CN106599044A - Recognition and processing method for road network target information - Google Patents
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Abstract
The embodiment of the invention discloses a recognition and processing method for road network target information. The processing method comprises the steps that A, deletion points on arc segments of a spatial data road network are searched for; B, the deletion points found in the step A are deleted; C, projection points and fitting points on the arc segments of the spatial data road network are searched for; D, the found projection points and fitting points are clustered to all designated processing units; E, the projection points and the fitting points in the processing units are subjected to projection processing and fitting processing respectively. Therefore, the embodiment is beneficial for repairing and accurately reflecting the spatial relation between a library entry and a road network entity, and a topological structure more complete in logic is formed to meet the demand for automatic cartographic generalization.
Description
Technical field
The present invention relates to GIS-Geographic Information System cartographic generaliztion field, more particularly to it is a kind of comprehensive to meet computer autodraft
The identification of the road network target information of conjunction and processing method.
Background technology
Map is that it is appreciated that the instrument of the world and reforming world, its appearance significantly improves people to objective reality
The awareness of things and its surrounding enviroment.GIS-Geographic Information System is built upon on digitized map basis.With
Economic constantly development, people require more and more higher to the abundant degree of geographical information space data under different scale, because
The geographical information space data for galore storing each scale as far as possible are generally required in this data base, such as：National basis ground
Reason information system.On existing basic geographic database basis, how by large-scale map data base quickly and with high fidelity
Derive arbitrary small scale map data base, it has also become a study hotspot of Modern Cartology.
Road network is geographic element most basic in Map Expression, is to constitute NSII frame data
An important part, is for splitting other space cut-off rules for artificially managing key element, its significance level and use frequency on map
Rate is all higher than other General maps key elements.At present, the expression of any type of map all relies on road network, and road network is data
Most important class Linear element in storehouse, the quality of automated cartographic generalization is heavily dependent on road network automatic Synthesis, because
This research road network automatic Synthesis is very necessary and crucial.
The content of the invention
In view of this, present invention is primarily targeted at being directed to existing Large-scale Urban map data base road network data
The spatial relationship Problem-Error of presence, proposes identification and the processing method of a kind of road network target information, by constituting road
The characteristic target information of net topology relation is identified and processes, and to repair and accurately reflects road network inter-entity in data base
Spatial relationship, forms more perfect topological structure so as to meet the demand of automated cartographic generalization.
The embodiment of the present invention provides a kind of recognition methodss of road network target information, comprises the following steps：
A, the segmental arc of spatial data road network is set up into segmental arc R tree；
B, each the pre- intersecting paired segmental arc in the segmental arc tree for finding is set up into array；
C, for the array in arbitrary paired segmental arc and arbitrary paired segmental arc the first segmental arc on arbitrary composition section
Point, performs following steps：
A composition node in c1, the first segmental arc for judging in paired segmental arc whether the second segmental arc in the paired segmental arc
On；
C2, if it is not, then by each two in second segmental arc it is adjacent point composition line segment set up line segment R trees；
C3, search obtain the line segment in the range of the outside specified first threshold of the outsourcing frame of current composition node；
C4, using the composition node in first segmental arc as centre point, to specify Second Threshold to make buffering circle as radius；
C5, judge that the buffering circle searches for whether the line segment that obtains intersects with described；If so, intersecting line segment, circle are then recorded
Heart point, first segmental arc and second segmental arc；
C6, intersected with this according to the centre point line segment two-end-point distance and specify the 3rd threshold value relation, identification should
The type of centre point.
By upper, the automatic identification to road network target information is realized by said method.
Preferably, step c6 includes：
The distance of any in the two-end-point that the centre point intersects line segment with this is judged be less than the 3rd threshold value, and this two
The point in end points is located in first segmental arc, and the match point after any point in the centre point and the two-end-point is fitted with
When the distance of the next point of the first segmental arc is less than three threshold values, then the centre point is deletion point to the centre point.
By upper, realize to deleting identification a little in road network.
Preferably, step c6 includes：
When judging that the centre point intersects the distance of the two-end-point of line segment with this and is both greater than three threshold values, then the centre point
For subpoint.
By upper, the identification to subpoint in road network is realized.
Preferably, step c6 includes：
The distance of any in the two-end-point that the centre point intersects line segment with this is judged be less than the 3rd threshold value, and with this
When the distance of another point in the two-end-point of intersecting line segment is more than three threshold values；Or
When the distance that the centre point intersects the two-end-point of line segment with this is both less than three threshold values；
Then the centre point is match point.
By upper, the identification to match point in road network is realized.
Based on the recognition methodss of above-mentioned road network target information, the embodiment of the present invention additionally provides a kind of road network target letter
The processing method of breath, comprises the following steps：
D, the deletion point searched in the segmental arc of spatial data road network；
E, by the deletion point deletion found in step A；
F, the subpoint and match point searched in the segmental arc of spatial data road network；
G, the subpoint for finding and match point are clustered；
H, the subpoint and match point after cluster is carried out into respectively projection process and process of fitting treatment.
By upper, the process to road network target information is realized by said method.
Preferably, step G includes：
It is the center of circle by the arbitrary subpoint for finding or match point, to specify the 4th threshold value to draw buffering circle as radius；
By the subpoint in the 4th threshold range and match point cluster to designated treatment unit.
By upper, the cluster to subpoint and match point is realized.
Preferably, step H includes：
H1, judge whether there is subpoint in each processing unit successively；
H2, when judge have subpoint when, whether the number for determining whether subpoint is 1；
H3, when the number for judging subpoint is for 1, the subpoint is carried out into projection process；
H4, when judging the number of subpoint not for 1, whether the line segment for determining whether to project segmental arc is same；
H5, when judge project segmental arc line segment for same when, each subpoint is done and obtain after process of fitting treatment match point,
Again the match point is done into projection process to the projection segmental arc；
H6, when judge project segmental arc line segment not for same when, by each subpoint do after process of fitting treatment obtain be fitted
Point, then the match point is done into projection process to for finding apart from the nearest Projection Line Segment of the match point；
H7, when there is no subpoint during processing unit is judged in step H1, further determined whether match point；Work as judgement
When having match point, process is fitted.
By upper, the projection process and process of fitting treatment to the subpoint after cluster and match point is realized.
In sum, the application is by the identification and process to target information, i.e. by deleting point, subpoint, fitting
The identification and process of point, is conducive to repairing and accurately reflecting the spatial relationship of road network inter-entity in data base, is formed more perfect
Topological structure so as to meeting the demand of automated cartographic generalization.
Description of the drawings
In order to be illustrated more clearly that the embodiment of the present invention or technical scheme of the prior art, below will be to embodiment or existing
The accompanying drawing to be used needed for having technology description is briefly described, it should be apparent that, drawings in the following description are these
Some bright embodiments, for those of ordinary skill in the art, without having to pay creative labor, can be with
Other accompanying drawings are obtained according to these accompanying drawings.
Fig. 1 is gap and fragment type figure present in the library road network data of the present invention；
Fig. 2 is road network target information classification chart of the present invention；
Fig. 3 is road network target information recognition methodss block diagram of the present invention；
Fig. 4 is road network target information processing method and step figure of the present invention；
Fig. 5 is road network target information clustering processing method and step figure of the present invention；
Fig. 6 is experimental results comparison diagram of the present invention.
Specific embodiment
To make purpose, technical scheme and the advantage of the embodiment of the present invention clearer, below in conjunction with the embodiment of the present invention
In accompanying drawing, the technical scheme in the embodiment of the present invention is clearly and completely described, it is clear that described embodiment is
The a part of embodiment of the present invention, rather than the embodiment of whole.Based on the embodiment in the present invention, those of ordinary skill in the art
The every other embodiment obtained under the premise of creative work is not made, belongs to the scope of protection of the invention.
Automated cartographic generalization this task is completed, the concordance of spatial data is it is critical only that.Topological relation is spatial data
Conforming important content, the present invention has found the library data with Large-scale Urban map data base at this stage as object of study
Topological relation generally existing some problems, although the area target such as between different layers visually meets drawing demand,
Gap and fragment on existed facts, but in prior art automatic identification and reparation cannot be carried out to it, it is difficult to and satisfaction is taken into account
Automated cartographic generalization demand during essential factors space restriction relation.
To overcome defect of the prior art, the embodiment of the present application is for road network entity in Large-scale Urban data base
Topological relation there is the inconsistent phenomenon of the spatial relationships such as gap and fragment, a kind of, phase intersecting to road is proposed first
From the method that, intertexture target classification is recognized and carries out respective handling, the method achieve the automatization to spatial relationship and repair, have
The topological integrity that ensure that road network spatial data of effect.
There is gap and fragment in the topological relation of the spatial entities in heretofore described Large-scale Urban data base, such as
Shown in Fig. 1, gap and fragment are roughly divided into three types, intersect, from, interweave.Wherein：
Illustrate 1, topological attribute.When the scale of map changes, the size and shape of map can occur accordingly
Change, some properties of map graph also will change accordingly, the length of such as map graph, area, angle and mutually
Between relative distance.But some graphical properties then will not change, the adjacency of such as map graph, inclusive, phase
Geometric type (the such as point, line, surface type) property of the property handed over and key element.These keep constant property in figure consecutive variations
Referred to as topological attribute.
Illustrate 2, topology element.Entity in geographical space is varied, and its shape is also ever-changing, but general on ground
Represented with three kinds of key elements on figure, that is, put key element, line feature dough-making powder key element.In two dimensional surface, they can correspond to respectively three kinds
Basic graphic element, i.e. node, segmental arc dough-making powder domain, these three graphic elements are referred to as topology element.
Illustrate that 2.1 nodes include isolated point, the end points of segmental arc, the junction point of segmental arc, polygonal interior point and polygon
Boundary point etc..
Illustrate that 2.2 segmental arcs refer to the orderly line segment between two nodes, two nodes of segmental arc can be different nodes,
It can also be identical node.
Illustrate that 2.3 face domains refer to the polygonal region surrounded by a plurality of closure segmental arc chain, can be with polygonal shape come table
Show.
As shown in Fig. 2 being spatial entities target information classification schematic diagram.In figure, the target information of identification is divided into three major types,
The tertiary target information, specially：First kind target information is subpoint, the 3rd classification to delete point, Equations of The Second Kind target information
Mark information is match point, wherein：
Illustrate 1, delete point.As shown in Fig. 2 (a), segmental arc A intersects at point O with segmental arc B, and the upper point P of segmental arc A is to segmental arc B
Distance is less than threshold value, and the distance between point P, point O less than threshold value, then point P is called deletion point.
Illustrate 2, subpoint.As shown in Fig. 2 (b), segmental arc A, B is non-intersect, and the distance of point P to segmental arc B in segmental arc A is little
In threshold value, but P is more than threshold value with the distance of the point O and point O' of segmental arc B, then point P is called subpoint.
Illustrate 3, match point.As shown in Fig. 2 (c), segmental arc A, B is non-intersect, the point P in segmental arc A to the point O in segmental arc B
The distance between be less than threshold value, then point P and point O are called match point.
Illustrate 4, based process model.Based process model is divided into three kinds, the first, it is directly to delete point P to delete point, such as
Point P in Fig. 2 (a)；Second, subpoint is to be the subpoint P' that point P projects to segmental arc, and subpoint P' is inserted in segmental arc,
Point P moves on to subpoint P', the point P in such as Fig. 2 (b)；The third, match point has two kinds of processing modes, and all match points are fitted
Process, fit a new point P' or select a maximum point P' of weight, P' then will be a little moved on to, in such as Fig. 2 (c)
Point P and point O.
(1) subpoint computing formula
First, straight line coefficient k is sought：If the beginning and end of straight line is respectively A (x1, y1)、B(x2, y2), outside straight line a bit
For C (x0, y0), intersection point is D；And set k=| AD |/| AB |.ThenAnd becauseInstitute
With,ThereforeBring coordinate into, obtain final product,
Then subpoint D coordinates (x, y) is sought：
X=X1+k*(X2*X1) (2)
Y=y1+k*(y2*y1) (3)
(2) match point computing formula
Wherein, (xi, yi) (i=0,1,2 ..., n) are all fitting origins, and n is fitting origin number.
As shown in figure 3, be road network target information recognition methodss block diagram of the present invention, in order to realize quickly finding mesh
Mark information, sets up the R trees of segmental arc arc minimum enclosed rectangle frame, finds the paired segmental arc of possibly target information, then to into
Detailed judgement is carried out to the point in segmental arc, the efficiency of algorithm is improved.Wherein, minimum enclosed rectangle is referred to and represented with two-dimensional coordinate
Some two-dimensional shapes (such as point, straight line, polygon) maximum magnitude, i.e., with give each summit of two-dimensional shapes in maximum
Abscissa, minimum abscissa, maximum ordinate, minimum vertical coordinate fix the rectangle on border.Road network target recognition concrete steps
It is as follows：
S301, set up segmental arc tree.Specifically, segmental arc R of all of segmental arc arc minimum enclosed rectangle frame of spatial data is set up
Tree.
The segmental arc that the possibility in all of segmental arc arc in S302, segmental arc R tree intersects is matched, and obtains each paired
Segmental arc, specially：To the outsourcing frame of each segmental arc arc in all segmental arcs arc to the scope for extending out a threshold value, built
Scan in vertical segmental arc R tree, obtain possible intersecting therewith segmental arc with search and matched, by each the paired arc for obtaining
Section is put in the array that segmental arc may intersect.
S303, from array select paired segmental arc arcA and arcB.
A P points in S304, segmental arc arcA for selecting in the paired segmental arc.
S305, judge current P points whether on arcB；If so, S306 is then performed；If it is not, then performing S312.
If S306, present node P show that arcA intersects with arcB on arcB, segmental arc arcA and arcB are identified as
Intersecting result, the process to the point terminates；
S307, the P points whether having without S305 judgement process judged in current segmental arc arcA；If so, S308 is performed；
If it is not, performing S309.
S308, selects undressed P points in current segmental arc arcA, and returns execution S305；
S309, judges whether each paired segmental arc has the paired segmental arc processed without the selection of S303；If so, then perform
S310；If it is not, then performing 311.
S310, selects the paired segmental arc selected without S304, and returns execution S304.
S311, process terminates.
S312, when judging current P points not on arcB in step S305, carry out segmental arc identification.To currently paired segmental arc
Segmental arc arcB on the outsourcing frame of line segment segment of the adjacent point composition of each two set up R trees.
S313, by the outsourcing frame of point P in current segmental arc arcA to the range searching for extending out a threshold value, judging whether can
Line segment segment is searched in the threshold range.If so, S314 is then performed；If it is not, then return performing S307.
S314, is centre point by the current P points, to specify first threshold to make buffering circle as radius；
S315, judges whether the line segment segment that the buffering circle is searched with S313 intersects；If it is not, then performing S316；If
It is then to perform S317.
S316, do not keep a record process.
S317, record intersecting line segment, centre point P, current segmental arc arcA and current segmental arc arcB；
Which kind of that centre point P is belonging in tertiary target information type S318, further identification tell.
Specifically, by the terminal A and B of point P and line segment AB between PA, PB and threshold value DistanceEpsilon
Relation come recognize and differentiate point P is belonging in tertiary target information type which kind of：
1st, when PA less than DistanceEpsilon and point A on arcA and P and A match point with point P in the next of arcA
The distance of individual point (prolonging AP directions) is less than DistanceEpsilon, then point P is deletion point；
When PB less than DistanceEpsilon and point B on arcA and P and B match point and point P arcA the next one
The distance of point (prolonging BP directions) is less than DistanceEpsilon, then point P is deletion point；
2nd, when PA is more than DistanceEpsilon and PB is more than DistanceEpsilon, then point P is subpoint；
3rd, when PA is less than or equal to DistanceEpsilon and PB is more than DistanceEpsilon, then point P is match point；
When PA is more than DistanceEpsilon and PB is less than or equal to DistanceEpsilon, then point P is match point；
When PA is less than DistanceEpsilon and PB is less than DistanceEpsilon, then point P is match point.
Return and perform S307.Until all of point P for determining all of paired segmental arc is complete identifying processing.
As shown in figure 4, above-mentioned road network target information recognition methodss are based on, present invention also offers a kind of road network target
Information processing method, comprises the following steps that：
Step 401, searches and deletes point.Spatial data target information is recognized, all segmental arcs search above-mentioned deletion point, and
Record；
Step 402, processes and deletes point.The processing mode adopted to deleting point is directly to delete the deletion point in segmental arc；Weight
Multiple 401, until searching less than first kind point, i.e. delete point；
Step 403, searches subpoint and match point.Spatial data target information is recognized, all segmental arcs are searched above-mentioned
Subpoint and match point, have, and record；Terminate if the algorithm without if, process is completed；
Step 404, subpoint and match point are clustered.Subpoint and match point in threshold range is put into one and processes single
Unit.
Step 405, classification judges.Judge whether all of processing unit the inside has subpoint, if there is subpoint, perform
Step 406；If without subpoint, execution step 411.
Step 406, judges the number of subpoint in processing unit, when the number for judging subpoint is for 1, execution step
407, do projection process；When judging the number of subpoint not for 1, execution step 408.
Step 407, does projection process.
Step 408, whether the line segment for judging to project segmental arc is same；When the line segment for judging to project segmental arc is for same
When, then execution step 409；When judge to project the line segment of segmental arc not for same when, then execution step 410.
Step 409, multiple subpoints are done after process of fitting treatment, then do projection process.
Specifically, multiple subpoints are fitted into new point P', and the new point P' to the line segment is done into projection process, and projected
Insertion point P in the middle of line segment ", updates index information of all of processing unit with regard to insertion point segmental arc；And return execution step
403。
Step 410, multiple subpoints are done after process of fitting treatment, are found one and are thrown apart from the nearest Projection Line Segment of match point
Shadow process, specifically, multiple subpoints are fitted into new point P', and searches and the new point P' closest line segment, and should
New point P' is insertion point P in the middle of projection process, and Projection Line Segment to the closest line segment ", update all of processing unit
With regard to the index information of insertion point segmental arc；And return execution step 403.
Step 411, judge whether there is match point in each processing unit.If so, then execution step 413.If nothing, return
Execution step 403.
Step 412, by all of match point in processing unit process of fitting treatment is done, and fits new point P ", or select weight
A maximum point P ", then will a little move on to P ", and return execution step 403.
As shown in figure 5, for above-mentioned steps 404, subpoint and match point are clustered.Embodiments of the invention additionally provide one
Target information clustering algorithm is planted, is each processing unit by target information cluster according to the spatial relationship of target information, concrete step
Rapid following (need not cluster due to deleting point, so the cluster of target information only considers subpoint and match point)：
Recognize that (embodiment midpoint P refers to subpoint and plan to tell one point P in step 501, selection S301-318
Chalaza).
Step 502, newly-built one processing unit comprising current point P.
Step 503, by the outsourcing frame of current point P to extending out a threshold range, by the subpoint in the range of this and/or
Match point is put into R trees.
Step 504, judge to have been recognized in S301-318 one by one and tell other points P whether in the R trees, if it is not, then holding
Row step 505；If so, then execution step 506.
Step 505, one point P not in the R trees of selection；Return execution step 502.
Step 506, will judge that the point P in R trees is put in processing unit newly-built in step 502 in step 504.
Step 507, judge to have recognized whether the point P for telling all is placed into place newly-built in step 502 in S301-318
In reason unit.If so, execution step 508；If it is not, returning execution step 504.
Step 508, clustering processing is completed.
As shown in fig. 6, be experimental results comparison diagram of the present invention, city road of this algorithm 650 square kilometres of Chengdu
Substantial amounts of test is done in road net data, given experimental threshold values are 0.1 meter, the result below figure for obtaining：
Fig. 6 (a) and the larger line of Fig. 6 (b) width represent the topological segmental arc of before processing road, the less line representative office of width
The topological segmental arc of road after reason；
Fig. 6 (c) and Fig. 6 (d) are the intersections on road network crossing and road surface, and Fig. 6 (c) is former data, and Fig. 6 (d) is to process
Data afterwards.
Fig. 6 (e) and Fig. 6 (f) are road network crossings, and Fig. 6 (e) is the topology of former data, and Fig. 6 (f) is the deletion after processing
Topology after redundant points.
The environment of experiment test is separate unit PC, and version of window is Windows XP, and system type is 32 bit manipulation systems
System, CPU is Intel Core2 Quad Q8400, and dominant frequency is 2.66GHz, and internal memory (RAM) is 3.25GB, and hard disk total size is
60GB (solid-state), test data is selected to centre of the city city 1：500 basic datas.Below with 650 square kilometres of road netting indexs
According to as a example by, in terms of efficiency：Topology constructing, topology preprocessing and 179 seconds total used times of topology reconstruction, wherein topology preprocessing used time
123 seconds；In terms of accuracy：The target information of identification 13049, the accuracy rate of identification is 100%, is shown in Table 1.
The road surface topology preprocessing results contrast table of table 1
Test result indicate that, it is largely effective to express and repair spatial data spatial relationship using topological relation, based on knowledge
Not with the method for processing model, spatial data is processed with topological relation, from accuracy rate, Auto-generalization of Maps pair is met
The demand of data consistency；From in efficiency, the requirement of actual production practice is met.
In sum, it is an advantage of the invention that realizing empty to existing Large-scale Urban map data base road network entity
Between relation there is automatic identification and the reparation of gap and fragment, the road network met when taking essential factors space restriction relation into account is automatic
Cartographic generaliztion demand.Overcome existing road network integrated approach to enter according to geometry feature, semantic feature, attribute character more
Trade road abbreviation, to road network topological characteristic, especially false target still do not formed comprehensively, system, consistent processing mode, enter
And affect the entirety in road network synthesis for road network structure to hold, road network integrated approach is prevented from being efficiently applied to many chis
The defect that the road charting of degree reaches.
Presently preferred embodiments of the present invention is the foregoing is only, not to limit the present invention, all essences in the present invention
Within god and principle, any modification, equivalent substitution and improvements made etc. should be included within the scope of the present invention.
Claims (7)
1. a kind of recognition methodss of road network target information, it is characterised in that comprise the following steps：
A, the segmental arc of spatial data road network is set up into segmental arc R tree；
B, each the pre- intersecting paired segmental arc in the segmental arc tree for finding is set up into array；
C, for the array in arbitrary paired segmental arc and arbitrary paired segmental arc the first segmental arc on arbitrary composition node, hold
Row following steps：
Whether the composition node in c1, the first segmental arc for judging in paired segmental arc is in the second segmental arc in the paired segmental arc；
C2, if it is not, then by each two in second segmental arc it is adjacent point composition line segment set up line segment R trees；
C3, search obtain the line segment in the range of the outside specified first threshold of the outsourcing frame of current composition node；
C4, using the composition node in first segmental arc as centre point, to specify Second Threshold to make buffering circle as radius；
C5, judge that the buffering circle searches for whether the line segment that obtains intersects with described；If so, then record intersecting line segment, centre point,
First segmental arc and second segmental arc；
C6, intersected with this according to the centre point line segment two-end-point distance and specify the 3rd threshold value relation, recognize the center of circle
The type of point.
2. method according to claim 1, it is characterised in that step c6 includes：
The distance of any in the two-end-point that the centre point intersects line segment with this is judged is less than the 3rd threshold value, and the two-end-point
In the point be located in first segmental arc, and the match point after any point in the centre point and the two-end-point is fitted and the circle
When the distance of the next point of the first segmental arc is less than three threshold values, then the centre point is deletion point to the heart o'clock.
3. method according to claim 1, it is characterised in that step c6 includes：
When judging that the centre point intersects the distance of the two-end-point of line segment with this and is both greater than three threshold values, then the centre point is to throw
Shadow point.
4. method according to claim 1, it is characterised in that step c6 includes：
The distance of any in the two-end-point that the centre point intersects line segment with this is judged is less than the 3rd threshold value, and intersects with this
When the distance of another point in the two-end-point of line segment is more than three threshold values；Or
When the distance that the centre point intersects the two-end-point of line segment with this is both less than three threshold values；
Then the centre point is match point.
5. a kind of processing method of the road network target information of the recognition methodss based on described in any one of claim 1-4, it is special
Levy and be, comprise the following steps：
D, the deletion point searched in the segmental arc of spatial data road network；
E, by the deletion point deletion found in step A；
F, the subpoint and match point searched in the segmental arc of spatial data road network；
G, the subpoint for finding and match point are clustered；
H, the subpoint and match point after cluster is carried out into respectively projection process and process of fitting treatment.
6. method according to claim 5, it is characterised in that step G includes：
It is the center of circle by the arbitrary subpoint for finding or match point, to specify the 4th threshold value to draw buffering circle as radius；
By the subpoint in the 4th threshold range and match point cluster to designated treatment unit.
7. method according to claim 6, it is characterised in that step H includes：
H1, judge whether there is subpoint in each processing unit successively；
H2, when judge have subpoint when, whether the number for determining whether subpoint is 1；
H3, when the number for judging subpoint is for 1, the subpoint is carried out into projection process；
H4, when judging the number of subpoint not for 1, whether the line segment for determining whether to project segmental arc is same；
H5, when judge project segmental arc line segment for same when, each subpoint is done and obtain after process of fitting treatment match point, then general
The match point to the projection segmental arc does projection process；
H6, when judge project segmental arc line segment not for same when, each subpoint is done and obtain after process of fitting treatment match point, then
The match point is done into projection process to for finding apart from the nearest Projection Line Segment of the match point；
H7, when there is no subpoint during processing unit is judged in step H1, further determined whether match point；When judgement has plan
During chalaza, process is fitted.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108629036A (en) * | 2018-05-10 | 2018-10-09 | 中国人民解放军战略支援部队信息工程大学 | A kind of road Generalization Method and device |
| CN109491984A (en) * | 2018-10-09 | 2019-03-19 | 湖北省农村信用社联合社网络信息中心 | Hash packet data library fragment poll method for sorting |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103150309A (en) * | 2011-12-07 | 2013-06-12 | 清华大学 | Method and system for searching POI (Point of Interest) points of awareness map in space direction |
| CN103390355A (en) * | 2013-07-30 | 2013-11-13 | 中国民用航空总局第二研究所 | Method for detecting taxiway conflict on basis of A-SMGCS (Advanced Surface Movement Guidance and Control System) |
-
2016
- 2016-11-09 CN CN201610985705.2A patent/CN106599044A/en active Pending
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103150309A (en) * | 2011-12-07 | 2013-06-12 | 清华大学 | Method and system for searching POI (Point of Interest) points of awareness map in space direction |
| CN103390355A (en) * | 2013-07-30 | 2013-11-13 | 中国民用航空总局第二研究所 | Method for detecting taxiway conflict on basis of A-SMGCS (Advanced Surface Movement Guidance and Control System) |
Non-Patent Citations (1)
| Title |
|---|
| 吴伟: "面向地图综合的城市道路数据多特征自动提联及等级智能识别模型", 《中国优秀硕士学位论文全文数据库基础科学辑》 * |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108629036A (en) * | 2018-05-10 | 2018-10-09 | 中国人民解放军战略支援部队信息工程大学 | A kind of road Generalization Method and device |
| CN109491984A (en) * | 2018-10-09 | 2019-03-19 | 湖北省农村信用社联合社网络信息中心 | Hash packet data library fragment poll method for sorting |
| CN109491984B (en) * | 2018-10-09 | 2020-12-15 | 湖北省农村信用社联合社网络信息中心 | Hash packet data base fragment polling sorting method |
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