CN115828110A - Water system spatial feature similarity detection method, equipment, storage medium and device - Google Patents
Water system spatial feature similarity detection method, equipment, storage medium and device Download PDFInfo
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Abstract
The invention discloses a method, equipment, a storage medium and a device for detecting spatial feature similarity of a water system, wherein the method comprises the steps of carrying out fusion processing on water system data after being subjected to shrinking editing through a root preset fusion model to obtain fused target water system data after being subjected to shrinking editing; performing spatial superposition on the target contracted water system data and the water system data before contraction through a preset superposition analysis model to generate a superposition result image layer; and performing similarity detection on the spatial features of the water system before and after the compilation according to the superposition result image layer and a preset model algorithm. According to the method, the superposition result layer is determined through the preset fusion processing model and the superposition analysis model, so that the similarity detection is performed on the spatial characteristics of the water system data according to the superposition result layer, compared with the situation that the similarity of the spatial layout of the water system data under different scale cannot be compared automatically in the prior art, the efficiency and the accuracy of manual detection are low, and the method realizes automatic detection and improves the detection efficiency and the accuracy of the detection result.
Description
Technical Field
The invention relates to the field of image processing, in particular to a method, equipment, a storage medium and a device for detecting similarity of spatial features of a water system.
Background
Based on homeland survey data, a series of scale reduction works are developed, and the three-tone data achievement is further enriched. The water system is an important land utilization type in homeland survey data and has a heavy proportion, so the comprehensive data quality of the water system is directly related to the quality of homeland survey compiled data products.
As China has various terrains and complicated geological structures, river water systems have various types. The main types are: dendritic water system, pinnate water system, braided water system, lattice water system, net water system, etc. Therefore, the spatial structure relationship and the geometric figure of water system data in the result of homeland survey data are usually complex, have obvious hierarchical structure and density characteristics, and the main stream and the tributary contain the main and secondary relationships in spatial distribution. The consistency of the spatial characteristics of the water system under different scales is a core control index of the quality of the result of the compilation data, and reflects the consistency of the spatial distribution characteristics of the water system in the compilation process.
Because the prior art lacks an implementable automatic inspection method, the inspection can be carried out only by manual visual inspection or by converting vector data into raster data, a large amount of manpower and time are consumed, the requirement on the service level of operators is high, the efficiency is low, the subjectivity and the randomness are high, the accuracy of an inspection result is not high, the inspection is easy to miss or miss, and the quality of a compiled data product is difficult to ensure.
The above is only for the purpose of assisting understanding of the technical solution of the present invention, and does not represent an admission that the above is the prior art.
Disclosure of Invention
The invention mainly aims to provide a method, equipment, a storage medium and a device for detecting the similarity of water system spatial features, and aims to solve the technical problems that the similarity of water system data spatial features in different scales of the same region cannot be automatically compared in the prior art, and the efficiency and the accuracy are low through manual detection.
In order to achieve the above object, the present invention provides a method for detecting spatial feature similarity of a water system, the method comprising the steps of:
acquiring water system data before editing and water system data after editing;
performing fusion processing on the edited water system data according to a preset fusion model to obtain fused target edited water system data;
performing spatial superposition on the target contracted water system data and the water system data before contraction through a preset superposition analysis model to generate a superposition result layer;
and performing similarity detection on the spatial features of the water system before and after the compilation according to the superposition result image layer and a preset model algorithm.
Optionally, the step of acquiring the pre-compilation water system data and the post-compilation water system data includes:
extracting water system data from target land type pattern spot data according to preset attribute screening conditions to obtain a flat water system data set before compiling;
performing layer fusion processing on the pre-compilation linear water system data set according to a preset fusion model to obtain fused pre-compilation water system data;
performing buffering treatment on the linear water system data after the contraction and weaving according to a preset buffer analysis model to obtain a planar image layer;
and determining the contracted and edited water system data according to the planar image layer.
Optionally, the step of performing fusion processing on the compiled water system data according to a preset fusion model to obtain fused target compiled water system data includes:
constructing a new target result layer according to the planar layer and the shortened back planar water system data;
and performing fusion processing on the target result layer according to the preset fusion model, and determining target edited water system data according to a fusion processing result.
Optionally, the step of performing spatial superposition on the target post-compilation water system data and the pre-compilation water system data through a preset superposition analysis model to generate a superposition result map layer includes:
performing spatial superposition on the target contracted water system data and the target contracted water system data before the contraction through a preset superposition analysis model, and constructing a vertex circular linked list;
determining a spatial overlapping relation between the target post-compilation water system data and the pre-compilation water system data according to the vertex cycle chain table and a preset intersection algorithm;
and generating a superposition result layer according to the spatial overlapping relation.
Optionally, the step of generating an overlay result image layer according to the spatial overlap relationship includes:
determining intersection point information between the target contracted water system data and the water system data before the contraction according to the space superposition relationship;
and tracking the vertexes in the vertex circular linked list according to the intersection point information and a preset tracking direction, and eliminating overlapped parts in the layers according to a tracking result and a preset intersection negation algorithm to obtain the layers with the overlapped results.
Optionally, the preset model algorithm includes a preset scatter model algorithm and a preset geometric algorithm, and the step of performing similarity detection on spatial features of the water system before and after the compilation according to the superposition result map layer and the preset model algorithm includes:
scattering the superposition result image layer through a preset scattering model algorithm to obtain an element result image layer corresponding to the target element;
dividing the element result image layer into a plurality of triangles, and calculating the area of the element image corresponding to the element result image layer according to a preset geometric algorithm;
screening the elements with the element image area not smaller than the minimum upper image area of the water system to generate a screening result image layer;
and carrying out similarity detection on the spatial features of the water system before and after the editing according to the screening result image layer.
Optionally, the step of performing similarity detection on the spatial features of the water system before and after the editing according to the screening result image layer includes:
performing negative buffering treatment on the screening result layer, and removing irregular graphs according to a negative buffering result;
scattering the layers after being removed, and performing positive buffer processing on each scattered element layer to obtain a target element layer;
calculating the area of each element graph corresponding to the target element graph layer according to a preset geometric algorithm;
and if the area of each element graph is not smaller than the minimum upper graph area of the water system, judging that the water system space characteristics of the area are inconsistent before and after the contraction and the weaving.
In addition, to achieve the above object, the present invention also proposes a water system spatial feature similarity detection apparatus comprising a memory, a processor and a water system spatial feature similarity detection program stored on the memory and executable on the processor, the water system spatial feature similarity detection program being configured to implement the steps of the water system spatial feature similarity detection as described above.
In order to achieve the above object, the present invention further provides a storage medium having stored thereon a water system spatial feature similarity detection program which, when executed by a processor, implements the steps of the water system spatial feature similarity detection method as described above.
In order to achieve the above object, the present invention also provides a water system spatial feature similarity detection device including:
the data acquisition module is used for acquiring water system data before editing and water system data after editing;
the fusion processing module is used for carrying out fusion processing on the edited water system data according to a preset fusion model to obtain fused target edited water system data;
the superposition analysis module is used for carrying out spatial superposition on the target post-compilation water system data and the pre-compilation water system data through a preset superposition analysis model to generate a superposition result map layer;
and the characteristic comparison module is used for carrying out similarity detection on the spatial characteristics of the water system before and after the compilation according to the superposition result image layer and a preset model algorithm.
According to the method, water system data before editing and water system data after editing are obtained; performing fusion processing on the edited water system data according to a preset fusion model to obtain fused target edited water system data; performing spatial superposition on the target contracted water system data and the water system data before contraction through a preset superposition analysis model to generate a superposition result layer; and performing similarity detection on the spatial features of the water system before and after the compilation according to the superposition result image layer and a preset model algorithm. According to the method, the superposition result layer is determined through the preset fusion processing model and the superposition analysis model, so that the similarity detection is performed on the spatial characteristics of the water system data according to the superposition result layer, compared with the situation that the similarity of the spatial layout of the water system data in the same region under different scale scales cannot be automatically compared in the prior art, the efficiency and accuracy of manual detection are low, the automatic detection is realized, and the detection efficiency and the accuracy of the detection result are improved.
Drawings
Fig. 1 is a schematic structural diagram of a water system spatial feature similarity detection device of a hardware operating environment according to an embodiment of the present invention;
FIG. 2 is a schematic flow chart of a method for detecting similarity of spatial characteristics of a water system according to a first embodiment of the present invention;
FIG. 3 is a schematic flow chart of a method for detecting similarity of spatial characteristics of a water system according to a second embodiment of the present invention;
FIG. 4 is a schematic flow chart of a method for detecting similarity of spatial characteristics of a water system according to a third embodiment of the present invention;
FIG. 5 is a schematic view of a similarity inspection process according to a third embodiment of the method for detecting similarity of spatial features of a water system according to the present invention;
fig. 6 is a block diagram illustrating a structure of a water system spatial feature similarity detection apparatus 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 water system spatial feature similarity detection device of a hardware operating environment according to an embodiment of the present invention.
As shown in fig. 1, the water system spatial feature similarity detection apparatus 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. The communication bus 1002 is used to implement connection communication among these components. The user interface 1003 may include a Display screen (Display), and the optional user interface 1003 may further include a standard wired interface and a wireless interface, and the wired interface for the user interface 1003 may be a USB interface in the present invention. 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 configuration shown in FIG. 1 does not constitute a limitation of the water system spatial feature similarity detection apparatus and may include more or fewer components than shown, or some components in combination, or a different arrangement of components.
As shown in FIG. 1, a memory 1005, identified as a computer storage medium, may include an operating system, a network communication module, a user interface module, and a water system spatial feature similarity detection program.
In the water system spatial feature similarity detection device shown in fig. 1, the network interface 1004 is mainly used for connecting a background server and performing data communication with the background server; the user interface 1003 is mainly used for connecting user equipment; the water system spatial feature similarity detection apparatus calls a water system spatial feature similarity detection program stored in the memory 1005 by the processor 1001 and executes the water system spatial feature similarity detection method provided by the embodiment of the present invention.
Based on the hardware structure, the embodiment of the method for detecting the similarity of the spatial characteristics of the water system is provided.
Referring to fig. 2, fig. 2 is a schematic flow chart of a first embodiment of the method for detecting similarity of spatial characteristics of a water system according to the present invention.
In this embodiment, the method for detecting similarity of spatial features of a water system includes the steps of:
step S10: acquiring water system data before editing and water system data after editing.
It is to be noted that the execution subject of the present embodiment may be an apparatus having a water system spatial feature similarity detection function, such as: the device may be a device for detecting spatial feature similarity of a water system in multi-scale data synthesis applied to homeland survey, and the embodiment does not limit the device. The present embodiment and the following embodiments will be described herein by taking the above-described computer as an example.
It should be understood that, in the technical scheme, aiming at the problems of low efficiency of checking spatial feature similarity of water system data, inaccurate checking result and the like in the existing multi-scale data of homeland survey, spatial superposition analysis is carried out on the water system data before and after compilation, and a part of the water system spatial distribution feature before and after compilation is extracted. By researching the spatial distribution characteristics and the change rules of water system data under different scales, a model algorithm is designed, automatic statistical analysis is carried out, and quick automatic check of spatial layout similarity is realized. And irregular patterns are eliminated through analysis of positive and negative buffer areas, so that accurate error reporting of inspection results is realized.
It can be understood that the pre-compilation water system data can be pre-compilation image data corresponding to water system accessory facilities and water system elements of various scale proportions extracted from land-like patches, wherein the water system element types comprise water area lines and water area surfaces, the water system elements comprise single-line rivers, double-line rivers representing rivers, canals, ditches, seasonal rivers, dry rivers and the like, and planar rivers such as lakes, reservoirs, ponds, seasonal lakes, dry lakes and the like which are crossed by the single-line rivers or the double-line rivers. The river network is formed by connecting a water system structure line collected along the center of a double line or a polygon with a single-line river. The water system structure line can be classified into (1) inflow structure line according to the position and topological relation: the river is flowing into the surface of the water area according to the direction of the structure line. Relating to the center line of a river outside the water area or the outflow structure of other water areas; (2) flowing out of the structure line: the river is flowing out of the water surface in the direction of the structure line. Relating to the center line of a river outside the water area or the inflow structure line of other water areas; (3) intermediate structure line: the structural line is completely in the water area, and the two ends of the structural line are associated with non-out-of-plane river structural lines; (4) individual structural lines: one water surface is associated and this water surface is associated with only one river structure line. The editing processing of the water system element data mainly comprises water system element topology preprocessing, water system surface selection, water system line selection, river gradual change, topology connection and the like, so that when the quality of the edited water system data is detected, similarity matching can be carried out on the water system space characteristics before and after editing through the water system surface data and the water system line data.
It can be understood that when a topological relation is constructed, topological preprocessing needs to be performed on basic data, and the preprocessing steps comprise arc self-intersection processing, node fitting processing, repeated line removal, redundant node removal, short suspension line removal, topological polygon construction and the like. And constructing a node topological relation tree according to topological characteristics in the water system data before compilation, wherein the topological relation tree can clearly express topological relevance among all nodes and arc sections.
It should be understood that the image data before the reduction includes parameter information corresponding to the water area line and the water area surface, and the parameter information includes parameter information such as length, space, width, area, minimum bend, density, importance level, and the like. The water system data after the contraction editing may be image data obtained by subjecting the water system data before the contraction editing to a series of process processes such as skeleton line extraction, melting of a long and narrow pattern spot, water system selection, simplification of a water system and the like. In the process of chart making synthesis, the chart object needs to be selected and summarized, and the information useful for the chart object is selected and kept in the map, so that when the chart quality is detected, the spatial feature similarity detection needs to be carried out by combining the water system data before the chart editing and the water coefficient data after the chart editing, and whether the chart quality reaches the standard can be determined according to the detection result.
In specific implementation, the water system data before the editing and the water system data after the editing can be obtained from a preset big data platform.
Further, the step S10 further includes: extracting water system data from target land type pattern spot data according to preset attribute screening conditions to obtain a flat water system data set before compiling; performing layer fusion processing on the pre-compilation linear water system data set according to a preset fusion model to obtain fused pre-compilation water system data; performing buffering treatment on the linear water system data after the contraction and weaving according to a preset buffer analysis model to obtain a planar image layer; and determining the contracted and edited water system data according to the planar image layer.
It should be noted that the preset fusion model may be a preset model for performing fusion processing on water coefficient data in the ground-based image patches, and the fusion model may be a model constructed based on a Pansharp (super resolution bayes) algorithm, an HPF algorithm and an SLIC superpixel segmentation algorithm, where the Pansharp algorithm is to calculate a gray value between a multispectral image and a panchromatic image based on a least square method principle, optimally match the gray value of a fusion band by using a minimum variance technique, and adjust the gray distribution of a single band to reduce the color deviation of the fusion image, and compared with other algorithms, the Pansharp algorithm can accurately acquire detail features, so as to ensure the accuracy of image fusion, the HPF algorithm may be to superimpose the multispectral image with low spatial resolution on the detail information of a high-resolution image by using a high-pass filter, so as to achieve the fusion of panchromatic and multispectral images, and the model constructed by the two algorithms may achieve the image fusion of the ground-based image patches before the editing under multiple scenes. The SLIC super-pixel segmentation algorithm can be used for pre-segmenting the ground-class image spot image before the editing, segmenting the ground-class image spot image into super-pixel blocks, and assisting a Panshirp algorithm and an HPF algorithm in processing water system data in the ground-class image spot. In the specific identification process, after the images are processed through the Panshirp algorithm and the HPF algorithm, similar pixel points are formed into small areas based on the SLIC superpixel segmentation algorithm and the characteristics of the images such as gray level, color, texture and shape.
It can be understood that water system data can be extracted from the land pattern data by taking a land code field as a screening field in a mode of screening the land pattern data according to attributes through a preset cluster analysis model aiming at the acquisition of the pre-compilation water system data to obtain a pre-compilation planar water system data set (DLTB _ SX 1), the pre-compilation planar water system data set planar water system layers are subjected to fusion processing through a preset fusion model, the fused pre-compilation water system data (SX 1) is obtained by combining adjacent surfaces, the preset cluster analysis model can be a model built based on a clustering algorithm, the clustering algorithm can be a k-means algorithm, and the data of the same type code field are classified together through the clustering algorithm to obtain the pre-compilation planar water system data set.
It should be understood that the preset buffer analysis model may be a preset model for forming a buffer polygonal entity around the point-like, line-like and planar elements within a preset buffer distance. Wherein the buffer value can be set according to the width attribute of the linear water system after the shrinking and knitting, and the side type is as follows: generating buffer areas at two sides of the line; end type: the ends of the buffer are straight or square and terminate at the end points of the input line elements.
Specifically, the contracted water system data can be acquired by acquiring contracted linear water system data (XZDW _ SX 2) and contracted water system data (DLTB _ SX 2). Ensuring that the linear water system after the contraction editing has a width attribute field, wherein the width field value is expressed as: w SX2 . And buffering the edited linear water system data (XZDW _ SX 2) by using a preset Buffer analysis model to form a planar layer (XZDW _ SX2_ Buffer), and updating the planar layer (XZDW _ SX2_ Buffer) into the edited linear water system data so as to determine the edited linear water system data. The above characters are used as labels for data, and are not limited to any material.
Step S20: and carrying out fusion processing on the edited water system data according to a preset fusion model to obtain fused target edited water system data.
The target contracted water system data is water system data obtained by fusing the contracted linear water system data and the contracted linear water system data. Therefore, it is necessary to determine the complete water system data after the contraction knitting by combining the linear water system data after the contraction knitting with the planar water system data.
Further, the step S20 further includes: constructing a new target result layer according to the planar layer and the shortened back planar water system data; and performing fusion processing on the target result image layer according to the preset fusion model, and determining target edited water system data according to a fusion processing result.
It should be noted that the contracted and edited water system data includes contracted and edited linear water system data and contracted and edited linear water system data, where the contracted and edited linear water system carries a width attribute field, for example: the width field value may be denoted as W SX2 。
It should be understood that the planar image layer obtained after the buffering processing is performed through the preset buffer analysis model in the above steps is updated to the reduced and edited rear planar data image layer, and a new target result image layer is created by overlapping the planar image layer and the reduced and edited rear planar data two groups of elements, wherein the elements of the planar image layer obtained after the buffering processing can replace the overlapped area in the reduced and edited rear planar data.
In the specific implementation, the obtained target result image layer is subjected to fusion processing through a preset fusion model, and a new surface image layer is created by combining adjacent surfaces with the same value of the geographical codes, so that the complete water system data after the target contraction editing, namely the water system data after the target contraction editing is obtained. For example: and creating a new layer by superposing two groups of elements of the planar layer (XZDW _ SX2_ Buffer) and the condensed rear-surface-shaped water system data (DLTB _ SX 2). The elements of the XZDW _ SX2_ Buffer layer replace their overlapping area in DLTB _ SX 2. And carrying out fusion processing on the new target result layer. And (4) creating a new surface image layer by combining adjacent surfaces with the same value of the ground type codes to obtain complete water system data (SX 2) after the contraction coding.
Step S30: and carrying out spatial superposition on the target contracted and compiled water system data and the water system data before contraction and compilation through a preset superposition analysis model to generate a superposition result image layer.
The preset superposition analysis model may be a preset model for performing spatial superposition analysis on the target edited water system data and the edited water system data, and the superposition result map layer is generated by determining a spatial intersection relationship between the target edited water system data and the edited water system data so as to determine an overlapping portion between the target edited water system data and the edited water system data.
In the specific implementation, the target post-editing water system data and the pre-editing water system data are subjected to spatial superposition analysis through a preset superposition analysis model, the superposition part between the target post-editing water system data and the pre-editing water system data is determined according to a spatial intersection relation, and a superposition result map layer is generated.
Step S40: and performing similarity detection on the spatial features of the water system before and after the compilation according to the superposition result image layer and a preset model algorithm.
It should be noted that the preset model algorithm is a preset algorithm for similarity detection of various types of spatial layouts, the preset model algorithm includes, but is not limited to, a preset scatter model algorithm and a preset geometric algorithm, and feature matching is performed on spatial features of the water system through the preset scatter model algorithm and the preset geometric algorithm, so that similarity of spatial features of the water system is determined.
It should be understood that the spatial characteristics of the water system before and after the compilation are detected through the preset model algorithm, so that whether the water system data after the compilation meets the compilation reduction standard is determined, and the automatic inspection is realized while the inspection efficiency and the accuracy of the inspection result are improved.
In the embodiment, water system data before editing and water system data after editing are acquired; performing fusion processing on the edited water system data according to a preset fusion model to obtain fused target edited water system data; carrying out spatial superposition on the target contracted and compiled water system data and the contracted and compiled water system data through a preset superposition analysis model to generate a superposition result layer; and performing similarity detection on the spatial features of the water system before and after the compilation according to the superposition result layer and a preset model algorithm. Because the superposition result layer is determined by the preset fusion processing model and the superposition analysis model, similarity detection is performed on the spatial characteristics of the water system data according to the superposition result layer, compared with the situation that the similarity of the spatial layout of the water system data in the same area under different scale scales cannot be automatically compared in the prior art, the efficiency is low and the accuracy is low through manual detection, and the accuracy of the detection efficiency and the detection result is improved while the automatic detection is realized.
Referring to fig. 3, fig. 3 is a schematic flow chart of a water system spatial feature similarity detection method according to a second embodiment of the present invention, which is proposed based on the first embodiment shown in fig. 2.
In this embodiment, the step S30 includes:
step S301: and carrying out spatial superposition on the target contracted water system data and the water system data before contraction through a preset superposition analysis model, and constructing a vertex circular linked list.
It should be noted that the target contracted water system data and the water system data before the contraction are subjected to spatial overlapping analysis through an intersection and negation calculation model in a preset overlapping analysis model, and a vertex circular linked list is created according to polygon nodes contained in an overlapping analysis result. The intersection reverse calculation model can be used for performing spatial superposition on the water system data before the compilation and the water system data after the compilation to obtain an area with inconsistent space between the water system data before the compilation and the water system data after the compilation, wherein the model input element is the water system data before the compilation, the updating element is the complete water system data after the compilation, and the output element is the area with inconsistent space between the water system data before the compilation and the water system data after the compilation. The intersection is inverted to eliminate the common part between the input element and the updated element, so as to obtain a new entity. The first selected object is the subtracted object, and the next selected object is the subtracted object.
It should be understood that the vertex circular link table may be used to describe planar polygons, each polygon being represented by a single linked list, each node of the single linked list storing one vertex of the polygon in order (the order in which the vertices of the polygon are input), the pointer of the last node being cast to the first node (the circular single linked list).
In the specific implementation, in the process of carrying out spatial superposition analysis on the target contracted water system data and the target contracted water system data through a preset superposition analysis model, nodes of a polygon are constructed through a preset intersection negation calculation model, and respective vertex circular linked lists are created.
Step S302: and determining a spatial overlapping relation between the target post-compilation water system data and the pre-compilation water system data according to the vertex cycle chain table and a preset intersection algorithm.
It should be noted that the preset intersection algorithm may be a preset algorithm for calculating an intersection between polygons, where the intersection algorithm may be an algorithm constructed based on an IOU algorithm, and when performing intersection operation on polygons, an area to be framed may be determined at a maximum value and a minimum value of two-dimensional coordinates by the IOU algorithm and each vertex corresponding to a polygon in a vertex circular linked list, so as to frame a preset rectangular area on the periphery of a polygon to perform intersection operation, and a spatial overlap relationship between the target contracted water system data and the contracted water system data is determined by an overlapping portion of the framed area of the IOU.
In a concrete implementation, a plurality ofAnd polygons R and S, wherein R and S respectively represent target post-editing water system data and pre-editing water system data, nodes of polygons R and S are established first in the process of calculating the intersection of the polygons R and S, respective vertex circular linked lists are established, and the x of the two polygons is sequentially solved min ，x max ， y min ，y max And recording the value into the R and S nodes. In the process of determining the spatial overlapping relationship between the target post-compilation water system data and the pre-compilation water system data, the area to be framed can be determined at the maximum value and the minimum value of two-dimensional coordinates by the IOU algorithm and each vertex corresponding to the polygon in the vertex circular linked list, and the spatial overlapping relationship between the target post-compilation water system data and the pre-compilation water system data can be determined by the overlapping part of the IOU framing area.
Step S303: and generating a superposition result layer according to the spatial overlapping relation.
It should be noted that if the spatial overlapping relationship shows that there is no intersection, it indicates that the two polygons are in a completely separated state, the intersection-solving algorithm is finished, and the superposition result map layer is determined according to the preset intersection inverse calculation model. If the spatial overlapping relationship shows that an intersection point exists, the fact that the two polygons have an overlapping area is indicated, therefore, a preset intersection inverse calculation model is used for eliminating a public part between the target contracted water system data and the water system data before contraction, and an overlapping result image layer is output.
Further, the step S303 further includes: determining intersection point information between the target post-compilation water system data and the pre-compilation water system data according to the spatial superposition relationship; and tracking the vertexes in the vertex circular linked list according to the intersection point information and a preset tracking direction, and eliminating overlapped parts in the layers according to a tracking result and a preset intersection negation algorithm to obtain the layers with the overlapped results.
It should be noted that the intersection information includes intersection information corresponding to the target post-compilation water system data and intersection information corresponding to the pre-compilation water system data, wherein different intersection negation calculation manners are corresponding to different tracking directions, and therefore the superposition result map layer is determined according to the intersection negation calculation result.
In the concrete implementation, the intersection negation calculation steps are as follows:
setp1: calculating the intersection point of the polygons R and S;
(A) Establishing nodes of R and S polygons, establishing respective vertex circular linked lists, and sequentially solving x of the two polygons min ，x max ，y min ，y max A value, recorded into the R and S nodes;
(B) And (3) checking whether the R and the S are completely separated or not by finding a rectangular bounding box of the R and the S (namely the maximum value and the minimum value found in the step (A)), if 2 rectangles have no intersection, the method is completely separated, the intersection algorithm is finished, and the result of the inversion of the intersection of the R and the S is the union of the R and the S graphs: RUS;
(C) Traversing each edge of the R polygon, solving the intersection point of the edge and each edge of the S polygon, respectively inserting the solved intersection points into the vertex chain tables of the R and S polygons, and simultaneously establishing a bidirectional pointer between the intersection points.
Setp2: calculate the difference between R and S: R-S;
(a) The method comprises the following steps Searching the vertexes of R in sequence to find a vertex (set as Ps) which is not in S;
(b) The method comprises the following steps The vertex of R is tracked and recorded clockwise from this vertex. If Ps is met, turning to (d); if meeting the intersection point of R and S, recording the intersection point, and turning to (c);
(c) The method comprises the following steps Tracing and recording the vertex of S counterclockwise from the point until the intersection point of R and S is encountered, recording the intersection point and returning to the step (b);
(d) The method comprises the following steps And connecting the points recorded in the tracking process to form a polygon. Checking the vertex of R, if the vertex is tracked once, executing (e); otherwise, searching an untracked vertex in the R, and returning to the step (b);
(e) The method comprises the following steps The sum of the n polygons formed in d is the difference between R and S: R-S.
Setp3: calculate the difference between S and R: S-R; exchanging R with S, and repeating the steps (a) to (e) in the Setp 2.
Setp4: the result of negating the intersection of R and S is (R-S) U (S-R).
In the embodiment, water system data before editing and water system data after editing are acquired; performing fusion processing on the edited water system data according to a preset fusion model to obtain fused target edited water system data; performing spatial superposition on the target contracted water system data and the target contracted water system data before the contraction through a preset superposition analysis model, and constructing a vertex circular linked list; determining a spatial overlapping relation between the target post-compilation water system data and the pre-compilation water system data according to the vertex cycle chain table and a preset intersection algorithm; generating a superposition result layer according to the spatial overlapping relation; and performing similarity detection on the spatial features of the water system before and after the compilation according to the superposition result image layer and a preset model algorithm. Because the superposition result layer is determined by the preset fusion processing model and the superposition analysis model, similarity detection is performed on the spatial characteristics of the water system data according to the superposition result layer, compared with the situation that the similarity of the spatial layout of the water system data in the same area under different scale scales cannot be automatically compared in the prior art, the efficiency is low and the accuracy is low through manual detection, and the accuracy of the detection efficiency and the detection result is improved while automatic detection is realized.
Referring to fig. 4, fig. 4 is a schematic flow chart of a water system spatial feature similarity detection method according to a third embodiment of the present invention, which is proposed based on the first embodiment shown in fig. 2.
In this embodiment, the preset model algorithm includes a preset breaking model algorithm and a preset geometric algorithm, and the step S40 includes:
step S401: and scattering the superposition result image layer through a preset scattering model algorithm to obtain an element result image layer corresponding to the target element.
It should be noted that the preset scattering model algorithm may be a preset model algorithm for scattering a superposition result layer (a result layer generated after an intersection inversion calculation), where the superposition result layer may be scattered according to a preset diameter range by the preset model algorithm, and each part of the scattered multi-part elements becomes an independent element to obtain an element result layer corresponding to the target element.
Step S402: and dividing the element result image layer into a plurality of triangles, and calculating the area of the element image corresponding to the element result image layer according to a preset geometric algorithm.
It should be noted that any polygon corresponding to the element result image layer is divided into a plurality of triangles according to a preset geometric algorithm, and the area of the element image corresponding to the element result image layer is calculated.
In a specific implementation, an arbitrary polygon can be divided into a plurality of triangles, and the area of the arbitrary polygon can be obtained according to a triangle area formula. The vertex coordinates of any polygon may be labeled in turn (x 0, y 0), (x 1, y 1), (x 2, y 2),., (xn, yn) (where n =2,3,4, \ 8230;), and its area S x Can be expressed as:
and recalculating the area Si of each element image of the suburb inversion result image layer.
Step S403: and screening the elements with the element image area not smaller than the minimum upper image area of the water system to generate a screening result image layer.
It should be noted that, by taking the inverse result map layer from the intersection, the screening pattern area Si is not less than the minimum upper pattern area a of the water system min Forming a screening result image layer.
Step S404: and performing similarity detection on the spatial features of the water system before and after the compiling according to the screening result layer.
It should be noted that, similarity detection is performed on the water system air conditioner characteristics before and after the editing according to the screening result map layer.
Further, the step S404 further includes: performing negative buffering treatment on the screening result layer, and removing irregular graphs according to a negative buffering result; scattering the layers after being removed, and performing positive buffer processing on each scattered element layer to obtain a target element layer; calculating the area of each element graph corresponding to the target element graph layer according to a preset geometric algorithm; and if the area of each element graph is not smaller than the minimum upper graph area of the water system, judging that the water system space characteristics of the area are inconsistent before and after the contraction and the weaving.
In a specific implementation, to further explain the processing procedure for the screening result image layer in the present embodiment, referring to the schematic diagram of the similarity checking flow shown in fig. 5, the specific steps of removing the elongated, dumbbell-shaped and other irregular figures are as follows: the step 1 comprises the following steps:
(1): and (3) carrying out negative buffering on the screening result layer, wherein the buffering value is as follows: -W min 2, half of the minimum upper graph width value of the graph spot;
(2): scattering the result image layer in the step (1), and then performing positive buffering, wherein the buffering value is as follows: + W min And/2, half of the minimum upper graph width value of the graph spot. Forming a buffer result layer;
step 2: recalculating the graphic area Si of each element of the buffer result layer;
and step 3: if the graphic area Si of the element of the buffer result graphic layer is more than or equal to A min And if the water system meets the minimum upper drawing area requirement and the minimum upper drawing width requirement before the shrink-weaving, the water system is combined into other places in the shrink-weaving process, namely the water system space characteristics of the area before and after the shrink-weaving are inconsistent.
In the embodiment, water system data before editing and water system data after editing are acquired; performing fusion processing on the edited water system data according to a preset fusion model to obtain fused target edited water system data; performing spatial superposition on the target contracted water system data and the water system data before contraction through a preset superposition analysis model to generate a superposition result layer; scattering the superposition result image layer through a preset scattering model algorithm to obtain an element result image layer corresponding to the target element; dividing the element result image layer into a plurality of triangles, and calculating the area of the element image corresponding to the element result image layer according to a preset geometric algorithm; screening the elements with the element image area not smaller than the minimum upper image area of the water system to generate a screening result image layer; and carrying out similarity detection on the spatial features of the water system before and after the editing according to the screening result image layer. Because the superposition result layer is determined by the preset fusion processing model and the superposition analysis model, similarity detection is performed on the spatial characteristics of the water system data according to the superposition result layer, compared with the situation that the similarity of the spatial layout of the water system data in the same area under different scale scales cannot be automatically compared in the prior art, the efficiency is low and the accuracy is low through manual detection, and the accuracy of the detection efficiency and the detection result is improved while automatic detection is realized.
In addition, to achieve the above object, the present invention also proposes a water system spatial feature similarity detection apparatus comprising a memory, a processor and a water system spatial feature similarity detection program stored on the memory and executable on the processor, the water system spatial feature similarity detection program being configured to implement the steps of the water system spatial feature similarity detection as described above.
In order to achieve the above object, the present invention further provides a storage medium having stored thereon a water system spatial feature similarity detection program which, when executed by a processor, implements the steps of the water system spatial feature similarity detection method as described above.
Referring to fig. 6, fig. 6 is a block diagram illustrating a structure of a water system spatial feature similarity detecting apparatus according to a first embodiment of the present invention.
As shown in fig. 6, the device for detecting similarity of spatial features of a water system according to an embodiment of the present invention includes:
the data acquisition module 10 is used for acquiring water system data before editing and water system data after editing;
the fusion processing module 20 is configured to perform fusion processing on the compiled water system data according to a preset fusion model to obtain fused target compiled water system data;
the superposition analysis module 30 is configured to perform spatial superposition on the target edited water system data and the edited water system data through a preset superposition analysis model to generate a superposition result map layer;
and the feature comparison module 40 is used for performing similarity detection on the spatial features of the water system before and after the compilation according to the superposition result image layer and a preset model algorithm.
In the embodiment, water system data before editing and water system data after editing are acquired; performing fusion processing on the edited water system data according to a preset fusion model to obtain fused target edited water system data; carrying out spatial superposition on the target contracted and compiled water system data and the contracted and compiled water system data through a preset superposition analysis model to generate a superposition result layer; and performing similarity detection on the spatial features of the water system before and after the compilation according to the superposition result image layer and a preset model algorithm. Because the superposition result layer is determined by the preset fusion processing model and the superposition analysis model, similarity detection is performed on the spatial characteristics of the water system data according to the superposition result layer, compared with the situation that the similarity of the spatial layout of the water system data in the same area under different scale scales cannot be automatically compared in the prior art, the efficiency is low and the accuracy is low through manual detection, and the accuracy of the detection efficiency and the detection result is improved while automatic detection is realized.
Further, the data acquisition module 10 is further configured to extract water system data from the target land type pattern data according to a preset attribute screening condition to obtain a pre-compilation planar water system data set; performing layer fusion processing on the pre-compilation linear water system data set according to a preset fusion model to obtain fused pre-compilation water system data; performing buffering processing on the linear water system data after the contraction and the weaving according to a preset buffer analysis model to obtain a planar image layer; and determining the contracted and edited water system data according to the planar image layer.
Further, the fusion processing module 20 is further configured to construct a new target result layer according to the planar layer and the scaled back planar water system data; and performing fusion processing on the target result layer according to the preset fusion model, and determining target edited water system data according to a fusion processing result.
Further, the superposition analysis module 30 is further configured to perform spatial superposition on the target contracted water system data and the target contracted water system data through a preset superposition analysis model to construct a vertex circular linked list; determining a spatial overlapping relation between the target post-compilation water system data and the pre-compilation water system data according to the vertex cycle chain table and a preset intersection algorithm; and generating a superposition result layer according to the spatial overlapping relation.
Further, the superposition analysis module 30 is further configured to determine intersection point information between the target contracted water system data and the pre-contracted water system data according to the spatial superposition relationship; and tracking the vertexes in the vertex circular linked list according to the intersection point information and a preset tracking direction, and eliminating overlapped parts in the layers according to a tracking result and a preset intersection negation algorithm to obtain the layers with the overlapped results.
Further, the feature comparison module 40 is further configured to scatter the overlay result image layer through a preset scattering model algorithm to obtain an element result image layer corresponding to the target element; dividing the element result image layer into a plurality of triangles, and calculating the area of an element image corresponding to the element result image layer according to a preset geometric algorithm; screening elements with the element image area not smaller than the minimum upper image area of the water system to generate a screening result image layer; and carrying out similarity detection on the spatial features of the water system before and after the editing according to the screening result image layer.
Further, the feature comparison module 40 is further configured to perform negative buffering on the screening result layer, and eliminate irregular graphs according to a negative buffering result; scattering the layers after being removed, and performing positive buffer processing on each scattered element layer to obtain a target element layer; calculating the area of each element graph corresponding to the target element graph layer according to a preset geometric algorithm; and if the area of each element graph is not smaller than the minimum upper graph area of the water system, judging that the water system space characteristics of the area are inconsistent before and after the contraction and the weaving.
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.
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 be referred to a method for detecting similarity of spatial features of a water system provided in any embodiment of the present invention, and are not described herein again.
It should 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 phrases "comprising a," "8230," "8230," or "comprising" does not exclude the presence of other like elements in a process, method, article, or system comprising the element.
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. In the unit claims enumerating several means, several of these means can be embodied by one and the same item of hardware. The use of the words first, second, third, etc. do not denote any order, but rather the words first, second, third, etc. are to be interpreted as names.
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 solutions 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., a Read Only Memory (ROM)/Random Access Memory (RAM), a magnetic disk, or an 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 detecting spatial feature similarity of a water system, the method comprising:
acquiring water system data before editing and water system data after editing;
performing fusion processing on the edited water system data according to a preset fusion model to obtain fused target edited water system data;
performing spatial superposition on the target contracted water system data and the water system data before contraction through a preset superposition analysis model to generate a superposition result layer;
and performing similarity detection on the spatial features of the water system before and after the compilation according to the superposition result layer and a preset model algorithm.
2. The method for detecting the similarity of spatial characteristics of water systems according to claim 1, wherein the step of acquiring the pre-compilation water system data and the post-compilation water system data includes:
extracting water system data from the target land type pattern spot data according to preset attribute screening conditions to obtain a planar water system data set before compiling;
performing layer fusion processing on the pre-compilation linear water system data set according to a preset fusion model to obtain fused pre-compilation water system data;
performing buffering treatment on the linear water system data after the contraction and weaving according to a preset buffer analysis model to obtain a planar layer;
and determining the contracted and edited water system data according to the planar image layer.
3. The method for detecting the similarity of the water system spatial features according to claim 2, wherein the step of performing fusion processing on the edited water system data according to a preset fusion model to obtain fused target edited water system data comprises the following steps:
constructing a new target result layer according to the planar layer and the shortened back planar water system data;
and performing fusion processing on the target result image layer according to the preset fusion model, and determining target edited water system data according to a fusion processing result.
4. The method for detecting the similarity of spatial features of the water system according to claim 1, wherein the step of performing spatial superposition on the target compiled water system data and the pre-compiled water system data through a preset superposition analysis model to generate a superposition result map layer includes:
performing spatial superposition on the target contracted water system data and the target contracted water system data before the contraction through a preset superposition analysis model, and constructing a vertex circular linked list;
determining a spatial overlapping relation between the target post-compilation water system data and the pre-compilation water system data according to the vertex cycle chain table and a preset intersection algorithm;
and generating a superposition result layer according to the spatial overlapping relation.
5. The water system spatial feature similarity detection method according to claim 4, wherein the step of generating a superimposed result map layer based on the spatial overlap relationship includes:
determining intersection point information between the target contracted water system data and the water system data before the contraction according to the space superposition relationship;
and tracking the vertexes in the vertex circular linked list according to the intersection point information and a preset tracking direction, and eliminating overlapped parts in the layers according to a tracking result and a preset intersection negation algorithm to obtain the layers with the overlapped results.
6. The water system spatial feature similarity detection method according to any one of claims 1 to 5, wherein the preset model algorithm comprises a preset scatter model algorithm and a preset geometric algorithm, and the step of performing similarity detection on the water system spatial features before and after compilation according to the superposition result map layer and the preset model algorithm comprises the steps of:
scattering the superposition result image layer through a preset scattering model algorithm to obtain an element result image layer corresponding to the target element;
dividing the element result image layer into a plurality of triangles, and calculating the area of an element image corresponding to the element result image layer according to a preset geometric algorithm;
screening the elements with the element image area not smaller than the minimum upper image area of the water system to generate a screening result image layer;
and carrying out similarity detection on the spatial features of the water system before and after the editing according to the screening result image layer.
7. The method for detecting the similarity of the spatial features of the water system according to claim 6, wherein the step of detecting the similarity of the spatial features of the water system before and after the compiling according to the screening result image layer comprises the following steps:
performing negative buffering treatment on the screening result layer, and removing irregular graphs according to a negative buffering result;
scattering the layers after being removed, and performing positive buffer processing on each scattered element layer to obtain a target element layer;
calculating the area of each element graph corresponding to the target element graph layer according to a preset geometric algorithm;
and if the area of each element graph is not smaller than the minimum upper graph area of the water system, judging that the water system space characteristics of the area are inconsistent before and after the contraction and the weaving.
8. A water system spatial feature similarity detection apparatus, characterized by comprising: a memory, a processor, and a water system spatial feature similarity detection program stored on the memory and executable on the processor, the water system spatial feature similarity detection program when executed by the processor implementing the steps of the water system spatial feature similarity detection method as recited in any one of claims 1 to 7.
9. A storage medium having stored thereon a water system spatial feature similarity detection program that, when executed by a processor, implements the steps of the water system spatial feature similarity detection method according to any one of claims 1 to 7.
10. A water system spatial feature similarity detection device, characterized by comprising:
the data acquisition module is used for acquiring water system data before editing and water system data after editing;
the fusion processing module is used for carrying out fusion processing on the edited water system data according to a preset fusion model to obtain fused target edited water system data;
the superposition analysis module is used for carrying out spatial superposition on the target post-compilation water system data and the pre-compilation water system data through a preset superposition analysis model to generate a superposition result map layer;
and the characteristic comparison module is used for carrying out similarity detection on the spatial characteristics of the water system before and after the compilation according to the superposition result image layer and a preset model algorithm.
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