CN111260521B - City boundary acquisition method and device, intelligent terminal and storage medium - Google Patents
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Abstract
The invention discloses a city boundary acquisition method, a city boundary acquisition device, an intelligent terminal and a storage medium. The method comprises the following steps: acquiring a road cross point set according to road network data, constructing a quadtree for road cross points in the point set, and storing one road cross point for each leaf node of the quadtree; calculating a density value according to the geometric information of the rectangle corresponding to the leaf node, and classifying the rectangle corresponding to the leaf node according to the density value; and carrying out non-transregional fusion Dissolve processing on the classified rectangles according to the topological relation of photographic adjacency to obtain city boundaries. The invention improves the spatial quadtree index processing method for spatial information of urban big data and clusters according to the nonlinear density characteristics of the type of data. Compared with the traditional urban data clustering method, the method does not need to set parameters in advance, and is high in speed and high in expandability.
Description
Technical Field
The invention relates to the technical field of spatial data analysis, in particular to a city boundary acquisition method, a city boundary acquisition device, an intelligent terminal and a storage medium.
Background
The city calculation means that valuable information is mined from the city multi-source heterogeneous big data by utilizing a computer technology, and scientific rules of connotation in the city are explored, so that efficient and healthy operation of the city is better promoted for human life service.
The city represents a region with high concentration of human living, and accurate description of city boundaries can help governments monitor whether urban construction regions are randomly expanded or not and coordinate planning of relations between urban resident trips and public facility configurations, and contributes to urban sustainable development, so that the city calculation is an important content.
Currently, the boundaries of cities are determined by local authorities or governments, and this top-down approach may be in-and-out with areas of real human activity. In addition, some technologies define the boundaries of cities through methods such as an information entropy method, a breakpoint analysis method and a remote sensing night light interpretation method, and the methods mainly have two defects: 1. the definition of the artificial threshold is fuzzy, the efficiency is high, the subjectivity is high, and the accuracy and objectivity are difficult to achieve 2. The construction efficiency of the data structure (such as triangular surface) supported by the definition of the range is very low, and the ideal result cannot be obtained when the data volume is large.
Accordingly, the prior art is still in need of improvement and development.
Disclosure of Invention
In view of the shortcomings of the prior art, the invention aims to provide a city boundary acquisition method, a city boundary acquisition device, an intelligent terminal and a storage medium, and aims to solve the problems of low efficiency and strong subjectivity of a city boundary dividing method in the prior art.
The technical scheme adopted by the invention for solving the technical problems is as follows:
in a first aspect, an embodiment of the present invention provides a city boundary obtaining method, where the method includes:
acquiring a road cross point set according to road network data, constructing a quadtree for road cross points in the road cross point set, and storing one road cross point for each leaf node of the quadtree;
calculating a density value according to the geometric information of the rectangle corresponding to each leaf node, and dividing the rectangle corresponding to each leaf node into a target class rectangle and a background class rectangle according to the density value; the density value is the reciprocal of the rectangular area;
and carrying out non-cross-region fusion processing on the classified target rectangles according to the topological relation of photographic adjacency to obtain city boundaries.
The city boundary obtaining method, wherein the building of the quadtree comprises the following specific building processes:
calculating outer envelope rectangles of all the road intersections, and taking the outer envelope rectangles as root nodes;
generating a full-N quadtree with depth, calculating the relation between each road intersection and each leaf node rectangle, and storing the rectangles with the points less than or equal to 1 in the leaf node rectangles; the N is a natural number greater than 1 and less than 9;
generating a quadtree independently for each rectangle with the number of points greater than 1 until the number of points in all the rectangle with leaf nodes is less than or equal to 1, and storing the rectangles; integrating all rectangles with the number of points less than or equal to 1, and ending.
The city boundary obtaining method includes the steps of calculating a density value according to geometric information of a rectangle corresponding to the leaf node, and dividing the rectangle corresponding to the leaf node into a target rectangle and a background rectangle according to the density value, wherein the method specifically includes the following steps:
dividing all rectangles into two parts of a target class rectangle and a background class rectangle by using a formula (1)
Wherein d is the segmentation threshold value of the target and the background, sigma w For the difference between the target and background density values, w 0 For the target point to account for the image proportion, sigma 0 For the variance of the target point number in the proportion of the image, w 1 For background points to account for image proportion, sigma 1 The variance of the background points in the proportion of the image; all rectangle information in the target class is extracted.
According to the city boundary obtaining method, the step carries out non-cross-region fusion processing on the classified target rectangles according to the topological relation of photographic adjacency to obtain city boundaries, wherein the non-cross-region fusion processing comprises the following steps:
screening adjacent rectangles with the rectangle from any rectangle in the classified target class rectangles, and storing the adjacent rectangles into a preset set;
traversing each adjacent rectangle, repeating the steps to screen adjacent rectangles corresponding to the rectangle, and storing the adjacent rectangles into a preset set until no new adjacent rectangle exists;
and eliminating the common edges of all rectangles in the set, generating a city boundary, and ending.
In the city boundary obtaining method, N is 7.
In a second aspect, an urban boundary acquisition device, the device comprising:
the four-way tree construction unit is used for acquiring a road cross point set according to road network data, constructing a four-way tree for road cross points in the road cross point set, and storing one road cross point for each leaf node of the four-way tree;
the rectangle classifying unit is used for calculating a density value according to the geometric information of the rectangle corresponding to each leaf node and classifying the rectangle corresponding to each leaf node into a target class and a background class according to the density value; the density value is the reciprocal of the rectangular area;
and the processing unit is used for carrying out non-cross-region fusion processing on the classified target rectangles according to the topological relation of photographic adjacency to obtain city boundaries.
The device, wherein the quadtree construction unit includes:
the calculating subunit is used for calculating the outer envelope rectangles of all the road intersections and taking the outer envelope rectangles as root nodes;
generating a subunit, namely generating a full quadtree with the depth N, calculating the relation between each road intersection and each leaf node rectangle, and storing the rectangles with the points less than or equal to 1 in the leaf node rectangles; the N is a natural number greater than 1 and less than 9;
a storage subunit, configured to independently generate a quadtree for each rectangle with a point number greater than 1, until the point number in all the leaf node rectangles is less than or equal to 1, and store the rectangles; integrating all rectangles with the number of points less than or equal to 1, and ending.
The apparatus, wherein the processing unit includes:
the screening unit is used for screening adjacent rectangles with any rectangle in the classified target class rectangles, and storing the adjacent rectangles into a preset set;
traversing subunit, configured to traverse each adjacent rectangle, repeat the steps to screen the adjacent rectangle with the rectangle, and store the adjacent rectangle into a preset set until no new adjacent rectangle exists;
and the elimination self unit is used for eliminating the common edges of all rectangles in the set, generating city boundaries and ending.
In a third aspect, embodiments of the present invention further provide an intelligent terminal, including a memory, and one or more programs, where the one or more programs are stored in the memory, and configured to be executed by the one or more processors, the one or more programs including for performing the method as described above.
In a fourth aspect, embodiments of the present invention also provide a non-transitory computer-readable storage medium, which when executed by a processor of an electronic device, causes the electronic device to perform a method as described above.
The invention has the beneficial effects that: the method for quickly clustering road intersections in the whole country and generating the city boundary range can accurately describe whether the urban construction area is disordered and expand or not and coordinate and plan the relationship between urban resident traveling and public facility configuration by establishing the space quadtree, and has important significance for urban sustainable development.
Drawings
Fig. 1 is a flowchart of a preferred embodiment of the city boundary acquiring method provided by the present invention.
Fig. 2 is a flowchart of step S100 of the city boundary obtaining method according to the preferred embodiment of the present invention.
Fig. 3 is a functional schematic diagram of the intelligent terminal provided by the invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more clear and clear, the present invention will be further described in detail below with reference to the accompanying drawings and examples. 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.
As the city boundaries are divided and acquired in the prior art, the following modes are adopted:
firstly, the related departments plan and set, and the mode of planning and setting is adopted, so that subjectivity is strong, and the obtained city boundary and the actual city boundary have access. And secondly, depending on data information, by means of an information entropy method, a breakpoint analysis method, remote sensing night light interpretation and the like, the construction efficiency of a data structure depending on the methods is very low when the range is defined, and a desired result cannot be obtained when the data quantity is large.
In order to solve the above technical problems, in the embodiment of the present invention, when a certain city needs to be defined by a boundary, national road network data or provincial road network data where the city is located may be acquired first. And analyzing road crossing points (road crossing points) according to the road network data, converging the road crossing points together to form a point set, and then constructing a quadtree for the road crossing points, so that each leaf node on the quadtree only stores one corresponding road crossing point. The area of the rectangle corresponding to the leaf node is calculated, so that the point density value is calculated, the rectangle corresponding to the leaf node is classified according to the point density value, the classification can be divided into two types, such as a target (city) and a background (non-city), namely, the points in the point set can be divided into a target type and a background type according to the point density value. And carrying out non-cross-region fusion Dissolve processing on the rectangles in the target class according to the topological relation of photographic adjacency to obtain the city boundary.
The method for quickly clustering road intersections in the whole country and generating the city boundary range can accurately describe whether the urban construction area is disordered and expand or not and coordinate and plan the relationship between urban resident traveling and public facility configuration by establishing the space quadtree, and has important significance for urban sustainable development.
For example, if the user wants to define Shenzhen boundaries, only road network data of the province of the entire Guangdong or the entire Guangdong is needed to obtain the road intersection point set, and then boundaries of all cities of the entire Guangdong or the entire Guangdong (including Shenzhen city) are generated according to the steps. Since city boundaries represent areas of relatively concentrated human activity, to determine the boundaries of a city, a larger area of division must be selected to define it. Otherwise, if only the road intersection in Shenzhen city is used, the result is not Shenzhen city boundaries, but boundaries of regions of Shenzhen.
Various non-limiting embodiments of the present invention are described in detail below with reference to the attached drawing figures.
Exemplary method
Referring to fig. 1, the embodiment provides a city boundary obtaining method, which includes the following steps:
step 100, acquiring a road cross point set according to road network data, constructing a quadtree for road cross points in the road cross point set, and storing one road cross point for each leaf node of the quadtree.
Specifically, the road network data is mainly road network data, or can be combined with railway network data, the road network data can be acquired through big data, and the road network data can be obtained in any mode in the prior art as long as the requirement can be met. And analyzing intersections of the roads from the known road network data, marking each intersection in the road network data as a point, and forming a point set by all the obtained intersection points of the roads. A quadtree is constructed for all points within the point set.
In this embodiment, the road intersection set in the road network is obtained, because the road intersection density reflects the construction condition of the urban infrastructure, and the actual spatial development of the urban land can be effectively determined by clustering the road intersection density.
Referring to fig. 2, in step S100, the quad tree is constructed as follows:
s110, calculating outer envelope rectangles of all the road intersections, and taking the outer envelope rectangles as root nodes;
s120, generating a full quadtree with the depth of N, calculating the relation between each road intersection and each leaf node rectangle, and storing the rectangles with the points less than or equal to 1 in the leaf node rectangles; the N is a natural number greater than 1 and less than 9;
s130, independently generating a quadtree for each rectangle with the number of points greater than 1 until the number of points in all the leaf node rectangles is less than or equal to 1, and storing the rectangles; integrating all rectangles with the number of points less than or equal to 1, and ending.
In particular, geospatial partitioning often uses a full quadtree structure to visualize spatial information of different scales (e.g., grid slices), but this approach has significant drawbacks. Generally, map levels range from global to street scale up to 15-20 layers, with up to 50 billion rectangles, up to several TB bytes, often exceeding the processing power of computer memory, if constructed in a full quadtree fashion, required to reach the sixteenth layer. The invention adopts a four-way tree construction method of firstly breadth and then depth, specifically, firstly generates a full four-way tree of N layers (such as 7-9 layers) by a breadth-first method, stores space entity information in a child node of the last layer, and then recursively carries out a depth-first mode on a region containing more elements in the child node until only one space element exists. In order to avoid unbalance of the quadtree structure and waste of storage space, the geographical entity information is stored in the smallest rectangular node which completely contains the geographical entity information and is not stored in the father node of the geographical entity information, and each geographical entity is stored in the tree only once, so that waste of storage space is avoided.
In this embodiment, the quad tree is constructed as a full quad tree with a depth of 7, wherein the number of nodes of the full quad tree of 7 layers is 16384, and subsequent steps can be smoothly performed on the basis of the full quad tree of 7 layers, for example, if the quad tree is 9 layers with a depth, about 32G exists.
S200, calculating a density value according to the geometric information of the rectangle corresponding to each leaf node, and dividing the rectangle corresponding to each leaf node into a target class rectangle and a background class rectangle according to the density value; the density value is the inverse of the rectangular area.
In step S200, each rectangle is regarded as a pixel, and the density value (1/rectangular area) corresponding to each point is calculated as a pixel value to calculate a threshold value, which is the maximum inter-class variance (common
The result calculated in formula 1). The formula (1) is as follows:
wherein d is the segmentation threshold value of the target and the background, sigma w For the difference between the target and background density values, w 0 For the target point to account for the image proportion, sigma 0 For the variance of the target point number in the proportion of the image, w 1 For background points to account for image proportion, sigma 1 The variance of the background points in the proportion of the image;
when the pixel value is smaller than the pixel threshold calculated by the formula (1), the rectangle is indicated as a target, and when the pixel value is larger than the pixel threshold calculated by the formula (1), the rectangle is indicated as a background, namely all rectangle information in the foreground is extracted by adopting the mode. Because of the two categories, the background rectangle information can be obtained after all rectangle information in the foreground category.
And S300, performing non-cross-region fusion processing on the classified rectangles according to the topological relation of photographic adjacency to obtain city boundaries.
Specifically, non-cross-region fusion Dissolve processing is performed on the classified rectangles according to a topological relation, wherein Dissolve processing represents fusing adjacent (co-edge) target rectangles, and the flow is as follows: in the foreground rectangle, starting from any rectangle, finding the rectangle adjacent to the rectangle, storing the rectangle in a designated set, traversing each adjacent rectangle, and repeating the steps until no new adjacent rectangle exists. And eliminating the common edges of all rectangles in the set to form a new polygon, wherein the obtained new polygon is the city boundary.
Based on the above embodiment, the present invention further provides an intelligent terminal, and a functional block diagram thereof may be shown in fig. 3. The intelligent terminal comprises a processor, a memory, a network interface, a display screen and a temperature sensor which are connected through a system bus. The processor of the intelligent terminal is used for providing computing and control capabilities. The memory of the intelligent terminal comprises a nonvolatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The network interface of the intelligent terminal is used for communicating with an external terminal through network connection. The computer program is executed by a processor to implement a city boundary acquisition method. The display screen of the intelligent terminal can be a liquid crystal display screen or an electronic ink display screen, and a temperature sensor of the intelligent terminal is arranged in the intelligent terminal in advance and used for detecting the current running temperature of internal equipment.
It will be appreciated by those skilled in the art that the schematic block diagram shown in fig. 3 is merely a block diagram of a portion of the structure associated with the present inventive arrangements and is not limiting of the smart terminal to which the present inventive arrangements are applied, and that a particular smart terminal may include more or fewer components than shown, or may combine some of the components, or have a different arrangement of components.
In one embodiment, a smart terminal is provided that includes a memory, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by one or more processors, the one or more programs comprising instructions for:
acquiring a road cross point set according to road network data, constructing a quadtree for road cross points in the road cross point set, and storing one road cross point for each leaf node of the quadtree;
calculating a density value according to the geometric information of the rectangle corresponding to each leaf node, and classifying the rectangle corresponding to the leaf node according to the density value; the density value is the reciprocal of the rectangular area;
and carrying out non-cross-region fusion processing on the classified rectangles according to the topological relation of the photographic adjacency to obtain city boundaries.
Those skilled in the art will appreciate that implementing all or part of the above described methods may be accomplished by way of a computer program stored on a non-transitory computer readable storage medium, which when executed, may comprise the steps of the embodiments of the methods described above. Any reference to memory, storage, database, or other medium used in embodiments provided herein may include non-volatile and/or volatile memory. The nonvolatile memory can include Read Only Memory (ROM), programmable ROM (PROM), electrically Programmable ROM (EPROM), electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms such as Static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double Data Rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous Link DRAM (SLDRAM), memory bus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), among others.
In summary, the invention discloses a city boundary acquisition method, a city boundary acquisition device, an intelligent terminal and a storage medium. The method comprises the following steps: acquiring a road cross point set according to road network data, constructing a quadtree for road cross points in the point set, and storing one road cross point for each leaf node of the quadtree; calculating a density value according to the geometric information of the rectangle corresponding to the leaf node, and classifying the rectangle corresponding to the leaf node according to the density value; the density value is the reciprocal of the rectangular area; and carrying out non-transregional fusion Dissolve processing on the classified rectangles according to the topological relation of photographic adjacency to obtain city boundaries. The invention improves the spatial quadtree index processing method for spatial information of urban big data and clusters according to the nonlinear density characteristics of the type of data. Compared with the traditional urban data clustering method, the method does not need to set parameters in advance, has high speed and high expandability, and can effectively help to mine the non-linear space rule of urban or human urban activities.
It is to be understood that the invention is not limited in its application to the examples described above, but is capable of modification and variation in light of the above teachings by those skilled in the art, and that all such modifications and variations are intended to be included within the scope of the appended claims.
Claims (7)
1. A city boundary acquisition method, the method comprising the steps of:
acquiring a road cross point set according to road network data, constructing a quadtree for road cross points in the road cross point set, and storing one road cross point for each leaf node of the quadtree;
calculating a density value according to the geometric information of the rectangle corresponding to each leaf node, and dividing the rectangle corresponding to each leaf node into a target class rectangle and a background class rectangle according to the density value; the density value is the inverse of the rectangular area of the moment;
carrying out non-cross-region fusion processing on the classified target rectangles according to the topological relation of photographic adjacency to obtain city boundaries;
the step of calculating a density value according to the geometric information of the rectangle corresponding to the leaf node, and dividing the rectangle corresponding to the leaf node into a target class rectangle and a background class rectangle according to the density value, wherein the method specifically comprises the following steps:
dividing all rectangles into two parts of a target class rectangle and a background class rectangle by using a formula (1)
Wherein d is the segmentation threshold value of the target and the background, sigma w For the difference between the target and background density values, w 0 For the target point to account for the image proportion, sigma 0 For the variance of the target point number in the proportion of the image, w 1 For background points to account for image proportion, sigma 1 The variance of the background points in the proportion of the image; extracting all rectangle information in the target class;
the step of carrying out non-cross-region fusion processing on the classified target rectangles according to the topological relation of photographic adjacency to obtain city boundaries, wherein the non-cross-region fusion processing comprises the following steps:
screening adjacent rectangles with the rectangle from any rectangle in the classified target class rectangles, and storing the adjacent rectangles into a preset set;
traversing each adjacent rectangle, repeating the steps to screen adjacent rectangles corresponding to the rectangle, and storing the adjacent rectangles into a preset set until no new adjacent rectangle exists;
and eliminating the common edges of all rectangles in the set, generating a city boundary, and ending.
2. The city boundary obtaining method of claim 1, wherein the building the quadtree comprises the specific building steps of:
calculating outer envelope rectangles of all the road intersections, and taking the outer envelope rectangles as root nodes;
generating a full-N quadtree with depth, calculating the relation between each road intersection and each leaf node rectangle, and storing the rectangles with the points less than or equal to 1 in the leaf node rectangles; the N is a natural number greater than 1 and less than 9;
generating a quadtree independently for each rectangle with the number of points greater than 1 until the number of points in all the rectangle with leaf nodes is less than or equal to 1, and storing the rectangles; integrating all rectangles with the number of points less than or equal to 1, and ending.
3. The city boundary acquisition method of claim 2, wherein N is 7.
4. An urban boundary acquisition device, the device comprising:
the four-way tree construction unit is used for acquiring a road cross point set according to road network data, constructing a four-way tree for road cross points in the road cross point set, and storing one road cross point for each leaf node of the four-way tree;
the rectangle classifying unit is used for calculating a density value according to the geometric information of the rectangle corresponding to each leaf node and classifying the rectangle corresponding to the leaf node into a target class rectangle and a background class rectangle according to the density value; the density value is the inverse of the rectangular area of the moment;
the processing unit is used for carrying out non-cross-region fusion processing on the classified target rectangles according to the topological relation of photographic adjacency to obtain city boundaries;
the rectangular classification unit includes: dividing all rectangles into two parts of a target class rectangle and a background class rectangle by using a formula (1)
Wherein d is the segmentation threshold value of the target and the background, sigma w For the difference between the target and background density values, w 0 For the target point to account for the image proportion, sigma 0 For the variance of the target point number in the proportion of the image, w 1 For background points to account for image proportion, sigma 1 The variance of the background points in the proportion of the image; extracting all rectangle information in the target class;
the processing unit includes:
the screening unit is used for screening adjacent rectangles with any rectangle in the classified target class rectangles, and storing the adjacent rectangles into a preset set;
traversing subunit, configured to traverse each adjacent rectangle, repeat the steps to screen the adjacent rectangle with the rectangle, and store the adjacent rectangle into a preset set until no new adjacent rectangle exists;
and the elimination subunit is used for eliminating the common edges of all rectangles in the set, generating city boundaries and ending.
5. The apparatus of claim 4, wherein the quadtree construction unit comprises:
the calculating subunit is used for calculating the outer envelope rectangles of all the road intersections and taking the outer envelope rectangles as root nodes;
generating a subunit, namely generating a full quadtree with the depth N, calculating the relation between each road intersection and each leaf node rectangle, and storing the rectangles with the points less than or equal to 1 in the leaf node rectangles; the N is a natural number greater than 1 and less than 9;
a storage subunit, configured to independently generate a quadtree for each rectangle with a point number greater than 1, until the point number in all the leaf node rectangles is less than or equal to 1, and store the rectangles; integrating all rectangles with the number of points less than or equal to 1, and ending.
6. An intelligent terminal comprising a memory, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by one or more processors, the one or more programs comprising instructions for performing the method of any of claims 1-3.
7. A storage medium, characterized in that instructions in the storage medium, when executed by a processor of an electronic device, enable the electronic device to perform the method of any one of claims 1-3.
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