CN103559213A - Efficient spatial nearest neighbor query method for highway networks - Google Patents

Abstract

The invention discloses an efficient spatial nearest neighbor query method for highway networks. The method includes the steps of A, storing all data objects and correspondences of coordinates thereof in a Hash table 1; B, calculating spatial nearest neighbors of apexes of a highway, and storing the highway apexes, the nearest neighbors and nearest distances into a Hash table 2; C, dividing a two-dimensional space into a plurality of grid units by a virtual grid, and numbering the grid units; D, calculating and storing correspondences between the grid units and the data objects or highway apexes, and storing the correspondences into a Hash table 3; E, calculating the grid units where query points locate and determining numbers of the grid units; F, searching for the data objects that the grid units correspond to, and calculating the nearest data objects and returning the nearest data objects to users. The method has the advantages that time complexity in spatial nearest neighbor query for highway networks can be reduced, query efficiency is improved greatly, and neighbor query time is shortened.

Description

A kind of network of highways efficient spatial arest neighbors querying method

Technical field

The present invention relates to a kind of space arest neighbors querying method, relate in particular to a kind of network of highways efficient spatial arest neighbors querying method.

Background technology

In recent years, along with popularizing of smart mobile phone, people use mobile phone to position, search, browse and sharing information more and more; Increasing facility can utilize the electronic chart in mobile phone to obtain as the geographic position of restaurant, shop, cinema etc.This service based on geographical location information towards smart phone user is accepted extensively by people.And along with the development of infotech, the application and service of this class based on geographical location information is also more and more.

In these application and service, the comparatively common service based on geographical location information is near search subscriber current location, meet the facility of user-defined keyword.In this class application, how efficiently to process the space querying of user's current geographic position, be also the inquiry of spatial key word, be an important research topic.Because a large number of users is initiated inquiry at one time by mobile terminal, and be desirably in the very short time and obtain answer, therefore, concurrent inquiry amount is large, inquiry requirement of real-time height is the main challenge that the current service application based on geographical location information faces.

How improving the search efficiency of space arest neighbors, is a basis and the very important key issue facing in the inquiry of spatial key word and a lot of service application based on geographical location information.In daily life, vehicle, pedestrian can only move conventionally on limited spatial network, also on network of highways, move and movably scope only limit to the existing road in network of highways.While searching the nearest object in the space of user's current location in network of highways, existing algorithm is conventionally all by means of the index structure of shortest path trees type.And the time complexity that uses the neighborhood of this class indexed search query point in network of highways is O (log N), the sum that wherein N is sub-section, this is by a large amount of query time of cost; And, after finding the neighborhood of query point on network of highways, existing method must be checked the candidate data object on all sub-sections in this neighborhood, and need to scan the index on network of highways and calculate shortest path and the length thereof from query point to each candidate target, this has further increased again the expense of calculating.

Summary of the invention

The object of this invention is to provide a kind of network of highways efficient spatial arest neighbors querying method, be applicable to that data volume is large, Real-time and Concurrent inquiry amount large and the high specific (special) requirements of response requirement of real-time, the time complexity of highway cyberspace arest neighbors inquiry can be reduced to O (1) from O (log N), greatly improve the efficiency of space arest neighbors inquiry.

The present invention adopts following technical proposals:

An efficient spatial arest neighbors querying method, is characterized in that, comprises the following steps:

A: the input data that comprise data object information on network of highways information and network of highways are provided by user, according to input data creation Hash table hashmap_1, and the corresponding relation of all data objects and its coordinate figure are stored in Hash table hashmap_1;

B: calculate respectively the space arest neighbors on every each summit of road, create Hash table hashmap_2, and the arest neighbors on spatial network and minimum distance are stored in Hash table hashmap_2 by link vertex, this summit;

C: use virtual grid that two-dimensional space is divided into the square net unit that several grades are large, determine the length of side of grid cell, and virtual grid is numbered;

D: calculate sub-section the save mesh unit that each grid cell comprises and control the data object in this sub-section or the corresponding relation between link vertex, and by result store in Hash table hashmap_3;

E: calculate the grid cell at place, query point position, and the numbering of the corresponding grid cell in definite query point position;

F: the grid cell numbering according to calculating in step e, search the data object that this grid cell is corresponding, and calculating wherein returns to user from the nearest data object of query point.

The major key of described Hash table hashmap_1 is unique indications oid of data object, is worth the coordinate figure for this data object.

The major key of described Hash table hashmap_2 is unique indications vid of link vertex, is worth for the oid of the arest neighbors of this summit on spatial network and the minimum distance s between vid and this oid.

Described step C comprises the following steps:

C1: use virtual grid that two-dimensional space is divided into the square net unit that several grades are large, pass through formula

(1) determine the length of side of grid cell, wherein, l is the grid cell length of side, establishes d ₁for the length of shortest path in all roads, establish d ₂for having on the road of data object, the minimum value of the distance between all two adjacent data objects, d is d ₁and d ₂in minimum value, ξ=10 ^-9;

C2: making R is the minimum boundary rectangle that all data objects of data centralization form, with the summit (x in the lower left corner of R ₀, y ₀) be starting point, set up virtual grid, and from coordinate (x ₀, y ₀) grid cell at place starts, from left to right, incrementally grid cell is numbered from bottom to top; The formula of the grid cell numbering at coordinates computed point (x, y) place is:

$\mathrm{id}=m^{*}(\mathrm{ceil}\left(\frac{y-y_0}{l}\right)-1)+\mathrm{ceil}\left(\frac{x-x_0}{l}\right)-1;---\left(2\right)$

Wherein, id is the grid cell numbering at coordinate points (x, y) place, the quantity of the grid cell that every a line that m is grid comprises, and ceil is upper bracket function.

Described step D comprises the following steps:

D1: for any road, be divided into some sub-sections;

D2: for any strip section, calculate the grid cell crossing with this sub-section;

D3: calculate all sub-section that each grid cell comprises and the set of data objects of controlling this little section, and the corresponding relation of the slope in the sub-section at grid cell numbering and set of data objects and data object place is stored in Hash table hashmap_3, the key of hashmap_3 is the numbering of grid cell, to value that should key, be a set, each element in this set is for controlling the oid of each data object of this grid cell and the slope in the sub-section at this data object place; If controlling the point of this grid cell is link vertex, using the slope in the sub-section of the vid on this summit and place as an element, be stored in the set of this grid cell correspondence in hashmap_3.

In described step D1, for any road, from its summit, first by this insertion point, summit set ps; Then take out successively the data object on this road and be inserted in a set ps; For the line segment that in a set ps, two adjacent data group of objects of front and back become, being divided equally is in two sub-line segments difference intron line segment aggregate rs; Finally by another summit of road also insertion point set ps.

In described step D2, for any one sub-section, establish its two-end-point and be respectively v ₁and v ₂, calculate limit v ₁→ v ₂intersection point with longitudinal network ruling and transverse grid line; According to v ₁to v ₂direction, according to the above-mentioned intersection point of magnitude relationship ordered arrangement and the v of abscissa value ₁and v ₂the set of the point that two summits form; To in the set after sequence the mid point of all adjacent 2 in set of computations a little successively, the numbering of formula (2) in each mid point use step C2 being calculated to the grid cell at this mid point place, the set that the grid cell at mid point place forms is and limit v ₁→ v ₂crossing grid cell.

In described step e, utilize the formula (2) in step C2, calculate the grid cell at place, query point position, and the numbering of the corresponding grid cell in definite query point position.

Described step F comprises the following steps:

F1: according to the grid cell numbering calculating in step e, search the slope in corresponding every the sub-section of this grid cell and control the oid of data object or the vid of link vertex in this sub-section in Hash table hashmap_3;

F2: the slope that calculates respectively the straight line that first object on query point and each strip section is formed by connecting; If the slope in corresponding a certain the sub-section of the slope calculating and grid cell equates, query point is positioned on the sub-section of this correspondence; If the slope calculating all equates with the slope in corresponding some the sub-sections of grid cell, calculate respectively the coordinate position relation between query point and the two-end-point of each sub-line segment, to determine sub-section, query point place;

F3: judge on the road of query point place whether have data object, if so, enter step F 4; If not, enter step F 5;

F4: if two end points in sub-section, query point place are respectively two data objects, calculate respectively the distance between these two data objects and query point, and will as Query Result, return to user with the nearest data object of query point, if these two data objects are identical with the distance between query point, these two data objects are returned to user as Query Result simultaneously;

If two end points in sub-section, query point place are respectively a data object and a link vertex, first calculate query point to the distance r of this data object ₁with the distance r of query point to this link vertex ₂, then from Hash table hashmap_2, inquire about arest neighbors and the minimum distance s of this link vertex on spatial network ₂, finally compare r ₁with r ₂+ s ₂size, if r ₁less, data object corresponding to sub-section, query point place end points returned to user as Query Result; If r ₂+ s ₂less, using inquire about the corresponding data object of the arest neighbors of this link vertex on spatial network from Hash table hashmap_2, as Query Result, return to user; If r ₁with r ₂+ s ₂size identical, simultaneously using data object corresponding to sub-section, query point place end points, from Hash table hashmap_2, inquire about the corresponding data object of the arest neighbors of this link vertex on spatial network as Query Result, return to user simultaneously;

F5: if two end points of query point place road are respectively two link vertex, first score is not calculated query point to the distance r of first link vertex ₃with the distance r4 of query point to the second link vertex, then from Hash table hashmap_2, inquire about respectively arest neighbors and the minimum distance s of first link vertex on spatial network ₃, second arest neighbors and the minimum distance s of link vertex on spatial network ₄, calculate r ₃+ s ₃with r ₄+ s ₄size, if r ₃+ s ₃less, using first link vertex inquiring from Hash table hashmap_2, the corresponding data object of arest neighbors on spatial network returns to user as Query Result; If r ₄+ s ₄less, using second link vertex inquiring from Hash table hashmap_2, the corresponding data object of arest neighbors on spatial network returns to user as Query Result; If r ₃+ s ₃with r ₄+ s ₄size identical, using first link vertex inquiring from Hash table hashmap_2, the corresponding data object of the corresponding data object of arest neighbors on spatial network, second link vertex inquiring from the Hash table hashmap_2 arest neighbors on spatial network returns to user as Query Result simultaneously simultaneously.

The present invention is applied as starting point with the space querying of the service based on geographical location information, spatial query processing is divided into off-line index and sets up and real-time two megastages of spatial query processing.Off-line index establishment stage is served the real-time spatial query processing stage, the response speed of the space arest neighbors inquiry for improve between real-time empty processing stage.Off-line index establishment stage is responsible for index and is set up, and in the present invention, at off-line index establishment stage, has created three Hash table: hashmap_1, hashmap_2, hashmap_3.Wherein, the indications of hashmap_1 for storing data object, i.e. corresponding relation between the oid of data object and the coordinate of data object; Hashmap_2 is for storing the indications of link vertex, i.e. the vid of link vertex and this summit value of the bee-line between the oid of nearest data object on the oid of nearest data object and the vid of link vertex and spatial network on spatial network; Hashmap_3 is for corresponding relation, this sub-section slope between save mesh unit and the data object in this grid cell neutron section of control.In the real-time spatial query processing stage, each space arest neighbors is inquired about, first calculate the corresponding grid cell of query point in two-dimensional space, then three Hash tables setting up by off-line index establishment stage find the data object of controlling the sub-section comprising in this grid cell, determine on the road at query point place and the space arest neighbors on the road of query point place, the space arest neighbors using it as query point returns to user.The present invention can make the time complexity of highway cyberspace arest neighbors inquiry be reduced to O (1) from O (log N), greatly improved arest neighbors search efficiency between network of highways real-time empty, reduce arest neighbors query time, the position-based service application system on the network of highways large for data volume, Real-time and Concurrent inquiry amount is large and response requirement of real-time is high is extremely important.

Accompanying drawing explanation

Fig. 1 is schematic flow sheet of the present invention;

Fig. 2 is that use virtual grid of the present invention is divided into two-dimensional space the schematic diagram of the square net unit that several grades are large;

The schematic diagram of Fig. 3 when to be sub-section, query point place slope equate with the slope in corresponding a certain the sub-section of grid cell;

The schematic diagram of Fig. 4 when to be sub-section, query point place slope equate with the slope in corresponding some the sub-sections of grid cell;

Fig. 5 is the schematic diagram of two end points of the sub-line segment at query point place while being respectively data object and link vertex;

Schematic diagram when two end points that Fig. 6 and Fig. 7 are the sub-line segment at query point place are link vertex.

Embodiment

As shown in Figure 1, network of highways efficient spatial arest neighbors querying method of the present invention comprises the following steps, and wherein A, B, C, D step are for off-line index establishment stage; E, F step are used for the real-time spatial query processing stage:

A: the input data that comprise data object information on network of highways information and network of highways are provided by user, according to input data creation Hash table hashmap_1, and the corresponding relation of all data objects and its coordinate figure are stored in Hash table hashmap_1; The major key of Hash table hashmap_1 is unique indications oid of data object, is worth the coordinate figure for this data object.

B: calculate respectively the space arest neighbors on every each summit of road, create Hash table hashmap_2, and the arest neighbors on spatial network and minimum distance are stored in Hash table hashmap_2 by link vertex, this summit; The major key of Hash table hashmap_2 is unique indications vid of link vertex, is worth for the oid of the arest neighbors of this summit on spatial network and the minimum distance s between vid and this oid.

C: use virtual grid that two-dimensional space is divided into the square net unit that several grades are large, determine the length of side of grid cell, and virtual grid is numbered; When carrying out C step, according to following concrete steps, carry out:

C1: use virtual grid that two-dimensional space is divided into the square net unit that several grades are large, pass through formula

(1) determine the length of side of grid cell, wherein, l is the grid cell length of side, establishes d ₁for the length of shortest path in all roads, establish d ₂for having on the road of data object, the minimum value of the distance between all two adjacent data objects, d is d ₁and d ₂in minimum value, ξ=10 ^-9;

C2: making R is the minimum boundary rectangle that all data objects of data centralization form, with the summit (x in the lower left corner of R ₀, y ₀) be starting point, set up virtual grid, and from coordinate (x ₀, y ₀) grid cell at place starts, from left to right, incrementally grid cell is numbered from bottom to top; The formula of the grid cell numbering at coordinates computed point (x, y) place is:

$\mathrm{id}=m^{*}(\mathrm{ceil}\left(\frac{y-y_0}{l}\right)-1)+\mathrm{ceil}\left(\frac{x-x_0}{l}\right)-1;---\left(2\right)$

Wherein, id is the grid cell numbering at coordinate points (x, y) place, the quantity of the grid cell that every a line that m is grid comprises, and ceil is upper bracket function.

D: calculate sub-section the save mesh unit that each grid cell comprises and control the data object in this sub-section or the corresponding relation between link vertex, and by result store in Hash table hashmap_3; When carrying out D step, according to following concrete steps, carry out:

D1: for any road, be divided into some sub-sections; For any road, from its summit, first by this insertion point, summit set ps; Then take out successively the data object on this road and be inserted in a set ps; For the line segment that in a set ps, two adjacent data group of objects of front and back become, being divided equally is in two sub-line segments difference intron line segment aggregate rs; Finally by another summit of road also insertion point set ps.

D2: for any strip section, make to calculate with the following method the grid cell crossing with this sub-section: make the two-end-point in this sub-section be respectively v ₁and v ₂, calculate limit v ₁→ v ₂intersection point with longitudinal network ruling and transverse grid line; According to v ₁to v ₂direction, according to the above-mentioned intersection point of magnitude relationship ordered arrangement and the v of abscissa value ₁and v ₂the set of the point that two summits form; To in the set after sequence the mid point of all adjacent 2 in set of computations a little successively, the numbering of formula (2) in each mid point use step C2 being calculated to the grid cell at this mid point place, the set that the grid cell at mid point place forms is and limit v ₁→ v ₂crossing grid cell.

D3: calculate all sub-section that each grid cell comprises and the set of data objects of controlling this little section, and the corresponding relation of the slope in the sub-section at grid cell numbering and set of data objects and data object place is stored in Hash table hashmap_3, the key of hashmap_3 is the numbering of grid cell, to value that should key, be a set, each element in this set is for controlling the oid of each data object of this grid cell and the slope in the sub-section at this data object place.If controlling the point of this grid cell is link vertex, using the slope in the sub-section of the vid on this summit and place as an element, be stored in the set of this grid cell correspondence in hashmap_3.

E: utilize the formula (2) in step C2, calculate the grid cell at place, query point position, and the numbering of the corresponding grid cell in definite query point position;

F: the grid cell numbering according to calculating in step e, search the data object that this grid cell is corresponding, and calculating wherein returns to user from the nearest data object of query point.When carrying out F step, according to following concrete steps, carry out:

F1: according to the grid cell numbering calculating in step e, search the slope in corresponding every the sub-section of this grid cell and control the oid of data object or the vid of link vertex in this sub-section in Hash table hashmap_3;

F2: the slope that calculates respectively the straight line that first object on query point and each strip section is formed by connecting; If the slope in corresponding a certain the sub-section of the slope calculating and grid cell equates, query point is positioned on the sub-section of this correspondence; If the slope calculating all equates with the slope in corresponding some the sub-sections of grid cell, calculate respectively the coordinate position relation between query point and the two-end-point of each sub-line segment, to determine sub-section, query point place;

F3: judge on the road of query point place whether have data object, if so, enter step F 4; If not, enter step F 5;

F4: if two end points in sub-section, query point place are respectively two data objects, calculate respectively the distance between these two data objects and query point, and will as Query Result, return to user with the nearest data object of query point, if these two data objects are identical with the distance between query point, these two data objects are returned to user as Query Result simultaneously;

If two end points in sub-section, query point place are respectively a data object and a link vertex, first calculate query point to the distance r of this data object ₁with the distance r of query point to this link vertex ₂, then from Hash table hashmap_2, inquire about arest neighbors and the minimum distance s of this link vertex on spatial network ₂, finally compare r ₁with r ₂+ s ₂size, if r ₁less, data object corresponding to sub-section, query point place end points returned to user as Query Result; If r ₂+ s ₂less, using inquire about the corresponding data object of the arest neighbors of this link vertex on spatial network from Hash table hashmap_2, as Query Result, return to user; If r ₁with r ₂+ s ₂size identical, simultaneously using data object corresponding to sub-section, query point place end points, from Hash table hashmap_2, inquire about the corresponding data object of the arest neighbors of this link vertex on spatial network as Query Result, return to user simultaneously;

F5: if two end points of query point place road are respectively two link vertex, first score is not calculated query point to the distance r of first link vertex ₃distance r with query point to the second link vertex ₄, then from Hash table hashmap_2, inquire about respectively arest neighbors and the minimum distance s of first link vertex on spatial network ₃, second arest neighbors and the minimum distance s of link vertex on spatial network ₄, calculate r ₃+ s ₃with r ₄+ s ₄size, if r ₃+ s ₃less, using first link vertex inquiring from Hash table hashmap_2, the corresponding data object of arest neighbors on spatial network returns to user as Query Result; If r ₄+ s ₄less, using second link vertex inquiring from Hash table hashmap_2, the corresponding data object of arest neighbors on spatial network returns to user as Query Result; If r ₃+ s ₃with r ₄+ s ₄size identical, using first link vertex inquiring from Hash table hashmap_2, the corresponding data object of the corresponding data object of arest neighbors on spatial network, second link vertex inquiring from the Hash table hashmap_2 arest neighbors on spatial network returns to user as Query Result simultaneously simultaneously.

Below in conjunction with specific embodiment, network of highways efficient spatial arest neighbors querying method of the present invention is explained:

A: the input data that comprise data object information on network of highways information and network of highways are provided by user, according to input data creation Hash table hashmap_1, and the corresponding relation of all data objects and its coordinate figure are stored in Hash table hashmap_1; The major key of Hash table hashmap_1 is unique indications oid of data object, is worth the coordinate figure for this data object.Like this, the oid of data-oriented object, by searching the respective value in Hash table hashmap_1, just can obtain the coordinate position of this data object.

B: calculate respectively the space arest neighbors on every each summit of road, create Hash table hashmap_2, and the arest neighbors on spatial network and minimum distance are stored in Hash table hashmap_2 by link vertex, this summit; The major key of Hash table hashmap_2 is unique indications vid of link vertex, is worth for the oid of the arest neighbors of this summit on spatial network and the minimum distance s between vid and this oid.

As shown in Figure 2, a, b, c, d, e, f, the summit that g is network of highways, o ₁to o ₇for the data object on network of highways, grid 0,1,2 ... for virtual grid element number hereinafter described, Q ₁, Q ₂for query point, P ₁,

P ₂, P ₃the mid point between adjacent two data object on road, wherein, P ₁the upper data object o of road ab ₁with data object o ₂mid point; P ₂data object o ₂with data object o ₃mid point, P ₃the upper data object o of road bc ₄with data object o ₅mid point.

Because every road all has two summits, and some roads can be shared a summit, therefore, in Fig. 2, have 11 roads, and the number on summit is 7.For each summit, use dijkstra algorithm to calculate the nearest data object of this summit on spatial network.In Fig. 2, b nearest data object on spatial network in summit is o ₇.Create Hash table hashmap_2, unique indications vid that major key is link vertex, is worth for the oid of the nearest data object of this summit on spatial network and the minimum distance s between vid and oid.Vid corresponding value in Hash table hashmap_2 is two tuples, and this two tuple is the oid of the nearest data object of vid and the bee-line between oid and vid.

C: use virtual grid that two-dimensional space is divided into the square net unit that several grades are large, determine the length of side of grid cell, and virtual grid is numbered; When carrying out C step, according to following concrete steps, carry out:

C1: use virtual grid that two-dimensional space is divided into the square net unit that several grades are large, pass through formula

(1) determine the length of side of grid cell, wherein, l is the grid cell length of side, establishes d ₁for the length of shortest path in all roads, establish d ₂for having on the road of data object, the minimum value of the distance between all two adjacent data objects, d is d ₁and d ₂in minimum value, ξ=10 ^-9;

C2: making R is the minimum boundary rectangle that all data objects of data centralization form, with the summit (x in the lower left corner of R ₀, y ₀) be starting point, set up virtual grid, and from coordinate (x ₀, y ₀) grid cell at place starts, from left to right, incrementally grid cell is numbered from bottom to top; The formula of the grid cell numbering at coordinates computed point (x, y) place is:

$\mathrm{id}=m^{*}(\mathrm{ceil}\left(\frac{y-y_0}{l}\right)-1)+\mathrm{ceil}\left(\frac{x-x_0}{l}\right)-1;---\left(2\right)$

Wherein, id is the grid cell numbering at coordinate points (x, y) place, the quantity of the grid cell that every a line that m is grid comprises, and ceil is upper bracket function.

In Fig. 2, use virtual grid to divide the two-dimensional space at network of highways place.This virtual grid one has 12 row, and every row comprises 16 grid cells, is also m=16.Wherein, 0,1,2,3 numberings that are respectively front 4 grid cells of the 1st row.And 16,17,18 be the numbering of first three grid cell of the 2nd row.119 and 120 is numberings of the 8th, the 9th grid cell of the 12nd row.

D: calculate sub-section the save mesh unit that each grid cell comprises and control the data object in this sub-section or the corresponding relation between link vertex, and by result store in Hash table hashmap_3; When carrying out D step, according to following concrete steps, carry out:

D1: for any road, be divided into some sub-sections; For any road, from its summit, first by this insertion point, summit set ps; Then take out successively the data object on this road and be inserted in a set ps; For the line segment that in a set ps, two adjacent data group of objects of front and back become, being divided equally is in two sub-line segments difference intron line segment aggregate rs; Finally by another summit of road also insertion point set ps.

In Fig. 2, a, b, c, d, e, f, the summit that g is network of highways, o ₁, o ₂..., o ₇for the data object on network of highways.For the line segment that in a set ps, two adjacent data group of objects of front and back become, being divided equally is in two sub-line segments difference intron line segment aggregate rs, as P ₁, P ₂for the mid point between the upper adjacent two data object of road ab.Wherein, P ₁data object o ₁with data object o ₂mid point; P ₂data object o ₂with data object o ₃mid point.And two, group section end points one of them while being link vertex, do not divide this sub-section, as upper in road ab in Fig. 2, a → o ₁, o ₃two sub-sections of → b are not split into two sub-sections.

D2: for any strip section, make to calculate with the following method the grid cell crossing with this sub-section: make the two-end-point in this sub-section be respectively v ₁and v ₂, calculate limit v ₁→ v ₂intersection point with longitudinal network ruling and transverse grid line; According to v ₁to v ₂direction, according to the above-mentioned intersection point of magnitude relationship ordered arrangement and the v of abscissa value ₁and v ₂the set of the point that two summits form; To in the set after sequence the mid point of all adjacent 2 in set of computations a little successively, the numbering of formula (2) in each mid point use step C2 being calculated to the grid cell at this mid point place, the set that the grid cell at mid point place forms is and limit v ₁→ v ₂crossing grid cell.

D3: calculate all sub-section that each grid cell comprises and the set of data objects of controlling this little section, and the corresponding relation of the slope in the sub-section at grid cell numbering and set of data objects and data object place is stored in Hash table hashmap_3, the key of hashmap_3 is the numbering of grid cell, to value that should key, be a set, each element in this set is for controlling the oid of each data object of this grid cell and the slope in the sub-section at this data object place; If controlling the point of this grid cell is link vertex, using the slope in the sub-section of the vid on this summit and place as an element, be stored in the set of this grid cell correspondence in hashmap_3.

E: utilize the formula (2) in step C2, calculate the grid cell at place, query point position, and the numbering of the corresponding grid cell in definite query point position; As in Fig. 2, utilize formula $\mathrm{id}=m^{*}(\mathrm{ceil}\left(\frac{y-y_0}{l}\right)-1)+\mathrm{ceil}\left(\frac{x-x_0}{l}\right)-1;$ Calculate query point Q ₁the grid cell at place is numbered 135.

F: the grid cell numbering according to calculating in step e, search the data object that this grid cell is corresponding, and calculating wherein returns to user from the nearest data object of query point.When carrying out F step, according to following concrete steps, carry out:

F1: according to the grid cell numbering calculating in step e, search the slope slope in corresponding every the sub-section of this grid cell and control the oid of data object or the vid of link vertex in this sub-section in Hash table hashmap_3;

F2: the slope that calculates respectively the straight line that first data object on query point and each strip section is formed by connecting; If the slope in corresponding a certain the sub-section of the slope calculating and grid cell equates, query point is positioned on the sub-section of this correspondence; If the slope calculating all equates with the slope in corresponding some the sub-sections of grid cell, calculate respectively the coordinate position relation between query point and the two-end-point of each sub-line segment, to determine sub-section, query point place;

For example, in Fig. 3, Q is query point, O ₁and O ₂for controlling the data object of the grid at query point place, the common vertex that a is two road.Difference calculated line Q → O ₁with Q → O ₂slope, known Q → O ₁slope with only with road a → O ₁slope identical, therefore, query point Q is at road a → O ₁on.

In Fig. 4, Q is query point, O ₁and O ₂for controlling the data object of the grid at query point place, the common vertex that a is two road.Difference calculated line Q → O ₁with Q → O ₂slope, known Q → O ₁with Q → O ₂slope identical, and with road a → O ₁and a → O ₂slope all identical, in the case, by calculating the coordinate position relation between the two-end-point of query point and each individual sub-line segment, can determine that query point Q is at road a → O ₂upper, and be not positioned at road a → O ₁on.

F3: judge on the road of query point place whether have data object, if so, enter step F 4; If not, enter step F 5;

F4: if two end points in sub-section, query point place are respectively two data objects, calculate respectively the distance between these two data objects and query point, and will as Query Result, return to user with the nearest data object of query point, if these two data objects are identical with the distance between query point, these two data objects are returned to user as Query Result simultaneously;

As the query point Q in Fig. 2 ₂position, Q ₂be positioned at sub-route O ₄→ O ₅between, and O ₄with O ₅be respectively two data objects, rather than summit, crossing.So only need to be from O ₄with O ₅in find out from query point Q ₂nearest data object returns to user as Query Result.In Fig. 2, data object O ₄for O ₄with O ₅in from query point Q ₂nearest data object, so in situation shown in Fig. 2, O ₄be the space arest neighbors inquiring.

If two end points in sub-section, query point place are respectively a data object and a link vertex, first calculate query point and arrive the distance r1 of this data object and the distance r that query point arrives this link vertex ₂, then from Hash table hashmap_2, inquire about arest neighbors and the minimum distance s of this link vertex on spatial network ₂, finally compare r ₁with r ₂+ s ₂size, if r ₁less, data object corresponding to sub-section, query point place end points returned to user as Query Result; If r ₂+ s ₂less, using inquire about the corresponding data object of the arest neighbors of this link vertex on spatial network from Hash table hashmap_2, as Query Result, return to user; If r ₁with r ₂+ s ₂size identical, simultaneously using data object corresponding to sub-section, query point place end points, from Hash table hashmap_2, inquire about the corresponding data object of the arest neighbors of this link vertex on spatial network as Query Result, return to user simultaneously;

Query point Q as shown in Figure 2 ₁position, query point Q ₁the grid cell at place is numbered 135, query point Q ₁the sub-line segment at place is o ₃→ b, two corresponding end points are respectively data object o ₃with link vertex b.For this situation, first calculate query point Q ₁to data object o ₃distance r ₁with the distance r of query point to link vertex b ₂, then from Hash table hashmap_2, inquire the arest neighbors o of link vertex b on spatial network ₇and link vertex b is to its arest neighbors o ₇minimum distance s ₂, finally compare r ₁with r ₂+ s ₂size, if r ₁less, by query point Q ₁data object o corresponding to sub-section, place end points ₃as Query Result, return to user; If r ₂+ s ₂less, will from Hash table hashmap_2, inquire about the corresponding data object o of the arest neighbors of this link vertex b on spatial network ₇as Query Result, return to user.As can be seen from Figure 2, r ₁< r ₂+ s ₂, therefore, by query point Q ₁data object o corresponding to sub-section, place end points ₃as the arest neighbors inquiring, return to user.

Query point Q as shown in Figure 5 again ₁position, query point Q ₁the sub-line segment at place is o ₂→ d, two corresponding end points are respectively data object o ₂with link vertex d.For this situation, first calculate query point Q ₁to data object o ₂distance r ₁with the distance r of query point to link vertex d ₂, then from Hash table hashmap_2, inquire the arest neighbors o of link vertex d on spatial network ₁and link vertex d is to its arest neighbors o ₁minimum distance s ₂, finally compare r ₁with r ₂+ s ₂size, if r ₁less, by query point Q ₁data object o corresponding to sub-section, place end points ₂as Query Result, return to user; If r ₂+ s ₂less, will from Hash table hashmap_2, inquire about the corresponding data object o of the arest neighbors of this link vertex d on spatial network ₁as Query Result, return to user.As can be seen from Figure 5, r ₁> r ₂+ s ₂, therefore, will from Hash table hashmap_2, inquire about the corresponding data object o of the arest neighbors of this link vertex d on spatial network ₁as Query Result, return to user.

F5: if two end points of query point place road are respectively two link vertex, first score is not calculated query point to the distance r of first link vertex ₃distance r with query point to the second link vertex ₄, then from Hash table hashmap_2, inquire about respectively arest neighbors and the minimum distance s of first link vertex on spatial network ₃, second arest neighbors and the minimum distance s of link vertex on spatial network ₄, calculate r ₃+ s ₃with r ₄+ s ₄size, if r ₃+ s ₃less, using first link vertex inquiring from Hash table hashmap_2, the corresponding data object of arest neighbors on spatial network returns to user as Query Result; If r ₄+ s ₄less, using second link vertex inquiring from Hash table hashmap_2, the corresponding data object of arest neighbors on spatial network returns to user as Query Result; If r ₃+ s ₃with r ₄+ s ₄size identical, using first link vertex inquiring from Hash table hashmap_2, the corresponding data object of the corresponding data object of arest neighbors on spatial network, second link vertex inquiring from the Hash table hashmap_2 arest neighbors on spatial network returns to user as Query Result simultaneously simultaneously.

Query point Q as shown in Figure 6 ₁position, query point Q ₁the sub-line segment at place is d → e, and two corresponding end points are respectively link vertex d and link vertex e.For this situation, first calculate query point Q ₁distance r to link vertex d ₃with the distance r of query point to link vertex e ₄, then from Hash table hashmap_2, inquire the arest neighbors o of link vertex d on spatial network ₁and link vertex d is to its arest neighbors o ₁minimum distance s ₃, the arest neighbors o of link vertex e on spatial network ₂and link vertex e is to its arest neighbors o ₂minimum distance s ₄, finally compare r ₃+ s ₃with r ₄+ s ₄size, if r ₃+ s ₃less, the corresponding data object o of arest neighbors on spatial network by first link vertex d inquiring from Hash table hashmap_2 ₁as Query Result, return to user; If r ₄+ s ₄less, will from Hash table hashmap_2, inquire about the corresponding data object o of the arest neighbors of this link vertex e on spatial network ₂as Query Result, return to user.As can be seen from Figure 6, r ₃+ s ₃> r ₄+ s ₄, therefore, will from Hash table hashmap_2, inquire about the corresponding data object o of the arest neighbors of this link vertex e on spatial network ₂as Query Result, return to user.

Query point Q as shown in Figure 7 again ₁position, query point Q ₁the sub-line segment at place is d → e, and two corresponding end points are respectively link vertex d and link vertex e.For this situation, first calculate query point Q ₁distance r to link vertex d ₃with the distance r of query point to link vertex e ₄, then from Hash table hashmap_2, inquire the arest neighbors o of link vertex d on spatial network ₁and link vertex d is to its arest neighbors o ₁minimum distance s ₃, the arest neighbors o of link vertex e on spatial network ₂and link vertex e is to its arest neighbors o ₂minimum distance s ₄, finally compare r ₃+ s ₃with r ₄+ s ₄size, if r ₃+ s ₃less, the corresponding data object o of arest neighbors on spatial network by first link vertex d inquiring from Hash table hashmap_2 ₁as Query Result, return to user; If r ₄+ s ₄less, will from Hash table hashmap_2, inquire about the corresponding data object o of the arest neighbors of this link vertex e on spatial network ₂as Query Result, return to user.As can be seen from Figure 6, r ₃+ s ₃> r ₄+ s ₄, therefore, will from Hash table hashmap_2, inquire about the corresponding data object o of the arest neighbors of this link vertex e on spatial network ₂as Query Result, return to user.

The present invention is applied as starting point with the space querying of the service based on geographical location information, spatial query processing is divided into off-line index and sets up and real-time two megastages of spatial query processing.

Off-line index establishment stage is served the real-time spatial query processing stage, the response speed of the space arest neighbors inquiry for improve between real-time empty processing stage.Off-line index establishment stage is responsible for index and is set up, and in the present invention, at off-line index establishment stage, has created three Hash table: hashmap_1, hashmap_2, hashmap_3.Wherein, the indications of hashmap_1 for storing data object, i.e. corresponding relation between the oid of data object and the coordinate of data object; Hashmap_2 is for storing the indications of link vertex, i.e. the vid of link vertex and this summit value of the bee-line between the oid of nearest data object on the oid of nearest data object and the vid of link vertex and spatial network on spatial network; Hashmap_3 is for corresponding relation, this sub-section slope between save mesh unit and the data object in this grid cell neutron section of control.

In the real-time spatial query processing stage, each space arest neighbors is inquired about, first calculate the corresponding grid cell of query point in two-dimensional space, then three Hash tables setting up by off-line index establishment stage find the data object of controlling the sub-section comprising in this grid cell, determine on the road at query point place and the space arest neighbors on the network of highways of query point place, the space arest neighbors using it as query point returns to user.

Claims (9)

1. a network of highways efficient spatial arest neighbors querying method, is characterized in that, comprises the following steps:

A: the input data that comprise data object information on network of highways information and network of highways are provided by user, according to input data creation Hash table hashmap_1, and the corresponding relation of all data objects and its coordinate figure are stored in Hash table hashmap_1;

B: calculate respectively the space arest neighbors on every each summit of road, create Hash table hashmap_2, and the arest neighbors on spatial network and minimum distance are stored in Hash table hashmap_2 by link vertex, this summit;

C: use virtual grid that two-dimensional space is divided into the square net unit that several grades are large, determine the length of side of grid cell, and virtual grid is numbered;

D: calculate sub-section the save mesh unit that each grid cell comprises and control the data object in this sub-section or the corresponding relation between link vertex, and by result store in Hash table hashmap_3;

E: calculate the grid cell at place, query point position, and the numbering of the corresponding grid cell in definite query point position;

F: the grid cell numbering according to calculating in step e, search the data object that this grid cell is corresponding, and calculating wherein returns to user from the nearest data object of query point.

2. network of highways efficient spatial arest neighbors querying method according to claim 1, is characterized in that: unique indications oid that the major key of described Hash table hashmap_1 is data object, is worth the coordinate figure for this data object.

3. network of highways efficient spatial arest neighbors querying method according to claim 2, it is characterized in that: unique indications vid that the major key of described Hash table hashmap_2 is link vertex, is worth for the oid of the arest neighbors of this summit on spatial network and the minimum distance s between vid and this oid.

4. network of highways efficient spatial arest neighbors querying method according to claim 3, is characterized in that, described step C comprises the following steps:

C1: use virtual grid that two-dimensional space is divided into the square net unit that several grades are large, pass through formula

(1) determine the length of side of grid cell, wherein, l is the grid cell length of side, establishes d ₁for the length of shortest path in all roads, establish d ₂for having on the road of data object, the minimum value of the distance between all two adjacent data objects, d is d ₁and d ₂in minimum value, ξ=10 ^-9;

C2: making R is the minimum boundary rectangle that all data objects of data centralization form, with the summit (x in the lower left corner of R ₀, y ₀) be starting point, set up virtual grid, and from coordinate (x ₀, y ₀) grid cell at place starts, from left to right, incrementally grid cell is numbered from bottom to top; The formula of the grid cell numbering at coordinates computed point (x, y) place is:

$\mathrm{id}=m^{*}(\mathrm{ceil}\left(\frac{y-y_0}{l}\right)-1)+\mathrm{ceil}\left(\frac{x-x_0}{l}\right)-1;---\left(2\right)$

Wherein, id is the grid cell numbering at coordinate points (x, y) place, the quantity of the grid cell that every a line that m is grid comprises, and ceil is upper bracket function.

5. network of highways efficient spatial arest neighbors querying method according to claim 4, is characterized in that, described step D comprises the following steps:

D1: for any road, be divided into some sub-sections;

D2: for any strip section, calculate the grid cell crossing with this sub-section;

D3: calculate all sub-section that each grid cell comprises and the set of data objects of controlling this little section, and the corresponding relation of the slope in the sub-section at grid cell numbering and set of data objects and data object place is stored in Hash table hashmap_3, the key of hashmap_3 is the numbering of grid cell, to value that should key, be a set, each element in this set is for controlling the oid of each data object of this grid cell and the slope in the sub-section at this data object place; If controlling the point of this grid cell is link vertex, using the slope in the sub-section of the vid on this summit and place as an element, be stored in the set of this grid cell correspondence in hashmap_3.

6. network of highways efficient spatial arest neighbors querying method according to claim 5, is characterized in that: in described step D1, for any road, from its summit, first by this insertion point, summit set ps; Then take out successively the data object on this road and be inserted in a set ps; For the line segment that in a set ps, two adjacent data group of objects of front and back become, being divided equally is in two sub-line segments difference intron line segment aggregate rs; Finally by another summit of road also insertion point set ps.

7. network of highways efficient spatial arest neighbors querying method according to claim 6, is characterized in that: in described step D2, for any one sub-section, establish its two-end-point and be respectively v ₁and v ₂, calculate limit v ₁→ v ₂intersection point with longitudinal network ruling and transverse grid line; According to v ₁to v ₂direction, according to the above-mentioned intersection point of magnitude relationship ordered arrangement and the v of abscissa value ₁and v ₂the set of the point that two summits form; To in the set after sequence the mid point of all adjacent 2 in set of computations a little successively, the numbering of formula (2) in each mid point use step C2 being calculated to the grid cell at this mid point place, the set that the grid cell at mid point place forms is and limit v ₁→ v ₂crossing grid cell.

8. network of highways efficient spatial arest neighbors querying method according to claim 7, it is characterized in that: in described step e, utilize the formula (2) in step C2, calculate the grid cell at place, query point position, and the numbering of the corresponding grid cell in definite query point position.

9. network of highways efficient spatial arest neighbors querying method according to claim 8, is characterized in that, described step F comprises the following steps:

F1: according to the grid cell numbering calculating in step e, search the slope in corresponding every the sub-section of this grid cell and control the oid of data object or the vid of link vertex in this sub-section in Hash table hashmap_3;

F2: the slope that calculates respectively the straight line that first object on query point and each strip section is formed by connecting; If the slope in corresponding a certain the sub-section of the slope calculating and grid cell equates, query point is positioned on the sub-section of this correspondence; If the slope calculating all equates with the slope in corresponding some the sub-sections of grid cell, calculate respectively the coordinate position relation between query point and the two-end-point of each sub-line segment, to determine sub-section, query point place;

F3: judge on the road of query point place whether have data object, if so, enter step F 4; If not, enter step F 5;

F4: if two end points in sub-section, query point place are respectively two data objects, calculate respectively the distance between these two data objects and query point, and will as Query Result, return to user with the nearest data object of query point, if these two data objects are identical with the distance between query point, these two data objects are returned to user as Query Result simultaneously;

If two end points in sub-section, query point place are respectively a data object and a link vertex, first calculate query point and arrive the distance r1 of this data object and the distance r that query point arrives this link vertex ₂, then from Hash table hashmap_2, inquire about arest neighbors and the minimum distance s of this link vertex on spatial network ₂, finally compare r ₁with r ₂+ s ₂size, if r ₁less, data object corresponding to sub-section, query point place end points returned to user as Query Result; If r ₂+ s ₂less, using inquire about the corresponding data object of the arest neighbors of this link vertex on spatial network from Hash table hashmap_2, as Query Result, return to user; If r ₁with r ₂+ s ₂size identical, simultaneously using data object corresponding to sub-section, query point place end points, from Hash table hashmap_2, inquire about the corresponding data object of the arest neighbors of this link vertex on spatial network as Query Result, return to user simultaneously;

F5: if two end points of query point place road are respectively two link vertex, first score is not calculated query point to the distance r of first link vertex ₃distance r with query point to the second link vertex ₄, then from Hash table hashmap_2, inquire about respectively arest neighbors and the minimum distance s of first link vertex on spatial network ₃, second arest neighbors and the minimum distance s of link vertex on spatial network ₄, calculate r ₃+ s ₃with r ₄+ s ₄size, if r ₃+ s ₃less, using first link vertex inquiring from Hash table hashmap_2, the corresponding data object of arest neighbors on spatial network returns to user as Query Result; If r ₄+ s ₄less, using second link vertex inquiring from Hash table hashmap_2, the corresponding data object of arest neighbors on spatial network returns to user as Query Result; If r ₃+ s ₃with r ₄+ s ₄size identical, using first link vertex inquiring from Hash table hashmap_2, the corresponding data object of the corresponding data object of arest neighbors on spatial network, second link vertex inquiring from the Hash table hashmap_2 arest neighbors on spatial network returns to user as Query Result simultaneously simultaneously.

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