CN103092926A - Multi-level mixed three-dimensional space index method - Google Patents
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Abstract
The invention relates to a multi-level mixed three-dimensional space index method which comprises the following steps: 1, building a united three-dimensional space index framework; 2, dividing a large-scale city space into a plurality of small regions, and building a first level grid index; 3, in the first level grid index region, building a second level multi-type mixing index for three-dimensional space data with different types; and 4, based on the multi-level mixed three-dimensional space index, carrying out multi-level query search on a three-dimensional space database. The three-dimensional space index method improves the searching efficiency of the large-scale three-dimensional space database through multi-level filter and search.
Description
Technical field
The present invention relates to a kind of geographical spatial system technical field, relate in particular to a kind of three dimensions indexing means of multi-level mixing.
Background technology
Unevenly be distributed in three dimensions with three dimensions objects such as underground ten hundreds of buildings, pipeline, geologic bodies on the ground in three-dimension GIS, how to inquire about efficiently the three dimensions object that meets the designated space condition from three dimensions, be that the conventional two-dimensional Spatial Data Index Technology is reluctant, need the three dimensions index badly and effectively support.Since the R tree space index method of proposition in 1984, R tree and mutation thereof have obtained research widely and have used from Guttma, set R* tree and HilbertR tree, and the combined index methods such as QR*, LOD-OR as R+.Present three dimensions indexing means mainly comprises: the Object Segmentation method, mainly realized by the level enclosure body; The rule split plot design mainly comprises regular grid, KD tree, KDB tree, BSP tree, Octree, R tree etc.; The combined index technology namely for the new demand of continuous appearance, is recombinated various index technologies and improve, and as R+ tree, R* tree, LOD-OR tree, CSR tree etc., but each space index method has its superiority, usable range and applicable object.Due to the difference of up and down three-dimensional space data all kinds of entities in ground in geometric shape, space distribution, spatial relationship each side, adopt any general three dimensions indexing means to be difficult to take into account the efficient retrieval of all kinds of three dimensions entities in up and down over the ground.
The spatial distribution characteristic of three-dimensional space data is to describe dissimilar three dimensions object in the distribution characteristics of whole city scope from whole, overall angle.The difference that has directly caused the each side such as its data structure, semantic topological relation just because of the notable difference of the features such as three-dimensional space data different types of data geometric shape and space distribution, for the characteristic of spatial distribution of three-dimensional city model data different types of data and the characteristics of data itself, need to select different three dimensions indexing means could realize efficient three-dimensional search.therefore, demand for the integrated retrieval of the extensive three-dimensional space data storehouse ground all kinds of three dimensions entity in up and down, due to BUILDINGS MODELS, model of geological structure body, the various thematic models such as pipeline model are at geometric configuration and texture feature, characteristic of spatial distribution, many-sided notable differences such as semantic topological relation, has discrete distribution as BUILDINGS MODELS, target shape is different, size is totally different, the characteristics of indoor semantic topological relation complexity, model of geological structure body has continuous entity description, the characteristics of spatial relationship complexity, the pipeline model has LINEAR CONTINUOUS and distributes, but pipe point and the clear and definite characteristics of the semantic topological relation of line sections, because every kind of three dimensions indexing means has its suitability and limitations, therefore adopt a kind of general three dimensions indexing means to be difficult to satisfy the efficient index of all types three-dimensional modeling data.
Summary of the invention
The present invention is directed to above-mentioned technical matters, a kind of three dimensions indexing means of multi-level mixing of the multi-level mixing that takes into account multi-level three dimensions entity efficient index be provided, comprise the following steps,
Step 1 is set up unified three dimensions index framework;
Step 2, the city space is divided into some zonules on a large scale, sets up the first level Grid Index;
Step 3 in the first level mesh region, is set up the second level polymorphic type hybrid index to dissimilar three-dimensional space data;
Step 4 is based on the three dimensions index of multi-level mixing, to the three-dimensional space data storehouse carrying out the multilayer query and search.
Preferably, described three-dimensional factor data comprises above and below ground BUILDINGS MODELS, city essay model and the vegetation model data of discrete distribution, and the three-dimensional R tree index of described above and below ground BUILDINGS MODELS, city essay model and vegetation model the data considering levels of detail is improved one's methods and carried out data directory.
Preferably, described three-dimensional factor data comprises the geologic model data of layering continuous distribution, and the three-dimensional R tree index of described geologic model the data clustering order is improved one's methods and carried out data directory.
Preferably, described three-dimensional factor data comprises the pipeline model data that LINEAR CONTINUOUS distributes, and the three-dimensional R tree index that topological relation is taken in described pipeline model data employing into account is improved one's methods and carried out data directory.
Preferably, described multilayer query and search includes following steps,
Step S1: input three dimensions query context;
Step S2: the numbering of calculating the first related level Grid Index of this three dimensions query context; Simultaneously this three dimensions query context is placed in the set of three dimensions query context, predicts the mobile trend of viewpoint according to several times three dimensions query context recently;
Step S3: calculate related second layer secondary index numbering, and judge whether to be loaded in the indexed cache pond, if in the indexed cache pond, directly enter S5, otherwise enter S4;
Step S4: according to second layer secondary index numbering, load not yet in the second layer secondary index data in indexed cache pond from the three-dimensional space data storehouse, be placed in the indexed cache pond;
Step S5: by second layer secondary index structure, the three-dimensional feature object ID that retrieval three dimensions query context is related and texture object ID set;
Step S6: according to the viewpoint mobile trend of prediction, calculate viewpoint and move related Grid Index numbering, and judge that second layer secondary index data in related graticule mesh are whether in the indexed cache pond, for second layer secondary index data in the indexed cache pond not, open special index prestrain thread and carry out prestrain;
Step S7: gather as querying condition take described three-dimensional feature object ID set and texture object ID, carry out the three-dimensional space data library inquiry, and return results.
Step S8: search end.
The three dimensions indexing means of multi-level mixing of the present invention is effectively realized fast filtering and the index of three-dimensional space data by multi-level index; Take simultaneously the demand of the semantic topological relation efficient retrieval of three-dimensional space data into account, realized the multiple retrieval tasks of three-dimensional factor kind data and semantic topological relation data thereof.
Description of drawings
Fig. 1 is the multi-level three dimensions indexing means one embodiment process flow diagram that mixes of the present invention;
Fig. 2 is multilayer query and search one embodiment process flow diagram of the present invention.
Embodiment
Below in conjunction with accompanying drawing and specific embodiment, the present invention is described in further detail.
As shown in Figure 1, be the multi-level three dimensions indexing means one embodiment process flow diagram that mixes of the present invention, the three dimensions indexing means of multi-level mixing of the present invention comprises the following steps,
Step 1 is set up unified three dimensions index framework;
Step 2, the city space is divided into some zonules on a large scale, sets up the first level Grid Index;
Step 3 in the first level mesh region, is set up the second level polymorphic type hybrid index to dissimilar three-dimensional space data;
In step 3, described three-dimensional factor data comprises above and below ground BUILDINGS MODELS, city essay model and the vegetation model data of discrete distribution, and the three-dimensional R tree index of described above and below ground BUILDINGS MODELS, city essay model and vegetation model the data considering levels of detail is improved one's methods and carried out data directory; Described three-dimensional factor data also comprises the geologic model data of layering continuous distribution, and the three-dimensional R tree index of described geologic model the data clustering order is improved one's methods and carried out data directory; Described three-dimensional factor data also comprises the pipeline model data that LINEAR CONTINUOUS distributes, and the three-dimensional R tree index that topological relation is taken in described pipeline model data employing into account is improved one's methods and carried out data directory.
Step 4 is based on the three dimensions index of multi-level mixing, to the three-dimensional space data storehouse carrying out the multilayer query and search.
In the above-described embodiments, the integrated Organization And Management of all types of three-dimensional space datas in data base management system (DBMS) realized in the three-dimensional space data storehouse, all types of three-dimensional space datas unified abstract expression in database model is three-dimensional factor kind, by all types of three-dimensional feature object of three-dimensional factor kind Unified Expression.In many-sided differences such as geometric configuration and texture feature, characteristic of spatial distribution, semantic topologys, the present invention adopts dissimilar three dimensions indexing means to satisfy the efficient index of different types of data for dissimilar model data.But, in order to guarantee high efficiency that all types of three dimensions indexing means are realized and the convenience of use, set up unified administrative mechanism and the index employment mechanism of polymorphic type index, therefore model unified three dimensions index framework, for all types of three dimensions index provide unified administrative mechanism and unified external calling interface etc.Provide on the basis of unified three dimensions index managing method and calling interface at unified three dimensions index framework, defined unified three dimensions index management base class, different three dimensions indexing means is inherited this base class and is carried out specific implementation and call.Under unified three dimensions index framework, by the abstractdesription to three dimensions indexing means commonly used, base class and basic structure and the unified external calling interface of various dissimilar three dimensions indexing means Unified Expressions have been defined, the realization of all types of three dimensions index all must be derived from from this index base class and get, thereby the implementation method of all kinds index has been carried out standard.Simultaneously when dissimilar index uses, the various unified index calling interfaces such as index creation interface, index warehousing interface, index fetch interface and index search interface have been defined, the use-pattern of all types index is all duplicate like this, thereby is very easy to user's calling various dissimilar index.Effectively reduced by unified three dimensions index framework the complicacy that multi-level hybrid three-dimensional spatial index is realized and called, also ensured the high efficiency that all types of three dimensions index are realized.
In the above-described embodiments, the first level of described multi-level hybrid index incites somebody to action on a large scale by Grid Index that the city space is divided into some zonules, and the corresponding graticule mesh in each zonule realizes scouting; Then the second level is realized the indexing means of various types of data again in each graticule mesh scope, realizes scrutinizing.Wherein, the Grid Index of the first level is different from General Two-Dimensional Grid Index commonly used, this Grid Index just is used for location second layer secondary index, but not direct index is to data itself, so Grid Index fundamental purpose herein is in order to reduce the data volume of second each space index structure of level, to avoid the deficiency that causes space querying efficient sharply to descend because of the overexpansion of spatial index data.Simultaneously, after dividing graticule mesh for the first level, for the three-dimensional feature object that falls into simultaneously two or more the first level graticule mesh, adopt the method for duplicate record to solve, the fundamental purpose that designs due to the first level graticule mesh can judge that duplicate record can not cause too many redundancy.
In the above-described embodiments, the three-dimensional R tree index of described above and below ground BUILDINGS MODELS, city essay model and vegetation model the data considering levels of detail is improved one's methods and is carried out data directory.Intermediate node is introduced the level of detail model, the dual role of realize target inquiry and level of detail inquiry, by first from bottom to top, after global search from top to bottom node selection algorithm and based on the node split algorithm of space clustering, guarantee node size evenly, regular shape and overlapping minimizing.Three-dimensional city model data for discrete distributions such as above and below ground building, city essay, vegetation, the present embodiment has adopted based on the dynamic 3 D R tree of global optimization and cluster analysis and has improved algorithm, and expands the management of three-dimensional R tree index structure considering levels of detail based on this.Detail is the multiple dimensioned description needs of extraterrestrial target in three dimensions, and the single 3 D extraterrestrial target has a plurality of LOD models and is traditional LOD model management mode as its satellite information.Improved three-dimensional R tree generating algorithm is that the index tuple with objective is inserted in the three-dimensional R tree structure successively, the node selection course is to select optimum leaf node to be used for inserting fresh target, the node split process is that the overflow node after the insertion target is implemented splitting operation, guarantees not overflow of node.The enforcement of the method mainly comprises the following steps:
Node is selected: adopted elder generation from bottom to top, rear top-down node selection algorithm search leaf node to be to insert the target tuple, this algorithm can be sought optimum leaf node in full tree scope, with the selection error problem of avoiding node overlapping to cause, and enable the evaluation criterion of the factors such as considering node overlapping, covering and shape, guarantee to insert target posterior nodal point shape reasonable.
Node selection algorithm specifically comprises following flow process, at first begins to search for from three-dimensional R tree leaf node place layer to comprise the node that is inserted into object fully; If Search Results is not empty, there is the leaf node that comprises object fully, select suitable node as finish node in Search Results; Otherwise, if Search Results is empty, in the above in one deck search comprise the node that is inserted into object fully, if this moment, Search Results was not empty, select suitable node as new root node in Search Results, call traditional node selection algorithm and adopt top-down mode to select suitable leaf node, if Search Results be still empty, comprise the node of object fully last layer search more; The rest may be inferred, until search root node.
node split: adopt conditionally two to be divided into three splitting-up method, namely when a node overflow, search overlaps the most serious brotgher of node, overflow node and the most serious overlapping brotgher of node are reassembled as three minor nodes, increased and reduced chance overlapping and optimization node shape, if there is no the overlapping with it brotgher of node, still adopt the divisional mode that is divided into two, wherein adopt the three dimensions clustering algorithm, enable and consider node overlapping, the division evaluation criterion of the factors such as covering and shape, make the rear brotgher of node regular shape of division, size uniform and overlapping minimizing.
Taking into account of detail: select and node split generation well tree-like based on node, the three-dimensional R tree structure is expanded to realize the detail function, the three-dimensional R tree middle layer allows to comprise target index tuple, important goal automatically is dispensed in the upper layer node of R tree.Adopt the depth balance hierarchical structure expansion detail function of R tree, different from classic method is that intermediate node is also managed the index tuple of important goal in the mode of management child node.Father node is selected several important goals from the goal set that its child node is managed, target numbers is defined as the number of child node, namely select a most important target from each child node, selecting foundation can be according to using the needs flexible choice, as height, volume or the projected area etc. of target bounding box.The rest may be inferred, the target index tuple that the node administration importance that tree hierachy is higher is larger.Each node can record the data volume of institute's management objectives model, has the situation of many parts of level of detail models for single target, and target index tuple comprises the index information of level of detail model, the i.e. identification number of each level of detail model and reach.
Simultaneously, because the geometric model of each three-dimensional feature object in three-dimensional space data has just recorded the material ID related with it, can find the texture ID related with it by material ID, but in the three-dimensional visualization process, not only to show the geometric model of three-dimensional feature object, also need to obtain fast its related data texturing by its related material, append to the effect of visualization that reaches the sense of reality in geometric model.Therefore, in the three-dimensional R tree index structure, leaf node structure to the R tree is expanded, increased the record of ID set of the data texturing of each feature object association, like this when carrying out the inquiry of three dimensions index, not only can retrieve the three-dimensional key element ID set that all meet this space querying, can also retrieve simultaneously the associated texture ID set of these three-dimensional feature objects, thereby can carry out simultaneously the scheduling of three-dimensional factor data and data texturing, effectively reduce the delay of data texturing scheduling, obviously improved efficient and the effect of three-dimensional visualization.
In the above-described embodiments, the three-dimensional R tree index of described geologic model the data clustering order is improved one's methods and is carried out data directory.Geologic model has the layering continuous distribution, the form random variation, interlaced, topological relation is complicated, the characteristics such as attribute is abundant, its random geometry form has obvious heterogeneous body and imparametrization feature, and topological relation has reflected connection, adjacency and the relation of inclusion of the continuous inter-entity of geology, be the important support data that realize that three-dimensional geological model is analyzed, usually adopt a plurality of topology relation table to come complex topology relation between maintenance point-line-face-body.In the present reality GEOLOGICAL APPLICATION, high-precision three-dimensional geologic model often scope is all smaller, as a dam body or an ore body, and it is not high for the common precision of large-scale three-dimensional geological model, therefore the disposable internal memory method that is loaded into of general employing just can satisfy application, minute file Grid Index modes that are similar to map relationship that adopt are managed more, are unfavorable for the scheduling of geologic model data efficient.But for the geologic model data of whole city scope, it is fully worthless adopting disposable load mode, must based on large-scale three-dimensional space data storehouse, set up dynamic queries and the search mechanism of geologic model data.For geologic body layering continuous distribution, the characteristics such as interlaced, the present embodiment has adopted the R tree Index Algorithm of space clustering sequence that traditional R tree index is improved, and fully take into account ground interlayer topological relation, distance similarity cluster analysis by the three-dimensional geologic object, spatial object close on three dimensions is placed under same node as far as possible, with the size of the minimum boundary rectangle that reduces node as far as possible, the overlapping situation of minimum boundary rectangle that as far as possible reduces between node occurs.At first by spatial object distance similarity cluster, each minute duration set to cluster carries out X, Y, (the X positive dirction is from left to right to the Z all directions, the Y positive dirction is from top to bottom, the Z positive dirction is from front to back, meet right-hand rule) carry out scan sorting, choose in twos the direction of distance accumulation minimum current component is carried out ascending sort; Recurrence Construction clustering order R sets index structure.This algorithm can take into full account the neighboring and correlative of geologic body spatial object, has reduced the node rectangular area, has reduced the overlapping probability of node rectangle, has improved search efficiency.
In the above-described embodiments, described pipeline model data adopts the three-dimensional R tree index of taking topological relation into account to improve one's methods to carry out data directory.Each pipeline of three-dimensional tube line model can resolve into some line sections according to pipe points such as point of crossing and reducing points, and line sections is connected by the respective tube point, forms a continual pipeline.Thus, the three-dimensional tube line model is a series of pipe point and line sections compositions that are communicated with topological relation that have.Although the pipeline model is a continuous linear model, but the pipe point data and the line sections data that form pipeline are all self-existent, so space distribution from whole city scope, the pipe point that forms pipeline separates with line sections, and pipe point and line sections all can be regarded as discrete distribution like this.The present invention adopts improved three-dimensional R tree Index Algorithm to set up respectively index to pipe point data and line sections data, and the three-dimensional R tree index structure is improved, increase record and manage the structure that is communicated with topological relation of putting with line sections, thereby can put quick-searching to the line sections that is communicated with it by pipe, also can be by the line sections quick-searching to the Guan Dian of associated etc., so both improve the efficient of geometric model data retrieval, also accelerated the recall precision of pipeline topological relation data.Therefore, this paper adopts and takes the three-dimensional R tree index improvement algorithm that is communicated with topological relation into account, by the three-dimensional R tree node structure is expanded, increase the description that is communicated with topological relation between pipe point and line sections, can realize the dual query task of three-dimensional factor data and semantic topological relation data.
The three-dimensional R tree node that the three-dimensional R tree index of taking topological relation into account improves algorithm select and node split algorithm and previously described three-dimensional R tree for the above and below ground BUILDINGS MODELS to improve algorithm consistent, the structure that is the leaf node set of R there are differences.Due to the topological relation that will record between pipe point and line sections, so increased in the structure of the leaf node of R tree the structure that storage is communicated with topological relation.
Multi-level hybrid three-dimensional spatial index is carrying out storage and management in the three-dimensional space data storehouse on a large scale, adopt following flow process to realize the loading of index data, at first need according to the engineering of opening, find the possessive case net index data of specifying engineering in the first level Grid Index table, and all read out from the three-dimensional space data storehouse and be loaded in internal memory; Then according to the position of system's initial viewpoint, calculate the graticule mesh numbering at initial viewpoint place, with this graticule mesh and on every side the second level polymorphic type three dimensions index data in adjacent graticule mesh take out from database read and be pre-loaded to internal memory; Then, in the viewpoint moving process, need the not yet index data of the second level in internal memory of dynamic load, the wait that causes in the dynamic load index data process, the information such as direction that need to move according to viewpoint, open special thread and carry out the prestrain of second layer secondary index data, can find in internal memory to guarantee the required second layer secondary index of real-time query, improve index search efficient.As shown in Figure 2, be multilayer query and search one embodiment process flow diagram of the present invention, multilayer query and search of the present invention includes following steps,
Step S1: input three dimensions query context;
Step S2: the numbering of calculating the first related level Grid Index of this three dimensions query context; Simultaneously this three dimensions query context is placed in the set of three dimensions query context, predicts the mobile trend of viewpoint according to several times three dimensions query context recently;
Step S3: calculate related second layer secondary index numbering, and judge whether to be loaded in the indexed cache pond, if in the indexed cache pond, directly enter S5, otherwise enter S4;
Step S4: according to second layer secondary index numbering, load not yet in the second layer secondary index data in indexed cache pond from the three-dimensional space data storehouse, be placed in the indexed cache pond;
Step S5: by second layer secondary index structure, the three-dimensional feature object ID that retrieval three dimensions query context is related and texture object ID set;
Step S6: according to the viewpoint mobile trend of prediction, calculate viewpoint and move related Grid Index numbering, and judge that second layer secondary index data in related graticule mesh are whether in the indexed cache pond, for second layer secondary index data in the indexed cache pond not, open special index prestrain thread and carry out prestrain;
Step S7: gather as querying condition take described three-dimensional feature object ID set and texture object ID, carry out the three-dimensional space data library inquiry, and return results.
Step S8: search end.
The three-dimensional space data storehouse that the present invention is directed to whole City-level has the characteristics such as data scale is huge, the data area span is large, by multi-level index, employing is by fast filtering and accurate retrieval of slightly effectively realizing three-dimensional space data to the method for essence, cascade filtration, can effectively improve inquiry and the recall precision in extensive three-dimensional space data storehouse, for efficient three-dimension GIS real-time visual provides the basis and ensures.
Be understandable that, for the person of ordinary skill of the art, can make other various corresponding changes and distortion by technical conceive according to the present invention, and all these change and distortion all should belong to the protection domain of claim of the present invention.
Claims (5)
1. multi-level three dimensions indexing means that mixes is characterized in that: comprises the following steps,
Step 1 is set up unified three dimensions index framework;
Step 2, the city space is divided into some zonules on a large scale, sets up the first level Grid Index;
Step 3 in the first level mesh region, is set up the second level polymorphic type hybrid index to dissimilar three-dimensional space data;
Step 4 is based on the three dimensions index of multi-level mixing, to the three-dimensional space data storehouse carrying out the multilayer query and search.
2. the three dimensions indexing means of multi-level mixing according to claim 1, it is characterized in that: described three-dimensional factor data comprises above and below ground BUILDINGS MODELS, city essay model and the vegetation model data of discrete distribution, and the three-dimensional R tree index of described above and below ground BUILDINGS MODELS, city essay model and vegetation model the data considering levels of detail is improved one's methods and carried out data directory.
3. the three dimensions indexing means of multi-level mixing according to claim 1, it is characterized in that: described three-dimensional factor data comprises the geologic model data of layering continuous distribution, and the three-dimensional R tree index of described geologic model the data clustering order is improved one's methods and carried out data directory.
4. the three dimensions indexing means of multi-level mixing according to claim 1, it is characterized in that: described three-dimensional factor data comprises the pipeline model data that LINEAR CONTINUOUS distributes, and the three-dimensional R tree index that topological relation is taken in described pipeline model data employing into account is improved one's methods and carried out data directory.
5. the three dimensions indexing means of multi-level mixing according to claim 1, it is characterized in that: described multilayer query and search includes following steps,
Step S1: input three dimensions query context;
Step S2: the numbering of calculating the first related level Grid Index of this three dimensions query context; Simultaneously this three dimensions query context is placed in the set of three dimensions query context, predicts the mobile trend of viewpoint according to several times three dimensions query context recently;
Step S3: calculate related second layer secondary index numbering, and judge whether to be loaded in the indexed cache pond, if in the indexed cache pond, directly enter S5, otherwise enter S4;
Step S4: according to second layer secondary index numbering, load not yet in the second layer secondary index data in indexed cache pond from the three-dimensional space data storehouse, be placed in the indexed cache pond;
Step S5: by second layer secondary index structure, the three-dimensional feature object ID that retrieval three dimensions query context is related and texture object ID set;
Step S6: according to the viewpoint mobile trend of prediction, calculate viewpoint and move related Grid Index numbering, and judge that second layer secondary index data in related graticule mesh are whether in the indexed cache pond, for second layer secondary index data in the indexed cache pond not, open special index prestrain thread and carry out prestrain;
Step S7: gather as querying condition take described three-dimensional feature object ID set and texture object ID, carry out the three-dimensional space data library inquiry, and return results.
Step S8: search end.
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| CN113535712A (en) * | 2021-06-04 | 2021-10-22 | 山东大学 | Method and system for supporting large-scale time sequence data interaction based on line segment KD tree |
| CN113535712B (en) * | 2021-06-04 | 2023-09-29 | 山东大学 | Method and system for supporting large-scale time sequence data interaction based on line segment KD tree |
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