CN114490899A - Multi-layer space big data superposition and combination method, device, equipment and storage medium - Google Patents
Multi-layer space big data superposition and combination method, device, equipment and storage medium Download PDFInfo
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Abstract
The invention discloses a method, a device, equipment and a storage medium for superposing and merging multi-layer spatial big data. The method comprises the following steps: dividing the space data set based on a quadtree, wherein each leaf node of the quadtree stores each divided subspace data set; creating base space data according to the minimum outer bounding boxes of the leaf nodes; carrying out superposition cross analysis on each space element in the subspace data set and the base space data in sequence, and combining the superposition cross analysis results of each space element to obtain the superposition cross analysis result of the leaf node; and combining the overlapping and cross-analyzing results of all the leaf nodes to obtain an overlapping and combining analyzing result. The invention introduces the base space data, converts the superposition merging analysis into the superposition cross analysis in the subspace, simplifies the judgment process of merging logic, iteratively cuts the data in each division range and the corresponding base space data, avoids data repeated calculation, saves calculation resources and improves the efficiency of the superposition merging analysis.
Description
Technical Field
The invention relates to the technical field of spatial information, in particular to a method, a device, equipment and a storage medium for superposition and combination of multi-layer spatial big data.
Background
Superposition analysis is a very important spatial analysis function in a Geographic Information System (GIS), and is based on the operation of intersection (cross analysis), union analysis and difference (difference analysis) of spatial logics of two or more layers, and the analysis and evaluation of attributes in a superposition range are carried out. With the explosive growth of spatial data, the traditional serial superposition analysis performance reaches the bottleneck, and the requirement of the existing spatial big data analysis cannot be met; meanwhile, the development of the existing parallel computing architecture and distributed architecture software and hardware technology provides basic support for superposition analysis to utilize more computing resources, the superposition analysis algorithm of the large spatial data is mainly based on the traditional superposition algorithm, distributed spatial indexes are introduced on the basis of spatial data division, the superposition analysis is realized in a task scheduling mode, and the method is mainly suitable for cross analysis and difference analysis in the superposition analysis and is mainly used for improving the efficiency of the cross analysis and the difference analysis. The existing big data superposition analysis method has a large amount of repeated calculation, and particularly when multiple image layers are superposed, combined and analyzed, the calculation resources are greatly wasted, and the efficiency of superposition, combination and analysis is reduced.
The above is only for the purpose of assisting understanding of the technical aspects of the present invention, and does not represent an admission that the above is prior art.
Disclosure of Invention
The invention mainly aims to provide a method, a device, equipment and a storage medium for superposition and merging of multi-layer space big data, and aims to solve the technical problem that the existing method is low in efficiency in superposition, merging and analysis of multiple layers.
In order to achieve the above object, the present invention provides a method for superposing and merging big data of multiple layer spaces, wherein the method comprises the following steps:
acquiring a spatial data set, and dividing the spatial data set based on a quadtree, wherein each leaf node of the quadtree stores each subspace data set divided by the spatial data set;
creating base space data according to the minimum outer bounding boxes corresponding to the leaf nodes;
carrying out superposition cross analysis on each space element in the target subspace data set and target substrate space data in sequence to obtain a superposition cross result corresponding to each space element;
merging the superposition cross results corresponding to the spatial elements to obtain a union of the superposition cross results as a superposition cross analysis result corresponding to a target leaf node, wherein the target leaf node stores the target subspace data set, and the target base spatial data correspond to the target leaf node;
and combining the overlapping cross analysis results corresponding to each leaf node to obtain a union of the overlapping cross analysis results as an overlapping and combining analysis result.
Optionally, the performing, by overlapping and cross-analyzing each spatial element in the target subspace data set and the target base spatial data in sequence to obtain an overlapping and cross-analyzing result corresponding to each spatial element includes:
traversing the target subspace data set, and determining intersection data and difference data between the target base space data and the traversed space elements;
taking the intersection data as a superposition intersection result corresponding to the traversed space elements;
taking the difference set data as updated basal space data;
and performing overlapping and crossing analysis on the next traversed space element according to the updated substrate space data, and so on to obtain an overlapping and crossing result corresponding to each space element.
Optionally, after the overlapping and crossing analysis results corresponding to each leaf node are merged to obtain a union of the overlapping and crossing analysis results as an overlapping and crossing analysis result, the method further includes:
when the target space element is obtained, obtaining the corresponding residual basal space data after the superposition cross analysis is carried out on the residual basal space data and the previous space data set;
determining current intersection data between the remaining base space data and the target space element;
taking the current intersection data as a superposition intersection result corresponding to the target space element;
and merging the superposition cross result corresponding to the target space element into the superposition merging analysis result to obtain an updated superposition merging analysis result.
Optionally, the performing, by overlapping and cross-analyzing each spatial element in the target subspace data set and the target base spatial data in sequence to obtain an overlapping and cross-analyzing result corresponding to each spatial element includes:
determining a computing node corresponding to each leaf node;
and carrying out superposition cross analysis on each spatial element in the target subspace data set and the target substrate spatial data in sequence in the computing node corresponding to the target leaf node to obtain a superposition cross result corresponding to each spatial element.
Optionally, the obtaining the spatial data set, and dividing the spatial data set based on a quadtree, includes:
acquiring a spatial data set, randomly acquiring a plurality of sample data from the spatial data set, and generating a sample data set;
creating a quadtree structure according to the spatial extent of the spatial data set;
determining the calculated quantity intensity corresponding to each sample data in the sample data set;
inserting each sample data in the sample data set into the quadtree structure according to the calculated quantity intensity to generate an initial quadtree;
clearing the space elements stored by each leaf node in the initial quadtree to obtain a space tree structure;
and inserting the spatial data set into the spatial tree structure to realize the division of the spatial data set based on the quadtree.
Optionally, the determining the computation intensity corresponding to each sample data in the sample data set includes:
and determining corresponding calculated quantity intensity according to the information quantity of each sample data in the sample data set.
Optionally, the inserting each sample data in the sample data set into the quadtree structure according to the calculated amount strength to generate an initial quadtree, includes:
taking the minimum bounding box corresponding to each sample data in the sample data set as a constraint condition;
taking the calculated quantity intensity corresponding to each sample data in the sample data set as a node splitting judgment condition;
and inserting each sample data into the quad-tree structure according to the constraint condition and the node splitting judgment condition to generate an initial quad-tree.
In addition, in order to achieve the above object, the present invention further provides a device for superimposing and merging big data in multiple layer spaces, where the device for superimposing and merging big data in multiple layer spaces includes:
the system comprises an acquisition module, a storage module and a processing module, wherein the acquisition module is used for acquiring a spatial data set and dividing the spatial data set based on a quadtree, and each leaf node of the quadtree stores each subspace data set divided by the spatial data set;
the determining module is used for creating substrate space data according to the minimum outer bounding boxes corresponding to the leaf nodes;
the superposition cross analysis module is used for carrying out superposition cross analysis on each space element in the target subspace data set and the target substrate space data in sequence to obtain a superposition cross result corresponding to each space element;
the superposition cross analysis module is further configured to merge superposition cross results corresponding to the spatial elements, and a union of the obtained superposition cross results is a superposition cross analysis result corresponding to a target leaf node, where the target leaf node stores the target subspace data set, and the target base spatial data corresponds to the target leaf node;
and the superposition merging analysis module is used for merging the superposition cross analysis results corresponding to the leaf nodes to obtain a union of the superposition cross analysis results as a superposition merging analysis result.
In addition, in order to achieve the above object, the present invention further provides a device for superimposing and merging big data in multiple layer space, where the device for superimposing and merging big data in multiple layer space includes: the device comprises a memory, a processor and a multi-layer space big data superposition and combination program which is stored on the memory and can run on the processor, wherein the multi-layer space big data superposition and combination program is configured to realize the multi-layer space big data superposition and combination method.
In addition, in order to achieve the above object, the present invention further provides a storage medium, where a multiple layer space big data superposition and merging program is stored, and when executed by a processor, the multiple layer space big data superposition and merging program implements the multiple layer space big data superposition and merging method as described above.
The method comprises the steps of dividing a space data set based on a quadtree by obtaining the space data set, wherein each leaf node of the quadtree stores each subspace data set divided by the space data set; creating base space data according to the minimum outer bounding boxes corresponding to the leaf nodes; carrying out superposition cross analysis on each space element in the target subspace data set and target substrate space data in sequence to obtain a superposition cross result corresponding to each space element; merging the superposition cross results corresponding to the spatial elements to obtain a union of the superposition cross results as a superposition cross analysis result corresponding to a target leaf node, wherein the target leaf node stores a target subspace data set, and the target base spatial data correspond to the target leaf node; and combining the overlapping cross analysis results corresponding to each leaf node to obtain a union of the overlapping cross analysis results as an overlapping and combining analysis result. By the method, the base space data are introduced, the superposition merging analysis is converted into the superposition cross analysis in the subspace, the judgment flow of merging logic is simplified, the space data in each division range and the corresponding base space data are subjected to iterative cutting, data repeated calculation is avoided, calculation resources are saved, and the efficiency of the superposition merging analysis is improved.
Drawings
Fig. 1 is a schematic structural diagram of a multiple layer space big data superposition merging device in a hardware operating environment according to an embodiment of the present invention;
fig. 2 is a schematic flowchart of a first embodiment of a method for superposing and merging multiple layer space big data according to the present invention;
FIG. 3 is a schematic diagram of a quadtree according to an embodiment of a method for merging and superimposing spatial big data of multiple layers;
fig. 4 is a schematic view of a superposition merging flow of an embodiment of a method for superposition merging of large data in a multi-layer space according to the present invention;
fig. 5 is a schematic flowchart of a second embodiment of a method for superposing and merging multiple layer space big data according to the present invention;
fig. 6 is a schematic view illustrating superposition and merging of leaf nodes according to an embodiment of a method for superposition and merging of large data in a multi-layer space according to the present invention;
fig. 7 is a schematic diagram of a quadtree superposition and merging according to an embodiment of the method for superposition and merging of large data in a multi-layer space;
fig. 8 is a schematic flowchart of a third embodiment of a method for superposing and merging multiple layer space big data according to the present invention;
fig. 9 is a structural block diagram of a device for superimposing and merging multiple layer space big data according to a first embodiment of the present invention.
The implementation, functional features and advantages of the objects of the present invention will be further explained with reference to the accompanying drawings.
Detailed Description
It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
Referring to fig. 1, fig. 1 is a schematic structural diagram of a multiple layer space big data superposition merging device in a hardware operating environment according to an embodiment of the present invention.
As shown in fig. 1, the multiple layer space big data superposition and merging device may include: a processor 1001, such as a Central Processing Unit (CPU), a communication bus 1002, a user interface 1003, a network interface 1004, and a memory 1005. Wherein a communication bus 1002 is used to enable connective communication between these components. The user interface 1003 may include a Display screen (Display), an input unit such as a Keyboard (Keyboard), and the optional user interface 1003 may also include a standard wired interface, a wireless interface. The network interface 1004 may optionally include a standard wired interface, a Wireless interface (e.g., a Wireless-Fidelity (Wi-Fi) interface). The Memory 1005 may be a Random Access Memory (RAM) or a Non-Volatile Memory (NVM), such as a disk Memory. The memory 1005 may alternatively be a storage device separate from the processor 1001.
Those skilled in the art will appreciate that the structure shown in fig. 1 does not constitute a limitation of the multiple layer space big data superposition and merging device, and may include more or less components than those shown, or combine some components, or arrange different components.
As shown in fig. 1, a memory 1005 as a storage medium may include an operating system, a network communication module, a user interface module, and a multi-layer space big data superposition and merging program.
In the multiple layer space big data superposition merging device shown in fig. 1, the network interface 1004 is mainly used for data communication with a network server; the user interface 1003 is mainly used for data interaction with a user; the processor 1001 and the memory 1005 in the device for superposing and merging the large data of the multiple layer space may be arranged in the device for superposing and merging the large data of the multiple layer space, and the device for superposing and merging the large data of the multiple layer space calls a program for superposing and merging the large data of the multiple layer space stored in the memory 1005 through the processor 1001 and executes the method for superposing and merging the large data of the multiple layer space provided by the embodiment of the present invention.
An embodiment of the present invention provides a method for superimposing and merging multiple layer space big data, and referring to fig. 2, fig. 2 is a schematic flowchart of a first embodiment of the method for superimposing and merging multiple layer space big data according to the present invention.
In this embodiment, the method for superposing and merging the big data in the multi-layer space includes the following steps:
step S10: the method comprises the steps of obtaining a spatial data set, and dividing the spatial data set based on a quadtree, wherein each leaf node of the quadtree stores each subspace data set divided by the spatial data set.
It can be understood that the execution main body of this embodiment is a multi-layer space big data superposition and merging device, and the multi-layer space big data superposition and merging device may be a device such as a computer and a server, and may also be another device with big data superposition analysis capability, which is not limited in this embodiment.
It should be noted that, in this embodiment, a bernoulli secondary distribution is adopted to obtain a sample data set from a spatial data set, a nonlinear spatial quadtree is created in a spatial range of the spatial data set, a preset computation intensity calculation function is used to determine a computation intensity corresponding to each spatial element in the sample data set, and the computation intensity corresponding to each spatial element in the sample data set is inserted into the nonlinear spatial quadtree as a determination condition to generate an initial quadtree. After the initial quad-tree is established, removing the spatial elements stored in the leaf nodes, reserving the generated spatial tree structure, inserting the spatial elements in the spatial data set into the quad-tree according to the minimum outer bounding box, wherein the spatial structure of the quad-tree is not changed, the spatial range of the leaf nodes is the division range of each sub-data block, and each leaf node in the quad-tree stores a subspace data set after the spatial data set is divided. Referring to fig. 3, fig. 3 is a schematic diagram of a quadtree according to an embodiment of the method for merging and superposing spatial big data of multiple image layers, where the quadtree in fig. 3 includes L1、L2、L3、L4Four leaf nodes, wherein L1The leaf nodes store subspace data sets { A, B, C, D }, L }2The leaf nodes store subspace data sets { G, E, F }, L }3The leaf nodes are stored with subspace data sets { L, J, K }, L }4The leaf node stores sub-nullsThe inter data set { I, F, H }.
Step S20: and creating base space data according to the minimum outer bounding boxes corresponding to the leaf nodes.
It will be appreciated that, with reference to FIG. 3, the minimum bounding box (x) according to each leaf node isi1,xi2,yi1,yi2) Creating base space data b1、b2、b3、b4。
Step S30: and carrying out superposition cross analysis on each space element in the target subspace data set and the target substrate space data in sequence to obtain a superposition cross result corresponding to each space element.
Step S40: and merging the superposition cross results corresponding to the spatial elements to obtain a union of the superposition cross results as a superposition cross analysis result corresponding to a target leaf node, wherein the target leaf node stores the target subspace data set, and the target base spatial data correspond to the target leaf node.
It should be noted that different leaf nodes store different spatial sub-data sets, for convenience of description, in this embodiment, a sub-space data set stored in any one leaf node is referred to as a target sub-space data set, that is, in this embodiment, the superposition intersection result corresponding to each leaf node is calculated through step S30.
It should be understood that, referring to fig. 3, the present embodiment uses leaf nodes L1For illustration purposes, leaf node L1Corresponding base data is b1The subspace data set is stored as { A, B, C, D }, and the computation set is determined as (B)1{ A, B, C, D }), and B are sequentially assigned to each spatial element in { A, B, C, D }, respectively1Performing a cross-over analysis, e.g. by combining the spatial elements A and b1Performing overlap cross analysis to determine the intersection A between the tworIs A, difference set b11Is b is1A, difference b11As the base space data in the superposition cross analysis of the next space element B, i.e. the space elements B and B11Performing superposition cross analysis to determine the intersection B between the tworIs b is11N, B, difference set B12Is b is11-(b11n.B), and so on, obtaining the superposition cross analysis result { A } corresponding to each space element in the subspace data set { A, B, C, D }r,Br,Cr,DrAnd combining results to obtain leaf nodes L1And (5) corresponding overlapping and cross-analyzing results.
It should be noted that, across a plurality of leaf node space elements, iterative clipping and cutting are performed on the base data set corresponding to each leaf node set, for example, the space element F in fig. 3, the space element F and the base data b2And b4Performing iterative cutting to obtain leaf nodes L2And leaf node L4And (5) corresponding overlapping and cross-analyzing results.
Further, in order to keep the superposition and merging independent from each other in each subspace and improve the efficiency of distributed or parallel computing, the step S30 includes: determining a computing node corresponding to each leaf node; and carrying out superposition cross analysis on each spatial element in the target subspace data set and the target substrate spatial data in sequence in the computing node corresponding to the target leaf node to obtain a superposition cross result corresponding to each spatial element.
It should be understood that the subspace data sets for each leaf node { C }iWith corresponding base space data biPerforming parallel computations in a distributed environment, and (b)1,C1)、(b2,C2)、(b3,C3)、(b4,C4) The calculation is performed in different calculation nodes according to step S30, and parallelization of the superposition-merging calculation is realized. In a specific implementation, the step S40 includes: and combining the superposition intersection results corresponding to the space elements in the calculation node corresponding to the target leaf node to obtain a union of the superposition intersection results as a superposition intersection analysis result corresponding to the target leaf node.
Step S50: and combining the overlapping cross analysis results corresponding to each leaf node to obtain a union of the overlapping cross analysis results as an overlapping and combining analysis result.
It should be noted that there is no spatial overlap relationship between the calculation results of the leaf nodes, the final results of the overlay cross analysis of the leaf nodes are directly merged, and the merged result is output, as shown in fig. 4, fig. 4 is a schematic view of an overlay merging process according to an embodiment of the method for overlay merging of multiple layer space big data of the present invention, data division is performed on the spatial big data based on a quadtree, base space data, i.e., a space range object, is created in each leaf node, overlay cross analysis is performed on subspace data stored in each leaf node and the base range object, overlay cross results corresponding to each space element are merged, and overlay cross analysis results corresponding to each leaf node are merged, so as to obtain an overlay merged result.
In this embodiment, a spatial data set is obtained, and the spatial data set is divided based on a quadtree, where each leaf node of the quadtree stores each subspace data set after the spatial data set is divided; creating base space data according to the minimum outer bounding boxes corresponding to the leaf nodes; carrying out superposition cross analysis on each space element in the target subspace data set and target substrate space data in sequence to obtain a superposition cross result corresponding to each space element; merging the superposition cross results corresponding to the spatial elements to obtain a union of the superposition cross results as a superposition cross analysis result corresponding to a target leaf node, wherein the target leaf node stores a target subspace data set, and the target base spatial data correspond to the target leaf node; and combining the overlapping cross analysis results corresponding to each leaf node to obtain a union of the overlapping cross analysis results as an overlapping and combining analysis result. By the method, the base space data are introduced, the superposition merging analysis is converted into the superposition cross analysis in the subspace, the judgment flow of merging logic is simplified, the space data in each division range and the corresponding base space data are subjected to iterative cutting, data repeated calculation is avoided, calculation resources are saved, and the efficiency of the superposition merging analysis is improved.
Referring to fig. 5, fig. 5 is a flowchart illustrating a method for superimposing and merging multiple spatial layers according to a second embodiment of the present invention.
Based on the first embodiment, step S30 of the method for superposing and merging multiple layer space big data in this embodiment includes:
step S301: and traversing the target subspace data set, and determining intersection data and difference data between the target base space data and the traversed space elements.
Step S302: and taking the intersection data as an overlapping and intersecting result corresponding to the traversed space elements.
Step S303: and taking the difference set data as updated base space data.
Step S304: and performing overlapping and crossing analysis on the next traversed space element according to the updated substrate space data, and so on to obtain an overlapping and crossing result corresponding to each space element.
It can be understood that, each spatial element in the target subspace data set is sequentially subjected to superposition cross analysis with the target base spatial data, referring to fig. 3 and fig. 6, fig. 6 is a schematic view of superposition and combination of leaf nodes according to an embodiment of the method for superposition and combination of large data in multiple layer spaces of the present invention, and a leaf node L is used as the leaf node L1For illustration purposes, leaf node L1Corresponding base data is b1The stored subspace data set is { A, B, C, D }, the space element A is taken as the first traversed space element, and intersection data A between the space element A and the target base space data is determinedrIs A, difference set data b11Is b is1-A, combining the difference set data b11The base space data corresponding to the next traversed space element is used as the base space data; the space element B is used as a second traversed space element, and the space element B and the base space data B are determined11Intersection data B betweenrIs b is11N, B, difference set data B12Is b is11-(b11N B), the difference set data B12The base space data corresponding to the next traversed space element is used as the base space data; the space element C is used as a third traversed space element, and the space element C and the base space data b are determined12Intersection data C betweenrIs b is12N C, difference set data b13Is b is12-(b12N.c), combining the difference set data b13As belowBase space data corresponding to the traversed space elements; the space element D is used as a fourth traversed space element, and the space element D and the base space data b are determined13Intersection data D betweenrIs b is13N C, difference set data b14Is b is13-(b13N D), determining the superposition intersection result corresponding to each spatial element as { Ar,Br,Cr,Dr}。
The result of the overlap and intersection corresponding to each spatial element is { A }r,Br,Cr,DrAre combined to obtain leaf nodes L1The corresponding overlapping cross analysis result is obtained through steps S301-S305 to obtain the leaf node L2、L3、L4Referring to fig. 7, fig. 7 is a schematic diagram of a quadtree superposition and merging according to an embodiment of the method for superposition and merging of large data in a multi-layer space according to the present invention, and L is calculated according to the result of the superposition cross analysis1、L2、L3、L4And combining the corresponding overlapping cross analysis results to obtain an overlapping and combining analysis result.
Further, after the step S50, the method further includes: when the target space element is obtained, obtaining the corresponding residual basal space data after the superposition cross analysis is carried out on the residual basal space data and the previous space data set; determining current intersection data between the remaining base space data and the target space element; taking the current intersection data as a superposition intersection result corresponding to the target space element; and merging the superposition cross result corresponding to the target space element into the superposition merging analysis result to obtain an updated superposition merging analysis result.
It should be understood that if there are other spatial element sets involved in the calculation, the remaining base data calculated in steps S301 to S305 is used as "base" data, intersection data and difference data between the remaining base data and the currently acquired target spatial element are determined, the intersection data is added to the previously obtained superposition and merging result, and an updated superposition and merging analysis result is generated. In specific implementation, if the range corresponding to the currently acquired target space element is completely covered by the previous stacking, the stacking, merging and analyzing result corresponding to the target space element is empty, and the subsequent target space element does not need to participate in calculation.
The embodiment divides the spatial data set based on the quadtree by acquiring the spatial data set; traversing the target subspace data set, and determining intersection data and difference data between the target base space data and the traversed space elements; taking the intersection data as a superposition intersection result corresponding to the traversed space elements; taking the difference set data as updated substrate space data; performing overlapping and crossing analysis on the next traversed space element according to the updated substrate space data, and so on to obtain an overlapping and crossing result corresponding to each space element; merging the superposition cross results corresponding to the space elements to obtain a union of the superposition cross results as a superposition cross analysis result corresponding to the target leaf node; and combining the overlapping cross analysis results corresponding to each leaf node to obtain a union of the overlapping cross analysis results as an overlapping and combining analysis result. By the method, the base space data are introduced, the superposition merging analysis is converted into the superposition cross analysis in the subspace, the judgment flow of merging logic is simplified, the intersection and the difference between each space element in the subspace data set and the base space data are determined, the difference is used as the base data when the next space element is subjected to the superposition cross analysis, the data repeated calculation is avoided, the calculation resources are saved, the parallel superposition cross analysis is performed on each leaf node in different calculation nodes, and the efficiency of the superposition merging analysis is improved.
Referring to fig. 8, fig. 8 is a schematic flowchart of a method for superposing and merging multiple layer space big data according to a third embodiment of the present invention.
Based on the first embodiment, step S10 of the method for superposing and merging multiple layer space big data in this embodiment includes:
step S101: the method comprises the steps of obtaining a spatial data set, randomly obtaining a plurality of sample data from the spatial data set, and generating a sample data set.
It will be appreciated that the present embodiment utilises Bernoulli secondary distribution to derive the spatial data set S { v }1,v2,v3,...,vnRandomly acquiring a plurality of sample data to generate a sample data set M { v }i,vb,vh,...,vj}。
Step S102: a quadtree structure is created from the spatial extent of the spatial data set.
Step S103: and determining the calculated quantity intensity corresponding to each sample data in the sample data set.
Specifically, the step S103 includes: and determining corresponding calculated quantity intensity according to the information quantity of each sample data in the sample data set.
It can be understood that the calculated quantity intensity corresponding to each sample data is calculated based on a preset calculated quantity intensity function according to the information quantity corresponding to each sample data, and the preset calculated quantity intensity function is shown in formula (1):
f(E)＝V(vi)*log(V(vi)) (1)
wherein v isiRepresents each sample data, V (V), in the sample data seti) Denotes viThe information amount (number of bytes) of the element object.
Step S104: and inserting each sample data in the sample data set into the quadtree structure according to the calculated quantity intensity to generate an initial quadtree.
Specifically, the step S104 includes: taking the minimum bounding box corresponding to each sample data in the sample data set as a constraint condition; taking the calculated quantity intensity corresponding to each sample data in the sample data set as a node splitting judgment condition; and inserting each sample data into the quad-tree structure according to the constraint condition and the node splitting judgment condition to generate an initial quad-tree.
It should be noted that, the maximum computation intensity in the leaf node is set to be N, and the sample data set M { v } is calculated according to the formula (1)i,vb,vh,...,vjIntensity of calculation amount of each element object { N }i,Nb,Nh,…,NjDetermine M { v }i,vb,vh,...,vjMinimum bounding Box of each element object in { r }i,rb,rh,...,rjAnd constructing an element object triple set { O }i(vi,Ni,ri) For each sample data with a minimum bounding box riInserted as constraints into the quadtree structure to compute the magnitude NiAs a judgment condition whether the leaf node is split or not, if the calculated quantity intensity is NiAfter the sample data is added into a certain leaf node, the total computation intensity corresponding to the leaf node is greater than N, the leaf node is split, the sample data is inserted into a newly generated leaf node, and if the computation intensity is NiAfter the sample data is added into a certain leaf node, the total computation intensity corresponding to the leaf node is not more than N, and the sample data v is directly addediInserting all sample data in the sample data set into the quad-tree structure to generate an initial quad-tree q (M).
Step S105: and clearing the space elements stored by each leaf node in the initial quadtree to obtain a space tree structure.
Step S106: and inserting the spatial data set into the spatial tree structure to realize the division of the spatial data set based on the quadtree.
It should be understood that, after the initial quadtree q (m) is created, the stored space elements in the leaf nodes are removed, the generated space tree structure is retained, and the space data set S is inserted into the space tree structure, specifically, the space data set S is inserted into the space tree structure according to the minimum outer bounding box corresponding to each space element in the space data set S, at this time, the structure of the quadtree q (m) is not changed, the space range of the leaf nodes is the division range of each sub data block, and each leaf node L in the quadtree q (m) is the division range of each leaf node LiThe stored spatial data set is a subspace data set Ci{vi1,vi2,vi3,...,vin}。
In the embodiment, a plurality of sample data are randomly acquired from a spatial data set by acquiring the spatial data set, and a sample data set is generated; creating a quadtree structure according to the spatial extent of the spatial data set; determining the calculated quantity intensity corresponding to each sample data in the sample data set; inserting each sample data in the sample data set into a quadtree structure according to the calculated quantity intensity to generate an initial quadtree; clearing spatial elements stored by each leaf node in the initial quadtree to obtain a spatial tree structure; inserting the spatial data set into a spatial tree structure to realize the division of the spatial data set based on the quadtree; creating base space data according to the minimum outer bounding boxes corresponding to the leaf nodes; carrying out superposition cross analysis on each space element in the target subspace data set and target substrate space data in sequence to obtain a superposition cross result corresponding to each space element; merging the superposition cross results corresponding to the space elements to obtain a union of the superposition cross results as a superposition cross analysis result corresponding to the target leaf node; and combining the overlapping cross analysis results corresponding to each leaf node to obtain a union of the overlapping cross analysis results as an overlapping and combining analysis result. By the method, the spatial data set is divided through the calculated quantity intensity, a data base is provided for independent calculation of a superposition merging analysis algorithm after data division, and efficiency of superposition merging analysis for spatial big data is improved. The base space data are introduced, the superposition merging analysis is converted into the superposition cross analysis in the subspace, the judgment process of merging logic is simplified, iterative cutting is carried out on the space data in each division range and the corresponding base space data, data repeated calculation is avoided, and calculation resources are saved.
In addition, an embodiment of the present invention further provides a storage medium, where a multiple layer space big data superposition and merging program is stored in the storage medium, and when executed by a processor, the multiple layer space big data superposition and merging program implements the multiple layer space big data superposition and merging method described above.
Since the storage medium adopts all technical solutions of all the embodiments, at least all the beneficial effects brought by the technical solutions of the embodiments are achieved, and no further description is given here.
Referring to fig. 9, fig. 9 is a structural block diagram of a first embodiment of a multiple layer space big data superposition merging device according to the present invention.
As shown in fig. 9, the apparatus for superposing and merging big data in multiple layer spaces according to the embodiment of the present invention includes:
an obtaining module 10, configured to obtain a spatial data set, and divide the spatial data set based on a quadtree, where each leaf node of the quadtree stores each subspace data set obtained by dividing the spatial data set.
And a determining module 20, configured to create the base space data according to the minimum outer bounding box corresponding to each leaf node.
The overlap analysis module 30 is configured to perform overlap analysis on each spatial element in the target subspace data set and the target base spatial data in sequence to obtain an overlap result corresponding to each spatial element;
the overlap-add cross analysis module 30 is further configured to merge the overlap-add cross results corresponding to the spatial elements, and obtain a union of the overlap-add cross results as an overlap-add cross analysis result corresponding to a target leaf node, where the target leaf node stores the target subspace data set, and the target base spatial data corresponds to the target leaf node.
And the superposition merging analysis module 40 is configured to merge the superposition cross analysis results corresponding to each leaf node to obtain a union of the superposition cross analysis results as a superposition merging analysis result.
It should be understood that the above is only an example, and the technical solution of the present invention is not limited in any way, and in a specific application, a person skilled in the art may set the technical solution as needed, and the present invention is not limited thereto.
In this embodiment, a spatial data set is obtained, and the spatial data set is divided based on a quadtree, where each leaf node of the quadtree stores each subspace data set after the spatial data set is divided; creating base space data according to the minimum outer bounding boxes corresponding to the leaf nodes; carrying out superposition cross analysis on each space element in the target subspace data set and target substrate space data in sequence to obtain a superposition cross result corresponding to each space element; merging the superposition cross results corresponding to the spatial elements to obtain a union of the superposition cross results as a superposition cross analysis result corresponding to a target leaf node, wherein the target leaf node stores a target subspace data set, and the target base spatial data correspond to the target leaf node; and combining the overlapping cross analysis results corresponding to each leaf node to obtain a union of the overlapping cross analysis results as an overlapping and combining analysis result. By the method, the base space data are introduced, the superposition merging analysis is converted into the superposition cross analysis in the subspace, the judgment flow of merging logic is simplified, the space data in each division range and the corresponding base space data are subjected to iterative cutting, data repeated calculation is avoided, calculation resources are saved, and the efficiency of the superposition merging analysis is improved.
It should be noted that the above-described work flows are only exemplary, and do not limit the scope of the present invention, and in practical applications, a person skilled in the art may select some or all of them to achieve the purpose of the solution of the embodiment according to actual needs, and the present invention is not limited herein.
In addition, the technical details that are not described in detail in this embodiment may refer to a method for superimposing and merging large spatial data of multiple image layers provided in any embodiment of the present invention, and are not described herein again.
In an embodiment, the overlap-add-cross analysis module 30 is further configured to traverse the target subspace data set, and determine intersection data and difference data between the target base space data and the traversed space elements; taking the intersection data as a superposition intersection result corresponding to the traversed space elements; taking the difference set data as updated basal space data; and performing overlapping and crossing analysis on the next traversed space element according to the updated substrate space data, and so on to obtain an overlapping and crossing result corresponding to each space element.
In an embodiment, the superposition merging analysis module 40 is further configured to, when the target spatial element is obtained, obtain remaining base spatial data corresponding to the previous spatial data set after the superposition cross analysis is performed; determining current intersection data between the remaining base space data and the target space element; taking the current intersection data as a superposition intersection result corresponding to the target space element; and merging the superposition cross result corresponding to the target space element into the superposition merging analysis result to obtain an updated superposition merging analysis result.
In an embodiment, the overlap-add cross analysis module 30 is further configured to determine a computing node corresponding to each leaf node; and carrying out superposition cross analysis on each spatial element in the target subspace data set and the target substrate spatial data in sequence in the computing node corresponding to the target leaf node to obtain a superposition cross result corresponding to each spatial element.
In an embodiment, the obtaining module 10 is further configured to obtain a spatial data set, randomly obtain a plurality of sample data from the spatial data set, and generate a sample data set; creating a quadtree structure according to the spatial extent of the spatial data set; determining the calculated quantity intensity corresponding to each sample data in the sample data set; inserting each sample data in the sample data set into the quadtree structure according to the calculated quantity intensity to generate an initial quadtree; clearing the space elements stored by each leaf node in the initial quadtree to obtain a space tree structure; and inserting the spatial data set into the spatial tree structure to realize the division of the spatial data set based on the quadtree.
In an embodiment, the obtaining module 10 is further configured to determine a corresponding calculated amount strength according to an information amount of each sample data in the sample data set.
In an embodiment, the obtaining module 10 is further configured to use a minimum bounding box corresponding to each sample data in the sample data set as a constraint condition; taking the calculated quantity intensity corresponding to each sample data in the sample data set as a node splitting judgment condition; and inserting each sample data into the quad-tree structure according to the constraint condition and the node splitting judgment condition to generate an initial quad-tree.
Further, it is to be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or system that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or system. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in the process, method, article, or system in which the element is included.
The above-mentioned serial numbers of the embodiments of the present invention are merely for description and do not represent the merits of the embodiments.
Through the above description of the embodiments, those skilled in the art will clearly understand that the method of the above embodiments can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware, but in many cases, the former is a better implementation manner. Based on such understanding, the technical solution of the present invention or portions thereof that contribute to the prior art may be embodied in the form of a software product, where the computer software product is stored in a storage medium (e.g. Read Only Memory (ROM)/RAM, magnetic disk, optical disk), and includes several instructions for enabling a terminal device (e.g. a mobile phone, a computer, a server, or a network device) to execute the method according to the embodiments of the present invention.
The above description is only a preferred embodiment of the present invention, and not intended to limit the scope of the present invention, and all modifications of equivalent structures and equivalent processes, which are made by using the contents of the present specification and the accompanying drawings, or directly or indirectly applied to other related technical fields, are included in the scope of the present invention.
Claims (10)
1. A method for superposing and combining big data of multiple layer spaces is characterized in that the method for superposing and combining the big data of the multiple layer spaces comprises the following steps:
acquiring a spatial data set, and dividing the spatial data set based on a quadtree, wherein each leaf node of the quadtree stores each subspace data set divided by the spatial data set;
creating base space data according to the minimum outer bounding boxes corresponding to the leaf nodes;
carrying out superposition cross analysis on each space element in the target subspace data set and target substrate space data in sequence to obtain a superposition cross result corresponding to each space element;
merging the superposition intersection results corresponding to the spatial elements to obtain a union of the superposition intersection results as a superposition intersection analysis result corresponding to a target leaf node, wherein the target leaf node stores the target subspace data set, and the target base spatial data correspond to the target leaf node;
and combining the overlapping cross analysis results corresponding to each leaf node to obtain a union of the overlapping cross analysis results as an overlapping and combining analysis result.
2. The method according to claim 1, wherein the step of performing overlap-add cross analysis on each spatial element in the target subspace data set and the target base spatial data in sequence to obtain an overlap-add cross result corresponding to each spatial element includes:
traversing the target subspace data set, and determining intersection data and difference data between the target base space data and the traversed space elements;
taking the intersection data as a superposition intersection result corresponding to the traversed space elements;
taking the difference set data as updated basal space data;
and performing overlapping and crossing analysis on the next traversed space element according to the updated base space data, and so on to obtain an overlapping and crossing result corresponding to each space element.
3. The method according to claim 1, wherein after the combining of the superposition cross analysis results corresponding to each leaf node to obtain a union of the superposition cross analysis results as the superposition combined analysis result, the method further comprises:
when the target space element is obtained, obtaining the corresponding residual basal space data after the superposition cross analysis is carried out on the residual basal space data and the previous space data set;
determining current intersection data between the remaining base space data and the target space element;
taking the current intersection data as a superposition intersection result corresponding to the target space element;
and merging the superposition cross result corresponding to the target space element into the superposition merging analysis result to obtain an updated superposition merging analysis result.
4. The method according to claim 1, wherein the step of performing overlap-add cross analysis on each spatial element in the target subspace data set and the target base spatial data in sequence to obtain an overlap-add cross result corresponding to each spatial element includes:
determining a computing node corresponding to each leaf node;
and carrying out superposition cross analysis on each spatial element in the target subspace data set and the target substrate spatial data in sequence in the computing node corresponding to the target leaf node to obtain a superposition cross result corresponding to each spatial element.
5. The method according to claim 1, wherein the obtaining a spatial data set and dividing the spatial data set based on a quadtree comprises:
acquiring a spatial data set, randomly acquiring a plurality of sample data from the spatial data set, and generating a sample data set;
creating a quadtree structure according to the spatial range of the spatial data set;
determining the calculated quantity intensity corresponding to each sample data in the sample data set;
inserting each sample data in the sample data set into the quadtree structure according to the calculated quantity intensity to generate an initial quadtree;
clearing the space elements stored by each leaf node in the initial quadtree to obtain a space tree structure;
and inserting the spatial data set into the spatial tree structure to realize the division of the spatial data set based on the quadtree.
6. The method according to claim 5, wherein the determining the computation power strength corresponding to each sample data in the sample data set comprises:
and determining corresponding calculated quantity intensity according to the information quantity of each sample data in the sample data set.
7. The method according to claim 5, wherein the generating an initial quadtree by inserting each sample data in the sample data set into the quadtree structure according to the calculated metric strength comprises:
taking the minimum bounding box corresponding to each sample data in the sample data set as a constraint condition;
taking the calculated quantity intensity corresponding to each sample data in the sample data set as a node splitting judgment condition;
and inserting each sample data into the quad-tree structure according to the constraint condition and the node splitting judgment condition to generate an initial quad-tree.
8. The utility model provides a many picture layer space big data stack merging devices which characterized in that, many picture layer space big data stack merging devices includes:
the system comprises an acquisition module, a storage module and a processing module, wherein the acquisition module is used for acquiring a spatial data set and dividing the spatial data set based on a quadtree, and each leaf node of the quadtree stores each subspace data set divided by the spatial data set;
the determining module is used for creating substrate space data according to the minimum outer bounding boxes corresponding to the leaf nodes;
the superposition cross analysis module is used for carrying out superposition cross analysis on each space element in the target subspace data set and the target substrate space data in sequence to obtain a superposition cross result corresponding to each space element;
the superposition cross analysis module is further configured to merge superposition cross results corresponding to the spatial elements, and a union of the obtained superposition cross results is a superposition cross analysis result corresponding to a target leaf node, where the target leaf node stores the target subspace data set, and the target base spatial data corresponds to the target leaf node;
and the superposition merging analysis module is used for merging the superposition cross analysis results corresponding to the leaf nodes to obtain a union of the superposition cross analysis results as a superposition merging analysis result.
9. The utility model provides a many picture layer space big data stack merge equipment which characterized in that, equipment includes: the device comprises a memory, a processor and a multi-layer space big data superposition and combination program which is stored on the memory and can run on the processor, wherein the multi-layer space big data superposition and combination program is configured to realize the multi-layer space big data superposition and combination method according to any one of claims 1 to 7.
10. A storage medium, wherein the storage medium stores thereon a multiple layer space big data superposition and merging program, and when the multiple layer space big data superposition and merging program is executed by a processor, the multiple layer space big data superposition and merging program implements the multiple layer space big data superposition and merging method according to any one of claims 1 to 7.
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