CN116205968A - Agricultural machinery operation area calculation method and system based on raster image - Google Patents
Agricultural machinery operation area calculation method and system based on raster image Download PDFInfo
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Abstract
The invention provides an agricultural machinery operation area calculation method and system based on raster images, comprising the following steps: acquiring behavior track data of an agricultural machine and preprocessing the behavior track data to obtain track Tian Lu data; dividing the track Tian Lushu by a clustering algorithm; converting longitude and latitude coordinates of each track point in the field operation track into plane Gaussian projection coordinates, and performing low-pass filtering to obtain filtered plane coordinates; calculating grid image coordinates corresponding to the farm machinery field operation track according to the plane coordinates, drawing a grid image, obtaining the number of pixel blocks of the initial farm machinery field operation, and calculating the real ground area corresponding to each pixel block; compensating the gaps of the agricultural machinery operation strips in the grid image to generate the number of pixel blocks of the final agricultural machinery field operation; and determining the agricultural machinery working area according to the product of the number of the pixel blocks of the final agricultural machinery field operation and the real ground area corresponding to each pixel block. The invention solves the problem of inaccurate measurement of the working area of the existing agricultural machinery.
Description
Technical Field
The invention relates to the technical field of agricultural machinery operation area measurement, in particular to an agricultural machinery operation area calculation method and system based on grid images.
Background
With the rapid development of agricultural machinery level and the continuous expansion of agricultural machinery scale in China, main crops in China such as wheat, rice, corn and the like have basically realized the whole mechanized level of field operation. The agricultural machinery operation area is an important basis for issuing agricultural machinery subsidies, collecting agricultural machinery operation fees, evaluating agricultural machinery operation efficiency and the like.
The traditional agricultural machinery work area is that the farmland area through artifical measurement is regarded as the work area of agricultural machinery. However, the method is time-consuming, labor-consuming and low in efficiency, and many farmlands are irregularly polygonal in shape and have larger measurement errors. With the continuous development of global positioning technology, agricultural work areas can be measured by positioning technology. The mode not only greatly liberates labor force, but also is convenient and quick, and improves the accuracy and efficiency of area measurement. For example, a positioning device (such as a Beidou/GNSS positioning terminal) is installed on the agricultural machine to record movement track information of the agricultural machine operation, and the movement track information is used as the area of the agricultural machine operation by calculating the area of the agricultural machine track.
At present, methods for measuring the operation area of an agricultural machine based on the track of the agricultural machine are mainly divided into three types, namely a distance breadth method, an outer envelope surface method and a raster image method. The distance width method is to connect track points of the agricultural machinery operation into a broken line, calculate the total length of the broken line to be used as the total length of the track of the agricultural machinery operation, and multiply the width of the agricultural machinery operation to obtain the area of the agricultural machinery operation. In addition, the students use a buffer method and an improved alpha+shapes algorithm to calculate the working area of the agricultural machinery. When the agricultural machinery works turn or turn around, the calculation of the folding line can generate larger error. Moreover, the method can lead to repeated calculation of the area of the overlapping region of the agricultural machinery track, and result in larger calculation errors. The outer envelope surface method starts from geometrical characteristics of an agricultural machine track, extracts longitude and latitude geographical coordinates of boundary points of an agricultural machine operation area, converts the longitude and latitude geographical coordinates into a plane rectangular coordinate system by adopting Gaussian projection forward calculation to obtain plane coordinates, sequentially connects the plane coordinates into an irregular polygonal area, namely an outer envelope surface, as the operation area of the agricultural machine, and further utilizes a Gaussian area formula or Delaunay triangle network method to realize the calculation of the operation area of the agricultural machine. The algorithm is greatly influenced by the complex shape of the farmland, and the non-operation area of the agricultural machinery is easily classified as the operation area of the agricultural machinery to calculate the operation area of the agricultural machinery, so that the calculated operation area error of the agricultural machinery is larger. The raster image method is to convert longitude and latitude geographical coordinates of agricultural machinery track points into plane projection coordinates through coordinate transformation, further visualizes the track points into a plane raster image, calculates the number of pixels occupied by the track in the raster image, and multiplies the actual area corresponding to a single pixel to obtain the agricultural machinery field operation area. In addition, there are also scholars that calculate an irregular polygonal area as an area of agricultural work by recognizing a boundary of a farmland surrounded by an agricultural work track in a raster image and using an area formula. The difficulty in the implementation process of the raster image method is that a gap omission phenomenon between two operation strips possibly occurs in the generated raster image, and the positioning error of the agricultural machine operation track data can cause a phenomenon of larger saw teeth and burrs at the edge of the agricultural machine track after the agricultural machine track is converted into an image. In addition, the grid size (i.e., the size of the image resolution) is poorly defined and is greatly affected by the track point concentration and resolution, resulting in difficult accurate detection of the agricultural work area.
Disclosure of Invention
The invention provides an agricultural machinery operation area calculation method and system based on grid images, which are used for solving the problem that the existing agricultural machinery operation area measurement is inaccurate.
The invention provides a grid image-based agricultural machinery operation area calculation method, which comprises the following steps:
acquiring behavior track data of an agricultural machine, and transmitting the behavior track data to an agricultural machine operation big data management service platform;
acquiring the behavior track data through the big data management service platform, and performing data preprocessing on the behavior track data to obtain track Tian Lu data;
dividing the track Tian Lushu according to a preset clustering algorithm, wherein the track is divided into a track of a plurality of sections of farm machinery operating in a field and a track transferred on a road;
converting longitude and latitude geographical coordinates of each track point in the field operation track into plane Gaussian projection coordinates, and performing low-pass filtering to obtain the filtered plane Gaussian projection coordinates;
calculating grid image coordinates corresponding to the operation track in the agricultural machinery field according to the plane Gaussian projection coordinates, drawing a grid image according to the grid image coordinates, obtaining the number of pixel blocks for initial operation in the agricultural machinery field, and calculating the real ground area corresponding to each pixel block;
Compensating the agricultural machinery operation strip gap in the grid image, obtaining the number of pixel blocks belonging to the agricultural machinery field operation in the compensated grid image, performing edge detection on the compensated grid image to correct the grid image, and generating the number of pixel blocks of the final agricultural machinery field operation;
and determining the agricultural machinery working area according to the product of the number of the pixel blocks of the final agricultural machinery field operation and the real ground area corresponding to each pixel block.
According to the grid image-based agricultural machinery operation area calculation method provided by the invention, the behavior track data of the agricultural machinery is obtained and sent to the agricultural machinery operation big data management service platform, and the method specifically comprises the following steps:
acquiring behavior track data of the agricultural machinery through positioning equipment arranged on the agricultural machinery;
the behavior track data of the agricultural machinery are transmitted back to the agricultural machinery Internet of things platform through the mobile communication network;
and forwarding the behavior track data of the agricultural machinery to an agricultural machinery operation big data management service platform in real time through the agricultural machinery Internet of things platform.
According to the grid image-based agricultural machinery operation area calculation method provided by the invention, the behavior track data is acquired through the big data management service platform, and the behavior track data is subjected to data preprocessing to obtain track Tian Lu data, which comprises the following steps:
Acquiring the behavior trace data through the big data management service platform;
by ascending the agricultural machinery behavior track data according to the time sequence, searching abnormal data according to the arrangement sequence, wherein the abnormal data comprise track data repetition, relay offset, dead point offset, speed abnormality, positioning point offset, periodic offset, track data drift and track data stop;
constructing a targeted track data anomaly identification and correction method according to coordinate discrimination, speed discrimination and vector discrimination methods, and realizing anomaly correction of track data;
and cleaning and deleting the found abnormal data or correcting the abnormal points according to the movement rule of the agricultural machinery.
According to the grid image-based agricultural machinery operation area calculation method provided by the invention, the track Tian Lushu is divided into a plurality of sections of tracks for agricultural machinery operation in the field and tracks for road transfer by a preset clustering algorithm, and the method specifically comprises the following steps:
calculating the sampling frequency of each track, and training a track classification model corresponding to each sampling frequency based on the track field data through a clustering algorithm;
and selecting the track classification model trained correspondingly according to the sampling frequency corresponding to each track to classify the tracks of the agricultural machine, and dividing the whole behavior track of the agricultural machine in one day into tracks of multiple sections of agricultural machine operation in the field and tracks of multiple sections of transfer on the road.
According to the agricultural machinery operation area calculation method based on the raster image, provided by the invention, longitude and latitude geographic coordinates of each track point in the field operation track are converted into plane Gaussian projection coordinates, and low-pass filtering is carried out to obtain the filtered plane Gaussian projection coordinates, and the method specifically comprises the following steps:
converting longitude and latitude geographic coordinates of each track point in the field operation track data into plane Gaussian projection coordinates through Gaussian projection according to the classified field operation track;
and filtering coordinates deviating from a set threshold value in the plane Gaussian projection coordinates through a low-pass filtering algorithm to generate filtered plane Gaussian projection coordinates.
According to the agricultural machinery operation area calculation method based on the grid image, which is provided by the invention, grid image coordinates corresponding to the operation track in the agricultural machinery field are calculated according to the plane Gaussian projection coordinates, the grid image is drawn according to the grid image coordinates, the number of pixel blocks of the initial agricultural machinery field operation is obtained, and the real ground area corresponding to each pixel block is calculated, and the method specifically comprises the following steps:
counting the maximum value and the minimum value of the abscissa and the maximum value and the minimum value of the ordinate in the filtered plane Gaussian projection coordinates according to the operation track data in each field;
Converting the plane Gaussian projection coordinates of each track point into coordinates on a grid image through a preset conversion algorithm;
calculating the size of the raster image, drawing the raster image, and counting the number of pixel blocks with set pixel values in the raster image according to the drawn raster image;
and calculating the real ground area corresponding to each pixel block according to the preset resolution of the raster image.
According to the agricultural machinery operation area calculating method based on the raster image, provided by the invention, the agricultural machinery operation strip gap in the raster image is compensated, the number of pixel blocks belonging to agricultural machinery field operation in the compensated raster image is obtained, the edge detection correction raster image is carried out on the compensated raster image, and the number of pixel blocks of the final agricultural machinery field operation is generated, and the method specifically comprises the following steps:
determining the row number and the column number of each pixel block in the grid image, judging the value of each pixel block in a grid with the current pixel block as the center and generating a judging result;
determining whether pixel blocks are added to the compensation block list according to the judging result until all the pixel blocks are judged, forming an agricultural machinery operation strip gap compensation block list, and obtaining the number of pixel blocks belonging to agricultural machinery field operation in the compensated grid image;
And carrying out edge detection correction on the compensated grid image through an image edge detection algorithm to generate the number of pixel blocks for the final farm machinery field operation.
According to the grid image-based agricultural machinery operation area calculation method provided by the invention, the agricultural machinery operation area is determined according to the product of the number of pixel blocks of the final agricultural machinery field operation and the real ground area corresponding to each pixel block, and the agricultural machinery operation area calculation method concretely comprises the following steps:
counting the number of the pixel blocks of the final farm work as the number of the pixel blocks with the set pixel value;
and obtaining the actual operation area of the agricultural machine by multiplying the determined number of pixel blocks by the real ground area corresponding to each pixel block.
The invention also provides an agricultural machinery operation area calculation system based on the raster image, which comprises:
the track data acquisition module is used for acquiring the behavior track data of the agricultural machinery and transmitting the behavior track data to the agricultural machinery operation big data management service platform;
the track data preprocessing module is used for acquiring the behavior track data through the big data management service platform, and performing data preprocessing on the behavior track data to obtain track Tian Lu data;
the track data Tian Lu dividing module is used for dividing the track Tian Lushu according to a preset clustering algorithm into tracks of a plurality of sections of agricultural machinery operating in the field and tracks transferred on roads;
The track data coordinate conversion module is used for converting longitude and latitude geographic coordinates of each track point in the field operation track into plane Gaussian projection coordinates and performing low-pass filtering to obtain the filtered plane Gaussian projection coordinates;
the grid image drawing module is used for calculating grid image coordinates corresponding to the farm machinery farm work track according to the plane Gaussian projection coordinates, drawing grid images according to the grid image coordinates, obtaining the number of pixel blocks for the initial farm machinery farm work, and calculating the real ground area corresponding to each pixel block;
the grid image compensation module is used for compensating the agricultural machinery operation strip gap in the grid image, acquiring the number of pixel blocks belonging to the agricultural machinery field operation in the compensated grid image, carrying out edge detection on the compensated grid image, correcting the grid image, and generating the number of pixel blocks of the final agricultural machinery field operation;
and the agricultural machinery operation area calculation module is used for determining the agricultural machinery operation area according to the product of the number of the pixel blocks operated in the final agricultural machinery field and the real ground area corresponding to each pixel block.
The invention also provides electronic equipment, which comprises a memory, a processor and a computer program stored on the memory and capable of running on the processor, wherein the processor realizes the grid image-based agricultural machinery working area calculating method when executing the program.
The present invention also provides a non-transitory computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements a raster image based agricultural work area calculation method as described in any one of the above.
The invention also provides a computer program product comprising a computer program which when executed by a processor implements the grid image based agricultural work area calculation method as described in any one of the above.
According to the agricultural machinery operation area calculating method and system based on the grid image, the agricultural machinery behavior track data are collected through the positioning equipment, the single-point positioning accuracy of the agricultural machinery positioning terminal is improved through low-pass filtering, the grid image of agricultural machinery operation is drawn according to the operation width of the agricultural machinery through simulating the behavior of the agricultural machinery operation, and gaps among agricultural machinery operation strips in the grid image are compensated. The method not only improves the precision of the operation track of the agricultural machinery, but also avoids repeated calculation of the operation area of the agricultural machinery, repairs the calculation of the missing area of the strip gap in the grid image, and reduces the calculation error of the operation area of the agricultural machinery.
Drawings
In order to more clearly illustrate the invention or the technical solutions of the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described, and it is obvious that the drawings in the description below are some embodiments of the invention, and other drawings can be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a schematic flow chart of an agricultural machinery operation area calculation method based on raster images;
FIG. 2 is a second flow chart of a method for calculating an agricultural work area based on raster images according to the present invention;
FIG. 3 is a third flow chart of a method for calculating an agricultural work area based on raster images according to the present invention;
FIG. 4 is a schematic flow chart of a grid image-based agricultural machinery working area calculation method provided by the invention;
FIG. 5 is a fifth flow chart of a grid image-based agricultural machinery work area calculation method provided by the invention;
FIG. 6 is a flowchart of a method for calculating an agricultural work area based on raster images according to the present invention;
FIG. 7 is a schematic diagram of a grid image-based agricultural machinery working area calculation method according to the present invention;
FIG. 8 is a schematic diagram of the modular connection of an agricultural work area computing system based on raster images provided by the present invention;
fig. 9 is a schematic structural diagram of an electronic device provided by the present invention;
FIG. 10 is a schematic view of a compensated raster image provided by the present invention;
reference numerals:
110: a track data acquisition module; 120: the track data preprocessing module; 130: trajectory data Tian Lu segmentation module; 140: a track data coordinate conversion module; 150: a raster image drawing module; 160: a raster image compensation module; 170: an agricultural machinery operation area calculation module;
910: a processor; 920: a communication interface; 930: a memory; 940: a communication bus.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the present invention more apparent, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings, and it is apparent that the described embodiments are some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
The following describes an agricultural machinery operation area calculating method based on raster images according to the present invention with reference to fig. 1 to 7, including:
s100, acquiring behavior track data of an agricultural machine, and transmitting the behavior track data to an agricultural machine operation big data management service platform;
s200, acquiring the behavior track data through the big data management service platform, and performing data preprocessing on the behavior track data to obtain track Tian Lu data;
s300, dividing the track Tian Lushu according to a preset clustering algorithm, wherein the track is divided into tracks of a plurality of sections of farm machinery operating in a field and tracks transferred on a road;
S400, converting longitude and latitude geographical coordinates of each track point in the field operation track into plane Gaussian projection coordinates, and performing low-pass filtering to obtain the filtered plane Gaussian projection coordinates;
s500, calculating grid image coordinates corresponding to the operation track in the agricultural machinery field according to the plane Gaussian projection coordinates, drawing a grid image according to the grid image coordinates, obtaining the number of pixel blocks for initial operation in the agricultural machinery field, and calculating the real ground area corresponding to each pixel block;
s600, compensating the agricultural machinery operation strip gap in the grid image, obtaining the number of pixel blocks belonging to the agricultural machinery field operation in the compensated grid image, performing edge detection correction grid image on the compensated grid image, and generating the number of pixel blocks of the final agricultural machinery field operation;
and S700, determining the agricultural machinery working area according to the product of the number of the pixel blocks of the final agricultural machinery field operation and the real ground area corresponding to each pixel block.
According to the invention, through the agricultural machinery operation area calculation based on the grid image, the single-point positioning precision of the positioning terminal can be improved, the problems of gaps and large saw teeth between operation strips after the track is converted into the image can be solved, the area of an agricultural machinery operation track repeated area can be avoided, and the calculation precision of the agricultural machinery operation area is improved.
Acquiring behavior track data of an agricultural machine and transmitting the behavior track data to an agricultural machine operation big data management service platform, wherein the method specifically comprises the following steps of:
s101, acquiring behavior track data of an agricultural machine through positioning equipment arranged on the agricultural machine;
s102, returning behavior track data of the agricultural machinery to an agricultural machinery Internet of things platform through a mobile communication network;
s103, forwarding the behavior track data of the agricultural machine to an agricultural machine operation big data management service platform in real time through the agricultural machine Internet of things platform.
According to the invention, the behavior track data of the agricultural machinery is acquired through the Beidou/GNSS positioning equipment, the acquired behavior track data is the most original preliminary data, the data is required to be processed through the data processing platform, the behavior track data of the agricultural machinery is sent to the Internet of things platform of the agricultural machinery manufacturing enterprise through the mobile communication network or the local area network, and the Internet of things platform of the agricultural machinery manufacturing enterprise gathers the data and then sends the data to the agricultural machinery operation big data service platform for data processing operation. Because the original preliminary data is influenced by environmental factors or other factors in the acquisition process, abnormal data exist in the acquired data, and the generated result is inaccurate easily, the acquired preliminary data needs to be preprocessed.
Acquiring the behavior trace data through the big data management service platform, and performing data preprocessing on the behavior trace data to obtain trace Tian Lu data, wherein the method specifically comprises the following steps of:
s201, acquiring the behavior trace data through the big data management service platform;
s202, by ascending the agricultural machinery behavior track data according to a time sequence, searching abnormal data according to the arrangement sequence, wherein the abnormal data comprise track data repetition, relay offset, dead point offset, speed abnormality, positioning point offset, periodic offset, track data drift and track data stop;
s203, distinguishing according to coordinates, speed and vector distinguishing methods, namely distinguishing according to coordinates, speed and vector constructed by the track points, constructing a targeted track data anomaly identification and correction method, and realizing anomaly correction of the track data;
s204, cleaning and deleting the found abnormal data or correcting the abnormal points according to the movement rule of the agricultural machinery, so that the accuracy and usability of the data are ensured.
The track Tian Lushu is divided into tracks of a plurality of sections of farm machinery operating in the field and tracks transferred on roads by a preset clustering algorithm, and specifically comprises the following steps:
S301, calculating sampling frequency of each track, and training a track classification model corresponding to each sampling frequency based on the track field data through a clustering algorithm;
s302, selecting a corresponding trained track classification model to classify tracks of the agricultural machine according to the sampling frequency corresponding to each track, and dividing the whole behavior track of the agricultural machine in one day into tracks of operation of the agricultural machine in the field and tracks of transfer of multiple segments on roads.
The invention adopts a DBSCAN clustering method to segment the farm machinery track. Because the DBSCAN clustering method is greatly influenced by the neighborhood radius (eps) and the minimum points (mints), the method firstly calculates the sampling frequency of each track, classifies each track according to the sampling frequency, and then manually marks the field data of the agricultural machinery operation tracks with different sampling frequencies, and then obtains the optimal model parameters corresponding to the different sampling frequencies through training. According to the method, a corresponding optimal track classification model is selected according to the sampling frequency corresponding to each track, the distribution density, speed and direction information of the track points of the agricultural machinery are utilized to classify the track data of the agricultural machinery in a field operation and road transfer, and the whole action track of the agricultural machinery in one day is divided into tracks of the agricultural machinery in the field operation and tracks transferred on the road.
Converting the longitude and latitude geographical coordinates of each track point in the field operation track into plane Gaussian projection coordinates and performing low-pass filtering to obtain the filtered plane Gaussian projection coordinates, wherein the method specifically comprises the following steps of:
s401, converting longitude and latitude geographic coordinates of each track point in the field operation track data into plane Gaussian projection coordinates through Gaussian projection according to the classified field operation track;
s402, filtering coordinates deviating from a set threshold value in the plane Gaussian projection coordinates through a low-pass filtering algorithm to generate filtered plane Gaussian projection coordinates.
In the invention, longitude and latitude geographical coordinates (lot, lat) of each track point in track data of each field operation are converted into plane Gaussian projection coordinates (X, Y) by WGS1984 geographical coordinates through a Gaussian projection forward calculation formula. The influence of various errors on the positioning result can be reduced as much as possible by the low-pass filtering algorithm, and the positioning precision of the GNSS is effectively improved. Therefore, the invention adopts a low-pass filtering algorithm to filter two groups of signals of X coordinate and Y coordinate of the plane Gaussian projection coordinate of the track data of each field operation respectively, and obtains the filtered plane Gaussian projection coordinate (X, Y). The plane Gaussian projection coordinates can be more in line with the actual situation through filtering processing.
Calculating grid image coordinates corresponding to the agricultural machinery field operation track according to the plane Gaussian projection coordinates, drawing a grid image according to the grid image coordinates, obtaining the number of pixel blocks of the initial agricultural machinery field operation, and calculating the real ground area corresponding to each pixel block, wherein the method specifically comprises the following steps:
s501, counting the maximum value and the minimum value of the abscissa and the maximum value and the minimum value of the ordinate in the filtered plane Gaussian projection coordinates according to the operation track data in each field;
s502, converting plane Gaussian projection coordinates of each track point into coordinates on a grid image through a preset conversion algorithm;
s503, calculating the size of the raster image, drawing the raster image, and counting the number of pixel blocks with the pixel value of 1 in the raster image according to the drawn raster image;
s504, calculating the real ground area corresponding to each pixel block according to the preset resolution of the raster image.
For the track data of each field operation, the minimum X of the X coordinate of the Gaussian projection coordinate after filtering is counted min And maximum value X max Filtered gaussian projection coordinate Y coordinate minimum Y min And maximum value Y max According to the formula:
image_Y(i) = round((X max -X (i))/resolution) +length of buffer
image_X(i) = round((Y(i) -Y min ) Resolution) +length of buffer
Calculating the coordinates (X image , Y image ) Wherein round functions to preserve the integer part of the input data by rounding.
Calculating the raster image size: setting the width of the raster image as X of the coordinates on the graph image The maximum value minus the minimum value of the coordinates plus 2 times the length of the buffer zone, the length of the raster image is set as Y of the coordinates on the image image The maximum value minus the minimum value of the coordinates plus 2 times the length of the buffer zone, the width of the painting brush is set as an integer n obtained by rounding the ratio of the farm work width to the resolution of the raster image.
Calculating the real ground area corresponding to the pixel block; setting the resolution corresponding to the raster image (namely the length of the real ground corresponding to each pixel block), and calculating the area of the real ground corresponding to each pixel block, namely the product of the resolution of the raster image.
And drawing a grid image, when the width of the agricultural machine is unknown, for a relatively regular field, calculating the operation direction of the agricultural machine by analyzing the space operation track data of the agricultural machine and counting the azimuth distribution frequency, extracting the operation line of the agricultural machine, and establishing an operation line spacing evaluation model of the agricultural machine to estimate the width. For irregular job fields, the corresponding job widths can be queried according to the agricultural machinery attribute table. By simulating the behavior of the agricultural machine operation, the grid image of the agricultural machine operation is drawn according to the operation width of the agricultural machine.
And determining the width of the line segment according to the operation width of the agricultural machine, sequentially connecting the track points of the agricultural machine operation by the line segment, namely simulating the range of the swept actual ground in the actual operation process of the agricultural machine, generating a black canvas, namely, taking out the first two track points from the agricultural machine operation track data, wherein the value of each pixel block is 0. And positioning in canvas according to the coordinates of the two points on the graph, determining the width of a line segment according to the operation width of the agricultural machine by taking the two track points as a starting point and an ending point, and connecting the track points of the agricultural machine operation by sequentially using the line segment formed by the pixel blocks with the pixel values of 1, namely simulating the range of the scanned actual ground in the actual operation process of the agricultural machine. Similarly, traversing agricultural machinery operation track data, sequentially taking out adjacent points and making line segments on canvas according to the method, generating a raster image, and counting the number of pixel blocks with 1 pixel value in the raster image, namely the number P of pixel blocks belonging to agricultural machinery field operation 0 。
Compensating the agricultural machinery operation strip gap in the raster image, acquiring the number of pixel blocks belonging to the agricultural machinery field operation in the compensated raster image, performing edge detection correction raster image on the compensated raster image, and generating the number of pixel blocks of the final agricultural machinery field operation, wherein the method specifically comprises the following steps:
S601, determining a row number and a column number of each pixel block in a grid image, judging a value of each pixel block in a grid with a set size taking a current pixel block as a center, and generating a judging result;
s602, determining whether pixel blocks are added into a compensation block list according to the judging result until all the pixel blocks are judged, forming an agricultural machinery operation strip gap compensation block list, and obtaining the number of pixel blocks belonging to agricultural machinery field operation in the compensated raster image;
and S603, performing edge detection correction on the compensated grid image through an image edge detection algorithm to generate the number of pixel blocks for the final farm machinery field operation.
Referring to fig. 9, agricultural work swath gaps in the raster image are compensated for: when n is an odd number, note m=n+2,when n is even, note m=n+1. For each black pixel block in the raster image, when both its row and column numbers are greater than k= (m-3)/2, it is determined whether the value of each pixel block in an m-by-m grid centered on that pixel block is 0. If the pixel block is not 0, the coordinates of the point are recorded in the compensation block list, and the next pixel block is continuously judged until all the pixel blocks are judged to be complete. Calculating the number of pixel blocks in the compensation block list as Q, and then calculating the number of pixel blocks belonging to agricultural machinery operation in the compensated image as P 1 = P 0 +Q。
Performing edge detection on the compensated raster image to correct the raster image: the contours of the raster images are circularly detected by utilizing an image edge detection algorithm, the total detection is carried out for k times, the number of pixel blocks occupied by each contour is recorded, the sum is recorded as S, and the number of the pixel blocks in the final agricultural machinery field operation in the corrected image is P 2 =P 1 -S。
Determining the agricultural machinery working area according to the product of the number of the pixel blocks of the final agricultural machinery field operation and the real ground area corresponding to each pixel block, wherein the method specifically comprises the following steps:
counting the number of pixel blocks of the final farm work as the number of pixel blocks with the set pixel value of 1;
and obtaining the actual operation area of the agricultural machine by multiplying the determined number of pixel blocks by the real ground area corresponding to each pixel block.
In the invention, the number of pixel blocks with a pixel value of 1, namely the number of pixel blocks for field operation is counted, and the number of pixel blocks for field operation is multiplied by the area A0 of the real ground corresponding to each pixel to obtain the area of the agricultural machinery operation area, namely the operation=P 2 *A 0 。
According to the agricultural machinery operation area calculation method based on the grid image, the agricultural machinery behavior track data are collected through the positioning equipment, the single-point positioning accuracy of the agricultural machinery positioning terminal is improved through low-pass filtering, the grid image of agricultural machinery operation is drawn according to the operation width of the agricultural machinery through simulating the behavior of the agricultural machinery operation, and gaps among agricultural machinery operation strips in the grid image are compensated. The method not only improves the precision of the operation track of the agricultural machinery, but also avoids repeated calculation of the operation area of the agricultural machinery, repairs the calculation of the missing area of the strip gap in the grid image, and reduces the calculation error of the operation area of the agricultural machinery.
Referring to fig. 8, the invention also discloses an agricultural machinery operation area calculating system based on raster image, which comprises:
the track data acquisition module 110 is configured to acquire behavior track data of an agricultural machine, and send the behavior track data to an agricultural machine operation big data management service platform;
the track data preprocessing module 120 is configured to acquire the behavior track data through the big data management service platform, and perform data preprocessing on the behavior track data to obtain track Tian Lu data;
the track data Tian Lu dividing module 130 is configured to divide the track Tian Lushu into a track of a plurality of segments of agricultural machinery operating in the field and a track transferred on a road by using a preset clustering algorithm;
the track data coordinate conversion module 140 is configured to convert the longitude and latitude geographical coordinates of each track point in the field operation track into plane gaussian projection coordinates and perform low-pass filtering, so as to obtain the filtered plane gaussian projection coordinates;
a raster image drawing module 150 for calculating raster image coordinates corresponding to the operation track in the farm machinery field according to the plane Gaussian projection coordinates, drawing a grid image according to the grid image coordinates, obtaining the number of pixel blocks of the initial agricultural machinery field operation, and calculating the real ground area corresponding to each pixel block;
The raster image compensation module 160 is configured to compensate for a farm machine operation stripe gap in the raster image, obtain the number of pixel blocks belonging to a farm machine field operation in the compensated raster image, perform edge detection on the compensated raster image, correct the raster image, and generate the number of pixel blocks of the final farm machine field operation;
the agricultural machinery working area calculating module 170 is configured to determine an agricultural machinery working area according to a product of the number of pixel blocks of the final agricultural machinery field operation and the real ground area corresponding to each pixel block.
The track data acquisition module acquires behavior track data of the agricultural machine through positioning equipment arranged on the agricultural machine;
the behavior track data of the agricultural machinery are transmitted back to the agricultural machinery Internet of things platform through the mobile communication network;
and forwarding the behavior track data of the agricultural machinery to an agricultural machinery operation big data management service platform in real time through the agricultural machinery Internet of things platform.
The track data preprocessing module is used for acquiring the behavior track data through the big data management service platform;
by ascending the agricultural machinery behavior track data according to the time sequence, searching abnormal data according to the arrangement sequence, wherein the abnormal data comprise track data repetition, relay offset, dead point offset, speed abnormality, positioning point offset, periodic offset, track data drift and track data stop;
Constructing a targeted track data anomaly identification and correction method according to coordinate discrimination, speed discrimination and vector discrimination methods, and realizing anomaly correction of track data;
and cleaning and deleting the found abnormal data or correcting the abnormal points according to the movement rule of the agricultural machinery.
Track data Tian Lu segmentation module, calculating the sampling frequency of each track, training a track classification model corresponding to each sampling frequency based on the track field data through a clustering algorithm;
and selecting the track classification model trained correspondingly according to the sampling frequency corresponding to each track to classify the tracks of the agricultural machine, and dividing the whole behavior track of the agricultural machine in one day into tracks of multiple sections of agricultural machine operation in the field and tracks of multiple sections of transfer on the road.
The track data coordinate conversion module converts longitude and latitude geographic coordinates of each track point in the field operation track data into plane Gaussian projection coordinates through Gaussian projection according to the classified field operation track;
and filtering coordinates deviating from a set threshold value in the plane Gaussian projection coordinates through a low-pass filtering algorithm to generate filtered plane Gaussian projection coordinates.
The grid image drawing module is used for counting the maximum value and the minimum value of the abscissa and the maximum value and the minimum value of the ordinate in the filtered plane Gaussian projection coordinates according to the operation track data in each field;
converting the plane Gaussian projection coordinates of each track point into coordinates on a grid image through a preset conversion algorithm;
calculating the size of the raster image, drawing the raster image, and counting the number of pixel blocks with set pixel values in the raster image according to the drawn raster image;
and calculating the real ground area corresponding to each pixel block according to the preset resolution of the raster image.
The grid image compensation module is used for determining the row number and the column number of each pixel block in the grid image, judging the value of each pixel block in a grid with the current pixel block as a center and generating a judging result;
determining whether pixel blocks are added to the compensation block list according to the judging result until all the pixel blocks are judged, forming an agricultural machinery operation strip gap compensation block list, and obtaining the number of pixel blocks belonging to agricultural machinery field operation in the compensated grid image;
and carrying out edge detection correction on the compensated grid image through an image edge detection algorithm to generate the number of pixel blocks for the final farm machinery field operation.
The agricultural machinery operation area calculation module is used for counting the number of the pixel blocks for final agricultural machinery field operation as the number of the pixel blocks with set pixel values;
and obtaining the actual operation area of the agricultural machine by multiplying the determined number of pixel blocks by the real ground area corresponding to each pixel block.
According to the agricultural machinery operation area computing system based on the grid image, agricultural machinery behavior track data are collected through the positioning equipment, and single-point positioning accuracy of an agricultural machinery positioning terminal is improved through low-pass filtering. When the width of the agricultural machinery is unknown, for the more regular field, the operation track data of the agricultural machinery space can be analyzed, the azimuth distribution frequency is counted, the operation direction of the agricultural machinery is calculated, the operation line of the agricultural machinery is extracted, and an operation row spacing evaluation model of the agricultural machinery is built to estimate the width. For irregular job fields, the corresponding job widths can be queried according to the agricultural machinery attribute table. By simulating the operation behavior of the agricultural machinery, a grid image of the agricultural machinery operation is drawn according to the operation width of the agricultural machinery, and gaps among the agricultural machinery operation strips in the grid image are compensated. The method not only improves the precision of the operation track of the agricultural machinery, but also avoids repeated calculation of the operation area of the agricultural machinery, repairs the calculation of the missing area of the strip gap in the grid image, and reduces the calculation error of the operation area of the agricultural machinery.
Fig. 9 illustrates a physical schematic diagram of an electronic device, as shown in fig. 9, which may include: processor 910, communication interface (Communications Interface), memory 930, and communication bus 940, wherein processor 910, communication interface 920, and memory 930 communicate with each other via communication bus 940. Processor 910 may invoke logic instructions in memory 930 to perform a raster image based agricultural work area calculation method including:
acquiring behavior track data of an agricultural machine, and transmitting the behavior track data to an agricultural machine operation big data management service platform;
acquiring the behavior track data through the big data management service platform, and performing data preprocessing on the behavior track data to obtain track Tian Lu data;
dividing the track Tian Lushu according to a preset clustering algorithm, wherein the track is divided into a track of a plurality of sections of farm machinery operating in a field and a track transferred on a road;
converting longitude and latitude geographical coordinates of each track point in the field operation track into plane Gaussian projection coordinates, and performing low-pass filtering to obtain the filtered plane Gaussian projection coordinates;
Calculating grid image coordinates corresponding to the operation track in the agricultural machinery field according to the plane Gaussian projection coordinates, drawing a grid image according to the grid image coordinates, obtaining the number of pixel blocks for initial operation in the agricultural machinery field, and calculating the real ground area corresponding to each pixel block;
compensating the agricultural machinery operation strip gap in the grid image, obtaining the number of pixel blocks belonging to the agricultural machinery field operation in the compensated grid image, performing edge detection on the compensated grid image to correct the grid image, and generating the number of pixel blocks of the final agricultural machinery field operation;
and determining the agricultural machinery working area according to the product of the number of the pixel blocks of the final agricultural machinery field operation and the real ground area corresponding to each pixel block.
Further, the logic instructions in the memory 930 described above may be implemented in the form of software functional units and may be stored in a computer-readable storage medium when sold or used as a stand-alone product. Based on this understanding, the technical solution of the present invention may be embodied essentially or in a part contributing to the prior art or in a part of the technical solution, in the form of a software product stored in a storage medium, comprising several instructions for causing a computer device (which may be a personal computer, a server, a network device, etc.) to perform all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a magnetic disk, or an optical disk, or other various media capable of storing program codes.
In another aspect, the present invention also provides a computer program product, the computer program product including a computer program, the computer program being storable on a non-transitory computer readable storage medium, the computer program, when executed by a processor, being capable of executing a grid image-based agricultural work area calculation method provided by the above methods, the method comprising:
acquiring behavior track data of an agricultural machine, and transmitting the behavior track data to an agricultural machine operation big data management service platform;
acquiring the behavior track data through the big data management service platform, and performing data preprocessing on the behavior track data to obtain track Tian Lu data;
dividing the track Tian Lushu according to a preset clustering algorithm, wherein the track is divided into a track of a plurality of sections of farm machinery operating in a field and a track transferred on a road;
converting longitude and latitude geographical coordinates of each track point in the field operation track into plane Gaussian projection coordinates, and performing low-pass filtering to obtain the filtered plane Gaussian projection coordinates;
calculating grid image coordinates corresponding to the operation track in the agricultural machinery field according to the plane Gaussian projection coordinates, drawing a grid image according to the grid image coordinates, obtaining the number of pixel blocks for initial operation in the agricultural machinery field, and calculating the real ground area corresponding to each pixel block;
Compensating the agricultural machinery operation strip gap in the grid image, obtaining the number of pixel blocks belonging to the agricultural machinery field operation in the compensated grid image, performing edge detection on the compensated grid image to correct the grid image, and generating the number of pixel blocks of the final agricultural machinery field operation;
and determining the agricultural machinery working area according to the product of the number of the pixel blocks of the final agricultural machinery field operation and the real ground area corresponding to each pixel block.
In yet another aspect, the present invention also provides a non-transitory computer readable storage medium having stored thereon a computer program which, when executed by a processor, is implemented to perform a raster image based agricultural work area calculation method provided by the above methods, the method comprising:
acquiring behavior track data of an agricultural machine, and transmitting the behavior track data to an agricultural machine operation big data management service platform;
acquiring the behavior track data through the big data management service platform, and performing data preprocessing on the behavior track data to obtain track Tian Lu data;
dividing the track Tian Lushu according to a preset clustering algorithm, wherein the track is divided into a track of a plurality of sections of farm machinery operating in a field and a track transferred on a road;
Converting longitude and latitude geographical coordinates of each track point in the field operation track into plane Gaussian projection coordinates, and performing low-pass filtering to obtain the filtered plane Gaussian projection coordinates;
calculating grid image coordinates corresponding to the operation track in the agricultural machinery field according to the plane Gaussian projection coordinates, drawing a grid image according to the grid image coordinates, obtaining the number of pixel blocks for initial operation in the agricultural machinery field, and calculating the real ground area corresponding to each pixel block;
compensating the agricultural machinery operation strip gap in the grid image, obtaining the number of pixel blocks belonging to the agricultural machinery field operation in the compensated grid image, performing edge detection on the compensated grid image to correct the grid image, and generating the number of pixel blocks of the final agricultural machinery field operation;
and determining the agricultural machinery working area according to the product of the number of the pixel blocks of the final agricultural machinery field operation and the real ground area corresponding to each pixel block.
The apparatus embodiments described above are merely illustrative, wherein the elements illustrated as separate elements may or may not be physically separate, and the elements shown as elements may or may not be physical elements, may be located in one place, or may be distributed over a plurality of network elements. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment. Those of ordinary skill in the art will understand and implement the present invention without undue burden.
From the above description of the embodiments, it will be apparent to those skilled in the art that the embodiments may be implemented by means of software plus necessary general hardware platforms, or of course may be implemented by means of hardware. Based on this understanding, the foregoing technical solution may be embodied essentially or in a part contributing to the prior art in the form of a software product, which may be stored in a computer readable storage medium, such as ROM/RAM, a magnetic disk, an optical disk, etc., including several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the method described in the respective embodiments or some parts of the embodiments.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solution of the present invention, and are not limiting; although the invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present invention.
Claims (10)
1. An agricultural machinery operation area calculating method based on raster images is characterized by comprising the following steps:
acquiring behavior track data of an agricultural machine, and transmitting the behavior track data to an agricultural machine operation big data management service platform;
acquiring the behavior track data through the big data management service platform, and performing data preprocessing on the behavior track data to obtain track Tian Lu data;
dividing the track Tian Lushu according to a preset clustering algorithm, wherein the track is divided into a plurality of sections of tracks for farm machinery to work in a field and tracks to transfer on a road;
converting longitude and latitude geographical coordinates of each track point in the field operation track into plane Gaussian projection coordinates, and performing low-pass filtering to obtain the filtered plane Gaussian projection coordinates;
calculating grid image coordinates corresponding to the operation track in the agricultural machinery field according to the plane Gaussian projection coordinates, drawing a grid image according to the grid image coordinates, obtaining the number of pixel blocks for initial operation in the agricultural machinery field, and calculating the real ground area corresponding to each pixel block;
compensating the agricultural machinery operation strip gap in the grid image, obtaining the number of pixel blocks belonging to the agricultural machinery field operation in the compensated grid image, performing edge detection on the compensated grid image to correct the grid image, and generating the number of pixel blocks of the final agricultural machinery field operation;
And determining the agricultural machinery working area according to the product of the number of the pixel blocks of the final agricultural machinery field operation and the real ground area corresponding to each pixel block.
2. The grid image-based agricultural machinery work area calculation method of claim 1, wherein the steps of acquiring the behavior trace data of the agricultural machinery and transmitting the behavior trace data to an agricultural machinery work big data management service platform comprise:
acquiring behavior track data of the agricultural machinery through positioning equipment arranged on the agricultural machinery;
the behavior track data of the agricultural machinery are transmitted back to the agricultural machinery Internet of things platform through the mobile communication network;
and forwarding the behavior track data of the agricultural machinery to an agricultural machinery operation big data management service platform in real time through the agricultural machinery Internet of things platform.
3. The grid image-based agricultural machinery working area calculation method of claim 1, wherein the behavior trace data is acquired through the big data management service platform, and the behavior trace data is subjected to data preprocessing to obtain trace Tian Lu data, and specifically comprises the following steps:
acquiring the behavior trace data through the big data management service platform;
by ascending the agricultural machinery behavior track data according to the time sequence, searching abnormal data according to the arrangement sequence, wherein the abnormal data comprise track data repetition, relay offset, dead point offset, speed abnormality, positioning point offset, periodic offset, track data drift and track data stop;
Constructing a targeted track data anomaly identification and correction method according to coordinate discrimination, speed discrimination and vector discrimination methods, and realizing anomaly correction of track data;
and cleaning and deleting the found abnormal data or correcting the abnormal points according to the movement rule of the agricultural machinery.
4. The grid image-based agricultural machinery operation area calculation method according to claim 1, wherein the track Tian Lushu is divided into a track of a multi-segment agricultural machinery operation in a field and a track transferred on a road by a preset clustering algorithm, and specifically comprises:
calculating the sampling frequency of each track, and training a track classification model corresponding to each sampling frequency based on the track field data through a clustering algorithm;
and selecting the track classification model trained correspondingly according to the sampling frequency corresponding to each track to classify the tracks of the agricultural machine, and dividing the whole behavior track of the agricultural machine in one day into tracks of multiple sections of agricultural machine operation in the field and tracks of multiple sections of transfer on the road.
5. The grid image-based agricultural machinery working area calculation method according to claim 1, wherein the converting the longitude and latitude geographical coordinates of each track point in the field working track into planar gaussian projection coordinates and performing low-pass filtering to obtain the filtered planar gaussian projection coordinates specifically comprises:
Converting longitude and latitude geographic coordinates of each track point in the field operation track data into plane Gaussian projection coordinates through Gaussian projection according to the classified field operation track;
and filtering coordinates deviating from a set threshold value in the plane Gaussian projection coordinates through a low-pass filtering algorithm to generate filtered plane Gaussian projection coordinates.
6. The grid image-based agricultural machinery operation area calculation method according to claim 1, wherein grid image coordinates corresponding to an agricultural machinery field operation track are calculated according to the plane gaussian projection coordinates, grid images are drawn according to the grid image coordinates, the number of pixel blocks of an initial agricultural machinery field operation is obtained, and a real ground area corresponding to each pixel block is calculated, specifically comprising:
counting the maximum value and the minimum value of the abscissa and the maximum value and the minimum value of the ordinate in the filtered plane Gaussian projection coordinates according to the operation track data in each field;
converting the plane Gaussian projection coordinates of each track point into coordinates on a grid image through a preset conversion algorithm;
calculating the size of the raster image, drawing the raster image, and counting the number of pixel blocks with set pixel values in the raster image according to the drawn raster image;
And calculating the real ground area corresponding to each pixel block according to the preset resolution of the raster image.
7. The raster image-based agricultural machinery operation area calculation method of claim 1, wherein compensating for an agricultural machinery operation stripe gap in the raster image, obtaining a number of pixel blocks belonging to an agricultural machinery field operation in the compensated raster image, performing edge detection correction on the compensated raster image, and generating a final number of pixel blocks for the agricultural machinery field operation, specifically comprising:
determining the row number and the column number of each pixel block in the grid image, judging the value of each pixel block in a grid with the current pixel block as the center and generating a judging result;
determining whether pixel blocks are added to the compensation block list according to the judging result until all the pixel blocks are judged, forming an agricultural machinery operation strip gap compensation block list, and obtaining the number of pixel blocks belonging to agricultural machinery field operation in the compensated grid image;
and carrying out edge detection correction on the compensated grid image through an image edge detection algorithm to generate the number of pixel blocks for the final farm machinery field operation.
8. The grid image-based agricultural machinery working area calculating method according to claim 1, wherein the determining the agricultural machinery working area according to the product of the number of pixel blocks of the final agricultural machinery field work and the real ground area corresponding to each pixel block specifically comprises:
Counting the number of the pixel blocks of the final farm work as the number of the pixel blocks with the set pixel value;
and obtaining the actual operation area of the agricultural machine by multiplying the determined number of pixel blocks by the real ground area corresponding to each pixel block.
9. An agricultural work area computing system based on raster images, the system comprising:
the track data acquisition module is used for acquiring the behavior track data of the agricultural machinery and transmitting the behavior track data to the agricultural machinery operation big data management service platform;
the track data preprocessing module is used for acquiring the behavior track data through the big data management service platform, and performing data preprocessing on the behavior track data to obtain track Tian Lu data;
the track data Tian Lu dividing module is used for dividing the track Tian Lushu according to a preset clustering algorithm into tracks of a plurality of sections of agricultural machinery operating in the field and tracks transferred on roads;
the track data coordinate conversion module is used for converting longitude and latitude geographic coordinates of each track point in the field operation track into plane Gaussian projection coordinates and performing low-pass filtering to obtain the filtered plane Gaussian projection coordinates;
The grid image drawing module is used for calculating grid image coordinates corresponding to the farm machinery farm work track according to the plane Gaussian projection coordinates, drawing grid images according to the grid image coordinates, obtaining the number of pixel blocks for the initial farm machinery farm work, and calculating the real ground area corresponding to each pixel block;
the grid image compensation module is used for compensating the agricultural machinery operation strip gap in the grid image, acquiring the number of pixel blocks belonging to the agricultural machinery field operation in the compensated grid image, carrying out edge detection on the compensated grid image, correcting the grid image, and generating the number of pixel blocks of the final agricultural machinery field operation;
and the agricultural machinery operation area calculation module is used for determining the agricultural machinery operation area according to the product of the number of the pixel blocks operated in the final agricultural machinery field and the real ground area corresponding to each pixel block.
10. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the raster image based agricultural work area calculation method of any one of claims 1 to 8 when the program is executed.
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