CN111475593B - Mapping method and device of electronic map, storage medium and terminal - Google Patents
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Abstract
The invention discloses a mapping method, a mapping device, a storage medium and a terminal of an electronic map, wherein the method comprises the following steps: acquiring electronic map road network data, wherein the electronic map road network data comprises first electronic map road network data and second electronic map road network data; performing semantic extraction on the first electronic map road network data and the second electronic map road network data to generate a first semantic set and a second semantic set, wherein the first semantic set comprises a first microscopic semantic, a first mesoscopic semantic and a first macroscopic semantic, and the second semantic set comprises a second microscopic semantic, a second mesoscopic semantic and a second macroscopic semantic; and mapping based on the first semantic set and the second semantic set, and generating an electronic map mapping result. Therefore, by adopting the embodiment of the application, the accuracy of the electronic map mapping can be improved.
Description
Technical Field
The present invention relates to the field of computer technologies, and in particular, to a mapping method and apparatus for an electronic map, a storage medium, and a terminal.
Background
With the improvement of social development and living standard, the space range of people's activities is continuously enlarged, and the map plays a vital role in people's life. Particularly in the times of the Internet and the mobile Internet, the electronic map can help users to search interest points, route planning, view road condition information and the like, and has the advantages of convenience in searching, convenience in carrying, quick data updating and the like.
The electronic map industry in China has long-term development and progress, but has great problems that the basic database cannot realize data exchange due to the lack of uniform data standards and uneven level of various electronic map manufacturers in the aspects of production process, platform construction and the like. For upper-layer applications such as traffic information service, which rely on electronic maps, because the basic databases between the map merchants cannot effectively exchange data, if services and contents of the map merchants are needed, mapping between electronic maps of different map merchants and different versions must be completed.
Different electronic maps have different degrees of difference in the road network detail degree, the positions of points and lines, the local road type details and the like, so that the mapping of the two maps is difficult to intuitively finish through geometric comparison. In addition, it is more difficult to map the area map where road networks such as main and auxiliary roads, overhead/ground and interchange are dense. At present, the road data mapping of different image merchants mostly adopts a mode of taking manpower as a main part and taking a machine as an auxiliary part, and a great deal of manpower and material resources are consumed.
Based on the above, the prior art provides an electronic map mapping method between different map merchants, and mapping is completed by finding some endpoints and road sections with obvious characteristics, continuously performing topology expansion on the basis, and iteratively completing mapping of all electronic map data. For example, in the prior art with patent number 201611262290.2, a series of preprocessing is performed on the electronic map, so as to bridge the difference of different electronic maps in the aspects of detail level, intersection description and the like. Although the method in the prior art reduces the difficulty of feature point mapping, the accuracy of electronic map mapping is improved. However, the core logic of the mapping method is based on feature point mapping, and the accuracy of the final electronic map mapping is strongly dependent on the accuracy of feature point mapping. However, the feature points cannot be mapped completely and accurately, and the reason why the map operators of different image manufacturers cannot map completely and accurately is that, based on different operation specifications and operation habits, when expressing the same geographic information elements, the description modes and the description accuracy adopted are different to some extent, as shown in fig. 1, at a small-sized roundabout intersection, a map a abstracts the intersection into 1 point, and a map B is depicted in more detail, wherein the dashed arrows are traffic flow directions, and the road sections of the map a in fig. 1 express bidirectional traffic flow. In this case, feature point mapping at the microscopic level cannot accurately describe differences of geographic information elements, and mapping accuracy is susceptible to map description differences. Therefore, a new technical idea and means are needed to more accurately realize the mapping between different versions of electronic maps.
Disclosure of Invention
The embodiment of the application provides a mapping method, a mapping device, a storage medium and a terminal of an electronic map. The following presents a simplified summary in order to provide a basic understanding of some aspects of the disclosed embodiments. This summary is not an extensive overview and is intended to neither identify key/critical elements nor delineate the scope of such embodiments. Its sole purpose is to present some concepts in a simplified form as a prelude to the more detailed description that is presented later.
In a first aspect, an embodiment of the present application provides a mapping method of an electronic map, where the method includes:
acquiring electronic map road network data, wherein the electronic map road network data comprises first electronic map road network data and second electronic map road network data;
performing semantic extraction on the first electronic map road network data and the second electronic map road network data to generate a first semantic set and a second semantic set, wherein the first semantic set comprises a first microscopic semantic, a first mesoscopic semantic and a first macroscopic semantic, and the second semantic set comprises a second microscopic semantic, a second mesoscopic semantic and a second macroscopic semantic;
and mapping based on the first semantic set and the second semantic set, and generating an electronic map mapping result.
Optionally, the semantic extraction of the first electronic map road network data and the second electronic map road network data generates a first semantic set and a second semantic set, which includes:
extracting endpoint data, communication relation data and azimuth relation data corresponding to road segments in the first electronic map road network data and the second electronic map road network data, and generating a first microscopic semantic and a second microscopic semantic;
extracting intersection structures and entrance structures corresponding to the first microscopic semantics and the second microscopic semantics to generate first mesoscopic semantics and second mesoscopic semantics;
and generating a first macroscopic semantic and a second macroscopic semantic based on the paths and the road networks corresponding to the first mesoscopic semantic and the second mesoscopic semantic.
Optionally, the mapping based on the first semantic set and the second semantic set generates a mapping result, including:
mapping the first macroscopic semantic map and the second macroscopic semantic map;
when the first macroscopic semantics and the second macroscopic semantics are mapped successfully, mapping the first mesoscopic semantics and the second mesoscopic semantics;
when the first mesoscopic semantic meaning and the second mesoscopic semantic meaning are mapped successfully, mapping the first microscopic semantic meaning and the second microscopic semantic meaning to generate an electronic map mapping result.
Optionally, the mapping the first macroscopic semantic map and the second macroscopic semantic map includes:
constructing a path in the first electronic map, and generating a macroscopic path;
and inputting the macroscopic path into the second electronic map for matching.
Optionally, when the mapping of the first macroscopic semantic meaning and the second macroscopic semantic meaning is successful, mapping the first mesoscopic semantic meaning and the second mesoscopic semantic meaning includes:
when the macro path is successfully matched, a matched macro path set is obtained;
calculating based on mesoscopic semantics of each macro path in the macro path set to generate similarity of each macro path;
and acquiring the target mapping paths according to the similarity of the macro paths.
Optionally, when the mapping between the first mesoscopic semantic meaning and the second mesoscopic semantic meaning is successful, mapping the first micro semantic meaning and the second micro semantic meaning to generate an electronic map mapping result, including:
when the first mesoscopic semantics and the second mesoscopic semantics are mapped successfully, a mapping road section in the target mapping path is obtained;
projecting the end points of all road sections in the constructed path and the mapped road sections;
When the projections overlap, outputting a projection overlapping road section and a distance value of the projection overlapping road section;
and determining the projection coincidence road section and the projection coincidence road section distance value as an electronic map mapping result.
In a second aspect, an embodiment of the present application provides a mapping apparatus for an electronic map, where the apparatus includes:
the data acquisition module is used for acquiring electronic map road network data, wherein the electronic map road network data comprises first electronic map road network data and second electronic map road network data;
the semantic set generation module is used for carrying out semantic extraction on the first electronic map road network data and the second electronic map road network data to generate a first semantic set and a second semantic set, wherein the first semantic set comprises a first micro semantic, a first mesoscopic semantic and a first macroscopic semantic, and the second semantic set comprises a second micro semantic, a second mesoscopic semantic and a second macroscopic semantic;
and the result generation module is used for mapping based on the first semantic set and the second semantic set to generate an electronic map mapping result.
Optionally, the semantic set generating module includes:
the micro-semantic generation unit is used for extracting endpoint data, communication relation data and azimuth relation data corresponding to road sections in the first electronic map road network data and the second electronic map road network data to generate a first micro-semantic and a second micro-semantic;
The mesoscopic semantic generation unit is used for extracting intersection structures and entrance structures corresponding to the first microscopic semantic and the second microscopic semantic to generate a first mesoscopic semantic and a second mesoscopic semantic;
the macroscopic semantic generation unit is used for generating a first macroscopic semantic and a second macroscopic semantic based on paths and road networks corresponding to the first mesoscopic semantic and the second mesoscopic semantic.
In a third aspect, embodiments of the present application provide a computer storage medium storing a plurality of instructions adapted to be loaded by a processor and to perform the above-described method steps.
In a fourth aspect, embodiments of the present application provide a terminal, which may include: a processor and a memory; wherein the memory stores a computer program adapted to be loaded by the processor and to perform the above-mentioned method steps.
The technical scheme provided by the embodiment of the application can comprise the following beneficial effects:
in the embodiment of the application, a user terminal firstly acquires electronic map road network data, wherein the electronic map road network data comprises first electronic map road network data and second electronic map road network data, then performs semantic extraction on the first electronic map road network data and the second electronic map road network data to generate a first semantic set and a second semantic set, the first semantic set comprises a first microscopic semantic, a first mesoscopic semantic and a first macroscopic semantic, the second semantic set comprises a second microscopic semantic, a second mesoscopic semantic and a second macroscopic semantic, and finally mapping is performed based on the first semantic set and the second semantic set to generate an electronic map mapping result. Because the three types of semantic electronic map mapping methods are fused, firstly, the three types of semantic electronic map mapping methods are abstracted layer by layer, microscopic, mesoscopic and macroscopic semantics of different maps are defined, then mapping is carried out on the macroscopic semantic level, the influence of description differences of different electronic maps is reduced, finally, mapping from a protocol to mesoscopic and microscopic semantics is gradually refined on the basis of successful macroscopic semantic mapping, and finally, an accurate microscopic-level electronic map mapping result is formed, so that the accuracy of electronic map mapping is improved.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the invention as claimed.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the invention and together with the description, serve to explain the principles of the invention.
Fig. 1 is a schematic diagram of different microscopic semantic expressions of a small roundabout intersection according to an embodiment of the present application;
fig. 2 is a flowchart of a mapping method of an electronic map according to an embodiment of the present application;
FIG. 3 is a schematic diagram of a map micro-semantic definition (points, lines, topology) provided by an embodiment of the present application;
fig. 4 is a schematic diagram of a parallel road network structure according to an embodiment of the present application;
FIG. 5 is a schematic diagram of a U-Turn structure according to an embodiment of the present application;
FIG. 6 is a schematic diagram of abstract view semantics of an intersection according to an embodiment of the present disclosure;
FIG. 7 is a generalized intersection abstraction schematic provided by embodiments of the present application;
FIG. 8 is a schematic diagram of a parallel switching mesosemantic abstraction provided in an embodiment of the present application;
fig. 9 is a diagram of a map semantic mapping function according to an embodiment of the present application;
Fig. 10 is a process schematic diagram of a mapping method of an electronic map according to an embodiment of the present application;
fig. 11 is a flowchart of another mapping method of an electronic map provided in an embodiment of the present application;
fig. 12 is a schematic structural diagram of a mapping device of an electronic map according to an embodiment of the present application;
FIG. 13 is a schematic structural diagram of a semantic collection generating module according to an embodiment of the present application;
fig. 14 is a schematic structural diagram of a terminal according to an embodiment of the present application.
Detailed Description
The following description and the drawings sufficiently illustrate specific embodiments of the invention to enable those skilled in the art to practice them.
It should be understood that the described embodiments are merely some, but not all, embodiments of the invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
When the following description refers to the accompanying drawings, the same numbers in different drawings refer to the same or similar elements, unless otherwise indicated. The implementations described in the following exemplary examples do not represent all implementations consistent with the invention. Rather, they are merely examples of apparatus and methods consistent with certain aspects of the invention as detailed in the accompanying claims.
In the description of the present invention, it should be understood that the terms "first," "second," and the like are used for descriptive purposes only and are not to be construed as indicating or implying relative importance. The specific meaning of the above terms in the present invention will be understood in specific cases by those of ordinary skill in the art. Furthermore, in the description of the present invention, unless otherwise indicated, "a plurality" means two or more. "and/or", describes an association relationship of an association object, and indicates that there may be three relationships, for example, a and/or B, and may indicate: a exists alone, A and B exist together, and B exists alone. The character "/" generally indicates that the context-dependent object is an "or" relationship.
So far, for electronic map mapping, the core logic of patent 201811496446.2 is based on feature point mapping, and the accuracy of the final electronic map mapping depends on the accuracy of the feature point mapping. Although the accuracy of the feature point mapping is greatly improved through technical means such as map preprocessing and multidimensional verification, mapping failure caused by large feature point microscopic description difference still cannot be avoided. For this reason, the present application provides a mapping method, apparatus, storage medium and terminal for an electronic map, so as to solve the problems in the related art. In the technical scheme provided by the application, as the three types of semantic electronic map mapping methods are fused, firstly, the three types of semantic electronic map mapping methods are abstracted layer by layer, the microscopic, mesoscopic and macroscopic semantics of different maps are defined, then mapping is carried out on the macroscopic semantic level, the influence of the description difference of different electronic maps is reduced, finally, the mapping from the protocol to the mesoscopic and microscopic semantics is gradually refined on the basis of successful macroscopic semantic mapping, and finally, the accurate microscopic-level electronic map mapping result is formed, so that the accuracy of the electronic map mapping is improved, and the detailed description is carried out by adopting an exemplary embodiment.
The following describes in detail the mapping method of the electronic map provided in the embodiment of the present application with reference to fig. 2 to fig. 11. The method may be implemented in dependence on a computer program, and may be run on a mapping device based on an electronic map of the von neumann system. The computer program may be integrated in the application or may run as a stand-alone tool class application. The mapping device of the electronic map in the embodiment of the present application may be a user terminal, including but not limited to: personal computers, tablet computers, handheld devices, vehicle mounted devices, wearable devices, computing devices, or other processing devices connected to a wireless modem, and the like. User terminals may be called different names in different networks, for example: a user equipment, an access terminal, a subscriber unit, a subscriber station, a mobile station, a remote terminal, a mobile device, a user terminal, a wireless communication device, a user agent or user equipment, a cellular telephone, a cordless telephone, a personal digital assistant (personal digitalassistant, PDA), a terminal device in a 5G network or a future evolution network, and the like.
Referring to fig. 2, a flowchart of a mapping method of an electronic map is provided in an embodiment of the present application. As shown in fig. 2, the method according to the embodiment of the present application may include the following steps:
S101, acquiring electronic map road network data, wherein the electronic map road network data comprises first electronic map road network data and second electronic map road network data;
in this embodiment of the present application, the first electronic map road network and the second electronic map road network may be electronic maps of different versions provided by the same map provider, or may be electronic maps provided by different map providers, where the first map road network and the second map road network all have differences of different degrees in terms of road network details, positions of points and lines, local road type details, and the like.
When mapping the first electronic map road network and the second electronic map road network, first acquiring the first electronic map road network data and the second electronic map road network data.
S102, performing semantic extraction on the first electronic map road network data and the second electronic map road network data to generate a first semantic set and a second semantic set, wherein the first semantic set comprises a first micro semantic, a first mesoscopic semantic and a first macroscopic semantic, and the second semantic set comprises a second micro semantic, a second mesoscopic semantic and a second macroscopic semantic;
in a possible implementation manner, based on the step S103, the first electronic map road network data and the second electronic map road network data may be obtained, then endpoint data, communication relation data and azimuth relation data corresponding to road segments in the first electronic map road network data and the second electronic map road network data are extracted, a first micro-semantic and a second micro-semantic are generated, then intersection structures and entrance structures corresponding to the first micro-semantic and the second micro-semantic are extracted, a first mesoscopic semantic and a second mesoscopic semantic are generated, and finally a first macro-semantic and a second macro-semantic are generated based on paths and road networks corresponding to the first mesoscopic semantic and the second mesoscopic semantic.
Specifically, as shown in table 1, the map semantics in the present application are divided into 3 layers of micro, mesoscopic and macro.
TABLE 1
From microscopic to macroscopic, on one hand, semantics are gradually abstract, and sensitivity to road network differences is reduced; on the other hand, the semantics are gradually aggregated, the information quantity is gradually enriched, and the mapping accuracy is improved. The map semantics are defined below at the micro, meso and macro 3 levels, respectively.
In microscopic semantic definition, for a given road network R<N,L>(where N represents a node set and L represents a road segment set), as shown in FIG. 3, in L 3 For example, from n 0 ,n 1 ,…,n m Consisting of m points in total, where n 0 And n m Referred to as endpoints, other points are referred to as interior points, where multiple road segments intersect.
Definition of endpoints
Wherein id uniquely identifies an endpoint; (x, y) is its longitude and latitude; phi is the incoming degree of the end point, i.e. the number of road sections (incident road sections) taking the end point as the end point; />
The degree of departure of the end point is the number of links (outgoing links) starting from the end point; Φ and ψ are road segment sets having an end point n as an end point and a start point, respectively.
With end point n 0 For example, n 0 .φ＝1，
n 0 .Φ＝{l 1 }，n 0 .Ψ＝{l 2 ,l 3 }。
Defining road segments
Wherein id uniquely identifies a road segment; len is the length of the road segment; n is n bgn Is the starting end point of the road section, n end Is the termination endpoint of the road segment; />
Is a function of the angle of (2); omega is the angle of the road end point, and theta is the angle of the road vector; kappa defines the curvature of a road segment, the ratio of the length of the road segment to the length of the vector of the start point and the end point of the road segment, and the higher the curvature, the more curved the road segment is represented; n is the set of all nodes of the road section.
By road section l 3 In the case of an example of this,
is->
Vector angle of (2); l (L) 3 Omega is->
Defining the function Angle () function as an orientation Angle operation, i.e.>
The vector angle is defined as 0 degree in the north direction, the vector angle is increased by rotating clockwise, and the range of the angle value is [0,360 ]. l (L) 3 Theta is->
Vector angle of (2);
l 3 .Ν＝{n 0 ,n 1 ,…,n m -1,n m }。
defining a topology: topology refers to the connectivity between road segments. In FIG. 3, because of l 3 Termination endpoint n of (2) m N of (2) m .Ψ＝{l 4 ,l 5 ,l 6 -so that the subsequent road segment is l 4 、l 5 And l 6 The method comprises the steps of carrying out a first treatment on the surface of the Wherein l 3 Omega and omega
The vector angle is the smallest, so called l 4 Is l 3 Is a straight-line successor segment; same principle l 3 The forward road section and the straight forward road section are both l 1 。
Definition of parallelism: parallel refers to the azimuth relationship between two road segments or groups of road segments. As shown in fig. 4, there are two sets of road segments ls=satisfying the topological order<l 1 →l 2 →…→l m >And LS' =<l′ 1 →l′ 2 →…→l′ n >If the Hausdroff distance of LS and LS 'is less than the set threshold, this indicates that the road segment sets LS and LS' satisfy the parallel relationship determination for any of LS and LS Two road sections l i Epsilon LS and l' j E LS', if l i And l' j Covering each other in the projection direction, description l i And l' j And (5) meeting the judgment of the parallel relation.
The Hausdroff distance is calculated by setting n and n ' to be any two points on LS and LS ', respectively, and Hausdroff distances of LS and LS ' to be H (LS, LS ')=max (H (LS, LS '), H (LS ', LS)), where H (LS, LS ') =max (n ε LS) { min (n ' ∈LS ') |n-n ' | } H (LS ', LS) =max (n ' ∈LS ') { min (n ε LS) |n ' -n| } where n is the distance paradigm of n and n ', the invention refers to Euclidean distance.
When the traffic flow directions of LS and LS 'are the same or similar, the LS and LS' are called to be parallel in the same direction, and the traffic flow directions are mainly expressed as main and auxiliary road, viaduct and other road network structures in reality; when the traffic flow directions of LS and LS 'are opposite, the LS and LS' are called antiparallel, and the traffic flow directions of the same road are mainly represented as two traffic flow directions isolated from each other in reality.
When LS is parallel to LS', if l i Epsilon LS and l' j E LS' satisfies the parallelism criterion, denoted spl (l i ,l′ j ) =true; when LS is antiparallel to LS', if l i Epsilon LS and l' j The epsilon LS' satisfies the parallelism criterion, denoted dpl (l i ,l′ j )＝True。
In mesoscopic semantic definition, mesoscopic semantic is aggregation and abstraction of microscopic semantic, and in the application, two mesoscopic semantic carriers are mainly defined, namely an intersection and an entrance.
In the definition of an intersection, the intersection is a convergence place of multi-directional traffic flows, and both the incoming traffic flow and the outgoing traffic flow exist. From the perspective of microscopic semantics, the description of the intersection is various, and still taking fig. 1 as an example, the same roundabout intersection has semantic descriptions of various forms at the microscopic level. However, if abstracted from the functional point of view of the intersection, i.e. the point of view of the traffic flow, the semantics of the intersection are similar.
Definition of the U-Turn structure:
as shown in FIG. 5 (a), the U-Turn structure consists of 3 road segments, for short segment l k If the length l k Len is smaller than the set threshold and is respectively located at l k Any two road sections upstream and downstream
And->
If->
If the determination is true, then l k 、/>
And->
A U-Turn structure is formed, denoted +.>
Wherein->
And->
Respectively the incoming and outgoing traffic streams. Further, U-Turn may have some modified morphology, as shown in FIG. 5 (b), where the incoming and outgoing traffic streams intersect at point n k The method comprises the steps of carrying out a first treatment on the surface of the As shown in fig. 5 (c), for a road segment with bi-directional traffic flow, it naturally forms a U-Turn structure around the end point.
Let the set of all U-turns in the road network be u= { U k K e (1, K), for a subset of U
If U 0 Short-circuit section U of all U-Turn structures in (B) k .l k (u k ∈U λ ) Can be clustered together based on topology, then U is clustered together based on topology λ Defined as an intersection CRS λ ＝<(x λ ,y λ ),U λ >Wherein (x) λ ,y λ ) The central point position after intersection aggregation is:
as shown in FIG. 6, the left graph is a road segment description of map B of FIG. 1, according to the intersection definition, due to l 2 、l 4 …l 16 Is a topologically continuous ring, so that the intersection shown in the right graph can be formed by aggregation and abstraction, and is abstracted into a central point and a plurality of incident and emergent traffic flows, and the abstracted intersection is the same as the description of the map A in fig. 1.
Further, from the function point of view of the intersection, as long as traffic flows in different directions meet, the structural condition that each direction strictly forms a U-Turn is not required, and the intersection structure can be defined broadly. As shown in fig. 7 (a), there is only one U-Turn structure; as shown in fig. 7 (b), the U-Turn structure does not exist, and has a distinct traffic flow change characteristic corresponding to a form such as a branch point, or may be defined as an intersection in an abstract manner.
In the road network structure, besides the intersection structure, the entrance structure can also generate divergence and convergence of traffic flow, so that the road network structure has obvious semantic characteristics. In a general entrance and exit structure, the vector characteristics of traffic flow divergence and convergence are obvious, and the traffic flow divergence and convergence can be distinguished at a microscopic semantic level (points and lines), but under a specific road network state such as a parallel road network structure, a special ramp entrance and exit structure exists, and mesosemantic assistance is needed for distinguishing and mapping matching. Therefore, the present invention defines the parallel switching semantics to describe the ramp entrance semantics under the parallel road network structure, and other general entrance structure semantics are not described herein (the prior art chinese patent No. 201611262290.2 has already been solved).
Under the structure of the same-direction parallel road network (such as main road and auxiliary road in urban road network, overhead road/ground, etc.), the traffic path can be switched between two groups of parallel road sections through ramp entrances and exits due to the adjacent positions of the two groups of road sections which are parallel to each other. Figure 8 illustrates several exemplary forms of parallel road network architecture,FIGS. 8 (a) and 8 (b) are typical down-ramp exit scenarios where traffic flow may be switched from a left-hand road to a right-hand parallel road; correspondingly, fig. 8 (c) and 8 (d) are typical on-ramp entrance scenarios, where traffic flow may be switched from right-hand to left-hand parallel roads. Similar to an intersection, road type descriptions often have differences due to different understanding of road shapes and road channeling by different operators. The description is different from that of the parallel road network structure of the same section described with reference to fig. 8 (a) and 8 (b), because in fig. 8 (b) the operator considers l in fig. 8 (a) 1 And l 4 Traffic flow is not split. In addition, the parallel road network structures often interfere with each other in the mapping task of the electronic map, and still taking fig. 8 (a) and fig. 8 (b) as an example, l in fig. 8 (b) is due to the fact that the traffic flow directions are consistent and the positions are adjacent 1 And l 2 Road segments, which can be mapped to l in FIG. 8 (a) 1 And l 2 Road segments, which can also be mapped as l in FIG. 8 (a) 4 And l 3 Road segments, resulting in reduced road network mapping accuracy.
In a parallel road network structure, the description is various and complex from the micro-semantic point of view, which makes the road network mapping task very difficult. However, if abstracting from the action angle of switching traffic flow between parallel road networks, the functional structure of each road section of the parallel road networks is easier to disassemble, and a foundation is laid for the subsequent road network mapping task.
Defining Parallel handover (pc for short):
given a road section l and its two subsequent road sections l ' and l ", if l ' and l" satisfy the same-direction parallel determination spl (l ', l ") =true, and assuming that l ' is on the left side of l" with respect to the traffic flow direction, pc (l→l ')=leftuut, pc (l→l ") =righttuut is defined. As in fig. 8 (a) and 8 (b), ramp l is ignored 5 ,pc(l 1 →l 2 )＝LeftOut，pc(l 1 →l 3 )＝RightOut。
Similarly given road section l and its two preceding road sections l 'and l ", if l' and l" satisfy the same-direction parallel determination spl (l ', l ") =true, and assuming that l' is on the left side of l" with reference to the traffic flow direction, thenPc (l' →l) =leftin, pc (l "→l) =rightin is defined. As in fig. 8 (c) and 8 (d), ramp l is ignored 5 ，pc(l 1 →l 2 )＝LeftIn，pc(l 3 →l 2 )＝RightIn。
In macro semantic definition, macro semantics are an aggregation of micro semantics and mesoscopic semantics. In the invention, a macroscopic semantic carrier is a path, and the path passes through a plurality of intersections or is subjected to parallel switching for a plurality of times from the macroscopic semantic level; at the microscopic level, a path may be described by a series of segments that satisfy a topological order, further by the end sequences that make up the segments.
In the present application, a path is a set of road segments meeting topological order, trip=<l 1 →l 2 →…→l m >The end sequences of the micro-level Trip are defined as Trip N ＝<l 1 .n bgn →l 2 .n bgn →…→l m-1 .n bgn →l m .n bgn →l m .n end >；
At mesoscopic level, if a certain sub-segment set sequence of a Trip
CRS for defining intersection λ If it meets->
Belonging to CRS λ Incident road section of certain U-Turn structure, < ->
Belonging to CRS λ An outgoing road section of a certain U-Turn structure, the Trip is indicated to pass through the crossing CRS λ . Defining the crossing sequence of the Trip as Trip C ＝<CRS 1 →CRS 2 →…→CRS λ >。
At mesoscopic level, if a certain sub-segment set sequence of a Trip
If the parallel switch definition is satisfied->
Record that the Trip is in road section +.>
Where a parallel switch occurs, denoted pc p . Defining parallel switching sequence passed by Trip as Trip P ＝<pc 1 →pc 2 →…→pc p >。
S103, mapping is conducted based on the first semantic set and the second semantic set, and an electronic map mapping result is generated.
In a possible implementation manner, based on the step S102, a first semantic set and the second semantic set may be obtained, mapping is performed on a first macroscopic semantic set in the first semantic set and a second macroscopic semantic set in the second semantic set, when the mapping of the first macroscopic semantic set and the second macroscopic semantic set is successful, mapping is performed on a first mesoscopic semantic set in the first semantic set and a second mesoscopic semantic set in the second semantic set, and when the mapping of the first mesoscopic semantic set and the second mesoscopic semantic set is successful, mapping is performed on a first microscopic semantic set in the first semantic set and a second microscopic semantic set in the first semantic set, so as to generate an electronic map mapping result.
Further, firstly constructing paths in a first electronic map, generating macroscopic paths, inputting the macroscopic paths into the second electronic map for matching, acquiring a matched macroscopic path set when the macroscopic paths are successfully matched, finally calculating based on mesoscopic semantics of each macroscopic path in the macroscopic path set, generating similarity of each macroscopic path, and acquiring a target mapping path based on the similarity of each macroscopic path. And projecting the end points of all the road sections in the constructed path and the mapped road sections, outputting the projection coincident road sections and the projection coincident road section distance values when the projections are coincident, and determining the projection coincident road sections and the projection coincident road section distance values as an electronic map mapping result.
Specifically, after extracting microscopic, mesoscopic and macroscopic semantics of the map, mapping the map semantics is completed according to the sequence of macroscopic, mesoscopic and microscopic.
When macroscopic semantic mapping is performed, firstly, a path Trip is constructed on a first electronic map, and the end sequence Trip is extracted according to a macroscopic semantic extraction method N Crossing sequence Trip C And parallel switching sequences Trip P . It should be noted that, in order to increase the information amount in the map mapping process, the Trip should be constructed through intersections and parallel switching as much as possible, and in order to control the computation complexity, the length of the Trip should be controlled within 5 km.
For ease of understanding, fig. 8 (a) and 8 (b) are illustrated herein. Fig. 8 (a) shows a local road network of the map a, and fig. 8 (B) shows a local road network of the map B corresponding to the same location. Firstly, constructing a Trip in a map A, and constructing the Trip as l according to the construction principle of the path 1 →l 5 →l 3 At l 3 Including a parallel switch of RightOut.
By Trip N As an input, a Map Matching (Map Matching) operation is performed in Map B, returning possible path Matching results { Trip' }. In FIG. 8 (b), the map matching returns a result of l' 1 →l′ 2 And l' 1 →l′ 3 (elements in map B are labeled with a' "symbol for ease of distinguishing the description).
In mesoscopic semantic mapping, mesoscopic semantic Trip is respectively extracted for a path Trip constructed in the map A and a path set { Trip' } returned by map matching operation in the map B C 、Trip P And { Trip' C }{Trip′ P And mapping.
For Trip C Intersection sequence Trip 'of any matching path' C Evaluate its similarity sim C (Trip C ,Trip′ C ). Similarity evaluation logic is for Trip C Each intersection of (a) describes CRS λ Whether or not to be able to be in Trip' C Find corresponding intersection description CRS' λ If the distance between the central points of the crossing and the included angle between the incident and emergent traffic flow directions are smaller than the set threshold value, the crossing is obtainedScore 1, otherwise, not score. Trip C The sum of the scores of all the intersections in the road junction is set as sim C (Trip C ,Trip′ C )。
For Trip P Parallel switching sequence Trip 'with any matching path' P Evaluate its similarity sim P (Trip P ,Trip′ P ). Similarity evaluation logic is for Trip P Each of which is a parallel switch description pc p Whether or not to be in Trip' P Find the corresponding parallel handover description pc' p If the distance meeting the switching point is smaller than the set threshold, the switching types are the same, the parallel is switched to be 1 score, otherwise, the parallel is not switched. Trip P The sum of the scores of all parallel switches in (a) is set to sim P (Trip P ,Trip′ P )。
Comprehensive sim C (Trip C ,Trip′ C ) And sim P (Trip P ,Trip′ P ) We can obtain that the similarity of Trip and Trip 'at mesoscopic semantic level is sim (Trip, trip')=sim C (Trip C ,Trip′ C )+sim P (Trip P ,Trip′ P ) Selecting the Trip ' with the highest similarity score in the { Trip ' }, wherein the similarity score is the same as that of the { Trip ' }. best As a result of mesoscopic semantic mapping.
Taking FIG. 8 (b) as an example, for path l' 1 →l′ 2 In l' 2 There is a parallel switch of LeftOut for path l' 1 →l′ 3 In l' 3 There is a parallel switch of right out, so sim (l 1 →l 5 →l 3 ,l′ 1 →l′ 3 )>sim(l 1 →l 5 →l 3 ,l′ 1 →l′ 2 ) Therefore choose l' 1 →l′ 3 For the final mapping path Trip' best 。
In the microscopic semantic mapping, under the condition that mesosemantic mapping is successful, the endpoint of each road section l in the Trip is directed to the Trip' best The road section l ' in the road section is projected, if the projection coincidence relation exists between the l and the l ' and the distance is d, the l ' and the d are recorded in the Matching Table to be input as a resultAnd (5) outputting.
For example, as shown in fig. 9, a set of links in the map a is first extracted, whether there are unprocessed links is determined, a path Trip is selected to be exited or constructed based on the determination result, and when the path Trip is constructed, the Trip needs to be extracted N Map matching is carried out in the electronic map B, a matching path { Trip' } is generated, and then Trip-based is carried out C Comparing mesoscopic semantic similarity with paths in the Trip and { Trip ' }, and selecting Trip ' with highest similarity ' best As a result of the mapping of mesoscopic semantics, the Trip and Trip 'are finally combined' best And performing microscopic semantic mapping, wherein the mapping result is supplemented into a road network mapping table of the map A and the map B.
For example, as shown in fig. 10, for the electronic map a and the electronic map B, semantic extraction is first performed, layer-by-layer abstraction is performed during semantic extraction, microscopic, mesoscopic and macroscopic semantics of different maps are defined, then semantic mapping is performed, when semantic mapping is performed, mapping is first performed on a macroscopic semantic layer, the influence of description differences of different electronic maps is reduced, and mapping from a protocol to mesoscopic and microscopic semantics is gradually refined on the basis of successful macroscopic semantic mapping, so that an accurate microscopic-layer electronic map mapping result is finally formed.
In the embodiment of the application, a user terminal firstly acquires electronic map road network data, wherein the electronic map road network data comprises first electronic map road network data and second electronic map road network data, then performs semantic extraction on the first electronic map road network data and the second electronic map road network data to generate a first semantic set and a second semantic set, the first semantic set comprises a first microscopic semantic, a first mesoscopic semantic and a first macroscopic semantic, the second semantic set comprises a second microscopic semantic, a second mesoscopic semantic and a second macroscopic semantic, and finally mapping is performed based on the first semantic set and the second semantic set to generate an electronic map mapping result. Because the three types of semantic electronic map mapping methods are fused, firstly, the three types of semantic electronic map mapping methods are abstracted layer by layer, microscopic, mesoscopic and macroscopic semantics of different maps are defined, then mapping is carried out on the macroscopic semantic level, the influence of description differences of different electronic maps is reduced, finally, mapping from a protocol to mesoscopic and microscopic semantics is gradually refined on the basis of successful macroscopic semantic mapping, and finally, an accurate microscopic-level electronic map mapping result is formed, so that the accuracy of electronic map mapping is improved.
Fig. 11 is a flowchart of a mapping method of an electronic map according to an embodiment of the present application. The embodiment is exemplified by the application of the data security storage method to the user terminal. The data security storage method may include the steps of:
s201, acquiring electronic map road network data, wherein the electronic map road network data comprises first electronic map road network data and second electronic map road network data;
s202, extracting endpoint data, communication relation data and azimuth relation data corresponding to road sections in the first electronic map road network data and the second electronic map road network data, and generating a first microscopic semantic and a second microscopic semantic;
s203, extracting intersection structures and entrance structures corresponding to the first microscopic semantics and the second microscopic semantics to generate first mesoscopic semantics and second mesoscopic semantics;
s204, generating a first macroscopic semantic meaning and a second macroscopic semantic meaning based on paths and road networks corresponding to the first mesoscopic semantic meaning and the second mesoscopic semantic meaning;
s205, mapping the first macroscopic semantic map and the second macroscopic semantic map;
s206, mapping the first mesoscopic semantics and the second mesoscopic semantics when the mapping of the first macroscopic semantics and the second macroscopic semantics is successful;
S207, when the first mesoscopic semantic and the second mesoscopic semantic are mapped successfully, mapping the first microscopic semantic and the second microscopic semantic to generate an electronic map mapping result.
In the embodiment of the application, a user terminal firstly acquires electronic map road network data, wherein the electronic map road network data comprises first electronic map road network data and second electronic map road network data, then performs semantic extraction on the first electronic map road network data and the second electronic map road network data to generate a first semantic set and a second semantic set, the first semantic set comprises a first microscopic semantic, a first mesoscopic semantic and a first macroscopic semantic, the second semantic set comprises a second microscopic semantic, a second mesoscopic semantic and a second macroscopic semantic, and finally mapping is performed based on the first semantic set and the second semantic set to generate an electronic map mapping result. Because the three types of semantic electronic map mapping methods are fused, firstly, the three types of semantic electronic map mapping methods are abstracted layer by layer, microscopic, mesoscopic and macroscopic semantics of different maps are defined, then mapping is carried out on the macroscopic semantic level, the influence of description differences of different electronic maps is reduced, finally, mapping from a protocol to mesoscopic and microscopic semantics is gradually refined on the basis of successful macroscopic semantic mapping, and finally, an accurate microscopic-level electronic map mapping result is formed, so that the accuracy of electronic map mapping is improved.
The following are examples of the apparatus of the present invention that may be used to perform the method embodiments of the present invention. For details not disclosed in the embodiments of the apparatus of the present invention, please refer to the embodiments of the method of the present invention.
Referring to fig. 12, a schematic structural diagram of a mapping device for an electronic map according to an exemplary embodiment of the present invention is shown. The mapping means of the electronic map may be implemented as all or part of the terminal by software, hardware or a combination of both. The device 1 comprises a data acquisition module 10, a semantic set generation module 20 and a result generation module 30.
The data acquisition module 10 is configured to acquire electronic map road network data, where the electronic map road network data includes first electronic map road network data and second electronic map road network data;
the semantic set generating module 20 is configured to perform semantic extraction on the first electronic map road network data and the second electronic map road network data to generate a first semantic set and a second semantic set, where the first semantic set includes a first micro-semantic, a first mesoscopic semantic, and a first macro-semantic, and the second semantic set includes a second micro-semantic, a second mesoscopic semantic, and a second macro-semantic;
The result generating module 30 is configured to generate an electronic map mapping result based on mapping the first semantic set and the second semantic set.
Optionally, as shown in fig. 13, the semantic set generating module 20 includes:
the micro-semantic generation unit 210 is configured to extract endpoint data, communication relationship data and azimuth relationship data corresponding to a road segment in the first electronic map road network data and the second electronic map road network data, and generate a first micro-semantic and a second micro-semantic;
the mesoscopic semantic generation unit 220 is configured to extract intersection structures and entrance structures corresponding to the first microscopic semantic and the second microscopic semantic, and generate a first mesoscopic semantic and a second mesoscopic semantic;
the macro-semantic generation unit 230 is configured to generate a first macro-semantic and a second macro-semantic based on paths and road networks corresponding to the first mesoscopic semantic and the second mesoscopic semantic.
It should be noted that, when the mapping device of the electronic map provided in the foregoing embodiment performs the mapping method of the electronic map, only the division of the foregoing functional modules is used as an example, in practical application, the foregoing functional allocation may be performed by different functional modules according to needs, that is, the internal structure of the device is divided into different functional modules, so as to complete all or part of the functions described above. In addition, the mapping device of the electronic map provided in the above embodiment and the mapping method embodiment of the electronic map belong to the same concept, which embody the detailed implementation process and are not described herein again.
In the embodiment of the application, a user terminal firstly acquires electronic map road network data, wherein the electronic map road network data comprises first electronic map road network data and second electronic map road network data, then performs semantic extraction on the first electronic map road network data and the second electronic map road network data to generate a first semantic set and a second semantic set, the first semantic set comprises a first microscopic semantic, a first mesoscopic semantic and a first macroscopic semantic, the second semantic set comprises a second microscopic semantic, a second mesoscopic semantic and a second macroscopic semantic, and finally mapping is performed based on the first semantic set and the second semantic set to generate an electronic map mapping result. Because the three types of semantic electronic map mapping methods are fused, firstly, the three types of semantic electronic map mapping methods are abstracted layer by layer, microscopic, mesoscopic and macroscopic semantics of different maps are defined, then mapping is carried out on the macroscopic semantic level, the influence of description differences of different electronic maps is reduced, finally, mapping from a protocol to mesoscopic and microscopic semantics is gradually refined on the basis of successful macroscopic semantic mapping, and finally, an accurate microscopic-level electronic map mapping result is formed, so that the accuracy of electronic map mapping is improved.
The invention also provides a computer readable medium, on which program instructions are stored, which when executed by a processor implement the mapping method of the electronic map provided by the above method embodiments.
The invention also provides a computer program product containing instructions which, when run on a computer, cause the computer to perform the mapping method of the electronic map described in the above method embodiments.
Referring to fig. 14, a schematic structural diagram of a terminal is provided in an embodiment of the present application. As shown in fig. 14, the terminal 1000 may include: at least one processor 1001, at least one network interface 1004, a user interface 1003, a memory 1005, at least one communication bus 1002.
Wherein the communication bus 1002 is used to enable connected communication between these components.
The user interface 1003 may include a Display screen (Display) and a Camera (Camera), and the optional user interface 1003 may further include a standard wired interface and a wireless interface.
The network interface 1004 may optionally include a standard wired interface, a wireless interface (e.g., WI-FI interface), among others.
Wherein the processor 1001 may include one or more processing cores. The processor 1001 connects various parts within the entire electronic device 1000 using various interfaces and lines, and performs various functions of the electronic device 1000 and processes data by executing or executing instructions, programs, code sets, or instruction sets stored in the memory 1005, and invoking data stored in the memory 1005. Alternatively, the processor 1001 may be implemented in at least one hardware form of digital signal processing (Digital Signal Processing, DSP), field programmable gate array (Field-Programmable Gate Array, FPGA), programmable logic array (Programmable Logic Array, PLA). The processor 1001 may integrate one or a combination of several of a central processing unit (Central Processing Unit, CPU), an image processor (Graphics Processing Unit, GPU), and a modem, etc. The CPU mainly processes an operating system, a user interface, an application program and the like; the GPU is used for rendering and drawing the content required to be displayed by the display screen; the modem is used to handle wireless communications. It will be appreciated that the modem may not be integrated into the processor 1001 and may be implemented by a single chip.
The Memory 1005 may include a random access Memory (Random Access Memory, RAM) or a Read-Only Memory (Read-Only Memory). Optionally, the memory 1005 includes a non-transitory computer readable medium (non-transitory computer-readable storage medium). The memory 1005 may be used to store instructions, programs, code, sets of codes, or sets of instructions. The memory 1005 may include a stored program area and a stored data area, wherein the stored program area may store instructions for implementing an operating system, instructions for at least one function (such as a touch function, a sound playing function, an image playing function, etc.), instructions for implementing the above-described respective method embodiments, etc.; the storage data area may store data or the like referred to in the above respective method embodiments. The memory 1005 may also optionally be at least one storage device located remotely from the processor 1001. As shown in fig. 14, an operating system, a network communication module, a user interface module, and a mapping application of an electronic map may be included in the memory 1005, which is one type of computer storage medium.
In the terminal 1000 shown in fig. 14, a user interface 1003 is mainly used for providing an input interface for a user, and acquiring data input by the user; and the processor 1001 may be configured to call a mapping application of the electronic map stored in the memory 1005, and specifically perform the following operations:
Acquiring electronic map road network data, wherein the electronic map road network data comprises first electronic map road network data and second electronic map road network data;
performing semantic extraction on the first electronic map road network data and the second electronic map road network data to generate a first semantic set and a second semantic set, wherein the first semantic set comprises a first microscopic semantic, a first mesoscopic semantic and a first macroscopic semantic, and the second semantic set comprises a second microscopic semantic, a second mesoscopic semantic and a second macroscopic semantic;
and mapping based on the first semantic set and the second semantic set, and generating an electronic map mapping result.
In one embodiment, when executing the semantic extraction on the first electronic map road network data and the second electronic map road network data to generate a first semantic set and a second semantic set, the processor 1001 specifically executes the following operations:
extracting endpoint data, communication relation data and azimuth relation data corresponding to road segments in the first electronic map road network data and the second electronic map road network data, and generating a first microscopic semantic and a second microscopic semantic;
extracting intersection structures and entrance structures corresponding to the first microscopic semantics and the second microscopic semantics to generate first mesoscopic semantics and second mesoscopic semantics;
And generating a first macroscopic semantic and a second macroscopic semantic based on the paths and the road networks corresponding to the first mesoscopic semantic and the second mesoscopic semantic.
In one embodiment, when performing the mapping based on the first semantic set and the second semantic set, the processor 1001 specifically performs the following operations to generate a mapping result:
mapping the first macroscopic semantic map and the second macroscopic semantic map;
when the first macroscopic semantics and the second macroscopic semantics are mapped successfully, mapping the first mesoscopic semantics and the second mesoscopic semantics;
when the first mesoscopic semantic meaning and the second mesoscopic semantic meaning are mapped successfully, mapping the first microscopic semantic meaning and the second microscopic semantic meaning to generate an electronic map mapping result.
In one embodiment, the processor 1001, when performing the mapping of the first macro-semantic and the second macro-semantic map, specifically performs the following operations:
constructing a path in the first electronic map, and generating a macroscopic path;
and inputting the macroscopic path into the second electronic map for matching.
In one embodiment, the processor 1001, when performing the mapping of the first mesoscopic semantic meaning and the second mesoscopic semantic meaning when the mapping of the first macroscopical semantic meaning and the second macroscopical semantic meaning is successful, specifically performs the following operations:
When the macro path is successfully matched, a matched macro path set is obtained;
calculating based on mesoscopic semantics of each macro path in the macro path set to generate similarity of each macro path;
and acquiring the target mapping paths according to the similarity of the macro paths.
In one embodiment, when the processor 1001 performs the mapping between the first microscopic semantic meaning and the second microscopic semantic meaning when the mapping between the first mesoscopic semantic meaning and the second mesoscopic semantic meaning is successful, the processor specifically performs the following operations when generating an electronic map mapping result:
when the first mesoscopic semantics and the second mesoscopic semantics are mapped successfully, a mapping road section in the target mapping path is obtained;
projecting the end points of all road sections in the constructed path and the mapped road sections;
when the projections overlap, outputting a projection overlapping road section and a distance value of the projection overlapping road section;
and determining the projection coincidence road section and the projection coincidence road section distance value as an electronic map mapping result.
In the embodiment of the application, a user terminal firstly acquires electronic map road network data, wherein the electronic map road network data comprises first electronic map road network data and second electronic map road network data, then performs semantic extraction on the first electronic map road network data and the second electronic map road network data to generate a first semantic set and a second semantic set, the first semantic set comprises a first microscopic semantic, a first mesoscopic semantic and a first macroscopic semantic, the second semantic set comprises a second microscopic semantic, a second mesoscopic semantic and a second macroscopic semantic, and finally mapping is performed based on the first semantic set and the second semantic set to generate an electronic map mapping result. Because the three types of semantic electronic map mapping methods are fused, firstly, the three types of semantic electronic map mapping methods are abstracted layer by layer, microscopic, mesoscopic and macroscopic semantics of different maps are defined, then mapping is carried out on the macroscopic semantic level, the influence of description differences of different electronic maps is reduced, finally, mapping from a protocol to mesoscopic and microscopic semantics is gradually refined on the basis of successful macroscopic semantic mapping, and finally, an accurate microscopic-level electronic map mapping result is formed, so that the accuracy of electronic map mapping is improved.
Those skilled in the art will appreciate that implementing all or part of the above-described methods in the embodiments may be accomplished by computer programs stored in a computer-readable storage medium, which when executed, may include the steps of the embodiments of the methods described above. The storage medium may be a magnetic disk, an optical disk, a read-only memory, a random access memory, or the like.
The foregoing disclosure is only illustrative of the preferred embodiments of the present application and is not intended to limit the scope of the claims herein, as the equivalent of the claims herein shall be construed to fall within the scope of the claims herein.
Claims (8)
1. A mapping method of an electronic map, the method comprising:
acquiring electronic map road network data, wherein the electronic map road network data comprises first electronic map road network data and second electronic map road network data;
performing semantic extraction on the first electronic map road network data and the second electronic map road network data to generate a first semantic set and a second semantic set, wherein the first semantic set comprises a first microscopic semantic, a first mesoscopic semantic and a first macroscopic semantic, and the second semantic set comprises a second microscopic semantic, a second mesoscopic semantic and a second macroscopic semantic; wherein,,
The semantic extraction is performed on the first electronic map road network data and the second electronic map road network data to generate a first semantic set and a second semantic set, and the method comprises the following steps:
extracting endpoint data, communication relation data and azimuth relation data corresponding to road segments in the first electronic map road network data and the second electronic map road network data, and generating a first microscopic semantic and a second microscopic semantic;
extracting intersection structures and entrance structures corresponding to the first microscopic semantics and the second microscopic semantics to generate first mesoscopic semantics and second mesoscopic semantics;
generating a first macroscopic semantic meaning and a second macroscopic semantic meaning based on paths and road networks corresponding to the first mesoscopic semantic meaning and the second mesoscopic semantic meaning;
and mapping according to the order of macroscopic, mesoscopic and microscopic based on the first semantic set and the second semantic set, and generating an electronic map mapping result.
2. The method of claim 1, wherein the mapping based on the first semantic set and the second semantic set to generate a mapping result comprises:
mapping the first macroscopic semantic map and the second macroscopic semantic map;
when the first macroscopic semantics and the second macroscopic semantics are mapped successfully, mapping the first mesoscopic semantics and the second mesoscopic semantics;
When the first mesoscopic semantic meaning and the second mesoscopic semantic meaning are mapped successfully, mapping the first microscopic semantic meaning and the second microscopic semantic meaning to generate an electronic map mapping result.
3. The method of claim 2, wherein said mapping the first macro-semantic and second macro-semantic mappings comprises:
constructing a path in the first electronic map, and generating a macroscopic path;
and inputting the macroscopic path into the second electronic map for matching.
4. The method of claim 3, wherein mapping the first mesoscopic semantic meaning and the second mesoscopic semantic meaning when the first macroscopic semantic meaning and the second macroscopic semantic meaning are successfully mapped comprises:
when the macro path is successfully matched, a matched macro path set is obtained;
calculating based on mesoscopic semantics of each macro path in the macro path set to generate similarity of each macro path;
and acquiring the target mapping paths according to the similarity of the macro paths.
5. The method of claim 4, wherein when the mapping of the first mesoscopic semantic meaning and the second mesoscopic semantic meaning is successful, mapping the first micro-semantic meaning and the second micro-semantic meaning to generate an electronic map mapping result, comprising:
When the first mesoscopic semantics and the second mesoscopic semantics are mapped successfully, a mapping road section in the target mapping path is obtained;
projecting the end points of all road sections in the constructed path and the mapped road sections;
when the projections overlap, outputting a projection overlapping road section and a distance value of the projection overlapping road section;
and determining the projection coincidence road section and the projection coincidence road section distance value as an electronic map mapping result.
6. A mapping apparatus for an electronic map, the apparatus comprising:
the data acquisition module is used for acquiring electronic map road network data, wherein the electronic map road network data comprises first electronic map road network data and second electronic map road network data;
the semantic set generation module is used for carrying out semantic extraction on the first electronic map road network data and the second electronic map road network data to generate a first semantic set and a second semantic set, wherein the first semantic set comprises a first micro semantic, a first mesoscopic semantic and a first macroscopic semantic, and the second semantic set comprises a second micro semantic, a second mesoscopic semantic and a second macroscopic semantic; wherein,,
the semantic collection generating module comprises:
The micro-semantic generation unit is used for extracting endpoint data, communication relation data and azimuth relation data corresponding to road sections in the first electronic map road network data and the second electronic map road network data to generate a first micro-semantic and a second micro-semantic;
the mesoscopic semantic generation unit is used for extracting intersection structures and entrance structures corresponding to the first microscopic semantic and the second microscopic semantic to generate a first mesoscopic semantic and a second mesoscopic semantic;
the macroscopic semantic generation unit is used for generating a first macroscopic semantic and a second macroscopic semantic based on paths and road networks corresponding to the first mesoscopic semantic and the second mesoscopic semantic;
the result generation module is used for mapping the first semantic set and the second semantic set according to the macroscopic, mesoscopic and microscopic sequences to generate an electronic map mapping result.
7. A computer storage medium storing a plurality of instructions adapted to be loaded by a processor and to perform the method steps of any of claims 1-5.
8. A terminal, comprising: a processor and a memory; wherein the memory stores a computer program adapted to be loaded by the processor and to perform the method steps of any of claims 1-5.
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