CN115828110B - Water system space feature similarity detection method, device, storage medium and apparatus - Google Patents
Water system space feature similarity detection method, device, storage medium and apparatus Download PDFInfo
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Abstract
The invention discloses a method, equipment, storage medium and device for detecting spatial feature similarity of a water system, which are used for carrying out fusion treatment on contracted and compiled water system data through a root preset fusion model to obtain fused target contracted and compiled water system data; carrying out space superposition on the water system data after shrinkage and editing and the water system data before shrinkage and editing on the target through a preset superposition analysis model to generate a superposition result layer; and carrying out similarity detection on the spatial characteristics of the water system before and after shrinkage editing according to the superposition result layer and a preset model algorithm. According to the invention, the superposition result image layer is determined by presetting the fusion processing model and the superposition analysis model, so that the similarity detection is carried out on the spatial characteristics of the water system data according to the superposition result image layer, and compared with the prior art that the similarity of the spatial layout of the water system data under different proportion scales cannot be automatically compared, the manual detection efficiency is low and the accuracy is low, and the automatic detection is realized and the detection efficiency and the accuracy of the detection result are improved.
Description
Technical Field
The present invention relates to the field of image processing, and in particular, to a method, an apparatus, a storage medium, and a device for detecting similarity of spatial features of a water system.
Background
Based on the territorial survey data, series scale shrinkage editing work is carried out, and triple data results are further enriched. The water system is an important land utilization type in the homeland investigation data and occupies a relatively heavy weight, so that the comprehensive data quality of the water system directly relates to the quality of the homeland investigation contracted and compiled data product.
Because of various terrains and complex geological structures in China, river water systems are various in types. The main types are: dendritic water systems, feathered water systems, braided water systems, lattice water systems, mesh water systems, and the like. Therefore, the spatial structure relationship and the geometric figure of the water system data in the homeland investigation data result are generally complex, the water system data has obvious hierarchical structure and density characteristics, and the main flow and the tributary contain main-secondary relationship 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 contracted and compiled data result, and reflects the consistency of the spatial distribution characteristics of the water system in the contracted and compiled process.
Because the existing technology lacks the automatic inspection method, it can only use manual visual inspection or change the vector data into raster data to inspect, and consume a lot of manpower and time, and has high service level requirement for operators, low efficiency, high subjectivity and randomness, and the inspection result has low accuracy, easy missing or error inspection, and difficult to ensure the quality of contracted data products.
The foregoing is provided merely for the purpose of facilitating understanding of the technical solutions of the present invention and is not intended to represent an admission that the foregoing is 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 the same area under different proportion scales cannot be automatically compared, and the manual detection efficiency is low and the accuracy is low in the prior art.
In order to achieve the above object, the present invention provides a method for detecting the similarity of spatial features of a water system, comprising the steps of:
acquiring water system data before shrinkage and water system data after shrinkage;
carrying out fusion processing on the contracted and compiled water system data according to a preset fusion model to obtain fused target contracted and compiled water system data;
performing spatial superposition on the target water system data after shrinkage and the water system data before shrinkage and braiding by a preset superposition analysis model to generate a superposition result layer;
and carrying out similarity detection on the spatial characteristics of the water system before and after shrinkage according to the superposition result layer and a preset model algorithm.
Optionally, the step of acquiring the water system data before shrinkage and the water system data after shrinkage includes:
Extracting water system data from the target ground pattern spot data according to preset attribute screening conditions to obtain a contracted front water system data set;
carrying out layer fusion processing on the contracted front water system data set according to a preset fusion model to obtain contracted front water system data after fusion is completed;
buffering the contracted linear water system data according to a preset buffering analysis model to obtain a planar image layer;
and determining the water system data after shrinkage and braiding according to the planar layer.
Optionally, the step of performing fusion processing on the contracted and compiled water system data according to a preset fusion model to obtain fused target contracted and compiled water system data includes:
constructing a new target result layer according to the planar layer and the contracted and compiled planar water system data;
and carrying out fusion processing on the target result layer according to the preset fusion model, and determining the water system data after target shrinkage according to the fusion processing result.
Optionally, the step of generating the superposition result layer by spatially superposing the target water system data after shrinkage and the water system data before shrinkage through a preset superposition analysis model includes:
performing spatial superposition on the target water system data after shrinkage and the water system data before shrinkage and braiding by a preset superposition analysis model to construct a vertex cyclic linked list;
Determining a spatial overlapping relation between the target water system data after shrinkage and the water system data before shrinkage according to the vertex circular linked list and a preset intersection algorithm;
and generating a superposition result layer according to the spatial superposition relation.
Optionally, the step of generating a superposition result layer according to the spatial overlapping relationship includes:
determining intersection point information between the target shrink-coded water system data and the water system data before shrink-coding according to the space superposition relation;
tracking the vertexes in the vertex circular chain table according to the intersection information and a preset tracking direction, and removing overlapped parts in the layers according to tracking results and a preset intersection inverse algorithm to obtain a layer of overlapped results.
Optionally, the preset model algorithm includes a preset break-up model algorithm and a preset geometric algorithm, and the step of performing similarity detection on the spatial features of the water system before and after shrinkage according to the superposition result layer and the preset model algorithm includes:
scattering the superimposed result layer through a preset scattering model algorithm to obtain an element result layer corresponding to the target element;
dividing the element result layer into a plurality of triangles, and calculating the element image area corresponding to the element result 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 characteristics of the water system before and after shrinkage editing according to the screening result layer.
Optionally, the step of performing similarity detection on the spatial features of the water system before and after shrinkage according to the screening result layer includes:
negative buffer processing is carried out on the screening result layer, and irregular patterns are removed according to the negative buffer result;
scattering the removed picture layers, and performing positive buffer treatment on each scattered element picture layer to obtain a target element picture layer;
calculating the areas of the element graphs corresponding to the target element graph layer according to a preset geometric algorithm;
if the area of each element figure is not smaller than the area of the minimum upper water system drawing, the water system space characteristics of the area before and after shrinkage is judged to be inconsistent.
In addition, in order to achieve the above object, the present invention also proposes a water system spatial feature similarity detection apparatus including 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 water system spatial feature similarity detection as described above.
In addition, in order to achieve the above object, the present invention also proposes a storage medium having stored thereon a water-based spatial feature similarity detection program which, when executed by a processor, implements the steps of the water-based spatial feature similarity detection method as described above.
In addition, 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 shrinkage and water system data after shrinkage;
the fusion processing module is used for carrying out fusion processing on the contracted and compiled water system data according to a preset fusion model to obtain fused target contracted and compiled water system data;
the superposition analysis module is used for carrying out space superposition on the target water system data after shrinkage and the water system data before shrinkage through a preset superposition analysis model to generate a superposition result layer;
and the characteristic comparison module is used for carrying out similarity detection on the spatial characteristics of the water system before and after shrinkage according to the superposition result layer and a preset model algorithm.
The method comprises the steps of obtaining water system data before shrinkage and water system data after shrinkage and braiding; carrying out fusion processing on the contracted and compiled water system data according to a preset fusion model to obtain fused target contracted and compiled water system data; performing spatial superposition on the target water system data after shrinkage and the water system data before shrinkage and braiding by a preset superposition analysis model to generate a superposition result layer; and carrying out similarity detection on the spatial characteristics of the water system before and after shrinkage according to the superposition result layer and a preset model algorithm. According to the invention, the superposition result image layer is determined by presetting the fusion processing model and the superposition analysis model, so that the similarity detection is carried out on the spatial characteristics of the water system data according to the superposition result image layer, and compared with the prior art that the similarity of the spatial layout of the water system data in the same area under different proportion scales cannot be automatically compared, the manual detection efficiency is low and the accuracy is low, and 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 device for detecting similarity of characteristics of water system space in a hardware operating environment according to an embodiment of the present invention;
FIG. 2 is a schematic flow chart of a first embodiment of the method for detecting the similarity of spatial features of a water system according to the present invention;
FIG. 3 is a schematic flow chart of a second embodiment of the method for detecting the similarity of spatial features of a water system according to the present invention;
FIG. 4 is a schematic flow chart of a third embodiment of a method for detecting similarity of spatial features of water system according to the present invention;
FIG. 5 is a schematic diagram of a similarity checking flow chart of a third embodiment of the method for detecting similarity of spatial features of water system according to the present invention;
fig. 6 is a block diagram showing a first embodiment of the apparatus for detecting spatial feature similarity of water system according to the present invention.
The achievement of the objects, functional features and advantages of the present invention will be further described with reference to the accompanying drawings, in conjunction with the embodiments.
Detailed Description
It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.
Referring to fig. 1, fig. 1 is a schematic structural diagram of a device for detecting similarity of water system spatial features in a hardware operation environment according to an embodiment of the present invention.
As shown in fig. 1, the apparatus for detecting spatial feature similarity of water system may include: a processor 1001, such as a central processing unit (Central Processing Unit, CPU), a communication bus 1002, a user interface 1003, a network interface 1004, a memory 1005. Wherein the communication bus 1002 is used to enable connected communication between these components. The user interface 1003 may include a Display (Display), and the optional user interface 1003 may also include a standard wired interface, 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 high-speed random access Memory (Random Access Memory, RAM) or a stable Memory (NVM), such as a disk Memory. The memory 1005 may also optionally be a storage device separate from the processor 1001 described above.
It will be appreciated by those skilled in the art that the structure 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 may combine certain components, or may be arranged in different components.
As shown in fig. 1, the memory 1005, which is assumed to be a computer storage medium, may include an operating system, a network communication module, a user interface module, and a water-based spatial feature similarity detection program.
In the apparatus for detecting the similarity of spatial features of water system shown in fig. 1, the network interface 1004 is mainly used for connecting to 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 device calls a water system spatial feature similarity detection program stored in the memory 1005 through the processor 1001, and executes the water system spatial feature similarity detection method provided by the embodiment of the invention.
Based on the above hardware structure, an 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 a method for detecting similarity of spatial characteristics of a water system according to the present invention.
In this embodiment, the method for detecting the similarity of spatial features of the water system includes the steps of:
step S10: and acquiring water system data before shrinkage and water system data after shrinkage.
Note that, the execution subject of the present embodiment may be an apparatus having a function of detecting similarity of spatial characteristics of water system, such as: the vehicle-mounted computer, the notebook computer, the tablet and the like can be other water system space feature similarity detection equipment capable of realizing the same or similar functions, and the equipment can be equipment applied to water system space feature similarity detection in multi-scale data synthesis of homeland investigation, and the embodiment is not limited to the detection. This embodiment and the following embodiments will be described herein by taking the above-described computer as an example.
It is to be understood that the technical scheme aims at solving the problems of low efficiency, inaccurate inspection results and the like of the spatial characteristic similarity inspection of the water coefficient data in the existing domestic soil investigation multi-scale data, and provides a method for carrying out spatial superposition analysis on the water system data before and after shrinkage editing and extracting the part of the change of the spatial distribution characteristics of the water system before and after shrinkage editing. By researching the spatial distribution characteristics and the change rules of the water system data under different scales and designing a model algorithm, automatic statistical analysis is performed, and the rapid automatic inspection of the spatial layout similarity is realized. Meanwhile, through analysis of the positive buffer area and the negative buffer area, irregular patterns are removed, and accurate error reporting of the inspection result is achieved.
It is understood that the water system data before the shrinkage may be image data before the shrinkage, which is extracted from the map spots of the land type and corresponds to the water system auxiliary facilities, wherein the water system elements include water area lines and water area surfaces, the water system elements include single-line rivers, double-line rivers representing rivers, canal, time-lapse rivers, dry rivers and the like, and planar rivers such as lakes, reservoirs, ponds, time-lapse lakes, dry lakes and the like traversed by the single-line rivers or the double-line rivers. The river network is formed by connecting a water system structure line collected along a double-line or polygonal center with a single-line river. The water system structural line can be divided into (1) inflow structural lines according to the position and topological relation: the river is collected into the water area according to the direction of the structural line. Correlating the river center line outside the water area or the outflow structure of other water areas; (2) outflow structural wire: the river flows out of the water area according to the direction of the structural line. Correlating the river center line outside the water area or the inflow structure line of other water areas; (3) intermediate structural wire: the structural line is completely in the water area, and the two ends of the structural line are related to non-out-of-plane river structural lines; (4) individual structural wires: associated with one water surface and this water surface is associated with only one river structure line. The contraction editing processing of the water system element data mainly comprises the processing of water system element topology pretreatment, water system surface selection, water system line selection, river gradual change, topology connection and the like, so that when quality detection is carried out on the water system data after contraction editing, similarity matching can be carried out on water system spatial characteristics before and after contraction editing through the water system surface data and the water system line data.
It can be understood that when constructing the topological relation, the basic data needs to be subjected to topological pretreatment, and the pretreatment steps comprise arc segment self-intersecting treatment, node fitting treatment, duplicate line removal, redundant node deletion, short suspension line deletion, topology polygon construction and the like. According to the topological characteristics in the water system data before shrinkage, a node topological relation tree is constructed, and the topological relation tree can clearly express the topological relevance among all nodes and arc segments.
It should be understood that the image data before shrinkage includes parameter information corresponding to the water area line and the water area, and the parameter information includes parameter information such as length, interval, width, area, minimum curvature, density, importance level, and the like. The water system data after shrinkage may be image data obtained by performing a series of processes such as skeleton line extraction, melting of long and narrow spots, water system selection, and water system simplification on the water system data before shrinkage. The contracted and compiled drawing can be a drawing comprehensive process, drawing objects need to be selected and summarized, and information which is useful for drawing purposes is selected to be reserved in a map, so that when the contracted and compiled drawing quality is detected, spatial feature similarity detection needs to be carried out by combining water system data before contracted and compiled water system data, and whether the contracted and compiled drawing quality meets the standard can be determined according to a detection result.
In a specific implementation, the water system data before shrinkage and the water system data after shrinkage can be obtained from a preset big data platform.
Further, the step S10 further includes: extracting water system data from the target ground pattern spot data according to preset attribute screening conditions to obtain a contracted front water system data set; carrying out layer fusion processing on the contracted front water system data set according to a preset fusion model to obtain contracted front water system data after fusion is completed; buffering the contracted linear water system data according to a preset buffering analysis model to obtain a planar image layer; and determining the water system data after shrinkage and braiding according to the planar layer.
It should be noted that, the preset fusion model may be a preset model for performing fusion processing on water-based data in the ground-based image spots, the fusion model may be a model constructed based on a Pansharp (super resolution bayesian) algorithm, an HPF algorithm and an SLIC super-pixel segmentation algorithm, where the Pansharp algorithm is a model constructed based on a least square method to calculate gray values between a multispectral image and a panchromatic image, and utilizes a minimum variance technique to optimally match gray values of fusion bands, and adjusts gray distribution of a single band to reduce color deviation of the fusion image, compared with other algorithms, the Pansharp algorithm can accurately acquire detail features, thereby ensuring accuracy of image fusion, the HPF algorithm may be a multispectral image with low spatial resolution superimposed on detail information of a high-resolution image to implement fusion of the panchromatic image and the multispectral image, and the model constructed by combining the above two algorithms can implement image fusion of the ground-based image before shrinkage under multiple scenes. The SLIC super-pixel segmentation algorithm can pre-segment the ground pattern spot image before shrinkage and divide the ground pattern spot image into super-pixel blocks, and assists the Pansharp algorithm and the HPF algorithm in processing water system data in the ground pattern spot, and compared with the existing edge contour segmentation algorithm based on the maximum similarity, the SLIC super-pixel segmentation algorithm has a better edge contour segmentation effect. In a specific recognition process, after the image is processed through the Pansharp algorithm and the HPF algorithm, similar pixel points are formed into a small area based on the SLIC super-pixel segmentation algorithm and the characteristics of gray scale, color, texture, shape and the like of the image.
It can be understood that, for the acquisition of the water system data before shrinkage, the water system data can be extracted from the ground pattern spot data by using the ground coding field as the screening field in a mode of attribute screening through a preset cluster analysis model, a pre-shrinkage front water system data set (DLTB_Sx1) is obtained, the front water system data set is subjected to fusion processing through a preset fusion model, the fused pre-shrinkage water system data (Sx 1) is obtained by combining adjacent surfaces, the preset cluster analysis model can be a model constructed based on a clustering algorithm, the clustering algorithm can be a k-means algorithm, and the data of the same class coding field are classified together through the clustering algorithm, so that the pre-shrinkage front water system data set is obtained.
It should be understood that the preset buffer analysis model may be a preset model for forming buffer polygon entities around elements for points, lines and planes within a preset buffer distance. The buffer value can be set according to the width attribute of the contracted linear water system, and the side type is as follows: generating buffer areas on two sides of the line; terminal type: the buffer ends straight or square and terminates at the end of the input line element.
Specifically, the contracted and woven water system data may be obtained by obtaining contracted and woven linear water system data (xzdw_sx2) and contracted and woven rear water system data (dltb_sx2). Ensuring that the contracted water is provided with a width attribute field, wherein the value of the width attribute field is recorded as follows: w (W) SX2 . And buffering the contracted and woven linear water system data (XZDW_SX2) by using a preset Buffer analysis model to form a planar layer (XZDW_SX2_buffer), so that the planar layer (XZDW_SX2_buffer) is updated into the contracted and woven linear water system data, and the contracted and woven water system data are determined. The above-mentioned characters are not limited in nature as labels for data.
Step S20: and carrying out fusion processing on the contracted and compiled water system data according to a preset fusion model to obtain fused target contracted and compiled water system data.
The target post-shrink-fit water system data is water system data obtained by performing fusion processing on the post-shrink-fit linear water system data and the post-shrink-fit planar water system data. Therefore, it is necessary to determine the complete water system data after shrink-knitting by combining the linear water system data after shrink-knitting and the planar water system data.
Further, the step S20 further includes: constructing a new target result layer according to the planar layer and the contracted and compiled planar water system data; and carrying out fusion processing on the target result layer according to the preset fusion model, and determining the water system data after target shrinkage according to the fusion processing result.
The contracted and woven water system data includes contracted and woven linear water system data and contracted and woven surface water system data, wherein the contracted and woven 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, updating the planar layer obtained by performing the buffer processing in the above step through the preset buffer analysis model to the contracted and compiled planar water system data layer, and creating a new target result layer by overlapping two groups of elements of the planar layer and the contracted and compiled planar water system data, wherein the elements of the planar layer obtained by performing the buffer processing replace overlapping areas in the contracted and compiled planar data.
In the specific implementation, fusion processing is carried out on the obtained target result layers through a preset fusion model, and a new surface layer is created by using the adjacent surfaces with the same value through merging ground class codes, so that the complete water system data after shrinkage is obtained, namely the water system data after shrinkage of the target. For example: a new layer is created by superimposing two sets of elements, the planar layer (XZDW_SX2_buffer) and the contracted planar water system data (DLTB_SX2). The elements of the xzdw_sx2_buffer layer replace their overlapping areas in dltb_sx2. And carrying out fusion processing on the new target result layer. And creating a new surface layer by merging adjacent surfaces with the same value by the ground class coding, and obtaining the complete water system data (SX 2) after shrinkage.
Step S30: and carrying out space superposition on the target water system data after shrinkage and the water system data before shrinkage and braiding by a preset superposition analysis model to generate a superposition result layer.
The preset superposition analysis model may be a preset model for performing spatial superposition analysis on the target water system data after shrinkage and the water system data before shrinkage, and the superposition result layer is generated by determining a spatial intersection relationship between the target water system data after shrinkage and the water system data before shrinkage, thereby determining an overlapping portion between the target water system data after shrinkage and the water system data before shrinkage.
In the specific implementation, spatial superposition analysis is performed on the target water system data after shrinkage and the water system data before shrinkage through a preset superposition analysis model, the superposition part between the target water system data after shrinkage and the water system data before shrinkage is determined according to the spatial intersection relation, and a superposition result layer is generated.
Step S40: and carrying out similarity detection on the spatial characteristics of the water system before and after shrinkage according to the superposition result layer and a preset model algorithm.
It should be noted that the preset model algorithm is a preset algorithm for performing similarity detection on each type of spatial layout, and the preset model algorithm includes, but is not limited to, a preset break-up model algorithm and a preset geometric algorithm, and features of the water system space features are matched through the preset break-up model algorithm and the preset geometric algorithm, so that similarity of the water system space features is determined.
It is understood that the spatial characteristics of the water system before and after shrinkage is detected through a preset model algorithm, so that whether the water system data after shrinkage accords with the shrinkage standard is determined, and the automatic inspection is realized, and meanwhile, the inspection efficiency and the accuracy of the detection result are improved.
In the embodiment, the water system data before shrinkage and the water system data after shrinkage are acquired; carrying out fusion processing on the contracted and compiled water system data according to a preset fusion model to obtain fused target contracted and compiled water system data; performing spatial superposition on the target water system data after shrinkage and the water system data before shrinkage and braiding by a preset superposition analysis model to generate a superposition result layer; and carrying out similarity detection on the spatial characteristics of the water system before and after shrinkage according to the superposition result layer and a preset model algorithm. Because the superposition result image layer is determined through the preset fusion processing model and the superposition analysis model, the similarity detection is carried out on the spatial characteristics of the water system data according to the superposition result image layer, and compared with the prior art that the similarity of the spatial layout of the water system data in the same area under different proportion scales cannot be automatically compared, the manual detection efficiency is low, the accuracy is low, and the detection efficiency and the accuracy of the detection result are improved while the automatic detection is realized.
Referring to fig. 3, fig. 3 is a schematic flow chart of a second embodiment of the method for detecting the similarity of spatial characteristics of a water system according to the present invention, and the second embodiment of the method for detecting the similarity of spatial characteristics of a water system according to the present invention is proposed based on the first embodiment shown in fig. 2.
In this embodiment, the step S30 includes:
step S301: and carrying out space superposition on the water system data after shrinkage and editing of the target and the water system data before shrinkage and editing by a preset superposition analysis model, and constructing a vertex circulation linked list.
The method is characterized in that space superposition analysis is carried out on the target water system data after shrinkage and the water system data before shrinkage through an intersection inversion calculation model in a preset superposition analysis model, and a vertex cyclic chain table is created according to polygonal nodes contained in superposition analysis results. The intersection inversion calculation model can be used for carrying out space superposition on water system data before shrinkage and water system data after shrinkage to obtain a region inconsistent with the water system data space before shrinkage and after shrinkage, wherein model input elements are water system data before shrinkage, update elements are complete water system data after shrinkage, and output elements are regions inconsistent with the water system data space before shrinkage and after shrinkage. The intersection inversion is to eliminate the common part between the input element and the update element, thereby obtaining a new entity. The first selected object is the subtracted object, and the later selected object is the subtracted object.
It should be appreciated that the vertex cyclic 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 (order of polygon vertex inputs), the pointer of the last node being directed to the first node (cyclic single linked list).
In the specific implementation, in the spatial superposition analysis process of the target water system data after shrinkage and the water system data before shrinkage through a preset superposition analysis model, nodes of a polygon are constructed through a preset intersection inverse calculation model, and each vertex cyclic chain table is created.
Step S302: and determining a spatial overlapping relation between the target water system data after shrinkage and the water system data before shrinkage according to the vertex circular linked list and a preset intersection point 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 the intersection operation is performed on the polygons, an area to be framed may be determined by a maximum value and a minimum value of two-dimensional coordinates of each vertex corresponding to the polygon in the IOU algorithm and the vertex circular linked list, so that the intersection operation is performed on a preset rectangular area around the periphery of the framed polygons, and a spatial overlapping relationship between the target water system data after shrinkage and the water system data before shrinkage is determined by an overlapping portion of the IOU framed area.
In the concrete implementation, a polygon R, S is provided, wherein R, S respectively represents the water system data after shrinkage and the water system data before shrinkage, in the process of calculating the intersection of R and S, the nodes of the R and S polygons are firstly established, the respective vertex circular linked lists are established, and the x of the two polygons is sequentially calculated min ，x max ， y min ，y max Values, recorded into R and S nodes. In the process of determining the spatial overlapping relation between the target contracted and woven water system data and the contracted and woven water system data, the area to be framed can be determined through the IOU algorithm and the maximum value and the minimum value of the two-dimensional coordinates of each vertex corresponding to the polygon in the vertex circular linked list, and the spatial overlapping relation between the target contracted and woven water system data and the contracted and woven water system data can be determined through the overlapping part of the IOU framed area.
Step S303: and generating a superposition result layer according to the spatial superposition relation.
It should be noted that if the spatial overlapping relationship is displayed without an intersection point, it is indicated that the two polygons are in a completely separated state, the intersection point calculating algorithm is ended, and the overlapping result layer is determined according to a preset intersection point inverse calculation model. If the spatial overlapping relation shows that the intersection points exist, the two polygons are indicated to have overlapping areas, so that a common part between the target water system data after shrinkage and the water system data before shrinkage is removed through a preset intersection inversion calculation model, and a superposition result layer is output.
Further, the step S303 further includes: determining intersection point information between the target shrink-coded water system data and the water system data before shrink-coding according to the space superposition relation; tracking the vertexes in the vertex circular chain table according to the intersection information and a preset tracking direction, and removing overlapped parts in the layers according to tracking results and a preset intersection inverse algorithm to obtain a layer of overlapped results.
It should be noted that the intersection information includes intersection information corresponding to the water system data after shrinkage and intersection information corresponding to the water system data before shrinkage, where different intersection inverse calculation modes are corresponding to different tracking directions, so that the superposition result layer is determined according to the intersection inverse calculation result.
In a specific implementation, the intersection inversion calculation steps are as follows:
setp1: calculating the intersection point of the polygon R, S;
(A) Establishing nodes of R and S polygons, establishing respective vertex cyclic linked lists, and sequentially solving x of two polygons min ，x max ，y min ，y max Values recorded into R and S nodes;
(B) Whether R and S are completely separated or not is checked, wherein the method is to calculate rectangular bounding boxes of R and S (namely, maximum and minimum values calculated in the step (A)), if 2 rectangles have no intersection points, the description is completely separated, the intersection point calculation algorithm is finished, and the result of the intersection point inversion of R and S is the union of R and S graphs: an RUS;
(C) Traversing each side of the R polygon, solving the intersection point of the side and each side of the S polygon, respectively inserting the solved intersection points into the vertex linked lists of the R and S polygons, and simultaneously establishing a bidirectional pointer between the intersection points.
Setp2: calculating the difference between R and S: R-S;
(a) The method comprises the following steps Searching the vertexes of R in turn to find a vertex (set as Ps) which is not in S;
(b) The method comprises the following steps The vertex of R is traced and recorded clockwise from that vertex. If Ps are encountered, go (d); if meeting the intersection point of R and S, recording the intersection point, and turning (c);
(c) The method comprises the following steps Tracking and recording the vertex of S counterclockwise from that point until the intersection of R and S is encountered, recording the intersection and returning to (b);
(d) The method comprises the following steps The points recorded during this tracking are connected to form a polygon. Checking the vertices of R, if all of the vertices are tracked once, then performing (e); otherwise, searching an untracked vertex in R, and returning to (b);
(e) The method comprises the following steps The n polygons formed in d are the differences between R and S: R-S.
Setp3: calculating the difference between S and R: S-R; r is exchanged with S, and steps (a) to (e) in the step of Setp2 are repeated.
Setp4: the intersection of R and S takes the inverse result as (R-S) U (S-R).
In the embodiment, the water system data before shrinkage and the water system data after shrinkage are acquired; carrying out fusion processing on the contracted and compiled water system data according to a preset fusion model to obtain fused target contracted and compiled water system data; performing spatial superposition on the target water system data after shrinkage and the water system data before shrinkage and braiding by a preset superposition analysis model to construct a vertex cyclic linked list; determining a spatial overlapping relation between the target water system data after shrinkage and the water system data before shrinkage according to the vertex circular linked list and a preset intersection algorithm; generating a superposition result layer according to the spatial superposition relation; and carrying out similarity detection on the spatial characteristics of the water system before and after shrinkage according to the superposition result layer and a preset model algorithm. Because the superposition result image layer is determined through the preset fusion processing model and the superposition analysis model, the similarity detection is carried out on the spatial characteristics of the water system data according to the superposition result image layer, and compared with the prior art that the similarity of the spatial layout of the water system data in the same area under different proportion scales cannot be automatically compared, the manual detection efficiency is low, the accuracy is low, and the detection efficiency and the accuracy of the detection result are improved while the automatic detection is realized.
Referring to fig. 4, fig. 4 is a schematic flow chart of a third embodiment of the method for detecting the similarity of spatial characteristics of a water system according to the present invention, and the third embodiment of the method for detecting the similarity of spatial characteristics of a water system according to the present invention is proposed based on the first embodiment shown in fig. 2.
In this embodiment, the preset model algorithm includes a preset break-up model algorithm and a preset geometry algorithm, and the step S40 includes:
step S401: and carrying out scattering treatment on 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 the superimposed result layer (the result layer generated after the intersection is performed with the inverse calculation), where the superimposed result layer may be scattered according to a preset diameter range by using the preset model algorithm, and after scattering, each part of the multiple parts of elements may become independent elements, so as to obtain an element result layer corresponding to the target element.
Step S402: and dividing the element result layer into a plurality of triangles, and calculating the element image area corresponding to the element result layer according to a preset geometric algorithm.
It should be noted that, according to the preset geometric figure algorithm, any polygon corresponding to the element result layer is divided into a plurality of triangles, and the element image area corresponding to the element result layer is calculated.
In a specific implementation, any polygon can be divided into a plurality of triangles, and the area of any polygon can be obtained according to a triangle area formula. Any polygon may be labeled, in order, with its vertex coordinates (x 0, y 0), (x 1, y 1), (x 2, y 2), (xn, yn) (where n=2, 3,4, …), then its area S x Can be expressed as:
and recalculating the areas Si of the element images 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.
The result layer is inverted from the intersection, and the screen pattern area Si is not smaller than the minimum upper pattern area A of the water system min Form a screening result layer.
Step S404: and carrying out similarity detection on the spatial characteristics of the water system before and after shrinkage editing according to the screening result layer.
The similarity detection is carried out on the water system air conditioner characteristics before and after shrinkage according to the screening result layer.
Further, the step S404 further includes: negative buffer processing is carried out on the screening result layer, and irregular patterns are removed according to the negative buffer result; scattering the removed picture layers, and performing positive buffer treatment on each scattered element picture layer to obtain a target element picture layer; calculating the areas of the element graphs corresponding to the target element graph layer according to a preset geometric algorithm; if the area of each element figure is not smaller than the area of the minimum upper water system drawing, the water system space characteristics of the area before and after shrinkage is judged to be inconsistent.
In a specific implementation, in order to further illustrate a processing procedure for a screening result layer in the present solution, referring to a schematic diagram of a similarity checking flow shown in fig. 5, specific steps for removing irregular patterns such as long and narrow, dumbbell-shaped patterns are as follows: the step 1 comprises the following steps:
(1): negative buffering is carried out on the screening result layer, and the buffering value is as follows: -W min 2, half of the value of the minimum upper graph width of the graph spots;
(2): scattering the result layer in the step (1), and then performing positive buffering to obtain a buffering value: +W min And/2, half the value of the width of the minimum upper graph of the graph spots. Forming a buffer result layer;
step 2: recalculating the graphic area Si of each element of the buffer result layer;
step 3: if the graphic area Si of the graphic layer element of the buffer result is more than or equal to A min It is shown that the water system before the contracted knitting meets the minimum upper graph area requirement and the minimum upper graph width requirement, but is used in the contracted knitting processThe water system space characteristics of the areas before and after shrinkage are not consistent.
In the embodiment, the water system data before shrinkage and the water system data after shrinkage are acquired; carrying out fusion processing on the contracted and compiled water system data according to a preset fusion model to obtain fused target contracted and compiled water system data; performing spatial superposition on the target water system data after shrinkage and the water system data before shrinkage and braiding by a preset superposition analysis model to generate a superposition result layer; scattering the superimposed result layer through a preset scattering model algorithm to obtain an element result layer corresponding to the target element; dividing the element result layer into a plurality of triangles, and calculating the element image area corresponding to the element result 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 characteristics of the water system before and after shrinkage editing according to the screening result layer. Because the superposition result image layer is determined through the preset fusion processing model and the superposition analysis model, the similarity detection is carried out on the spatial characteristics of the water system data according to the superposition result image layer, and compared with the prior art that the similarity of the spatial layout of the water system data in the same area under different proportion scales cannot be automatically compared, the manual detection efficiency is low, the accuracy is low, and the detection efficiency and the accuracy of the detection result are improved while the automatic detection is realized.
In addition, in order to achieve the above object, the present invention also proposes a water system spatial feature similarity detection apparatus including 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 water system spatial feature similarity detection as described above.
In addition, in order to achieve the above object, the present invention also proposes a storage medium having stored thereon a water-based spatial feature similarity detection program which, when executed by a processor, implements the steps of the water-based spatial feature similarity detection method as described above.
Referring to fig. 6, fig. 6 is a block diagram showing the configuration of a first embodiment of the apparatus for detecting the similarity of spatial characteristics of a water system according to the present invention.
As shown in fig. 6, the apparatus for detecting the 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 shrinkage and water system data after shrinkage;
the fusion processing module 20 is configured to perform fusion processing on the contracted and compiled water system data according to a preset fusion model, so as to obtain fused target contracted and compiled water system data;
The superposition analysis module 30 is configured to spatially superimpose the target water system data after shrinkage and the water system data before shrinkage by using a preset superposition analysis model, so as to generate a superposition result layer;
and the feature comparison module 40 is used for detecting the similarity of the spatial features of the water system before and after shrinkage according to the superposition result layer and a preset model algorithm.
In the embodiment, the water system data before shrinkage and the water system data after shrinkage are acquired; carrying out fusion processing on the contracted and compiled water system data according to a preset fusion model to obtain fused target contracted and compiled water system data; performing spatial superposition on the target water system data after shrinkage and the water system data before shrinkage and braiding by a preset superposition analysis model to generate a superposition result layer; and carrying out similarity detection on the spatial characteristics of the water system before and after shrinkage according to the superposition result layer and a preset model algorithm. Because the superposition result image layer is determined through the preset fusion processing model and the superposition analysis model, the similarity detection is carried out on the spatial characteristics of the water system data according to the superposition result image layer, and compared with the prior art that the similarity of the spatial layout of the water system data in the same area under different proportion scales cannot be automatically compared, the manual detection efficiency is low, the accuracy is low, and the detection efficiency and the accuracy of the detection result are improved while the automatic detection is realized.
Further, the data acquisition module 10 is further configured to extract water system data from the target land pattern spot data according to a preset attribute screening condition, so as to obtain a contracted front water system data set; carrying out layer fusion processing on the contracted front water system data set according to a preset fusion model to obtain contracted front water system data after fusion is completed; buffering the contracted linear water system data according to a preset buffer analysis model to obtain a planar image layer; and determining the water system data after shrinkage and braiding according to the planar layer.
Further, the fusion processing module 20 is further configured to construct a new target result layer according to the planar layer and the contracted planar water system data; and carrying out fusion processing on the target result layer according to the preset fusion model, and determining the water system data after target shrinkage according to the fusion processing result.
Further, the superposition analysis module 30 is further configured to spatially superimpose the target water system data after shrinkage and the water system data before shrinkage by using a preset superposition analysis model, so as to construct a vertex circular linked list; determining a spatial overlapping relation between the target water system data after shrinkage and the water system data before shrinkage according to the vertex circular linked list and a preset intersection algorithm; and generating a superposition result layer according to the spatial superposition relation.
Further, the superposition analysis module 30 is further configured to determine intersection information between the target water system data after shrinkage and the water system data before shrinkage according to the spatial superposition relationship; tracking the vertexes in the vertex circular chain table according to the intersection information and a preset tracking direction, and removing overlapped parts in the layers according to tracking results and a preset intersection inverse algorithm to obtain a layer of overlapped results.
Further, the feature comparison module 40 is further configured to perform a break-up process on the superimposed result layer by using a preset break-up model algorithm, so as to obtain an element result layer corresponding to the target element; dividing the element result layer into a plurality of triangles, and calculating the element image area corresponding to the element result 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 characteristics of the water system before and after shrinkage editing according to the screening result layer.
Further, the feature comparison module 40 is further configured to perform negative buffer processing on the filtering result layer, and reject an irregular graph according to a negative buffer result; scattering the removed picture layers, and performing positive buffer treatment on each scattered element picture layer to obtain a target element picture layer; calculating the areas of the element graphs corresponding to the target element graph layer according to a preset geometric algorithm; if the area of each element figure is not smaller than the area of the minimum upper water system drawing, the water system space characteristics of the area before and after shrinkage is judged to be inconsistent.
It should be understood that the foregoing is illustrative only and is not limiting, and that in specific applications, those skilled in the art may set the invention as desired, and the invention is not limited thereto.
It should be noted that the above-described working procedure is merely illustrative, and does not limit the scope of the present invention, and in practical application, a person skilled in the art may select part or all of them according to actual needs to achieve the purpose of the embodiment, which is not limited herein.
In addition, technical details not described in detail in the present embodiment may refer to the method for detecting the similarity of water system spatial features provided in any embodiment of the present invention, which is 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 phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article, or system that comprises the element.
The foregoing embodiment numbers of the present invention are merely for the purpose of description, and do not represent the advantages or disadvantages of the embodiments. In the unit claims enumerating several means, several of these means may be embodied by one and the same item of hardware. The use of the terms first, second, third, etc. do not denote any order, but rather the terms first, second, third, etc. are used to interpret the terms as names.
From the above description of the embodiments, it will be clear to those skilled in the art that the above-described embodiment method may be implemented by means of software plus a necessary general hardware platform, but of course may also be implemented by means of hardware, but in many cases the former is a preferred embodiment. Based on such understanding, the technical solution of the present invention may be embodied essentially or in a part contributing to the prior art in the form of a software product stored in a storage medium (e.g. read only memory mirror (Read Only Memory image, ROM)/random access memory (Random Access Memory, RAM), magnetic disk, optical disk), comprising instructions for causing a terminal device (which may be a mobile phone, a computer, a server, or a network device, etc.) to perform the method according to the embodiments of the present invention.
The foregoing description is only of the preferred embodiments of the present invention, and is not intended to limit the scope of the invention, but rather is intended to cover any equivalents of the structures or equivalent processes disclosed herein or in the alternative, which may be employed directly or indirectly in other related arts.
Claims (6)
1. The water system space feature similarity detection method is characterized by comprising the following steps of:
acquiring water system data before shrinkage and water system data after shrinkage;
carrying out fusion processing on the contracted and compiled water system data according to a preset fusion model to obtain fused target contracted and compiled water system data;
performing spatial superposition on the target water system data after shrinkage and the water system data before shrinkage and braiding by a preset superposition analysis model to generate a superposition result layer;
performing similarity detection on the spatial characteristics of the water system before and after shrinkage according to the superposition result layer and a preset model algorithm;
the step of performing spatial superposition on the target water system data after shrinkage and the water system data before shrinkage and braiding through a preset superposition analysis model to generate a superposition result layer comprises the following steps:
Performing spatial superposition on the target water system data after shrinkage and the water system data before shrinkage and braiding by a preset superposition analysis model to construct a vertex cyclic linked list;
determining a spatial overlapping relation between the target water system data after shrinkage and the water system data before shrinkage according to the vertex circular linked list and a preset intersection algorithm;
determining intersection point information between the target water system data after shrinkage and the water system data before shrinkage according to the spatial overlapping relation;
tracking the vertexes in the vertex circular chain table according to the intersection point information and a preset tracking direction, and removing overlapped parts in the layers according to tracking results and a preset intersection inverse algorithm to obtain a layer of overlapped results;
the preset model algorithm comprises a preset scattering model algorithm and a preset geometric algorithm, and the step of carrying out similarity detection on the spatial characteristics of the water system before and after shrinkage according to the superposition result layer and the preset model algorithm comprises the following steps:
scattering the superimposed result layer through a preset scattering model algorithm to obtain an element result layer corresponding to the target element;
dividing the element result layer into a plurality of triangles, and calculating the element image area corresponding to the element result 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;
negative buffer processing is carried out on the screening result layer, and irregular patterns are removed according to the negative buffer result;
scattering the removed picture layers, and performing positive buffer treatment on each scattered element picture layer to obtain a target element picture layer;
calculating the areas of the element graphs corresponding to the target element graph layer according to a preset geometric algorithm;
if the area of each element figure is not smaller than the minimum upper-level area of the water system, the water system space characteristics before and after shrinkage is judged to be inconsistent.
2. The method for detecting spatial feature similarity of water system according to claim 1, wherein the step of acquiring water system data before and after the shrink-fit comprises:
extracting water system data from the target ground pattern spot data according to preset attribute screening conditions to obtain a contracted front water system data set;
carrying out layer fusion processing on the contracted front water system data set according to a preset fusion model to obtain contracted front water system data after fusion is completed;
buffering the contracted linear water system data according to a preset buffering analysis model to obtain a planar image layer;
And determining the water system data after shrinkage and braiding according to the planar layer.
3. The method for detecting spatial feature similarity of water system according to claim 2, wherein the step of performing fusion processing on the contracted and compiled water system data according to a preset fusion model to obtain fused target contracted and compiled water system data comprises the steps of:
constructing a new target result layer according to the planar layer and the contracted and compiled planar water system data;
and carrying out fusion processing on the target result layer according to the preset fusion model, and determining the water system data after target shrinkage according to the fusion processing result.
4. A water system spatial feature similarity detection apparatus, characterized in that the water system spatial feature similarity detection apparatus comprises: a memory, a processor, and a water-based spatial feature similarity detection program stored on the memory and executable on the processor, which when executed by the processor, implements the steps of the water-based spatial feature similarity detection method according to any one of claims 1 to 3.
5. A storage medium having stored thereon a water-based spatial feature similarity detection program which, when executed by a processor, implements the steps of the water-based spatial feature similarity detection method according to any one of claims 1 to 3.
6. A water system spatial feature similarity detection device, characterized in that the water system spatial feature similarity detection device comprises:
the data acquisition module is used for acquiring water system data before shrinkage and water system data after shrinkage;
the fusion processing module is used for carrying out fusion processing on the contracted and compiled water system data according to a preset fusion model to obtain fused target contracted and compiled water system data;
the superposition analysis module is used for carrying out space superposition on the target water system data after shrinkage and the water system data before shrinkage through a preset superposition analysis model to generate a superposition result layer;
the feature comparison module is used for carrying out similarity detection on the spatial features of the water system before and after shrinkage according to the superposition result layer and a preset model algorithm;
the superposition analysis module is further used for performing spatial superposition on the target water system data after shrinkage and the water system data before shrinkage through a preset superposition analysis model to construct a vertex circulation linked list; determining a spatial overlapping relation between the target water system data after shrinkage and the water system data before shrinkage according to the vertex circular linked list and a preset intersection algorithm; determining intersection point information between the target water system data after shrinkage and the water system data before shrinkage according to the spatial overlapping relation; tracking the vertexes in the vertex circular chain table according to the intersection point information and a preset tracking direction, and removing overlapped parts in the layers according to tracking results and a preset intersection inverse algorithm to obtain a layer of overlapped results;
The feature comparison module is further used for carrying out scattering processing on the superimposed result image layer through the preset scattering model algorithm to obtain an element result image layer corresponding to the target element; dividing the element result layer into a plurality of triangles, and calculating the element image area corresponding to the element result 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; negative buffer processing is carried out on the screening result layer, and irregular patterns are removed according to the negative buffer result; scattering the removed picture layers, and performing positive buffer treatment on each scattered element picture layer to obtain a target element picture layer; calculating the areas of the element graphs corresponding to the target element graph layer according to a preset geometric algorithm; if the area of each element figure is not smaller than the minimum upper-level area of the water system, the water system space characteristics before and after shrinkage is judged to be inconsistent.
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