CN111693006B - Method and device for determining number and positions of sensors in coral sand soil monitoring area - Google Patents
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- 235000014653 Carica parviflora Nutrition 0.000 title claims abstract description 181
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Abstract
The disclosure relates to a method and a device for determining the number and the positions of sensors in a coral sand soil monitoring area, wherein the method comprises the following steps: obtaining a coral sand soil monitoring area, and dividing the soil monitoring area into M grid units with the same size by adopting a spatial grid; uniformly selecting the central positions of N target grid units as soil sampling points in a coral sand soil monitoring area, and acquiring coral sand content attribute values of coral sand soil at the soil sampling points; calculating coral sand content attribute values of other grid units except the target grid unit in the coral sand soil monitoring area according to the coral sand content attribute values at the soil sampling points; dividing a coral sand soil monitoring area into a plurality of block polygons with different soil textures according to the coral sand content attribute value of each grid unit; calculating the total number of the block polygons and the centroid coordinate of each block polygon; and determining the positions and the number of the sensors according to the mass center coordinates and the total number of the block polygons.
Description
Technical Field
The disclosure relates to the technical field of geographic monitoring, in particular to a method and a device for determining the number and the positions of sensors in a coral sand soil monitoring area.
Background
Under the current globalization, the research value and the practical strategic significance of the reef are increasingly prominent. The island of south China sea is far away from the continent, and the round trip time of personnel is long; the island environment is severe and is not suitable for long-term residence; island reef land areas are scarce, and management personnel on the island are few. This kind of land is little, the people is few, the special characteristics far away for south sea island reef ecological environment monitoring and protection are difficult to rely on traditional artifical mode to manage, need to introduce internet of things, and real-time supervision, acquisition island reef ecological environment data and its abnormal conditions of analysis to long-range intelligent equipment such as vegetation watering water valve of opening or closing realizes real-time supervision and intelligent control to island reef ecological environment. At present, for the application demand of real-time monitoring of the ecological environment of the island in south China sea, a domestic research institution develops the arrangement work of soil monitoring sensors on the island, acquires the monitoring data such as the temperature, the humidity and the water potential of the island coral sand soil, analyzes the drought degree of the island coral sand soil, and further realizes the remote intelligent control of a vegetation irrigation water valve. Due to the spatial heterogeneity of the island coral sand soil, how to reasonably determine the arrangement positions and the arrangement quantity of the soil monitoring sensors is an important and difficult part in the whole island coral sand soil monitoring process. At present, in the application of the Internet of things, the arrangement positions and the arrangement quantity of the sensing instruments are mainly determined according to the experience of a user, so that the real-time monitoring data is not completely representative due to strong subjectivity.
Disclosure of Invention
In order to solve the problems in the related art, the invention provides a coral sand soil monitoring area sensor quantity and position determination method and device, which can automatically determine the optimal arrangement positions and quantity of soil monitoring sensors according to coral sand soil attribute data, so that the soil monitoring area can be fully covered by using the optimal quantity of sensors, the obtained real-time soil monitoring data is more reliable, and the problems of redundant arrangement of the soil monitoring sensors or unrepresentative monitoring data and the like are avoided.
According to a first aspect of the embodiments of the present disclosure, there is provided a method for determining the number and positions of sensors in a coral sand soil monitoring area, including:
obtaining a coral sand soil monitoring area, and dividing the soil monitoring area into M grid units with the same size by adopting a spatial grid;
uniformly selecting the central positions of N target grid units as soil sampling points in the coral sand soil monitoring area, and acquiring coral sand content attribute values of coral sand soil at the soil sampling points;
calculating coral sand content attribute values of other grid units in the coral sand soil monitoring area except the target grid unit according to the coral sand content attribute values at the soil sampling points;
dividing the coral sand soil monitoring area into a plurality of block polygons with different soil textures according to the coral sand content attribute value of each grid unit;
calculating the total number of the block polygons and the centroid coordinate of each block polygon;
and determining the positions and the number of the sensors according to the centroid coordinates of each block polygon and the total number.
In one embodiment, preferably, dividing the coral sand soil monitoring area into a plurality of partitioned polygons with different soil textures according to the coral sand content attribute value of each grid cell comprises:
obtaining the interval from the minimum value to the maximum value of the coral sand content attribute values of all the grid units;
according to the arrangement quantity of preset sensors, dividing the interval into attribute value intervals with corresponding quantity at equal intervals, and sequentially numbering each attribute value interval;
sequentially determining a target attribute value interval in which the coral sand content attribute value of each grid unit is located;
modifying the attribute value of the coral sand content of each grid unit into a serial number value corresponding to the target attribute value interval in which the coral sand content is positioned;
and extracting corresponding region boundaries according to the modified attribute values of the coral sand content of each grid unit, and converting all the region boundaries into vector polygons, thereby obtaining the block polygons.
In one embodiment, preferably, extracting corresponding region boundaries according to the modified coral sand content attribute values of the respective grid cells, and converting all the region boundaries into vector polygons, thereby obtaining the block polygons, includes:
aiming at the serial number i of each attribute value interval, carrying out boundary tracking on all grid units at the junction where the coral content attribute value i changes;
removing boundary regions outside the coral sand soil monitoring region by using a space surface-surface cutting method to obtain all boundary regions with attribute value interval numbers i;
obtaining a boundary polygon set according to all the boundary areas;
sequentially extracting first target polygons with the minimum areas from the boundary polygon set;
judging whether a second target polygon including the first target polygon exists or not by using a face-face inclusion relation;
when the second target polygon does not exist, converting the first target polygon into an independent vector polygon and deleting the independent vector polygon from the boundary polygon set; when the second target polygon exists, judging whether the number of the second target polygons is an even number;
when the number is an even number, converting the first target polygon into an independent vector polygon and deleting the independent vector polygon from the boundary polygon set; when the number is an odd number, taking a third target polygon with the smallest area from a plurality of second target polygons, combining the third target polygon and the first target polygon into a vector polygon with holes together, and deleting the third target polygon and the first target polygon from the boundary polygon set;
and counting the number of the vector polygons corresponding to each attribute value interval, directly taking the vector polygons as the block polygons when the number is one, and selecting the vector polygons with the largest areas as the block polygons when the number is multiple.
In one embodiment, preferably, calculating coral sand content attribute values of grid cells in the coral sand soil monitoring area except for the target grid cell according to the coral sand content attribute values at the soil sampling points includes:
and according to the coral sand content attribute values at the soil sampling points, interpolating and calculating the coral sand content attribute values of other grid units except the target grid unit in the coral sand soil monitoring area by adopting a Krigin interpolation method, wherein during interpolation calculation, a spherical model is adopted for half-variance fitting.
In one embodiment, preferably, determining the positions and the number of sensors according to the coordinates of the centroid of each of the blocking polygons and the total number comprises:
determining the total number of the blocking polygons as the number of the sensors, and determining the centroid coordinate of each blocking polygon as the position of the sensor.
According to a second aspect of the embodiments of the present disclosure, there is provided a coral sand soil monitoring area sensor number and position determination apparatus, including:
the coral sand monitoring system comprises an acquisition module, a data acquisition module and a data processing module, wherein the acquisition module is used for acquiring a coral sand soil monitoring area and dividing the soil monitoring area into M grid units with the same size by adopting a spatial grid;
the selecting module is used for uniformly selecting the central positions of N target grid units as soil sampling points in the coral sand soil monitoring area and acquiring coral sand content attribute values of coral sand soil at the soil sampling points;
the first calculation module is used for calculating coral sand content attribute values of other grid units in the coral sand soil monitoring area except the target grid unit according to the coral sand content attribute value at the soil sampling point;
the dividing module is used for dividing the coral sand soil monitoring area into a plurality of block polygons with different soil textures according to the coral sand content attribute value of each grid unit;
the second calculation module is used for calculating the total number of the block polygons and the mass center coordinate of each block polygon;
and the determining module is used for determining the positions and the number of the sensors according to the centroid coordinate of each block polygon and the total number.
In one embodiment, preferably, the dividing module includes:
the acquiring unit is used for acquiring the interval from the minimum value to the maximum value of the coral sand content attribute values of all the grid units;
the numbering unit is used for dividing the interval into attribute value intervals with corresponding number at equal intervals according to the arrangement number of the preset sensors and sequentially numbering the attribute value intervals;
the interval determining unit is used for sequentially determining a target attribute value interval in which the coral sand content attribute value of each grid unit is located;
the assignment unit is used for modifying the coral sand content attribute value of each grid unit into a serial number value corresponding to the target attribute value interval in which the coral sand content attribute value is positioned;
and the extracting unit is used for extracting corresponding region boundaries according to the modified attribute values of the coral sand content of each grid unit, and converting all the region boundaries into vector polygons so as to obtain the block polygons.
In one embodiment, preferably, the extraction unit is configured to:
aiming at the serial number i of each attribute value interval, carrying out boundary tracking on all grid units at the junction where the coral sand content attribute value i changes;
removing boundary regions outside the coral sand soil monitoring region by using a space surface-surface cutting method to obtain all boundary regions with attribute value interval numbers i;
obtaining a boundary polygon set according to all the boundary areas;
sequentially extracting first target polygons with the minimum areas from the boundary polygon set;
judging whether a second target polygon including the first target polygon exists or not by using a face-face inclusion relation;
when the second target polygon does not exist, converting the first target polygon into an independent vector polygon and deleting the independent vector polygon from the boundary polygon set; when the second target polygon exists, judging whether the number of the second target polygons is an even number;
when the number is an even number, converting the first target polygon into an independent vector polygon and deleting the independent vector polygon from the boundary polygon set; when the number is an odd number, taking a third target polygon with the smallest area from a plurality of second target polygons, combining the third target polygon and the first target polygon into a vector polygon with holes together, and deleting the third target polygon and the first target polygon from the boundary polygon set;
and counting the number of the vector polygons corresponding to each attribute value interval, directly taking the vector polygons as the block polygons when the number is one, and selecting the vector polygons with the largest areas as the block polygons when the number is multiple.
In one embodiment, preferably, the first calculation module is configured to:
and according to the coral sand content attribute values at the soil sampling points, interpolating and calculating the coral sand content attribute values of other grid units except the target grid unit in the coral sand soil monitoring area by adopting a Krigin interpolation method, wherein during interpolation calculation, a spherical model is adopted for half-variance fitting.
In one embodiment, preferably, the determining module is configured to:
determining the total number of the blocking polygons as the number of the sensors, and determining the centroid coordinate of each blocking polygon as the position of the sensor.
The technical scheme provided by the embodiment of the disclosure can have the following beneficial effects:
in the embodiment of the invention, the coral sand content attribute data of the island soil monitoring area are sampled in advance, the island soil monitoring area is divided into a plurality of blocks with different soil textures according to the coral sand content attribute data, and the arrangement position and the arrangement number of the soil monitoring instruments are determined by calculating the mass center of each block polygon, so that the island soil monitoring area can be completely covered by the optimal number of the soil monitoring sensors, the arrangement position of the soil monitoring sensors is representative, and the data such as the soil temperature, the soil humidity and the water potential obtained by monitoring can more fully reflect the drought degree of the soil area.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present disclosure and together with the description, serve to explain the principles of the disclosure.
FIG. 1 is a flow chart illustrating a method for sensor quantity and location determination for a coral sand soil monitoring area in accordance with an exemplary embodiment.
FIG. 2 is a schematic diagram illustrating the division of a soil monitoring area into grid cells according to an exemplary embodiment.
FIG. 3 is a flow chart illustrating yet another method for sensor quantity and location determination for a coral sand soil monitoring area in accordance with an exemplary embodiment.
FIG. 4 is a diagram illustrating attribute values of grid cells in accordance with an exemplary embodiment.
FIG. 5 is a flowchart illustrating step S35 of a method for determining a number of sensors and a location of a coral sand soil monitoring area in accordance with an exemplary embodiment.
FIG. 6 is a schematic diagram illustrating a bounding region in accordance with an exemplary embodiment.
FIG. 7 is a schematic diagram of a vector polygon shown in accordance with an exemplary embodiment.
FIG. 8 is a flow chart illustrating another method of sensor number and location determination for a coral sand soil monitoring area in accordance with an exemplary embodiment.
FIG. 9 is a schematic diagram illustrating the location and number of sensors according to an exemplary embodiment.
FIG. 10 is a block diagram illustrating a sensor number and location determination apparatus for a coral sand soil monitoring area in accordance with an exemplary embodiment.
FIG. 11 is a block diagram illustrating a partitioning module in a coral sand soil monitoring area sensor number and location determination device in accordance with an exemplary embodiment.
Detailed Description
Reference will now be made in detail to the exemplary embodiments, examples of which are illustrated in the accompanying drawings. When the following description refers to the accompanying drawings, like numbers in different drawings represent the same or similar elements unless otherwise indicated. The implementations described in the exemplary embodiments below are not intended to represent all implementations consistent with the present disclosure. Rather, they are merely examples of apparatus and methods consistent with certain aspects of the present disclosure, as detailed in the appended claims.
FIG. 1 is a flow chart illustrating a method for determining the number and location of sensors in a coral sand soil monitoring area, as shown in FIG. 1, comprising the following steps, according to an exemplary embodiment.
Step S11, obtaining a coral sand soil monitoring area, and dividing the soil monitoring area into M grid units with the same size by adopting a spatial grid.
For example, a soil monitoring area range in which sensors are to be arranged on an island is extracted from a high-resolution remote sensing image, and a spatial grid is adopted to divide the soil monitoring area into regular grids with the same size of m rows × n columns, as shown in fig. 2, and a calculation method of the number m of rows, the number n of columns, and the size cellsize of grid cells of the grids is shown in formula (1).
In the above formula, the function max is to find the maximum value of the two parameters, the function sqrt is to find the square root, s is to represent the area of the soil monitoring area, and w and h respectively represent the width and height of the minimum rectangular bounding box of the soil monitoring area.
And step S12, uniformly selecting the central positions of N target grid units as soil sampling points in the coral sand soil monitoring area, and acquiring coral sand content attribute values of coral sand soil at the soil sampling points.
Uniformly selecting the central positions of a plurality of grid units in the soil monitoring area as coral sand soil sampling points, wherein the number of the selected grid units is defined to be 1/3 times of the total number of the grid units in the soil monitoring area; then, according to the selected sample points, field sampling of the coral sand soil is carried out, and the coral sand soil sample is obtained at a position 25cm away from the ground at the sampling points; and finally, obtaining sample data of the coral sand content of the soil at each sampling point through indoor analysis.
And step S13, calculating coral sand content attribute values of other grid units in the coral sand soil monitoring area except the target grid unit according to the coral sand content attribute value at the soil sampling point.
In one embodiment, preferably, calculating coral sand content attribute values of grid cells in the coral sand soil monitoring area except for the target grid cell according to the coral sand content attribute values at the soil sampling points includes:
and according to the coral sand content attribute values at the soil sampling points, interpolating and calculating the coral sand content attribute values of other grid units except the target grid unit in the coral sand soil monitoring area by adopting a Krigin interpolation method, wherein during interpolation calculation, a spherical model is adopted for half-variance fitting.
And step S14, dividing the coral sand soil monitoring area into a plurality of block polygons with different soil textures according to the coral sand content attribute value of each grid unit.
Step S15, the total number of block polygons and the centroid coordinates of each block polygon are calculated.
And step S16, determining the positions and the number of the sensors according to the centroid coordinates of each block polygon and the total number.
In this embodiment, coral sand content attribute data through sampling island reef soil monitoring area in advance, and divide into the different piecemeal of a plurality of soil texture with island reef soil monitoring area according to coral sand content attribute data, thereby confirm laying the position and laying the quantity of soil monitoring instrument through calculating the polygonal barycenter of every piecemeal, thus, can just can cover island reef soil monitoring area completely with the optimal soil monitoring sensor quantity, and the laying position of soil monitoring sensor has more the representativeness, the soil temperature that the monitoring obtained, data such as humidity and water potential more can fully reflect the regional arid degree of soil.
FIG. 3 is a flow chart illustrating a method for sensor quantity and location determination for a coral sand soil monitoring area in accordance with an exemplary embodiment.
As shown in fig. 3, in one embodiment, preferably, the step S14 includes:
and step S31, acquiring the interval from the minimum value to the maximum value of the coral sand content attribute values of all the grid units.
Specifically, all grid cells in the soil monitoring area are traversed to obtain the minimum value and the maximum value interval [ x ] of the coral sand content attribute valuemin，xmax]。
And step S32, dividing the interval into attribute value intervals with corresponding number at equal intervals according to the preset sensor distribution number, and sequentially numbering the attribute value intervals.
The interval is divided into n attribute value intervals [ x ] at equal intervalsmin，x1)1、[x1，x2)2、[x2，x3)3、…、[xn-1，xmax]nAnd the n attribute value intervals are numbered as 1, 2, 3, … and n in sequence. Wherein n represents the preset sensor layout number.
And step S33, sequentially determining a target attribute value interval in which the coral sand content attribute value of each grid cell is located.
And step S34, modifying the attribute value of the coral sand content of each grid unit into the number value corresponding to the target attribute value interval in which the coral sand content is positioned.
And traversing all grid units in the soil monitoring area, judging the attribute value interval of the coral sand content attribute value of the grid unit, and assigning the serial number of the attribute value interval as the attribute value to the grid unit again. Fig. 4 shows assignment of attributes of grid cells in a soil monitoring area by using the numbers of attribute value intervals.
And step S35, extracting corresponding region boundaries according to the modified attribute values of the coral sand content of each grid unit, and converting all the region boundaries into vector polygons, thereby obtaining the block polygons.
FIG. 5 is a flowchart illustrating step S35 of a method for determining a number of sensors and a location of a coral sand soil monitoring area in accordance with an exemplary embodiment.
As shown in fig. 5, in one embodiment, preferably, the step S35 includes:
and step S51, carrying out boundary tracking on all grid cells at the boundary of the change of the coral sand content attribute value i aiming at each attribute value interval number i.
And step S52, removing the boundary regions outside the coral sand soil monitoring region by using a space surface-surface cutting method to obtain all the boundary regions with the attribute value interval number i.
And traversing the attribute value interval numbers in sequence, setting the current attribute value interval number as i, carrying out boundary tracking on all grid units in the soil monitoring area at the junction of the change of the attribute value i, and removing the boundary areas outside the soil monitoring area by using a space surface-surface cutting method to obtain all the boundary areas when the attribute value interval number is i. Fig. 6 shows boundary tracing when the attribute value of a grid cell in a soil monitoring area is 2, and 2 boundary areas (bold black lines in the figure) are obtained.
And step S53, obtaining a boundary polygon set according to all the boundary areas.
Step S54, sequentially extracting the first target polygon with the smallest area from the boundary polygon set.
Step S55, using the face-face inclusion relationship, determines whether or not there is a second target polygon including the first target polygon.
Step S56, when the second target polygon does not exist, converting the first target polygon into an independent vector polygon and deleting the first target polygon from the boundary polygon set; and judging whether the number of the second target polygons is an even number or not when the second target polygons exist.
Step S57, when the number is even, converting the first target polygon into independent vector polygon, and deleting the independent vector polygon from the boundary polygon set; and when the number of the second target polygons is an odd number, taking a third target polygon with the smallest area from the plurality of second target polygons, combining the third target polygon and the first target polygon into a vector polygon with holes together, and deleting the third target polygon and the first target polygon from the boundary polygon set.
Step S58, counting the number of vector polygons corresponding to each attribute value interval, and when the number is one, directly using the vector polygon as the block polygon, and when the number is multiple, selecting the vector polygon with the largest area as the block polygon.
Specifically, a polygon set polygon is formed by all boundary regions tracked when the attribute value interval is numbered i, and a polygon with the smallest area is sequentially taken out from the polygon set for judgment, wherein the judgment method comprises the following steps:
firstly, judging whether other boundary polygons including polygon exist by using a face-face inclusion relation, if not, directly converting the polygon into an independent vector polygon, and deleting the polygon from a set Polygonset; if so, judging whether the number of the boundary polygons containing the polygon is an even number, if so, directly converting the polygon into an independent vector polygon and deleting the polygon from the set PolygonSet, otherwise, taking the polygon with the smallest area in the boundary polygons containing the polygon, combining the polygon and the polygon into a vector polygon with 'holes', and deleting the two boundary polygons from the set PolygonSet.
Repeating the steps until the set PolygonSet is empty, obtaining all vector polygons with the attribute value interval number of i, and assigning the attribute values of all the vector polygons obtained at the moment to the attribute value interval number of i.
As shown in fig. 7, 2 boundary regions (bold black lines in the figure) are obtained by tracking when the grid cell attribute value is 6, and it can be determined that the 2 boundary regions jointly form a vector polygon with "holes" (filled regions in the figure) by using the above method.
And then sequentially traversing the attribute value intervals, and screening out the vector polygons corresponding to the attribute value intervals. If a plurality of vector polygons exist in the attribute value interval, only calculating the centroid of the vector polygon with the largest area according to the principle that the soil texture in the attribute value interval is approximately the same, and otherwise, continuously traversing the next attribute value interval.
FIG. 8 is a flow chart illustrating another method of sensor number and location determination for a coral sand soil monitoring area in accordance with an exemplary embodiment.
As shown in fig. 8, in one embodiment, preferably, the step S16 includes:
step S81, determining the total number of the blocking polygons as the number of the sensors, and determining the centroid coordinates of each blocking polygon as the position of the sensor.
In the embodiment, the number of the centroids is counted, and the soil monitoring sensors are arranged at the positions of the centroids in the soil monitoring area according to the coordinate positions of the centroids. FIG. 9 shows that the method of the present invention is used to divide the sand-soil monitoring area of the island coral into several segments with different soil textures, and the centroid point of the polygon of the segment is the optimal position for the arrangement of the soil monitoring sensors (shown by the dots in the figure).
Based on the same conception, the embodiment of the disclosure also provides a device for determining the number and the positions of the sensors in the coral sand soil monitoring area.
It is understood that the sensor number and position determining device for the coral sand soil monitoring area provided by the embodiment of the disclosure comprises hardware structures and/or software modules corresponding to the respective functions in order to realize the functions. The disclosed embodiments can be implemented in hardware or a combination of hardware and computer software, in combination with the exemplary elements and algorithm steps disclosed in the disclosed embodiments. Whether a function is performed as hardware or computer software drives hardware depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present disclosure.
FIG. 10 is a block diagram illustrating a sensor number and location determination apparatus for a coral sand soil monitoring area in accordance with an exemplary embodiment. As shown in fig. 10, the device for determining the number and position of sensors in a coral sand soil monitoring area according to the present invention comprises:
the coral sand monitoring system comprises an acquisition module 101, a data acquisition module and a data processing module, wherein the acquisition module 101 is used for acquiring a coral sand soil monitoring area and dividing the soil monitoring area into M grid units with the same size by adopting a spatial grid;
a selecting module 102, configured to uniformly select center positions of N target grid units as soil sampling points in the coral sand soil monitoring area, and obtain coral sand content attribute values of coral sand soil at the soil sampling points;
a first calculating module 103, configured to calculate coral sand content attribute values of grid cells in the coral sand soil monitoring area, except for the target grid cell, according to the coral sand content attribute value at the soil sampling point;
the dividing module 104 is used for dividing the coral sand soil monitoring area into a plurality of block polygons with different soil textures according to the coral sand content attribute value of each grid unit;
a second calculating module 105, configured to calculate a total number of blocked polygons and a centroid coordinate of each blocked polygon;
a determining module 106, configured to determine the positions and the number of the sensors according to the coordinates of the centroid of each of the blocking polygons and the total number.
FIG. 11 is a block diagram illustrating a partitioning module in a coral sand soil monitoring area sensor number and location determination device in accordance with an exemplary embodiment.
As shown in fig. 11, in one embodiment, preferably, the dividing module 104 includes:
the acquiring unit 111 is used for acquiring the interval from the minimum value to the maximum value of the coral sand content attribute values of all the grid units;
the numbering unit 112 is used for dividing the interval into attribute value intervals with corresponding number at equal intervals according to the arrangement number of the preset sensors, and sequentially numbering the attribute value intervals;
an interval determination unit 113, configured to sequentially determine a target attribute value interval in which the coral sand content attribute value of each grid cell is located;
the assignment unit 114 is used for modifying the coral sand content attribute value of each grid unit into a serial number value corresponding to the target attribute value interval in which the coral sand content attribute value is positioned;
and the extracting unit 115 is configured to extract corresponding region boundaries according to the modified attribute values of the coral sand content of each grid cell, and convert all the region boundaries into vector polygons, thereby obtaining the block polygons.
In one embodiment, preferably, the extracting unit 115 is configured to:
aiming at the serial number i of each attribute value interval, carrying out boundary tracking on all grid units at the junction where the coral sand content attribute value i changes;
removing boundary regions outside the coral sand soil monitoring region by using a space surface-surface cutting method to obtain all boundary regions with attribute value interval numbers i;
obtaining a boundary polygon set according to all the boundary areas;
sequentially extracting first target polygons with the minimum areas from the boundary polygon set;
judging whether a second target polygon including the first target polygon exists or not by using a face-face inclusion relation;
when the second target polygon does not exist, converting the first target polygon into an independent vector polygon and deleting the independent vector polygon from the boundary polygon set; when the second target polygon exists, judging whether the number of the second target polygons is an even number;
when the number is an even number, converting the first target polygon into an independent vector polygon and deleting the independent vector polygon from the boundary polygon set; when the number is an odd number, taking a third target polygon with the smallest area from a plurality of second target polygons, combining the third target polygon and the first target polygon into a vector polygon with holes together, and deleting the third target polygon and the first target polygon from the boundary polygon set;
and counting the number of the vector polygons corresponding to each attribute value interval, directly taking the vector polygons as the block polygons when the number is one, and selecting the vector polygons with the largest areas as the block polygons when the number is multiple.
In one embodiment, preferably, the first calculation module 103 is configured to:
and according to the coral sand content attribute values at the soil sampling points, interpolating and calculating the coral sand content attribute values of other grid units except the target grid unit in the coral sand soil monitoring area by adopting a Krigin interpolation method, wherein during interpolation calculation, a spherical model is adopted for half-variance fitting.
In one embodiment, preferably, the determining module 106 is configured to:
determining the total number of the blocking polygons as the number of the sensors, and determining the centroid coordinate of each blocking polygon as the position of the sensor.
It is further understood that the use of "a plurality" in this disclosure means two or more, as other terms are analogous. "and/or" describes the association relationship of the associated objects, meaning that there may be three relationships, e.g., a and/or B, which may mean: a exists alone, A and B exist simultaneously, and B exists alone. The character "/" generally indicates that the former and latter associated objects are in an "or" relationship. The singular forms "a", "an", and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise.
It will be further understood that the terms "first," "second," and the like are used to describe various information and that such information should not be limited by these terms. These terms are only used to distinguish one type of information from another and do not denote a particular order or importance. Indeed, the terms "first," "second," and the like are fully interchangeable. For example, first information may also be referred to as second information, and similarly, second information may also be referred to as first information, without departing from the scope of the present disclosure.
It is further to be understood that while operations are depicted in the drawings in a particular order, this is not to be understood as requiring that such operations be performed in the particular order shown or in serial order, or that all illustrated operations be performed, to achieve desirable results. In certain environments, multitasking and parallel processing may be advantageous.
Other embodiments of the disclosure will be apparent to those skilled in the art from consideration of the specification and practice of the disclosure disclosed herein. This application is intended to cover any variations, uses, or adaptations of the disclosure following, in general, the principles of the disclosure and including such departures from the present disclosure as come within known or customary practice within the art to which the disclosure pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the disclosure being indicated by the following claims.
It will be understood that the present disclosure is not limited to the precise arrangements described above and shown in the drawings and that various modifications and changes may be made without departing from the scope thereof. The scope of the present disclosure is limited only by the appended claims.
Claims (10)
1. A method for determining the number and the positions of sensors in a coral sand soil monitoring area is characterized by comprising the following steps:
obtaining a coral sand soil monitoring area, and dividing the soil monitoring area into M grid units with the same size by adopting a spatial grid;
uniformly selecting the central positions of N target grid units as soil sampling points in the coral sand soil monitoring area, and acquiring coral sand content attribute values of coral sand soil at the soil sampling points;
calculating coral sand content attribute values of other grid units in the coral sand soil monitoring area except the target grid unit according to the coral sand content attribute values at the soil sampling points;
dividing the coral sand soil monitoring area into a plurality of block polygons with different soil textures according to the coral sand content attribute value of each grid unit;
calculating the total number of the block polygons and the centroid coordinate of each block polygon;
and determining the positions and the number of the sensors according to the centroid coordinates of each block polygon and the total number.
2. The method of claim 1, wherein dividing the coral sand soil monitoring area into a plurality of partitioned polygons of different soil textures according to coral sand content attribute values for each grid cell comprises:
obtaining the interval from the minimum value to the maximum value of the coral sand content attribute values of all the grid units;
according to the arrangement quantity of preset sensors, dividing the interval into attribute value intervals with corresponding quantity at equal intervals, and sequentially numbering each attribute value interval;
sequentially determining a target attribute value interval in which the coral sand content attribute value of each grid unit is located;
modifying the attribute value of the coral sand content of each grid unit into a serial number value corresponding to the target attribute value interval in which the coral sand content is positioned;
and extracting corresponding region boundaries according to the modified attribute values of the coral sand content of each grid unit, and converting all the region boundaries into vector polygons, thereby obtaining the block polygons.
3. The method of claim 2, wherein extracting corresponding region boundaries from the modified coral sand content attribute values for each grid cell and converting all region boundaries into vector polygons to obtain the block polygons, comprises:
aiming at the serial number i of each attribute value interval, carrying out boundary tracking on all grid units at the junction where the coral sand content attribute value i changes;
removing boundary regions outside the coral sand soil monitoring region by using a space surface-surface cutting method to obtain all boundary regions with attribute value interval numbers i;
obtaining a boundary polygon set according to all the boundary areas;
sequentially extracting first target polygons with the minimum areas from the boundary polygon set;
judging whether a second target polygon including the first target polygon exists or not by using a face-face inclusion relation;
when the second target polygon does not exist, converting the first target polygon into an independent vector polygon and deleting the independent vector polygon from the boundary polygon set; when the second target polygon exists, judging whether the number of the second target polygons is an even number;
when the number is an even number, converting the first target polygon into an independent vector polygon and deleting the independent vector polygon from the boundary polygon set; when the number is an odd number, taking a third target polygon with the smallest area from a plurality of second target polygons, combining the third target polygon and the first target polygon into a vector polygon with holes together, and deleting the third target polygon and the first target polygon from the boundary polygon set;
and counting the number of the vector polygons corresponding to each attribute value interval, directly taking the vector polygons as the block polygons when the number of the vector polygons is one, and selecting the vector polygons with the largest areas as the block polygons when the number of the vector polygons is multiple.
4. The method as claimed in claim 1, wherein calculating coral sand content attribute values of grid cells other than the target grid cell in the coral sand soil monitoring area based on the coral sand content attribute values at the soil sampling points comprises:
and according to the coral sand content attribute values at the soil sampling points, interpolating and calculating the coral sand content attribute values of other grid units except the target grid unit in the coral sand soil monitoring area by adopting a Krigin interpolation method, wherein during interpolation calculation, a spherical model is adopted for half-variance fitting.
5. The method of any one of claims 1 to 4, wherein determining the location and number of sensors from the centroid coordinates and the total number of each blocking polygon comprises:
determining the total number of the blocking polygons as the number of the sensors, and determining the centroid coordinate of each blocking polygon as the position of the sensor.
6. A coral sand soil monitoring area sensor quantity and position determination device, comprising:
the coral sand monitoring system comprises an acquisition module, a data acquisition module and a data processing module, wherein the acquisition module is used for acquiring a coral sand soil monitoring area and dividing the soil monitoring area into M grid units with the same size by adopting a spatial grid;
the selecting module is used for uniformly selecting the central positions of N target grid units as soil sampling points in the coral sand soil monitoring area and acquiring coral sand content attribute values of coral sand soil at the soil sampling points;
the first calculation module is used for calculating coral sand content attribute values of other grid units in the coral sand soil monitoring area except the target grid unit according to the coral sand content attribute value at the soil sampling point;
the dividing module is used for dividing the coral sand soil monitoring area into a plurality of block polygons with different soil textures according to the coral sand content attribute value of each grid unit;
the second calculation module is used for calculating the total number of the block polygons and the mass center coordinate of each block polygon;
and the determining module is used for determining the positions and the number of the sensors according to the centroid coordinate of each block polygon and the total number.
7. The apparatus of claim 6, wherein the partitioning module comprises:
the acquiring unit is used for acquiring the interval from the minimum value to the maximum value of the coral sand content attribute values of all the grid units;
the numbering unit is used for dividing the interval into attribute value intervals with corresponding number at equal intervals according to the arrangement number of the preset sensors and sequentially numbering the attribute value intervals;
the interval determining unit is used for sequentially determining a target attribute value interval in which the coral sand content attribute value of each grid unit is located;
the assignment unit is used for modifying the coral sand content attribute value of each grid unit into a serial number value corresponding to the target attribute value interval in which the coral sand content attribute value is positioned;
and the extracting unit is used for extracting corresponding region boundaries according to the modified attribute values of the coral sand content of each grid unit, and converting all the region boundaries into vector polygons so as to obtain the block polygons.
8. The apparatus of claim 7, wherein the extraction unit is configured to:
aiming at the serial number i of each attribute value interval, carrying out boundary tracking on all grid units at the junction where the coral sand content attribute value i changes;
removing boundary regions outside the coral sand soil monitoring region by using a space surface-surface cutting method to obtain all boundary regions with attribute value interval numbers i;
obtaining a boundary polygon set according to all the boundary areas;
sequentially extracting first target polygons with the minimum areas from the boundary polygon set;
judging whether a second target polygon including the first target polygon exists or not by using a face-face inclusion relation;
when the second target polygon does not exist, converting the first target polygon into an independent vector polygon and deleting the independent vector polygon from the boundary polygon set; when the second target polygon exists, judging whether the number of the second target polygons is an even number;
when the number is an even number, converting the first target polygon into an independent vector polygon and deleting the independent vector polygon from the boundary polygon set; when the number is an odd number, taking a third target polygon with the smallest area from a plurality of second target polygons, combining the third target polygon and the first target polygon into a vector polygon with holes together, and deleting the third target polygon and the first target polygon from the boundary polygon set;
and counting the number of the vector polygons corresponding to each attribute value interval, directly taking the vector polygons as the block polygons when the number of the vector polygons is one, and selecting the vector polygons with the largest areas as the block polygons when the number of the vector polygons is multiple.
9. The apparatus of claim 6, wherein the first computing module is configured to:
and according to the coral sand content attribute values at the soil sampling points, interpolating and calculating the coral sand content attribute values of other grid units except the target grid unit in the coral sand soil monitoring area by adopting a Krigin interpolation method, wherein during interpolation calculation, a spherical model is adopted for half-variance fitting.
10. The apparatus of any one of claims 6-9, wherein the determining module is configured to:
determining the total number of the blocking polygons as the number of the sensors, and determining the centroid coordinate of each blocking polygon as the position of the sensor.
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