CN102222066A - Conflict shifting processing method for multi-source spatial data combination - Google Patents

Abstract

The invention relates to a conflict shifting processing method for multi-source spatial data combination. The method comprises the following steps of: (1) reading data, and constructing topological relationships among entities; (2) performing entity matching; (3) performing the regulation and combination of entity coordinates; (4) checking whether combination results comprise mismatching or not, correcting the mismatching and returning to the step (3) if the combination results comprise the mismatching, and executing the step (5) if the combination results do not comprise the mismatching; and (5) automatically detecting the existence or inexistence of a picture conflict, and if the picture conflict is automatically detected, performing conflict shifting by adopting a least square adjustment method. Compared with the prior art, the method has the advantages of saving manpower and material resources, eliminating differences among data sets, solving the spatial conflict, maintaining geometrical characteristics of the entities, and the like.

Description

The conflict shifting processing method that a kind of multi-source spatial data merges

Technical field

The present invention relates to the conflict shifting processing method that a kind of data merge, especially relate to the conflict shifting processing method that a kind of multi-source spatial data merges.

Background technology

Along with the development of Surveying and Mapping Industry and Geographic Information System (GIS), abundant GIS data resource has been accumulated by each department, and these data have various differences.Why producing difference between data set mainly is because data from different production divisions, each production division has different emphasis for self needs to data.Emphasize the positional information in plot as land administration department, thereby the database that produces has the higher spatial positional precision, and economic department is studied at space distribution information, more focuses on the attribute information of non-space, the database of its generation has abundant attribute data, but precision is relatively poor.Wherein, of paramount importance a little species diversity is as follows:

1, the difference of data model.Existing GIS data model is divided into vector data model and raster data model, even model of the same race, the data organization mode also can be different, may use noodles shape structure as the line entity, also may use node segmental arc topological structure;

2, database structure difference, as the field difference that database comprises, the same field data type is not equal;

3, data the level of detail difference mainly due to the map scale difference, is subjected to the map combined influence, varies in size for same entity information amount.As, river under large scale than under small scale, having more multiple spot information, or because data obtain asynchronism(-nization), cause that some entity does not exist or the like on the new figure on old figure;

4, the differences in spatial location of entity of the same name;

5, data precision difference, for example large scale topographical map is higher to the accuracy requirement of the volume coordinate position of atural object, and the cadastre of same ratio chi only requires that then there is higher precision in the ownership boundary line.

Because above-mentioned kind of species diversity, realize data integrated and shared be difficulty comparatively, therefore most departments take the method for repeated acquisition, have wasted lot of manpower and material resources, also caused the idle of a large amount of useful datas.Realize separate sources, have the integrated and shared important topic that has become present stage GIS development of the data of each species diversity.In order to utilize these data better, therefore the difference of eliminating each data set " spatial data merging " this notion occurred to form the new data set that can satisfy certain requirement.

The spatial data merging is meant on the basis of spatial entities coupling of the same name, adjusts the geometric position of relevant atural object entity, realizes the integrated and information fusion of areal separate sources spatial data.In the spatial data merging process, because the displacement of entity can cause and the conflicting of other entity.Be illustrated in fig. 1 shown below, entity comprises buildings entity and road entity among the figure, and in the merging process, path layer carries out the adjustment conversion of entity according to different maps respectively with the buildings layer, stacked and together the time as two figure, the too small figure that causes of distance conflicts between some buildings and road.

Existing big quantity research attempts to solve the conflict displacement problem of figure.Lichtner (1976) has proposed the displacement formula of putting under the perfect condition, and promptly the off-line entity is far away more, and shift amount is more little, and this method does not consider that a displacement is subjected to the influence of many line entities, only is applicable to the displacement that the single line entity causes.Many afterwards methods are all based on this thought, and expand, and for example consider the road turnover to every combined effect in the displacement district, again according to road corner, the floor area of building to whole building or turning point displacement.Mackness (1994) has set up a kind of by the conflict of target interphase interaction space exploration, thereby implements the attenuation type shifting function away from the conflict center.Another kind method is that polar plot is converted into grid map, Li Zhilin (1996) has proposed the map comprehensive key element displacement model based on mathematical morphology, Fei Lifan (2004,2002) propose to adopt grid one vector MIDAS mixed data structure to be shifted and distinguish interior owned building classification, the operations such as displacement and controlled-deformation of charting.In the additive method, Burghardt ﹠amp; Meier (1997) has proposed " snake " model, has considered that simultaneously the external energy (conflict) of entity and internal energy (single order, second order inverses that entity is how much) are to reach the energy minimum.It is some triangles with whole Region Segmentation that Hojholt (1998) uses finite element method, so that survey and the problem that manages conflict.Ai Tinghua (2004) sets up the subdivision structure that is similar to Voronoi figure in the polygon group space, obtain the effect that " equidistant relation curve " is used for expressing a displacement power, obtains direction of displacement and displacement by vector calculus.

About seldom mentioning the follow-up collision problem that causes because of merging in the research of spatial data, complete spatial data combination system should be able to detect the conflict and the displacement that conflicts automatically both at home and abroad.

The topological relation that must keep inter-entity when managing conflict the displacement problem, the geometrical property of as far as possible preserving entity can't be brought new collision problem simultaneously.

Summary of the invention

The conflict shifting processing method that purpose of the present invention is exactly the difference that provides a kind of in order to overcome the defective that above-mentioned prior art exists and use manpower and material resources sparingly, eliminate each data set, solved the space conflict, kept the multi-source spatial data of entity geometric properties to merge.

Purpose of the present invention can be achieved through the following technical solutions:

The conflict shifting processing method that a kind of multi-source spatial data merges is characterized in that, may further comprise the steps:

(1) reading of data, the topological relation of structure inter-entity;

(2) carry out the entity coupling;

(3) adjustment of carrying out the entity coordinate merges;

(4) whether the check amalgamation result exists the mistake coupling, and if yes, correct the mistake coupling, and return step (3), if not, execution in step (5);

(5) automatically whether detection exists the figure conflict, if yes, adopts the displacement that manages conflict of least square adjustment method.

Entity coupling in the described step (2) comprises the coupling of an entity, the coupling of line entity, the coupling of face entity.

Employing least square adjustment method in the described step (5) manage conflict the displacement as follows:

If point coordinate is (x among the former figure _i ⁰, y _i ⁰) (i=1,2 ..., n) as the parameter approximate value, the position coordinates after point moves

As adjusted value, then have

$\left\{\begin{array}{c}{\hat{x}}_i=x_i^0+{\mathrm{\Δx}}_i\\ {\hat{y}}_i=y_i^0+{\mathrm{\Δy}}_i\end{array}\right.(i=\mathrm{1,2},\·\·\·,n)---\left(1\right)$

(Δ x in the formula _i, Δ y _i) be the coordinate translation amount, be parameter to be found the solution is the adjusted value correction, the adjustment equation of use comprises as follows:

1) coordinate amount of movement error equation

The point that constitutes entity should remain on original position as far as possible, and promptly the coordinate amount of movement will approach 0, obtains following error equation

$\left\{\begin{array}{c}{v}_{x_i}={\mathrm{\Δx}}_i\\ v_{y_i}={\mathrm{\Δy}}_i\end{array}\right.---\left(2\right)$

All corresponding two the coordinate amount of movement error equations of each point, therefore the number of whole coordinate amount of movement error equation is total number of the point of twice;

2) relative shift error equation

In order to keep the geometry of entity inside, should make that the relative shift of consecutive point approaches 0 on the entity, can get following error equation

$\left\{\begin{array}{c}{v}_{x_{i,j+1}}={\mathrm{\Δx}}_i-{\mathrm{\Δx}}_{i+1}\\ v_{y_{i,i+1}}={\mathrm{\Δy}}_i-{\mathrm{\Δy}}_{i+1}\end{array}\right.---\left(3\right)$

3) shape error equation

Keep the original shape of entity when coordinate is adjusted, need carry out the shape restriction,, can get error equation and be if keep the turning value of entity frontier point to entity to be adjusted

$v_{{\mathrm{\β}}_i}=a_{\mathrm{ik}}{\mathrm{\Δx}}_k+b_{\mathrm{ik}}{\mathrm{\Δy}}_k-(a_{\mathrm{ik}}-a_{\mathrm{ij}}){\mathrm{\Δx}}_i-(b_{\mathrm{ik}}-b_{\mathrm{ij}}){\mathrm{\Δy}}_i-a_{\mathrm{ij}}{\mathrm{\Δx}}_j-b_{\mathrm{ij}}{\mathrm{\Δy}}_j-l_{{\mathrm{\β}}_i}---\left(4\right)$

In the formula $a_{\mathrm{ij}}=-\frac{\sin {\mathrm{\α}}_{\mathrm{ij}}}{s_{\mathrm{ij}}};$ $b_{\mathrm{ij}}=\frac{\cos {\mathrm{\α}}_{\mathrm{ij}}}{s_{\mathrm{ij}}};$ $a_{\mathrm{ik}}=-\frac{\sin {\mathrm{\α}}_{\mathrm{ik}}}{s_{\mathrm{ik}}};$ $b_{\mathrm{ik}}=\frac{\cos {\mathrm{\α}}_{\mathrm{ik}}}{s_{\mathrm{ik}}}$

$s_{\mathrm{ij}}=\sqrt{{({x_j}^0-{x_i}^0)}^2+{({y_j}^0-{y_i}^0)}^2};$ $s_{\mathrm{ik}}=\sqrt{{({x_k}^0-{x_i}^0)}^2+{({y_k}^0-{y_i}^0)}^2}$

$l_{{\mathrm{\β}}_i}={\mathrm{\β}}_i-{\mathrm{\β}}_i^0;$ ${\mathrm{\β}}_i^0=\arctan \frac{{y_k}^0-{y_i}^0}{{x_k}^0-{x_i}^0}-\arctan \frac{{y_j}^0-{y_i}^0}{{x_j}^0-{x_i}^0}$

Wherein, α _Ik, α _IjBe respectively the position angle of ik, ij direction, obtained by the approximation calculation of coordinate adjustment value, β is the former angle value of corner point, and the number of shape error equation is the number of entity corner point;

4) conflict shift error equation

The figure conflict can be divided into a little and conflicting with face with line, line with face, line with line, point with point, point,

Point is tried to achieve with the range formula of point

$v_{D(P_i,P_j)}+D(P_i,P_j)=\sqrt{{({\hat{x}}_j-{\hat{x}}_i)}^2+{({\hat{y}}_j-{\hat{y}}_i)}^2}---\left(5\right)$

Wherein,

Be the error of distance observation, D (P _i, P _j) be a P _i(x _i, y _i) and P _j(x _j, y _j) distance,

With

It is the coordinate after its displacement;

Linearization obtains its error equation:

v _dist＝-a _ijΔx _i+b _ijΔy _i+a _ijΔx _j-b _ijΔy _j-l _dist (6)

A wherein _Ij=cos α _Ij, b _Ij=-sin α _Ij, α _IjApproximation calculation by the coordinate adjustment value obtains, l _DistValue be divided into two kinds of situations: if the distance D of point and point before moving _Point-pointLess than minimum threshold of distance mindist, then l _Dist=min dist-D _Point-point, wherein

If D _Point-pointGreater than mindist and less than 1.5*mindist l then _Dist=0;

Point with the range equation of line segment is:

$v_{D(P_i,L_{P_j{P}_k})}+D_{(P_i,L_{P_j{P}_k})}=\frac{|({\hat{y}}_j-{\hat{y}}_k){\hat{x}}_i+({\hat{x}}_k-{\hat{x}}_j){\hat{y}}_i+({\hat{x}}_j{\hat{y}}_k-{\hat{x}}_k{\hat{y}}_j)|}{\sqrt{{({\hat{y}}_k-{\hat{y}}_j)}^2+{({\hat{x}}_k-{\hat{x}}_j)}^2}}---\left(7\right)$

Wherein,

With

Be respectively apart from observed reading and error,

With Two end points of adjusted point and line segment;

Linearization obtains its error equation:

$v_{D(P_i,L_{P_j{P}_k})}=a_{\mathrm{ijk}}{\mathrm{\δx}}_i+b_{\mathrm{ijk}}{\mathrm{\δy}}_i+c_{\mathrm{ijk}}{\mathrm{\δx}}_j+d_{\mathrm{ijk}}{\mathrm{\δy}}_j+e_{\mathrm{ijk}}{\mathrm{\δx}}_k+f_{\mathrm{ijk}}{\mathrm{\δy}}_k-l_{D(P_i,P_j{P}_k)}---\left(8\right)$

Wherein, (x _i ⁰, y _i ⁰), (x _j ⁰, y _j ⁰) and (x _k ⁰, y _k ⁰) be the approximate value of coordinate, and

$a_{\mathrm{ijk}}=\frac{y_j^0-y_k^0}{{({(y_k^0-y_j^0)}^2+{(x_k^0-x_j^0)}^2)}^{1/2}};$

$b_{\mathrm{ijk}}=\frac{x_k^0-x_j^0}{{({(y_k^0-y_j^0)}^2+{(x_k^0-x_j^0)}^2)}^{1/2}};$

$c_{\mathrm{ijk}}=\frac{(y_k^0-y_i^0)[{(y_k^0-y_j^0)}^2+{(x_k^0-x_j^0)}^2]+(x_k^0-x_j^0)[(y_j^0-y_k^0)x_i^0-(x_k^0-x_j^0)y_i^0+(x_j^0{y}_k^0-x_k^0{y}_j^0)]}{{({(y_k^0-y_j^0)}^2+{(x_k^0-x_j^0)}^2)}^{3/2}};$

$d_{\mathrm{ijk}}=\frac{(x_i^0-x_k^0)[{(y_k^0-y_j^0)}^2+{(x_k^0-x_j^0)}^2]+(y_k^0-y_j^0)[(y_j^0-y_k^0)x_i^0-(x_k^0-x_j^0)y_i^0+(x_j^0{y}_k^0-x_k^0{y}_j^0)]}{{({(y_k^0-y_j^0)}^2+{(x_k^0-x_j^0)}^2)}^{3/2}};$

$e_{\mathrm{ijk}}=\frac{(y_i^0-y_j^0)[{(y_k^0-y_j^0)}^2+{(x_k^0-x_j^0)}^2]+(x_j^0-x_k^0)[(y_j^0-y_k^0)x_i^0-(x_k^0-x_j^0)y_i^0+(x_j^0{y}_k^0-x_k^0{y}_j^0)]}{{({(y_k^0-y_j^0)}^2+{(x_k^0-x_j^0)}^2)}^{3/2}};$

$f_{\mathrm{ijk}}=\frac{(x_j^0-x_i^0)[{(y_k^0-y_j^0)}^2+{(x_k^0-x_j^0)}^2]+(y_j^0-y_k^0)[(y_j^0-y_k^0)x_i^0-(x_k^0-x_j^0)y_i^0+(x_j^0{y}_k^0-x_k^0{y}_j^0)]}{{({(y_k^0-y_j^0)}^2+{(x_k^0-x_j^0)}^2)}^{3/2}};$

$l_{D(P_i,L_{P_j{P}_k})}=D_{(P_i,L_{P_j{P}_k})}-\frac{|(y_j^0-y_k^0)x_i^0+(x_k^0-x_j^0)y_i^0+(x_j^0{y}_k^0-x_k^0{y}_j^0)|}{\sqrt{{(y_k^0-y_j^0)}^2+{(x_k^0-x_j^0)}^2}}.$

L in the formula _DistValue identical with point with the error equation of point, if D _Point-line＜mindist, then l _Dist=min dist-D _Point-line, if mindist＜D _Point-line＜1.5*mindist, then l _Dist=0;

Point with the distance definition of face is: this minor increment in the distance of all polylines of forming this face thus, can be similar to formula (8) and set up the distance error equation that point arrives face;

Line to the distance definition of line is: for line segment L _iAnd L _j, its distance is

$D_{(L_i,L_j)}=\underset{P_i\∈L_i}{\min }\left\{\underset{P_j\∈L_j}{\min }\right\{D(P_i,P_j)\left\}\right\}---\left(9\right)$

Wherein, P _iBe line segment L _iOn more arbitrarily, P _jBe line segment L _jOn more arbitrarily, D (P _i, P _j) be a P _iAnd P _jDistance, calculate according to formula (5);

Line to the distance definition of face is: for line segment L _iWith polygon A _j, its distance is

$D_{(L_i,A_j)}=\underset{P_i\∈L_i}{\min }\left\{\underset{P_j\∈A_j}{\min }\right\{D(P_i,P_j)\left\}\right\}---\left(10\right)$

Wherein, P _iBe line segment L _iOn more arbitrarily, P _jBe polygon A _jIn more arbitrarily, the minimum value that line is a line segment in the distance of forming this polygonal polyline to the distance definition of face,

Line to the distance definition of face is: for line segment A _iWith polygon A _j, its distance is

$D_{(A_i,A_j)}=\underset{P_i\∈A_i}{\min }\left\{\underset{P_j\∈A_j}{\min }\right\{D(P_i,P_j)\left\}\right\}---\left(11\right)$

Wherein, P _iBe polygon A _iIn more arbitrarily, P _jBe polygon A _jIn more arbitrarily, the various equations of simultaneous are the indirect adjustment problem, its general model is:

v＝Ax-l (12)

X is a parameter vector in the formula, x=(Δ x ₁Δ y ₁Δ x _nΔ y _n) ^TA is a factor arrays; L is a constant vector; 1 is constant vector; V is a residual vector, by the principle of least square, can get normal equation and is

N _Ax-U＝0 (13)

In the formula: N _A=A ^TPA; U=A ^TPl, P are weight matrix;

Separating normal equation can get:

$x=N_A^{-1}\·U---\left(14\right)$

Parameter x substitution error equation with trying to achieve promptly gets residual error v and adjusted value The coordinate adjustment value is the coordinate after a little moving;

The inverse of weight matrix computing formula of coordinate adjustment value is

$Q_{\hat{x}\hat{x}}{\left(A^T\mathrm{PA}\right)}^{-1}=N_A^{-1}---\left(15\right)$

Wherein A is the factor arrays of error equation, and p is the power battle array, and then the accuracy formula of adjusted value is

${\widehat{\mathrm{\σ}}}_{{\hat{x}}_i}=\±{\widehat{\mathrm{\σ}}}_0\sqrt{Q_{{\hat{x}}_i{\hat{x}}_i}}---\left(16\right)$

Wherein

For

The pivot of matrix, error in the weight unit

Compared with prior art, the present invention has the difference that uses manpower and material resources sparingly, eliminate each data set, has solved the space conflict, keeps the entity geometric properties.

Description of drawings

Fig. 1 is a process flow diagram of the present invention;

Fig. 2 is a structure table precedence diagram of the present invention.

Embodiment

The present invention is described in detail below in conjunction with the drawings and specific embodiments.

Embodiment

As shown in Figure 1, the conflict shifting processing method that a kind of multi-source spatial data merges may further comprise the steps:

(1) reading of data, the topological relation of structure inter-entity;

(2) carry out the entity coupling;

(3) adjustment of carrying out the entity coordinate merges;

(4) whether the check amalgamation result exists the mistake coupling, and if yes, correct the mistake coupling, and return step (3), if not, execution in step (5);

(5) automatically whether detection exists the figure conflict, if yes, adopts the displacement that manages conflict of least square adjustment method.

Entity coupling in the described step (2) comprises the coupling of an entity, the coupling of line entity, the coupling of face entity.

Employing least square adjustment method in the described step (5) manage conflict the displacement as follows:

If point coordinate is (x among the former figure _i ⁰, y _i ⁰) (i=1,2 ..., n) as the parameter approximate value, the position coordinates after point moves

As adjusted value, then have

$\left\{\begin{array}{c}{\hat{x}}_i=x_i^0+{\mathrm{\Δx}}_i\\ {\hat{y}}_i=y_i^0+{\mathrm{\Δy}}_i\end{array}\right.(i=\mathrm{1,2},\·\·\·,n)---\left(1\right)$

(Δ x in the formula _i, Δ y _i) be the coordinate translation amount, be parameter to be found the solution is the adjusted value correction, the adjustment equation of use comprises as follows:

1) coordinate amount of movement error equation

The point that constitutes entity should remain on original position as far as possible, and promptly the coordinate amount of movement will approach 0, obtains following error equation

$\left\{\begin{array}{c}{v}_{x_i}={\mathrm{\Δx}}_i\\ v_{y_i}={\mathrm{\Δy}}_i\end{array}\right.---\left(2\right)$

All corresponding two the coordinate amount of movement error equations of each point, therefore the number of whole coordinate amount of movement error equation is total number of the point of twice;

2) relative shift error equation

In order to keep the geometry of entity inside, should make that the relative shift of consecutive point approaches 0 on the entity, can get following error equation

$\left\{\begin{array}{c}{v}_{x_{i,j+1}}={\mathrm{\Δx}}_i-{\mathrm{\Δx}}_{i+1}\\ v_{y_{i,i+1}}={\mathrm{\Δy}}_i-{\mathrm{\Δy}}_{i+1}\end{array}\right.---\left(3\right)$

3) shape error equation

Keep the original shape of entity when coordinate is adjusted, need carry out the shape restriction,, can get error equation and be if keep the turning value of entity frontier point to entity to be adjusted

$v_{{\mathrm{\β}}_i}=a_{\mathrm{ik}}{\mathrm{\Δx}}_k+b_{\mathrm{ik}}{\mathrm{\Δy}}_k-(a_{\mathrm{ik}}-a_{\mathrm{ij}}){\mathrm{\Δx}}_i-(b_{\mathrm{ik}}-b_{\mathrm{ij}}){\mathrm{\Δy}}_i-a_{\mathrm{ij}}{\mathrm{\Δx}}_j-b_{\mathrm{ij}}{\mathrm{\Δy}}_j-l_{{\mathrm{\β}}_i}---\left(4\right)$

In the formula $a_{\mathrm{ij}}=-\frac{\sin {\mathrm{\α}}_{\mathrm{ij}}}{s_{\mathrm{ij}}};$ $b_{\mathrm{ij}}=\frac{\cos {\mathrm{\α}}_{\mathrm{ij}}}{s_{\mathrm{ij}}};$ $a_{\mathrm{ik}}=-\frac{\sin {\mathrm{\α}}_{\mathrm{ik}}}{s_{\mathrm{ik}}};$ $b_{\mathrm{ik}}=\frac{\cos {\mathrm{\α}}_{\mathrm{ik}}}{s_{\mathrm{ik}}}$

$s_{\mathrm{ij}}=\sqrt{{({x_j}^0-{x_i}^0)}^2+{({y_j}^0-{y_i}^0)}^2};$ $s_{\mathrm{ik}}=\sqrt{{({x_k}^0-{x_i}^0)}^2+{({y_k}^0-{y_i}^0)}^2}$

$l_{{\mathrm{\β}}_i}={\mathrm{\β}}_i-{\mathrm{\β}}_i^0;$ ${\mathrm{\β}}_i^0=\arctan \frac{{y_k}^0-{y_i}^0}{{x_k}^0-{x_i}^0}-\arctan \frac{{y_j}^0-{y_i}^0}{{x_j}^0-{x_i}^0}$

Wherein, α _Ik, α _IjBe respectively the position angle of ik, ij direction, obtained by the approximation calculation of coordinate adjustment value, β is the former angle value of corner point, and the number of shape error equation is the number of entity corner point;

4) conflict shift error equation

The figure conflict can be divided into a little and conflicting with face with line, line with face, line with line, point with point, point,

Point is tried to achieve with the range formula of point

$v_{D(P_i,P_j)}+D(P_i,P_j)=\sqrt{{({\hat{x}}_j-{\hat{x}}_i)}^2+{({\hat{y}}_j-{\hat{y}}_i)}^2}---\left(5\right)$

Wherein,

Be the error of distance observation, D (P _i, P _j) be a P _i(x _i, y _i) and P _j(x _j, y _j) distance,

With

It is the coordinate after its displacement;

Linearization obtains its error equation:

v _dist＝-a _ijΔx _i+b _ijΔy _i+a _ijΔx _j-b _ijΔy _j-l _dist (6)

A wherein _Ij=cos α _Ij, b _Ij=-sin α _Ij, α _IjApproximation calculation by the coordinate adjustment value obtains, l _DistValue be divided into two kinds of situations: if the distance D of point and point before moving _Point-pointLess than minimum threshold of distance mindist, then l _Dist=mindist-D _Point-point, wherein If D _Point-pointGreater than mindist and less than 1.5*mindist l then _Dist=0;

Point with the range equation of line segment is:

$v_{D(P_i,L_{P_j{P}_k})}+D_{(P_i,L_{P_j{P}_k})}=\frac{|({\hat{y}}_j-{\hat{y}}_k){\hat{x}}_i+({\hat{x}}_k-{\hat{x}}_j){\hat{y}}_i+({\hat{x}}_j{\hat{y}}_k-{\hat{x}}_k{\hat{y}}_j)|}{\sqrt{{({\hat{y}}_k-{\hat{y}}_j)}^2+{({\hat{x}}_k-{\hat{x}}_j)}^2}}---\left(7\right)$

Wherein,

With

Be respectively apart from observed reading and error,

With

Two end points of adjusted point and line segment;

Linearization obtains its error equation:

$v_{D(P_i,L_{P_j{P}_k})}=a_{\mathrm{ijk}}{\mathrm{\δx}}_i+b_{\mathrm{ijk}}{\mathrm{\δy}}_i+c_{\mathrm{ijk}}{\mathrm{\δx}}_j+d_{\mathrm{ijk}}{\mathrm{\δy}}_j+e_{\mathrm{ijk}}{\mathrm{\δx}}_k+f_{\mathrm{ijk}}{\mathrm{\δy}}_k-l_{D(P_i,P_j{P}_k)}---\left(8\right)$

Wherein, (x _i ⁰, y _i ⁰, (x _j ⁰, y _j ⁰) and (x _k ⁰, y _k ⁰) be the approximate value of coordinate, and

$a_{\mathrm{ijk}}=\frac{y_j^0-y_k^0}{{({(y_k^0-y_j^0)}^2+{(x_k^0-x_j^0)}^2)}^{1/2}};$

$b_{\mathrm{ijk}}=\frac{x_k^0-x_j^0}{{({(y_k^0-y_j^0)}^2+{(x_k^0-x_j^0)}^2)}^{1/2}};$

$c_{\mathrm{ijk}}=\frac{(y_k^0-y_i^0)[{(y_k^0-y_j^0)}^2+{(x_k^0-x_j^0)}^2]+(x_k^0-x_j^0)[(y_j^0-y_k^0)x_i^0-(x_k^0-x_j^0)y_i^0+(x_j^0{y}_k^0-x_k^0{y}_j^0)]}{{({(y_k^0-y_j^0)}^2+{(x_k^0-x_j^0)}^2)}^{3/2}};$

$d_{\mathrm{ijk}}=\frac{(x_i^0-x_k^0)[{(y_k^0-y_j^0)}^2+{(x_k^0-x_j^0)}^2]+(y_k^0-y_j^0)[(y_j^0-y_k^0)x_i^0-(x_k^0-x_j^0)y_i^0+(x_j^0{y}_k^0-x_k^0{y}_j^0)]}{{({(y_k^0-y_j^0)}^2+{(x_k^0-x_j^0)}^2)}^{3/2}};$

$e_{\mathrm{ijk}}=\frac{(y_i^0-y_j^0)[{(y_k^0-y_j^0)}^2+{(x_k^0-x_j^0)}^2]+(x_j^0-x_k^0)[(y_j^0-y_k^0)x_i^0-(x_k^0-x_j^0)y_i^0+(x_j^0{y}_k^0-x_k^0{y}_j^0)]}{{({(y_k^0-y_j^0)}^2+{(x_k^0-x_j^0)}^2)}^{3/2}};$

$f_{\mathrm{ijk}}=\frac{(x_j^0-x_i^0)[{(y_k^0-y_j^0)}^2+{(x_k^0-x_j^0)}^2]+(y_j^0-y_k^0)[(y_j^0-y_k^0)x_i^0-(x_k^0-x_j^0)y_i^0+(x_j^0{y}_k^0-x_k^0{y}_j^0)]}{{({(y_k^0-y_j^0)}^2+{(x_k^0-x_j^0)}^2)}^{3/2}};$

$l_{D(P_i,L_{P_j{P}_k})}=D_{(P_i,L_{P_j{P}_k})}-\frac{|(y_j^0-y_k^0)x_i^0+(x_k^0-x_j^0)y_i^0+(x_j^0{y}_k^0-x_k^0{y}_j^0)|}{\sqrt{{(y_k^0-y_j^0)}^2+{(x_k^0-x_j^0)}^2}}.$

L in the formula _DistValue identical with point with the error equation of point, if D _Point-line＜mindist, then l _Dist=min dist-D _Point-line, if mindist＜D _Point-line＜1.5*mindist, then l _Dist=0;

Point with the distance definition of face is: this minor increment in the distance of all polylines of forming this face thus, can be similar to formula (8) and set up the distance error equation that point arrives face;

Line to the distance definition of line is: for line segment L _iAnd L _j, its distance is

$D_{(L_i,L_j)}=\underset{P_i\∈L_i}{\min }\left\{\underset{P_j\∈L_j}{\min }\right\{D(P_i,P_j)\left\}\right\}---\left(9\right)$

Wherein, P _iBe line segment L _iOn more arbitrarily, P _jBe line segment L _jOn more arbitrarily, D (P _i, P _j) be a P _iAnd P _jDistance, calculate according to formula (5);

Line to the distance definition of face is: for line segment L _iWith polygon A _j, its distance is

$D_{(L_i,A_j)}=\underset{P_i\∈L_i}{\min }\left\{\underset{P_j\∈A_j}{\min }\right\{D(P_i,P_j)\left\}\right\}---\left(10\right)$

Wherein, P _iBe line segment L _iOn more arbitrarily, P _jBe polygon A _jIn more arbitrarily, the minimum value that line is a line segment in the distance of forming this polygonal polyline to the distance definition of face,

Line to the distance definition of face is: for line segment A _iWith polygon A _j, its distance is

$D_{(A_i,A_j)}=\underset{P_i\∈A_i}{\min }\left\{\underset{P_j\∈A_j}{\min }\right\{D(P_i,P_j)\left\}\right\}---\left(11\right)$

Wherein, P _iBe polygon A _iIn more arbitrarily, P _jBe polygon A _jIn more arbitrarily, the various equations of simultaneous are the indirect adjustment problem, its general model is:

v＝Ax-l (12)

X is a parameter vector in the formula, x=(Δ x ₁Δ y ₁Δ x _nΔ y _n) ^TA is a factor arrays; L is a constant vector; 1 is constant vector; V is a residual vector, by the principle of least square, can get normal equation and is

N _Ax-U＝0 (13)

In the formula: N _A=A ^TPA; U=A ^TPl, P are weight matrix;

Separating normal equation can get:

$x=N_A^{-1}\·U---\left(14\right)$

Parameter x substitution error equation with trying to achieve promptly gets residual error v and adjusted value

The coordinate adjustment value is the coordinate after a little moving;

The inverse of weight matrix computing formula of coordinate adjustment value is

$Q_{\hat{x}\hat{x}}{\left(A^T\mathrm{PA}\right)}^{-1}=N_A^{-1}---\left(15\right)$

Wherein A is the factor arrays of error equation, and p is the power battle array, and then the accuracy formula of adjusted value is

${\widehat{\mathrm{\σ}}}_{{\hat{x}}_i}=\±{\widehat{\mathrm{\σ}}}_0\sqrt{Q_{{\hat{x}}_i{\hat{x}}_i}}---\left(16\right)$

Wherein

For

The pivot of matrix, error in the weight unit

Realize that hardware system of the present invention comprises that data organization module, pattern process module, entity matching module, adjustment merge module, conflict shift module 5 modules altogether.The function of each module is as follows:

1, data organization module

This module mainly is to read the GIS data file, generates the topological relation of a some entity, line entity, face entity, sets up the data structure on summit after vertex data structure, polyline data structure, polygon data structure, the adjustment, store graphics data.

2, pattern process module

Pattern process module comprises the basic operation of GIS polar plot, as amplify, dwindle, the control of translation, figure layer etc., also be included in the map merging process function to graphics process, as manual matching feature, remove matching relationship, the graphic result after being combined adds up or the like.

3, entity matching module

The entity matching module can carry out the coupling of an entity, line entity, face entity according to matching algorithm used herein respectively, and can know the matching relationship between display entity.

4, adjust the merging module

The adjustment of entity merges module not only can realize adjusting the indirect adjustment method of merging, and clearly illustrates the result who adjusts after merging, and can also realize the algorithm that some other is classical, so as with the method for this paper relatively.

5, conflict shift module

The conflict shift module mainly is that the figure collision problem that map merges in the contingency question that causes is carried out shifting processing, and can demonstrate the result after the displacement.

System has adopted point-line or point-face form, and promptly only storage constitutes the point coordinate of line or face, so also needs to make up the topological relation of inter-entity after having read file data.Make up topological relation and be primarily aimed at line entity and face entity.Set up a some table, line segment table, line segment-line string list, line string list in the building process, at this, the initial end points of line string must be node or the end end points that do not link to each other with other any line, and the face entity also needs to generate line string-face table and face table.The coupling period of the coordinate of the main memory point of some table, the degree of point, this point, the line segment number that connects at this point etc.; The starting point of line segment table storage line segment and termination period; What line segment-line string list played is the effect of a connecting line segment table and line string list, each section line segment number that comprises according to line string of end to end sequential storage; The line string list is stored this line and is strung the position of initial line section in line segment-line string list, and actual is an index value, also comprises initial period in addition, stops period, the matched line string of left and right sides polygon number, this line string number etc.; Line string-face table also has been the effect of a connecting line string list and face table, mainly stores line string that a face entity comprises number; The face table has been stored the start line string number in online string-face table.The order such as the table 1, shown in Figure 2 of the information of each table and structure table.

Table 1 makes up the information of all kinds of tables of topological relation

The point table	The line segment table	Line segment-line string list
			This coupling period of the line segment number that the degree of the coordinate points of point connects at this point	Initial period terminating point number	The line segment number
The line string list	Line string-face table	The face table
			The initial period terminating point of the index value of initial segment left and right sides polygon number	Line string number	The index value of start line string

Claims (3)

1. the conflict shifting processing method that the multi-source spatial data merges is characterized in that, may further comprise the steps:

(1) reading of data, the topological relation of structure inter-entity;

(2) carry out the entity coupling;

(3) adjustment of carrying out the entity coordinate merges;

(4) whether the check amalgamation result exists the mistake coupling, and if yes, correct the mistake coupling, and return step (3), if not, execution in step (5);

(5) automatically whether detection exists the figure conflict, if yes, adopts the displacement that manages conflict of least square adjustment method.

2. the conflict shifting processing method that a kind of multi-source spatial data according to claim 1 merges is characterized in that, the entity coupling in the described step (2) comprises the coupling of an entity, the coupling of line entity, the coupling of face entity.

3. the conflict shifting processing method that a kind of multi-source spatial data according to claim 1 merges is characterized in that, the displacement that manages conflict of the employing least square adjustment method in the described step (5) is as follows:

If point coordinate is (x among the former figure _i ⁰, y _i ⁰) (i=1,2 ..., n) as the parameter approximate value, the position coordinates after point moves (i=1,2 ...,, then have n) as adjusted value

(Δ x in the formula _i, Δ y _i) be the coordinate translation amount, be parameter to be found the solution is the adjusted value correction, the adjustment equation of use comprises as follows:

1) coordinate amount of movement error equation

The point that constitutes entity should remain on original position as far as possible, and promptly the coordinate amount of movement will approach 0, obtains following error equation

All corresponding two the coordinate amount of movement error equations of each point, therefore the number of whole coordinate amount of movement error equation is total number of the point of twice;

2) relative shift error equation

In order to keep the geometry of entity inside, should make that the relative shift of consecutive point approaches 0 on the entity, can get following error equation

3) shape error equation

Keep the original shape of entity when coordinate is adjusted, need carry out the shape restriction,, can get error equation and be if keep the turning value of entity frontier point to entity to be adjusted

In the formula

Wherein, α _Ik, α _IjBe respectively the position angle of ik, ij direction, obtained by the approximation calculation of coordinate adjustment value, β is the former angle value of corner point, and the number of shape error equation is the number of entity corner point;

4) conflict shift error equation

The figure conflict can be divided into a little and conflicting with face with line, line with face, line with line, point with point, point,

Point is tried to achieve with the range formula of point

Wherein, Be the error of distance observation, D (P _i, P _j) be a P _i(x _i, y _i) and P _j(x _j, y _j) distance, With It is the coordinate after its displacement;

Linearization obtains its error equation:

v _dist＝-a _ijΔx _i+b _ijΔy _i+a _ijΔx _j-b _ijΔy _j-l _dist (6)

A wherein _Ij=cos α _Ij, b _Ij=-sin α _Ij, α _IjApproximation calculation by the coordinate adjustment value obtains, l _DistValue be divided into two kinds of situations: if the distance D of point and point before moving _Point-pointLess than minimum threshold of distance mindist, then l _Dist=min dist-D _Point-point, wherein

If D _Point-pointGreater than mindist and less than 1.5*mindist l then _Dist=0;

Point with the range equation of line segment is:

Wherein, With

Be respectively apart from observed reading and error,

With

Two end points of adjusted point and line segment;

Linearization obtains its error equation:

Wherein, (x _i ⁰, y _i ⁰), (x _j ⁰, y _j ⁰) and (x _k ⁰, y _k ⁰) be the approximate value of coordinate, and

L in the formula _DistValue identical with point with the error equation of point, if D _Point-line＜mindist, then l _Dist=min dist-D _Point-line, if mindist＜D _Point-line＜1.5*mindist, then l _Dist=0;

Point with the distance definition of face is: this minor increment in the distance of all polylines of forming this face thus, can be similar to formula (8) and set up the distance error equation that point arrives face;

Line to the distance definition of line is: for line segment L _iAnd L _j, its distance is

Wherein, P _iBe line segment L _iOn more arbitrarily, P _jBe line segment L _jOn more arbitrarily, D (P _i, P _j) be a P _iAnd P _jDistance, calculate according to formula (5);

Line to the distance definition of face is: for line segment L _iWith polygon A _j, its distance is

Wherein, P _iBe line segment L _iOn more arbitrarily, P _jBe polygon A _jIn more arbitrarily, the minimum value that line is a line segment in the distance of forming this polygonal polyline to the distance definition of face,

Line to the distance definition of face is: for line segment A _iWith polygon A _j, its distance is

Wherein, P _iBe polygon A _iIn more arbitrarily, P _jBe polygon A _jIn more arbitrarily, the various equations of simultaneous are the indirect adjustment problem, its general model is:

v＝Ax-l (12)

X is a parameter vector in the formula, x=(Δ x ₁Δ y ₁Δ x _nΔ y _n) ^TA is a factor arrays; L is a constant vector; 1 is constant vector; V is a residual vector, by the principle of least square, can get normal equation and is

N _Ax-U＝0 (13)

In the formula: N _A=A ^TPA; U=A ^TPl, P are weight matrix;

Separating normal equation can get:

Parameter x substitution error equation with trying to achieve promptly gets residual error v and adjusted value

The coordinate adjustment value is the coordinate after a little moving;

The inverse of weight matrix computing formula of coordinate adjustment value is

Wherein A is the factor arrays of error equation, and p is the power battle array, and then the accuracy formula of adjusted value is

Wherein

For

The pivot of matrix, error in the weight unit

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