CN109145171A - A kind of multiple dimensioned map data updating method - Google Patents
A kind of multiple dimensioned map data updating method Download PDFInfo
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Abstract
The invention discloses a kind of multiple dimensioned map data updating method, include the following steps: to carry out multiscale target matching to new large scale data and small scale data to be updated；Establishing element grade incidence relation；Carry out factor change infomation detection；Cartographic generaliztion is carried out to redefine；Carry out the incremental update of object-oriented；Carry out the detection of space inconsistency and processing.Multiple dimensioned map data updating method disclosed by the invention, which can effectively solve the prior art, can not accurately identify change information and prediction variation updated core elements transmitting scale, and element matching range is smaller, the problem for having efficiency when map elements update lower.
Description
Technical field
The present invention relates to multiple dimensioned map rejuvenation technical field more particularly to a kind of multiple dimensioned map data updating methods.
Background technique
For national Major Strategic project and wisdom cities such as the monitoring of geographical national conditions, National land space optimization, ecological red line protections
City's construction, it is reliable, applicable and timely geographical spatial data has great importance.The Up-to-date state of geo-spatial data is to measure
One of important logo of its application value directly restricts its use value and use scope.Currently, all ground level in the whole nation with
It lists and a county-level city has all carried out the construction of digital city more than 400, wherein digital city construction more than ground level is complete
It completes in face.The base surveying focus of work is produced by data and turns to data maintenance, especially to existing multi-scale map vector
The Up-to-date state of database updates and consistency maintenance.
Scale and Up-to-date state are the most basic features of map datum, and the map datum of different scale can express earth sky
Between phenomenon or entity different levels form, structure and details；The data of different tenses can then express terrestrial space phenomenon
Or entity is with time-varying process, trend and rule.Multiple dimensioned expression of the various scale maps as real world, holds
Different social applications is carried on a shoulder pole, is gradually being promoted and is being goed deep into every profession and trade application.Therefore, one with real world is kept
Cause property, realizing that multiple dimensioned electronic map quickly updates is the core work content of modern base surveying.
It is existing that matched method is carried out to multiple dimensioned map are as follows:
(1) map trunk road network to be matched is chosen, map space to be matched is divided into several network meshes, and match not
Network meshes between ruler in proportion；(2) white space skeleton line is constructed under the network meshes after matching, by network meshes subdivision
At group and white space skeleton line mesh；(3) sky of each network meshes of small scale map is completed by same steps
Between subdivision；(4) the white space skeleton line mesh of large-scale map is constructed according to the above method, and is extracted in each network meshes
Settlement place group；(5) settlement place group, white space skeleton line mesh and the settlement place in different scale map are matched step by step
Entity obtains the map architecture option using white space skeleton line mesh between the different scale map of unit.
The present inventor has found in the practice of the invention, and following technical problem exists in the prior art:
It can only be matched by image information to map element；If map elements generate variation, to small scale map
When being updated, needs all elements in map to be matched again, lead to matching efficiency when having map elements update
It is lower；It is unable to get forecasting model database, the matching of variation element when to map rejuvenation next time is predicted；Using trunk roads
Net building skeleton line reduces element matching range since the area coverage of road is smaller to a certain extent.
Summary of the invention
The embodiment of the present invention provides a kind of multiple dimensioned map data updating method, and it is unpredictable to can effectively solve the prior art
Change element, element matching range is smaller, the problem for having matching efficiency when map elements update lower.
One embodiment of the invention provides a kind of multiple dimensioned map data updating method, includes the following steps:
Multiscale target matching is carried out to new large scale data and small scale data to be updated；
Establishing element grade incidence relation；
Carry out factor change infomation detection；
Cartographic generaliztion is carried out to redefine；
Carry out the incremental update of object-oriented；
Carry out the detection of space inconsistency and processing.
As an improvement of the above scheme, described more to new large scale data and small scale data progress to be updated
The matched method of scaled target is as follows:
When matching target is dotted entity, matched using semantic similarity and Euclidean distance similarity；
When matching target is Linear Entity, matched by calculating Hausdorff distance and node；
When matching target is planar entity, matched by position adjacent degree or overlapping similarity.
As an improvement of the above scheme, carry out that matched the specific method is as follows using semantic similarity and Distance conformability degree:
Proper noun is extracted from reference data place name, address and POI data library, obtains proprietary vocabulary and near synonym group；
Proprietary vocabulary and near synonym group are added to dictionary；
Dictionary is imported into segmenter, obtains word segmentation result；
By word segmentation result creation inverted entry index, and save to index document；
Location information is extracted from reference data place name, address, POI data library and other data containing location information, is obtained
To position character information；
Position character information is imported into segmenter, obtains word segmentation result；
Trie tree is established according to word segmentation result；
Search result is obtained in index document by the search index in trie tree；
According to the judgment formula (1) of following semantic similarity, is sorted, matched to search result by semantic similarity
As a result:
In formula, sim (wk, Si) | i ∈ (1,2,3 ... n) indicates query term search term SiWith the W of reference data document dk's
Semantic similarity, max { sim (wk, Si) | i ∈ (1,2,3 ... n) } indicate and WkThe maximum value of semantic similarity.
As an improvement of the above scheme, the specific method of the establishing element grade incidence relation is using alternative manner, tool
Body construction step is as follows:
The target collection for enabling new large scale data include is OL={ OL1, OL2 ..., OLm }, small scale to be updated
Footage according to comprising target collection be OS={ OS1, OS2 ..., OSn }, set TL and set TS are defined as storing respectively
From the interim set of set OL and the target of set OS, and initialize TL=Φ, TS=Φ；
Step 1: if meeting OS ≠ Φ, taking out target in OS and store to TS, and execute step 4；If being unsatisfactory for OS ≠ Φ, hold
Row step 2；
Step 2: if meeting OL ≠ Φ, executing step 3；If being unsatisfactory for OL ≠ Φ, terminate matching process；
Step 3: traversing each target Oi (i=S1, S2 ..., Sn) in interim set TS, pass through object matching query set
The target that matches with Oi in OL is closed, by the target to match from taking out and be saved in array A in set OL；If meeting array
A is sky, executes step 5；If being unsatisfactory for array A as sky, interim set TL is emptied, and by the goal displacement in array A to temporarily
In set TL；
Step 4: traversing each target Oj (j=L1, L2 ..., Lm) in interim set TL, pass through object matching query set
The target to match in OS with Oj is closed, the target to match is taken out from set OS and is saved in array A；If meeting array A
For sky, step 5 is executed；If being unsatisfactory for array A as sky, interim set TS is emptied, and the goal displacement in array A is collected to interim
It closes in TS, executes step 3；
Step 5: taking out the target in interim set TL and interim set TS respectively, be recorded as matching pair, establishing element closes
Connection relationship；TL and TS are emptied simultaneously, execute step 1.
As an improvement of the above scheme, poor using variation relation type, overlapping when carrying out the factor change infomation detection
Different degree, shape similarity, size similitude and geometric area judge that factor change is believed as characteristic index, by decision-tree model
Breath；
Wherein, the variation relation type is judged by spatial object overlay analysis, including 1:0,0:1,1:1, m:
1 (m > 1), m:0 (m > 1), m:n (m >=1, n > 1) six seed types, specific mode classification are as follows:
Enabling new large scale data is D1, and small scale data to be updated are D2；
If meet some target in D1 does not have matching object in D2, shows as single element target and increase newly, point
Class is Class1: 0；
If meet some target in D2 does not have matching object in D1, shows as single element target and disappear, point
Class is type 0:1；
If meeting in D1 and D2 has object matching to correspond to, but expansion, shrinkage phenomenon is being locally present in the target, is classified as
Class1: 1；
If meeting in D1, multiple adjacent targets are corresponding with single target matching in D2, show as the merging of adjacent target, point
Class is type m:1 (m > 1)；
If meeting the type variation to treat adjacent targets multiple in D1 as a whole, there is no matching correspondence in D2
Target or target complex, show as the newly-increased of element group, be classified as type m:0 (m > 1).
If meeting, single or multiple adjacent targets in D1 are corresponding with object matchings multiple in D2, and the structure between element target is closed
System changes, and is classified as type m:n (m >=1, n > 1)；
The following formula of calculation formula (2) of the overlapping diversity factor:
In formula, φ is overlapping diversity factor, Ni(i=1,2 ..., I) is include I in match group from new large scale
The target of footage evidence, Pj(j=1,2 ..., J) it is the J mesh from small scale data to be updated for including in match group
Calculation is shipped in mark, ∩ expression, and ∪ indicates union；
The following formula of single target shape index calculation formula (3) of the shape similarity:
In formula, OaFor single target, ShapeIndex (Oa) it is single target shape index, Perimeter (Oa) it is mesh
Target perimeter, Area (Oa) be target area；
The size similitude is target and small scale footage to be updated in large scale data new in match group
The area ratio of target in；
The geometric area includes target area (new_area) and small ratio to be updated in new large scale data
The area (old_area) of target of the example footage in.
As an improvement of the above scheme, the process of the judgement factor change of the decision-tree model is as follows:
The variation relation of element is classified；
If change type is Class1: 0, the target area (new_area) in new large scale data is calculated, if meeting
New_area > area change standard value determines that the element belongs to changed new element, if being unsatisfactory for new_area > area
Change standard value, determines that the element is not belonging to changed new element；
If change type is type 0:1, determine that the element belongs to changed new element；
If change type is Class1: 1, overlapping diversity factor φ is calculated, if being unsatisfactory for φ > overlapping diversity factor standard value, is determined
The element is not belonging to changed new element, if meeting φ > overlapping diversity factor standard value, calculates shape index ShapeIndex
(Oa), if being unsatisfactory for ShapeIndex (Oa) > shape similarity standard value determines that the element belongs to changed new element, if
Meet ShapeIndex (Oa) > shape similarity standard value calculates size similitude, if it is similar to meet size similitude > size
Property standard value, determines that the element is not belonging to changed new element, if being unsatisfactory for size similitude > size similarity standard
Value, determines that the element belongs to changed new element；
If change type is type m:1 (m > 1), overlapping diversity factor φ is calculated, if meeting φ > overlapping diversity factor standard value,
Determine that the element belongs to changed new element, if being unsatisfactory for φ > overlapping diversity factor standard value, determines that the element is not belonging to send out
The new element for changing；
If change type is type m:0 (m > 1), the target area (new_area) in new large scale data is calculated,
If meeting new_area > area change standard value, determine that the element belongs to changed new element, if being unsatisfactory for new_area
> area change standard value determines that the element is not belonging to changed new element；
If change type is type m:n (m >=1, n > 1), determine that the element belongs to changed new element.
As an improvement of the above scheme, the progress cartographic generaliztion is redefined is described in the form of hexa-atomic group, is specifically retouched
It states are as follows: (< layer identification code >, < operation operator >, < attribute code >, < index item >, < lower limit >, < upper limit >)；
Wherein, < layer identification code > determines that the property layer that this rule is applicable in, < operation operator > determine the synthetic operation of this rule,
The operation includes deletion, merging and abbreviation；< attribute code > determines the objectives that this rule is applicable under certain layer；< index item > is true
The characteristic item that set pattern is then directed to；< upper limit > and < lower limit > determines the value range of index item；
Hexa-atomic group of the general meaning can be expressed as: when the target in < layer identification code > has < attribute code >, and its < index
When item > is less than < upper limit > and is greater than < lower limit >, < operation operator > is executed.
As an improvement of the above scheme, in the increment updating method for carrying out object-oriented, the update operation of object can
It is divided into creation, deletion, geometric modification and attribute modification；
For newly-increased object, operated using creation；
For the object of disappearance, delete operation is used；
For the object of geometry or attribute change occurs, geometric modification or attribute modification are carried out；
For the object of merging, decomposition and polymerization, carries out deleting former object, create matching target object.
As an improvement of the above scheme, the specific method is as follows for the progress space inconsistency detection and processing:
Area target is shared into the inconsistent type in side and is divided into intersection type, mutually release, intertexture type；
The neutrodyne that unification processing mode is divided into biting connecions processing and precision equality that positioning accuracy is dominant is handled；
Establish six kinds of modes of spatial relationship consistency maintenance: intersection type+biting connecions,<2>intersection type+neutrodyne,<3>phase
Release+biting connecions,<4>are mutually release+neutrodyne,<5>intertexture type+biting connecions and<6>intertexture type+neutrodyne；
The inconsistent regional area in boundary expressed based on the neighbouring analysis detection of Delaunay triangulation network model by triangle collection；
It is inconsistent to boundary to correct by triangulation network skeleton line drawing；
Boundary is made to correct loss of significance by equal part feature of the triangulation network skeleton line on uniformly subdivision minimum；
Mesh data topological relation connectivity correcting method is divided into point-wire connectivity conflict correction, line-wire connectivity punching
Prominent correction and line-face connectivity conflict correction；
The linear river endpoint that conflict relationship will be present by extending segmental arc extends to the position of point target, makes linear river
The connectivity conflict correction between point-line is realized in the connection of stream and dotted well；
By moving back a displacement connection method and mobile endpoint location method, the connectivity conflict correction between line-line is realized；
Using mobile endpoint location method and newly-increased segmental arc method, line-face connectivity conflict correction is realized.
As an improvement of the above scheme, it if element to be updated is road element, can also be carried out by BP neural network
It updates, specific update method is as follows:
Obtain the object composition of large scale data and the training sample in small scale data；
Sample training is carried out, the update being passed and the update not being passed；
The training of BP nerve is carried out, weight matrix and bias vector are obtained, constructs forecasting model database；
The change information in new large scale data and small scale data to be updated is obtained, variation characteristic is calculated and refers to
Mark；
In conjunction with the forecasting model database obtained through the training of BP nerve, the scale transmitting of change information is differentiated；
Small scale data to be updated are updated；
Wherein, it is as follows to carry out the housebroken process of BP mind:
Using training sample as input value and target value；
Initialization weight is combined to obtain predicted value by prediction process target value；
By predicted value and target value entrance loss function, penalty values are obtained；
Penalty values are calculated into gradient by backpropagation, obtain new initialization weight.
A kind of multiple dimensioned map data updating method provided in an embodiment of the present invention has as follows compared with prior art
The utility model has the advantages that
Matched using multiscale target, including semantic similarity matching, Euclidean distance similarity mode, Hausdorff away from
From matching, Knot Searching and position adjacent degree or overlapping similarity mode, improve the matched range of target component, accuracy and
Accuracy, wherein the comparison based on semantic similarity, can effectively solve because data source it is inconsistent caused by with put it is not of the same name
Phenomenon greatly improves matched accuracy；Factor change is judged by establishing element grade incidence relation and decision-tree model
Information carries out part update when map elements change, and existing element is avoided to repeat to update, and improves map match effect
Rate, the standard value of decision-tree model can be obtained or are manually set by the training in not same area, to adapt to different zones environment
Under the conditions of variation identification；Cartographic generaliztion is described by way of hexa-atomic group to redefine, and keeps the matching operation of map more succinct；It is logical
The detection of space inconsistency and processing are crossed, cartographic accuracy, stability and reliability are improved；When element to be updated is wanted for road
It when plain, is updated by BP neural network, the demand of multi-Scale Intelligentization update can be substantially met, operation week is greatly reduced
Phase reduces operating cost, improves operating efficiency, saves the activity duration.
Detailed description of the invention
Fig. 1 is a kind of flow diagram of multiple dimensioned map data updating method provided in an embodiment of the present invention.
Fig. 2 is a kind of signal of the decision-tree model of multiple dimensioned map data updating method provided in an embodiment of the present invention
Figure.
Fig. 3 is that a kind of cartographic generaliztion of multiple dimensioned map data updating method provided in an embodiment of the present invention redefines front and back
Comparison diagram.
Fig. 4 is a kind of BP neural network change information of multiple dimensioned map data updating method provided in an embodiment of the present invention
Matched flow diagram.
Specific embodiment
Following will be combined with the drawings in the embodiments of the present invention, and technical solution in the embodiment of the present invention carries out clear, complete
Site preparation description, it is clear that described embodiments are only a part of the embodiments of the present invention, instead of all the embodiments.It is based on
Embodiment in the present invention, it is obtained by those of ordinary skill in the art without making creative efforts every other
Embodiment shall fall within the protection scope of the present invention.
It is a kind of flow diagram of multiple dimensioned map data updating method provided in an embodiment of the present invention referring to Fig. 1, tool
Steps are as follows for body update:
S1, multiscale target matching is carried out to new large scale data and small scale data to be updated；
Wherein, it when matching target is dotted entity, is matched using semantic similarity and Euclidean distance similarity: from
Proper noun is extracted in reference data place name, address and POI data library, obtains proprietary vocabulary and near synonym group；By proprietary vocabulary
Dictionary is added to near synonym group；Dictionary is imported into segmenter, obtains word segmentation result；Word segmentation result is created into inverted entry rope
Draw, and saves to index document；It is mentioned from reference data place name, address, POI database and other data containing location information
Location information is taken, position character information is obtained；Position character information is imported into segmenter, obtains word segmentation result；It is tied according to participle
Fruit establishes trie tree；Search result is obtained in index document by the search index in trie tree；It is similar according to following semanteme
The judgment formula (1) of degree sorts to search result by semantic similarity, obtains matching result:
In formula, sim (wk, Si) | i ∈ (1,2,3 ... n) indicates query term search term SiWith the W of reference data document dk's
Semantic similarity.max{sim(wk, Si) | i ∈ (1,2,3 ... n) } indicate and WkThe maximum value of semantic similarity.
When matching target is Linear Entity, matched by calculating Hausdorff distance and node；
When matching target is planar entity, matched by position adjacent degree or overlapping similarity.
S2, establishing element grade incidence relation；
Wherein, the specific method of establishing element grade incidence relation is using alternative manner, and specific construction step is as follows:
The target collection for enabling new large scale data include is OL={ OL1, OL2 ..., OLm }, small scale to be updated
Footage according to comprising target collection be OS={ OS1, OS2 ..., OSn }, set TL and set TS are defined as storing respectively
From the interim set of set OL and the target of set OS, and initialize TL=Φ, TS=Φ；
Step 1: if meeting OS ≠ Φ, taking out target in OS and store to TS, and execute step 4；If being unsatisfactory for OS ≠ Φ, hold
Row step 2；
Step 2: if meeting OL ≠ Φ, executing step 3；If being unsatisfactory for OL ≠ Φ, terminate matching process；
Step 3: traversing each target Oi (i=S1, S2 ..., Sn) in interim set TS, pass through object matching query set
The target that matches with Oi in OL is closed, by the target to match from taking out and be saved in array A in set OL；If meeting array
A is sky, executes step 5；If being unsatisfactory for array A as sky, interim set TL is emptied, and by the goal displacement in array A to temporarily
In set TL；
Step 4: traversing each target Oj (j=L1, L2 ..., Lm) in interim set TL, pass through object matching query set
The target to match in OS with Oj is closed, the target to match is taken out from set OS and is saved in array A；If meeting array A
For sky, step 5 is executed；If being unsatisfactory for array A as sky, interim set TS is emptied, and the goal displacement in array A is collected to interim
It closes in TS, executes step 3；
Step 5: taking out the target in interim set TL and interim set TS respectively, be recorded as matching pair, establishing element closes
Connection relationship；TL and TS are emptied simultaneously, execute step 1.
S3, factor change infomation detection is carried out；
It referring to fig. 2, is a kind of decision-tree model of multiple dimensioned map data updating method provided in an embodiment of the present invention
Schematic diagram；
Wherein, using variation relation type, overlapping diversity factor, shape similarity, size similitude and geometric area conduct
Characteristic index judges factor change information by decision-tree model；
Judge variation relation type by spatial object overlay analysis, including 1:0,0:1,1:1, m:1 (m > 1), m:0 (m >
1), m:n (m >=1, n > 1) six seed types, specific mode classification are as follows:
Enabling new large scale data is D1, and small scale data to be updated are D2；
If meet some target in D1 does not have matching object in D2, shows as single element target and increase newly, point
Class is Class1: 0；
If meet some target in D2 does not have matching object in D1, shows as single element target and disappear, point
Class is type 0:1；
If meeting in D1 and D2 has object matching to correspond to, but expansion, shrinkage phenomenon is being locally present in the target, is classified as
Class1: 1；
If meeting in D1, multiple adjacent targets are corresponding with single target matching in D2, show as the merging of adjacent target, point
Class is type m:1 (m > 1)；
If meeting the type variation to treat adjacent targets multiple in D1 as a whole, there is no matching correspondence in D2
Target or target complex, show as the newly-increased of element group, be classified as type m:0 (m > 1).
If meeting, single or multiple adjacent targets in D1 are corresponding with object matchings multiple in D2, and the structure between element target is closed
System changes, and is classified as type m:n (m >=1, n > 1)；
The following formula of calculation formula (2) of the overlapping diversity factor:
In formula, φ is overlapping diversity factor, Ni(i=1,2 ..., I) is include I in match group from new large scale
The target of footage evidence, Pj(j=1,2 ..., J) it is the J mesh from small scale data to be updated for including in match group
Calculation is shipped in mark, ∩ expression, and ∪ indicates union；
The following formula of single target shape index calculation formula (3) of the shape similarity:
In formula, OaFor single target, ShapeIndex (Oa) is single target shape index, Perimeter (Oa) it is mesh
Target perimeter, Area (Oa) be target area；
The size similitude is target and small scale footage to be updated in large scale data new in match group
The area ratio of target in；
The geometric area includes target area (new_area) and small ratio to be updated in new large scale data
The area (old_area) of target of the example footage in.
Referred to using variation relation type, overlapping diversity factor, shape similarity, size similitude and geometric area as characteristic
The process of the judgement factor change of target decision-tree model is as follows:
The variation relation of element is classified；
If change type is Class1: 0, the target area (new_area) in new large scale data is calculated, if meeting
New_area > area change standard value determines that the element belongs to changed new element, if being unsatisfactory for new_area > area
Change standard value, determines that the element is not belonging to changed new element；
If change type is type 0:1, determine that the element belongs to changed new element；
If change type is Class1: 1, overlapping diversity factor φ is calculated, if being unsatisfactory for φ > overlapping diversity factor standard value, is determined
The element is not belonging to changed new element, if meeting φ > overlapping diversity factor standard value, calculates shape index ShapeIndex
(Oa), if being unsatisfactory for ShapeIndex (Oa) > shape similarity standard value determines that the element belongs to changed new element, if
Meet ShapeIndex (Oa) > shape similarity standard value calculates size similitude, if it is similar to meet size similitude > size
Property standard value, determines that the element is not belonging to changed new element, if being unsatisfactory for size similitude > size similarity standard
Value, determines that the element belongs to changed new element；
If change type is type m:1 (m > 1), overlapping diversity factor φ is calculated, if meeting φ > overlapping diversity factor standard value,
Determine that the element belongs to changed new element, if being unsatisfactory for φ > overlapping diversity factor standard value, determines that the element is not belonging to send out
The new element for changing；
If change type is type m:0 (m > 1), the target area (new_area) in new large scale data is calculated,
If meeting new_area > area change standard value, determine that the element belongs to changed new element, if being unsatisfactory for new_area
> area change standard value determines that the element is not belonging to changed new element；
If change type is type m:n (m >=1, n > 1), determine that the element belongs to changed new element.
S4, progress cartographic generaliztion redefine；
Wherein, it carries out cartographic generaliztion and redefines the description in the form of hexa-atomic group, specifically describe are as follows: (< layer identification code >, < behaviour
Make operator >, < attribute code >, < index item >, < lower limit >, < upper limit >)；
< layer identification code > determines the property layer that this rule is applicable in, and < operation operator > determines the synthetic operation of this rule, described
Operation includes deletion, merging and abbreviation；< attribute code > determines the objectives that this rule is applicable under certain layer；< index item > determines rule
The characteristic item being then directed to；< upper limit > and < lower limit > determines the value range of index item；
Hexa-atomic group of the general meaning can be expressed as: when the target in < layer identification code > has < attribute code >, and its < index
When item > is less than < upper limit > and is greater than < lower limit >, executing < operation operator > referring to Fig. 3 is that one kind provided in an embodiment of the present invention is more
The cartographic generaliztion of scale map data updating method redefines the comparison diagram of front and back.
S5, the incremental update for carrying out object-oriented；
Wherein, the update operation of object can be divided into creation, deletion, geometric modification and attribute modification；For newly-increased object,
It is operated using creation；For the object of disappearance, delete operation is used；For the object of geometry or attribute change occurs, into
Row geometric modification or attribute modification；For the object of merging, decomposition and polymerization, carries out deleting former object, create matching
Target object.
S6, the detection of space inconsistency and processing are carried out；
Wherein, the specific method is as follows:
Area target is shared into the inconsistent type in side and is divided into intersection type, mutually release, intertexture type；
The neutrodyne that unification processing mode is divided into biting connecions processing and precision equality that positioning accuracy is dominant is handled；
Establish six kinds of modes of spatial relationship consistency maintenance: intersection type+biting connecions,<2>intersection type+neutrodyne,<3>phase
Release+biting connecions,<4>are mutually release+neutrodyne,<5>intertexture type+biting connecions and<6>intertexture type+neutrodyne；
The inconsistent regional area in boundary expressed based on the neighbouring analysis detection of Delaunay triangulation network model by triangle collection；
It is inconsistent to boundary to correct by triangulation network skeleton line drawing；
Boundary is made to correct loss of significance by equal part feature of the triangulation network skeleton line on uniformly subdivision minimum；
Mesh data topological relation connectivity correcting method is divided into point-wire connectivity conflict correction, line-wire connectivity punching
Prominent correction and line-face connectivity conflict correction；
The linear river endpoint that conflict relationship will be present by extending segmental arc extends to the position of point target, makes linear river
The connectivity conflict correction between point-line is realized in the connection of stream and dotted well；
By moving back a displacement connection method and mobile endpoint location method, the connectivity conflict correction between line-line is realized；
Using mobile endpoint location method and newly-increased segmental arc method, line-face connectivity conflict correction is realized.
It further, referring to fig. 4, is a kind of BP mind of multiple dimensioned map data updating method provided in an embodiment of the present invention
Flow diagram through network change information matches can also pass through BP neural network if element to be updated is road element
It is updated, specific update method is as follows:
Obtain the object composition of large scale data and the training sample in small scale data；
Sample training is carried out, the update being passed and the update not being passed；
The training of BP nerve is carried out as follows: using training sample as input value and target value；Target value is combined
It initializes weight and predicted value is obtained by prediction process；By predicted value and target value entrance loss function, penalty values are obtained；It will damage
Mistake value calculates gradient by backpropagation, obtains new initialization weight.
Terminate the training of BP nerve, obtain weight matrix and bias vector, constructs forecasting model database；
The change information in new large scale data and small scale data to be updated is obtained, variation characteristic is calculated and refers to
Mark, variation characteristic index include length, width, grade, degree of communication, reticular density, roading density and center Jie of road etc.；
In conjunction with the forecasting model database obtained through the training of BP nerve, the scale transmitting of change information is differentiated；
Small scale data to be updated are updated.
A kind of multiple dimensioned map data updating method provided in an embodiment of the present invention has as follows compared with prior art
The utility model has the advantages that
Matched using multiscale target, including semantic similarity matching, Euclidean distance similarity mode, Hausdorff away from
From matching, Knot Searching and position adjacent degree or overlapping similarity mode, improve the matched range of target component, accuracy and
Accuracy, wherein the comparison based on semantic similarity, can effectively solve because data source it is inconsistent caused by with put it is not of the same name
Phenomenon greatly improves matched accuracy；Factor change is judged by establishing element grade incidence relation and decision-tree model
Information carries out part update when map elements change, and existing element is avoided to repeat to update, and improves map match effect
Rate, the standard value of decision-tree model can be obtained or are manually set by the training in not same area, to adapt to different zones environment
Under the conditions of variation identification；Cartographic generaliztion is described by way of hexa-atomic group to redefine, and keeps the matching operation of map more succinct；It is logical
The detection of space inconsistency and processing are crossed, cartographic accuracy, stability and reliability are improved；When element to be updated is wanted for road
It when plain, is updated by BP neural network, the demand of multi-Scale Intelligentization update can be substantially met, operation week is greatly reduced
Phase reduces operating cost, improves operating efficiency, saves the activity duration.
The above is a preferred embodiment of the present invention, it is noted that for those skilled in the art
For, various improvements and modifications may be made without departing from the principle of the present invention, these improvements and modifications are also considered as
Protection scope of the present invention.
Claims (10)
1. a kind of multiple dimensioned map data updating method, which comprises the steps of:
Multiscale target matching is carried out to new large scale data and small scale data to be updated；
Establishing element grade incidence relation；
Carry out factor change infomation detection；
Cartographic generaliztion is carried out to redefine；
Carry out the incremental update of object-oriented；
Carry out the detection of space inconsistency and processing.
2. a kind of multiple dimensioned map data updating method as described in claim 1, which is characterized in that described to new large scale
It is as follows that footage evidence and small scale data to be updated carry out the matched method of multiscale target:
When matching target is dotted entity, matched using semantic similarity and Euclidean distance similarity；
When matching target is Linear Entity, matched by calculating Hausdorff distance and node；
When matching target is planar entity, matched by position adjacent degree or overlapping similarity.
3. a kind of multiple dimensioned map data updating method as claimed in claim 2, which is characterized in that using semantic similarity and
Distance conformability degree progress is matched, and the specific method is as follows:
Proper noun is extracted from reference data place name, address and POI data library, obtains proprietary vocabulary and near synonym group；
Proprietary vocabulary and near synonym group are added to dictionary；
Dictionary is imported into segmenter, obtains word segmentation result；
By word segmentation result creation inverted entry index, and save to index document；
Location information is extracted from reference data place name, address, POI data library and other data containing location information, is obtained in place
Set character information；
Position character information is imported into segmenter, obtains word segmentation result；
Trie tree is established according to word segmentation result；
Search result is obtained in index document by the search index in trie tree；
According to the judgment formula (1) of following semantic similarity, is sorted by semantic similarity to search result, obtain matching knot
Fruit:
In formula, sim (wk, Si) | i ∈ (1,2,3 ... n) indicates query term search term SiWith the W of reference data document dkSemantic phase
Like degree, max { sim (wk, Si) | i ∈ (1,2,3 ... n) } indicate and WkThe maximum value of semantic similarity.
4. a kind of multiple dimensioned map data updating method as described in claim 1, which is characterized in that the establishing element grade is closed
The specific method of connection relationship is using alternative manner, and specific construction step is as follows:
The target collection for enabling new large scale data include is OL={ OL1, OL2 ..., OLm }, small scale footage to be updated
According to comprising target collection be OS={ OS1, OS2 ..., OSn }, set TL and set TS are defined as storing respectively coming from and gathered
The interim set of the target of OL and set OS, and initialize TL=Φ, TS=Φ；
Step 1: if meeting OS ≠ Φ, taking out target in OS and store to TS, and execute step 4；If being unsatisfactory for OS ≠ Φ, step is executed
Rapid 2；
Step 2: if meeting OL ≠ Φ, executing step 3；If being unsatisfactory for OL ≠ Φ, terminate matching process；
Step 3: traversing each target Oi (i=S1, S2 ..., Sn) in interim set TS, pass through object matching query set OL
In the target that matches with Oi, by the target to match from taking out and be saved in array A in set OL；It is if meeting array A
Sky executes step 5；If being unsatisfactory for array A as sky, interim set TL is emptied, and the goal displacement in array A is gathered to interim
In TL；
Step 4: traversing each target Oj (j=L1, L2 ..., Lm) in interim set TL, pass through object matching query set OS
In the target that matches with Oj, the target to match is taken out from set OS and is saved in array A；If meeting array A is sky,
Execute step 5；If being unsatisfactory for array A as sky, interim set TS is emptied, and by the goal displacement in array A to temporarily set TS
In, execute step 3；
Step 5: taking out the target in interim set TL and interim set TS respectively, be recorded as matching pair, establishing element association is closed
System；TL and TS are emptied simultaneously, execute step 1.
5. a kind of multiple dimensioned map data updating method as described in claim 1, which is characterized in that carry out the factor change
When infomation detection, using variation relation type, overlapping diversity factor, shape similarity, size similitude and geometric area as special
Property index, judges factor change information by decision-tree model；
Wherein, the variation relation type is judged by spatial object overlay analysis, including 1:0,0:1,1:1, m:1 (m >
1), m:0 (m > 1), m:n (m >=1, n > 1) six seed types, specific mode classification are as follows:
Enabling new large scale data is D1, and small scale data to be updated are D2；
If meet some target in D1 does not have matching object in D2, shows as single element target and increase newly, be classified as
Class1: 0；
If meet some target in D2 does not have matching object in D1, shows as single element target and disappear, be classified as
Type 0:1；
If meeting in D1 and D2 has object matching to correspond to, but expansion, shrinkage phenomenon is being locally present in the target, is classified as type
1:1；
If meeting in D1, multiple adjacent targets are corresponding with single target matching in D2, show as the merging of adjacent target, are classified as
Type m:1 (m > 1)；
If meeting the type variation to treat adjacent targets multiple in D1 as a whole, there is no matching corresponding mesh in D2
Mark or target complex show as the newly-increased of element group, are classified as type m:0 (m > 1)；
If meeting, single or multiple adjacent targets in D1 are corresponding with object matchings multiple in D2, the structural relation hair between element target
It is raw to change, it is classified as type m:n (m >=1, n > 1)；
The following formula of calculation formula (2) of the overlapping diversity factor:
In formula, φ is overlapping diversity factor, Ni(i=1,2 ..., I) is include I in match group from new large scale data
Target, Pj(j=1,2 ..., J) is the J targets from small scale data to be updated for including, ∩ table in match group
Show and ship calculation, ∪ indicates union；
The following formula of single target shape index calculation formula (3) of the shape similarity:
In formula, OaFor single target, ShapeIndex (Oa) it is single target shape index, Perimeter (Oa) be target week
It is long, Area (Oa) be target area；
The size similitude is in target and small scale data to be updated in large scale data new in match group
Target area ratio；
The geometric area includes target area (new_area) and small scale to be updated in new large scale data
The area (old_area) of target in data.
6. a kind of multiple dimensioned map data updating method as claimed in claim 5, which is characterized in that the decision-tree model
Judge that the process of factor change is as follows:
The variation relation of element is classified；
If change type is Class1: 0, the target area (new_area) in new large scale data is calculated, if meeting new_
Area > area change standard value determines that the element belongs to changed new element, if being unsatisfactory for new_area > area change
Standard value determines that the element is not belonging to changed new element；
If change type is type 0:1, determine that the element belongs to changed new element；
If change type is Class1: 1, overlapping diversity factor φ, which is calculated, if being unsatisfactory for φ > overlapping diversity factor standard value determines that this is wanted
Element is not belonging to changed new element, if meeting φ > overlapping diversity factor standard value, calculates shape index ShapeIndex
(Oa), if being unsatisfactory for ShapeIndex (Oa) > shape similarity standard value determines that the element belongs to changed new element, if
Meet ShapeIndex (Oa) > shape similarity standard value calculates size similitude, if it is similar to meet size similitude > size
Property standard value, determines that the element is not belonging to changed new element, if being unsatisfactory for size similitude > size similarity standard
Value, determines that the element belongs to changed new element；
If change type is type m:1 (m > 1), overlapping diversity factor φ is calculated, if meeting φ > overlapping diversity factor standard value, is determined
The element belongs to changed new element, if being unsatisfactory for φ > overlapping diversity factor standard value, determines that the element is not belonging to become
The new element changed；
If change type is type m:0 (m > 1), the target area (new_area) in new large scale data is calculated, if full
Sufficient new_area > area change standard value determines that the element belongs to changed new element, if being unsatisfactory for new_area > face
Product variation standard value, determines that the element is not belonging to changed new element；
If change type is type m:n (m >=1, n > 1), determine that the element belongs to changed new element.
7. a kind of multiple dimensioned map data updating method as described in claim 1, which is characterized in that the carry out cartographic generaliztion
It redefines and is described in the form of hexa-atomic group, specifically described are as follows: (< layer identification code >, < operation operator >, < attribute code >, < index item >,
< lower limit >, < upper limit >)；
Wherein, < layer identification code > determines the property layer that this rule is applicable in, and < operation operator > determines the synthetic operation of this rule, described
Operation includes deletion, merging and abbreviation；< attribute code > determines the objectives that this rule is applicable under certain layer；< index item > determines rule
The characteristic item being then directed to；< upper limit > and < lower limit > determines the value range of index item；
Hexa-atomic group of the general meaning can be expressed as: when the target in < layer identification code > has < attribute code >, and its < index item > is small
In < upper limit > and be greater than < lower limit > when, execute < operation operator >.
8. a kind of multiple dimensioned map data updating method as described in claim 1, which is characterized in that the carry out object-oriented
Increment updating method in, the update of object operation can be divided into creation, deletion, geometric modification and attribute modification；
For newly-increased object, operated using creation；
For the object of disappearance, delete operation is used；
For the object of geometry or attribute change occurs, geometric modification or attribute modification are carried out；
For the object of merging, decomposition and polymerization, carries out deleting former object, create matching target object.
9. a kind of multiple dimensioned map data updating method as described in claim 1, which is characterized in that the progress space is different
The specific method is as follows for the detection of cause property and processing:
Area target is shared into the inconsistent type in side and is divided into intersection type, mutually release, intertexture type；
The neutrodyne that unification processing mode is divided into biting connecions processing and precision equality that positioning accuracy is dominant is handled；
Establish six kinds of modes of spatial relationship consistency maintenance: intersection type+biting connecions,<2>intersection type+neutrodyne,<3>are mutually release
+ biting connecions,<4>are mutually release+neutrodyne,<5>intertexture type+biting connecions and<6>intertexture type+neutrodyne；
The inconsistent regional area in boundary expressed based on the neighbouring analysis detection of Delaunay triangulation network model by triangle collection；
It is inconsistent to boundary to correct by triangulation network skeleton line drawing；
Boundary is made to correct loss of significance by equal part feature of the triangulation network skeleton line on uniformly subdivision minimum；
Mesh data topological relation connectivity correcting method is divided into point-wire connectivity conflict correction, line-wire connectivity conflict changes
Just corrected with line-face connectivity conflict；
The linear river endpoint that conflict relationship will be present by extending segmental arc extends to the position of point target, make linear river with
The connectivity conflict correction between point-line is realized in the connection of dotted well；
By moving back a displacement connection method and mobile endpoint location method, the connectivity conflict correction between line-line is realized；
Using mobile endpoint location method and newly-increased segmental arc method, line-face connectivity conflict correction is realized.
10. a kind of multiple dimensioned map data updating method as described in claim 1, which is characterized in that if element to be updated
When for road element, it can be also updated by BP neural network, specific update method is as follows:
Obtain the object composition of large scale data and the training sample in small scale data；
Sample training is carried out, the update being passed and the update not being passed；
The training of BP nerve is carried out, weight matrix and bias vector are obtained, constructs forecasting model database；
The change information in new large scale data and small scale data to be updated is obtained, variation characteristic index is calculated；
In conjunction with the forecasting model database obtained through the training of BP nerve, the scale transmitting of change information is differentiated；
Small scale data to be updated are updated；
Wherein, it is as follows to carry out the housebroken process of BP mind:
Using training sample as input value and target value；
Initialization weight is combined to obtain predicted value by prediction process target value；
By predicted value and target value entrance loss function, penalty values are obtained；
Penalty values are calculated into gradient by backpropagation, obtain new initialization weight.
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