CN117437254B - Grid division method, device, equipment and medium based on environment space-time data - Google Patents
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Abstract
The embodiment of the invention provides a grid dividing method, device, equipment and medium based on environment space-time data, relating to the technical field of data processing, wherein the method comprises the following steps: acquiring ground environment data and POI data of a target area; determining a ground environment sensitivity index in the target area according to the ground environment data; dividing a map of the target area into a plurality of sub-area images according to the type of the ground environment sensitivity index; in each sub-area image, calculating the environmental sensitivity value distributed in each sub-area image according to the density distribution of the POI points of different types in the POI data; determining initial grid cells with preset sizes on a map of a target area, and calculating the environmental sensitivity value of each initial grid cell according to the environmental sensitivity values distributed in each sub-area image; and adjusting the size of the initial grid unit according to the size relation between the environment sensitivity value of the initial grid unit and the threshold value. The scheme improves the fineness of grid division.
Description
Technical Field
The present invention relates to the field of data processing technologies, and in particular, to a method, an apparatus, a device, and a medium for meshing based on environmental spatiotemporal data.
Background
With the rapid development of sensor technology, various sensors can acquire environmental information such as the earth surface, the atmosphere, the ocean and the like in real time and at high frequency. This includes meteorological sensors, remote sensing satellites, environmental monitoring equipment, etc., providing a continuous stream of data for spatio-temporal data. With the increasingly prominent problems of climate change, natural disasters, environmental pollution and the like, the demands of society on environmental data are dramatically increased. Governments, enterprises and institutions, research institutions, etc. need more environmental data to support decisions and address challenges.
Environmental spatiotemporal data typically involves extensive information, including weather, geographic, and ecological data, and how to make the data easier to process, analyze, visualize, while maintaining consistency and continuity of the data is a significant challenge.
Currently, the space-time data of the environment is divided mainly by using a grid method, and the geographic space is divided into regular or irregular grid cells. These grid cells are typically of a fixed size and are generally square, hexagonal in shape. This division may result in loss of information finesse and some details may not be accurately reflected.
Disclosure of Invention
In view of the above, the embodiment of the invention provides a grid division method based on environmental space-time data, so as to solve the technical problem of low fineness in grid division in the prior art. The method comprises the following steps:
acquiring ground environment data and POI data of a target area;
Determining a ground environment sensitivity index in the target area according to the ground environment data, wherein the ground environment sensitivity index refers to a ground related index affecting the change of the ground environment data;
Dividing the map of the target area into a plurality of sub-area images according to the type of the ground environment sensitivity index;
In each sub-region image, calculating environmental sensitivity values distributed in each sub-region image according to density distribution of POI points of different types in the POI data, wherein the size of the environmental sensitivity values represents the sensitivity degree to different change factors in the ground environment;
Determining initial grid cells with preset sizes on a map of the target area, and calculating the environmental sensitivity value of each initial grid cell according to the environmental sensitivity value distributed in each sub-area image;
And adjusting the size of the initial grid unit on the map according to the size relation between the environment sensitivity value of the initial grid unit and the threshold value.
The embodiment of the invention also provides a grid dividing device based on the environmental space-time data, so as to solve the technical problem of low fineness in the grid division in the prior art. The device comprises:
the data acquisition module is used for acquiring ground environment data and POI data of the target area;
The index determining module is used for determining a ground environment sensitivity index in the target area according to the ground environment data, wherein the ground environment sensitivity index is a ground related index influencing the change of the ground environment data;
the subarea dividing module is used for dividing the map of the target area into a plurality of subarea images according to the type of the ground environment sensitivity index;
The first environmental sensitivity value determining module is used for calculating environmental sensitivity values distributed in each sub-region image according to the density distribution of POI points of different types in the POI data, wherein the size of the environmental sensitivity values represents the sensitivity degree to different change factors in the ground environment;
the second environment sensitivity value determining module is used for determining initial grid cells with preset sizes on a map of the target area, and calculating the environment sensitivity value of each initial grid cell according to the environment sensitivity value distributed in each sub-area image;
and the grid adjustment module is used for adjusting the size of the initial grid unit on the map according to the size relation between the environment sensitivity value of the initial grid unit and the threshold value.
The embodiment of the invention also provides computer equipment, which comprises a memory, a processor and a computer program stored on the memory and capable of running on the processor, wherein the processor realizes the arbitrary grid division method based on the environmental space-time data when executing the computer program so as to solve the technical problem of low fineness in the grid division in the prior art.
The embodiment of the invention also provides a computer readable storage medium which stores a computer program for executing any grid division method based on environment space-time data, so as to solve the technical problem of low fineness of grid division in the prior art.
Compared with the prior art, the beneficial effects that above-mentioned at least one technical scheme that this description embodiment adopted can reach include at least: the method comprises the steps of determining a ground environment sensitivity index in a target area based on ground environment data, dividing a map of the target area into a plurality of sub-area images based on the type of the ground environment sensitivity index, further calculating an environment sensitivity value distributed in each sub-area image according to density distribution of POI points of different types in the POI data in each sub-area image, further calculating an environment sensitivity value of each initial grid unit, and finally adjusting the size of the initial grid unit on the map based on the size relation between the environment sensitivity value of each initial grid unit and a threshold value. Dividing subareas based on the ground environment sensitivity indexes corresponding and conforming to ground environment data, dividing the same type of ground environment areas into the same subareas, further calculating environment sensitivity values distributed in each subarea to quantify environment sensitivity values distributed at different positions or data points in each subarea, further quantifying the environment sensitivity values of each initial grid unit to determine the sensitivity degree of each initial grid unit to different change factors in the ground environment, further adjusting the size of each initial grid unit based on the environment sensitivity values of each initial grid unit, further adjusting the size and shape of each initial grid unit based on the sensitivity degree to different change factors in the ground environment, enabling the adjusted grid units to reflect severe environment changes, being more beneficial to accurately reflecting differences between environments of certain details or areas of the environment changes, improving the fineness of grid division, and further providing data basis for more refined assessment of geographic areas.
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In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are needed in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and that other drawings can be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of a meshing method based on environmental spatiotemporal data provided by an embodiment of the invention;
FIG. 2 is a schematic diagram of type division subareas based on a ground environment sensitivity index according to an embodiment of the present invention;
Fig. 3 is a schematic view of density distribution of POI points of each type in each sub-area image according to an embodiment of the present invention;
FIG. 4 is a schematic diagram of the distribution of environmental sensitivity values in each sub-area image according to an embodiment of the present invention;
FIG. 5 is a schematic diagram of a meshing result provided by an embodiment of the present invention;
FIG. 6 is a block diagram of a computer device according to an embodiment of the present invention;
fig. 7 is a block diagram of a meshing apparatus based on environmental spatiotemporal data according to an embodiment of the present invention.
Detailed Description
Embodiments of the present application will be described in detail below with reference to the accompanying drawings.
Other advantages and effects of the present application will become apparent to those skilled in the art from the following disclosure, which describes the embodiments of the present application with reference to specific examples. It will be apparent that the described embodiments are only some, but not all, embodiments of the application. The application may be practiced or carried out in other embodiments that depart from the specific details, and the details of the present description may be modified or varied from the spirit and scope of the present application. It should be noted that the following embodiments and features in the embodiments may be combined with each other without conflict. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
In an embodiment of the present invention, a meshing method based on environmental spatiotemporal data is provided, as shown in fig. 1, where the method includes:
step S101: acquiring ground environment data and POI data (i.e., point of interest data, including various types of geographic features, such as stores, restaurants, banks, attractions, hospitals, gas stations, etc.) for a target area;
Step S102: determining a ground environment sensitivity index in the target area according to the ground environment data, wherein the ground environment sensitivity index refers to a ground related index affecting the change of the ground environment data;
Step S103: dividing the map of the target area into a plurality of sub-area images according to the type of the ground environment sensitivity index;
Step S104: in each sub-region image, calculating environmental sensitivity values distributed in each sub-region image according to density distribution of POI points of different types in the POI data, wherein the size of the environmental sensitivity values represents the sensitivity degree to different change factors in the ground environment;
Step S105: determining initial grid cells with preset sizes on a map of the target area, and calculating the environmental sensitivity value of each initial grid cell according to the environmental sensitivity value distributed in each sub-area image;
Step S106: and adjusting the size of the initial grid unit on the map according to the size relation between the environment sensitivity value of the initial grid unit and the threshold value.
As can be seen from the flow shown in fig. 1, in the embodiment of the present invention, the division of the subareas based on the corresponding and conforming ground environment sensitivity indexes with the ground environment data is realized, the same type of ground environment area is divided into the same subareas, and the environment sensitivity values distributed in each subarea are calculated, so as to quantify the environment sensitivity values distributed in different positions or data points in each subarea, and further, the environment sensitivity values of each initial grid unit can be quantified, so as to determine the sensitivity degree of each initial grid unit to different variation factors in the ground environment, and further, the adjustment of the size of each initial grid unit based on the size of the environment sensitivity values of each initial grid unit is realized, and further, the adjustment of the size and shape of each initial grid unit based on the sensitivity degree to different variation factors in the ground environment is realized, so that the adjusted grid units can reflect the severe environment variation, and are more favorable for accurately reflecting the differences between certain details of the environment variation or the regional environments, and further, the fineness of grid division is improved, and further, the data basis is favorable for more finely evaluating the geographic area.
In specific implementation, the above ground environment data is any ground environment related data, and may include, for example, data of climate, soil, vegetation, water resources, and the like. The data can be obtained by means of remote sensing, sensors, field investigation and the like.
In particular implementations, the POI data may include information of various geographic entities or locations at particular geographic locations on the ground.
In specific implementation, after the ground environment data are acquired, the ground environment sensitivity index in the target area may be determined according to the ground environment data, for example, the ground environment sensitivity index may be determined according to pollution influence factors, change factors and the like of the ground environment data in the target area, for example, the ground environment sensitivity index may include any one or any combination of the following:
vegetation coverage, soil texture, topography relief, precipitation, temperature variation, and land function.
In the implementation, after the ground environment sensitivity index in the target area is determined, the map of the target area can be divided into a plurality of sub-area images according to the type of the ground environment sensitivity index, specifically, the map of the target area can be divided into a plurality of sub-area images according to different types of each ground environment sensitivity index, when a plurality of ground environment sensitivity indexes exist, the map of the target area can be divided into a plurality of sub-area images according to different types of one ground environment sensitivity index, and then the plurality of sub-area images are continuously divided in each sub-area image based on different types of other ground environment sensitivity indexes.
For example, from the viewpoint of pollution, the ground environment sensitivity index is exemplified by the land function, and the land function can be generally classified into different types, such as residential areas, agricultural lands, industrial areas, and the like, as shown in fig. 2, and further, the target area can be classified into three different land types: residential area, agricultural land and industrial area, part (1) in fig. 2 is the agricultural land, part (2) in fig. 2 is the residential area, and part (3) in fig. 2 is the industrial area. When the ground environment sensitivity index includes other ground environment sensitivity indexes in addition to the ground function, taking the vegetation coverage as an example, different sub-areas may be divided based on the interval values of different vegetation coverage in the parts (1), (2), and (3) in fig. 2, respectively.
In particular, in order to calculate an accurate and effective environmental sensitivity value to reflect environmental sensitivity degrees of different data points or positions in the sub-region images, it is proposed to calculate environmental sensitivity values distributed in each sub-region image through a nuclear density distribution of POI points, for example, calculate a density distribution of each type of POI point in each sub-region image by using a nuclear density estimation method; and calculating the environmental sensitivity value distributed in each sub-region image according to the density distribution of the POI points of each type in each sub-region image and the pollution influence degree of the POI points of each type on the environment.
In the implementation, the environmental sensitivity value distributed in each sub-area image is calculated through the nuclear density distribution of the POI points, and statistical analysis, space analysis and other methods can be used for processing and analyzing the POI data to obtain the environmental sensitivity value of each sub-area. In the process of calculating the density distribution condition of each type of POI point on each sub-region image by using the kernel density estimation method, firstly, a kernel function is selected, and then the density distribution is calculated based on the kernel function, wherein the density distribution of the kernel function is shown in figure 3. For example, common kernel functions include gaussian kernels (also known as normal kernels), uniform kernels, triangular kernels, and the like. Taking a gaussian kernel as an example, the calculation method is as follows:
The gaussian kernel function has the expression:
The formula for the kernel density estimation is:
Wherein, Is the coordinates of the point to be estimated,/>For the coordinates of the mth point of interest (i.e., POI point), K is a kernel function,/>Is bandwidth,/>Is the total number of points of interest,/>And estimating a probability density function for Gaussian kernel density estimation of the point to be estimated. The bandwidth controls the width of the kernel and thus affects the degree of smoothness of the estimate.
It is important to select a suitable bandwidth. Too little bandwidth may result in an over-rugged estimate, while too much bandwidth may result in an over-smooth estimate. Common bandwidth selection methods include Silverman's rule and Scott's rule, and suitable bandwidths may be selected by cross-validation or the like.
In specific implementation, after the density distribution condition of each type of interest point on different sub-region images is obtained by calculation, the environmental sensitivity value distributed in each sub-region image can be calculated by the following formula:
Wherein, For the environmental sensitivity value distributed in the ith sub-region image,/>For the density distribution of the j-th class POI point in the i-th sub-region image,/>The pollution influence of the j-th POI points on the environment is graded, and the higher the grade is, the greater the pollution influence of the higher the grade on the environment is, the greater the pollution influence of the j-th POI points on the environment isIs the total number of types of POI points.
In specific implementation, the level of the pollution effect on the environment can be divided into multiple levels according to actual requirements, for example, a level system from 1 to 10 can be set, wherein 1 level indicates no effect on the environment and 10 level indicates great effect on the environment.
In practice, certain types of POI spots are prone to contamination. For example, paint stores use paints and solvents that may produce organic volatile compounds (VOCs) to cause environmental pollution. Chemical plants may emit toxic chemicals, resulting in atmospheric and water pollution. Petroleum refineries may discharge petroleum products, wastewater, and gases, causing air and water pollution, etc. After calculation, as shown in fig. 4, the distribution of environmental sensitivity values around the target area is shown in fig. 4.
In implementation, before calculating the environmental sensitivity value distributed in each sub-area image by the nuclear density distribution of POI points, the area of each sub-area image and the number of POI data points may be normalized to a relative value, which may be achieved using the following formula:
relative value= (actual value-minimum)/(maximum value-minimum).
In particular, in setting the initial grid cell, the size of the initial grid cell may be selected according to the characteristics of the target area and the ground environment data.
In particular, the process of calculating the environmental sensitivity value of each initial grid cell includes the steps of:
Mapping (e.g. according to a coordinate mapping) according to the environmental sensitivity values distributed in each sub-region image to obtain environmental sensitivity values distributed in each initial grid unit;
And averaging the environmental sensitivity values distributed in each initial grid unit, and determining the average value as the environmental sensitivity value of each initial grid unit.
In practice, after obtaining the environmental sensitivity value of the initial grid cell, in order to improve the fineness of grid division and reflect the environmental change condition, it is proposed to adjust the size of the initial grid cell based on the size relationship between the environmental sensitivity value of the initial grid cell and the threshold, for example,
Calculating standard deviations of the environmental sensitivity values of all the initial grid cells;
for each initial grid cell, calculating a difference between the product of the threshold and a coefficient multiplied by the standard deviation, and if the environmental sensitivity value of the initial grid cell is smaller than the difference, merging the initial grid cell with grid cells around the initial grid cell.
In the implementation, according to the size relation between the environmental sensitivity value and the threshold value of the initial grid unit, the size of the initial grid unit is adjusted on the map, which comprises the following steps:
Calculating, for each of the initial grid cells, a sum of the products of the threshold and a coefficient multiplied by the standard deviation, dividing the initial grid cell into smaller sub-grids, e.g., equally dividing the initial grid cell into four sub-grid cells, if the environmental sensitivity value of the initial grid cell is greater than the sum;
calculating an average value of the environmental sensitivity values distributed in each of the sub-grid cells;
Calculating gradients of each sub-grid unit along an x-axis and a y-axis respectively according to an average value of environmental sensitivity values of each sub-grid unit;
If the gradient of each sub-grid unit along the x axis and the gradient of each sub-grid unit along the y axis are both greater than a preset gradient threshold value, continuing to execute the steps to divide grids of each sub-grid unit; if not, grid division is not performed on each sub-grid unit.
Specifically, the initial grid unit is initially sub-divided, the initial grid unit is uniformly divided into four parts, four sub-grid units are obtained, and the environmental sensitivity values distributed in each uniformly divided sub-grid unit are averaged. The environmental sensitivity values within each sub-grid cell are measured by gradients. Since each sub-grid cell is adjacent to the other two sub-grid cells, the gradient is calculated along the x, y axes. Assuming that the bottom left corner coordinate of the sub-grid cell is (x left,ybottom), and the top right corner coordinate is (x right,ytop) (the coordinate position takes the grid center point).
The gradient of each sub-grid cell is calculated as follows:
Gradient along x-direction:
gradient along y-direction:
Wherein, 、/>The gradient of each sub-grid cell along the x, y axes is shown, and f (x, y) is the average of the environmental sensitivities of each sub-grid cell.
If the gradient of a certain sub-grid unit along the x direction and the gradient of the sub-grid unit along the y direction are both larger than a preset gradient threshold value, the sub-grid unit needs to be subdivided according to the method; if not, no subdivision is required.
In specific implementation, the magnitudes of the coefficients and the threshold values can be adjusted as required to control the degree of grid refinement.
In specific implementation, after the process of adjusting the size of the initial grid unit based on the size relation between the environmental sensitivity value and the threshold value of the initial grid unit is iterated for several times, the grid division can meet the fine requirement, and the grid division result is shown in fig. 5.
In this embodiment, a computer device is provided, as shown in fig. 6, including a memory 601, a processor 602, and a computer program stored on the memory and executable on the processor, where the processor implements any of the above-mentioned meshing methods based on environmental spatiotemporal data when the computer program is executed.
In particular, the computer device may be a computer terminal, a server or similar computing means.
In the present embodiment, a computer-readable storage medium storing a computer program for executing any of the above-described meshing methods based on environmental spatiotemporal data is provided.
In particular, computer-readable storage media, including both permanent and non-permanent, removable and non-removable media, may be used to implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of computer-readable storage media include, but are not limited to, phase-change memory (PRAM), static Random Access Memory (SRAM), dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), read Only Memory (ROM), electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), digital Versatile Disks (DVD) or other optical storage, magnetic cassettes, magnetic tape disk storage or other magnetic storage devices, or any other non-transmission medium, which can be used to store information that can be accessed by a computing device. Computer-readable storage media, as defined herein, does not include transitory computer-readable media (transmission media), such as modulated data signals and carrier waves.
Based on the same inventive concept, the embodiment of the invention also provides a grid dividing device based on the environmental space-time data, as described in the following embodiment. Because the principle of solving the problem by the grid dividing device based on the environment space-time data is similar to that of the grid dividing method based on the environment space-time data, the implementation of the grid dividing device based on the environment space-time data can be referred to the implementation of the grid dividing method based on the environment space-time data, and the repetition is omitted. As used below, the term "unit" or "module" may be a combination of software and/or hardware that implements the intended function. While the means described in the following embodiments are preferably implemented in software, implementation in hardware, or a combination of software and hardware, is also possible and contemplated.
FIG. 7 is a block diagram of a meshing apparatus based on environmental spatiotemporal data according to an embodiment of the invention, as shown in FIG. 7, the apparatus comprising:
a data acquisition module 701, configured to acquire ground environment data and POI data of a target area;
an index determination module 702, configured to determine a ground environment sensitivity index in the target area according to the ground environment data, where the ground environment sensitivity index is a ground related index that affects a change of the ground environment data;
A sub-region dividing module 703, configured to divide the map of the target region into a plurality of sub-region images according to the type of the ground environment sensitivity index;
a first environmental sensitivity value determining module 704, configured to calculate, in each of the sub-area images, environmental sensitivity values distributed in each of the sub-area images according to density distribution of POI points of different types in the POI data, where a magnitude of the environmental sensitivity values represents a sensitivity degree to different variation factors in a ground environment;
A second environmental sensitivity value determining module 705, configured to determine an initial grid cell of a preset size on a map of the target area, and calculate an environmental sensitivity value of each initial grid cell according to the environmental sensitivity values distributed in each sub-area image;
And the grid adjustment module 706 is configured to adjust the size of the initial grid unit on the map according to the size relationship between the environmental sensitivity value and the threshold value of the initial grid unit.
In one embodiment, the first environmental sensitivity value determining module is configured to calculate a density distribution of each type of POI point in each of the sub-region images by using a kernel density estimating method; and calculating the environmental sensitivity value distributed in each sub-region image according to the density distribution of the POI points of each type in each sub-region image and the pollution influence degree of the POI points of each type on the environment.
In one embodiment, the first environmental sensitivity value determining module is configured to calculate the environmental sensitivity value distributed in each of the sub-region images by the following formula:
Wherein, For the environmental sensitivity value distributed in the ith sub-region image,/>For the density distribution of the j-th class POI point in the i-th sub-region image,/>The pollution influence of the j-th POI points on the environment is graded, and the higher the grade is, the greater the pollution influence of the higher the grade on the environment is, the greater the pollution influence of the j-th POI points on the environment isIs the total number of types of POI points.
In one embodiment, a second environmental sensitivity value determining module is configured to map environmental sensitivity values distributed in each initial grid cell according to environmental sensitivity values distributed in each sub-region image; and averaging the environmental sensitivity values distributed in each initial grid unit, and determining the average value as the environmental sensitivity value of each initial grid unit.
In one embodiment, the grid adjustment module is configured to calculate standard deviations of the environmental sensitivity values of all the initial grid cells; for each initial grid cell, calculating a difference between the product of the threshold and a coefficient multiplied by the standard deviation, and if the environmental sensitivity value of the initial grid cell is smaller than the difference, merging the initial grid cell with grid cells around the initial grid cell.
In one embodiment, the grid adjustment module is configured to calculate, for each initial grid cell, a sum of the products of the threshold and a coefficient multiplied by the standard deviation, and if the environmental sensitivity value of the initial grid cell is greater than the sum, divide the initial grid cell into four sub-grid cells;
calculating an average value of the environmental sensitivity values distributed in each of the sub-grid cells; calculating gradients of each sub-grid unit along an x-axis and a y-axis respectively according to an average value of environmental sensitivity values of each sub-grid unit; if the gradient of each sub-grid unit along the x axis and the gradient of each sub-grid unit along the y axis are both greater than a preset gradient threshold value, continuing to execute the steps to divide grids of each sub-grid unit; if not, grid division is not performed on each sub-grid unit.
The embodiment of the invention realizes the following technical effects: dividing subareas based on the ground environment sensitivity indexes corresponding and conforming to ground environment data, dividing the same type of ground environment areas into the same subareas, further calculating environment sensitivity values distributed in each subarea to quantify environment sensitivity values distributed at different positions or data points in each subarea, further quantifying the environment sensitivity values of each initial grid unit to determine the sensitivity degree of each initial grid unit to different change factors in the ground environment, further adjusting the size of each initial grid unit based on the environment sensitivity values of each initial grid unit, further adjusting the size and shape of each initial grid unit based on the sensitivity degree to different change factors in the ground environment, enabling the adjusted grid units to reflect severe environment changes, being more beneficial to accurately reflecting differences between environments of certain details or areas of the environment changes, improving the fineness of grid division, and further providing data basis for more refined assessment of geographic areas.
It will be apparent to those skilled in the art that the modules or steps of the embodiments of the invention described above may be implemented in a general purpose computing device, they may be concentrated on a single computing device, or distributed across a network of computing devices, they may alternatively be implemented in program code executable by computing devices, so that they may be stored in a storage device for execution by computing devices, and in some cases, the steps shown or described may be performed in a different order than what is shown or described, or they may be separately fabricated into individual integrated circuit modules, or a plurality of modules or steps in them may be fabricated into a single integrated circuit module. Thus, embodiments of the invention are not limited to any specific combination of hardware and software.
The above description is only of the preferred embodiments of the present invention and is not intended to limit the present invention, and various modifications and variations can be made to the embodiments of the present invention by those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (8)
1. A meshing method based on environmental spatiotemporal data, comprising:
acquiring ground environment data and POI data of a target area;
Determining a ground environment sensitivity index in the target area according to the ground environment data, wherein the ground environment sensitivity index refers to a ground related index affecting the change of the ground environment data;
Dividing the map of the target area into a plurality of sub-area images according to different types of each ground environment sensitivity index;
In each sub-region image, calculating environmental sensitivity values distributed in each sub-region image according to density distribution of POI points of different types in the POI data, wherein the size of the environmental sensitivity values represents the sensitivity degree to different change factors in the ground environment;
Determining initial grid cells with preset sizes on a map of the target area, and calculating the environmental sensitivity value of each initial grid cell according to the environmental sensitivity value distributed in each sub-area image;
According to the size relation between the environment sensitivity value of the initial grid unit and the threshold value, the size of the initial grid unit is adjusted on the map;
According to the density distribution of the POI points of different types in the POI data, calculating the environmental sensitivity value distributed in each sub-region image, wherein the environmental sensitivity value comprises the following steps:
calculating probability density distribution of each type of POI point in each sub-region image by using a kernel density estimation method;
Calculating an environmental sensitivity value distributed in each sub-region image according to probability density distribution of each type of POI point in each sub-region image and the pollution influence degree of each type of POI point on the environment;
Calculating the environmental sensitivity value of each initial grid unit according to the environmental sensitivity value distributed in each sub-region image, wherein the environmental sensitivity value comprises the following steps:
According to the coordinates of the environmental sensitivity values distributed in each sub-region image, mapping to obtain the environmental sensitivity values distributed in each initial grid unit;
And averaging the environmental sensitivity values distributed in each initial grid unit, and determining the average value as the environmental sensitivity value of each initial grid unit.
2. The meshing method based on environmental spatiotemporal data according to claim 1, wherein calculating environmental sensitivity values distributed in each of the sub-area images based on a density distribution of each type of POI points in each of the sub-area images and a degree of pollution effect of each type of POI points on the environment, comprises:
Calculating the environmental sensitivity values distributed in each of the sub-region images by the following formula:
Wherein, For the environmental sensitivity value distributed in the ith sub-region image,/>For the density distribution of the j-th class POI point in the i-th sub-region image,/>The pollution influence of the j-th POI points on the environment is graded, and the higher the grade is, the greater the pollution influence of the higher the grade on the environment is, the greater the pollution influence of the j-th POI points on the environment isIs the total number of types of POI points.
3. The environmental spatiotemporal data based meshing method of any one of claims 1 to 2, wherein adjusting the size of the initial grid cell on the map according to the magnitude relationship of the environmental sensitivity value of the initial grid cell to a threshold value includes:
Calculating standard deviations of the environmental sensitivity values of all the initial grid cells;
for each initial grid cell, calculating a difference between the product of the threshold and a coefficient multiplied by the standard deviation, and if the environmental sensitivity value of the initial grid cell is smaller than the difference, merging the initial grid cell with grid cells around the initial grid cell.
4. The environmental spatiotemporal data based meshing method of claim 3, wherein adjusting the size of the initial grid cell on the map according to the size relationship of the environmental sensitivity value of the initial grid cell to a threshold value comprises:
Calculating a sum of products of the threshold and a coefficient multiplied by the standard deviation for each initial grid cell, and equally dividing the initial grid cell into four sub-grid cells if the environmental sensitivity value of the initial grid cell is greater than the sum;
calculating an average value of the environmental sensitivity values distributed in each of the sub-grid cells;
Calculating gradients of each sub-grid unit along an x-axis and a y-axis respectively according to an average value of environmental sensitivity values of each sub-grid unit;
If the gradient of each sub-grid unit along the x axis and the gradient of each sub-grid unit along the y axis are both greater than a preset gradient threshold value, continuing to grid-divide each sub-grid unit; if not, grid division is not performed on each sub-grid unit.
5. The environmental spatiotemporal data based meshing method of any of claims 1 to 2, wherein the ground environmental sensitivity index includes any one or any combination of the following:
vegetation coverage, soil texture, topography relief, precipitation, temperature variation, and land function.
6. A meshing apparatus based on environmental spatiotemporal data, comprising:
the data acquisition module is used for acquiring ground environment data and POI data of the target area;
The index determining module is used for determining a ground environment sensitivity index in the target area according to the ground environment data, wherein the ground environment sensitivity index is a ground related index influencing the change of the ground environment data;
The subarea dividing module is used for dividing the map of the target area into a plurality of subarea images according to different types of each ground environment sensitivity index;
The first environmental sensitivity value determining module is used for calculating environmental sensitivity values distributed in each sub-region image according to the density distribution of POI points of different types in the POI data, wherein the size of the environmental sensitivity values represents the sensitivity degree to different change factors in the ground environment;
the second environment sensitivity value determining module is used for determining initial grid cells with preset sizes on a map of the target area, and calculating the environment sensitivity value of each initial grid cell according to the environment sensitivity value distributed in each sub-area image;
the grid adjustment module is used for adjusting the size of the initial grid unit on the map according to the size relation between the environment sensitivity value of the initial grid unit and the threshold value;
The first environment sensitivity value determining module is used for calculating probability density distribution of each type of POI point in each sub-region image by using a kernel density estimating method; calculating an environmental sensitivity value distributed in each sub-region image according to probability density distribution of each type of POI point in each sub-region image and the pollution influence degree of each type of POI point on the environment;
The second environmental sensitivity value determining module is used for mapping to obtain environmental sensitivity values distributed in each initial grid unit according to coordinates of the environmental sensitivity values distributed in each sub-region image; and averaging the environmental sensitivity values distributed in each initial grid unit, and determining the average value as the environmental sensitivity value of each initial grid unit.
7. A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor implements the ambient spatiotemporal data based meshing method of any one of claims 1 to 5 when the computer program is executed.
8. A computer-readable storage medium, characterized in that the computer-readable storage medium stores a computer program for executing the grid dividing method based on environmental spatiotemporal data of any one of claims 1 to 5.
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Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106557869A (en) * | 2016-10-20 | 2017-04-05 | 北京市劳动保护科学研究所 | A kind of air pollutant emission inventory space allocation method and device based on POI points |
| CN107172626A (en) * | 2017-04-18 | 2017-09-15 | 中国矿业大学 | A kind of CO2The sensing network node positioning and optimizing method in geological storage region |
| CN109583723A (en) * | 2018-11-19 | 2019-04-05 | 北京英视睿达科技有限公司 | Method, electronic equipment and the device of water environment hot spot grid are determined based on pollution sources |
| CN109613182A (en) * | 2018-12-21 | 2019-04-12 | 北京英视睿达科技有限公司 | Monitoring location site selecting method and device based on atmosphere pollution |
| CN110705830A (en) * | 2019-09-06 | 2020-01-17 | 中国平安财产保险股份有限公司 | Risk assessment method and device and server |
| CN115546237A (en) * | 2021-06-29 | 2022-12-30 | 富泰华工业（深圳）有限公司 | Point cloud density adjusting method, electronic device and storage medium |
| CN116341961A (en) * | 2023-03-09 | 2023-06-27 | 广东省土地调查规划院 | Ocean resource homogeneous region division method and device and electronic equipment |
| CN116580123A (en) * | 2023-03-29 | 2023-08-11 | 云南省生态环境科学研究院 | Regional atmospheric environment photochemical grid model image generation system |
| WO2023168781A1 (en) * | 2022-03-10 | 2023-09-14 | 浙江大学 | Soil cadmium risk prediction method based on spatial-temporal interaction relationship |
Family Cites Families (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP7013512B2 (en) * | 2020-03-30 | 2022-01-31 | 本田技研工業株式会社 | Information processing device, route guidance device, program, and information processing method |
| CN111737392B (en) * | 2020-06-22 | 2024-04-02 | 北京百度网讯科技有限公司 | Method, device, equipment and storage medium for merging building data |
-
2023
- 2023-12-21 CN CN202311764862.7A patent/CN117437254B/en active Active
Patent Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106557869A (en) * | 2016-10-20 | 2017-04-05 | 北京市劳动保护科学研究所 | A kind of air pollutant emission inventory space allocation method and device based on POI points |
| CN107172626A (en) * | 2017-04-18 | 2017-09-15 | 中国矿业大学 | A kind of CO2The sensing network node positioning and optimizing method in geological storage region |
| CN109583723A (en) * | 2018-11-19 | 2019-04-05 | 北京英视睿达科技有限公司 | Method, electronic equipment and the device of water environment hot spot grid are determined based on pollution sources |
| CN109613182A (en) * | 2018-12-21 | 2019-04-12 | 北京英视睿达科技有限公司 | Monitoring location site selecting method and device based on atmosphere pollution |
| CN110705830A (en) * | 2019-09-06 | 2020-01-17 | 中国平安财产保险股份有限公司 | Risk assessment method and device and server |
| CN115546237A (en) * | 2021-06-29 | 2022-12-30 | 富泰华工业（深圳）有限公司 | Point cloud density adjusting method, electronic device and storage medium |
| WO2023168781A1 (en) * | 2022-03-10 | 2023-09-14 | 浙江大学 | Soil cadmium risk prediction method based on spatial-temporal interaction relationship |
| CN116341961A (en) * | 2023-03-09 | 2023-06-27 | 广东省土地调查规划院 | Ocean resource homogeneous region division method and device and electronic equipment |
| CN116580123A (en) * | 2023-03-29 | 2023-08-11 | 云南省生态环境科学研究院 | Regional atmospheric environment photochemical grid model image generation system |
Non-Patent Citations (1)
| Title |
|---|
| 基于GIS的海洋敏感区溢油敏感度分析研究;王会蒙;季民;孙勇;;海洋信息;20150515(第02期);全文 * |
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