CN113409240B - Agricultural machinery behavior analysis and operation area statistical method based on Beidou positioning data - Google Patents
Agricultural machinery behavior analysis and operation area statistical method based on Beidou positioning data Download PDFInfo
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Abstract
An agricultural machinery behavior analysis and operation area statistical method based on Beidou positioning data comprises the steps of automatic identification of an agricultural machinery operation area, area calculation, overlapped area analysis and omitted area analysis; the method for automatically identifying the agricultural machinery operation area is an agricultural machinery operation area automatic identification algorithm based on spatial clustering; the method of calculating the area includes a grid-based area calculation method and a contour-based area calculation method. In order to improve the accuracy and efficiency of area statistics of agricultural machinery operation areas, reduce the investment of manpower, material resources and time and meet the requirements of modern agricultural development, the invention provides a method for automatically analyzing agricultural machinery behaviors and counting the areas of the operation areas through Beidou positioning data, which can automatically identify each subarea of agricultural machinery operation and is suitable for area statistics under the condition of simultaneous existence of overlapping operation and omission operation.
Description
Technical Field
The invention relates to an agricultural machinery behavior analysis and operation area statistical method based on Beidou positioning data, and belongs to the technical field of agricultural machinery operation data acquisition and analysis.
Background
Most of traditional agriculture in China is still finished in a manual intervention mode, the automation degree is low, for example, in the aspects of statistics and calculation of the operation area of the agricultural machinery, most of the traditional agriculture is still realized in a manual division and tape measurement mode, a large amount of manpower and time are consumed, certain errors exist in manual measurement, the manual measurement is a static measurement mode, and the operation area of the agricultural machinery cannot be calculated in real time.
The precision agriculture is the most important part in the development of the science and technology of the agriculture in the twenty-first century, has high science and technology content and strong integration comprehensiveness, greatly improves the agricultural production efficiency, and becomes an important target of modern agricultural production management. The Beidou positioning and navigation system is one of core supporting technologies, and plays an irreplaceable role in the aspects of fine agricultural seeding and harvesting, real-time agricultural machine positioning and monitoring, automatic agricultural machine operation navigation, remote command and scheduling and the like. With the popularization of large-scale, intensive and regional agricultural production processes, timely mastering of agricultural machinery operation areas and task completion conditions has important significance on overall efficiency evaluation and subsequent operation scheduling. Most of traditional agricultural machinery operation area statistical methods are realized by manually reporting by agricultural machinery operating personnel or entrusting a third party to carry out on-site surveying and mapping, and relate to the problems of more human factors, larger errors and consumption of a large amount of manpower, material resources and time. In view of the above problems, the technical problems proposed by those skilled in the art are: how to use dynamic metering to achieve automatic and accurate measurement of the working area of agricultural machinery?
At present, in the aspect of dynamic measurement of the working area of agricultural machinery, wheel revolution measurement methods, ultrasonic measurement methods, laser measurement methods and measurement methods based on a positioning navigation system (such as GPS and Beidou) are mainly used. The wheel revolution measuring method is simple in principle and low in cost, but large errors occur in area measurement due to the fact that wheels slip in fields of agricultural machines, and the operation area of the agricultural machines under the condition of overlapping operation cannot be accurately measured. The method can effectively measure the area of the agricultural machinery during overlapping operation, but needs crops as reference to obtain the actual operation width, and is mostly used for calculating the operation area during harvesting. The measurement method based on positioning and navigation mainly comprises a boundary-based measurement method and a track-based measurement method. Most boundary-based measurement methods are that the agricultural machinery operation area is wound for a circle, and then a triangle segmentation algorithm or a Simpson algorithm is adopted to realize area calculation.
The boundary-based measurement method can be used for a working area with an arbitrary shape, and the larger the area is, the higher the calculation accuracy is.
The track-based measuring method mainly comprises a width method, a buffer area vector method and other calculation methods. The breadth method is to obtain the area of an operation area according to the multiplication of the length of an operation track of the agricultural machine and the operation breadth, the operation length is regarded as the sum of the distances between two adjacent data points, the method can dynamically calculate the operation area of the agricultural machine in real time, but the operation area cannot be accurately calculated when the overlapped operation area exists, and the method is mainly used for calculating the area when the agricultural machine with accurate autonomous navigation performs full-breadth operation. The vector method of the buffer area is mainly to construct the buffer area of the track line entity, which essentially translates the distance of half the operation width along the vertical direction to two sides of the track (assuming that the positioning terminal is on the central axis of the agricultural machinery) to obtain two parallel lines according to the running track of the agricultural machinery, and fits the two ends or one end of the parallel lines by adopting a smooth curve, and the finally obtained closed area is the buffer area of the line entity.
The buffer area vector method can be used for operation areas in any shapes, effective operation and missing operation area can be counted, but the design has high calculation complexity and relates to multiple intersection calculation.
In order to solve the above technical problems, the chinese patent literature discloses the following technical contents:
chinese patent document CN107036572B discloses a method and a device for obtaining the working area of an agricultural machine, which includes: receiving agricultural machinery operation track data sent by an agricultural machinery positioning device; improving a neighborhood radius determination method of a dbscan clustering algorithm based on the operation speed; filtering road driving points and field transition points in the agricultural machinery operation track data by adopting an improved dbscan clustering algorithm; determining the number of agricultural machinery operation field pieces according to the filtered agricultural machinery operation track data; and respectively calculating the area of each agricultural machine operation field by using a distance method. However, the method described in the patent document is not suitable for the acquisition requirement of the existing agricultural machinery operation track data: the agricultural machinery operation track data is required to contain speed and course information, so that the distance method is not suitable for area statistical analysis during overlapping operation.
Chinese patent document CN107462208A discloses an agricultural machine and an agricultural machine working area measuring device and measuring method, which collects longitude and latitude data in real time by the working area measuring device on the agricultural machine, and records the running track of the agricultural machine; removing the longitude and latitude information points with the deviation, and determining an agricultural machinery operation track; determining the contour line of the agricultural machinery operation track graph as an operation area boundary line; the work area is calculated based on the plurality of measurement reference points and the work area boundary line. The invention accurately determines the boundary line of the operation area by removing the offset point and supplementing the blank point, further calculates the area of the operation area by multiple measurement reference points, and has the advantages of high measurement speed and high precision. The technique of this document essentially uses a boundary-based area calculation method, and thus cannot analyze and count the area of the missing work area.
Chinese patent document CN107843228B discloses a method for acquiring a spatial trajectory area of a multi-layer scanning timing sequence, which includes: performing Gauss-Krueger projection on a running track point on an agricultural machinery operation track; acquiring a first external rectangle of the coordinates of the operation track points; respectively generating line buffer areas aiming at the coordinates of every two adjacent running track points; scanning and rasterizing each line buffer area, and calculating the sum of the grid areas covered by each line buffer area to obtain a first operation area; scanning each line buffer area again, regrooving the grids which are not completely covered in each line buffer area, and calculating the sum of the areas of the grids covered by each line buffer area to obtain a second operation area; and when the absolute value of the difference value between the second working area and the first working area is smaller than a set error threshold value, taking the second working area as the actual working area of the agricultural machine. The method for calculating the area in the patent document adopts a mode of combining a buffer vector method and a grid method, and relates to twice measurement, the calculation complexity is high, the operation area cannot be automatically identified, and the area of the omitted operation area cannot be analyzed and counted.
In conclusion, the invention utilizes the Beidou positioning terminal to realize real-time acquisition of the travel track of the agricultural machine, and further provides a method for analyzing the behavior of the agricultural machine, which can automatically identify the operation area of the agricultural machine only by depending on the longitude and latitude information, the time and the operation width of the operation of the agricultural machine, and analyze and count the areas of the agricultural machine during effective operation, missed operation and overlapped operation.
Disclosure of Invention
Aiming at the defects of the prior art, the invention discloses an agricultural machinery behavior analysis and operation area statistical method based on Beidou positioning data.
The invention aims to: in order to improve the accuracy and efficiency of statistics of the area of an agricultural machine operation area, reduce investment of manpower, material resources and time and meet the requirements of modern agricultural development, a method for automatically analyzing agricultural machine behaviors and counting the area of the operation area through Beidou positioning data is provided, each subarea of agricultural machine operation can be automatically identified, and the method is suitable for area statistics under the conditions of overlapping operation and missing operation.
The detailed technical scheme of the invention is as follows:
an agricultural machinery behavior analysis and operation area statistical method based on Beidou positioning data is characterized by comprising the steps of automatically identifying an agricultural machinery operation area, calculating the area, analyzing the overlapped area and the omitted area;
the method for automatically identifying the agricultural machinery operation area is an agricultural machinery operation area automatic identification algorithm based on spatial clustering;
the method for calculating the area comprises a grid-based area calculation method and a contour-based area calculation method;
the method for analyzing the overlapping area is calculated by subtracting the area obtained by the grid-based area calculation method from the area obtained by multiplying the track length by the width:
wherein d (Q) i ，Q i+1 ) Representing the distance between adjacent track points of the agricultural machinery operation;
the method for analyzing the missing area comprises the following steps:
S miss ＝S contour -S grid ；
wherein the Scontour is an area based on a contour; the Sgrid is to calculate the grid-based area.
According to the invention, the agricultural machinery operating area automatic identification algorithm based on spatial clustering preferably comprises the following steps:
1-1) agricultural machinery operation data acquisition
Track data set P ═ P of agricultural machinery operation is obtained through vehicle-mounted Beidou positioning terminal and GPRS mobile communication equipment 1 (t 1 ，lat 1 ，long 1 )，P 2 (t 2 ，lat 2 ，long 2 )，…，P n (t n ，lat n ，long n ) Wherein t represents time, lat represents latitude, long represents longitude, and n represents the total number of track points;
1-2) data preprocessing
The preprocessing refers to removing data abnormal points, drift points, stop points and random noise points;
1-3) projection
Obtaining a data point set Q ═ Q under a UTM coordinate system 1 (t 1 ，x 1 ，y 1 )，Q 2 (t 2 ，x 2 ，y 2 )，…，Q n (t n ，x n ，y n )；
1-4) spatial clustering
Identifying the operation area by utilizing spatial clustering, wherein the identification comprises the following specific steps:
1-4-1) drawing a circle by a certain radius r with each preprocessed data point as the center of the circle, wherein the density value of the point is formed by how many adjacent data points in the circle;
1-4-2) if the density value of the point is less than a set threshold value min _ pts, marking the point as a low density point, otherwise, marking the point as a high density point;
1-4-3) connecting two points if a high density point is within the circle of another high density point; if a certain low-density point is in the circle of another high-density point, connecting the low-density point to the high-density point closest to the low-density point to form a boundary point;
1-4-4) repeating the steps 1-4-2) and 1-4-3), removing low-density points which are not in the circle of any high-density point, and reserving a high-density point set as a track point of an agricultural machinery operation area;
1-5) calculating the contour-based area Scontour;
1-6) calculating the grid-based area Sgrid;
1-7) the method of analyzing the analysis overlap area is:
the area obtained by subtracting the area obtained by the grid-based area calculation method from the area obtained by multiplying the track length by the width is calculated as follows:
wherein d (Q) i ，Q i+1 ) Representing the distance between adjacent track points of the agricultural machinery operation;
the method for analyzing the missing area comprises the following steps:
S miss ＝S contour -S grid 。
according to the invention, the method for preprocessing the data in the step 1-2) comprises, but is not limited to, removing the data in the following order:
1-2-1) eliminating abnormal points: any agricultural machinery track point should satisfy P (t, lat, long):
lat∈[-90°，90°]
long∈[-180°，180°]
data points which do not meet the formula are taken as abnormal points to be removed;
1-2-2) removing drift points: for 2 adjacent track points P i (t i ，lat i ，long i )，P i+1 (t i+1 ，lat i+1 ，long i+1 ) Calculating the running speed of the agricultural machine:
wherein d (P) i ，P i+1 ) Representing adjacent track points P i 、P i+1 A distance between, v (P) i P i+1 )＞v max Trace point elimination of (v), wherein max The maximum operating speed of the agricultural machine;
1-2-3) elimination stop point: calculating the average speed of k continuous agricultural machine operation track points:
removing track points with the average speed smaller than a certain threshold value delta;
1-2-4) eliminating random noise points: for 2 adjacent track points P i (t i ，lat i ，long i )，P i+1 (t i+1 ，lat i+1 ，long i+1 ) Calculate its direction:
the expression method for converting the vector into the unit vector comprises the following steps: theta.theta. i，i+1 →(cos(θ i，i+1 )，sin(θ i，i+1 ) Then, calculating the direction mean value of k continuous agricultural machine operation track points as follows:
the standard deviation was calculated as:
and eliminating points with standard deviation larger than a certain threshold value.
According to a preferred embodiment of the present invention, the method 15) for calculating the contour-based area Scontour includes the following steps:
1-5-1) foveal bag calculation
And performing concave packet calculation on each type of data points according to the data points obtained by clustering, wherein the specific steps are as follows:
(1) finding out the point with the minimum y value, and taking the point with the maximum x value as a starting point O if the y values are the same;
(2) starting from an initial point O, taking (1, 0) as a reference vector, firstly finding an edge with a radius of R smaller than that of the initial edge, and taking the point as A;
(3) looping to find the next edge, assuming the previous edge is AB, then the next edge must start at point B and connect to a point C in the R neighborhood of B, using the following rule: firstly, the points in the R neighborhood of B are sorted in the polar coordinate direction by taking B as the center and BA vector as the reference, and then the points C in the R neighborhood of B are sequentially sorted 0 ～C n Set up with BC i The circle is a chord, whether other neighborhood points are included is checked, if the other neighborhood points do not exist, the chord is a new edge, and a cycle is jumped out;
(4) finding all edges in sequence until no new edge can be found or a point which is used as an edge before is encountered;
1-5-2) area calculation
Computing the concave packet of each category to obtain a plurality of polygons, and thenCalculating the area of the polygon by adopting a triangle segmentation algorithm or Simpson algorithm to obtain S contour_in I.e. by
Wherein w represents the working width, d (Q) i ，Q i+1 ) Indicating adjacent boundary points Q i 、Q i+1 The distance between them.
Preferably, according to the present invention, the method for calculating the grid-based area Sgrid in step 16) is as follows:
1-6-1), wherein the area expansion is to perform rasterization processing on an actual operation area according to agricultural machinery track data and breadth, and the specific steps are as follows:
(a-1) finding the minimum x of x, y min ，y min And a maximum value x max ，y max ；
(a-2) determining the size of the grid matrix to be opened up according to the ratio mu of the pixel elements to the actual size:
where ε represents an additional boundary that ensures that the data points are all located within the matrix; opening up a two-dimensional array representing a grid matrix and initializing to 0;
(a-3) calculating the area to be expanded according to the agricultural machine track and the width:
known adjacent agricultural machinery operation track point Q i (t i ，x i ，y i )，Q i+1 (t i+1 ，x i+1 ，y i+1 ) And an operating width w, the region expansion of which is substantially Q' i ，Q″ i ，Q′ i+1 ，Q″ i+1 Coordinates of four points:
Q′ i (x′ i ，y′ i )＝(x i +Δ x ，y i -Δ y )
Q″ i (x″ i ，y″ i )＝(x i -Δ x ，y i +Δ y )
Q′ i+1 (x′ i+1 ，y′ i+1 )＝(x i+1 +Δ x ，y i+1 -Δ y )
Q″ i+1 (x″ i+1 ，y″ i+1 )＝(x i+1 -Δ x ，y i+1 +Δ y )
according to Q' i ，Q″ i ，Q′ i+1 ，Q″ i+1 Rectangles and Q 'generated from these four points' i+1 ，Q″ i+1 Generated by the two points
Superposing the semi-circle with the radius and the grid matrix: obtaining a rasterized agricultural machinery operation track diagram;
1-6-2) calculated area
According to the grid matrix, counting the number of grid unit values of 1, and then calculating the area according to the proportion of the pixel elements to the actual size:
S grid ＝N*w 2
where N is the number of grid cell values 1.
The technical advantages of the invention are as follows:
1. the agricultural machinery operation area can be automatically identified, the investment of manpower, material resources and time is reduced, and the identification result is shown in the attached drawing.
2. The invention can carry out real-time statistical analysis on the effective farming area, the area of the lost and lost area and the overlapping farming area of the agricultural machinery operation area, and provides a foundation for accurate agricultural analysis.
3. The invention is suitable for the positioning terminal with low cost and is insensitive to the noise and drift of the positioning data.
4. The agricultural machinery operation area can be effectively analyzed only by positioning data and operation width of the agricultural machinery all day long, and other hardware is not needed to inform the algorithm model whether the agricultural machinery is in an operation state or not.
5. The method can not only count the total effective operation area of the agricultural machinery all day, but also effectively count the area of each sub-operation area.
Drawings
FIG. 1 is a flow chart of the scheme of the present invention;
FIG. 2 is a schematic view of an agricultural machinery supervision platform;
FIG. 3 is a schematic view of an agricultural machine travel track;
FIG. 4 is a peripheral profile of an agricultural machine travel track;
FIG. 5 is a schematic illustration of zone expansion;
FIG. 6 is a schematic diagram of a rasterized agricultural machine trajectory;
FIG. 7 is a schematic illustration of finding a new edge during the computation of a notch;
FIG. 8 is a schematic coordinate diagram of a boundary-based work area calculation method;
fig. 9a to 9f are images of agricultural machinery working areas obtained after eliminating interference of overlapping areas and missing areas in the embodiment of the invention.
Detailed Description
The invention is described in detail below with reference to the following examples and drawings, but is not limited thereto.
Examples of the following,
As shown in fig. 1, an agricultural machinery behavior analysis and operation area statistical method based on Beidou positioning data comprises automatic identification of an agricultural machinery operation area, area calculation, overlapped area analysis and missing area analysis;
the method for automatically identifying the agricultural machinery operation area is an agricultural machinery operation area automatic identification algorithm based on spatial clustering;
the method for calculating the area comprises a grid-based area calculation method and a contour-based area calculation method;
the method for analyzing the overlapping area is calculated by subtracting the area obtained by the grid-based area calculation method from the area obtained by multiplying the track length by the width:
wherein d (Q) i ，Q i+1 ) Representing the distance between adjacent track points of the agricultural machinery operation;
the method for analyzing the missing area comprises the following steps:
S miss ＝S contour -S grid ；
wherein the Scontour is an area based on a contour; the Sgrid is to calculate the grid-based area.
The agricultural machinery operation area automatic identification algorithm based on spatial clustering comprises the following steps:
1-1) agricultural machinery operation data acquisition
As shown in fig. 2, a trajectory data set P ═ P of agricultural machinery operation is obtained through a vehicle-mounted Beidou positioning terminal and a GPRS mobile communication device 1 (t 1 ，lat 1 ，long 1 )，P 2 (t 2 ，lat 2 ，long 2 )，…，P n (t n ，lat n ，long n ) Wherein t represents time, lat represents latitude, long represents longitude, and n represents the total number of track points;
1-2) data preprocessing
The preprocessing refers to removing data abnormal points, drift points, stop points and random noise points;
1-3) projection
In order to facilitate the calculation of the subsequent area and the distance between two points, the longitude and latitude information (WGS84 coordinate system) needs to be converted into a plane rectangular coordinate system (UTM coordinate system)System) to obtain a data point set Q ═ Q in the UTM coordinate system 1 (t 1 ，x 1 ，y 1 )，Q 2 (t 2 ，x 2 ，y 2 )，…，Q n (t n ，x n ，y n )；
1-4) spatial clustering
According to the division of the agricultural machinery driving data points, the road driving points and the field transfer driving points are different from the actual operation points in the spatial distribution: as shown in fig. 3 and 4, the track points in the working area are distributed more densely, while the track points in the road driving and field transfer areas are distributed sparsely, and the working area is identified by using spatial clustering, wherein the identification comprises the following specific steps:
1-4-1) drawing a circle by a certain radius r with each preprocessed data point as the center of the circle, wherein the density value of the point is formed by how many adjacent data points in the circle;
1-4-2) if the density value of the point is less than a set threshold value min _ pts, marking the point as a low density point, otherwise, marking the point as a high density point;
1-4-3) connecting two points if a high density point is within the circle of another high density point; if a certain low-density point is in the circle of another high-density point, connecting the low-density point to the high-density point closest to the low-density point to form a boundary point;
1-4-4) repeating the steps 1-4-2) and 1-4-3), eliminating low-density points which are not in the circle of any high-density point, reserving a high-density point set as track points of an agricultural machine operation area, wherein all the points which can be connected together form a class, and the low-density points which are not in the circle of any high-density point are abnormal points (namely road driving points or field transfer points) and can be eliminated, so that the high-density points (namely the track points of the agricultural machine operation area) are obtained;
1-5) calculating the contour-based area Scontour;
1-6) calculating the grid-based area Sgrid;
1-7) the method of analyzing the analysis overlap area is:
the area obtained by subtracting the area obtained by the grid-based area calculation method from the area obtained by multiplying the track length by the width is calculated as follows:
wherein d (Q) i ，Q i+1 ) Representing the distance between adjacent track points of the agricultural machinery operation;
the method for analyzing the missing area comprises the following steps:
since the contour-based area calculation method may take into account the missing operation region, the area of the missing region is:
S miss ＝S contour -S grid 。
the data preprocessing method of the step 1-2) comprises, but is not limited to, the data removing sequence:
1-2-1) rejecting abnormal points: any agricultural machinery track point should satisfy P (t, lat, long):
lat∈[-90°，90°]
long∈[-180°，180°]
data points which do not meet the formula are taken as abnormal points to be removed;
1-2-2) removing drift points: for 2 adjacent track points P i (t i ，lat i ，long i )，P i+1 (t i+1 ，lat i+1 ，long i+1 ) Calculating the operating speed of the agricultural machine:
wherein d (P) i ，P i+1 ) Representing adjacent track points P i 、P i+1 A distance between, v (P) i P i+1 )＞v max Trace point elimination of (v), wherein max The maximum operating speed of the agricultural machine;
1-2-3) elimination stop point: because the agricultural machinery is when the stall condition, the big dipper positioning terminal is the upload data that still does not stop, so need eliminate the stall point, when the agricultural machinery is stopped, the velocity of motion lasts for 0 in certain time, but when the agricultural machinery turns, speed also can be close to 0, so adopt average speed when eliminating the stall point, calculate its average speed to k continuous agricultural machinery operation track points:
removing track points with the average speed smaller than a certain threshold value delta (a small value can be preset by a user according to the actual working condition of the agricultural machine);
1-2-4) eliminating random noise points: the Beidou positioning and navigation system inevitably generates certain random noise due to various interferences, particularly when the agricultural machinery is in a stopped state, the data points of the agricultural machinery are not always fixed at one position but randomly walk around a certain central point, so that the noise needs to be eliminated, the influence of the noise on a subsequent clustering algorithm is reduced, and the random noise is characterized in that the direction of the data points changes randomly within a certain time, namely the variance is large; for 2 adjacent track points P i (t i ，lat i ，long i )，P i+1 (t i+1 ，lat i+1 ，long i+1 ) Calculate its direction:
the expression method for converting the vector into the unit vector comprises the following steps: theta.theta. i，i+1 →(cos(θ i，i+1 )，sin(θ i，i+1 ) Then, the mean direction value of k continuous agricultural machine operation track points is calculated as:
the standard deviation was calculated as:
and eliminating points with standard deviation larger than a certain threshold (preset by a user according to the actual working condition of the agricultural machine).
According to the present invention, preferably, the method 1-5) of calculating the contour-based area Scontour can obtain the actual working area of the agricultural machinery through spatial clustering, because there may be a plurality of working areas (i.e. the clustering result is a plurality of categories), the area calculation is performed on each working area according to the category, and the contour-based area calculation method includes the following steps:
1-5-1) foveal bag calculation
Performing the notch bag calculation on each type of data points according to the data points obtained by clustering, as shown in fig. 4, the specific steps are as follows:
(1) finding out the point with the minimum y value, and taking the point with the maximum x value as the starting point O if the y values are the same, wherein the x and y points are coordinate values of a data point and the point is fixed on the concave bag;
(2) starting from an initial point O, taking (1, 0) as a reference vector, firstly finding an edge with a radius of R smaller than that of the initial edge, and taking the point as A;
(3) looping to find the next edge, assuming the previous edge is AB, then the next edge must start at point B and connect to a point C in the R neighborhood of B, using the following rule: firstly, the points in the R neighborhood of B are sorted in the polar coordinate direction by taking B as the center and BA vector as the reference, and then the points C in the R neighborhood of B are sequentially sorted 0 ～C n Set up with BC i The circle is a chord circle, whether other neighborhood points are included in the circle is checked, if the circle does not exist, the chord is a new edge, a loop is formed, fig. 7 shows how to find a point C, namely, a circle with BC as the chord is established, and then whether other neighborhood points are included is judged, and the point is found if the other neighborhood points do not exist;
(4) finding all edges in sequence until no new edge can be found or a point which is used as an edge before is encountered;
1-5-2) calculated area
Calculating the number of pockets for each category results in a plurality of pockets similar to that shown in FIG. 8Then calculating the area of the polygon by adopting a triangle segmentation algorithm or Simpson algorithm to obtain S contour_in If the Beidou positioning terminal is installed on the central axis of the agricultural machinery, the actual area also comprises the area of multiplying the length of the peripheral outline by the width/2, namely
Wherein w represents the working width, d (Q) i ，Q i+1 ) Indicating adjacent boundary points Q i 、Q i+1 The distance between them.
Preferably, according to the present invention, the method for calculating the grid-based area Sgrid in step 16) is as follows:
the grid data structure is array data composed of pixels (grid units) which are equal in size, uniform in distribution and closely connected, can be used for representing the distribution of space ground objects or phenomena, can be used for realizing the calculation of the operation area of the agricultural machinery, and is easier to process an overlapped operation area, and the specific steps are as follows:
1-6-1), wherein the area expansion is to perform rasterization processing on an actual operation area according to agricultural machinery track data and breadth, and the specific steps are as follows:
(a-1) finding the minimum x of x, y min ，y min And a maximum value x max ，y max ；
(a-2) determining the size of the grid matrix to be opened up according to the ratio mu of the pixel elements to the actual size:
where ε represents an additional boundary that ensures that the data points are all located within the matrix; opening up a two-dimensional array representing a grid matrix and initializing to 0;
(a-3) calculating the area to be expanded according to the agricultural machine track and the width:
as shown in figure 5, the running track points Q of the known adjacent agricultural machinery i (t i ，x i ，y i )，Q i+1 (t i+1 ，x i+1 ，y i+1 ) And an operating width w, the region expansion of which is substantially Q' i ，Q″ i ，Q′ i+1 ，Q″ i+1 Coordinates of four points:
Q′ i (x′ i ，y′ i )＝(x i +Δ x ，y i -Δ y )
Q″ i (x″ i ，y″ i )＝(x i -Δ x ，y i +Δ y )
Q′ i+1 (x′ i+1 ，y′ i+1 )＝(x i+1 +Δ x ，y i+1 -Δ y )
Q″ i+1 (x″ i+1 ，y″ i+1 )＝(x i+1 -Δ x ，y i+1 +Δ y )
according to Q' i ，Q″ i ，Q′ i+1 ，Q″ i+1 Rectangles and Q 'generated from these four points' i+1 ，Q″ i+1 Generated by the two points
The half circle of radius (for the smoothing of the working area) is superimposed with the grid matrix: the value of the grid cell in the rectangular and semicircular ranges is modified to 1, and if it is already 1 (overlap operation), it is not necessary to have the grid cell in the rectangular and semicircular rangesModifying to obtain a rasterized agricultural machinery operation track diagram, as shown in FIG. 6;
1-6-2) calculated area
According to the grid matrix, counting the number of grid unit values of 1, and then calculating the area according to the proportion of the pixels to the actual size:
S grid ＝N*w 2
where N is the number of grid cell values 1.
Application of comparative example,
According to the agricultural machinery behavior analysis and operation area statistical method based on Beidou positioning data, the method is applied to 6 different plots and used for detecting the operation tracks of agricultural machinery during operation of the different plots, and the method is formed as shown in fig. 9a to 9 f.
In order to verify the accuracy of the analysis and operation area statistical method, the actual operation area of the 6 plots is artificially measured to obtain the actual operation area.
Meanwhile, a boundary method and a breadth method in the prior art are introduced to process the agricultural machinery operation data respectively to obtain corresponding area data respectively.
The working areas of the agricultural machinery obtained by the method of the invention and the boundary method and the breadth method are compared, and are shown in table 1:
table 1:
wherein "195001018747 _0, 195001018747_1, 195001018747_2, 195001018747_ 3" refer to three identified areas in a parcel 195001018747;
"195001018965 _0, 195001018965_1, 195001018965_2, 195001018965_3, 195001018965_4, 195001018965_ 5" refers to six identification areas in the parcel 195001018965;
"206003470996 _0, 206003470996_ 1" refers to two identification regions in parcel 206003470996.
As can be seen from table 1, the "boundary method" has a large error in area statistics when there is a missing operation; the 'breadth method' takes overlapping operation into consideration, and has a large error; according to the area calculation method based on the contour and the grid, the overlapping area and the missing area can be respectively counted, then the interference of the overlapping area and the missing area is eliminated, and the obtained actual effective area error is smaller than the error of the comparison algorithm.
Claims (2)
1. An agricultural machinery behavior analysis and operation area statistical method based on Beidou positioning data is characterized by comprising the steps of automatically identifying an agricultural machinery operation area, calculating the area, analyzing the overlapped area and the omitted area;
the method for automatically identifying the agricultural machinery operation area is an agricultural machinery operation area automatic identification algorithm based on spatial clustering;
the method for calculating the area comprises a grid-based area calculation method and a contour-based area calculation method;
the method for analyzing the overlapping area is calculated by subtracting the area obtained by the grid-based area calculation method from the area obtained by multiplying the track length by the width:
wherein d (Q) i ，Q i+1 ) Representing the distance between adjacent track points of the agricultural machinery operation;
the method for analyzing the missing area comprises the following steps:
S miss ＝S contour -S grid ；
wherein the Scontour is a contour-based area; the Sgrid is used for calculating the area based on the grid;
the agricultural machinery operation area automatic identification algorithm based on spatial clustering comprises the following steps:
1-1) agricultural machinery operation data acquisition
Through on-vehicle big dipper positioning terminal and GPRS mobile communicationThe equipment obtains a track data set P ═ P of agricultural machinery operation 1 (t 1 ，lat 1 ，long 1 )，P 2 (t 2 ，lat 2 ，long 2 )，…，P n (t n ，lat n ，long n ) Wherein t represents time, lat represents latitude, long represents longitude, and n represents the total number of track points;
1-2) data preprocessing
The preprocessing refers to removing data abnormal points, drift points, stop points and random noise points;
1-3) projection
Obtaining a data point set Q ═ Q under the UTM coordinate system 1 (t 1 ，x 1 ，y 1 )，Q 2 (t 2 ，x 2 ，y 2 )，…，Q n (t n ，x n ，y n )；
1-4) spatial clustering
Identifying the operation area by using spatial clustering, wherein the specific steps of the identification are as follows:
1-4-1) drawing a circle by a certain radius r with each preprocessed data point as the center of the circle, wherein the density value of the point is formed by how many adjacent data points in the circle;
1-4-2) if the density value of the point is less than a set threshold value min _ pts, marking the point as a low density point, otherwise, marking the point as a high density point;
1-4-3) connecting two points if a high density point is within the circle of another high density point; if a certain low-density point is in the circle of another high-density point, connecting the low-density point to the high-density point closest to the low-density point to form a boundary point;
1-4-4) repeating the steps 1-4-2) and 1-4-3), removing low-density points which are not in the circle of any high-density point, and reserving a high-density point set as a track point of an agricultural machinery operation area;
1-5) calculating the area Scontour based on the contour;
1-6) calculating an area Sgrid based on the grid;
1-7) the method for analyzing the overlapping area is as follows:
the area obtained by subtracting the area obtained by the grid-based area calculation method from the area obtained by multiplying the track length by the width is calculated as follows:
wherein d (Q) i ，Q i+1 ) Representing the distance between adjacent track points of the agricultural machinery operation;
the method for analyzing the missing area comprises the following steps:
S miss ＝S contour -S grid ；
1-5) method for calculating area Scontour based on contour, wherein the method for calculating area based on contour comprises the following steps:
1-5-1) foveal bag calculation
Carrying out concave packet calculation on each type of data points according to the data points obtained by clustering, and specifically comprising the following steps:
(1) finding out the point with the minimum y value, and taking the point with the maximum x value as a starting point O if the y values are the same;
(2) starting from an initial point O, taking (1, 0) as a reference vector, firstly finding an edge with a radius smaller than R as an initial edge, and then taking the point as A;
(3) looping to find the next edge, assuming the previous edge is AB, then the next edge must start at point B and connect to a point C in the R neighborhood of B, finding point C using the following rule: firstly, the points in the R neighborhood of B are sorted in the polar coordinate direction by taking B as the center and BA vector as the reference, and then the points C in the R neighborhood of B are sequentially sorted 0 ～C n Set up with BC i The circle is a chord, whether other neighborhood points are included is checked, if the other neighborhood points do not exist, the chord is a new edge, and a cycle is jumped out;
(4) finding all edges in sequence until no new edge can be found or a point which is used as an edge before is encountered;
1-5-2) calculated area
Calculating the concave packet of each category to obtain a plurality of polygons, and then calculating the polygonal surface by adopting a triangle segmentation algorithm or a Simpson algorithmAccumulating to obtain S contour_in I.e. by
Wherein w represents the working width, d (Q) i ，Q i+1 ) Indicating adjacent boundary points Q i 、Q i+1 The distance between them;
the method for calculating the grid-based area Sgrid in the step 1-6) is as follows:
1-6-1), wherein the area expansion is to perform rasterization processing on an actual operation area according to agricultural machinery track data and breadth, and the specific steps are as follows:
(a-1) finding the minimum x of x, y min ，y min And a maximum value x max ，y max ；
(a-2) determining the size of the grid matrix to be opened up according to the ratio mu of the pixel elements to the actual size:
where ε represents an additional boundary that ensures that the data points are all located within the matrix; opening up a two-dimensional array representing a grid matrix and initializing to 0;
(a-3) calculating the area to be expanded according to the agricultural machine track and the width:
known adjacent agricultural machinery operation track point Q i (t i ，x i ，y i )，Q i+1 (t i+1 ，x i+i ，y i+1 ) And the working width w, the region expansion of which is actually Q' i ，Q″ i ，Q′ i+1 ，Q″ i+1 Coordinates of four points:
Q′ i (x′ i ，y′ i )＝(x i +Δ x ，y i -Δ y )
Q″ i (x″ i ，y″ i )＝(x i -Δ x ，y i +Δ y )
Q′ i+1 (x′ i+1 ，y′ i+1 )＝(x i+1 +Δ x ，y i+1 -Δ y )
Q″ i+1 (x″ i+1 ，y″ i+1 )＝(x i+1 -Δ x ，y i+1 +Δ y )
according to Q' i ，Q″ i ，Q′ i+1 ，Q″ i+1 Rectangles and Q 'generated from these four points' i+1 ，Q″ i+1 Generated by the two points
Superposing the semi-circle with the radius and the grid matrix: obtaining a rasterized agricultural machinery operation track diagram;
1-6-2) calculated area
According to the grid matrix, counting the number of grid unit values of 1, and then calculating the area according to the proportion of the pixel elements to the actual size:
S grid ＝N*μ 2
where N is the number of grid cell values 1.
2. The agricultural machinery behavior analysis and working area statistical method based on Beidou positioning data as set forth in claim 1, wherein the data preprocessing method of step 1-2) comprises the sequence of data elimination:
1-2-1) rejecting abnormal points: any agricultural machinery track point should satisfy P (t, lat, long):
lat∈[-90°，90°]
long∈[-180°，180°]
data points which do not meet the formula are taken as abnormal points to be removed;
1-2-2) removing drift points: for 2 adjacent track points P i (t i ，lat i ，long i )，P i+1 (t i+1 ，lat i+1 ，long i+1 ) Calculating the running speed of the agricultural machine:
wherein d (P) i ，P i+1 ) Representing adjacent track points P i 、P i+1 A distance between, v (P) i P i+1 )＞v max Trace point elimination of (v), wherein max The maximum operating speed of the agricultural machine;
1-2-3) eliminating stop points: calculating the average speed of k continuous agricultural machine operation track points:
eliminating track points with the average speed smaller than a certain threshold value delta;
1-2-4) eliminating random noise points: for 2 adjacent track points P i (t i ，lat i ，long i )，P i+1 (t i+1 ，lat i+1 ，long i+1 ) Calculating the direction:
the expression method for converting the vector into the unit vector comprises the following steps: theta i，i+1 →(cos(θ i，i+1 )，sin(θ i，i+1 ) Then, the mean direction value of k continuous agricultural machine operation track points is calculated as:
the standard deviation was calculated as:
and eliminating points with standard deviation larger than a certain threshold value.
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Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104992072A (en) * | 2015-07-21 | 2015-10-21 | 江苏北斗卫星应用产业研究院有限公司 | Operation land parcel automatic identification and area statistics method based on spatial mesh division |
| CN106503433A (en) * | 2016-10-18 | 2017-03-15 | 广州极飞科技有限公司 | Sprinkling area determination method and device based on unmanned machine operation |
| CN107036572A (en) * | 2017-04-12 | 2017-08-11 | 中国农业大学 | A kind of agricultural machinery working area acquisition methods and device |
| CN107462208A (en) * | 2017-08-15 | 2017-12-12 | 河北农业大学 | Agricultural machine and agricultural machine operation area measuring device and measuring method |
| CN111336980A (en) * | 2018-12-18 | 2020-06-26 | 江苏北斗卫星应用产业研究院有限公司 | Repeated operation area calculation and alarm method |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2020102763A1 (en) * | 2018-11-15 | 2020-05-22 | Raven Industries, Inc. | Integrated platform and common software structural architecture for autonomous agricultural vehicle and machinery operation. |
-
2020
- 2020-09-01 CN CN202010902540.4A patent/CN113409240B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104992072A (en) * | 2015-07-21 | 2015-10-21 | 江苏北斗卫星应用产业研究院有限公司 | Operation land parcel automatic identification and area statistics method based on spatial mesh division |
| CN106503433A (en) * | 2016-10-18 | 2017-03-15 | 广州极飞科技有限公司 | Sprinkling area determination method and device based on unmanned machine operation |
| CN107036572A (en) * | 2017-04-12 | 2017-08-11 | 中国农业大学 | A kind of agricultural machinery working area acquisition methods and device |
| CN107462208A (en) * | 2017-08-15 | 2017-12-12 | 河北农业大学 | Agricultural machine and agricultural machine operation area measuring device and measuring method |
| CN111336980A (en) * | 2018-12-18 | 2020-06-26 | 江苏北斗卫星应用产业研究院有限公司 | Repeated operation area calculation and alarm method |
Non-Patent Citations (1)
| Title |
|---|
| "农机远程智能管理平台研发及其应用";朱登胜 等;《智能农业》;20200630;第2卷(第2期);第67-81页 * |
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