CN117875091B - Optimization method and device of high-precision curved surface modeling method based on adaptive algorithm - Google Patents
Optimization method and device of high-precision curved surface modeling method based on adaptive algorithm Download PDFInfo
- Publication number
- CN117875091B CN117875091B CN202410276207.5A CN202410276207A CN117875091B CN 117875091 B CN117875091 B CN 117875091B CN 202410276207 A CN202410276207 A CN 202410276207A CN 117875091 B CN117875091 B CN 117875091B
- Authority
- CN
- China
- Prior art keywords
- sub
- calculation
- hasm
- calculation domain
- domain
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 238000000034 method Methods 0.000 title claims abstract description 120
- 238000004422 calculation algorithm Methods 0.000 title claims abstract description 52
- 230000003044 adaptive effect Effects 0.000 title claims abstract description 48
- 238000005457 optimization Methods 0.000 title claims abstract description 29
- 238000004364 calculation method Methods 0.000 claims abstract description 260
- 238000005070 sampling Methods 0.000 claims abstract description 141
- 238000004088 simulation Methods 0.000 claims abstract description 78
- 238000003860 storage Methods 0.000 claims abstract description 17
- 238000007781 pre-processing Methods 0.000 claims abstract description 15
- 239000011159 matrix material Substances 0.000 claims description 61
- 230000008569 process Effects 0.000 claims description 14
- 230000008520 organization Effects 0.000 claims description 12
- 230000017105 transposition Effects 0.000 claims description 7
- 238000010276 construction Methods 0.000 claims description 4
- 238000004590 computer program Methods 0.000 claims description 3
- 238000002939 conjugate gradient method Methods 0.000 claims description 3
- 238000012545 processing Methods 0.000 abstract description 6
- 238000013500 data storage Methods 0.000 abstract description 3
- 238000013461 design Methods 0.000 abstract description 2
- 238000012876 topography Methods 0.000 abstract description 2
- 238000004891 communication Methods 0.000 description 7
- CURLTUGMZLYLDI-UHFFFAOYSA-N Carbon dioxide Chemical compound O=C=O CURLTUGMZLYLDI-UHFFFAOYSA-N 0.000 description 4
- 238000010586 diagram Methods 0.000 description 4
- 238000000638 solvent extraction Methods 0.000 description 4
- 238000009825 accumulation Methods 0.000 description 3
- 230000004048 modification Effects 0.000 description 3
- 238000012986 modification Methods 0.000 description 3
- 230000035772 mutation Effects 0.000 description 3
- 238000004904 shortening Methods 0.000 description 3
- 238000004458 analytical method Methods 0.000 description 2
- 229910002092 carbon dioxide Inorganic materials 0.000 description 2
- 239000001569 carbon dioxide Substances 0.000 description 2
- 230000008859 change Effects 0.000 description 2
- 230000000694 effects Effects 0.000 description 2
- 238000005516 engineering process Methods 0.000 description 2
- 230000006870 function Effects 0.000 description 2
- 230000006872 improvement Effects 0.000 description 2
- 238000010295 mobile communication Methods 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000005094 computer simulation Methods 0.000 description 1
- 230000007423 decrease Effects 0.000 description 1
- 238000009826 distribution Methods 0.000 description 1
- 230000007613 environmental effect Effects 0.000 description 1
- 238000002474 experimental method Methods 0.000 description 1
- 230000004927 fusion Effects 0.000 description 1
- 239000005431 greenhouse gas Substances 0.000 description 1
- 230000003993 interaction Effects 0.000 description 1
- 238000013507 mapping Methods 0.000 description 1
- 238000005192 partition Methods 0.000 description 1
- 238000001556 precipitation Methods 0.000 description 1
- 230000000750 progressive effect Effects 0.000 description 1
- 238000011160 research Methods 0.000 description 1
- 239000002689 soil Substances 0.000 description 1
- 108020001568 subdomains Proteins 0.000 description 1
Landscapes
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The application relates to the technical field of digital data processing, and provides an optimization method and device of a high-precision curved surface modeling method (HASM) based on an adaptive algorithm. Taking the target ecological environment area as a calculation domain according to sampling points in the target ecological environment area; based on the topography or other ecological environment characteristics of the target ecological environment area shown by the sampling points, carrying out area quadtree subdivision on the calculation domain by adopting a preprocessing idea in an adaptive algorithm to obtain a plurality of sub-calculation domains; and performing curved surface simulation on each sub-calculation domain by using a HASM method to obtain a simulation result of the target ecological environment region. By applying the idea of an adaptive algorithm to the HASM method design, the uniform resolution is not used any more, the curved surface is firstly partitioned based on the characteristics of the target curved surface to be simulated, different resolutions are used for different subareas according to the characteristics of the subareas, a quadtree structure is used for data storage, the storage space is saved, and the HASM running speed under the condition of large-scale data simulation is optimized.
Description
Technical Field
The application relates to the technical field of digital data processing, in particular to an optimization method and device of a high-precision curved surface modeling method based on an adaptive algorithm, a computer readable storage medium and electronic equipment.
Background
In the ecological environment informatics research, an area or an ecological environment element thereof can be abstracted into a mathematical 'surface', and the mathematical surface can be used for representing an ecological environment surface, including a natural system surface, a natural system contribution surface to human and a natural system change driving force surface, so that the searching of an efficient and accurate ecological environment modeling method has important significance.
In the beginning of the century, yue Tianxiang developed a high-precision curved surface modeling method (High Accuracy Surface Modeling, HASM), realized the organic combination of the external and internal quantities, and solved the key problems of ecological environment surface modeling such as error, multi-scale and nonlinearity. HASM has been successfully applied to surface modeling of ecological environmental elements of various spatial scales, including digital elevation model construction, carbon dioxide concentration data fusion, climate change simulation analysis, population space distribution dynamic simulation, food supply simulation analysis, digital soil mapping, and other fields.
The main equation set of HASM is a typical differential equation set, and at present, a finite difference method is mainly used for solving the equation set, and when the finite difference method is used, it is widely believed that the accuracy can be improved by using smaller resolution, but the improvement of the resolution causes the sharp decline of the calculation performance, so that the effect is poor when simulating the ecological environment curved surface mutation phenomena such as cliffs, fracture zones and the like.
Therefore, it is needed to provide an optimization method capable of ensuring the simulation accuracy of the HASM, improving the operation speed and shortening the calculation time.
Disclosure of Invention
The application aims to provide an optimization method, an optimization device, a computer-readable storage medium and electronic equipment of a high-precision curved surface modeling method based on an adaptive algorithm, so as to solve the problem of how to ensure the simulation precision of a HASM method and improve the operation performance when a huge amount of data is used in the curved surface modeling process.
In order to achieve the above object, the present application provides the following technical solutions:
the application provides an optimization method of a high-precision curved surface modeling method based on an adaptive algorithm, which comprises the following steps:
Acquiring sampling points in a target ecological environment area, and taking the target ecological environment area as a calculation domain of a high-precision curved surface modeling method (HASM);
Based on the topography or other ecological environment characteristics of the target ecological environment area represented by the sampling points, carrying out area quadtree subdivision on the calculation domain by adopting a preprocessing idea in an adaptive algorithm to obtain a plurality of sub-calculation domains, and recording the sampling points contained in each sub-calculation domain and the subdivision times experienced by each sub-calculation domain ；
Determining an initial field, parameters and an initial resolution H of a HASM method;
according to the initial resolution And the number of subdivisions experienced by each of the sub-computational domains/>Determining the resolution h used when each sub-calculation domain performs surface simulation, wherein/>Constructing a HASM main equation of each sub-calculation domain based on the resolution h used when the sub-calculation domains perform curved surface simulation and the sampling points of each sub-calculation domain;
The expression of the HASM main equation is as follows:
，
Wherein, The resolution used in performing surface simulation for the sub-computational domain; /(I)Representing the number of iterations of the HASM principal equation,/>Is a positive integer; matrix/>、/>Coefficient matrix in the sub-calculation domain for the HASM main equation, T represents matrix transposition; Simulation of the first/>, for curved surfaces Performing iteration values; vector/>、/>Is the right end term of the sub-calculation domain of the HASM main equation in the nth iteration; /(I)The weight coefficient of the sampling points is calculated according to the mutual position relation among the sampling points; matrix/>Sum vector/>Respectively establishing a sampling coefficient matrix and a sampling right-end item according to the sampling points;
Is provided with ，/>The HASM main process is simplified as:
，
Solving a HASM main equation of each sub-calculation domain, and recording simulation values of each sub-calculation domain, resolution used in surface simulation and region origin coordinates of each sub-calculation domain to obtain simulation results of the target ecological environment region.
Preferably, the area quadtree subdivision is performed on the calculation domain by adopting a preprocessing idea in an adaptive algorithm based on the area range where the sampling points in the target ecological environment area are located, so as to obtain a plurality of sub-calculation domains, which are specifically:
step S21: taking the calculation domain as a current calculation domain, or taking any one of the sub calculation domains from a plurality of sub calculation domains as the current calculation domain;
step S22: judging whether sampling points contained in the current calculation domain meet preset judging conditions or not;
Step S23: if the sampling points contained in the current calculation domain meet the preset judging conditions, recording the sampling points contained in the current calculation domain and the subdivision times experienced by the current calculation domain in a data organization form of a quadtree;
Step S24: if the sampling points contained in the current calculation domain do not meet the preset judgment conditions, further judging whether the subdivision times corresponding to the current calculation domain reach the preset subdivision times upper limit or not;
step S25: if the subdivision times corresponding to the current calculation domain reach the preset subdivision times upper limit, recording sampling points contained in the current calculation domain and subdivision times experienced by the current calculation domain in a data organization form of a quadtree;
Step S26: if the subdivision times corresponding to the current calculation domain do not reach the preset subdivision times upper limit, continuously conducting regional quadtree subdivision on the current calculation domain to obtain a plurality of new sub-calculation domains, and iteratively executing the step S21 and the following steps until sampling points of all calculation domains/sub-calculation domains meet preset judgment conditions, or the subdivision times corresponding to the calculation domains/sub-calculation domains reach the preset subdivision times upper limit, and ending iteration.
Preferably, the HASM main equation for each of said sub-computational domains is solved using a HASM direct solution or a conjugate gradient solution.
The embodiment of the application provides an optimization device of a high-precision curved surface modeling method based on an adaptive algorithm, which comprises the following steps:
the acquisition unit is configured to acquire sampling points in the target ecological environment area and take the target ecological environment area as a calculation domain of the high-precision curved surface modeling method HASM;
The subdivision unit is configured to subdivide the computation domain into a plurality of sub-computation domains by adopting a preprocessing idea in an adaptive algorithm based on the region range of the sampling points in the target ecological environment region to obtain a plurality of sub-computation domains, and record the sampling points contained in each sub-computation domain and the subdivision times experienced by each sub-computation domain ；
A determining unit configured to determine an initial field, parameters, and an initial resolution H of the HASM method;
A construction unit configured to, according to the initial resolution And the number of subdivisions experienced by each of the sub-computational domains/>Determining the resolution h used when each sub-calculation domain performs surface simulation, wherein/>Constructing a HASM main equation of each sub-calculation domain based on the resolution h used when the sub-calculation domains perform curved surface simulation and the sampling points of each sub-calculation domain;
The expression of the HASM main equation is as follows:
，
Wherein, The resolution used in performing surface simulation for the sub-computational domain; /(I)Representing the number of iterations of the HASM principal equation,/>Is a positive integer; matrix/>、/>Coefficient matrix in the sub-calculation domain for the HASM main equation, T represents matrix transposition; Simulation of the first/>, for curved surfaces Performing iteration values; vector/>、/>Is the right end term of the sub-calculation domain of the HASM main equation in the nth iteration; /(I)The weight coefficient of the sampling points is calculated according to the mutual position relation among the sampling points; matrix/>Sum vector/>Respectively establishing a sampling coefficient matrix and a sampling right-end item according to the sampling points;
Is provided with ，/>The HASM main process is simplified as:
，
And the simulation unit is configured to solve a HASM main equation of each sub-calculation domain, record simulation values of each sub-calculation domain, resolution used in curved surface simulation and region origin coordinates of each sub-calculation domain, and obtain a simulation result of the target ecological environment region.
Preferably, the subdivision unit is further configured to:
step S21: taking the calculation domain as a current calculation domain, or taking any one of the sub calculation domains from a plurality of sub calculation domains as the current calculation domain;
step S22: judging whether sampling points contained in the current calculation domain meet preset judging conditions or not;
Step S23: if the sampling points contained in the current calculation domain meet the preset judging conditions, recording the sampling points contained in the current calculation domain and the subdivision times experienced by the current calculation domain in a data organization form of a quadtree;
Step S24: if the sampling points contained in the current calculation domain do not meet the preset judgment conditions, further judging whether the subdivision times corresponding to the current calculation domain reach the preset subdivision times upper limit or not;
step S25: if the subdivision times corresponding to the current calculation domain reach the preset subdivision times upper limit, recording sampling points contained in the current calculation domain and subdivision times experienced by the current calculation domain in a data organization form of a quadtree;
Step S26: if the subdivision times corresponding to the current calculation domain do not reach the preset subdivision times upper limit, continuously conducting regional quadtree subdivision on the current calculation domain to obtain a plurality of new sub-calculation domains, and iteratively executing the step S21 and the following steps until sampling points of all calculation domains/sub-calculation domains meet preset judgment conditions, or the subdivision times corresponding to the calculation domains/sub-calculation domains reach the preset subdivision times upper limit, and ending iteration.
Preferably, the HASM main equation for each of said sub-computational domains is solved using a HASM direct algorithm or a conjugate gradient method.
The embodiment of the application also provides a computer readable storage medium, on which a computer program is stored, which when being executed by a processor, implements the optimization method of the high-precision curved surface modeling method based on the adaptive algorithm according to any embodiment.
The embodiment of the application also provides electronic equipment, which comprises: the system comprises a memory, a processor and a program stored in the memory and capable of running on the processor, wherein the processor executes the program to realize the optimization method of the high-precision curved surface modeling method based on the adaptive algorithm according to any embodiment.
The beneficial effects are that:
According to the optimization method of the high-precision curved surface modeling method based on the adaptive algorithm, sampling points in a target ecological environment area are taken, and the target ecological environment area is used as a calculation domain of a high-precision curved surface modeling method HASM; based on the region range of the sampling point in the target ecological environment region, carrying out regional quadtree subdivision on the calculation region by adopting a preprocessing idea in an adaptive algorithm to obtain a plurality of sub-calculation regions; and performing curved surface simulation on each sub-calculation domain by using a HASM method to obtain a simulation result of the target ecological environment region. The technical scheme fully utilizes the advantages of the adaptive algorithm and the tree structure, the whole calculation domain is divided into a plurality of sub-calculation domains by using the adaptive algorithm, the purpose of shortening the calculation time under the condition of ensuring the accuracy is achieved, and meanwhile, the original numerical model is not changed when the method is applied, so that the main equation of the HASM is not changed to a great extent when the technology is implemented in the curved surface modeling process, and the characteristics of combination of the external accumulation amount and the internal accumulation amount of the HASM and high accuracy are ensured.
Drawings
The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this specification, illustrate embodiments of the application and together with the description serve to explain the application. Wherein:
Fig. 1 is a flow chart of an optimization method of a high-precision curved surface modeling method based on an adaptive algorithm according to some embodiments of the present application.
Fig. 2 is a flow chart of an optimization method of a high-precision curved surface modeling method based on an adaptive algorithm according to other embodiments of the present application.
Fig. 3 is a schematic diagram of quadtree partitioning at a subdivision number of 3 provided in accordance with some embodiments of the present application.
FIG. 4 is a schematic diagram showing the data storage sequence and structure of the quadtree partitioning result according to FIG. 3.
FIG. 5 is a schematic flow chart of solving a HASM master equation, provided in accordance with some embodiments of the application.
FIG. 6 is a flow chart of a direct HASM solution provided in accordance with some embodiments of the present application.
Fig. 7 is a flow chart of a conjugate gradient PCG method according to some embodiments of the present application.
Detailed Description
The application will be described in detail below with reference to the drawings in connection with embodiments. The examples are provided by way of explanation of the application and not limitation of the application. Indeed, it will be apparent to those skilled in the art that modifications and variations can be made in the present application without departing from the scope or spirit of the application. For example, features illustrated or described as part of one embodiment can be used on another embodiment to yield still a further embodiment.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this disclosure belongs. The terminology used herein is for the purpose of describing embodiments of the present disclosure only and is not intended to be limiting of the present disclosure.
Method embodiment:
The embodiment of the application provides an optimization method of a high-precision curved surface modeling method based on an adaptive algorithm, which is shown in fig. 1-7, and comprises the following steps:
Step S1: and acquiring sampling points in the target ecological environment area, and taking the target ecological environment area as a calculation domain of the high-precision curved surface modeling method HASM.
In this embodiment, the sampling point may be a sampling point of a digital elevation model, or may be a sampling point of climate elements such as air temperature, precipitation, or may be a sampling point of elements such as greenhouse gas, carbon dioxide concentration, etc., and the type of the sampling point is not limited in the present application.
The target ecological environment area can be any area in a geographical space range on the earth, and the application does not limit the geographical position and the size of the simulated target ecological environment area.
The application takes the space range of the target ecological environment area as the calculation domain of the HASM, namely, the application aims to perform high-precision simulation on ecological environment elements of the area by using the HASM method to obtain a simulation result.
The sampling points of the target ecological environment area are usually multiple, and the multiple sampling points form a sampling set, so that the target ecological environment area comprisesThe set of sample points can be denoted {/>The computational domain of which is denoted/>。
Step S2: based on the area range of the sampling points in the target ecological environment area, carrying out area quadtree subdivision on the calculation domain by adopting the preprocessing idea in the adaptive algorithm to obtain a plurality of sub-calculation domains, and recording the sampling points contained in each sub-calculation domain and the subdivision times undergone by each sub-calculation domain。
Step S3: the initial field, parameters and initial resolution H of the HASM method are determined.
As shown in fig. 1, the parameters, i.e. input parameters, may for example include: parameters required to run the HASM method, sample point storage path, initial field storage path (which may be empty), and result storage path.
In order to ensure smooth operation of the algorithm, the method provided by the embodiment automatically judges whether the parameters input by the user meet the operation conditions, for example, judges whether the parameter format and the numerical value meet the requirements, and if not, allows the user to input again until all the parameters meet the operation conditions of the algorithm.
Step S4: according to the initial resolutionAnd the number of subdivisions experienced by each of the sub-computational domains/>Determining the resolution h used when each sub-calculation domain performs surface simulation, wherein/>And constructing a HASM main equation of each sub-calculation domain based on the resolution h used when the sub-calculation domains perform curved surface simulation and the sampling points of each sub-calculation domain.
The expression of the HASM master equation is:
，
Wherein, The resolution used in performing surface simulation for the sub-computational domain; /(I)Representing the number of iterations of the HASM principal equation,/>Is a positive integer; matrix/>、/>Calculating a coefficient matrix of a domain for the HASM main equation in the sub-calculation domain; /(I)Simulation of the first/>, for curved surfacesPerforming iteration values; vector/>、/>Is the right-hand term of the HASM main equation in the sub-computation domain; /(I)The weight coefficient of the sampling points is calculated according to the mutual position relation among the sampling points; matrix/>Sum vector/>The sampling coefficient matrix and the sampling right term established according to the sampling points are respectively. /(I)、/>、/>Respectively matrix/>、/>、/>Is a transposed matrix of (a). For each sub-calculation region and its corresponding resolution/>Is provided withIs the orthogonal division of the sub-calculation area, and each grid unit in the sub-area can find the corresponding grid unit/>, according to the corresponding abscissa number i, j. Use/>Represents the/>The sampling points are in the unit grid/>Sampling value at the location, matrix/>Sum vector/>The elements in (a) respectively satisfy/>And/>Wherein i, j is the transverse and longitudinal numbers of the orthogonal subdivision grids in the sub-calculation domain corresponding to the sampling points,/>For the sub-calculation of the number of parts of the region divided equally in the lateral and longitudinal directions,/>Numbering sample points is also vector/>Corresponding to the element number in the database.
Is provided with，/>The above HASM master equation can be reduced to:
。
Step S5: and solving a HASM main equation of each sub-calculation domain, and recording simulation values of each sub-calculation domain, resolution used in surface simulation and region origin coordinates of each sub-calculation domain to obtain a simulation result of the target ecological environment region.
It should be noted that, the origin coordinates of the subregions refer to coordinates of lower left corner points of the subregions.
In the finite difference algorithm, the simulation accuracy can be improved by using smaller resolution, but for some parts of the calculation domain, even small changes of the local truncation error of the part can lead to a large increase of the total truncation error, while the contribution of the local truncation error of other parts is not so significant, so that the use of a suitable fine grid only in those subfields with a larger influence on the global truncation error can greatly reduce the additional cost in the simulation process. The embodiment combines the adaptive method with the HASM, divides the whole calculation domain into a plurality of subdomains by using the adaptive method, and refines only certain selected subdomains so as to improve the solving efficiency and accuracy.
Meanwhile, special attention needs to be paid to the fact that an adaptive method is combined with the HASM, an original HASM numerical model is not changed, so that the main equation of the HASM cannot be changed to a great extent when the technology is implemented in the curved surface modeling process, and the characteristics of combination of the external and internal accumulation amounts of the HASM and high precision are guaranteed.
In the embodiment, before the HASM method is operated to perform surface simulation, a preprocessing idea is adopted, and a quadtree is used in advance to subdivide a calculation domain so as to obtain a plurality of sub-calculation domains. The tree structure is utilized to enhance the memory locality and realize continuous direct memory access, thereby improving the calculation efficiency of the HASM method.
In summary, the optimization method of the high-precision curved surface modeling method based on the adaptive algorithm provided by the embodiment is a new optimization method for the high-precision curved surface modeling method, the method can achieve the purpose of shortening the calculation time under the condition of guaranteeing the precision, and the method has more obvious advantages on the simulation precision for the small-range surface of the curved surface with the steep rise and fall phenomenon, and the advantages indicate that the method has positive significance on solving the high-precision simulation problem of the ecological environment curved surface mutation phenomenon such as cliffs and fracture zones.
In some alternative embodiments, based on the region range where the sampling points in the target ecological environment region are located, the computing domain is subdivided by adopting a preprocessing idea in an adaptive algorithm to obtain a plurality of sub-computing domains, which specifically includes:
step S21: taking the computing domain as a current computing domain, or taking any one of the sub-computing domains from a plurality of sub-computing domains as the current computing domain;
Step S22: judging whether sampling points contained in the current calculation domain meet preset judging conditions or not;
step S23: if the sampling points contained in the current calculation domain meet the preset judging conditions, recording the sampling points contained in the current calculation domain and the subdivision times experienced by the current calculation domain in a data organization form of a quadtree;
Step S24: if the sampling points contained in the current calculation domain do not meet the preset judgment conditions, further judging whether the subdivision times corresponding to the current calculation domain reach the preset subdivision times upper limit or not;
Step S25: if the subdivision times corresponding to the current calculation domain reach the preset subdivision times upper limit, recording sampling points contained in the current calculation domain and subdivision times experienced by the current calculation domain in a data organization form of a quadtree;
Step S26: if the subdivision times corresponding to the current calculation domain do not reach the preset subdivision times upper limit, continuously conducting regional quadtree subdivision on the current calculation domain to obtain a plurality of new sub-calculation domains, and iteratively executing the step S21 and the following steps until sampling points of all calculation domains/sub-calculation domains meet the preset judgment conditions, or the subdivision times corresponding to the calculation domains/sub-calculation domains reach the preset subdivision times upper limit, and ending iteration.
Illustratively, fig. 3 is a schematic diagram of quadtree partitioning at a subdivision number of 3 according to some embodiments of the present application, and fig. 4 is a schematic diagram of data storage sequence and structure according to the quadtree partitioning result of fig. 3.
As shown in fig. 3 and 4, in the plane xoy, the calculation domain consisting of all the sampling points is calculatedBased on the preprocessing thought in the adaptive algorithm, the regional quadtree is divided through the steps S21-S26, and the whole calculation domain/>Split into multiple sub-computational domains (also called sub-domains): /(I)I.e./>And/>And l is the number of sub-calculated domains obtained by dividing.
For example, in the first division, the entire computational domainIs quartered to obtain the/>, for the sub-calculation domain、/>、/>、To represent; on the basis, traversing the four sub-calculation domains, judging whether further subdivision is carried out one by one, and judging the sub-calculation domains/>When further refinement is required, it is then second sub-divided into four equal parts to obtain a new sub-calculation domain, use/>、、/>、/>A representation; continuing to traverse the new sub-computation domain/>、/>、/>、/>When judging the sub-calculation domainWhen further subdivision is required, a third quarter is performed on it, and so on.
The above-described dividing process can also be exemplarily described as:
(a) Whether the sampling points in the calculation domain meet the judgment condition (for example, the number of the sampling points is smaller than a preset threshold value) or not and whether the subdivision times reach the set subdivision times upper limit or not are judged.
(B) And if the sampling points in the calculation domain do not meet the judgment conditions and the subdivision times do not reach the subdivision times upper limit, performing quartering on the calculation domain.
Repeating (a) and (b) until all the sampling points of the calculation sub-areas meet the judgment condition or the subdivision upper limit is reached.
Stopping subdivision and taking the whole calculation domainAs the root node of the quadtree, the sampling point of each block sub-region and the subdivision times/>, which are experienced by the sampling point, are organized by data of the quadtree according to the subdivision sequenceRecording is performed and the memory structure is shown in fig. 4. Preferably, QTree in the Python program may be used for storage.
In some alternative embodiments, the HASM principal equations for each subregion are solved using a HASM direct solution or a conjugate gradient solution.
FIG. 5 is a schematic flow chart of solving a HASM master equation, provided in accordance with some embodiments of the application. As shown in fig. 5, solving the HASM master equation for each sub-computational domain may include the steps of:
Step S41: reading sub-region sampling data and resolution h corresponding to the sub-region;
Step S42: initializing data;
step S43: sampling to generate a trend surface (i.e., a matrix consistent with the final result scale generated from the input data);
step S44: calculating lambda% ) A weight coefficient;
Step S45: calculating a sample vector And sampling matrix/>；
Step S46: judging whether the matrix scale corresponding to the trend surface is smaller than the input scale parameter;
step S47: if yes, solving a HASM main equation by using a HASM direct solution;
step S48: if not, solving a HASM main equation by using a PCG solution;
step S49: and outputting the result.
In step S45, to reduce the storage space, a CSR (Compressed Sparse Row) matrix may be used to store the calculated sample vectorAnd sampling matrix/>。
In the steps S46 to S47, the corresponding solution method is selected according to the matrix scale currently participating in the operation, and the HASM direct solution is selected for the matrix with smaller scale, and the PCG solution is selected for the solution in the case of larger scale, so that the calculation efficiency can be further ensured.
FIG. 6 is a flow chart of a direct HASM solution provided in accordance with some embodiments of the present application. As shown in fig. 6, the HASM direct solution includes the steps of:
step S61: generating a coefficient matrix;
step S62: generating an upper and lower bound constraint matrix;
Step S63: judging whether the current iteration exceeds the set iteration times, if so, outputting a result and ending the iteration;
Step S64: if the iteration times do not exceed the set iteration times, generating a right end term of the HASM equation;
step S65: solving a matrix equation by using a HASM direct solution;
Step S66: and (5) performing inequality constraint judgment and entering the next iteration.
It should be noted that, the HASM direct solution refers to a HASM traditional solution method, and related steps may refer to the prior art, and are not described herein in detail.
For larger matrix sizes, the solution is performed using the conjugate gradient PCG method shown in fig. 7, which may include the following steps:
step S71: generating a coefficient matrix corresponding to the PCG solution by using the main diagonal;
Step S72: generating an upper and lower bound constraint matrix;
step S73: judging whether the current iteration exceeds the set iteration times, if so, outputting a result and ending the iteration;
Step S74: if the iteration times do not exceed the set iteration times, generating a right end term of the HASM equation;
Step S75: solving a matrix equation by using a PCG solution;
Step S76: and (5) performing inequality constraint judgment and entering the next iteration.
It should be noted that, the specific implementation process of the PCG method may refer to the prior art, and will not be described herein in detail.
In summary, the present application aims to optimize the existing HASM method, and provides an optimization method of a high-precision curved surface modeling method based on an adaptive algorithm, which can greatly improve the operation speed and simultaneously realize high-precision simulation of the curved surface mutation phenomenon of ecological environments such as cliffs and fracture zones under the condition that the simulation precision is not lost.
In addition, the method can effectively reduce the scale of an operation matrix, and the numerical experiment proves that the method can shorten the operation time to 1/100 or even shorter.
In a word, the application applies the idea of the adaptive algorithm to the HASM method design, does not use uniform resolution, but partitions the curved surface firstly based on the characteristics of the curved surface to be simulated, uses different resolutions for different subareas according to the characteristics of the curved surface, stores data by using a quadtree structure, saves storage space, simultaneously searches and accesses data more effectively, and optimizes the HASM operation speed under the condition of large-scale data simulation.
According to the method, the used HASM algorithm is stored by using the python sparse matrix CSR matrix when the large matrix is stored, so that the storage space is reduced, and the matrix operation speed is ensured.
Exemplary System:
the embodiment of the application provides an optimization device of a high-precision curved surface modeling method based on an adaptive algorithm, which comprises the following steps: an acquisition unit, a subdivision unit and an analog unit. Wherein:
the acquisition unit is configured to acquire sampling points in the target ecological environment area and take the target ecological environment area as a calculation domain of the high-precision curved surface modeling method HASM;
The subdivision unit is configured to subdivide the computation domain into a plurality of sub-computation domains by adopting a preprocessing idea in an adaptive algorithm based on the region range of the sampling points in the target ecological environment region to obtain a plurality of sub-computation domains, and record the sampling points contained in each sub-computation domain and the subdivision times experienced by each sub-computation domain ；
A determining unit configured to determine an initial field, parameters, and an initial resolution H of the HASM method;
A construction unit configured to, according to the initial resolution And the number of subdivisions experienced by each of the sub-computational domains/>Determining the resolution h used when each sub-calculation domain performs surface simulation, wherein/>Constructing a HASM main equation of each sub-calculation domain based on the resolution h used when the sub-calculation domains perform curved surface simulation and the sampling points of each sub-calculation domain;
The expression of the HASM main equation is as follows:
，
Wherein, The resolution used in performing surface simulation for the sub-computational domain; /(I)Representing the number of iterations of the HASM principal equation,/>Is a positive integer; matrix/>、/>Coefficient matrix in the sub-calculation domain for the HASM main equation, T represents matrix transposition; Simulation of the first/>, for curved surfaces Performing iteration values; vector/>、/>Is the right end term of the sub-calculation domain of the HASM main equation in the nth iteration; /(I)The weight coefficient of the sampling points is calculated according to the mutual position relation among the sampling points; matrix/>Sum vector/>Respectively establishing a sampling coefficient matrix and a sampling right-end item according to the sampling points;
Is provided with ，/>The HASM main process is simplified as:
，
And the simulation unit is configured to solve a HASM main equation of each sub-calculation domain, record simulation values of each sub-calculation domain, resolution used in curved surface simulation and region origin coordinates of each sub-calculation domain, and obtain a simulation result of a target ecological environment region.
Preferably, the subdivision unit is further configured to:
for a plurality of sub-computing domains, the following steps are performed:
Taking any one sub-calculation domain from the plurality of sub-calculation domains as a current sub-calculation domain;
judging whether sampling points contained in the current sub-calculation domain meet the preset sampling point conditions or not;
If the sampling points contained in the current sub-calculation domain meet the preset sampling point conditions, recording the sampling points contained in the current sub-calculation domain and the subdivision times experienced by the current sub-calculation domain in a data organization form of a quadtree;
If the sampling points contained in the current sub-calculation domain do not meet the preset sampling point conditions, further judging whether the subdivision times corresponding to the current sub-calculation domain reach the preset subdivision times upper limit;
If the corresponding subdivision times of the current sub-calculation domain reach the preset subdivision times upper limit, recording sampling points contained in the current sub-calculation domain and subdivision times experienced by the current sub-calculation domain in a data organization form of a quadtree;
If the corresponding subdivision times of the current sub-calculation domain do not reach the preset subdivision times upper limit, continuously subdividing the area quadtree of the current sub-calculation domain to obtain a plurality of new sub-calculation domains, and performing iterative execution: taking any sub-calculation domain from the plurality of sub-calculation domains as the current sub-calculation domain, and then, until sampling points of all the sub-calculation domains meet the preset sampling point condition, or the corresponding subdivision times of the current sub-calculation domain reach the preset subdivision times upper limit, ending the iteration.
Preferably, the HASM main equation for each sub-computational domain is solved using a HASM direct algorithm or a conjugate gradient method.
The optimization device of the high-precision curved surface modeling method based on the adaptive algorithm provided by the embodiment of the application can realize the steps and the flow of the optimization method of the high-precision curved surface modeling method based on the adaptive algorithm provided by any embodiment, and achieve the same technical effects, and are not described in detail herein.
Exemplary device:
Some embodiments of the application provide an electronic device; the electronic device includes:
One or more processors;
A computer readable storage medium, which may be configured to store one or more programs, which when executed by one or more processors, implement the steps of: acquiring sampling points in a target ecological environment area, and taking the target ecological environment area as a calculation domain of a high-precision curved surface modeling method HASM; based on the area range of the sampling points in the target ecological environment area, carrying out area quadtree subdivision on the calculation domain by adopting the preprocessing idea in the adaptive algorithm to obtain a plurality of sub-calculation domains, and recording the sampling points contained in each sub-calculation domain and the subdivision times undergone by each sub-calculation domain ；
Determining an initial field, parameters and an initial resolution H of a HASM method;
according to the initial resolution And the number of subdivisions experienced by each of the sub-computational domains/>Determining the resolution h used when each sub-calculation domain performs surface simulation, wherein/>Constructing a HASM main equation of each sub-calculation domain based on the resolution h used when the sub-calculation domains perform curved surface simulation and the sampling points of each sub-calculation domain;
The expression of the HASM main equation is as follows:
，
Wherein, The resolution used in performing surface simulation for the sub-computational domain; /(I)Representing the number of iterations of the HASM principal equation,/>Is a positive integer; matrix/>、/>Coefficient matrix in the sub-calculation domain for the HASM main equation, T represents matrix transposition; Simulation of the first/>, for curved surfaces Performing iteration values; vector/>、/>Is the right end term of the sub-calculation domain of the HASM main equation in the nth iteration; /(I)The weight coefficient of the sampling points is calculated according to the mutual position relation among the sampling points; matrix/>Sum vector/>Respectively establishing a sampling coefficient matrix and a sampling right-end item according to the sampling points;
Is provided with ，/>The HASM main process is simplified as:
，
And solving a HASM main equation of each sub-calculation domain, and recording the simulation value of each sub-calculation domain, the resolution used in the curved surface simulation and the region origin coordinates of each sub-calculation domain to obtain the simulation result of the target ecological environment region.
Some embodiments of the application provide a hardware structure of an electronic device; the hardware structure of the electronic device may include: a processor, a communication interface, a computer-readable storage medium (also referred to as memory), and a communication bus.
Wherein the processor, the communication interface, and the computer readable storage medium communicate with each other via a communication bus.
A computer readable storage medium may be configured to store one or more programs.
Alternatively, the communication interface may be an interface of a communication module, such as an interface of a GSM module.
Wherein the processor executes one or more programs that implement the steps of: acquiring sampling points in a target ecological environment area, and taking the target ecological environment area as a calculation domain of a high-precision curved surface modeling method HASM; based on the area range of the sampling points in the target ecological environment area, carrying out area quadtree subdivision on the calculation domain by adopting the preprocessing idea in the adaptive algorithm to obtain a plurality of sub-calculation domains, and recording the sampling points contained in each sub-calculation domain and the subdivision times undergone by each sub-calculation domain；
Determining an initial field, parameters and an initial resolution H of a HASM method;
according to the initial resolution And the number of subdivisions experienced by each of the sub-computational domains/>Determining the resolution h used when each sub-calculation domain performs surface simulation, wherein/>Constructing a HASM main equation of each sub-calculation domain based on the resolution h used when the sub-calculation domains perform curved surface simulation and the sampling points of each sub-calculation domain;
The expression of the HASM main equation is as follows:
，
Wherein, The resolution used in performing surface simulation for the sub-computational domain; /(I)Representing the number of iterations of the HASM principal equation,/>Is a positive integer; matrix/>、/>Coefficient matrix in the sub-calculation domain for the HASM main equation, T represents matrix transposition; Simulation of the first/>, for curved surfaces Performing iteration values; vector/>、/>Is the right end term of the sub-calculation domain of the HASM main equation in the nth iteration; /(I)The weight coefficient of the sampling points is calculated according to the mutual position relation among the sampling points; matrix/>Sum vector/>Respectively establishing a sampling coefficient matrix and a sampling right-end item according to the sampling points;
Is provided with ，/>The HASM main process is simplified as:
，
And solving a HASM main equation of each sub-calculation domain, and recording the simulation value of each sub-calculation domain, the resolution used in the curved surface simulation and the region origin coordinates of each sub-calculation domain to obtain the simulation result of the target ecological environment region.
The processor may be a general purpose processor including a central processing unit (central processing unit, CPU for short), a network processor (Network Processor, NP for short), etc., or may be a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), an off-the-shelf programmable gate array (FPGA) or other programmable logic device, discrete gate or transistor logic device, discrete hardware components. The disclosed methods, steps, and logic blocks in the embodiments of the present application may be implemented or performed. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The electronic device of the embodiments of the present application exists in a variety of forms including, but not limited to:
(1) A mobile communication device: such devices are characterized by mobile communication capabilities and are primarily aimed at providing voice, data communications. Such terminals include: smart phones (e.g., iPhone), multimedia phones, functional phones, and low-end phones, etc.
(2) Ultra mobile personal computer device: such devices are in the category of personal computers, having computing and processing functions, and generally also having mobile internet access characteristics. Such terminals include: PDA, MID, and UMPC devices, etc., such as iPad.
(3) Portable entertainment device: such devices may display and play multimedia content. The device comprises: audio, video players (e.g., iPod), palm game consoles, electronic books, and smart toys and portable car navigation devices.
(4) And (3) a server: the configuration of the server includes a processor, a hard disk, a memory, a system bus, and the like, and the server is similar to a general computer architecture, but is required to provide highly reliable services, and thus has high requirements in terms of processing capacity, stability, reliability, security, scalability, manageability, and the like.
(5) Other electronic devices with data interaction function.
It should be noted that, according to implementation requirements, each component/step described in the embodiments of the present application may be split into more components/steps, and two or more components/steps or part of operations of the components/steps may be combined into new components/steps, so as to achieve the purposes of the embodiments of the present application.
The above-described methods according to embodiments of the present application may be implemented in hardware, firmware, or as software or computer code storable in a recording medium such as a CD ROM, RAM, floppy disk, hard disk, or magneto-optical disk, or as computer code originally stored in a remote recording medium or a non-transitory machine storage medium downloaded through a network and to be stored in a local recording medium, so that the methods described herein may be stored in such software processes on a recording medium using a general purpose computer, a special purpose processor, or programmable or dedicated hardware such as an ASIC or FPGA. It is understood that a computer, processor, microprocessor controller, or programmable hardware includes a memory component (e.g., RAM, ROM, flash memory, etc.) that can store or receive software or computer code that, when accessed and executed by the computer, processor, or hardware, implements the methods of optimizing the adaptive algorithm-based high-precision surface modeling methods described herein.
Furthermore, when a general purpose computer accesses code for implementing the methods illustrated herein, execution of the code converts the general purpose computer into a special purpose computer for performing the methods illustrated herein.
Those of ordinary skill in the art will appreciate that the elements and method steps of the examples described in connection with the embodiments disclosed herein can be implemented as electronic hardware, or as a combination of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the embodiments of the present application.
It should be noted that, all embodiments in this specification are described in a progressive manner, and identical and similar parts between the embodiments are all mutually referred to. In particular, for the apparatus and system embodiments, since they are substantially similar to the method embodiments, the description is relatively simple, with reference to the description of the method embodiments in part.
The above-described apparatus and system embodiments are merely illustrative, in which elements illustrated as separate elements may or may not be physically separate, and elements illustrated as elements may or may not be physical elements, may be located in one place, or may be distributed over a plurality of network elements. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment. Those of ordinary skill in the art will understand and implement the present invention without undue burden.
The above description is only of the preferred embodiments of the present application and is not intended to limit the present application, but various modifications and variations can be made to the present application by those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the protection scope of the present application.
Claims (8)
1. An optimization method of a high-precision curved surface modeling method based on an adaptive algorithm is characterized by comprising the following steps:
Acquiring sampling points in a target ecological environment area, and taking the target ecological environment area as a calculation domain of a high-precision curved surface modeling method HASM;
based on the area range of the sampling points in the target ecological environment area, carrying out area quadtree subdivision on the calculation domains by adopting a preprocessing idea in an adaptive algorithm to obtain a plurality of sub-calculation domains, and recording the sampling points contained in each sub-calculation domain and the subdivision times experienced by each sub-calculation domain ；
Determining an initial field, parameters and an initial resolution H of a HASM method;
according to the initial resolution And the number of subdivisions experienced by each of the sub-computational domains/>Determining the resolution h used when each sub-calculation domain performs surface simulation, wherein/>Constructing a HASM main equation of each sub-calculation domain based on the resolution h used when the sub-calculation domains perform curved surface simulation and the sampling points of each sub-calculation domain;
The expression of the HASM main equation is as follows:
，
Wherein, The resolution used in performing surface simulation for the sub-computational domain; /(I)Representing the number of iterations of the HASM main equation,Is a positive integer; matrix/>、/>Coefficient matrix in the sub-calculation domain for the HASM main equation, T represents matrix transposition; /(I)Simulation of the first/>, for curved surfacesPerforming iteration values; vector/>、/>Is the right end term of the sub-calculation domain of the HASM main equation in the nth iteration; /(I)The weight coefficient of the sampling points is calculated according to the mutual position relation among the sampling points; matrix/>Sum vector/>Respectively establishing a sampling coefficient matrix and a sampling right-end item according to the sampling points;
Is provided with ，/>The HASM main process is simplified as:
，
Solving a HASM main equation of each sub-calculation domain, and recording simulation values of each sub-calculation domain, resolution used in surface simulation and region origin coordinates of each sub-calculation domain to obtain simulation results of the target ecological environment region.
2. The optimization method of the high-precision curved surface modeling method based on the adaptive algorithm according to claim 1, wherein the computing domain is subdivided into a plurality of sub-computing domains by adopting a preprocessing idea in the adaptive algorithm based on the region range of the sampling points in the target ecological environment region, and the sub-computing domains are specifically:
step S21: taking the calculation domain as a current calculation domain, or taking any one of the sub calculation domains from a plurality of sub calculation domains as the current calculation domain;
step S22: judging whether sampling points contained in the current calculation domain meet preset judging conditions or not;
Step S23: if the sampling points contained in the current calculation domain meet the preset judging conditions, recording the sampling points contained in the current calculation domain and the subdivision times experienced by the current calculation domain in a data organization form of a quadtree;
Step S24: if the sampling points contained in the current calculation domain do not meet the preset judgment conditions, further judging whether the subdivision times corresponding to the current calculation domain reach the preset subdivision times upper limit or not;
step S25: if the subdivision times corresponding to the current calculation domain reach the preset subdivision times upper limit, recording sampling points contained in the current calculation domain and subdivision times experienced by the current calculation domain in a data organization form of a quadtree;
step S26: if the subdivision times corresponding to the current calculation domain do not reach the preset subdivision times upper limit, continuously conducting regional quadtree subdivision on the current calculation domain to obtain a plurality of new sub-calculation domains, and iteratively executing the step S21 and the following steps until sampling points of all calculation domains or sub-calculation domains meet preset judgment conditions, or the subdivision times corresponding to the calculation domains or the sub-calculation domains reach the preset subdivision times upper limit, and ending iteration.
3. The optimization method of the adaptive algorithm-based high-precision surface modeling method according to claim 1, wherein a HASM main equation of each of the sub-calculation domains is solved using a HASM direct solution or a conjugate gradient solution.
4. An optimization device of a high-precision curved surface modeling method based on an adaptive algorithm is characterized by comprising the following components:
the acquisition unit is configured to acquire sampling points in the target ecological environment area and take the target ecological environment area as a calculation domain of the high-precision curved surface modeling method HASM;
The subdivision unit is configured to subdivide the computation domain into a plurality of sub-computation domains by adopting a preprocessing idea in an adaptive algorithm based on the region range of the sampling points in the target ecological environment region to obtain a plurality of sub-computation domains, and record the sampling points contained in each sub-computation domain and the subdivision times experienced by each sub-computation domain ；
A determining unit configured to determine an initial field, parameters, and an initial resolution H of the HASM method;
A construction unit configured to, according to the initial resolution And the number of subdivisions experienced by each of the sub-computational domainsDetermining the resolution h used when each sub-calculation domain performs surface simulation, wherein/>Constructing a HASM main equation of each sub-calculation domain based on the resolution h used when the sub-calculation domains perform curved surface simulation and the sampling points of each sub-calculation domain;
The expression of the HASM main equation is as follows:
，
Wherein, The resolution used in performing surface simulation for the sub-computational domain; /(I)Representing the number of iterations of the HASM main equation,Is a positive integer; matrix/>、/>Coefficient matrix in the sub-calculation domain for the HASM main equation, T represents matrix transposition; /(I)Simulation of the first/>, for curved surfacesPerforming iteration values; vector/>、/>Is the right end term of the sub-calculation domain of the HASM main equation in the nth iteration; /(I)The weight coefficient of the sampling points is calculated according to the mutual position relation among the sampling points; matrix/>Sum vector/>Respectively establishing a sampling coefficient matrix and a sampling right-end item according to the sampling points;
Is provided with ，/>The HASM main process is simplified as:
，
And the simulation unit is configured to solve a HASM main equation of each sub-calculation domain, record simulation values of each sub-calculation domain, resolution used in curved surface simulation and region origin coordinates of each sub-calculation domain, and obtain a simulation result of the target ecological environment region.
5. The optimization apparatus of the adaptive algorithm-based high-precision surface modeling method according to claim 4, wherein the subdivision unit is further configured to:
step S21: taking the calculation domain as a current calculation domain, or taking any one of the sub calculation domains from a plurality of sub calculation domains as the current calculation domain;
step S22: judging whether sampling points contained in the current calculation domain meet preset judging conditions or not;
Step S23: if the sampling points contained in the current calculation domain meet the preset judging conditions, recording the sampling points contained in the current calculation domain and the subdivision times experienced by the current calculation domain in a data organization form of a quadtree;
Step S24: if the sampling points contained in the current calculation domain do not meet the preset judgment conditions, further judging whether the subdivision times corresponding to the current calculation domain reach the preset subdivision times upper limit or not;
step S25: if the subdivision times corresponding to the current calculation domain reach the preset subdivision times upper limit, recording sampling points contained in the current calculation domain and subdivision times experienced by the current calculation domain in a data organization form of a quadtree;
step S26: if the subdivision times corresponding to the current calculation domain do not reach the preset subdivision times upper limit, continuously conducting regional quadtree subdivision on the current calculation domain to obtain a plurality of new sub-calculation domains, and iteratively executing the step S21 and the following steps until sampling points of all calculation domains or sub-calculation domains meet preset judgment conditions, or the subdivision times corresponding to the calculation domains or the sub-calculation domains reach the preset subdivision times upper limit, and ending iteration.
6. The optimization device of the adaptive algorithm-based high-precision surface modeling method according to claim 4, wherein a HASM direct algorithm or a conjugate gradient method is used to solve a HASM main equation of each of the sub-calculation domains.
7. A computer-readable storage medium having stored thereon a computer program, wherein the computer program is the optimization method of the adaptive algorithm-based high-precision surface modeling method according to any one of claims 1 to 3.
8. An electronic device, comprising: the optimization method for the high-precision curved surface modeling method based on the adaptive algorithm according to any one of claims 1-3 is realized when the processor executes the program.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202410276207.5A CN117875091B (en) | 2024-03-12 | 2024-03-12 | Optimization method and device of high-precision curved surface modeling method based on adaptive algorithm |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202410276207.5A CN117875091B (en) | 2024-03-12 | 2024-03-12 | Optimization method and device of high-precision curved surface modeling method based on adaptive algorithm |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN117875091A CN117875091A (en) | 2024-04-12 |
| CN117875091B true CN117875091B (en) | 2024-05-10 |
Family
ID=90595251
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202410276207.5A Active CN117875091B (en) | 2024-03-12 | 2024-03-12 | Optimization method and device of high-precision curved surface modeling method based on adaptive algorithm |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN117875091B (en) |
Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR100848304B1 (en) * | 2007-02-14 | 2008-07-24 | 중앙대학교 산학협력단 | Apparatus for nurbs surface rendering of sculpting deformation effect using the multi-resolution curved surface trimming |
| CN106683185A (en) * | 2017-01-09 | 2017-05-17 | 中国科学院地理科学与资源研究所 | High accuracy surface modeling method based on big data |
| CN108629135A (en) * | 2018-05-11 | 2018-10-09 | 中国水利水电科学研究院 | Non- unified high-precision curved grid flow simulation of water quality and method for visualizing and system |
| CN114373058A (en) * | 2021-12-29 | 2022-04-19 | 四川知周科技有限责任公司 | Sea surface mesh dynamic division and height field generation method based on illusion engine |
| CN115374682A (en) * | 2022-10-25 | 2022-11-22 | 中国科学院地理科学与资源研究所 | Space-time collaborative high-precision curved surface modeling method and system |
| CN116310135A (en) * | 2023-04-11 | 2023-06-23 | 广州市易鸿智能装备有限公司 | Curved surface display method and system based on multi-resolution LOD model |
| CN117115393A (en) * | 2023-07-24 | 2023-11-24 | 浙江大学 | NURBS curved surface parallel intersection method, equipment and storage medium based on GPU |
-
2024
- 2024-03-12 CN CN202410276207.5A patent/CN117875091B/en active Active
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR100848304B1 (en) * | 2007-02-14 | 2008-07-24 | 중앙대학교 산학협력단 | Apparatus for nurbs surface rendering of sculpting deformation effect using the multi-resolution curved surface trimming |
| CN106683185A (en) * | 2017-01-09 | 2017-05-17 | 中国科学院地理科学与资源研究所 | High accuracy surface modeling method based on big data |
| CN108629135A (en) * | 2018-05-11 | 2018-10-09 | 中国水利水电科学研究院 | Non- unified high-precision curved grid flow simulation of water quality and method for visualizing and system |
| CN114373058A (en) * | 2021-12-29 | 2022-04-19 | 四川知周科技有限责任公司 | Sea surface mesh dynamic division and height field generation method based on illusion engine |
| CN115374682A (en) * | 2022-10-25 | 2022-11-22 | 中国科学院地理科学与资源研究所 | Space-time collaborative high-precision curved surface modeling method and system |
| CN116310135A (en) * | 2023-04-11 | 2023-06-23 | 广州市易鸿智能装备有限公司 | Curved surface display method and system based on multi-resolution LOD model |
| CN117115393A (en) * | 2023-07-24 | 2023-11-24 | 浙江大学 | NURBS curved surface parallel intersection method, equipment and storage medium based on GPU |
Non-Patent Citations (2)
| Title |
|---|
| Wenjiao Shi ; Tianxiang Yue ; Zong Wang.High accuracy surface modeling method combined with auxiliary variables for soil mapping.2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS).2017,6083-6086. * |
| 高精度曲面建模的三维地形可视化研究;王世海;岳天祥;;武汉大学学报(信息科学版);20090105(第01期);全文 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN117875091A (en) | 2024-04-12 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Ronchi et al. | The “cubed sphere”: A new method for the solution of partial differential equations in spherical geometry | |
| Nino Ruiz et al. | An efficient implementation of the ensemble Kalman filter based on an iterative Sherman–Morrison formula | |
| CN106021567A (en) | Mass vector data partition method and system based on Hadoop | |
| CN106683185B (en) | High-precision curved surface modeling method based on big data | |
| CN113918837B (en) | Method and system for generating city interest point category representation | |
| CN111489447B (en) | Right-angle grid adaptive modeling method suitable for lattice Boltzmann method | |
| Deng et al. | An efficient online direction-preserving compression approach for trajectory streaming data | |
| Vermeulen et al. | A comparative study of k‐nearest neighbour techniques in crowd simulation | |
| CN110084865B (en) | Method for fitting regular grid by discrete point grading weighting | |
| JP2022533704A (en) | Classifying Patterns in Electronic Circuit Layouts Using Machine Learning-Based Encoding | |
| CN116629079B (en) | Method and device for constructing mixed finite element space and solving linear elastic mechanical problem | |
| Behrens et al. | Efficiency considerations in triangular adaptive mesh refinement | |
| CN109974716A (en) | Geographical location matching process and device, storage medium, server | |
| Zhou et al. | A fast multipole method accelerated adaptive background cell-based domain integration method for evaluation of domain integrals in 3D boundary element method | |
| CN117875091B (en) | Optimization method and device of high-precision curved surface modeling method based on adaptive algorithm | |
| CN107506572B (en) | Method and device for acquiring height of target point | |
| CN117540552A (en) | Simulation space open-view analysis method, device and equipment based on grid subdivision | |
| CN116051699B (en) | Dynamic capture data processing method, device, equipment and storage medium | |
| Huck et al. | Visualizing patterns in spatially ambiguous point data | |
| CN111898819B (en) | Space grid dividing method and device | |
| Qi et al. | A Bio-Inspired Algorithm for Maximum Matching in Bipartite Graphs. | |
| Hudek et al. | Ancestral sequence alignment under optimal conditions | |
| Lv et al. | The application of a complex composite fractal interpolation algorithm in the seabed terrain simulation | |
| KR20210074550A (en) | Spatial Bigdata Processing System for SMART CITY SERVICE | |
| CN117234524B (en) | Quantum cloud computing compiling method and device |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |