Urban and rural planning data efficient storage method and system

Abstract

The invention relates to the technical field of data processing, in particular to an urban and rural planning data efficient storage method and system, and the method comprises the steps: obtaining all index data of an urban and rural region; performing hierarchical division according to the attribute of each index data, and dividing the urban and rural region based on each index data after hierarchical division to obtain a plurality of first regions; calculating the stability condition of the data in the first region along with the time change, and dividing the index data in the first region of which the stability condition is greater than a first threshold value into a plurality of second regions through a quadtree; dividing the second areas according to the stability conditions of the second areas to obtain a plurality of third areas; determining the condition that iterative segmentation needs to be carried out in the third region; and constructing a tree structure based on the urban and rural region division result and carrying out data storage. In this way, the reliability and efficiency of the index data during storage are improved, and the efficiency of data retrieval is further improved.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and particularly to an efficient storage method and system for urban and rural planning data. Background Art

[0002] Urban and rural planning is to achieve the effective utilization of resources, environmental protection, and the sustainable development of social economy through the rational layout and development planning of urban and rural areas. It mainly focuses on the physical space structure design of urban and rural areas, including land use zoning, traffic network construction, public service facility allocation, etc. To support urban planning, a large amount of urban and rural planning data needs to be collected for urban and rural planning.

[0003] In some scenarios, it is necessary to store the above-obtained urban and rural planning data. In the data storage link, the traditional method of simply storing the obtained urban and rural planning data in a database in a list form has obvious drawbacks. Since urban and rural planning data shows significant temporal and spatial distribution characteristics when displayed and queried, such as the historical development dynamic changes of a region, the list storage method will seriously ignore the internal relationship of data in space, resulting in low efficiency in analyzing and using data and being difficult to meet the requirements of urban and rural planning for rapid data retrieval and in-depth analysis. To solve the above problems, constructing a tree structure to store data becomes a feasible idea. By storing urban and rural planning data in different leaf nodes, the spatial correlation between data can be effectively reflected, and rapid retrieval can be achieved at the same time. In the tree structure construction algorithm, the quadtree algorithm is widely used. However, the quadtree algorithm also has certain limitations. During the construction process, it only directly divides the obtained urban and rural planning data into four regions, often ignoring the overall change situation of data in a region and being unable to fully reflect the characteristics of urban and rural planning data. In addition, urban and rural planning data has different change characteristics at different scales. The quadtree algorithm fails to fully consider this characteristic and directly divides it into four regions, resulting in low reliability and efficiency of data storage, and further leading to low efficiency of data retrieval. Summary of the Invention

[0004] In order to solve the technical problems of low reliability and efficiency of data storage, which further leads to low efficiency of data retrieval, the purpose of the present invention is to provide an efficient storage method and system for urban and rural planning data. The specific technical solutions adopted are as follows: In a first aspect, an embodiment of the present invention provides an efficient storage method for urban and rural planning data, including: obtaining various index data of urban and rural areas; performing hierarchical division according to the attributes of each index data, and dividing the urban and rural areas based on the index data after hierarchical division to obtain multiple first regions; calculating the stability of the data in the first region over time according to the index data of each index collected each time in the first region, and dividing the index data in the first region with a stability greater than a first threshold into multiple second regions through a quadtree; dividing the second regions according to the stability of each second region to obtain multiple third regions; determining the situation that needs to be iteratively divided within the third region according to the first quantity of the basic regions within the third region, the overlapping times of the minimum bounding rectangles constructed in the R-tree within the third region, and the variance of each index data within the third region; when the situation that needs to be iteratively divided within the third region is less than a second threshold, stop dividing the third region, and construct a tree structure based on the division result of the urban and rural areas and perform data storage.

[0005] Optionally, calculating the stability of the data in the first region over time according to the index data of each index collected each time in the first region includes: calculating the absolute value of the first difference between adjacent index data of each index, and the first sum value between adjacent index data of each index; superimposing the first ratio of the absolute value of each first difference to the first sum value to obtain the stability of the data in the first region over time.

[0006] Optionally, dividing the second regions according to the stability of each second region to obtain multiple third regions includes: classifying each index data in the second region according to the stability of each second region to obtain a first cluster class and a second cluster class, and the data fluctuation situation of the first cluster class is greater than that of the second cluster class; determining the preferred situation when dividing at the current position in the second region according to the shortest distance from the current position in the second region to the boundary of the first cluster class, the second quantity of the index data in the first cluster class, and the third quantity of the index data in the second cluster class; dividing the second region through the data point corresponding to the position with the largest preferred situation to obtain multiple third regions.

[0007] Optionally, determining the preferred situation when dividing at the current position in the second region according to the shortest distance from the current position in the second region to the boundary of the first cluster class, the second quantity of the index data in the first cluster class, and the third quantity of the index data in the second cluster class includes: calculating the absolute value of the second difference between the second quantity and the third quantity, and normalizing the absolute value of the second difference to obtain a normalized value; superimposing each normalized value to obtain a first superimposed value; determining the first product between the shortest distance and the first superimposed value as the preferred situation when dividing at the current position.

[0008] Optionally, determining the cases that need iterative segmentation within the third region based on the first quantity of the basic regions within the third region, the overlapping times of the minimum bounding rectangles constructed in the R-tree within the third region, and the variances of the respective index data within the third region includes: calculating a second superposition value of the variances of the respective index data within the third region, and a second product among the first quantity, the overlapping times, and the second superposition value; performing inverse proportional normalization on the second product to obtain the cases that need iterative segmentation within the third region.

[0009] Optionally, constructing a tree structure and storing data based on the division result of the urban-rural regions includes: using the urban-rural region and the index data of all its corresponding indicators as the root node, and using the first region and its corresponding index data, the second region and its corresponding index data, and the third region and its corresponding index data as the child nodes of the tree in sequence; storing data using a grid structure at the root node and the child nodes.

[0010] In a second aspect, an embodiment of the present invention provides an efficient storage system for urban-rural planning data, including: an acquisition module for acquiring the respective index data of the urban-rural regions; a division module for performing hierarchical division according to the attributes of the respective index data, and dividing the urban-rural regions based on the respective index data after hierarchical division to obtain multiple first regions; the division module is further configured to calculate the stability of the data in the first region changing with time according to the index data of each index collected each time in the first region, and divide the index data in the first region with a stability greater than a first threshold into multiple second regions through a quadtree; the division module is further configured to divide the second regions according to the stability of each second region to obtain multiple third regions; a determination module for determining the cases that need iterative segmentation within the third region based on the first quantity of the basic regions within the third region, the overlapping times of the minimum bounding rectangles constructed in the R-tree within the third region, and the variances of the respective index data within the third region; a storage module for stopping dividing the third region when the cases that need iterative segmentation within the third region are less than a second threshold, and constructing a tree structure and storing data based on the division result of the urban-rural regions.

[0011] Optionally, the division module is further configured to calculate the absolute value of the first difference between adjacent index data of each index, and the first sum value between adjacent index data of each index; superimpose the first ratio of the absolute value of each first difference to the first sum value to obtain the stability of the data in the first region changing with time.

[0012] Optionally, the partitioning module is further configured to classify the index data in each second region according to the stability of each second region, to obtain a first cluster and a second cluster, where the data fluctuation of the first cluster is greater than that of the second cluster; according to the shortest distance from the current position in the second region to the boundary of the first cluster, the second quantity of the index data in the first cluster, and the third quantity of the index data in the second cluster, to determine the preferred situation when the current position in the second region is partitioned; and partition the second region through the data point corresponding to the position with the largest preferred situation, to obtain a plurality of third regions.

[0013] In a third aspect, an embodiment of the present invention provides an efficient storage system for urban and rural planning data, including: a processor and a memory; wherein, the memory is used to store a computer program that can run on the processor; the processor is configured to execute the program stored in the memory to implement the steps of the efficient storage method for urban and rural planning data as mentioned in the first aspect.

[0014] The present invention has the following beneficial effects: First, obtain various index data of urban and rural areas; perform hierarchical partitioning according to the attributes of each index data, and partition the urban and rural areas based on the index data after hierarchical partitioning to obtain a plurality of first regions; calculate the stability of the data in the first region changing with time according to the index data of each index collected each time in the first region, and divide the index data in the first region with a stability greater than a first threshold into a plurality of second regions through a quadtree; partition the second regions according to the stability of each second region to obtain a plurality of third regions; determine the situation that needs to be iteratively partitioned in the third region according to the first quantity of the basic regions in the third region, the overlapping times of the minimum bounding rectangles constructed in the R-tree in the third region, and the variance of each index data in the third region; when the situation that needs to be iteratively partitioned in the third region is less than a second threshold, stop partitioning the third region, and construct a tree structure based on the partitioning result of the urban and rural areas and store the data.

[0015] In this way, the embodiment of the present invention can construct data of different scales by different depths of the tree. The greater the depth of the tree, the larger the scale it constructs and the more detailed the information. When constructing the tree, the index data at different scales can be partitioned according to the change situation of the attributes of the dimensions where the obtained data are located, and the urban and rural areas are sequentially partitioned into first regions, second regions, and third regions, so as to construct a tree structure based on the first regions, second regions, and third regions, ensuring that data with the same attributes are in the same region, improving the reliability and efficiency of the index data during storage, and further improving the efficiency of data retrieval. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] To more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the following will briefly introduce the accompanying drawings required for the description of the embodiments or the prior art. Obviously, the accompanying drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0017] Figure 1 Flowchart of an efficient storage method for urban and rural planning data provided by an embodiment of the present invention; Figure 2 Structure diagram of an efficient storage system for urban and rural planning data provided by an embodiment of the present invention; Figure 3 Structure diagram of an efficient storage system for urban and rural planning data provided by another embodiment of the present invention. Detailed implementation manners

[0018] To further elaborate on the technical means and effects adopted by the present invention to achieve the intended invention purpose, the following, in conjunction with the accompanying drawings and preferred embodiments, details the specific implementation manners, structures, features, and effects of an efficient storage method for urban and rural planning data proposed according to the present invention. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. In addition, the specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0019] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those of ordinary skill in the technical field to which the present invention belongs.

[0020] The following specifically describes the specific solution of an efficient storage method for urban and rural planning data provided by the present invention in conjunction with the accompanying drawings.

[0021] Embodiment 1: Please refer to Figure 1 , which shows the flowchart of an efficient storage method for urban and rural planning data provided by an embodiment of the present invention, including: S101, Obtain various index data of the urban and rural areas.

[0022] Specifically, in the embodiment of the present invention, the map information of the urban and rural areas can be obtained through the Geographic Information System (GIS). The obtained map information includes, but is not limited to, road planning, community distribution, green vegetation, and public facilities, etc.

[0023] Furthermore, the embodiments of the present invention collect data by performing statistics according to small areas. The data of an area larger than a small area can be obtained by accumulating the data of the small area. These small areas can be used as basic areas, and the basic area is the smallest unit of each area in the following embodiments.

[0024] Furthermore, the various indicator data of urban and rural areas can be obtained through data published on the Internet, such as population data, economic data, and data on policies and regulations. The various indicator data can also be obtained through each grid unit of the urban grid, and each grid unit contains the indicator data associated with it. For example, grid unit 1 may contain information such as population density and land use type in urban and rural areas. Grid unit 2 may contain information such as road information and green vegetation.

[0025] S102, hierarchical division is performed according to the attributes of each indicator data, and urban and rural areas are divided based on each indicator data after hierarchical division to obtain a plurality of first areas.

[0026] Specifically, when storing the acquired planning data of urban and rural areas, since the acquired data structure has strong spatial distribution characteristics when displayed, it is necessary to construct a tree and construct strong spatial connection data through the relationship between tree nodes when storing data. When constructing data for a region, there are changes in scale. When constructing a tree, data of different scales can be constructed according to the different depths of the tree. The greater the depth of the tree, the larger the scale of the construction and the more detailed the information. When constructing a tree, the data at different scales can be divided according to the attribute changes of the dimension in which the acquired data is located. For example, the changes in the attributes of a region in the time series are relatively stable and the different locations in the region are also relatively similar. The scale of data division for this region can be smaller, thereby improving the efficiency of data storage.

[0027] Furthermore, in an embodiment of the present invention, when constructing a quadtree, one region is divided into four, and each of the four divided regions is further divided into four. The greater the number of divisions, the greater the depth of the tree of this region. The region can be divided into multiple regions by constructing an R-tree, and data with the same attributes in each region can be kept in the same region as much as possible. At this time, the efficiency of data retrieval can be improved.

[0028] Further, the data in the same area has various attributes. When performing data hierarchical division, for example, an area has information such as agricultural land, residential land, and commercial land. The indicator data can be stratified according to the types of the above-obtained indicator data. Through stratification, the obtained indicator data can be classified into the same attribute dimension. According to the change in the hierarchical structure of the data after stratification, the change stability of the indicator data at the current position can be determined. At the same time, after stratification, regional division can ensure the consistency of the indicator data in each obtained area. For example, the embodiments of the present invention have different stratification results when stratifying the urban-rural planning data of an obtained urban-rural area. For example, through the inclusion relationship of regions, it can be divided into scales such as cities, counties, and districts, and at the same time, it can also be divided into a parallel relationship according to different attributes in the same dimension.

[0029] Further, according to the analysis, when storing data, in order to ensure the relevance of the obtained data in terms of spatial distribution, indicator data at different scales need to be stored. First, the collected indicator data is divided according to the changes between scales. The entire urban-rural area is preliminarily divided according to the announced location and scale relationship to obtain the inclusion relationship between regions, that is, the inclusion relationship between each first region. Then, directly constructing the nodes of the tree through the obtained division levels above, using other data as the attributes of each node is not conducive to querying between data. Therefore, each dimension of the obtained data needs to be stratified once. At this time, the dimension data is not time-series data, but its attribute data. Among them, the attribute data of each node includes but is not limited to transportation, housing, greening, etc. Further, the obtained attribute division levels are used to divide the entire urban-rural area into small areas, and the small areas are the first regions. For example, an urban-rural area has attributes such as agricultural land, residential land, and commercial land. These attributes are stratified and divided into three small areas: agricultural land, residential land, and commercial land.

[0030] S103, calculate the stability of the data in the first region over time according to the indicator data of each indicator collected in the first region, and divide the indicator data in the first region with a stability greater than the first threshold into multiple second regions through a quadtree.

[0031] Specifically, the above embodiments of the present invention stratify the obtained index data, and divide the data of the entire urban-rural area obtained through the stratified results into multiple first regions with the same attributes. The above-constructed first regions serve as the main regions when constructing the tree. Subsequently, each main region needs to be further divided into units of different sizes when constructing the tree. When dividing the data, the update situation of the obtained index data needs to be considered. Due to the correlation between layers during the data update of the tree structure, the index data at a smaller scale is statistically obtained through the index data at a larger scale within the current region. When the index data at a larger scale changes, all the index data related to the current changed data will also change. Rapid updates are not conducive to the storage of index data. Therefore, for rapidly changing regions, they can be independent of the previous level of association, reducing the complexity during updates. The regions divided according to the above embodiments of the present invention are relatively large regions, and the currently divided regions can form the main part of the tree. The embodiments of the present invention obtain the minimum bounding rectangle of the obtained first region and construct an R-tree. Among them, the number of the second regions can be determined according to the actual situation, and the value is taken as 4 in the embodiments of the present invention.

[0032] Further, after constructing the branches of the tree through the R-tree, the quadtree division method is used to construct the leaves on the branches of the constructed tree. However, during the process of constructing the R-tree, there will be an overlapping phenomenon between the minimum bounding rectangles. The overlapping data interferes with the subsequent quadtree division on the plane. The overlapping regions in the R-tree may cause some quadrants of the quadtree to contain more data, thus affecting the balance and query efficiency of the quadtree. When performing subsequent calculations on the current region, more refined division operations can be performed on the index data in the current region, and at the same time, the obtained index data is divided into different branches.

[0033] Further, as an optional embodiment of the present invention, calculating the stability of the data in the first region over time according to the index data of each index collected each time in the first region includes: calculating the absolute value of the first difference between adjacent index data of each index, and the first sum value between adjacent index data of each index; superimposing the first ratios of the absolute values of the first differences to the first sum values to obtain the stability of the data in the first region over time.

[0034] Specifically, the embodiments of the present invention analyze the change situation of the index data collected in each first region over the time series for the above-obtained first regions, and judge the stability situation of the entire current first region. Among them, the stability of the data in the first region over time can be calculated using the following formula: In the above formula, represents the obtained The stability of the data in the first region over time. Indicates the number of dimensions of the acquired indicator data. Indicates the number of acquisitions of the acquired indicator data, that is, the number of acquisitions of the indicator data at the current position. Indicates the th indicator data acquired at the Indicates the th indicator data acquired at the

[0035] Further, in the embodiments of the present invention, the first threshold can be determined according to the actual situation, and the value in the embodiments of the present invention is 0.6. When the stability of the data in the acquired first region over time is not greater than 0.6, the indicator data can be directly divided by a quadtree. However, when the stability of the data in the acquired first region over time is greater than 0.6, simply dividing by a quadtree will result in too deep a splitting depth of the subsequent tree, leading to poor retrieval efficiency. Therefore, in the embodiments of the present invention, the following embodiments are used to divide each second region according to the stability of each second region.

[0036] S104, divide each second region according to the stability of each second region to obtain a plurality of third regions.

[0037] Specifically, it can be determined whether each second region is in a stable situation through the acquired data. When dividing the second region, the unstable part in the second region can be divided into an interval, which is an uneven division when performing a quadtree division. In the embodiments of the present invention, the indicator data in the acquired second region is scaled to determine the change situation of the small regions under each second region, and the fluctuation change of the second region may be caused by the change of only a small part of the data in the second region.

[0038] Further, as an optional embodiment of the present invention, dividing each second region according to the stability of each second region to obtain a plurality of third regions includes: classifying each indicator data in the second region according to the stability of each second region to obtain a first cluster and a second cluster, and the data fluctuation situation of the first cluster is greater than that of the second cluster; determining the preferred situation when dividing the current position in the second region according to the shortest distance from the current position in the second region to the boundary of the first cluster, the second quantity of the indicator data in the first cluster, and the third quantity of the indicator data in the second cluster; dividing the second region through the data point corresponding to the position with the largest preferred situation to obtain a plurality of third regions.

[0039] Specifically, the embodiment of the present invention analyzes the obtained basic region to determine the influence range of the basic region, obtains the stability of each second region, and performs clustering analysis through Density-Based Spatial Clustering of Applications with Noise (DBSCAN) to obtain two clusters. Among them, the data fluctuation of the first cluster is greater than that of the second cluster. And the second regions are divided into larger connected domains composed of basic regions by clustering. That is, the stability of each second region is classified into two clusters, and each cluster includes second regions with similar stability and the index data in the second regions. The data fluctuation refers to the fluctuation degree of the index data in the cluster. Among them, the fluctuation degree of the data can be measured by variance and standard deviation. The calculation methods of variance and standard deviation can refer to the prior art, and the embodiments of the present invention will not elaborate here.

[0040] Further, as an optional embodiment of the present invention, according to the shortest distance from the current position in the second region to the boundary of the first cluster, the second quantity of the index data in the first cluster, and the third quantity of the index data in the second cluster, the preferred situation when dividing the current position in the second region is determined as follows: calculate the absolute value of the second difference between the second quantity and the third quantity, and perform normalization processing on the absolute value of the second difference to obtain a normalized value; superimpose the normalized values to obtain a first superimposed value; determine that the first product between the shortest distance and the first superimposed value is the preferred situation when dividing the current position.

[0041] Specifically, the current position in the second region refers to the position where the current index data in the second region is located. The embodiment of the present invention takes the current position in the th second region as an example, and specifically uses the following formula to calculate the preferred situation when dividing the current position: In the above formula, represents the preferred situation when dividing the th position in the th second region obtained. represents the shortest distance from the th position to the boundary of the cluster with larger fluctuations in the th second region obtained, that is, the shortest distance from the current position in the second region to the boundary of the first cluster; when the th position belongs to the first cluster with larger fluctuations, the shortest distance takes the value of 0, represents the The clarity of the two indicator data in the second region after segmentation. Indicates the number of segmentations. Among them, S can be predefined according to the actual situation, and its value is set to 4 in this embodiment of the present invention, which is not limited herein. Indicates the second quantity of the indicator data in the first cluster obtained. Indicates the third quantity of the indicator data in the second cluster obtained. Indicates a normalization function, which is used to Perform normalization processing.

[0042] S105. Determine the situation that needs to be iteratively segmented within the third region according to the first quantity of the basic region within the third region, the overlapping times of the minimum bounding rectangles constructed in the R-tree within the third region, and the variance of each indicator data within the third region.

[0043] Specifically, in this embodiment of the present invention, by analyzing the division of all the indicator data in the second region, and selecting the optimal situation at the maximum current position segmentation as the data segmentation situation in the current stage. The second region obtained is divided into four third regions by using the data points at this position. At this time, the third region is the updated small region. Analyze the data distribution situation within the third region after segmentation, and judge whether the current third region stops dividing according to the obtained distribution situation.

[0044] Further, as an optional embodiment of the present invention, determining the situation that needs to be iteratively segmented within the third region according to the first quantity of the basic region within the third region, the overlapping times of the minimum bounding rectangles constructed in the R-tree within the third region, and the variance of each indicator data within the third region includes: calculating the second superposition value of the variances of each indicator data within the third region, and the second product between the first quantity, the overlapping times, and the second superposition value; performing inverse proportional normalization processing on the second product to obtain the situation that needs to be iteratively segmented within the third region.

[0045] Specifically, this embodiment of the present invention specifically uses the following formula to calculate the situation that needs to be iteratively segmented within the third region: In the above formula, Indicates the obtained Situation that needs to be iteratively segmented within the z-th third region. Indicates the obtained current First quantity of the basic region within the z-th third region. Indicates the obtained current Overlapping times in the minimum bounding rectangle constructed in the R-tree within the z-th third region, and the minimum value is 1. Indicates the variance composed of the indicator data of the z-th dimension of the obtained third region. Indicates the number of dimensions of the index data. Indicates the inverse proportional normalization function, which is based on the exponential function with the natural constant as the base to Perform inverse proportional normalization processing.

[0046] S106. When the number of cases that need iterative segmentation in the third region is less than the second threshold, stop dividing the third region, construct a tree structure based on the division result of the urban-rural region, and store the data.

[0047] Specifically, in the embodiment of the present invention, the second threshold can take a value of 0.75. When the number of cases that need iterative segmentation in the third region is not less than 0.75, the above iterative operation needs to be performed until the number of cases that need iterative segmentation in the obtained third region is less than 0.75, and then stop dividing the third region. In this way, by performing similar operations on the index data of each third region, the tree structure for storing the obtained index data can be constructed.

[0048] Further, as an optional embodiment of the present invention, constructing a tree structure based on the division result of the urban-rural region and storing the data includes: taking the urban-rural region and the index data of all its corresponding indicators as the root node, and taking the first region and its corresponding index data, the second region and its corresponding index data, and the third region and its corresponding index data as the child nodes of the tree in sequence; using a grid structure to store data at the root node and the child nodes.

[0049] Specifically, according to the above embodiment, the present invention obtains the constructed tree structure. When storing data, through the combination of a grid and a tree structure, the root node represents the entire data set or the largest geographical region of the urban-rural region, and the child nodes represent smaller regions or more specific data. Such as the first region, the second region, and the third region divided in sequence. In the embodiment of the present invention, by recursively delving into different levels of the tree, data at different scales can be obtained. Each node uses a grid structure to store the index data at this current scale. When performing data query, the depth of the tree retrieved is different according to the different query scales, meeting faster and more convenient queries.

[0050] Embodiment 2: Corresponding to the efficient storage method of urban-rural planning data provided in the above embodiment, based on the same technical concept, the embodiment of the present invention also provides an efficient storage system for urban-rural planning data. This efficient storage system for urban-rural planning data is used to execute the above efficient storage method of urban-rural planning data. Figure 2 For the structural schematic diagram of an efficient storage system for urban-rural planning data provided by an embodiment of the present invention, as Figure 2 shown, the efficient storage system 200 for urban-rural planning data includes: An acquisition module 201 for acquiring various index data of urban and rural areas; a division module 202 for performing hierarchical division according to the attributes of each index data, and dividing the urban and rural areas based on the index data after hierarchical division to obtain a plurality of first regions; the division module 202 is further configured to calculate the stability of the data in the first region changing with time according to the index data of each index collected each time in the first region, and divide the index data in the first region with a stability greater than a first threshold into a plurality of second regions by using a quadtree; the division module 202 is further configured to divide the second regions according to the stability of each second region to obtain a plurality of third regions; a determination module 203 for determining the situation that needs to be iteratively divided in the third region according to the first quantity of the basic regions in the third region, the overlapping times of the minimum circumscribed rectangles constructed in the R-tree in the third region, and the variance of each index data in the third region; a storage module 204 for stopping dividing the third region when the situation that needs to be iteratively divided in the third region is less than a second threshold, constructing a tree structure based on the division result of the urban and rural areas, and storing data.

[0051] In the embodiment of the present invention, different-scale data can be constructed according to the different depths of the tree. The greater the depth of the tree, the larger the scale and the more detailed the information constructed. When constructing the tree, the index data at different scales can be divided according to the change situation of the attributes of the dimensions where the acquired data is located. The urban and rural areas are sequentially divided into first regions, second regions, and third regions, so as to construct a tree structure based on the first regions, second regions, and third regions, ensuring that data with the same attributes are in the same region, improving the reliability and efficiency of index data storage, and further improving the efficiency of data retrieval.

[0052] Optionally, the division module 202 is further configured to calculate the absolute value of the first difference between adjacent index data of each index, and the first sum value between adjacent index data of each index; superimpose the first ratio of the absolute value of each first difference to the first sum value to obtain the stability of the data in the first region changing with time.

[0053] Optionally, the division module 202 is further configured to classify the index data in each second region according to the stability of each second region to obtain a first cluster class and a second cluster class, and the data fluctuation situation of the first cluster class is greater than that of the second cluster class; determine the preferred situation when dividing at the current position in the second region according to the nearest distance from the current position in the second region to the boundary of the first cluster class, the second quantity of the index data in the first cluster class, and the third quantity of the index data in the second cluster class; divide the second region through the data point corresponding to the position with the largest preferred situation to obtain a plurality of third regions.

[0054] Embodiment 3: Corresponding to the efficient urban and rural planning data storage method provided in the above embodiments, based on the same technical concept, the embodiments of the present invention also provide an efficient urban and rural planning data storage system, which is used to execute the above-mentioned efficient urban and rural planning data storage method. Figure 3 FIG. is a schematic structural diagram of an efficient urban and rural planning data storage system provided in another embodiment of the present invention, as Figure 3 shown. The efficient urban and rural planning data storage system may vary greatly due to configuration or performance, and may include one or more processors 301 and a memory 302. The memory 302 is used to store computer programs that can run on the processor 301. The processor 301 is used to execute the programs stored on the memory 302 to implement the above Figure 1 steps in the method embodiments. Among them, the memory 302 can be short-term storage or persistent storage. The application programs stored in the memory 302 may include one or more modules (not shown in the figure), and each module may include a series of computer-executable instructions in the efficient urban and rural planning data storage system.

[0055] Furthermore, the processor 301 can be set to communicate with the memory 302 and execute a series of computer-executable instructions in the memory 302 on the efficient urban and rural planning data storage system. The efficient urban and rural planning data storage system may further include one or more power supplies 303, one or more wired or wireless network interfaces 304, one or more input / output interfaces 305, and one or more keyboards 306.

[0056] Specifically, in this embodiment, the efficient urban and rural planning data storage system includes a processor, a communication interface, a memory, and a communication bus. Among them, the processor, the communication interface, and the memory complete mutual communication through the bus. The memory is used to store computer programs. The processor is used to execute the programs stored on the memory to implement the above Figure 1 steps in the method embodiments, and has the beneficial effects of the above method embodiments. To avoid repetition, the embodiments of the present invention will not be described in detail here.

[0057] It should be noted that the efficient urban and rural planning data storage system provided in the embodiments of the present invention and the efficient urban and rural planning data storage method provided in the embodiments of the present invention are based on the same application concept. Therefore, the specific implementation of this embodiment can refer to the implementation of the above-mentioned efficient urban and rural planning data storage method, and has the same or similar beneficial effects. The repeated parts will not be described again.

[0058] It should be noted that the above order of the embodiments of the present invention is only for description and does not represent the superiority or inferiority of the embodiments. The processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0059] Each embodiment in this specification is described in a progressive manner. For the same or similar parts among the embodiments, reference can be made to each other. The key point of each embodiment is to illustrate the differences from other embodiments.

Claims

1. A method for efficiently storing urban and rural planning data, characterized in that: The method for efficiently storing urban and rural planning data comprises: Obtain data on various indicators in urban and rural areas; Performing hierarchical division according to the attributes of each of the indicator data, and dividing the urban and rural areas based on the hierarchical divided indicator data to obtain a plurality of first areas; Calculate the stability of the data in the first region over time according to the indicator data of each indicator collected in the first region each time, and divide the indicator data in the first region whose stability is greater than a first threshold into a plurality of second regions through a quadtree; Dividing the second region according to the stability of each of the second regions to obtain a plurality of third regions; Determining the need for iterative segmentation within the third area according to the first number of basic areas within the third area, the number of overlaps of minimum circumscribed rectangles constructed in the R-tree within the third area, and the variance of each indicator data within the third area; When the number of cases where iterative segmentation is required in the third area is less than a second threshold, the segmentation of the third area is stopped, and a tree structure is constructed based on the segmentation result of the urban and rural areas and data is stored.

2. The method for efficiently storing urban and rural planning data according to claim 1, characterized in that: The calculating, according to the indicator data of each indicator collected in the first area each time, the stability of the data in the first area over time includes: Calculating the absolute value of the first difference between the adjacent indicator data of each indicator, and the first sum value between the adjacent indicator data of each indicator; The absolute value of each of the first difference values ​​is superimposed with a first ratio of the first sum value to obtain a stability of the data in the first region over time.

3. The efficient storage method for urban and rural planning data according to claim 1 is characterized in that: The second area is divided according to the stability of each of the second areas to obtain a plurality of third areas, including: Classifying each indicator data in the second area according to the stability of each second area to obtain a first cluster and a second cluster, wherein the data fluctuation of the first cluster is greater than the data fluctuation of the second cluster; Determine a preferred situation for segmenting the current position in the second area according to the shortest distance from the current position in the second area to the boundary of the first cluster, the second amount of indicator data in the first cluster, and the third amount of indicator data in the second cluster; The second area is divided according to the data points corresponding to the position where the preferred situation is the largest, to obtain a plurality of third areas.

4. The method for efficiently storing urban and rural planning data according to claim 3 is characterized in that: The determining of the preferred situation when the current position in the second area is segmented according to the shortest distance from the current position in the second area to the boundary of the first cluster, the second amount of indicator data in the first cluster, and the third amount of indicator data in the second cluster includes: Calculating an absolute value of a second difference between the second quantity and the third quantity, and normalizing the absolute value of the second difference to obtain a normalized value; Superimposing the normalized values ​​to obtain a first superimposed value; Determining a first product between the shortest distance and the first superposition value is a preferred case when the current position is segmented.

5. The method for efficiently storing urban and rural planning data according to claim 1, characterized in that: Determining the need for iterative segmentation in the third area according to the first number of basic areas in the third area, the number of overlaps of the minimum circumscribed rectangles constructed in the R-tree in the third area, and the variance of each indicator data in the third area includes: Calculate a second superposition value of the variance of each indicator data in the third area, and a second product of the first number, the number of overlaps, and the second superposition value; The second product is subjected to inverse normalization processing to obtain a situation where iterative segmentation is required in the third area.

6. The method for efficiently storing urban and rural planning data according to claim 1, characterized in that: The step of constructing a tree structure based on the division result of the urban and rural areas and storing the data comprises: The indicator data of the urban and rural areas and all the corresponding indicators are used as the root node, and the first area and the corresponding indicator data, the second area and the corresponding indicator data, and the third area and the corresponding indicator data are used as child nodes of the tree in sequence; A grid structure is used to store data in the root node and the child nodes.

7. An efficient storage system for urban and rural planning data, characterized in that: include: The acquisition module is used to obtain various indicator data of urban and rural areas; A division module, used for performing hierarchical division according to the attributes of each of the indicator data, and dividing the urban and rural areas based on the hierarchical divided indicator data to obtain a plurality of first areas; The division module is further used to calculate the stability of the data in the first region over time according to the indicator data of each indicator collected in the first region each time, and divide the indicator data in the first region whose stability is greater than the first threshold into multiple second regions through a quadtree; The division module is further used to divide the second area according to the stability of each of the second areas to obtain a plurality of third areas; A determination module, configured to determine whether iterative segmentation is required in the third area according to a first number of basic areas in the third area, a number of overlaps of minimum circumscribed rectangles constructed in the R-tree in the third area, and a variance of each indicator data in the third area; The storage module is used to stop dividing the third area when the number of iterative segmentations required within the third area is less than a second threshold, and to construct a tree structure based on the division results of the urban and rural areas and store data.

8. The efficient storage system for urban and rural planning data according to claim 7 is characterized in that: The division module is further used to calculate the absolute value of the first difference between the adjacent indicator data of each indicator, and the first sum value between the adjacent indicator data of each indicator; The absolute value of each of the first difference values ​​is superimposed with a first ratio of the first sum value to obtain a stability of the data in the first region over time.

9. The efficient storage system for urban and rural planning data according to claim 7, characterized in that: The classification module is further used to classify each indicator data in the second area according to the stability of each second area to obtain a first cluster and a second cluster, and the data fluctuation of the first cluster is greater than the data fluctuation of the second cluster; Determine a preferred situation for segmenting the current position in the second area according to the shortest distance from the current position in the second area to the boundary of the first cluster, the second amount of indicator data in the first cluster, and the third amount of indicator data in the second cluster; The second area is divided according to the data points corresponding to the position where the preferred situation is the largest, to obtain a plurality of third areas.

10. An efficient storage system for urban and rural planning data, characterized in that: include: A processor and a memory; wherein the memory is used to store a computer program that can be run on the processor; A processor is used to execute the program stored in the memory to implement the steps of the method for efficient storage of urban and rural planning data as described in any one of claims 1 to 6.

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