CN103440280A - Retrieval method and device applied to massive spatial data retrieval - Google Patents
Retrieval method and device applied to massive spatial data retrieval Download PDFInfo
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Abstract
The invention discloses a retrieval method and a retrieval device applied to massive spatial data retrieval. The method comprises the following steps: calculating a layer range to be indexed to generate a minimum boundary matrix; judging the category of the layer; if the layer belongs to a dot facility, calling a generating algorithm of an MCER (Maximal Contrast Enhancement Ratio) tree to generate an MCER index tree; and if the layer belongs to a line facility, extracting an end point of a line to generate an MCER index tree of the end point and adding the line to a pointer list of an existing MCER tree leaf child node. According to the method and the device, the generating efficiency of the index tree and the retrieval query efficiency of the equipment are improved. A tree-shaped structure of the line is constructed by using the index tree of the dot equipment, so that the topological relevancy between pieces of equipment is improved, and the generating efficiency of the index tree of the line is increased. A clustering technology is applied to generating and insertion algorithms of the MCER tree, the semantic relevance between the pieces of equipment is considered, and the generating and updating efficiencies of the index tree are improved.
Description
Technical field
The present invention relates to the data directory technical field, particularly relate to a kind of indexing means that is applied to the massive spatial data retrieval.
Background technology
Fast development along with spatial information infrastructure construction and Spatial data capture technology, the spatial data scale is increasing, requirement to Spatial Data Sharing is more and more higher, in the situation that rely on hardware, to improve the Database Systems performance more and more difficult, to improve the Spatial Data Sharing ability, the index efficiency that strengthens spatial data becomes the focus forward position of current research.
Spatial index refers to a kind of data structure of arranging in sequence according to certain spatial relationship between the position of spatial object and shape or spatial object, the summary info that wherein comprises spatial object, as the pointer of sign, boundary rectangle and the pointing space object entity of object.As a kind of complementary spatial data structure, spatial index is between spatial operation algorithm and spatial object, and it is by the screening effect, and a large amount of and particular space operates irrelevant spatial object and is excluded, thereby improves speed and the efficiency of spatial operation.
At present in the GIS platform, the indexing means of main flow is quaternary tree index, R tree index and for expansion and the fusion of this indexing means.The quaternary tree index is exactly recursively geographical space to be carried out to four minutes, (such as the number of each node association diagram element is no more than 3, surpasses 3, just four minutes again) until the end condition set up on their own, finally forms a stratified quaternary tree.The R tree is the another kind of form of B tree to the hyperspace development, it is divided spatial object by scope, the corresponding zone of each node and a disk page, the regional extent of its all child nodes of storage in the disk page of non-leaf node, within its regional extent is all dropped in the zone of all child nodes of non-leaf node; The boundary rectangle of all spatial objects in the disk page of leaf node within its regional extent of storage.Based on this, the indexing means of many mixing and mutation is studied, such as R+ tree, R* tree, QR tree, SS tree, X tree etc.
Due to the magnanimity geographical spatial data except thering are the features such as General Spatial data space feature, spatial relationship, sorting code number, also there are the characteristics such as destructuring, large data characteristic, multiresolution, multi-level, multidate, therefore traditional space index method exists a lot of problems in processing the massive spatial data process, such as lacking fast access ability to mass data, lack mass data access dirigibility, weak to the unstructured data processing power, be difficult to control ever-increasing storage and maintenance cost etc.
Summary of the invention
Goal of the invention: for the Traditional Space indexing means lack fast access ability to mass data, lack mass data access dirigibility, weak to the unstructured data processing power, be difficult to control the problem such as ever-increasing storage and maintenance cost, the present invention proposes a kind of indexing means and device that is applied to the massive spatial data retrieval.
Technical scheme: a kind of indexing means that is applied to the massive spatial data retrieval comprises:
Calculate the figure layer scope that lithol yet to be built draws, generate minimum boundary matrix;
Judge the classification of described figure layer, if described figure layer belongs to the point-like facility, the generating algorithm of calling the MCER tree generates the MCER index tree; If described figure layer belongs to line facility, extract the circuit end points, generate the MCER index tree of end points, then circuit is added to the pointer chained list of existing MCER leaf node.
Preferably, described MCER tree comprises the M rank, M is the maximal value of a node Spatial Objects number, make 2≤m≤M/2, m is a regulated variable, sets the minimum value of any hierarchy node Spatial Objects number, the MCER tree node is defined as a four-tuple: (Identifier, I, Object_ID, Pointers); In described MCER tree,
1. the index entry that each node comprises is between m and M, unless it is root node simultaneously;
2. the rarest two children of root node, unless it is leafy node simultaneously;
3. non-leaf node is as index list, and the storage space object, be not defined as tlv triple (Identifier, I, Pointers), wherein, Identifier is node identification, and I is the minimum boundary rectangle that comprises all child nodes, and Pointer is for pointing to the pointer of child nodes;
4. allow leaf node to appear at the different levels of tree, the degree of depth of high-grade spatial object in tree is little, and the m value is relatively little, and the degree of depth of low-grade spatial object in tree is large, and the m value is relatively large;
5. leaf node storage space object, four-tuple (Identifier, I, ObjectID, Pointers), Identifier is node identification, I is the position coordinates of spatial object, ObjectID is the sign of spatial object, and the description of Pointers is divided into two kinds of situations: the semantic association point of the current spatial object of point-like facility record, and the line identification that current spatial object is beginning or end be take in line facility record;
6. leaf node points to all circuits associated with node, and in two kinds of situation: if two nodes belong to the child nodes of same father node, corresponding line stores in the pointer chained list of the nearer leaf node of decentering point; If two nodes belong to different father nodes, corresponding line stores respectively the pointer chained list of two nodes into.
Preferably, the figure layer scope that described calculating lithol yet to be built draws, generate minimum boundary matrix and adopt cluster algorithm, considers the semantic distance of spatial data.
Preferably, the computing method of described semantic distance are, the Euclidean distance that the semantic distance of current point-like facility and i cluster centre is current point-like facility and i cluster centre deducts the topological connection relation number of existing point-like facility in current point-like facility and i cluster.
Preferably, described MCER tree comprises generation, insertion, deletion, renewal, query function.
The invention still further relates to a kind of indexing unit that is applied to the massive spatial data retrieval, comprising:
First module, the figure layer scope of drawing for calculating lithol yet to be built, generate minimum boundary matrix;
Second unit, for judging the classification of described figure layer, if described figure layer belongs to the point-like facility, the generating algorithm of calling the MCER tree generates the MCER index tree; If described figure layer belongs to line facility, extract the circuit end points, generate the MCER index tree of end points, then circuit is added to the pointer chained list of existing MCER leaf node.
The present invention adopts technique scheme, has following beneficial effect: 1. the embodiment of the present invention carries out multiple dimensioned index to the equipment of different electric pressures, has improved the formation efficiency of index tree, and the retrieval and inquisition efficiency of equipment.2. utilize the tree structure of the index tree structure circuit of point-like equipment, improved the topological relevance between equipment, accelerated the formation efficiency of circuit index tree.Apply clustering technique in the generation of 3. setting at MCER, insertion algorithm, considered the semantic relevance between equipment, improved the efficiency of generation and the renewal of index tree.
The accompanying drawing explanation
The method flow diagram that Fig. 1 is the embodiment of the present invention;
The functional structure chart of the MCER tree that Fig. 2 is the embodiment of the present invention;
The electric system wiring diagram that Fig. 3 is an embodiment of the present invention, thick line means the 500kV facility, and fine rule means the 220kV facility, and point means transformer station, and line means transmission line of electricity, empty wire frame representation minimum boundary rectangle;
Fig. 4 is the MCER tree construction that Fig. 3 utilizes the embodiment of the present invention to generate.
Embodiment
Below in conjunction with specific embodiment, further illustrate the present invention, should understand these embodiment only is not used in and limits the scope of the invention for the present invention is described, after having read the present invention, those of ordinary skills all fall within the application's claims limited range to the modification of the various equivalent form of values of the present invention.
Describe in conjunction with Fig. 1-4, at first define the MCER tree:
MCER tree for the M rank, M is the maximal value of a node Spatial Objects number, make 2≤m≤M/2, m is a regulated variable, set the minimum value of any hierarchy node Spatial Objects number, the MCER tree node can be defined as a four-tuple: (Identifier, I, Object_ID, Pointers)
1. the index entry that each node comprises is between m and M, unless it is root node simultaneously;
2. the rarest two children of root node, unless it is leafy node simultaneously;
3. non-leaf node is as index list, and the storage space object, therefore can not be reduced to tlv triple (Identifier, I, Pointers), wherein, Identifier is node identification, and I is the minimum boundary rectangle that comprises all child nodes, and Pointer is for pointing to the pointer of child nodes;
4. allow leaf node to appear at the different levels of tree, the degree of depth of high-grade spatial object in tree is little, and the m value is relatively little, and the degree of depth of low-grade spatial object in tree is large, and the m value is relatively large;
5. leaf node storage space object, four-tuple (Identifier, I, ObjectID, Pointers), Identifier is node identification, I is the position coordinates of spatial object, ObjectID is the sign of spatial object, and the description of Pointers is divided into two kinds of situations: the semantic association point of the current spatial object of point-like facility record, and the line identification of current spatial object as starting point (or terminal) be take in line facility record;
6. leaf node points to all circuits associated with node, due to two nodes of every connection, therefore in two kinds of situation: if two nodes belong to the child nodes of same father node, corresponding line stores in the pointer chained list of the nearer leaf node of decentering point; If two nodes belong to different father nodes, corresponding line stores respectively the pointer chained list of two nodes into.
According to above-mentioned definition, the difference of MCER and traditional R tree is mainly at rear 2 points, this also just traditional R tree be applied to the limitation place in electric power GIS, by expanding traditional R tree, can realize the efficient index of electric power GIS Spatial Objects.
The method of the embodiment of the present invention comprises:
Calculate the figure layer scope that lithol yet to be built draws, generate minimum boundary matrix;
Judge the classification of described figure layer, if described figure layer belongs to the point-like facility, the generating algorithm of calling the MCER tree generates the MCER index tree; If described figure layer belongs to line facility, extract the circuit end points, generate the MCER index tree of end points, then circuit is added to the pointer chained list of existing MCER leaf node.
Also relate to a kind of device of realizing said method, comprise first module, the figure layer scope of drawing for calculating lithol yet to be built, generate minimum boundary matrix;
Second unit, for judging the classification of described figure layer, if described figure layer belongs to the point-like facility, the generating algorithm of calling the MCER tree generates the MCER index tree; If described figure layer belongs to line facility, extract the circuit end points, generate the MCER index tree of end points, then circuit is added to the pointer chained list of existing MCER leaf node.
Index tree with electrical network 500kV transformer station, circuit and 220kV transformer station, circuit generates and is inserted as example, and implementation process of the present invention is described:
1. for the transformer station of 500kV, call the generating algorithm of MCER tree, generate the MCER tree of 500kV transformer station.
2. according to the topological connection relation between transformer station and circuit, on the basis of the MCER of 500kV transformer station tree, the circuit of 500kV is inserted in the chained list of leaf node in the MCER tree.
3. for the transformer station of 220kV, call the generating algorithm of MCER tree, after cluster, with the MCER tree of existing 500kV transformer station, merge, make it expansion.
4. according to the annexation of 220kV circuit and transformer station, on the basis of the MCER of expansion tree, the circuit of 220kV is inserted in the chained list of leaf node in the MCER tree.
So far, the Mass production work of MCER tree completes, after newly adding an equipment, need to upgrade operation to index tree, and we take the circuit that newly adds a 500kV is example, illustrates that MCER sets the implementation process of insertion algorithm:
5. call the insertion algorithm of MCER tree, complete the insertion of circuit two ends transformer station or upgrade operation.If transformer station's insertion process generation node overflows, call node overflow algorithm, complete the division of node, and the growth course of MCER tree.
6. the close principle according to semanteme, be inserted into circuit the pointer chained list of leaf node corresponding in the MCER tree.
Concrete, the MCER tree constructing method is: transmission line of electricity is example, the building process of spatial index is described in detail in detail, transmission line of electricity is the line between transformer station, point-like facility with transformer station builds the MECR tree, power transmission facility comprises 500kV, 220kV, tri-electric pressures of 110kV in addition, and building process need to consider these factors, and the GenerateTree algorithmic procedure is as follows:
GT1 by transformer station's difference cluster, establishes the number that N is 500kV transformer station according to electric pressure, and m and M are minimum number and the maximum number that in the MCER-tree, each node can hold entity, and k is the number of division cluster, span [N/M, N/m], and initial value is [N/M].
1.1 the introducing distance threshold, choose k cluster centre node1 by the representative point method, node2 ... nodek;
1.2 semantic-based is apart from computing formula (1), remaining 500kV transformer station is assigned to most suitable cluster C1, C2 ... Ck, wherein SemD (node, nodei) mean the semantic distance of current transformer station and i cluster centre, EuD (node, nodei) means the Euclidean distance of current transformer station and i cluster centre, T (node, Ci) mean the topological connection relation number of existing transformer station in current transformer station and i cluster, upgrade each cluster centre;
SemD(node，node
1)＝EuD(node，node
1)-T(node，C
1) (1)
1.3 repeating step 1.2, until cluster centre is constant, if fruit exists cluster Spatial Objects number to be greater than M, corresponding spatial object is continued to cluster operation, calculate the value of evaluation function P according to the magnetic disc access times of unit area in the site polling performance model of R-tree, if exist cluster Spatial Objects number to be less than m, remember that P is ∞;
1.4k++, if k<[N/m] re-executes above-mentioned steps;
1.5 finally choose the number of the k of evaluation function value minimum as clustering, and record cluster result;
1.6, with same clustering method, the transformer station of each electric pressure is carried out to cluster.
GT2 generates the R tree construction by the 500kV transformer station of display resolution minimum.
2.1, according to the cluster of the 500kV transformer station generated in GT1, carry out bottom-up R tree index construct, guarantee that the degree of depth of all leaf nodes in tree is identical, the fan leaves coefficient of each node is in [m, M].
2.2 describe according to the MCER tree construction, be each leaf node generation circuit chained list, and by the pointed circuit chained list of all leaf nodes.
GT3 is according to the cluster situation of 220kV transformer station, and the R of 500kV transformer station that expansion has generated sets.
3.1 the minimum boundary rectangle (MBR) that the cluster of the 220kV transformer station that generates in step 1 of take forms is basis, then carries out cluster operation, the individual initial cluster centre MBR1 of selected k, and MBR2 ... MBRk;
3.2 selected not cluster minimum boundary rectangle MBR, calculate respectively the semantic dependency of MBR and each cluster centre, the topological connection relation between the transformer station of take in minimum boundary rectangle is Rule of judgment;
3.3 recalculate cluster centre, repeat 3.2, until cluster result is constant, record cluster result;
3.4 the R of 500kV transformer station that the cluster result by 3.3 and step 2 generate tree merges, the area coverage of take is minimum is condition, guarantees leaf node same level in tree of each grade, the leaf node of different brackets different levels in tree;
3.5 describe according to the MCER tree construction, be each leaf node generation circuit chained list of 220kV transformer station, and by the pointed circuit chained list of all leaf nodes.
GT4 repeating step 3, until the facility of all electric pressures all incorporates the MCER tree.
Claims (6)
1. an indexing means that is applied to the massive spatial data retrieval, is characterized in that, comprising:
Calculate the figure layer scope that lithol yet to be built draws, generate minimum boundary matrix;
Judge the classification of described figure layer, if described figure layer belongs to the point-like facility, the generating algorithm of calling the MCER tree generates the MCER index tree; If described figure layer belongs to line facility, extract the circuit end points, generate the MCER index tree of end points, then circuit is added to the pointer chained list of existing MCER leaf node.
2. a kind of indexing means that is applied to massive spatial data retrieval according to claim 1, it is characterized in that: described MCER tree comprises the M rank, M is the maximal value of a node Spatial Objects number, make 2≤m≤M/2, m is a regulated variable, sets the minimum value of any hierarchy node Spatial Objects number, the MCER tree node is defined as a four-tuple: (Identifier, I, Object_ID, Pointers); In described MCER tree,
1. the index entry that each node comprises is between m and M, unless it is root node simultaneously;
2. the rarest two children of root node, unless it is leafy node simultaneously;
3. non-leaf node is as index list, and the storage space object, be not defined as tlv triple (Identifier, I, Pointers), wherein, Identifier is node identification, and I is the minimum boundary rectangle that comprises all child nodes, and Pointer is for pointing to the pointer of child nodes;
4. allow leaf node to appear at the different levels of tree, the degree of depth of high-grade spatial object in tree is little, and the m value is relatively little, and the degree of depth of low-grade spatial object in tree is large, and the m value is relatively large;
5. leaf node storage space object, four-tuple (Identifier, I, ObjectID, Pointers), Identifier is node identification, I is the position coordinates of spatial object, ObjectID is the sign of spatial object, and the description of Pointers is divided into two kinds of situations: the semantic association point of the current spatial object of point-like facility record, and the line identification that current spatial object is beginning or end be take in line facility record;
6. leaf node points to all circuits associated with node, and in two kinds of situation: if two nodes belong to the child nodes of same father node, corresponding line stores in the pointer chained list of the nearer leaf node of decentering point; If two nodes belong to different father nodes, corresponding line stores respectively the pointer chained list of two nodes into.
3. a kind of indexing means that is applied to massive spatial data retrieval according to claim 1 is characterized in that: the figure layer scope that described calculating lithol yet to be built draws, and generate minimum boundary matrix and adopt cluster algorithm, consider the semantic distance of spatial data.
4. a kind of indexing means that is applied to massive spatial data retrieval according to claim 3, it is characterized in that: the computing method of described semantic distance are, the Euclidean distance that the semantic distance of current point-like facility and i cluster centre is current point-like facility and i cluster centre deducts the topological connection relation number of existing point-like facility in current point-like facility and i cluster.
5. according to the arbitrary described a kind of indexing means that is applied to the massive spatial data retrieval of claim 1-4, it is characterized in that: described MCER tree comprises generation, insertion, deletion, renewal, query function.
6. realize a kind of indexing unit that is applied to the massive spatial data retrieval as described as claim 1-5 any one, it is characterized in that, comprising:
First module, the figure layer scope of drawing for calculating lithol yet to be built, generate minimum boundary matrix;
Second unit, for judging the classification of described figure layer, if described figure layer belongs to the point-like facility, the generating algorithm of calling the MCER tree generates the MCER index tree; If described figure layer belongs to line facility, extract the circuit end points, generate the MCER index tree of end points, then circuit is added to the pointer chained list of existing MCER leaf node.
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| CN107636639A (en) * | 2015-09-24 | 2018-01-26 | 谷歌有限责任公司 | Quick rectangular projection |
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| CN109993334A (en) * | 2017-12-29 | 2019-07-09 | 顺丰科技有限公司 | Quota prediction technique, device, equipment and storage medium |
| CN108921774A (en) * | 2018-06-08 | 2018-11-30 | 广州虎牙信息科技有限公司 | The storage organization and related edit method, apparatus, equipment and storage medium of model |
| CN111026754B (en) * | 2019-12-05 | 2022-12-02 | 中国科学院软件研究所 | Safe and efficient circular range data uploading and querying method, corresponding storage medium and electronic device |
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| CN111723096B (en) * | 2020-06-23 | 2022-08-05 | 重庆市计量质量检测研究院 | Spatial data indexing method integrating GeoHash and Quadtree |
| CN113312436A (en) * | 2020-07-27 | 2021-08-27 | 阿里巴巴集团控股有限公司 | Spatial index processing method and device |
| CN113312436B (en) * | 2020-07-27 | 2024-04-19 | 阿里巴巴集团控股有限公司 | Spatial index processing method and device |
| CN113836445A (en) * | 2021-09-16 | 2021-12-24 | 北京百度网讯科技有限公司 | Semantization method and device, electronic equipment and readable storage medium |
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