CN102110137B - Method and device for detecting consistency of multidimensional vector map - Google Patents
Method and device for detecting consistency of multidimensional vector map Download PDFInfo
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Abstract
The invention provides a method and a device for detecting consistency of a multidimensional vector map, wherein the detecting method comprises steps as follows: 1) multidimensional object correspondence detecting step: extracting multidimensional vector data and matching the vector data with different dimensions, so as to find out a detailed object and a rough object corresponding to a detailed dimension and a rough dimension respectively in a same geographical object; 2) neighboring object detecting step: choosing the neighboring object within the certain neighboring range of the detailed object and the rough object respectively, and computing a detailed spatial relationship and a rough spatial relationship between the detailed object as well as the rough object and the neighboring objects of the detailed object and the rough object; and 3) consistency detecting step: judging whether the geographical objects are consistent in the vector data with different dimensions according to the detailed spatial relationship and the rough spatial relationship. The invention adopts the spatial relationship between the geographical objects as the basis for detecting the consistency of the multidimensional vector data, the detecting efficiency is high, and the consistency detection of the large scale vector data is realized.
Description
Technical field
The present invention relates to the multiscale space technical field, more specifically, relate to a kind of consistency detecting method to multiple dimensioned map vector and device.
Background technology
Spatial data based on geographic object (such as highway, soil, building etc.) can be set up visual vector data, and inquiry and processing service to geographical object-related information are provided in vector data.Because the user of different departments has demand from macroscopic view to the various different levels of microcosmic to spatial information, thereby for same geographic area, often needs to set up the vector data of different scale standard.For example, the user need to set up visual vector data with rough yardstick (such as 1:10 ten thousand) and inquire about and manage for macro-plan; And for certain regional physical planning, can set up vector data with detailed yardstick (such as 1:5000), and in detail inquiry and the management of the enterprising row space information of yardstick.Aspect practical application, the vector data of multiple yardstick has been set up in different industries and field.For example, the basic-scale topographic maps such as 1:5000,1:1 ten thousand, 1:5 ten thousand, 1:10 ten thousand have been set up at survey field; At intelligent transportation field, in gps system, can use the navigation traffic map of multiple yardstick; At Field of Land Resources, also formed the land management data system based on the Land resources classification map of multiple yardstick, the planning of land resources is integrated and taken precautions against illegal land used have remarkable effect.Aspect based on network multiscale space information service, Google Earth (Google Earth) provides a kind of multi-scale self-adaptive vector data platform, and the three-dimensional network Visualization Service based on the multiscale space data can be provided.
For multiple dimensioned vector data, along with dimensional variation, the geological information of the geographic object on the map also can change.On vector data, the geological information that geographic object shows comprises area, length, coordinate position etc.Geological information is the quantificational description to geographic object, depends on the map yardstick.On multiple dimensioned map, the geological information of identical geographic object under different scale is widely different, comprise: (1) is when the yardstick leap is larger, identical geographic object has different space dimensionalities at the vector data of different scale, for example geometric configuration is certain geographic object of planar (2 dimension) under detailed yardstick, and geometric configuration may show as planar (2 dimension), wire (1 dimension) or point-like (0 ties up) in the map of rough yardstick; Geometric configuration is certain geographic object of wire (1 dimension) under detailed yardstick, and geometric configuration may show as wire (1 dimension) or point-like (0 dimension) in the map of rough yardstick; (2) identical geographic object geometric configuration and relative position in the different scale map can change; (3) structure of geographic object also can change in the multiple dimensioned map, and complicated geographic object is comprised of the subobject of some separation in the map such as detailed yardstick, and subobject comprehensively is single geographic object in the map of rough yardstick.
Although there are difference in the space dimensionality that identical geographic object shows on the vector data of different scale, shape, structure, but be the spatial information of same geographic area because multiple dimensioned vector data reflects, therefore there is consistance in it to the vector data of different scale also inevitable requirement.If in multiple dimensioned vector data, the vector data of different scale exists serious inconsistent, then not only can run counter to the objectivity of real world, also can cause the mistake of the aspects such as extraction of spatial information and inquiry in the application of multiple dimensioned vector data.So for the multiple dimensioned vector data of the same geographic area of reflection, we need to detect the consistance of vector data under the different scale wherein.
In existing multiple dimensioned vector data consistency detection technology, normally extract the geological information data (such as data such as area, length, coordinate positions) of geographic object in the vector data, and the geological information data of identical geographic object in the vector data of different scale are mated to detect the consistance of multiple dimensioned vector data.Yet as mentioned before, the geological informations such as the space dimensionality of same geographic object, shape and structure all change on the vector data of different scale.When the yardstick span was little, the geological information of geographic object changed little, still can also be used for consistency detection.But when the yardstick span was larger, the geological information difference of geographic object was too large, can not be used further to consistency analysis.
To sum up, consistency detection for multiple dimensioned vector data, particularly between the larger vector data of its mesoscale span conforming detection, technological means accurately and effectively not in the prior art, this is a problem that needs to be resolved hurrily in the existing multiple dimensioned vector data technology.
Summary of the invention
Problem for the consistency detection that solves multiple dimensioned vector data described in the background technology the invention provides a kind of multiple dimensioned vector data consistency detecting method and related device.Unlike the prior art, the present invention utilizes the geological information of geographic object in the multiple dimensioned vector data to realize consistency detection, but with the spatial relationship between the geographic object in the multiple dimensioned vector data as detecting conforming foundation.
The invention provides a kind of multiple dimensioned map vector consistency detecting method, comprising:
Multiple dimensioned object correspondence detecting step mates the different scale map vector, finds same geographic object difference in the vector data of detailed yardstick and rough yardstick corresponding detailed object and rough object;
The adjacent object detecting step, for described detailed object and rough object, be chosen in respectively the adjacent object in its particular neighborhood scope, and calculate respectively detailed spatial relationship and rough spatial relationship between described detailed object and rough object and its adjacent object separately, wherein, described detailed spatial relationship and rough spatial relationship are topological relation and the direction relations between detailed object and rough object and its adjacent object separately;
The consistency detection step is calculated the spatial relationship set that it may be corresponding on rough yardstick according to described detailed spatial relationship, if described rough spatial relationship is contained in described spatial relationship set, then judges consistent.
Preferably, in described multiple dimensioned object correspondence detecting step, if two geographic object in the vector data of detailed yardstick and rough yardstick are the wire geographic object, then judge according to following steps whether the two is corresponding: at first calculate projection on another in the two, if the ratio of projection and original length, judges then that the wire geographic object on this detailed yardstick and the rough yardstick is matching relationship greater than certain threshold level; Secondly, make up wire geographic object cyberrelationship according to the annexation between the wire geographic object; Again, judge whether the wire geographic object with matching relationship satisfies described annexation, if satisfy then judge the two be same geographic object in the vector data of in detail yardstick and rough yardstick corresponding detailed object and rough object respectively.
Preferably, in described multiple dimensioned object correspondence detecting step, if two geographic object in the vector data of detailed yardstick and rough yardstick are planar geographic object, then judge according to following steps whether the two is corresponding: at first, if the area similarity of planar geographic object satisfies certain threshold value on the described detailed and rough yardstick, then the planar geographic object on described detailed yardstick and the rough yardstick is matching relationship; Then according to the border of described planar geographic object adjacency whether, make up the syntople network; Again, judge whether the described planar geographic object with matching relationship satisfies adjacency and connected relation, if satisfy then judge the two be same geographic object in the vector data of in detail yardstick and rough yardstick corresponding detailed object and rough object respectively.
Preferably, in described multiple dimensioned object correspondence detecting step, if the geographic object in the vector data of detailed yardstick is wire and planar geographic object, geographic object in the vector data of rough yardstick is wire and planar geographic object, then judge according to following steps whether the two is corresponding: at first do the line object buffer zone, if then a wire or the planar geographic object length/area ratio in buffer zone is less than certain threshold level, judge that then the two is matching relationship; Then make up geographical network according to wire with connection and the topological relation of being connected between geographic object; Again, connection and topological property according to described geographical network, judge whether described geographic object with matching relationship satisfies described connection and topological property on detailed and the yardstick of being connected, if satisfy then judge the two be same geographic object in the vector data of in detail yardstick and rough yardstick corresponding detailed object and rough object respectively.
Preferably, in described consistency detection step, set up the corresponding table of multi-scale Spatial Relationship, inquire about the corresponding table of this multi-scale Spatial Relationship according to described detailed spatial relationship and obtain space set of relationship on the described rough yardstick.
A kind of multiple dimensioned map vector consistency detection device comprises:
Multiple dimensioned object correspondence detection module, extract the data of multiple dimensioned vector data and the data of different scale vector data are mated, find same geographic object corresponding detailed object and rough object respectively in the vector data of in detail yardstick and rough yardstick;
The adjacent object detection module, for described detailed object and rough object, be chosen in respectively the adjacent object in its particular neighborhood scope, and calculate respectively detailed spatial relationship and rough spatial relationship between described detailed object and rough object and its adjacent object separately, wherein, described detailed spatial relationship and rough spatial relationship are topological relation and the direction relations between detailed object and rough object and its adjacent object separately; The consistency detection module, respectively on detailed and rough yardstick, set up network of spatial relationship according to object and adjacent object thereof, calculate the spatial relationship set that it may be corresponding on rough yardstick according to described detailed spatial relationship, if described rough spatial relationship is contained in described spatial relationship set, then judge consistent.
Preferably, described consistency detection module comprises the corresponding table of multi-scale Spatial Relationship, inquires about the corresponding table of this multi-scale Spatial Relationship according to described detailed spatial relationship and obtains described spatial relationship set.
Multiple dimensioned vector data consistency detecting method of the present invention and device, the dimensional variation of utilizing the spatial relationship between the geographic object is stable, limited and foreseeable characteristic, calculating is in the spatial relationship between the geographic object on the multiple dimensioned vector data, and with the conforming foundation of spatial relationship as detection different scale vector data, compare the detection mode with traditional dependence geographic object geological information, detection efficiency is higher, testing result is more accurate, and can realize the consistency detection to the larger vector data of yardstick span.
Description of drawings
The present invention is further detailed explanation below in conjunction with the drawings and specific embodiments:
Fig. 1 is the process flow diagram of multiple dimensioned vector data consistency detecting method of the present invention;
Fig. 2 is the structural representation of a kind of multiple dimensioned vector data consistency detection device of the present invention;
Fig. 3 shows the direction relations situation of geographic object in the vector data of different scale.
Embodiment
In order to make those skilled in the art person understand better technical scheme of the present invention, and above-mentioned purpose of the present invention, feature and advantage can be become apparent more, the present invention is further detailed explanation below in conjunction with example and example figure.
Vector data all is to be made of the geometrical symbol that represents various geographic object usually, and said geographic object comprises highway, water system, building etc. here.In multiple dimensioned vector data, the vector data of different scale is independently, not direct correlation between the data each other.But, the reflection of the vector data of these different scales all be spatial relationship in the same geographic area.For example, what multiple dimensioned vector data may reflect all is same urban area, it is its ratio scale different (from 1:5000 to 1:10 ten thousand), thereby the spatial information about identical geographic object that the vector data of inevitable requirement different scale reflects has significantly consistance, and Query Result was consistent when guarantee was used multiple dimensioned vector data like this.Otherwise if in multiple dimensioned vector data, for identical geographic object, the spatial information that reflects in the vector data under the different scale exists seriously inconsistent, then will inevitably cause the mistake of Query Result.
By extracting the data relevant with various geographic object in the vector data, we can obtain the information of two aspects: the one, and the geological information of geographic object is such as the length of geographic object, area, coordinate position etc.; The 2nd, the spatial relationship between the geographic object, spatial relationship is described mutual relationship between the geographic object from angle qualitatively.Common described spatial relationship comprises topological relation and direction relations.Topological relation has reflected the space structure between the geographic object, such as geographic object A separate with geographic object B, geographic object A and geographic object B is overlapping, geographic object A comprises geographic object B, geographic object A and passes through geographic object B etc. and all belong to topological relation.Relative direction between the direction relations reflection geographic object, as: geographic object A is positioned at the north of geographic object B etc.Ever-changing than the geographic object geological information, the kind of the spatial relationship between the geographic object is that finite sum is exhaustible.For example, in vector data, the topological relation between two planar geographic object A and the B has 8 kinds of situations, and the topological relation between wire geographic object A and the planar geographic object B has 19 kinds.
As described in the background art, on the vector data of different scale, the geological informations such as the shape of identical geographic object, length, coordinate position even space dimensionality also can change.Than geological information, the spatial relationship between the geographic object can keep stronger stability at the vector data of different scale.
For example, on detailed yardstick vector data, geographic object A is positioned at the north of geographic object B; On corresponding rough yardstick vector data, structure and the shape of geographic object A and geographic object B all can change, even might be become by original two-dimensional shapes wire or point-like; But geographic object A still is positioned at the north of geographic object B on rough yardstick, so the spatial relationship between the two should not change.
For example, as shown in Figure 3, comprise geographic object A, B, C at the detailed vector data of yardstick; On the vector data of corresponding rough yardstick, have geographic object A*, B*, C* on the rough yardstick corresponding with A, B, C.Wherein variation has all occured in the geometric configuration of A*, B*, C*, and this is so that utilize its geometric configuration to judge that consistance has difficulties.But, as can see from Figure 3, direction relations between yardstick geographic object A and the B is R(A in detail, B), direction relations is R(A* between the geographic object A* of rough yardstick and the B*, B*), the two is to have very high similarity (being the north that B and B* lay respectively at A and A*), and it is consistent can being judged as; Utilize this direction relation, can judge that geographic object A and the A* on the vector data of different scale has consistance, and B and B* have consistance.Otherwise, as shown in Figure 3, direction relations R(A, the C of geographic object A and C) with direction relations R(A*, the C* of geographic object A* and C*) obviously inconsistent, and direction relations R(B, C) and R(B*, C*) also obviously inconsistent.When therefore utilizing vector data inquiry direction relations, may there be mistake in the Query Result relevant with object C or C*.
Certainly along with the increase of yardstick span, it is in full accord that the spatial relationship between the geographic object can't keep.But in the vector data of different scale, the spatial relationship situation of change of geographic object also is predictable, and it changes kind also is limited to exhaustible.For example, as indicated above, have 8 kinds of situations at the topological relation between planar geographic object A and the B on the detailed yardstick; Geographic object A becomes wire and geographic object B keeps planar in the vector data of rough yardstick.At this moment, 8 kinds of faces on 19 kinds of lines between the two/face topological relation and the detailed yardstick/face topological relation has specific correspondence, be that a certain/face topological on the detailed yardstick closes and to tie up on the rough yardstick only corresponding to a certain or several specific lines/face topological relation, and this corresponding relation is limited, and mode that can question blank is in addition exhaustive.Therefore, we can judge in the vector data of different scale, whether the variation of spatial relationship meets the rule of this correspondence between the geographic object, if the variation of spatial relationship meets correspondence, think that then the spatial relationship in the multiple dimensioned vector data has consistance, otherwise, then think not have consistance.
Utilize the above characteristic of the spatial relationship between the geographic object, the present invention the spatial relationship between the geographic object on the multiple dimensioned vector data as detecting conforming foundation, thereby a kind of multiple dimensioned vector data consistency detecting method is provided.
Fig. 1 is the process flow diagram of multiple dimensioned vector data consistency detecting method of the present invention.As shown in Figure 1, described multiple dimensioned vector data consistency detecting method may further comprise the steps:
(1) multiple dimensioned object correspondence detecting step 101 extracts multiple dimensioned vector data and the different scale vector data is mated, and finds same geographic object corresponding detailed object and rough object respectively in the vector data of in detail yardstick and rough yardstick.
In consistency detection, so-called spatial relationship must be the same geographic object spatial relationship that corresponding each object has in the vector data of different scale.Therefore described multiple dimensioned object correspondence detecting step 101 be must carry out, same geographic object difference in the vector data of detailed yardstick and rough yardstick corresponding detailed object and rough object found.Usually, each geographic object in the vector data of rough yardstick has corresponding object in the vector data of detailed yardstick, but some map at rough yardstick of the geographic object in the vector data of detailed yardstick does not have corresponding object.Thereby, only have by above-mentioned steps, find same geographic object corresponding detailed object and rough object respectively in the vector data of in detail yardstick and rough yardstick, the spatial relationship that could utilize detailed object and rough object to have is respectively carried out consistency detection.If certain geographic object has detailed object, but do not have corresponding rough object at rough yardstick, then can not be used for consistency detection.
In carrying out the process of described multiple dimensioned object correspondence detecting step 101, read respectively the data in the vector data of detailed yardstick and rough yardstick, carry out object extraction.We can extract the spatial data corresponding with each geographic object from spatial database corresponding to vector data, such as coordinate data etc., thereby finish object extraction; Perhaps, can utilize image recognition algorithm from vector data, to extract the related data of geographic object.Can from the map of detailed yardstick and rough yardstick, respectively extract a geographic object.Whether then the data of these two geographic object are mated, be corresponding detailed object and rough object of same geographic object difference in the vector data of detailed yardstick and rough yardstick in order to judge the two.
Be the wire geographic object if in the vector data of detailed yardstick and rough yardstick, extract two geographic object, then adopt the second degree matches technology to judge, specifically, at first to calculate projection on another in the two, if the ratio of projection and original length greater than certain threshold level, then should be set up the initial matching relation with rough object by detailed object; Then the wire geographic object is made up network, carry out the global optimization coupling according to wire geographical network characteristic.Shadow casting technique is a kind of technological means known in those skilled in the art, and the length limit is introduced no longer in detail at this.
If two geographic object extracting respectively in the vector data of detailed yardstick and rough yardstick are planar geographic object, then utilize first multiple dimensioned area similarity in the face of elephant to carry out elementary coupling; Then the syntople according to planar geographic object makes up network, and the global optimization coupling of carrying out Network Based.
If the geographic object that extracts in the vector data of detailed yardstick is wire and planar geographic object, the geographic object of extracting in the vector data of rough yardstick is wire and planar geographic object, then at first line object is done buffer zone, then utilize buffer zone to carry out elementary the matching analysis, the mixing match condition of complexity is reduced to the calculating of area degree of overlapping; Then wire and area feature are made up the mixing geographical network, according to the hybrid network characteristic, carry out the global optimization coupling.
(2) the adjacent object detecting step 102, for described detailed object and rough object, be chosen in respectively the adjacent object in its particular neighborhood scope, and calculate respectively detailed spatial relationship and rough spatial relationship between described detailed object and rough object and its adjacent object separately.
Owing to comprising a large amount of geographic object in the vector data, obviously can not all calculate and detect the spatial relationship between the every pair of geographic object wherein.In fact, the spatial relationship between the geographic object of apart from each other is quite loose, utilizes them to carry out consistance and judges nonsensical.So we will be chosen in adjacent object in its particular neighborhood scope at detailed yardstick vector data for described detailed object; Select adjacent object for described rough object at the vector data of rough yardstick simultaneously.When selecting adjacent object, can according to the distance between certain geographic object and described detailed object or the rough object whether less than certain threshold level, determine described adjacent object.Perhaps, will be defined as described adjacent object with the object of described detailed object or the rough direct adjacency of object.
After the adjacent object of having determined respectively described detailed object and rough object, calculate respectively detailed spatial relationship and rough spatial relationship between described detailed object and rough object and its adjacent object separately.For example, the A corresponding detailed object that is a geographic object on detailed yardstick, A* is same geographic object corresponding rough object on rough yardstick, the N(A of their adjacent object set on yardstick separately) and N(A*); Then for a pair of object A and B(B on the detailed yardstick
N(A)) and a pair of object A* and B*(B* ∈ N(A* on the rough yardstick)), calculate detailed object A and be adjacent detailed spatial relationship R(A between the object B, B), and calculate rough object A* and be adjacent rough spatial relationship R(A*, B* between the object B *).
Here, described detailed spatial relationship R(A, B) and rough spatial relationship R(A*, B*) can be detailed object A and rough object A* with its separately adjacent object B and the direction relations between the B*, also can be detailed object A and roughly object A* and its separately adjacent object B and the topological relation between the B*.As mentioned before, the direction relations between the geographic object and topological relation all are limited, can exhaustively list.For example, if detailed object A and adjacent object B are planar geographic object, the topological relation between the two has 8 kinds of possible situations; If rough object A* becomes wire, and adjacent object B* maintenance is planar, then the topological relation between the two has 19 kinds of possible situations.The computing method of direction relations and topological relation belong to a kind of technological means known in those skilled in the art, and the length limit is also introduced no longer in detail at this.
(3) consistency detection step 103 judges with rough spatial relationship whether described geographic object is consistent in the vector data of different scale according to described detailed spatial relationship.
As mentioned before, in the vector data of different scale, the spatial relationship between the geographic object remains unchanged often; Perhaps, the variation of spatial relationship meets the rule of correspondence between the geographic object, and this corresponding relation can be exhaustive, and therefore, we can utilize detailed spatial relationship and rough spatial relationship to judge consistance.
Specifically, accept top for example, be adjacent detailed spatial relationship R(A, B between the object B according to detailed object A), we can calculate R(A, B) the set σ of spatial relationship that may be corresponding on rough yardstick [R(A, B)].R(A, B) with σ [R(A, B)] have certain correspondence, because detailed spatial relationship R(A, B) and set σ [R(A, B)] corresponding relation between is that finite sum is exhaustible, therefore can set up the corresponding table of a multi-scale Spatial Relationship, according to described detailed spatial relationship R(A, B) inquire about the corresponding table of this multi-scale Spatial Relationship, thereby acquisition set σ [R(A, B)].For example, object A and adjacent object B are planar geographic object, then R(A, B in detail) be a kind of of 8 kinds of faces/face topological relation; And corresponding rough object A* becomes wire on rough yardstick, and it is planar that adjacent object B* keeps, then R(A, B) corresponding σ on rough yardstick [R(A, B)] can only be a certain in whole 19 kinds of lines/face topological relation or certain is several; Can set up the corresponding table of multi-scale Spatial Relationship, according to R(A, B) inquiry obtains it may corresponding whole topological relations on rough yardstick, namely gather σ [R(A, B)].Judge that rough object A* is adjacent rough spatial relationship R(A*, the B* between the object B *) whether be contained in σ [R(A, B)]; R(A* even, B*) ∈ σ [R(A, B)] illustrates that then described rough spatial relationship meets the dimensional variation rule of spatial relationship, can judge that the corresponding same geographic object of A and A* is consistent in the vector data of different scale, thereby finish the consistency detection to vector data.
In order to carry out described consistency detecting method, as shown in Figure 2, the present invention also provides a kind of multiple dimensioned vector data consistency detection device, specifically comprises:
Multiple dimensioned object correspondence detection module 201, (for example extract multiple dimensioned vector data, detailed yardstick vector data 204 among Fig. 2 and rough yardstick vector data 205) data and the data of different scale vector data are mated, find same geographic object corresponding detailed object and rough object respectively in the vector data of in detail yardstick and rough yardstick;
Adjacent object detection module 202, for described detailed object and rough object, be chosen in respectively the adjacent object in its particular neighborhood scope, and calculate respectively detailed spatial relationship and rough spatial relationship between described detailed object and rough object and its adjacent object separately;
Consistency detection module 203 judges with rough spatial relationship whether described geographic object is consistent in the vector data of different scale according to described detailed spatial relationship.
Wherein, if detect on the rough and detailed yardstick consistance of spatial relationship between 2 objects, described consistency detection module 203 is calculated the spatial relationship set that it may be corresponding on rough yardstick according to fine space relation between described detailed two objects, if described rough spatial relationship is contained in described spatial relationship set, then judge consistent.As shown in Figure 2, described consistency detection module 203 comprises the corresponding table of multi-scale Spatial Relationship 203a, inquires about the corresponding table of this multi-scale Spatial Relationship 203a according to described detailed spatial relationship and obtains described rough spatial relationship set.
If detect the consistance of the spatial relationship between a plurality of objects on the rough and detailed yardstick, then make up geographical network according to object and adjacent object thereof; According to the connectivity in the network, according to said method, detect 2 spatial relationship consistance between the object at every turn, finish the Conformance Assessment of all objects.
As seen, consistency detecting method of the present invention and device are as judging conforming foundation with the spatial relationship between the geographic object.Because spatial relationship is that finite sum is exhaustible with the situation of dimensional variation, therefore can be mainly finish the judgement that space on the different scale concerns correspondence by the mode of tabling look-up, counting yield is higher; And, because the stability of spatial relationship between the geographic object goes for the consistency detection between the larger vector data of yardstick span.
The above only is the specific embodiment of the present invention, and the present invention can also be applied in other opertaing device.Protection scope of the present invention is not limited to this, anyly is familiar with those skilled in the art in the technical scope that the present invention discloses, and the variation that can expect easily or replacement all should be encompassed within protection scope of the present invention.Therefore, protection scope of the present invention should be as the criterion with the protection domain that claim was defined.
Claims (7)
1. a multiple dimensioned map vector consistency detecting method is characterized in that, comprising:
Multiple dimensioned object correspondence detecting step mates the different scale map vector, finds same geographic object difference in the vector data of detailed yardstick and rough yardstick corresponding detailed object and rough object;
The adjacent object detecting step, for described detailed object and rough object, be chosen in respectively the adjacent object in its particular neighborhood scope, and calculate respectively detailed spatial relationship and rough spatial relationship between described detailed object and rough object and its adjacent object separately, wherein, described detailed spatial relationship and rough spatial relationship are topological relation and the direction relations between detailed object and rough object and its adjacent object separately;
The consistency detection step is calculated the spatial relationship set that it may be corresponding on rough yardstick according to described detailed spatial relationship, if described rough spatial relationship is contained in described spatial relationship set, then judges consistent.
2. multiple dimensioned map vector consistency detecting method according to claim 1, it is characterized in that, in described multiple dimensioned object correspondence detecting step, if two geographic object in the vector data of detailed yardstick and rough yardstick are the wire geographic object, then judge according to following steps whether the two is corresponding: at first calculate projection on another in the two, if the ratio of projection and original length, judges then that the wire geographic object on this detailed yardstick and the rough yardstick is matching relationship greater than certain threshold level; Secondly, make up wire geographic object cyberrelationship according to the annexation between the wire geographic object; Again, judge whether the wire geographic object with matching relationship satisfies described annexation, if satisfy then judge the two be same geographic object in the vector data of in detail yardstick and rough yardstick corresponding detailed object and rough object respectively.
3. multiple dimensioned vector data consistency detecting method according to claim 1, it is characterized in that, in described multiple dimensioned object correspondence detecting step, if two geographic object in the vector data of detailed yardstick and rough yardstick are planar geographic object, then judge according to following steps whether the two is corresponding: at first, if the area similarity of planar geographic object satisfies certain threshold value on the described detailed and rough yardstick, then the planar geographic object on described detailed yardstick and the rough yardstick is matching relationship; Then according to the border of described planar geographic object adjacency whether, make up the syntople network; Again, judge whether the described planar geographic object with matching relationship satisfies adjacency and connected relation, if satisfy then judge the two be same geographic object in the vector data of in detail yardstick and rough yardstick corresponding detailed object and rough object respectively.
4. multiple dimensioned vector data consistency detecting method according to claim 1, it is characterized in that, in described multiple dimensioned object correspondence detecting step, if the geographic object in the vector data of detailed yardstick is wire and planar geographic object, geographic object in the vector data of rough yardstick is wire and planar geographic object, then judge according to following steps whether the two is corresponding: at first do the line object buffer zone, if then a wire or the planar geographic object length/area ratio in buffer zone is less than certain threshold level, judge that then the two is matching relationship; Then make up geographical network according to wire with connection and the topological relation of being connected between geographic object; Again, connection and topological property according to described geographical network, judge whether described geographic object with matching relationship satisfies described connection and topological property on detailed and the yardstick of being connected, if satisfy then judge the two be same geographic object in the vector data of in detail yardstick and rough yardstick corresponding detailed object and rough object respectively.
5. multiple dimensioned vector data consistency detecting method according to claim 1, it is characterized in that, in described consistency detection step, set up the corresponding table of multi-scale Spatial Relationship, inquire about the corresponding table of this multi-scale Spatial Relationship according to described detailed spatial relationship and obtain space set of relationship on the described rough yardstick.
6. a multiple dimensioned map vector consistency detection device is characterized in that, comprising:
Multiple dimensioned object correspondence detection module extracts multiple dimensioned vector data and the different scale vector data is mated, and finds same geographic object corresponding detailed object and rough object respectively in the vector data of in detail yardstick and rough yardstick;
The adjacent object detection module, for described detailed object and rough object, be chosen in respectively the adjacent object in its particular neighborhood scope, and calculate respectively detailed spatial relationship and rough spatial relationship between described detailed object and rough object and its adjacent object separately, wherein, described detailed spatial relationship and rough spatial relationship are topological relation and the direction relations between detailed object and rough object and its adjacent object separately;
The consistency detection module, respectively on detailed and rough yardstick, set up network of spatial relationship according to object and adjacent object thereof, calculate the spatial relationship set that it may be corresponding on rough yardstick according to described detailed spatial relationship, if described rough spatial relationship is contained in described spatial relationship set, then judge consistent.
7. multiple dimensioned vector data consistency detection device according to claim 6, it is characterized in that, described consistency detection module comprises the corresponding table of multi-scale Spatial Relationship, inquires about the corresponding table of this multi-scale Spatial Relationship according to described detailed spatial relationship and obtains described spatial relationship set.
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| CN101246597A (en) * | 2007-11-30 | 2008-08-20 | 北京师范大学 | Method for commercial scale vector map data gradual transmission and dynamic reconstruction |
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| CN101246597A (en) * | 2007-11-30 | 2008-08-20 | 北京师范大学 | Method for commercial scale vector map data gradual transmission and dynamic reconstruction |
| CN101359341A (en) * | 2008-09-08 | 2009-02-04 | 中国科学院地理科学与资源研究所 | Dimension setting method based on vector data traffic |
| CN101923542A (en) * | 2009-06-12 | 2010-12-22 | 如临其境创意(上海)有限公司 | MDDRQ-Tree index structure for network multidimensional space data expression |
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