CN112527916A - Grid visual definition and intelligent feature analysis method based on remote sensing image - Google Patents
Grid visual definition and intelligent feature analysis method based on remote sensing image Download PDFInfo
- Publication number
- CN112527916A CN112527916A CN202011358237.9A CN202011358237A CN112527916A CN 112527916 A CN112527916 A CN 112527916A CN 202011358237 A CN202011358237 A CN 202011358237A CN 112527916 A CN112527916 A CN 112527916A
- Authority
- CN
- China
- Prior art keywords
- grid
- data
- point
- information
- area
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000004458 analytical method Methods 0.000 title claims abstract description 31
- 230000000007 visual effect Effects 0.000 title claims abstract description 10
- 238000000034 method Methods 0.000 claims abstract description 13
- 238000004422 calculation algorithm Methods 0.000 claims description 20
- 238000007726 management method Methods 0.000 claims description 11
- 238000007405 data analysis Methods 0.000 claims description 6
- 238000000605 extraction Methods 0.000 claims description 6
- 230000001788 irregular Effects 0.000 claims description 6
- 238000012545 processing Methods 0.000 claims description 6
- 238000005516 engineering process Methods 0.000 claims description 5
- 238000012800 visualization Methods 0.000 claims description 5
- 230000005484 gravity Effects 0.000 claims description 4
- 238000010998 test method Methods 0.000 claims description 4
- 238000013500 data storage Methods 0.000 claims description 3
- 238000000926 separation method Methods 0.000 claims description 3
- 238000004141 dimensional analysis Methods 0.000 claims description 2
- 238000005065 mining Methods 0.000 claims description 2
- 230000008676 import Effects 0.000 claims 1
- 238000010586 diagram Methods 0.000 description 13
- 238000005457 optimization Methods 0.000 description 3
- 230000008901 benefit Effects 0.000 description 2
- 230000008859 change Effects 0.000 description 2
- 238000001914 filtration Methods 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 230000003068 static effect Effects 0.000 description 2
- 239000013598 vector Substances 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000004364 calculation method Methods 0.000 description 1
- 238000010276 construction Methods 0.000 description 1
- 125000004122 cyclic group Chemical group 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 230000018109 developmental process Effects 0.000 description 1
- 230000002349 favourable effect Effects 0.000 description 1
- 238000011835 investigation Methods 0.000 description 1
- 239000000463 material Substances 0.000 description 1
- 238000004806 packaging method and process Methods 0.000 description 1
- 238000012216 screening Methods 0.000 description 1
- 230000011218 segmentation Effects 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/28—Databases characterised by their database models, e.g. relational or object models
- G06F16/284—Relational databases
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/24—Querying
- G06F16/245—Query processing
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/29—Geographical information databases
Landscapes
- Engineering & Computer Science (AREA)
- Databases & Information Systems (AREA)
- Theoretical Computer Science (AREA)
- Data Mining & Analysis (AREA)
- Physics & Mathematics (AREA)
- General Engineering & Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Remote Sensing (AREA)
- Computational Linguistics (AREA)
- Information Retrieval, Db Structures And Fs Structures Therefor (AREA)
Abstract
The invention discloses a grid visual definition and intelligent feature analysis method based on remote sensing images, which comprises a plurality of paralleling data acquisition schemes integrated by a data acquisition part and a method for realizing grid judgment and analysis, and comprises the following specific steps: s1: reading data; s2: dotting the geographical information of the household registration; s3: planning a grid area range, drawing a grid in real time by a map, and customizing a display style; s4: associating the collected information points with the grid area; s5: and intelligently analyzing grid data. The invention can be applied to visual area definition based on an electronic map, realizes accurate coordinate dotting marking on the map for the household registration information, family information, land information and the like of a specific user, is convenient for directly displaying the position of the specific user or specific personnel and checking the details of the information on the map, realizes the drawing of a user-defined image grid on the map, and can also store the specific personnel information in the image grid in the same area range through the drawing adjustment of the grid.
Description
Technical Field
The invention relates to the technical field of remote sensing image information technical processing, extraction and feature analysis, in particular to a grid visual definition and intelligent feature analysis method based on remote sensing images.
Background
With the rapid advance of social informatization construction, the geographic spatial pattern is increasingly complex while economic development is promoted, and some household registration information, land information, population information data and the like are more complicated and complicated. If the search or the fixed point marking of a specific household registration or a certain field on the map is to be realized, the direct search by depending on the existing satellite sky and land image map cannot be finished. The remote sensing image technology has the advantages of wide observation range, large information amount, manpower and material resource saving, few man-made interference factors and the like, but has the characteristics of various data types, large range and the like, so that the high-precision information extraction and positioning of small information cannot be realized in a map.
Aiming at the demand of visually and intensively displaying information in a specific community range under the condition of ensuring high-precision positioning and information visualization, a grid visual definition and intelligent feature analysis method based on remote sensing images is provided.
Disclosure of Invention
The invention aims to provide a grid visual definition and intelligent feature analysis method based on a remote sensing image, which aims to solve the problem that the high-precision information extraction and positioning of small information cannot be realized in a map due to the characteristics of various data types, large range and the like in the prior art of the remote sensing image, which are provided by the background technology.
In order to achieve the purpose, the invention provides the following technical scheme:
a grid visual definition and intelligent feature analysis method based on remote sensing images comprises a plurality of parallelable data acquisition schemes integrated by a data acquisition part and a method for carrying out data acquisition and grid judgment and analysis aiming at various data sources, and specifically comprises the following steps:
s1: a data acquisition part: the information collector collects community visiting data, and can extract data from MySQL relational databases of mass multi-source data of various industry departments in database synchronization-based, log-based database increment extraction, manual import-in and other modes;
s2: and (3) dotting the geographical information of the household registration: the map coordinate system adopts a 2000 national geodetic coordinate system, a group of CGCS2020 coordinates are necessarily same-frame homoepoch coordinates of ITRF97 frames and 2000 epochs, point coordinates formed by the same-frame homoepoch coordinates are instantaneous and can never change, and the point coordinates are static, through the map frame, the retrieval range is reduced according to the planning of a designated administrative area, and finally, the geographical position coordinates of the household cadastre are accurately positioned;
when dotting, if a plurality of divided grid areas exist on the map, judging the area of the current point, if the current dotting position is contained in a certain grid area, automatically associating the current point into the corresponding grid area, otherwise, setting the point as a scattered point;
s3: planning the area range of the grid, drawing the grid in real time by a map, and customizing a display style: the grid drawing can be realized by carrying out point and point connection according to the drawing sequence to generate a grid after any coordinate point is dotted on a map;
s4: associating the collected information points with the grid area: the method comprises the steps that community grid management service is achieved, an information platform of a street/town-community/village-grid three-level grid system is built by a mobile intelligent terminal through an interconnection information technology, a jurisdiction region is divided into a plurality of grid-shaped units, people, land, things, affairs, organizations and the like are all brought into grid management, urban and rural community autonomy and service functions are enhanced aiming at government affairs villages and convenience for people, and a novel community management and service system is perfected;
s5: intelligent analysis of grid data: after the four steps, the system analyzes and deeply excavates the household registration data in the grid by adopting an intelligent analysis algorithm after the grid area and the household registration information points generate the association relationship, reveals implicit and previously unknown information with potential value from a large amount of data in the database, and binds the data with the associated value as much as possible.
Preferably, in step S1, the mass multi-source heterogeneous data storage data warehouse is mainly composed of two independent clusters of "current application" and "history archive", where the "history archive" is used to store outdated archive history data, and the "current application" is used to store effective data of current big data analysis application, and includes data exchange cache, community household information, community household land information, and other big data areas.
Preferably, in the step S2, to determine whether the point is on the surface, the following algorithm is required to determine: and acquiring irregular grids in the effective range area, taking out the grid areas as few as possible from the determined range, and performing circular traversal processing on the grid array.
Preferably, the circular traversal processing on the grid array comprises two methods: firstly, making up upper, lower, left and right extension lines for a positioned coordinate point, recording the current grid when the extension lines are intersected with the boundary of an adjacent grid area, and making the same intersection record for the extension lines in the remaining directions, wherein if the extension lines in 4 directions are intersected in the same grid area, a point can be preliminarily judged to be contained in the grid, otherwise, the positioned coordinate point belongs to a scattered point; and secondly, circularly traversing the grid area array, judging the grids one by using an algorithm of 'included angle sum check method' between the positioning point and each grid area, and if the point cannot be determined even when the circulation is finished, representing that the point is a scattered point.
Preferably, in step S3, the mesh generation includes that the mesh region is not overlapped and the center of gravity of the mesh region is calculated, and the name of the mesh is displayed, the mesh polygon region is overlapped and divided into vertices contained in other regions, which are definitely overlapped regions, the judgment point in the region can be judged by using the "included angle sum test method", and the part overlap, which can be demonstrated by using the reverse side of the part separation.
Compared with the prior art, the invention has the beneficial effects that:
the invention realizes the accurate coordinate dotting marking of the household registration information, the family information, the land information and the like of a specific user in a certain area on the map in work, is convenient for directly displaying the position of the specific user or a specific person and checking the details of the information on the map, realizes the drawing of a self-defined image grid on the map, can summarize and summarize the information, and simultaneously stores the information of the specific person in the image grid in the same area range by drawing and adjusting the grid.
Drawings
FIG. 1 is a schematic diagram of the intersection of an extended line and a grid region according to the present invention.
Fig. 2 is a schematic diagram of the present invention where the intersection points all occur at the boundaries of the regions.
FIG. 3 is a flowchart of an algorithm for cyclically traversing an array of grid regions in accordance with the present invention.
FIG. 4 is a schematic diagram of the sum of included angles test method of the present invention.
FIG. 5 is a schematic view of the intersection determination of two parts of the present invention.
FIG. 6 is a schematic diagram of y2 of the barycentric coordinate G (x2, y2) of the polygon of the present invention.
FIG. 7 is a schematic diagram of x2 of the barycentric coordinate G (x2, y2) of the polygon of the present invention.
FIG. 8 is a schematic diagram of region point data after region segmentation by sequentially connecting the region critical points which are drawn by traversal according to the present invention.
FIG. 9 is a schematic view of a region cut by a vertex-edge connecting line according to the present invention.
FIG. 10 is an analysis diagram of the algorithm for performing the outer closure operation on the vertices of the region by the grid data according to the present invention.
FIG. 11 is a diagram of a first embodiment of the present invention.
Fig. 12 is a diagram of a data acquisition case of an application example of the present invention.
Fig. 13 is a diagram of a data acquisition case of an application example of the present invention.
Fig. 14 is a diagram of a data acquisition case of an application example of the present invention.
Fig. 15 is a scene diagram of a positioning information associated grid according to an exemplary application of the present invention.
Fig. 16 is a schematic view showing intravillage profile data of an exemplary application of the present invention.
Fig. 17 is a detailed information data presentation diagram in village of an application example of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is apparent that the described embodiments are only one mechanical embodiment of the present invention, and not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Example one
Referring to fig. 1 to 11, the present invention provides a technical solution: a grid visual definition and intelligent feature analysis method based on remote sensing images comprises a plurality of parallelable data acquisition schemes integrated by a data acquisition part and a method for carrying out data acquisition and grid judgment and analysis aiming at various data sources, and comprises the following specific steps:
s1: a data acquisition part: the information collector collects community visiting data, and can extract data from MySQL relational databases of mass multi-source data of various industry departments in database synchronization-based, log-based database increment extraction, manual import-in and other modes.
The massive multi-source heterogeneous data storage data warehouse is mainly composed of two independent clusters of current application and historical filing, wherein the historical filing is used for storing outdated filing historical data, and the current application is used for storing effective data of current big data analysis application and comprises data exchange cache regions, community household registration information, community household registration land information and other big data regions. By constructing a data warehouse supporting non-structures, the platform has the capability of multi-dimensional analysis (OLAP) of ultra-large scale data, a cube can be generated from mass data, and operations such as slicing, dicing, rotating and rotating can be performed.
S2: and (3) dotting the geographical information of the household registration: the map coordinate system adopts a 2000 national geodetic coordinate system, a group of CGCS2020 coordinates are necessarily same-frame homodrome coordinates of ITRF97 frame and 2000 epoch, point coordinates consisting of same-frame homodrome coordinates are instantaneous and never change, and the point coordinates are static. And through a map frame, the retrieval range is narrowed according to the planning of a designated administrative area, and finally, the coordinates of the household registration geographical position are accurately positioned.
When dotting, a plurality of divided grid areas exist on a map, the area of a current point needs to be judged, if the current dotting position is contained in a certain grid area, the current point needs to be automatically associated into the corresponding grid area, otherwise, the point is set to be a scattered point, and whether the point is on the surface or not needs to be judged by the following algorithm:
acquiring an irregular grid in the effective range area: actually, in numerous grids, it is difficult to calculate which region a geographic position coordinate point is located in, because the number of polygons has uncertainty and it is difficult to calculate any point in an irregular region, so that the idea needs to be changed here, grid regions as few as possible are taken out from a determined range first, and then a grid array is subjected to cyclic traversal processing, and two schemes are provided here to solve the problem:
the first scheme is as follows: and (3) making up vertical and horizontal extension lines for the positioned coordinate points, recording the current grid when the extension lines are intersected with the boundary of the adjacent grid area, and making the same intersection record for the extension lines in the rest directions, wherein if the extension lines in the 4 directions are intersected in the same grid area, the points can be preliminarily judged to be contained in the grid, otherwise, the positioned coordinate points belong to scattered points.
As shown in FIG. 1, the outer points represent the points where the extended lines intersect the grid area, PointLeft side ofAnd PointRight sideIt is obvious that two intersection points do not belong to the same region, and therefore it can be judged that the point is necessarily a scatter point.
As shown in fig. 2, the outer 4 intersection points all belong to the same grid region boundary, and therefore it can be preliminarily determined that a point is included in a certain grid.
Since the intersection points all occur on the boundary of the area, the coordinates Point (x, y) can be substituted By the straight line formula Ax + By + c being 0 to calculate 4 intersection points, and further to calculate which grid area the coordinates Point is located in.
Scheme II: the grid area array is traversed in a circulating mode, the grids are judged one by using an algorithm of 'included angle sum check method' carried out on the positioning points and each grid area until the points cannot be determined even when the circulation is finished, the points are scattered points, and the algorithm flow chart is shown in figure 3:
the efficiency of the second scheme is much higher than that of the first scheme because the relational database only stores the sequential vertex set of the mesh when the mesh region object is taken out, and if the first scheme is used, the sequential vertex set is required to generate the boundary vector V in turnPoint 1 Point 2、VPoint 1 Point 2、……VPoint n-1 point nWhen the number of the vertices of the grid area is large, it takes time to traverse the vertices of the area to generate vectors, and the corresponding grid area is searched from the database according to the side information, which are very popular in the calculation of server resources. In the second scheme, only one single cycle is needed to be carried out on the area list, and the point inclusion judgment is carried out on the traversal grid objectCan be prepared.
S3: planning the area range of the grid, drawing the grid in real time by a map, and customizing a display style: the grid drawing can be realized by carrying out point and point connection according to the drawing sequence to generate grids after carrying out point dotting on any coordinate point on a map. The grid generation includes the following:
1. the grid areas can not be overlapped, and the polygon areas are overlapped and are subjected to algorithm analysis in two conditions:
first, if the vertex is included in other area, it is determined to be an overlapping area, and the judgment point can be judged in the area by adopting an "included angle sum test method".
Suppose there is a vertex p0，p0With the vertex p of another regioniForm an included angle alphaiAs in FIG. 1, if
The point is outside the region, shown in fig. 4 (a);
the point is located outside the area as shown in fig. 4 (b).
Second, the overlap of regions is also the case where parts overlap, which can be demonstrated by the reverse side of the part separation. The basic criteria are two points: the method comprises the following steps that 1, all vertexes of a part A are outside a part B, and all vertexes of the part B are outside A; criterion 2, each edge of A does not intersect with the edge of B. It should be noted that:
if it is merely a that all the vertices are outside B, it cannot be said A, B are separated, a may contain B, as shown in fig. 5 (a).
Part A, B only satisfies criterion 1 and does not guarantee that the two are certainly separated, as shown in fig. 5 (b), and therefore criterion 2 needs to be introduced.
In many cases, the abutting part is far from the "abutted" part, and the relative position of the two parts can be quickly determined by the bounding box of the two parts.
2. Calculating the gravity center of the grid area, and displaying the grid name:
general centroid formula for the polytypes surrounded by the discrete data points: with Ai(xiyi) Arbitrary N modification a with (i ═ 1,2,3 …, N) as the apex1A2…AnIt is divided into N-2 triangles, as shown in figure 1, the gravity center G of each trianglei(xiyi) Then the barycentric coordinates G (x2, y2) of the polygon are as shown in fig. 6 and 7.
S4: associating the collected information points with the grid area: the community grid management service utilizes an interconnection information technology, utilizes a mobile intelligent terminal to build an information platform of a three-level grid system of 'street/town-community/village-grid', divides a jurisdiction region into a plurality of grid-shaped units, brings people, land, things, affairs, organizations and the like into grid management, strengthens urban and rural community autonomy and service functions aiming at government affairs villages, convenience for people and people, and perfects a novel community management and service system.
And 4, calculating all coordinate points including the closed packet through the irregular grid area generated in the step three by the following algorithm.
1. And calculating all dotting information coordinate points in the current screen display range. Obtaining the boundary coordinate value (x) of the current screen display rangeLeft side ofyOn the upper partxRight sideyLower part) And searching the point object which is in line with the boundary range through a condition constructor of the relational database mysql. Sql conditions:
Point.x>＝xleft side ofandPoint.x<＝xRight sideandPoint.y<＝Point.y>＝yLower partThis benefit greatly optimizes the filtering dead point step.
2. The household points filtered by the algorithm 1 still have a great optimization problem. For example, when the current screen display range has been stretched to the full screen, the algorithm 1 has lost the meaning of the filter points, and since the points in the range are all points, a second filter point algorithm optimization is needed here:
traversing all vertexes of the drawn region, finding out the leftmost point, the uppermost point, the rightmost point and the lowermost point respectively, sequentially connecting the critical points to perform region cutting, and only keeping the data of the cut region points, as shown in fig. 8.
The area cut out by the vertex edge connecting line can reduce the search point range area again, but this may cause a bad situation, and if the area is inclined, the boundary range after the vertex connecting line is obtained, and the filtering area is more than the effective area, as shown in fig. 9.
However, in combination with an actual service scene, the probability of occurrence of the situation is not high, and the performance optimization of the database retrieval point is enough according to the range obtained by the vertex connecting area, if detailed algorithm analysis is performed on the irregular graph, and then the effective area is recalculated, the calculated amount is too large, the program processing speed is affected, and thus, the situation is not compensated.
S5: intelligent analysis of grid data: after the four steps, after the grid area and the household registration information points generate the association relationship, the system adopts an intelligent analysis algorithm to perform data analysis and deep mining on the household registration data in the grid, reveals implicit, previously unknown and potentially valuable information from a large amount of data in the database, and binds the data with the associated value as much as possible, wherein the following is an example of a result set generated by intelligent analysis:
assuming that 3 adjacent grid areas often receive the same community problem information feedback, but the adjacent grid areas do not report similar problems, the range of the generated problems can be roughly determined, and the areas are subjected to the outer closure operation and the algorithm analysis (as shown in fig. 10):
1. separately acquire
Maximum boundary point of grid 1: p1, p2, p7, p 8;
maximum boundary point of grid 2: p3, p4, p10, p 9;
maximum boundary point of grid 3: p11, p5, p6, p 12;
2. selecting outer closed points
As can be derived from fig. 10, p2, p3, p11 are the leftmost vertices of the three graphs, but the x coordinate of the rightmost vertex p10 of mesh 2 < the leftmost vertex p11 of mesh 3, so p11 will not be able to be the outer closure area vertex. Repeating the above thought, calculating the top vertex, the bottom vertex and the right vertex in turn, and connecting the vertexes to obtain the required area.
After the approximate range with problems is calculated, the new range area can be pushed to a platform manager and a grid manager, and then corresponding workers are arranged from top to bottom for investigation and analysis, so that an efficient and high-quality solution is provided, and an intelligent management community is realized.
The following detailed description of the present invention will be made in conjunction with the accompanying drawings and examples. The following examples are intended to illustrate the invention but are not intended to limit the scope of the invention.
The following are examples of applications that utilize the techniques of the present invention:
the method is applied to a system to realize data acquisition, and as shown in the figure 12, the figure 13 and the figure 14, after an information acquirer inputs a name and an identity card number of a user, automatic retrieval and analysis can be performed according to mass data, and automatic associated filling is performed on basic data.
The name 'old XX' and the identity card number '4509021996081 XXXX' of the householder are input, the system associates corresponding basic data, automatically improves family information, member information, housing information and the like of the householder 'old XX', and greatly improves the acquisition efficiency of data acquirers.
Location information association grid
Referring to fig. 15, after the information acquirer checks the household registration information, it can perform corresponding range retrieval according to the positioning information to query a reasonable grid set, and there are two scenarios of range extension according to the positioning information:
scenario 1: when the map is zoomed too far, the number of contained grids may be very large, which is not favorable for program calculation and also increases the screening difficulty of the information collector. Therefore, the grids need to be screened in administrative villages, and after the grids are subdivided into village-level grids, data needing to be traversed can be reduced to a great extent;
scenario 2: when the map is zoomed out, a central point can be made according to the positioning point, a circle is drawn by taking 2 kilometers as a radius, all grids are inquired by taking 2 kilometers of the square circle as a target, the grids are traversed, and the grids where the points are located are finally obtained according to a point-containing polygon algorithm.
Grid management concrete presentation
As shown in fig. 16 and 17, after the household registration information and the grid management, intelligent data analysis can be performed, and through art packaging, the data is visually displayed on a large screen, so as to provide clear data report display for the user.
Although the present invention has been described in detail with reference to the foregoing embodiments, it will be apparent to those skilled in the art that various changes, modifications, equivalents, improvements and the like can be made therein without departing from the spirit and scope of the invention.
Claims (5)
1. A grid visual definition and intelligent feature analysis method based on remote sensing images is characterized in that: the method comprises a plurality of data acquisition schemes which are integrated by a data acquisition part and can be paralleled, and is used for carrying out data acquisition and grid judgment and analysis aiming at various data sources, and comprises the following specific steps:
s1: a data acquisition part: the information collector collects community visiting data, and can adopt database synchronization and log-based database increment extraction and manual import modes to extract data from a MySQL relational database of massive multi-source data of various industry departments;
s2: and (3) dotting the geographical information of the household registration: the map coordinate system adopts a 2000 national geodetic coordinate system, the retrieval range is reduced according to the planning of the designated administrative area through a map frame, and finally the geographical position coordinates of the registered household books are accurately positioned; when dotting, if a plurality of divided grid areas exist on the map, judging the area of the current point, if the current dotting position is contained in a certain grid area, automatically associating the current point into the corresponding grid area, otherwise, setting the point as a scattered point;
s3: planning the area range of the grid, drawing the grid in real time by a map, and customizing a display style: the grid drawing can be realized by carrying out point-point connection on points in any coordinate point on a map according to the drawing sequence to generate a grid;
s4: associating the collected information points with the grid area: the community grid management service adopts the internet information technology, builds an informatization platform of a three-level grid system of 'street/town-community/village-grid' through a mobile intelligent terminal, divides a jurisdiction region into a plurality of grid-shaped units, and brings 'people, places, things and organizations' into grid management;
s5: intelligent analysis of grid data: after the four steps, the grid area and the household registration information points generate the association relationship, the system adopts an intelligent analysis algorithm to carry out data analysis and deep mining on the household registration data in the grid, reveals implicit and previously unknown information with potential value from a large amount of data in the database, and binds the data with the associated value as much as possible.
2. The grid visualization definition and intelligent feature analysis method based on remote sensing images as claimed in claim 1, wherein: the mass multi-source heterogeneous data storage data warehouse in the step S1 is mainly composed of two independent clusters of "current application" and "historical archiving"; the 'current application' is used for storing effective data of the current big data analysis application, and the 'history archive' is used for storing outdated archive history data, including data exchange cache regions, community household information, community household land information and other big data regions; by constructing a data warehouse supporting non-structures, the platform has the multi-dimensional analysis capability of super-large-scale data, a cube can be generated from mass data, and operations such as slicing, dicing, rotating and rotating can be performed.
3. The grid visualization definition and intelligent feature analysis method based on remote sensing images as claimed in claim 1, wherein: in step S2, to determine whether the point is on the surface, the following algorithm needs to be performed to determine: and acquiring irregular grids in the effective range area, firstly acquiring the grid areas as few as possible from the determined range, and then performing circular traversal processing on the grid array.
4. The grid visualization definition and intelligent feature analysis method based on remote sensing images as claimed in claim 3, wherein: the circular traversal processing of the grid array comprises two methods: firstly, making up upper, lower, left and right extension lines for a positioned coordinate point, recording the current grid when the extension lines are intersected with the boundary of an adjacent grid area, and making the same intersection record for the extension lines in the rest directions, wherein if the extension lines in 4 directions are intersected in the same grid area, the point can be preliminarily judged to be contained in the grid, otherwise, the positioned coordinate point belongs to a scattered point; and secondly, circularly traversing the grid area array, carrying out judgment one by using an algorithm of 'included angle sum check method' on the positioning points and each grid area, and if the point cannot be determined even when the circulation is finished, representing that the point is a scattered point.
5. The grid visualization definition and intelligent feature analysis method based on remote sensing images as claimed in claim 1, wherein: in step S3, the mesh generation includes that the mesh region is not overlapped and the center of gravity of the mesh region is calculated, and the mesh name is displayed, the mesh polygon region is overlapped and divided into vertices contained in other regions, and it is determined that the vertices are overlapped regions, and the determination points can be determined by "sum of included angles test method" in the regions, and the parts are overlapped, and the part overlapping can be demonstrated by the reverse side of the part separation.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011358237.9A CN112527916A (en) | 2020-11-27 | 2020-11-27 | Grid visual definition and intelligent feature analysis method based on remote sensing image |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011358237.9A CN112527916A (en) | 2020-11-27 | 2020-11-27 | Grid visual definition and intelligent feature analysis method based on remote sensing image |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN112527916A true CN112527916A (en) | 2021-03-19 |
Family
ID=74994172
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202011358237.9A Pending CN112527916A (en) | 2020-11-27 | 2020-11-27 | Grid visual definition and intelligent feature analysis method based on remote sensing image |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN112527916A (en) |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114187428A (en) * | 2021-10-21 | 2022-03-15 | 广西电网有限责任公司 | Drawing method and system based on parcel service grid |
| CN114494519A (en) * | 2022-02-18 | 2022-05-13 | 上海歆广数据科技有限公司 | Electronic map grid drawing method and system in grid data system |
| CN114706931A (en) * | 2022-03-30 | 2022-07-05 | 海南视联通信技术有限公司 | Data processing method and device |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102103635A (en) * | 2011-01-05 | 2011-06-22 | 安徽兴博远实信息科技有限公司 | Household condition grid management mode of population social affair comprehensive service information |
| CN107480175A (en) * | 2017-06-30 | 2017-12-15 | 广州数鹏通科技有限公司 | A kind of geographic information data screening technique, electronic equipment, storage medium and system based on gridding |
| CN108806473A (en) * | 2018-05-25 | 2018-11-13 | 重庆中地六合测绘技术有限公司 | Plot data collecting system |
-
2020
- 2020-11-27 CN CN202011358237.9A patent/CN112527916A/en active Pending
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102103635A (en) * | 2011-01-05 | 2011-06-22 | 安徽兴博远实信息科技有限公司 | Household condition grid management mode of population social affair comprehensive service information |
| CN107480175A (en) * | 2017-06-30 | 2017-12-15 | 广州数鹏通科技有限公司 | A kind of geographic information data screening technique, electronic equipment, storage medium and system based on gridding |
| CN108806473A (en) * | 2018-05-25 | 2018-11-13 | 重庆中地六合测绘技术有限公司 | Plot data collecting system |
Non-Patent Citations (1)
| Title |
|---|
| 刘虓: "基于HAPE的二维不规则零件排样算法及其性能研究", 《中国博士学位论文全文数据库 工程科技Ⅱ辑》, pages 029 - 10 * |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114187428A (en) * | 2021-10-21 | 2022-03-15 | 广西电网有限责任公司 | Drawing method and system based on parcel service grid |
| CN114494519A (en) * | 2022-02-18 | 2022-05-13 | 上海歆广数据科技有限公司 | Electronic map grid drawing method and system in grid data system |
| CN114494519B (en) * | 2022-02-18 | 2023-08-22 | 上海歆广数据科技有限公司 | Method and system for drawing electronic map grids in grid data system |
| CN114706931A (en) * | 2022-03-30 | 2022-07-05 | 海南视联通信技术有限公司 | Data processing method and device |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN109977179B (en) | Method for realizing convergence and dispersion visualization of mass ground object mark points through layered grid division | |
| CN112527916A (en) | Grid visual definition and intelligent feature analysis method based on remote sensing image | |
| LU102117B1 (en) | Method and system for measuring mountain view visible area in city | |
| US7024402B2 (en) | Spatial data mining method, spatial data mining apparatus and storage medium | |
| CN116310192A (en) | Urban building three-dimensional model monomer reconstruction method based on point cloud | |
| CN116089555B (en) | CIM platform-based three-dimensional space data acquisition and light weight system and method | |
| CN112115198A (en) | Urban remote sensing intelligent service platform | |
| CN111597170B (en) | Method for building spatial semantic database from BIM model without damage | |
| CN111221867B (en) | Protective building information management system | |
| CN108765567A (en) | A kind of system for building urban design digitlization sand table | |
| CN106842193A (en) | The processing method of Road Detection information, device and system | |
| CN114049462B (en) | Three-dimensional model monomer method and device | |
| Yadav et al. | Identification of trees and their trunks from mobile laser scanning data of roadway scenes | |
| CN114820975B (en) | Three-dimensional scene simulation reconstruction system and method based on all-element parameter symbolization | |
| CN110660125A (en) | Three-dimensional modeling device for power distribution network system | |
| CN110659369A (en) | On-orbit high-precision lightweight global image control point database construction method and system | |
| Janečka et al. | Registering the underground objects in the 3D cadastre: a case study of wine cellar located in the vineyard area Tokaj. | |
| CN116796083B (en) | Space data partitioning method and system | |
| CN113361786B (en) | Intelligent planning method for power line fusing multi-source multi-dimensional heterogeneous big data | |
| Xing et al. | Continuous monitoring of nearest neighbors on land surface | |
| CN114490907A (en) | Method and device for constructing famous city management database and storage medium | |
| CN113626551A (en) | GIS-based query and display method for applied geological elements | |
| CN113313101B (en) | Building contour automatic aggregation method, device, equipment and storage medium | |
| Vo | Spatial data storage and processing strategies for urban laser scanning | |
| CN109947884A (en) | A kind of high-efficiency tissue querying method of whole world ICESat/GLAS point cloud |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |