; lz is the size of the pipeline grid in the Z-axis direction; calculating by the formula:
startx is an initial index of the pipeline model bounding box occupying the X-axis direction of the pipeline grid; endx is an index of the pipeline model bounding box occupying the X-axis direction of the pipeline grid; stary is the initial index of the pipeline model bounding box occupying the Y-axis direction of the pipeline grid; endy is an index of the pipeline model bounding box occupying the Y-axis direction of the pipeline grid; startz is an index of the pipeline model bounding box occupying the pipeline grid in the Z-axis direction; endz is an index of the pipeline model bounding box occupying the Z-axis direction of the pipeline grid;
traversing the initial grid index: and (3) traversing the initial index of the spatial grid calculated in the formula (3), calculating a grid bounding volume according to the grid index, and performing collision detection with the pipeline model: judging that the pipeline body model and the grid enclosure body form intersection, calculating the spatial grid index through a formula, and filling pipelines into a set with the same grid index; if the pipeline body model is not intersected with the grid surrounding body, traversing a next space grid index;
the formula is as follows:
Grid＝Gridz＜＜48+Gridy＜＜24+Gridx (4)
wherein:
gridz is grid index in Z-axis direction; gridy is a grid index in the Y-axis direction; gridx is grid index in the X-axis direction;
calculating by the formula: grid is a Grid index of the spatial Grid;
obtaining a pipeline grid set: and sequentially processing all pipelines to be detected to obtain a pipeline grid set in the specified area range.
A rapid collision detection method for massive underground pipelines is disclosed, wherein the collision detection task group is constructed by the following steps:
creating a collision detection task group: acquiring a single grid from a pipeline grid set in a specified region range, and constructing a detection collision task group of the single grid;
constructing a collision detection task: two pipelines which are not communicated are taken out from the pipeline grid, a collision detection task object is constructed, and a task is added into a task group;
obtaining a set of collision detection task groups: and constructing a collision detection task group for all grids in the formulated region range, and acquiring all detection task group sets to be executed.
A rapid collision detection method for massive underground pipelines is disclosed, wherein the execution of collision detection tasks comprises the following steps:
initializing a running environment: detecting the support condition of a graphics processor device in a running environment to a unified computing device architecture; judging whether the unified computing equipment architecture is supported or not, and starting a unified computing equipment architecture object if the unified computing equipment architecture is supported; and if the collision detection task group is not supported, the host computer terminal accelerates the execution of the collision detection task group by utilizing the parallel computing based on the shared memory.
Transmission collision detection task group set: distributing a device side video memory in the graphic processor device, and transmitting the task group set to the device side video memory;
the equipment end executes a collision detection task: starting a kernel object, and executing a collision detection task at the equipment end of the graphics processor;
acquiring a collision detection result: allocating a host-side memory, and transmitting a collision detection result executed in the graphics processor equipment side to the host-side memory;
clearing data: emptying the video memory distributed in the graphics processor equipment end;
the host end executes a collision detection task: and judging the execution environment, and accelerating the execution of the collision detection task group by utilizing the parallel computing based on the shared memory at the host end if the execution environment does not support the unified computing equipment architecture.
A rapid collision detection method for massive underground pipelines is disclosed, wherein the collision detection task executed by the equipment end comprises the following steps:
the thread block acquires the task group object: each thread block of the image processor equipment end acquires a collision detection task group corresponding to the index number from the task group set according to the thread block index number;
thread number 0 calculates the number of iterations: index thread No. 0 in the thread block at the image processor device end, calculating the iteration number of each thread for executing the task according to the formula (5),
wherein the calculation formula is as follows:
Num＝(taskSize+blockDim.x-1)/blockDim.x (5)
wherein:
taskSize is the number of tasks in each task block; the block dim.x is the number of threads owned by each thread block;
the formula is calculated to obtain:
num is the number of tasks required to be calculated by each thread;
all threads in a thread block are synchronized: all threads in the thread block at the image processor device end are synchronously processed, so that each thread obtains correct iteration times;
thread computation task index in thread block: each thread in the image processor device end thread block calculates a processing task index according to a formula (6);
the formula is as follows:
taskIdx＝blockDim.x×proIdx+threadIdx.x (6)
wherein:
the block dim.x is the number of threads owned by the thread block; proIdx is the number of tasks that the current thread has already processed; the thread Idx.x is the current thread index;
the formula is calculated to obtain:
taskIdx is a task index processed by the current thread;
thread execution collision detection in thread blocks: each thread in the image processor device side thread block performs collision detection on two pipelines in the detection task.
A rapid collision detection method for massive underground pipelines, wherein a thread in a thread block performs collision detection, comprises the following steps:
and (3) filtering the bounding box structure: judging whether the surrounding bodies of the two pipelines are intersected; if the bounding bodies are not intersected, judging that the two pipelines have no collision; if the bounding box structural bodies are intersected, performing collision detection according to the pipeline body model;
and (3) collision detection of the cylinder body model and the cylinder body model: calculating the shortest distance between the central axes of the two cylinders, and judging whether the shortest distance is greater than the sum of the radii of the two cylinder models; if the shortest distance is greater than the sum of the radii, judging that the two cylinder models do not have collision; if the shortest distance is smaller than the sum of the radii, judging that the two cylinder models collide;
and (3) detecting the collision between the cuboid model and the cuboid model: sequentially judging whether each surface of the two cuboid models has collision or not; if the collision does not exist between the cuboid surfaces, judging that the two cuboid models do not have collision; if the collision exists between the cuboid surfaces, the collision of the two cuboid models is judged;
and (3) detecting the collision between the cuboid body model and the cylinder body model: sequentially calculating the shortest distance between each surface of the cuboid and the cylinder, and if the shortest distance is greater than the radius of the cylinder, judging that no collision exists between the cuboid model and the cylinder model; if the shortest distance is smaller than the radius of the cylinder, judging that the cuboid model and the cylinder model are collided;
writing a collision detection result: and writing the pipeline collision detection result into a collision detection task according to the collision detection result of the pipeline body model.
A rapid collision detection method for massive underground pipelines comprises the following steps: the pipeline information includes: the number of the pipeline, the pipe diameter, the coordinates of the initial point of the pipeline and the coordinates of the end point of the pipeline; the collision task group includes: task group index, grid block index, and task set.
It can be seen from this that:
the rapid collision detection method of the massive underground pipelines in the embodiment of the invention comprises the following steps: the method solves the problems that a cylinder model, a cuboid model and other body models are created for the pipeline according to the pipe diameter and other information of the pipeline, on one hand, the complexity and the data volume for creating three-dimensional models such as a triangular mesh model and the like are reduced, and on the other hand, compared with a method based on analytic geometry, the calculation complexity of collision detection is reduced under the condition that the collision detection accuracy is not reduced. The method for establishing the space grid is used for removing a large amount of pipeline collision detection which is far away and obviously has no collision, so that the detection efficiency is improved. A method based on cooperative computing of a CPU and a GPU is used for accelerating a large number of pipeline collision detection tasks with the same operation process, so that the overall detection speed is improved.
Drawings
Fig. 1 is a schematic overall flow chart of a method for detecting rapid collision of a mass of underground pipelines according to an embodiment of the present invention;
FIG. 2 is a schematic flow chart illustrating steps of inputting pipeline data and generating a body model for the pipeline data in the method for detecting rapid collision of massive underground pipelines according to the embodiment of the present invention;
FIG. 3 is a schematic flowchart illustrating a dividing step of a pipeline space grid in the method for detecting rapid collision of mass underground pipelines according to an embodiment of the present invention;
FIG. 4 is a schematic flow chart illustrating a step of constructing a collision detection task group in the method for rapidly detecting collision of mass underground pipelines according to an embodiment of the present invention;
FIG. 5 is a schematic flow chart illustrating a step of executing a collision detection task group in the method for rapidly detecting collision of mass underground pipelines according to an embodiment of the present invention;
fig. 6 is a schematic flow chart illustrating a step of performing a collision detection task at a device end in the method for rapidly detecting collision of mass underground pipelines according to the embodiment of the present invention;
fig. 7 is a schematic flow chart illustrating a step of performing collision detection on a thread in a thread block in the method for rapidly detecting collision of massive underground pipelines according to the embodiment of the present invention.
Detailed Description
In order that those skilled in the art will better understand the technical solution of the present invention, the present invention will be described in detail with reference to the accompanying drawings and specific embodiments, wherein the exemplary embodiments and the description of the present invention are provided to explain the present invention, but not to limit the present invention.
Example 1: fig. 1 is a schematic flow chart of a method for detecting a fast collision of a large number of underground pipelines according to this embodiment, and as shown in fig. 1, the method for detecting a fast collision of a large number of underground pipelines includes the following steps:
inputting pipeline data, generating a body model for the pipeline data: acquiring pipeline information from a pipeline data file according to a set pipeline data format and a set region range; generating a corresponding body model for the pipeline according to the pipeline information, and calculating an enclosure for the body model; counting all the information of the pipeline bounding volumes;
dividing a pipeline space grid: calculating the scale information of the pipeline space grid, and filling the pipeline into the space grid by using the information of the pipeline body model bounding body; acquiring a pipeline grid set in a designated area range;
constructing a collision detection task group: constructing a collision detection task set for each pipeline grid in the formulated region range to obtain all detection task groups to be executed;
performing a collision detection task group: detecting the support condition of the running environment to the unified computing equipment architecture, and if the running environment supports the unified computing equipment architecture, executing a collision detection task in a way of cooperative processing of a central processing unit and a graphic processor; if the operating environment does not support the unified computing equipment architecture, accelerating the execution of the collision detection task in the central processing unit by using a parallel computing mode based on a shared memory;
merging collision detection results: and traversing the collision detection task groups, combining collision results of pipelines with the same ID in each collision detection task group, and acquiring collision detection results of all pipelines in the whole range.
As shown in fig. 2, the method for detecting a fast collision of a massive underground pipeline, wherein the step of inputting pipeline data and generating a body model for the pipeline data comprises the following steps:
acquiring pipeline information: acquiring pipeline information from a pipeline data file according to a set pipeline data format;
generating a pipeline body model: generating a corresponding body model for the pipeline according to the acquired pipeline starting point coordinate, the acquired pipeline ending point coordinate and the acquired pipeline diameter information: if the pipe diameter information is the radius, generating a cylinder model for the pipeline; if the pipe diameter information is the product of the side lengths, a cuboid model is generated for the pipeline;
generating a pipeline enclosure: generating an enclosure for the pipeline according to the acquired pipeline starting point coordinate, the acquired pipeline ending point coordinate and the acquired pipeline diameter information;
the calculation formula is as follows:
wherein:
x is the X-axis component of the pipeline coordinate; y is a pipeline coordinate Y-axis component; z is a Z-axis component of a pipeline coordinate; subscript start represents the pipeline start point coordinates; subscript end represents the pipeline termination point coordinates; MIN is a method for obtaining a smaller value; MAX is a method for obtaining a larger value; MAX { GJ } is a larger value in the obtained pipe diameter, if the pipe diameter is a radius, the larger value is the radius, and if the pipe diameter is a product of side lengths, the larger value is the larger value in the side lengths;
calculating a pipeline body model enclosure through a formula:
wherein: xmin is the minimum value of the bounding volume in the X-axis direction; xmax is the maximum value of the bounding body in the X-axis direction; ymin is the minimum value of the enclosing body in the Y-axis direction; ymax is the maximum value of the enclosing body in the Y-axis direction; zmin is the minimum value of the bounding volume in the Z-axis direction; zmax is the maximum value of the enclosing body in the Z-axis direction;
counting pipeline information: and counting the information of the surrounding body of the pipeline and acquiring the occupied space range of all the pipelines.
As shown in fig. 3, a method for detecting fast collision of massive underground pipelines, where the partitioning of the pipeline space grid includes the following steps:
calculating the space grid scale: calculating the space grid scale according to a formula by using pipeline information;
which has the formula of
Wherein:
li is the ith line length; GJi is the pipe diameter of the ith pipeline; n is the number of pipelines;
calculating by the formula:
lx is the size of the space grid in the X-axis direction; ly is the size of the space grid in the Y-axis direction; lz is the size of the space grid in the Z-axis direction;
calculating the initial grid index of the pipeline: calculating the initial index of the space grid occupied by the pipeline by utilizing the information of the pipeline bounding volume, wherein the formula is as follows:
wherein:
xmin is the minimum value of the pipeline model on the X axis; ymin is the minimum value of the pipeline model on the Y axis; zmin is the minimum value of the pipeline model on the Z axis; xmax is the maximum value of the pipeline model on the X-axis; ymax is the maximum value of the pipeline model on the Y axis; zmax is the maximum value of the pipeline model on the Z axis; xo is the X-axis coordinate of the starting point of the pipeline grid; yo is the Y-axis coordinate of the starting point of the pipeline grid; zo is the Z-axis coordinate of the starting point of the pipeline grid; lx is the size of the pipeline grid in the X-axis direction; ly is the size of the pipeline grid in the Y-axis direction; lz is the size of the pipeline grid in the Z-axis direction; calculating by the formula:
startx is an initial index of the pipeline model bounding box occupying the X-axis direction of the pipeline grid; endx is an index of the pipeline model bounding box occupying the X-axis direction of the pipeline grid; stary is the initial index of the pipeline model bounding box occupying the Y-axis direction of the pipeline grid; endy is an index of the pipeline model bounding box occupying the Y-axis direction of the pipeline grid; startz is an index of the pipeline model bounding box occupying the pipeline grid in the Z-axis direction; endz is an index of the pipeline model bounding box occupying the Z-axis direction of the pipeline grid;
traversing the initial grid index: and (3) traversing the initial index of the spatial grid calculated in the formula (3), calculating a grid bounding volume according to the grid index, and performing collision detection with the pipeline model: judging that the pipeline body model and the grid enclosure body form intersection, calculating the spatial grid index through a formula, and filling pipelines into a set with the same grid index; if the pipeline body model is not intersected with the grid surrounding body, traversing a next space grid index;
the formula is as follows:
Grid＝Gridz＜＜48+Gridy＜＜24+Gridx (4)
wherein:
gridz is grid index in Z-axis direction; gridy is a grid index in the Y-axis direction; gridx is grid index in the X-axis direction;
calculating by the formula: grid is a Grid index of the spatial Grid;
obtaining a pipeline grid set: and sequentially processing all pipelines to be detected to obtain a pipeline grid set in the specified area range.
As shown in fig. 4, the method for rapidly detecting collision of massive underground pipelines includes the following steps:
creating a collision detection task group: acquiring a single grid from a pipeline grid set in a specified region range, and constructing a detection collision task group of the single grid;
constructing a collision detection task: two pipelines which are not communicated are taken out from the pipeline grid, a collision detection task object is constructed, and a task is added into a task group;
obtaining a set of collision detection task groups: and constructing a collision detection task group for all grids in the formulated region range, and acquiring all detection task group sets to be executed.
As shown in fig. 5, a method for rapidly detecting collision of a mass of underground pipelines, where the performing of a collision detection task group includes the following steps:
initializing a running environment: detecting the support condition of a graphics processor device in a running environment to a unified computing device architecture; judging whether the unified computing equipment architecture is supported or not, and starting a unified computing equipment architecture object if the unified computing equipment architecture is supported; if the collision detection task is not supported, accelerating the execution of the collision detection task in a central processing unit by using a parallel computing mode based on memory sharing;
transmission collision detection task group set: distributing a device side video memory in the graphic processor device, and transmitting the task group set to the device side video memory;
the equipment end executes a collision detection task: starting a kernel object, and executing a collision detection task at the equipment end of the graphics processor;
acquiring a collision detection result: allocating a host-side memory, and transmitting a collision detection result executed in the graphics processor equipment side to the host-side memory;
clearing data: emptying the video memory distributed in the graphics processor equipment end;
the host end executes a collision detection task: and judging the execution environment, and accelerating the execution of the collision detection task group by using a parallel computing method based on a shared memory at the host end if the execution environment does not support the unified computing equipment architecture.
As shown in fig. 6, a method for rapidly detecting collision of a mass of underground pipelines, where the device end executes a collision detection task, includes the following steps:
the thread block acquires the task group object: each thread block of the image processor equipment end acquires a collision detection task group corresponding to the index number from the task group set according to the thread block index number;
thread number 0 calculates the number of iterations: index thread No. 0 in the thread block at the image processor device end, calculating the iteration number of each thread for executing the task according to the formula (5),
wherein the calculation formula is as follows:
Num＝(taskSize+blockDim.x-1)/blockDim.x (5)
wherein:
taskSize is the number of tasks in each task block; the block dim.x is the number of threads owned by each thread block;
the formula is calculated to obtain:
num is the number of tasks required to be calculated by each thread;
all threads in a thread block are synchronized: all threads in the thread block at the image processor device end are synchronously processed, so that each thread obtains correct iteration times;
thread computation task index in thread block: each thread in the image processor device end thread block calculates a processing task index according to a formula (6);
the formula is as follows:
taskIdx＝blockDim.x×proIdx+threadIdx.x (6)
wherein:
the block dim.x is the number of threads owned by the thread block; proIdx is the number of tasks that the current thread has already processed; the thread Idx.x is the current thread index;
the formula is calculated to obtain:
taskIdx is a task index processed by the current thread;
thread execution collision detection in thread blocks: each thread in the image processor device side thread block performs collision detection on two pipelines in the detection task.
As shown in fig. 7, a method for fast collision detection of massive underground pipelines, where a thread in a thread block performs collision detection, includes the following steps:
and (3) filtering the bounding box structure: judging whether the surrounding bodies of the two pipelines are intersected; if the bounding bodies are not intersected, judging that the two pipelines have no collision; if the bounding box structural bodies are intersected, performing collision detection according to the pipeline body model;
and (3) collision detection of the cylinder body model and the cylinder body model: calculating the shortest distance between the central axes of the two cylinders, and judging whether the shortest distance is greater than the sum of the radii of the two cylinder models; if the shortest distance is greater than the sum of the radii, judging that the two cylinder models do not have collision; if the shortest distance is smaller than the sum of the radii, judging that the two cylinder models collide;
and (3) detecting the collision between the cuboid model and the cuboid model: sequentially judging whether each surface of the two cuboid models has collision or not; if the collision does not exist between the cuboid surfaces, judging that the two cuboid models do not have collision; if the collision exists between the cuboid surfaces, the collision of the two cuboid models is judged;
and (3) detecting the collision between the cuboid body model and the cylinder body model: sequentially calculating the shortest distance between each surface of the cuboid and the cylinder, and if the shortest distance is greater than the radius of the cylinder, judging that no collision exists between the cuboid model and the cylinder model; if the shortest distance is smaller than the radius of the cylinder, judging that the cuboid model and the cylinder model are collided;
writing a collision detection result: and writing the pipeline collision detection result into a collision detection task according to the collision detection result of the pipeline body model.
In an embodiment, the pipeline information includes: the number of the pipeline, the pipe diameter, the coordinates of the initial point of the pipeline and the coordinates of the end point of the pipeline; the collision task group includes: task group index, grid block index, and task set.
The following is described in one specific case:
the following describes a specific embodiment of the method for performing fast collision detection on massive underground pipeline data. By the method, the rapid collision detection of pipeline data such as massive communication pipeline data, electric power pipeline data, water supply pipeline data, water drainage pipeline data, gas pipeline data, thermal pipeline data, industrial pipeline data and the like can be realized.
Acquiring pipeline data such as underground communication pipeline data, electric power pipeline data, water supply pipeline data, water drainage pipeline data, gas pipeline data, thermal pipeline data, industrial pipeline data and the like of a certain region; according to a set pipeline data format, pipeline information is obtained from a pipeline data file, wherein the pipeline information comprises: pipeline number, pipe diameter, pipeline starting point coordinate and pipeline ending point coordinate; generating a corresponding body model for the pipeline according to the acquired pipeline starting point coordinate, the acquired pipeline ending point coordinate and the acquired pipeline diameter information: if the pipe diameter information is the radius, generating a cylindrical body model for the pipeline, and if the pipe diameter information is the product of side lengths, generating a cuboid body model for the pipeline; and generating an enclosure for the pipeline according to the acquired pipeline initial point coordinate, the acquired pipeline end point coordinate and the acquired pipe diameter information, and counting the information of all the pipeline enclosures.
Calculating the scale information of the spatial grid according to all the pipeline enclosure information obtained by statistics, and obtaining the scale sizes of the pipeline spatial grid in the X-axis direction, the Y-axis direction and the Z-axis direction; calculating the starting and stopping index numbers of the pipelines crossing the pipeline grids by utilizing the pipeline bounding bodies, the scale information of the pipeline grids and the coordinates of the starting points; traversing pipelines to cross the grid indexes, obtaining a grid bounding volume by utilizing the grid indexes, the grid scales and the initial point coordinates, and carrying out collision detection with a pipeline body model: if the pipeline body model is intersected with the grid enclosure body, calculating the spatial grid index through a formula, and filling pipelines into a set with the same grid index; traversing the spatial grid index if the pipeline model does not want to intersect with the grid enclosure; the above process is repeated until all pipelines are added into the grids corresponding to the indexes, and a pipeline data set for spatial grid division is obtained, so that the conditions of long distance and no collision are obviously removed, and the detection efficiency is improved.
Acquiring single grid data from a grid set for grid division, and creating a collision detection task group object for the grid; then acquiring two pipeline data with different IDs from the grid set, creating a collision detection task object for the two pipeline data, and adding the collision detection task object into a collision detection task group object of the grid; the above process is repeated until collision detection task group objects are created for all the mesh sets, thereby acquiring all the sets of collision detection task group objects to be executed.
Since the collision detection task group object set to be executed and the task sets in the collision detection task group have the same or similar operation flows, the collision detection efficiency can be improved by parallel. Due to the special architecture of the graphics processor, the method provides a new solution for many problems which are difficult to solve originally. Firstly, judging the support condition of a graphics processor to a unified computing device architecture: if the graphics processor supports a unified computing device architecture, accelerating the collision detection process by adopting a CPU and GPU cooperative processing mode; if the graphics processor does not support the unified computing device architecture, a parallel computing method based on a shared memory is adopted at the CPU end to accelerate the collision detection process.
In the process of cooperatively processing collision detection based on a CPU and a GPU, a central processing unit firstly initializes a unified computing device architecture kernel object; then, distributing a video memory object in a graphics processor, and transmitting the acquired collision detection task group object set to a video memory; finally, starting the kernel object, and executing a specific collision detection task at the graphic processor end; and after the execution of all the collision detection tasks is finished, the central processing unit acquires the detection results of the collision detection tasks from the graphic processor and combines the collision results of the pipelines with the same ID, so that the collision conditions of all the pipelines are acquired. When the graphics processor executes a specific collision detection task, each thread block acquires a collision detection task group object with the same ID from a video memory through the ID of each thread block; then the number 0 thread calculates the number of detection tasks to be executed by each thread according to the number of thread blocks and the number of tasks in the collision detection task group object according to a formula; and synchronizing the threads in the thread block, so that each thread obtains the number of tasks to be executed, then acquiring and executing the corresponding collision detection task from the task group object according to the thread ID of the thread, and writing back the detection result after the execution is finished.
In the process of processing collision detection by a parallel computing method based on a central processing unit shared memory, firstly, the number of usable threads is obtained, and the number of task group objects to be executed by each thread is calculated; then each thread executes the corresponding collision detection task and writes back the detection result.
Finally, the collision detection results through parallel acceleration are retrieved, and the collision results of the pipelines with the same ID are combined, so that the correct collision detection results of all the pipelines are obtained.
It can be seen from this that: the rapid collision detection method of the massive underground pipelines in the embodiment of the invention comprises the following steps: the method solves the problems that a cylinder model, a cuboid model and other body models are created for the pipeline according to the pipe diameter and other information of the pipeline, on one hand, the complexity and the data volume for creating three-dimensional models such as a triangular mesh model and the like are reduced, and on the other hand, compared with a method based on analytic geometry, the calculation complexity of collision detection is reduced under the condition that the collision detection accuracy is not reduced. The method for establishing the space grid is used for removing a large amount of pipeline collision detection which is far away and obviously has no collision, so that the detection efficiency is improved. A method based on cooperative computing of a CPU and a GPU is used for accelerating a large number of pipeline collision detection tasks with the same operation process, so that the overall detection speed is improved.
While the embodiments of the present invention have been described by way of example, those skilled in the art will appreciate that there are numerous variations and permutations of the present invention without departing from the spirit of the invention, and it is intended that the appended claims cover such variations and modifications as fall within the true spirit of the invention.
Claims (8)
1. A rapid collision detection method for massive underground pipelines is characterized by comprising the following steps:
inputting pipeline data, generating a body model for the pipeline data: acquiring pipeline information from a pipeline data file according to a set pipeline data format and a set region range; generating a corresponding body model for the pipeline according to the pipeline information, and calculating an enclosure for the body model; counting all the information of the pipeline bounding volumes;
dividing a pipeline space grid: calculating the scale information of the pipeline space grid, and filling the pipeline into the space grid by using the information of the pipeline body model bounding body; acquiring a pipeline grid set in a designated area range;
constructing a collision detection task group: constructing a collision detection task set for each pipeline grid in the specified region range to obtain all detection task groups to be executed;
performing a collision detection task: detecting the support condition of the running environment to the unified computing equipment architecture, and executing a collision detection task in a mode of cooperative processing of a central processing unit and a graphic processor when the running environment supports the unified computing equipment architecture; when the operating environment does not support a unified computing device architecture, a collision detection task is accelerated to be executed in a central processing unit by utilizing parallel computing based on a shared memory;
merging collision detection results: and traversing the collision detection task groups, combining collision results of pipelines with the same ID in each collision detection task group, and acquiring collision detection results of all pipelines in the whole range.
2. The method for rapidly detecting collision of massive underground pipelines according to claim 1, wherein the step of inputting pipeline data and generating a body model for the pipeline data comprises the following steps:
acquiring pipeline information: acquiring pipeline information from a pipeline data file according to a set pipeline data format;
generating a pipeline body model: generating a corresponding body model for the pipeline according to the acquired pipeline starting point coordinate, ending point coordinate and pipe diameter information, and generating a cylindrical body model for the pipeline if the pipe diameter information is judged to be the radius; judging that the pipe diameter information is the product of side lengths, and generating a cuboid model for the pipeline;
generating a pipeline enclosure: generating an enclosure for the pipeline according to the acquired pipeline starting point coordinate, the acquired pipeline ending point coordinate and the acquired pipeline diameter information;
the calculation formula is as follows:
wherein:
x is the X-axis component of the pipeline coordinate; y is a pipeline coordinate Y-axis component; z is a Z-axis component of a pipeline coordinate; subscript start represents the pipeline start point coordinates; subscript end represents the pipeline termination point coordinates; MIN is a method for obtaining a smaller value; MAX is a method for obtaining a larger value; MAX { GJ } is a larger value in the obtained pipe diameter, if the pipe diameter is a radius, the larger value is the radius, and if the pipe diameter is a product of side lengths, the larger value is the larger value in the side lengths;
calculating a pipeline body model enclosure through a formula:
wherein: xmin is the minimum value of the bounding volume in the X-axis direction; xmax is the maximum value of the bounding body in the X-axis direction; ymin is the minimum value of the enclosing body in the Y-axis direction; ymax is the maximum value of the enclosing body in the Y-axis direction; zmin is the minimum value of the bounding volume in the Z-axis direction; zmax is the maximum value of the enclosing body in the Z-axis direction;
counting pipeline information: and counting the information of the surrounding body of the pipeline and acquiring the occupied space range of all the pipelines.
3. The method for rapidly detecting collision of massive underground pipelines according to claim 1, wherein the dividing of the pipeline space grid comprises the following steps:
calculating the space grid scale: calculating the space grid scale according to a formula by using pipeline information;
which has the formula of
Wherein:
li is the ith line length; GJi is the pipe diameter of the ith pipeline; n is the number of pipelines;
calculating by the formula:
lx is the size of the space grid in the X-axis direction; ly is the size of the space grid in the Y-axis direction; lz is the size of the space grid in the Z-axis direction;
calculating the initial grid index of the pipeline: calculating the initial index of the space grid occupied by the pipeline by utilizing the information of the pipeline bounding volume, wherein the formula is as follows:
wherein:
xmin is the minimum value of the pipeline model on the X axis; ymin is the minimum value of the pipeline model on the Y axis; zmin is the minimum value of the pipeline model on the Z axis; xmax is the maximum value of the pipeline model on the X-axis; ymax is the maximum value of the pipeline model on the Y axis; zmax is the maximum value of the pipeline model on the Z axis; xo is the X-axis coordinate of the starting point of the pipeline grid; yo is the Y-axis coordinate of the starting point of the pipeline grid; zo is the Z-axis coordinate of the starting point of the pipeline grid; lx is the size of the space grid in the X-axis direction; ly is the size of the space grid in the Y-axis direction; lz is the size of the space grid in the Z-axis direction;
calculating by the formula:
startx is an initial index of the pipeline model bounding box occupying the X-axis direction of the pipeline grid; endx is an index of the pipeline model bounding box occupying the X-axis direction of the pipeline grid; stary is the initial index of the pipeline model bounding box occupying the Y-axis direction of the pipeline grid; endy is an index of the pipeline model bounding box occupying the Y-axis direction of the pipeline grid; startz is an index of the pipeline model bounding box occupying the pipeline grid in the Z-axis direction; endz is an index of the pipeline model bounding box occupying the Z-axis direction of the pipeline grid;
traversing the initial grid index: and (3) traversing the initial index of the spatial grid calculated in the formula (3), calculating a grid bounding volume according to the grid index, and performing collision detection with the pipeline model: judging that the pipeline body model and the grid enclosure body form intersection, calculating the spatial grid index through a formula, and filling pipelines into a set with the same grid index; if the pipeline body model is not intersected with the grid surrounding body, traversing a next space grid index;
the formula is as follows:
Grid＝Gridz＜＜48+Gridy＜＜24+Gridx (4)
wherein:
gridz is grid index in Z-axis direction; gridy is a grid index in the Y-axis direction; gridx is grid index in the X-axis direction;
calculating by the formula: grid is a Grid index of the spatial Grid;
obtaining a pipeline grid set: and sequentially processing all pipelines to be detected to obtain a pipeline grid set in the specified area range.
4. The rapid collision detection method for the mass underground pipelines according to claim 1, characterized in that: the constructing of the collision detection task group includes the steps of:
creating a collision detection task group: acquiring a single grid from a pipeline grid set in a specified region range, and constructing a detection collision task group of the single grid;
constructing a collision detection task: two pipelines which are not communicated are taken out from the pipeline grid, a collision detection task object is constructed, and a task is added into a task group;
obtaining a set of collision detection task groups: and constructing collision detection task groups for all grids in the specified region range, and acquiring all detection task group sets to be executed.
5. The rapid collision detection method for the mass underground pipelines according to claim 1, characterized in that: the performing of the collision detection task comprises the steps of:
initializing a running environment: detecting the support condition of a graphics processor device in a running environment to a unified computing device architecture; judging whether the unified computing equipment architecture is supported or not, and starting a unified computing equipment architecture object if the unified computing equipment architecture is supported; if the collision detection task group is judged not to be supported, the collision detection task group is executed at the host terminal by using the parallel computation based on the shared memory in an accelerated manner;
transmission collision detection task group set: distributing a device side video memory in the graphic processor device, and transmitting the task group set to the device side video memory;
the equipment end executes a collision detection task: starting a kernel object, and executing a collision detection task at the equipment end of the graphics processor;
acquiring a collision detection result: allocating a host-side memory, and transmitting a collision detection result executed in the graphics processor equipment side to the host-side memory;
clearing data: emptying the video memory distributed in the graphics processor equipment end;
the host end executes a collision detection task: and judging the execution environment, and accelerating the execution of the collision detection task group by utilizing the parallel computing based on the shared memory at the host end if the execution environment does not support the unified computing equipment architecture.
6. The rapid collision detection method for the mass underground pipelines according to claim 5, characterized in that: the device side executing the collision detection task comprises the following steps:
the thread block acquires the task group object: each thread block of the image processor equipment end acquires a collision detection task group corresponding to the index number from the task group set according to the thread block index number;
thread number 0 calculates the number of iterations: index thread No. 0 in the thread block at the image processor device end calculates the number of tasks required to be calculated by each thread according to formula (5),
wherein the calculation formula is as follows:
Num＝(taskSize+blockDim.x-1)/blockDim.x (5)
wherein:
taskSize is the number of tasks in each task group; x is the number of threads owned in each thread block;
the formula is calculated to obtain:
num is the number of tasks required to be calculated by each thread;
all threads in a thread block are synchronized: all threads in the thread block at the image processor device end are synchronously processed, so that each thread obtains correct iteration times;
thread computation task index in thread block: each thread in the image processor device end thread block calculates a processing task index according to a formula (6);
the formula is as follows:
taskIdx＝blockDim.x×proIdx+threadIdx.x (6)
wherein:
the block dim.x is the number of threads owned by the thread block; proIdx is the number of tasks that the current thread has already processed; the thread Idx.x is the current thread index;
the formula is calculated to obtain:
taskIdx is a task index processed by the current thread;
thread execution collision detection in thread blocks: each thread in the image processor device side thread block performs collision detection on two pipelines in the detection task.
7. The method for rapidly detecting collision of mass underground pipelines according to claim 6, characterized in that: the thread execution collision detection in the thread block comprises the following steps:
and (3) filtering the bounding box structure: judging whether the surrounding bodies of the two pipelines are intersected; judging that the surrounding bodies do not intersect, and judging that the two pipelines do not have collision; judging the intersection of the bounding box structural bodies, and performing collision detection according to the pipeline body model;
and (3) collision detection of the cylinder body model and the cylinder body model: calculating the shortest distance between the central axes of the two cylinders, and judging whether the shortest distance is greater than the sum of the radii of the two cylinder models; judging that the shortest distance is greater than the sum of the radii, and judging that the two cylinder models do not have collision; judging that the shortest distance is smaller than the sum of the radii, and judging that the two cylinder models collide;
and (3) detecting the collision between the cuboid model and the cuboid model: sequentially judging whether each surface of the two cuboid models has collision or not; judging that the two cuboid models do not have collision when the cuboid surfaces do not have collision; judging that the two cuboid models have collision when the cuboid surfaces have collision;
and (3) detecting the collision between the cuboid body model and the cylinder body model: sequentially calculating the shortest distance between each surface of the cuboid and the cylinder, wherein the shortest distance is greater than the radius of the cylinder, and judging that no collision exists between the cuboid model and the cylinder model; the shortest distance is smaller than the radius of the cylinder, and the cuboid model and the cylinder model are judged to have collision;
writing a collision detection result: and writing the pipeline collision detection result into a collision detection result set according to the collision detection result of the pipeline body model.
8. The method for rapidly detecting collision of mass underground pipelines according to any one of claims 1 to 7, characterized in that: the pipeline information includes: the number of the pipeline, the pipe diameter, the coordinates of the initial point of the pipeline and the coordinates of the end point of the pipeline; the collision detection task group includes: task group index, grid block index, and task set.
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