CN102034270A - Chinese road spectrum database-based typical pavement extraction and synthesis method - Google Patents
Chinese road spectrum database-based typical pavement extraction and synthesis method Download PDFInfo
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- CN102034270A CN102034270A CN 201010595348 CN201010595348A CN102034270A CN 102034270 A CN102034270 A CN 102034270A CN 201010595348 CN201010595348 CN 201010595348 CN 201010595348 A CN201010595348 A CN 201010595348A CN 102034270 A CN102034270 A CN 102034270A
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Abstract
The invention discloses a Chinese road spectrum database-based typical pavement extraction and synthesis method, which is characterized by comprising the following steps of: extracting pavement characteristic parameters; calculating a normalized area by using a radar chart analysis method; extracting an optimized pavement sample by using a mean target analysis method and extracting an optimized pavement sample again by using a standard deviation target analysis method; synthesizing a representative typical road, and the like. The invention has the advantages that: due to a typical pavement extraction and synthesis algorithm, by extracting and statistically analyzing the characteristic parameters of mass pavement test data in a road spectrum database, a representative highly-concentrated typical road is synthesized under the matching of a radar chart analysis tool. The typical road can be used for performance test of various test stands in automotive engineering, and also can be used for a variety of virtual simulation software to form a representative three-dimensional digital road with regional characteristics.
Description
Technical field
The present invention relates to the extraction and the synthetic technology of road data, specifically, is that extract and synthetic method on a kind of typical road surface based on Chinese road spectrum database.
Background technology
Along with developing rapidly of automobile industry, China has become world car commercial production big country, has passed by imitated, technology introduction and processing stage, has entered the new stage of independent research, autonomous innovation.In the automobile engineering field in independent research stage, the road surface data are the indoor various test stand performance tests or the support of all too busy to get away basic road surface of the virtual emulation analysis in computing machine data as the main external source of vehicle.
China's road spectrum database is under Ministry of Science and Technology's 863 Program project support, launch in China to carry out the achievement that statistical measurement obtains at each regional typical pavement of road of China, promptly measure in measurement collecting device road pavement by special use, obtain after a large amount of road surface data, these road surface data are carried out preliminary pre-service calculate directly available basic road surface data, the organic organization and management of these road surface data is got up just to have formed Chinese road spectrum database.
In present Vehicle Engineering, a large amount of uses all is certain very local somewhere bar Surface of Trial Pavement in the test simulation, perhaps directly in computing machine emulation generate a road surface, these road surface data all can not these regional pavement of road characteristics of authentic representative, therefore in engineering, be starved of a typical road surface, can not be oversize but have the various road surface characteristic of this area's road in this road surface, promptly has typicalness, can represent this regional road surface characteristics, and also lack a kind of method that typical road surface is extracted and synthesized that is used in the prior art.
Summary of the invention
Needs based on the engineering application, the purpose of this invention is to provide a kind of extraction of typical road surface and synthetic method, in at Chinese road spectrum database on the basis of a large amount of pavement test data statistic analysis, refine and synthesized the typical road surface that to represent certain area, be widely used in various tests of automobile and virtual emulation analysis.
For reaching above purpose, the technical solution used in the present invention is as follows:
Extract and synthetic method on a kind of typical road surface based on Chinese road spectrum database, may further comprise the steps:
The first step (S1): road surface characteristic parameter extraction
What therefrom N the index in M sample highway section of extraction extracted as typical road surface in state's road spectrum database
Underlying parameter, M and N are positive integer;
Second step (S2): utilize the radar map analytic approach to calculate normalized area, comprising:
S2-1: the data of N the index in M sample highway section among the step S1 are drawn in the radar map, calculate
The N dimension average closed loop curve of underlying parameter;
S2-2: according to
N index of underlying parameter carried out the dimension unification, wherein
Represent i the sample value of data highway section on the j dimension,
Represent the average on the j dimension,
Represent i sample highway section dimension sample parameter value after reunification, i=1 ..., M, j=1 ..., N;
S2-3: according to
Calculate the closed loop area in M sample highway section respectively, wherein,
Be the closed loop area in i data highway section,
The 3rd step (S3): use average target analysis method to extract optimum highway section sample, comprising:
S3-1: according to the closed loop area that draws M sample highway section among the S2-3
Size sort, obtain the sample area collating sequence;
S3-2: the length on the synthetic road surface of target setting is
Km, the length in combination highway section is
Km, thus determine to make up the number in highway section
, according to combination highway section number
On the sample area collating sequence be with length
Carry out the equal length grouping, wherein
Integral part;
S3-3: in each sample group of step S3-2 gained
Utilize average target analysis method to determine optimized in the bar highway section
Bar sample highway section,
Value as required the configuring condition of the length of composite links and computing machine determine, and
The 4th step (S4): use standard deviation target analysis method to extract optimization highway section sample once more:
S4-1: what every group was chosen from S3-3
Randomly draw a highway section and other in the bar sample highway section
Group
The middle highway section of extracting combines, and forms
The target sample highway section of bar highway section combination reaches the length in the synthetic highway section of target, and calculates the standard deviation of this combination highway section supplemental characteristic, selects standard deviation in all combinations near the combination of the sample target sample highway section as final extraction;
S4-2: with the time domain sequences data random alignment in the target sample highway section that extracts among the S4-1 and be connected smoothly, generate the synthetic road surface on final typical road surface.
The user is by analyzing a large amount of pavement test data in the Chinese typical automotive road spectrum database, on the basis of analyzing at the road surface characteristic parametric statistics, use the radar map analysis tool, progressively extract the typical highway section that can represent this area's pavement of road characteristics, can obtain the typical road surface that needs by the typical highway section of selecting being carried out organic smooth connection.
A described N index comprises 8 scale-up factors of road surface standard fluctuating, eight grades of classifications of international roughness index and road surface.
Described average target analysis method extract optimum highway section sample according to
Described standard deviation target analysis method is extracted in the sample of optimization highway section once more, according to
Remarkable result of the present invention is: at the great number tested data of Chinese road spectrum database, acquisition can characterize the characteristic parameter of road surface characteristics, on the basis of statistical study, use the radar map analysis tool, rationally synthesized typical road surface representative, that highly concentrate efficiently.This typical case road surface can be used for the road surface input of various test stand performance tests in the automobile engineering and uses; Also can be used in the various virtual emulation softwares, be formed with region characteristic, representational three-dimensional digital road surface.
Description of drawings
Fig. 1 is that Chinese road spectrum database is formed structural representation;
Fig. 2 is ten parameter lists of feature of pavement of road;
Fig. 3 is the radar map of all highway section characteristic parameters of Beijing area;
Fig. 4 is the typical highway section synoptic diagram that extracts;
Fig. 5 is synthetic later typical road surface curve synoptic diagram.
Embodiment
Hereinafter with reference to accompanying drawing, embodiments of the invention and principle of work thereof are described in detail.
Extract and synthetic method on a kind of typical road surface based on Chinese road spectrum database, may further comprise the steps:
The first step (S1): road surface characteristic parameter extraction
What therefrom N the index in M sample highway section of extraction extracted as typical road surface in state's road spectrum database
Underlying parameter, M and N are positive integer;
As shown in Figure 1, the structure of Chinese road spectrum database has shown the coverage and the level of database
Structure in specific implementation process, is analyzed great number tested data in the database by the query interface of Chinese road spectrum database, and here N gets 10, promptly extracts the underlying parameter that 10 indexs are extracted as this typical case road surface.
As shown in Figure 2, described 10 indexs comprise that the road surface standard rises and falls (standard deviation of road roughness curve), 8 scale-up factors of eight grades of classifications of international roughness index and road surface.
Second step (S2): utilize the radar map analytic approach to calculate normalized area, comprising:
S2-1: the data of N the index in M sample highway section among the step S1 are drawn in the radar map, calculate
The N dimension average closed loop curve of underlying parameter;
Road surface data instance with the Beijing area, it is 831.9 kilometers that the highway section is effectively measured in whole Beijing area, be one with 100 meters and analyze the segment length, just will have 8319 analyzing samples data, be M=8319,10 achievement datas of these road surface data all are drawn in the radar map, and its result as shown in Figure 3.
S2-2: according to
10 indexs of underlying parameter are carried out the dimension unification, wherein
Represent i the sample value of data highway section on the j dimension,
Represent the average on the j dimension,
Represent i sample highway section dimension sample parameter value after reunification, i=1 here ..., 8319, and j=1 ..., 10;
S2-3: according to
Calculate the closed loop area in M sample highway section respectively, wherein, N=10, M=8319,
Be the closed loop area in i data highway section,
The 3rd step (S3): use average target analysis method to extract optimum highway section sample, comprising:
S3-1: according to the closed loop area that draws M sample highway section among the S2-3
Size sort, obtain the sample area collating sequence;
S3-2: the length on the synthetic road surface of target setting is
Km, the length in combination highway section is
Km, thus determine to make up the number in highway section
, according to combination highway section number
On the sample area collating sequence be with length
Carry out the equal length grouping, wherein
Integral part, 8319 highway sections that are about in the area collating sequence are divided into 20 groups,
, in concrete processing procedure, remove preceding 9 data and back 10 data in the sequence, 8300 data in the middle of choosing are by every group
=415 data are divided into 20 groups;
S3-3: in each sample group of step S3-2 gained
Utilize average target analysis method to determine optimized in the bar highway section
Bar sample highway section,
Value as required the configuring condition of the length of composite links and computing machine determine, and
, generalized case
, we get here
So-called average target analysis method extract optimum highway section sample according to
The 4th step (S4): use standard deviation target analysis method to extract optimization highway section sample once more:
S4-1: randomly draw a highway section and other in 2 sample highway sections that every group is chosen from S3-3
Group
Middle extraction
The bar highway section combines, form the target sample highway section of n bar highway section combination, reach the length in the synthetic highway section of target, and calculate the standard deviation of this combination highway section supplemental characteristic, select standard deviation in all combinations near the combination of sample as the final target sample highway section of extracting, finally choose 20 sample highway sections shown in Fig. 4 as the target sample highway section;
Described standard deviation target analysis method is extracted in the sample of optimization highway section once more, according to
S4-2: with the time domain sequences data random alignment in the target sample highway section that extracts among the S4-1 and be connected smoothly, generate the synthetic road surface on final typical road surface, its result as shown in Figure 5.
Principle of work of the present invention is:
The extraction on typical case road surface is actually a multi-objective optimization question with synthetic, and promptly ten dimension data in the radar map are ten objective optimization problems.In order to make optimization more effective succinct with simplification, if multi-objective optimization question can be reduced to the single goal optimization problem, then can simplify calculating greatly, therefore expect calculating the area of the broken line closed loop that ten dimension road surface supplemental characteristics form, use this area to weigh and estimate and extract the highway section, so just multiple goal (ten) optimization problem is reduced to the single goal optimization problem.When reference area, because the meaning and the unit of the representative of the physical quantity on each dimension data axle of radar map are all inequality, so each dimension goes up the full scale of supplemental characteristic and the step-length of scale has just seriously influenced broken line closed loop area size, therefore need to adopt a kind of method to unify the dimension of whole ten dimension data, even find a relation of equivalence between the scale of each dimension.This algorithm has adopted the dimension unified approach of " average equivalence ", and the data on every dimension data axle are all divided by the average of this data axle, and the area dimension that reaches the broken line closed loop is unified.
In the sample data of each grouping, suppose to influence the road surface factor have ten dimensions promptly 10 targets be respectively
,
...,
, its optimal value is all arranged for each objective function
,
Be the mean value of each dimension.If the optimum solution correspondence of all these targets
All identical, be made as
, need only so
This point, all targets all reach optimal value separately simultaneously, otherwise, with regard to vector function
, vector
Only be an ideal point, what we will do looks for a bit on every dimension exactly, make with
The deviation minimum, this moment, the optimal value of target was just near more from ideal point, separated just more excellently, so we can define a mould at this 10 dimension space:
Utilize the normalization principle once more, can get:
Herein, weight coefficient
=1, get noninferior solution
=2,
,
,
Represent maximal value, minimum value and mean value on the decagon i dimension respectively.
This distance of the more little expression of value more little, just more near ideal point.Its optimization method more can embody the otherness between the data.
Adopt standard deviation target analysis method, n ' the bar candidate road section that each grouping is selected is randomly drawed out one, the highway section of extracting with other grouping combines, and forms the synthetic highway section of target.Calculate the standard deviation of this each dimension data of synthetic highway section then, pass through again
Calculate the difference degree of synthetic each dimension data standard deviation of highway section and each dimension data standard deviation of whole sample, in the formula
The standard deviation on each dimension of highway section data, the i.e. standard deviation of small sample are selected in expression;
Represent the standard deviation on each dimension of all highway section data, i.e. the standard deviation of large sample.
Be worth more for a short time, the difference of small sample standard deviation and large sample standard deviation is just more little.At the various combined situation of candidate road section, choose
Highway section combination for hour is the typical highway section combination of finally picking out.
The above embodiment of the present invention only is in order to explain and to illustrate, its purpose is not that the present invention is limited to the scope that specifies, can also carry out conspicuous change or modification according to mentioned above principle, therefore, all these type of modifications and changes are all within the claim that the present invention limited.
Claims (4)
1. extract and synthetic method on the typical road surface based on Chinese road spectrum database, and its feature is in may further comprise the steps:
The first step (S1): road surface characteristic parameter extraction
What therefrom N the index in M sample highway section of extraction extracted as typical road surface in state's road spectrum database
Underlying parameter, M and N are positive integer;
Second step (S2): utilize the radar map analytic approach to calculate normalized area, comprising:
S2-1: the data of N the index in M sample highway section among the step S1 are drawn in the radar map, calculate
The N dimension average closed loop curve of underlying parameter;
S2-2: according to
N index of underlying parameter carried out the dimension unification, wherein
Represent i the sample value of data highway section on the j dimension,
Represent the average on the j dimension,
Represent i sample highway section dimension sample parameter value after reunification, i=1 ..., M, j=1 ..., N;
S2-3: according to
Calculate the closed loop area in M sample highway section respectively, wherein,
Be the closed loop area in i data highway section,
The 3rd step (S3): use average target analysis method to extract optimum highway section sample, comprising:
S3-1: according to drawing M sample highway section closed loop area among the S2-3
Size sort, obtain the sample area collating sequence;
S3-2: the length on the synthetic road surface of target setting is
Km, the length in combination highway section is
Km, thus determine to make up the number in highway section
, according to combination highway section number
On the sample area collating sequence be that L carries out the equal length grouping, wherein with length
Integral part;
S3-3: in each sample group of step S3-2 gained
Utilize average target analysis method to determine optimized in the bar highway section
Bar sample highway section,
Value as required the configuring condition of the length of composite links and computing machine determine, and
The 4th step (S4): use standard deviation target analysis method to extract optimization highway section sample once more:
S4-1: what every group was chosen from S3-3
Randomly draw a highway section and other in the bar sample highway section
Group
The middle highway section of extracting is combined to form
The target sample highway section of bar highway section combination reaches the length in the synthetic highway section of target, and calculates the standard deviation of this combination highway section supplemental characteristic, selects standard deviation in all combinations near the combination of the sample target sample highway section as final extraction;
S4-2: with the time domain sequences data random alignment in the target sample highway section that extracts among the S4-1 and be connected smoothly, generate the synthetic road surface on final typical road surface.
2. extract and synthetic method on the typical road surface based on Chinese road spectrum database according to claim 1, and it is characterized in that: a described N index comprises 8 scale-up factors of road surface standard fluctuating, eight grades of classifications of international roughness index and road surface.
3. extract and synthetic method on the typical road surface based on Chinese road spectrum database according to claim 1, it is characterized in that: described average target analysis method extract optimum highway section sample according to
4. extract and synthetic method on the typical road surface based on Chinese road spectrum database according to claim 1, and it is characterized in that: described standard deviation target analysis method is extracted in the sample of optimization highway section once more, according to
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102222141A (en) * | 2011-06-10 | 2011-10-19 | 中国汽车工程研究院股份有限公司 | Method for generation of typical road spectrum |
| CN103981795A (en) * | 2014-05-28 | 2014-08-13 | 江苏科技大学 | Method for implementing road spectrum soft measurement by using vehicle suspension sensor |
| CN104233935A (en) * | 2014-08-28 | 2014-12-24 | 吉林大学 | Identification method for pavement quality grade on basis of information of longitudinal section of road |
| CN116479718A (en) * | 2023-05-06 | 2023-07-25 | 宁波中海建材有限公司 | Intelligent concrete processing system based on area detection |
Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101877007A (en) * | 2010-05-18 | 2010-11-03 | 南京师范大学 | Remote sensing image retrieval method with integration of spatial direction relation semanteme |
-
2010
- 2010-12-20 CN CN 201010595348 patent/CN102034270B/en active Active
Patent Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101877007A (en) * | 2010-05-18 | 2010-11-03 | 南京师范大学 | Remote sensing image retrieval method with integration of spatial direction relation semanteme |
Non-Patent Citations (5)
| Title |
|---|
| 《2010 2nd International Conference on Signal Processing Systems (ICSPS)》 20100707 Duan Hu-ming et al Signal Trend Extraction of Road Surface Profile Measurement 694-698 , * |
| 《IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY》 20091130 Wenjing Wang et al Small-Scale and Large-Scale Routing in Vehicular Ad Hoc Networks 5200-5213 第58卷, 第9期 * |
| 《中国测试》 20090731 段虎明 等 中国典型汽车道路谱研究的构建及应用 76-79 第35卷, 第4期 * |
| 《电子测量与仪器学报》 20100131 段虎明 等 道路谱测量检测技术研究综述 72-79 第24卷, 第1期 * |
| 《电子测量与仪器学报》 20100930 马颖 等 道路路面测量数据中奇异信号识别和修正方法 878-884 第24卷, 第9期 * |
Cited By (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102222141A (en) * | 2011-06-10 | 2011-10-19 | 中国汽车工程研究院股份有限公司 | Method for generation of typical road spectrum |
| CN102222141B (en) * | 2011-06-10 | 2012-12-19 | 中国汽车工程研究院股份有限公司 | Method for generation of typical road spectrum |
| CN103981795A (en) * | 2014-05-28 | 2014-08-13 | 江苏科技大学 | Method for implementing road spectrum soft measurement by using vehicle suspension sensor |
| CN104233935A (en) * | 2014-08-28 | 2014-12-24 | 吉林大学 | Identification method for pavement quality grade on basis of information of longitudinal section of road |
| CN104233935B (en) * | 2014-08-28 | 2016-05-11 | 吉林大学 | A kind of pavement quality grade discrimination method based on profile of road information |
| CN116479718A (en) * | 2023-05-06 | 2023-07-25 | 宁波中海建材有限公司 | Intelligent concrete processing system based on area detection |
| CN116479718B (en) * | 2023-05-06 | 2023-09-19 | 宁波中海建材有限公司 | Intelligent concrete processing system based on area detection |
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