CN116227209A - Multi-dimensional linear difference method for point cloud data, terminal equipment and storage medium - Google Patents
Multi-dimensional linear difference method for point cloud data, terminal equipment and storage medium Download PDFInfo
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Abstract
The invention relates to a point cloud data multidimensional linear difference method, terminal equipment and a storage medium, wherein the method comprises the following steps: extracting the maximum value and the minimum value of all node coordinates under each coordinate dimension based on the point cloud data file; determining a grid coverage area corresponding to a grid file to be interpolated; selecting a maximum basic point and a minimum basic point of the node; constructing an assigned grid corresponding to the node and vertices on the boundary of the assigned grid; assigning a value to the vertex; based on the dimension of the grid, calculating the enclosing area of each vertex on the boundary of the node and the assigned grid respectively; and calculating the value of the node based on the value of each enclosing area and each vertex. The invention can rapidly and efficiently realize the data interpolation in the numerical simulation platform and has the advantages of small error, high efficiency and the like.
Description
Technical Field
The present invention relates to the field of data processing, and in particular, to a point cloud data multidimensional linear difference method, a terminal device, and a storage medium.
Background
Multi-physical field coupling is a key technology of modern digital systems, and the essence of multi-physical field coupling is coupling between physical quantities described in partial differential equations or ordinary differential equations, and also simultaneous solution of these equations. Along with the rapid industrial development worldwide, the appearance and development of the multi-physical simulation platform can well help technicians and scientific researchers to complete the modeling and simulation of multiple physical fields in the industrial field. This is of great importance for both industrial research and production. Multiple physical field simulation software such as COMSOL and ANYSIS has been widely used in various corporations, universities and scientific research institutions. An open-source object-oriented multi-physical-field simulation environment MOOSE platform developed by the national laboratories of Edaha is used for solving the calculation problem of all systems in a fully coupled mode and focusing on realizing a multi-physical-field coupling and trans-scale physical calculation simulation process. MOOSE provides a new approach to computing efficiency for handling multi-physical-field problems in the nuclear and broader scientific community. The MOOSE platform is one of the most popular multi-physical-field development and application platforms at present.
The image post-processing serves as a simulation result visualization module to play a vital role in a man-machine interaction interface of the multi-physical-field simulation software. Interpolation largely determines the quality and reliability of the processed image. Most of the multi-physical field simulation platforms provide modules to help users to better process the obtained simulation data and images. However, as the numerical simulation platform is widely applied to various large fields, most of interpolation methods are gradually unable to meet the needs of users. These requirements include a need for variety of interpolation methods, a need for expansion of functions of interpolation methods, and the like. In particular, linear interpolation has found wide application as a common, reliable, and efficient interpolation in various fields including computer, electronic information, and geology. Its use requires the user to provide meshing input data, which is often not met in real-world use scenarios. Therefore, the research is suitable for the linear interpolation method with irregular data such as point cloud and the like as input data, and the function of perfecting the linear interpolation method has important significance for perfecting the simulation calculation of multiple physical fields and improving the working efficiency of users.
Disclosure of Invention
In order to solve the problems, the invention provides a point cloud data multidimensional linear difference method, terminal equipment and a storage medium.
The specific scheme is as follows:
a point cloud data multidimensional linear difference method comprises the following steps:
s1: extracting the maximum value and the minimum value of all node coordinates under each coordinate dimension based on the point cloud data file;
s2: determining a grid coverage area corresponding to a grid file to be interpolated based on the maximum value and the minimum value of the extracted node coordinates, and setting the value of a node outside the coverage area to be 0;
s3: for any node in the coverage area, two nodes are selected from other nodes in the point cloud data file to serve as a maximum basic point and a minimum basic point of the node respectively, wherein the maximum basic point and the minimum basic point are required to meet the following requirements: the coordinates of the node in each dimension are larger than or equal to the coordinates of the minimum basic point in the corresponding dimension, and smaller than or equal to the coordinates of the maximum basic point in the corresponding dimension; if the maximum basic point and/or the minimum basic point which meet the conditions do not exist in the point cloud data file, taking the vertex of the coverage area, which consists of the maximum value in each coordinate dimension, as the maximum basic point, and/or taking the vertex of the coverage area, which consists of the minimum value in each coordinate dimension, as the minimum basic point;
s4: constructing an assignment grid corresponding to the node and a vertex on an assignment grid boundary according to the maximum basic point and the minimum basic point;
s5: assigning a value to the vertex on the assigned grid boundary of the node;
s6: based on the dimension of the grid, calculating the enclosing area of each vertex on the boundary of the node and the assigned grid respectively;
s7: calculating the value of the node based on the value of each enclosing area and each vertex:
wherein U is a The value of the node is represented by,
the value of the ith vertex is represented, i represents the number of the vertex, n represents the total number of the vertices, S i Representing the area enclosed by the node and the ith vertex on the assigned grid boundary;
s8: and traversing all nodes in the grid coverage area, and repeating the steps S3-S7 to realize the value of all the nodes.
Further, the method for determining the grid coverage area in step S2 is as follows: if the grid is one-dimensional, the coverage area is a line segment x min ，x max ]The method comprises the steps of carrying out a first treatment on the surface of the If the grid is two-dimensional, the coverage area is a rectangle, and the coordinates corresponding to the two diagonal points are (x min ，y min 0) and (x) max ，y max 0); if the grid is three-dimensional, the coverage area is a cuboid, and the coordinates corresponding to the two opposite corners are (x min ，y min ，z min ) And (x) max ，y max ，z max )，x min ，y min ，z min Respectively, the minimum value and x under X, Y, Z three coordinate dimensions max ，y max ，z max Each being the maximum in three coordinate dimensions of X, Y, Z.
Further, the construction method of the assignment grid of the node and the vertex on the boundary of the assignment grid in the step S4 is as follows: the coordinates of the maximum base point and the minimum base point of the node are set as (x) 2 ，y 2 ，z 2 ) And (x) 1 ，y 1 ，z 1 ) The following steps are:
if the network is one-dimensional, the node's assignment grid is a segment [ x ] 1 x 2 ]Assigning vertices on the grid boundary as two endpoints of the line segment, and respectively assigning coordinates as (x) 1 0, 0) and (x) 2 ，0，0)；
If the grid is two-dimensional, the assigned grid of points is a rectangle, the vertexes on the boundary of the assigned grid are four vertexes of the rectangle, and the coordinates are respectively: (x) 1 ，y 1 ，0)、(x 2 ，y 2 ，0)、(x 1 ，y 2 0) and (x) 2 ，y 1 ，0)；
If the grid is three-dimensional, the assigned grid of the node is a cuboid, the vertexes on the boundary of the assigned grid are 8 vertexes of the cuboid, and the coordinates are respectively: (x) 1 ，y 1 ，z 1 )、(x 2 ，y 2 ，z 2 )、(x 1 ，y 1 ，z 2 )、(x 1 ，y 2 ，z 1 )、(x 1 ，y 2 ，z 2 )、(x 2 ，y 1 ，z 1 ）、（x 2 ，y 1 ，z 2 )、(x 2 ，y 2 ，z 1 )。
Further, the vertex assignment method in step S5 is as follows: if the vertex is a node in the point cloud data file, the value of the vertex takes a corresponding value in the point cloud data file; otherwise, searching a node closest to the vertex from the point cloud data file, and giving the value of the searched node to the vertex.
Further, the calculation method of the surrounding area in step S6 is as follows:
if the grid is one-dimensional, the area enclosed by the nodes and the vertexes is the length of a line segment formed from the nodes to the vertexes;
if the grid is two-dimensional, the area enclosed by the nodes and the vertexes is the area of a rectangle formed by taking the nodes and the vertexes as diagonal points;
if the grid is three-dimensional, the area enclosed by the nodes and the vertexes is the volume of a cuboid formed by taking the nodes and the vertexes as opposite vertex points.
The point cloud data multidimensional linear difference terminal equipment comprises a processor, a memory and a computer program which is stored in the memory and can run on the processor, wherein the processor realizes the steps of the method according to the embodiment of the invention when executing the computer program.
A computer readable storage medium storing a computer program which, when executed by a processor, implements the steps of the method described above for embodiments of the present invention.
By adopting the technical scheme, the invention can rapidly and efficiently realize the data interpolation in the numerical simulation platform. When the method of the embodiment is used for carrying out data interpolation, the method has the advantages of small error, high efficiency and the like.
Drawings
Fig. 1 is a flowchart of a first embodiment of the present invention.
Fig. 2 is a schematic diagram of content of a point cloud data file according to an embodiment of the present invention.
Fig. 3 is a schematic diagram showing the linear difference result obtained by the method according to the first embodiment of the present invention.
Fig. 4 is a graph showing the comparison of the results of the linear interpolation algorithm using the method of the present embodiment with the mose in the first embodiment of the present invention.
Detailed Description
For further illustration of the various embodiments, the invention is provided with the accompanying drawings. The accompanying drawings, which are incorporated in and constitute a part of this disclosure, illustrate embodiments and together with the description, serve to explain the principles of the embodiments. With reference to these matters, one of ordinary skill in the art will understand other possible embodiments and advantages of the present invention.
The invention will now be further described with reference to the drawings and detailed description.
Embodiment one:
the embodiment of the invention provides a point cloud data multidimensional linear difference method which is used for a numerical simulation software platform, such as MOOSE, SALOME and the like. Fig. 1 is a flowchart of a method for multidimensional linear difference of point cloud data according to an embodiment of the present invention, the method includes the following steps:
s1: and extracting the maximum value and the minimum value of all the node coordinates in each coordinate dimension based on the point cloud data file.
The dimension of the node coordinates may be one-dimensional, two-dimensional or three-dimensional, assuming that the node coordinates are uniformly expressed as (x, y, z), if one-dimensional, then default y=z=0; if a two-dimensional node, then default z=0.
The content of the point cloud data file is shown in fig. 2, and each row of the point cloud data file corresponds to a node, and the point cloud data file comprises coordinate values of the node in three coordinate dimensions of X, Y, Z and the value of the node.
The maximum value of the coordinates in the three coordinate dimensions of X, Y, Z is set as x in the embodiment max ，y max ，z max Minimum value x min ，y min ，z min 。
S2: and determining a grid coverage area corresponding to the grid file to be interpolated based on the maximum value and the minimum value of the extracted node coordinates, and setting the value of the node outside the coverage area to be 0.
The method for determining the set grid coverage area in this embodiment is as follows: if the grid is one-dimensional, the coverage area is a line segment x min ，x max ]The coordinates of the two end points are (x) min 0, 0) and (x) max 0, 0); if the grid is two-dimensional, the coverage area is a rectangle, and the coordinates corresponding to the two diagonal points are (x min ，y min 0) and (x) max ，y max 0); if the grid is three-dimensional, the coverage area is a cuboid, and the coordinates corresponding to the two opposite corners are (x min ，y min ，z min ) And (x) max ，y max ，z max )。
S3: for any node in the coverage area, two nodes are selected from other nodes in the point cloud data file to serve as a maximum basic point and a minimum basic point of the node respectively, wherein the maximum basic point and the minimum basic point are required to meet the following requirements: the coordinates of the node in each dimension are larger than or equal to the coordinates of the minimum basic point in the corresponding dimension, and smaller than or equal to the coordinates of the maximum basic point in the corresponding dimension; if the maximum base point and/or the minimum base point which meet the conditions are not present in the point cloud data file, a vertex of the coverage area, which consists of the maximum value in each coordinate dimension, is taken as the maximum base point, and/or a vertex of the coverage area, which consists of the minimum value in each coordinate dimension, is taken as the minimum base point.
The maximum base point and the minimum base point of the node a (x, y, z) are K respectively a2 (x 2 ，y 2 ，z 2 ) And K a1 (x 1 ，y 1 ，z 1 ) Then there is x 1 ≤x≤x 2 ，y 1 ≤y≤y 2 And z 1 ≤z≤z 2 。
In the case where the maximum base point and/or the minimum base point satisfying the above condition does not exist in the point cloud data file, the coordinates of the maximum base point are set to (x) based on different mesh dimensions max 0, 0) or (x) max ，y max 0) or (x) max ，y max ，z max ) The coordinates of the minimum base point are (x min 0, 0) or (x) min ，y min 0) or (x) min ，y min ，z min )。
S4: and constructing an assignment grid corresponding to the node and vertexes on the boundary of the assignment grid according to the maximum basic point and the minimum basic point.
In this embodiment, the method for constructing the assignment grid of the node and the vertex on the boundary of the assignment grid includes:
if the network is one-dimensional, the node's assignment grid is a segment [ x ] 1 x 2 ]Assigning vertices on the grid boundary as two endpoints of the line segment, and respectively assigning coordinates as (x) 1 0, 0) and (x) 2 ，0，0)；
If the grid is two-dimensional, the assigned grid of points is a rectangle, the vertexes on the boundary of the assigned grid are four vertexes of the rectangle, and the coordinates are respectively: (x) 1 ，y 1 ，0)、(x 2 ，y 2 ，0)、(x 1 ，y 2 0) and (x) 2 ，y 1 ，0)；
If the grid is three-dimensional, the assigned grid of the node is a cuboid, the vertexes on the boundary of the assigned grid are 8 vertexes of the cuboid, and the coordinates are respectively: (x) 1 ，y 1 ，z 1 )、(x 2 ，y 2 ，z 2 )、(x 1 ，y 1 ，z 2 )、(x 1 ，y 2 ，z 1 )、(x 1 ，y 2 ，z 2 )、(x 2 ，y 1 ，z 1 )、(x 2 ，y 1 ，z 2 ）、(x 2 ，y 2 ，z 1 )。
S5: and assigning the vertex on the assigned grid boundary of the node.
The vertex assignment method in this embodiment is: if the vertex is a node in the point cloud data file, the value of the vertex takes a corresponding value in the point cloud data file; otherwise, searching a node closest to the vertex from the point cloud data file, and giving the value of the searched node to the vertex.
S6: and respectively calculating the enclosing area of the node and each vertex on the assigned grid boundary based on the dimension of the grid.
The calculation method of the surrounding area in this embodiment is:
if the grid is one-dimensional, the area enclosed by the nodes and the vertexes is the length of a line segment formed from the nodes to the vertexes, and the two vertexes are arranged, so that the two enclosing areas are respectively marked as S 1 And S is 2 ；
If the grid is two-dimensional, the area enclosed by the nodes and the vertexes is the area of a rectangle formed by the nodes and the vertexes serving as diagonal points, and the area of four enclosing cities is respectively marked as S because the grid has four vertexes 1 ,S 2 ,S 3 And S is 4 ；
If the grid is three-dimensional, the area enclosed by the nodes and the vertexes is the volume of a cuboid formed by the nodes and the vertexes as opposite vertex points, and since eight vertexes exist, eight enclosing areas are respectively marked as S 1 ,S 2 ,S 3 ,……,S 8 。
S7: calculating the value of the node based on the value of each enclosing area and each vertex:
wherein U is a The value of the node is represented by,
the value of the ith vertex is represented, i represents the number of the vertex, n represents the total number of the vertices, and the value is represented according to the netThe dimension of the lattice being 2,4 or 8,S i Representing the bounding area of the node with the ith vertex on the assigned mesh boundary.
S8: and traversing all nodes in the grid coverage area, and repeating the steps S3-S7 to realize the value of all the nodes.
The steps S2-S8 are to conduct difference values on the grid files to be interpolated, wherein the grid files to be interpolated are applied to a numerical simulation software platform and comprise Medcoupling grid files, exodus files, gmsh grid files or the like.
In this embodiment, the method of this embodiment is adopted in the MOOSE platform, the point cloud data file as shown in fig. 2 is adopted, the screenshot at x=5 is shown in fig. 3, and the linear difference result is shown in table 1.
TABLE 1
| Sampling node coordinates | Linear interpolation result |
| (0.5，8.9，2.8) | 5.38 |
| (0.5，4.2，0.9) | 6.4 |
| (4.1，0.6，2.8) | 4.63 |
| (1.1，0.2，3.1) | 4.53 |
| (2.6，5.4，7.8) | 5.06 |
| (3.2，8.1，2.1) | 5.28 |
| (4.9，2.1，6.2) | 4.82 |
| (5.6，9.2，4.7) | 4.25 |
| (8.3，8.6，7.7) | 4.09 |
| (9.3，3.5，8.6) | 7.25 |
The comparison of the interpolation results of the linear interpolation algorithm (left in fig. 4) of the embodiment and the linear interpolation algorithm (right in fig. 4) in the MOOSE is shown in fig. 4, which are applied in the MOOSE platform under the same gridding point cloud data file.
The data comparison result of the linear interpolation algorithm proposed in this embodiment and the random grid node of the linear interpolation algorithm in the MOOSE under the same gridding input data shows that the error is zero (as shown in table 2), which indicates that the function of the linear interpolation algorithm proposed in this embodiment can completely cover the linear interpolation algorithm in the MOOSE in this case.
TABLE 2
The embodiment of the invention solves the problem that the prior multi-physical field simulation software needs a user to provide gridding input data to realize interpolation, and provides a linear interpolation mode which is not only suitable for gridding input data, but also suitable for irregular data such as point cloud and the like as input data. The embodiment can rapidly and efficiently realize data interpolation in the numerical simulation platform. When the method of the embodiment is used for carrying out data interpolation, the method has the advantages of small error, high efficiency and the like.
Embodiment two:
the invention also provides a point cloud data multidimensional linear difference terminal device, which comprises a memory, a processor and a computer program stored in the memory and capable of running on the processor, wherein the steps in the method embodiment of the first embodiment of the invention are realized when the processor executes the computer program.
Further, as an executable scheme, the point cloud data multidimensional linear difference terminal device may be a computing device such as a desktop computer, a notebook computer, a palm computer, a cloud server, and the like. The point cloud data multidimensional linear difference terminal device may include, but is not limited to, a processor, a memory. It will be appreciated by those skilled in the art that the above-described composition structure of the point cloud data multidimensional linear difference terminal device is merely an example of the point cloud data multidimensional linear difference terminal device, and does not constitute limitation of the point cloud data multidimensional linear difference terminal device, and may include more or fewer components than the above-described components, or combine certain components, or different components, for example, the point cloud data multidimensional linear difference terminal device may further include an input/output device, a network access device, a bus, and the like, which is not limited by the embodiment of the present invention.
Further, as an implementation, the processor may be a central processing unit (Central Processing Unit, CPU), other general purpose processor, digital signal processor (Digital Signal Processor, DSP), application specific integrated circuit (Application Specific Integrated Circuit, ASIC), field programmable gate array (Field-Programmable Gate Array, FPGA) or other programmable logic device, discrete gate or transistor logic device, discrete hardware components, etc. The general processor may be a microprocessor or any conventional processor, etc., and the processor is a control center of the point cloud data multidimensional linear difference terminal device, and connects various parts of the whole point cloud data multidimensional linear difference terminal device by using various interfaces and lines.
The memory may be used to store the computer program and/or the module, and the processor may implement various functions of the point cloud data multidimensional linear difference terminal device by running or executing the computer program and/or the module stored in the memory and invoking data stored in the memory. The memory may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, at least one application program required for a function; the storage data area may store data created according to the use of the cellular phone, etc. In addition, the memory may include high-speed random access memory, and may also include non-volatile memory, such as a hard disk, memory, plug-in hard disk, smart Media Card (SMC), secure Digital (SD) Card, flash Card (Flash Card), at least one disk storage device, flash memory device, or other volatile solid-state storage device.
The present invention also provides a computer readable storage medium storing a computer program which when executed by a processor implements the steps of the above-described method of an embodiment of the present invention.
The modules/units integrated with the point cloud data multidimensional linear difference terminal device can be stored in a computer readable storage medium if implemented in the form of software functional units and sold or used as independent products. Based on such understanding, the present invention may implement all or part of the flow of the method of the above embodiment, or may be implemented by a computer program to instruct related hardware, where the computer program may be stored in a computer readable storage medium, and when the computer program is executed by a processor, the computer program may implement the steps of each of the method embodiments described above. Wherein the computer program comprises computer program code which may be in source code form, object code form, executable file or some intermediate form etc. The computer readable medium may include: any entity or device capable of carrying the computer program code, a recording medium, a U disk, a removable hard disk, a magnetic disk, an optical disk, a computer Memory, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a software distribution medium, and so forth.
While the invention has been particularly shown and described with reference to a preferred embodiment, it will be understood by those skilled in the art that various changes in form and details may be made therein without departing from the spirit and scope of the invention as defined by the appended claims.
Claims (7)
1. The multi-dimensional linear difference method of the point cloud data is used for a numerical simulation software platform and is characterized by comprising the following steps of:
s1: extracting the maximum value and the minimum value of all node coordinates under each coordinate dimension based on the point cloud data file;
s2: determining a grid coverage area corresponding to a grid file to be interpolated based on the maximum value and the minimum value of the extracted node coordinates, and setting the value of a node outside the coverage area to be 0;
s3: for any node in the coverage area, two nodes are selected from other nodes in the point cloud data file to serve as a maximum basic point and a minimum basic point of the node respectively, wherein the maximum basic point and the minimum basic point are required to meet the following requirements: the coordinates of the node in each dimension are larger than or equal to the coordinates of the minimum basic point in the corresponding dimension, and smaller than or equal to the coordinates of the maximum basic point in the corresponding dimension; if the maximum basic point and/or the minimum basic point which meet the conditions do not exist in the point cloud data file, taking the vertex of the coverage area, which consists of the maximum value in each coordinate dimension, as the maximum basic point, and/or taking the vertex of the coverage area, which consists of the minimum value in each coordinate dimension, as the minimum basic point;
s4: constructing an assignment grid corresponding to the node and a vertex on an assignment grid boundary according to the maximum basic point and the minimum basic point;
s5: assigning a value to the vertex on the assigned grid boundary of the node;
s6: based on the dimension of the grid, calculating the enclosing area of each vertex on the boundary of the node and the assigned grid respectively;
s7: calculating the value of the node based on the value of each enclosing area and each vertex:
wherein U is a The value of the node is represented by,
the value of the ith vertex is represented, i represents the number of the vertex, n represents the total number of the vertices, S i Representing the area enclosed by the node and the ith vertex on the assigned grid boundary;
s8: and traversing all nodes in the grid coverage area, and repeating the steps S3-S7 to realize the value of all the nodes.
2. The method of multi-dimensional linear difference of point cloud data according to claim 1, wherein: the method for determining the grid coverage area in the step S2 is as follows: if the grid is one-dimensional, the coverage area is a line segment x min ，x max ]The method comprises the steps of carrying out a first treatment on the surface of the If the grid is two-dimensional, the coverage area is a rectangle, and the coordinates corresponding to the two diagonal points are (x min ，y min 0) and (x) max ，y max 0); if the grid is three-dimensional, the coverage area is a cuboid, and the coordinates corresponding to the two opposite corners are (x min ，y min ，z min ) And (x) max ，y max ，z max )，x min ，y min ，z min Respectively, the minimum value and x under X, Y, Z three coordinate dimensions max ，y max ，z max Each being the maximum in three coordinate dimensions of X, Y, Z.
3. The point cloud data of claim 1The dimension linear difference method is characterized in that: the construction method of the vertices on the assigned mesh boundary of the node in the step S4 is as follows: the coordinates of the maximum base point and the minimum base point of the node are set as (x) 2 ，y 2 ，z 2 ) And (x) 1 ，y 1 ，z 1 ) The following steps are:
if the network is one-dimensional, the node's assignment grid is a segment [ x ] 1 x 2 ]Assigning vertices on the grid boundary as two endpoints of the line segment, and respectively assigning coordinates as (x) 1 0, 0) and (x) 2 ，0，0)；
If the grid is two-dimensional, the assigned grid of points is a rectangle, the vertexes on the boundary of the assigned grid are four vertexes of the rectangle, and the coordinates are respectively: (x) 1 ，y 1 ，0)、(x 2 ，y 2 ，0)、(x 1 ，y 2 0) and (x) 2 ，y 1 ，0)；
If the grid is three-dimensional, the assigned grid of the node is a cuboid, the vertexes on the boundary of the assigned grid are 8 vertexes of the cuboid, and the coordinates are respectively: (x) 1 ，y 1 ，z 1 )、(x 2 ，y 2 ，z 2 )、(x 1 ，y 1 ，z 2 )、(x 1 ，y 2 ，z 1 )、(x 1 ，y 2 ，z 2 )、(x 2 ，y 1 ，z 1 )、(x 2 ，y 1 ，z 2 )、(x 2 ，y 2 ，z 1 )。
4. The method of multi-dimensional linear difference of point cloud data according to claim 1, wherein: the vertex assignment method in the step S5 is as follows: if the vertex is a node in the point cloud data file, the value of the vertex takes a corresponding value in the point cloud data file; otherwise, searching a node closest to the vertex from the point cloud data file, and giving the value of the searched node to the vertex.
5. The method of multi-dimensional linear difference of point cloud data according to claim 1, wherein: the calculation method of the surrounding area in the step S6 is as follows:
if the grid is one-dimensional, the area enclosed by the nodes and the vertexes is the length of a line segment formed from the nodes to the vertexes;
if the grid is two-dimensional, the area enclosed by the nodes and the vertexes is the area of a rectangle formed by taking the nodes and the vertexes as diagonal points;
if the grid is three-dimensional, the area enclosed by the nodes and the vertexes is the volume of a cuboid formed by taking the nodes and the vertexes as opposite vertex points.
6. The point cloud data multidimensional linear difference terminal equipment is characterized in that: comprising a processor, a memory and a computer program stored in the memory and running on the processor, which processor, when executing the computer program, carries out the steps of the method according to any one of claims 1 to 5.
7. A computer-readable storage medium storing a computer program, characterized in that: the computer program implementing the steps of the method according to any one of claims 1 to 5 when executed by a processor.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117094268A (en) * | 2023-10-17 | 2023-11-21 | 北京智芯微电子科技有限公司 | Inter-grid data transmission method and device, storage medium and electronic equipment |
| CN117235832A (en) * | 2023-11-16 | 2023-12-15 | 中国空气动力研究与发展中心计算空气动力研究所 | Method, device, equipment and medium for selecting aeroelastic coupling simulation interface point |
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117094268A (en) * | 2023-10-17 | 2023-11-21 | 北京智芯微电子科技有限公司 | Inter-grid data transmission method and device, storage medium and electronic equipment |
| CN117094268B (en) * | 2023-10-17 | 2024-01-19 | 北京智芯微电子科技有限公司 | Inter-grid data transmission method and device, storage medium and electronic equipment |
| CN117235832A (en) * | 2023-11-16 | 2023-12-15 | 中国空气动力研究与发展中心计算空气动力研究所 | Method, device, equipment and medium for selecting aeroelastic coupling simulation interface point |
| CN117235832B (en) * | 2023-11-16 | 2024-03-12 | 中国空气动力研究与发展中心计算空气动力研究所 | Method, device, equipment and medium for selecting aeroelastic coupling simulation interface point |
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