A method for locating land boundary data based on intelligent perception

Abstract

The present invention relates to the field of intelligent perception, and in particular to a method for locating land boundary data based on intelligent perception, comprising: step S1, real-time acquisition of land boundary data sources; step S2, correction of abnormal data in the land boundary data sources; step S3, acquisition of land boundary point coordinates; step S4, adjustment of land boundary points; step S5, acquisition of land boundary lines, and optimization of smoothness and continuity of land boundary lines; step S6, judgment of the terrain of an operating land; step S7, judgment of the attributes of an operating land; step S8, adjustment of the judgment process of the attributes of an operating land; and step S9, adjustment of the judgment process of the operation frequency of agricultural machinery. The present invention realizes accurate positioning of land boundary through intelligent perception technology, reduces labor costs, and improves work efficiency.

Description

Technical Field

[0001] The present invention relates to the field of intelligent perception, and in particular to a method for locating land boundary data based on intelligent perception. Background Art

[0002] Traditional methods of surveying and mapping land boundaries mainly rely on manual measurement and manual recording. This method usually requires surveyors to carry professional equipment (such as total stations, GPS equipment) to the site for measurement, and then complete the positioning of land boundary through manual recording and drawing. With the development of remote sensing technology, high-resolution satellite images combined with image processing technology can automatically extract land boundary. The cost of acquiring satellite images is high, and the resolution and accuracy are not enough to meet the needs of agricultural machinery operation plots. The cost of acquiring high-precision surveying and mapping equipment and satellite images is high, which increases the economic burden of land boundary surveying and mapping.

[0003] Chinese Patent Publication No.: CN104240022A discloses an agricultural machinery vehicle-mounted terminal and an agricultural machinery scheduling method, system and server, the method comprising: when the agricultural machinery is operating across regions, the agricultural machinery holder collects agricultural machinery operation information, determines the operation workload, generates an operation plan through the agricultural machinery scheduling server, and sends it to the agricultural machinery vehicle-mounted terminal installed on the agricultural machinery; the agricultural machinery vehicle-mounted terminal sends the identification and current location information of the agricultural machinery vehicle-mounted terminal to the agricultural machinery server; after the agricultural machinery scheduling server receives the identification and location information of the agricultural machinery vehicle-mounted terminal; sends the identification of the farmland to be operated to the agricultural machinery vehicle-mounted terminal with the identification, determines the farmland containing the farmland and the agricultural machinery vehicle-mounted terminal The map data within the geographical range of the location of the terminal and the driving route from the location to the farmland; the agricultural machinery vehicle-mounted terminal displays the map and highlights the driving route according to the received response information. The technical solution of the present invention can dispatch agricultural machinery to the farmland to be operated, avoid blindly searching for the operation area, and is conducive to improving the dispatching efficiency of agricultural machinery; it can be seen that the agricultural machinery vehicle-mounted terminal and the agricultural machinery dispatching method, system and server have the following problems: the system is mainly based on the location information of the agricultural machinery and the farmland identification for dispatching, but the perception of the boundary of the plot is not accurate, and the specific attributes of the plot are not considered enough. These attributes have a great influence on the applicability and efficiency of agricultural machinery operations. Summary of the invention

[0004] To this end, the present invention provides a method for locating land boundary data based on intelligent perception, so as to overcome the problems in the prior art of inaccurate perception of land boundary and lack of real-time perception of internal attributes of land during agricultural machinery operation.

[0005] To achieve the above object, the present invention provides a method for locating land boundary data based on intelligent perception, comprising:

[0006] Step S1, real-time collection of land parcel data sources, cleaning of the collected land parcel data sources, and determination of abnormal data in the land parcel data sources;

[0007] Step S2, correcting the abnormal data in the land data source to obtain corrected abnormal data, and using the corrected abnormal data as normal data;

[0008] Step S3, obtaining the coordinates of the boundary points of the plot according to the longitude and latitude data in the plot data source;

[0009] Step S4, using GIS software to load the initially mapped work plot, obtaining the plot boundary points according to the plot data source, and adjusting the plot boundary points;

[0010] Step S5, using the "point to line" tool in the GIS software to connect the outputted plot boundary point coordinates into a line, obtain the plot boundary line, and optimize the smoothness and continuity of the plot boundary line;

[0011] Step S6, obtaining the grid color distribution according to the plot boundary line and the plot data source, and judging the terrain of the operation plot;

[0012] Step S7, according to the data source of the plot, the crop type, soil fertility, irrigation conditions and historical yield of the operation plot are obtained, the attributes of the operation plot are judged, and the number of grid color divisions is adjusted according to the result of the judgment of the attributes of the operation plot;

[0013] Step S8, judging the frequency of agricultural machinery operation according to the number of operations on the plot, and adjusting the process of judging the attributes of the operation plot;

[0014] Step S9, adjusting the agricultural machinery operation frequency determination process according to the agricultural machinery operation distance.

[0015] Furthermore, in step S1, when the land data source is collected in real time, the collected land data source is cleaned, duplicate data is removed, missing data is recollected, and the data format is ensured to be consistent. The abnormal data is judged by the 3σ principle, where n is the number of samples, xi is each sample data, and:

[0016] When xi≥3σ, the data value is determined to be abnormal data and removed;

[0017] When xi<3σ, the data value is determined to be normal data and uploaded to the server via wireless transmission.

[0018] Furthermore, in step S2, when correcting the abnormal data, the abnormal data and correction method of the longitude and latitude coordinates, working distance, crop type, soil fertility, irrigation conditions and historical yield are added to the abnormal detection model, the output result of the abnormal detection model is obtained, and the correction method for the abnormal data is output, including deleting duplicate values, replacing abnormal values ​​with the mean method, unifying the data format, manually correcting the abnormal longitude and latitude coordinates, comparing the satellite image data source of the actual location of the working plot with the abnormal longitude and latitude coordinates, adjusting the abnormal longitude and latitude coordinates, and converting the corrected abnormal data into a binary grid as normal data.

[0019] Furthermore, in step S3, when obtaining the coordinates of the boundary points of the plot, the maximum longitude xmax, the minimum longitude xmin, the maximum latitude ymax, and the minimum latitude ymin are obtained according to the longitude and longitude data in the plot data source, and the number of grid cells a1 in the longitude direction and the number of grid cells a2 in the latitude direction are calculated according to the maximum longitude xmax, the minimum longitude xmin, the maximum latitude ymax, and the minimum latitude ymin, and a1=(xmax-xmin) / d, a2=(ymax-ymin) / d, d is the grid step size, and the initial value of the two-dimensional array is set to 0, the size is a1×a2, and the initial value of the two-dimensional array is initialized to 0, the size is a1×a2, and the traversal is performed. Plot data source, obtain longitude and latitude data (x1, y1), set x2 = (x1-xmin) / d, y2 = (y1-ymin) / d, set the value of the two-dimensional array at index x2 and y2 to 1, indicating that the two-dimensional array is covered by the plot data source, set the value of (x2, y2) to initialize the two-dimensional array data information A0, pass the two-dimensional array as a parameter to the opencv2 boundary search function findContours, and obtain the boundary point data cluster c1. According to the index information of the elements in c1, the data information A1 corresponding to the index position of the initialized two-dimensional array data information A0 can be obtained, and the data information A1 can be recorded in the set c2. The set c2 is the plot boundary point coordinate data set.

[0020] Further, in the step S4, the satellite image data source of the actual position of the work plot is input into the GIS software to load the preliminary mapped work plot, obtain the three-dimensional model of the work plot, obtain the coordinates of the plot boundary points of the three-dimensional model of the work plot, set the coordinates of the plot boundary points to (x1, y1, z1), and the actual plot coordinates obtained by drone mapping are (x2, y2, z2). The X-axis error is A, A=x1-x2, the Y-axis error is B, B=y1-y2, the Z-axis error is C, C=z1-z2, and the preset error threshold is D0. The X-axis error A is compared with the preset error threshold D0, and the validity of the coordinates of the plot boundary points is judged according to the comparison result, and output according to the judgment result, wherein:

[0021] When A falls outside D0, the X-axis coordinate of the boundary point of the plot is determined to be invalid, and the output result is that the boundary point of the plot is invalid. x1 is adjusted to x1=x2 to obtain the coordinates of the boundary point of the second plot, and the preliminary mapped operation plot is loaded using GIS software according to the coordinates of the boundary point of the second plot to obtain the three-dimensional model of the second operation plot. When the X-axis coordinate of the boundary point of the plot is determined to be valid, the validity of the Y-axis coordinate of the boundary point of the plot is judged;

[0022] When A falls within D0, the X-axis coordinate of the boundary point of the plot is determined to be valid, and the validity of the Y-axis coordinate of the boundary point of the plot is determined;

[0023] In step S4, when judging the validity of the Y-axis coordinates of the boundary point of the plot, the Y-axis error B is compared with the preset error threshold D0, and the validity of the coordinates of the boundary point of the plot is judged according to the comparison result, and output according to the judgment result, wherein:

[0024] When B falls outside D0, the Y-axis coordinate of the boundary point of the plot is determined to be invalid, and the output result is that the boundary point of the plot is invalid. y1 is adjusted to y1=y2 to obtain the coordinates of the boundary point of the third plot, and the preliminary mapped operation plot is loaded using GIS software according to the coordinates of the boundary point of the third plot to obtain the three-dimensional model of the third operation plot. When the Y-axis coordinate of the boundary point of the plot is determined to be valid, the validity of the Z-axis coordinate of the boundary point of the plot is judged;

[0025] When B falls within D0, the Y-axis coordinate of the boundary point of the plot is determined to be valid, and the validity of the Z-axis coordinate of the boundary point of the plot is determined;

[0026] In step S4, when judging the validity of the Z-axis coordinates of the boundary point of the plot, the Z-axis error C is compared with the preset error threshold D0, and the validity of the coordinates of the boundary point of the plot is judged according to the comparison result, and output according to the judgment result, wherein:

[0027] When C falls outside D0, the Z-axis coordinate of the boundary point of the plot is determined to be invalid, and the output result is that the boundary point of the plot is invalid. z1 is adjusted to z1=z2 to obtain the coordinates of the fourth boundary point of the plot, and the preliminary mapped operation plot is loaded using GIS software according to the coordinates of the boundary point of the fourth plot to obtain the three-dimensional model of the fourth operation plot. When the Z-axis coordinate of the boundary point of the plot is determined to be valid, the coordinates of the boundary point of the plot are output;

[0028] When C falls within D0, the Z-axis coordinate of the boundary point of the plot is determined to be valid, and the coordinates of the boundary point of the plot are output.

[0029] Furthermore, in step S5, the outputted plot boundary point coordinates are connected into lines using the "point to line" tool in the GIS software. When obtaining the plot boundary line, the plot boundary line feature line segment Y is compared with the preset non-smooth line segment YO, and the overlap threshold △Y is set. The smoothness of the plot boundary line is judged according to the comparison result, and the plot boundary line is optimized according to the judgment result, wherein:

[0030] When the overlap between Y and Y0 is ≥ △Y, the smoothness of the boundary line of the plot is determined to be abnormal, and the smoothness of the boundary line of the plot is optimized. The output optimized coordinates are (xi,yi,zi) is the coordinate of the i-th original point, Ni is the set of points within 5m of the i-th point and its surroundings;

[0031] When the coincidence degree between Y and Y0 is less than △Y, the smoothness of the boundary line of the plot is judged to be normal;

[0032] In step S5, the Euclidean distance d between the starting point (x3, y3, z3) and the end point (x4, y4, z4) is compared with the preset distance d0, and the continuity of the plot boundary line is judged according to the comparison result, and the plot boundary line is optimized according to the judgment result, wherein:

[0033] When d>d0, the land parcel boundary line is determined to be discontinuous, the continuity of the land parcel boundary line is optimized, an intermediate point is generated between the starting point and the end point, the distance between the starting point and the end point and the intermediate point is set to d1≤d0, so that the land parcel boundary line is continuous, and the land parcel boundary line is output;

[0034] When d≤d0, the boundary line of the land parcel is determined to be continuous and the boundary line of the land parcel is output.

[0035] Furthermore, in step S6, when judging the terrain of the working plot, the grid color distribution is obtained according to the plot boundary line and the plot data source to identify the concave and convex surface of the working plot, wherein:

[0036] When the grid color changes from light to dark and then to light again, the work plot is convex;

[0037] When the grid color changes from dark to light and then to dark again, the work plot is concave.

[0038] Furthermore, in step S7, when judging the attributes of the operation plot, the crop type, soil fertility, irrigation conditions and historical yield of the operation plot are obtained according to the plot data source, and a comprehensive evaluation is performed on the plot. The comprehensive score V is compared with the preset score V0, and the attributes of the operation plot are judged according to the comparison result, wherein:

[0039] When V>V0, the operation plot is determined to be an important area;

[0040] When V≤V0, the operation plot is determined to be a non-important area.

[0041] Furthermore, in step S8, when analyzing the frequency of agricultural machinery operation according to the number of operations on the plot, the agricultural machinery operation frequency T is compared with the preset agricultural machinery operation frequency T0, and the operation plot attribute judgment process is adjusted according to the comparison result, wherein:

[0042] When T>T0, it is determined that the frequency of agricultural machinery operation is high and the correction coefficient is set The preset color division number S0 is adjusted according to the correction coefficient, and the adjusted preset color division number is Sa, and Sa=S0×a is set;

[0043] When T≤T0, it is determined that the operation frequency of agricultural machinery is low, and the correction coefficient b=-2lnS-S0 is set.

[0044] The preset color division number S0 is adjusted according to the correction coefficient, and the adjusted preset color division number is Sb, and Sb=S0×b is set.

[0045] Furthermore, in step S9, when the agricultural machinery operation frequency judgment process is adjusted according to the agricultural machinery operation distance, the effective operation distance M is compared with the preset effective operation distance M0, and the effectiveness of the agricultural machinery operation is judged according to the comparison result, and the agricultural machinery operation frequency judgment process is adjusted according to the judgment result, wherein:

[0046] When M>M0, the agricultural machinery operation is judged to be valid and recorded in the agricultural machinery operation frequency statistics;

[0047] When M≤M0, the agricultural machinery operation is judged to be invalid and ignored in the agricultural machinery operation frequency statistics.

[0048] Compared with the existing technology, the beneficial effect of the present invention lies in that it is based on intelligent sensing technology, adopts automated data collection and processing procedures, and combines intelligent analysis algorithms to achieve real-time perception and precise positioning of land boundaries, reduce labor costs, and improve work efficiency.

[0049] In particular, the step S1 collects the land parcel data source in real time, cleans the collected information, and judges the abnormal data, so as to obtain an accurate, reliable and clean land parcel data source and ensure the integrity of the land parcel data source.

[0050] In particular, the step S2 corrects the abnormal data judgment result according to the land parcel data source, so as to ensure the accuracy and reliability of the data, and provide a solid foundation for subsequent data analysis and positioning of land parcel boundary data.

[0051] In particular, step S3 obtains the coordinates of the plot boundary points according to the latitude and longitude data in the plot data source, so as to reduce manual intervention through automation technology, improve surveying and mapping efficiency and reduce costs, and can work stably under complex terrain and various environmental conditions, providing high-precision and high-reliability plot boundary data.

[0052] In particular, the step S4 uses GIS software to load the preliminarily mapped work plot, obtains the plot boundary points according to the plot data source, and adjusts the plot boundary points to optimize the spatial representation of the work plot and ensure that the boundary of the work plot is accurate and reflects the actual situation.

[0053] In particular, in step S5, the output plot boundary point coordinates are connected into lines using the "point to line" tool in the GIS software to adjust the boundary points, obtain the plot boundary line, and optimize the smoothness and continuity of the plot boundary line, so as to improve the representation accuracy of the plot boundary and reduce errors caused by the collection or processing of the plot data source.

[0054] In particular, step S6 obtains the grid color distribution according to the plot boundary line and the plot data source, and judges the terrain of the operation plot, so as to display the terrain characteristics of the plot in a visual manner, and provide a basis for intelligent perception of the plot boundary data.

[0055] In particular, the step S7 obtains the crop type, soil fertility, irrigation conditions and historical yield of the operating plot based on the plot data source, judges the attributes of the operating plot, and adjusts the number of grid color divisions based on the result of the judgment of the operating plot attributes, so as to analyze the plot attributes in a quantitative manner and provide a scientific basis for subsequent agricultural production, land planning, resource management, etc.

[0056] In particular, the step S8 judges the frequency of agricultural machinery operation according to the number of land operations, and adjusts the process of judging the attributes of the operation land to ensure the efficiency and rationality of agricultural machinery operations. At the same time, the judgment of land attributes is adjusted according to the actual operation conditions to better guide agricultural production.

[0057] In particular, the step S9 adjusts the agricultural machinery operation frequency judgment process according to the agricultural machinery operation distance, so as to accurately identify the agricultural machinery operation frequency and avoid the influence of recognition errors on the judgment of the operation plot attributes. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] Figure 1 The figure is a flow chart of a method for locating land boundary data based on intelligent perception according to the present embodiment. DETAILED DESCRIPTION

[0059] In order to make the objects and advantages of the present invention more clearly understood, the present invention is further described below in conjunction with embodiments; it should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0060] The preferred embodiments of the present invention are described below with reference to the accompanying drawings. It should be understood by those skilled in the art that these embodiments are only used to explain the technical principles of the present invention and are not intended to limit the protection scope of the present invention.

[0061] It should be noted that, in the description of the present invention, terms such as "up", "down", "left", "right", "inside" and "outside" indicating directions or positional relationships are based on the directions or positional relationships shown in the drawings. This is merely for the convenience of description and does not indicate or imply that the device or element must have a specific orientation, be constructed and operated in a specific orientation. Therefore, it cannot be understood as a limitation on the present invention.

[0062] In addition, it should be noted that in the description of the present invention, unless otherwise clearly specified and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For those skilled in the art, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0063] See also Figure 1 As shown, it is a flow chart of the method for locating land boundary data based on intelligent perception in this embodiment, and the method includes:

[0064] Step S1, real-time collection of land parcel data sources, cleaning of the collected land parcel data sources, and determination of abnormal data in the land parcel data sources;

[0065] Step S2, correcting the abnormal data in the land data source to obtain corrected abnormal data, and using the corrected abnormal data as normal data;

[0066] Step S3, obtaining the coordinates of the boundary points of the plot according to the longitude and latitude data in the plot data source;

[0067] Step S4, using GIS software to load the initially mapped work plot, obtaining the plot boundary points according to the plot data source, and adjusting the plot boundary points;

[0068] Step S5, using the "point to line" tool in the GIS software to connect the outputted plot boundary point coordinates into a line, obtain the plot boundary line, and optimize the smoothness and continuity of the plot boundary line;

[0069] Step S6, obtaining the grid color distribution according to the plot boundary line and the plot data source, and judging the terrain of the operation plot;

[0070] Step S7, according to the data source of the plot, the crop type, soil fertility, irrigation conditions and historical yield of the operation plot are obtained, the attributes of the operation plot are judged, and the number of grid color divisions is adjusted according to the result of the judgment of the attributes of the operation plot;

[0071] Step S8, judging the frequency of agricultural machinery operation according to the number of operations on the plot, and adjusting the process of judging the attributes of the operation plot;

[0072] Step S9, adjusting the agricultural machinery operation frequency determination process according to the agricultural machinery operation distance.

[0073] Specifically, the plot boundary data positioning method collects the plot data source in real time through step S1, and cleans the collected plot data source to ensure that an accurate, reliable and clean plot data source is obtained; the plot boundary data positioning method corrects the abnormal data judgment result through step S2 to ensure the accuracy and reliability of the data; the plot boundary data positioning method obtains the coordinates of the plot boundary points through step S3 to reduce manual intervention through automation technology, and the plot boundary data positioning method adjusts the plot boundary points through step S4 to ensure that the boundary of the operating plot is accurate and reflects the actual situation; the plot boundary data positioning method corrects the abnormal data judgment result through step S2 to ensure the accuracy and reliability of the data; the plot boundary data positioning method obtains the coordinates of the plot boundary points through step S3 to reduce manual intervention through automation technology, and adjusts the plot boundary points through step S4 to ensure that the boundary of the operating plot is accurate and reflects the actual situation; the plot boundary data positioning method corrects the abnormal data judgment result through step S5 to ensure that the abnormal data judgment result is accurate and reflects the actual situation; The smoothness and continuity of the plot boundary line are optimized to improve the representation accuracy of the plot boundary; the plot boundary data positioning method judges the terrain of the working plot through step S6 to display the terrain characteristics of the plot in a visual manner; the plot boundary data positioning method judges the attributes of the working plot through step S7 to analyze the plot attributes in a quantitative manner; the plot boundary data positioning method adjusts the working plot attribute judgment process through step S8 to ensure the efficiency and rationality of agricultural machinery operations; the plot boundary data positioning method adjusts the agricultural machinery operation frequency judgment process through step S9 to accurately identify the agricultural machinery operation frequency.

[0074] Specifically, the land boundary data positioning method is based on intelligent perception technology, adopts automated data collection and processing procedures, and combines intelligent analysis algorithms to achieve real-time perception and precise positioning of land boundary, reduce labor costs, and improve work efficiency.

[0075] Specifically, in step S1, when the land data source is collected in real time, the collected land data source is cleaned, duplicate data is removed, missing data is recollected, and the data format is ensured to be consistent. The abnormal data is judged by the 3σ principle, n is the number of samples, xi is each sample data, where:

[0076] When xi≥3σ, the data value is determined to be abnormal data and removed;

[0077] When xi<3σ, the data value is determined to be normal data and uploaded to the server via wireless transmission.

[0078] Specifically, the data source of the plot includes longitude and latitude coordinates, working distance, crop type, soil fertility, irrigation conditions and historical yield. This embodiment does not limit the collection method of the plot data source. Those skilled in the art can freely set it according to actual conditions, and only need to meet the collection requirements. For example, the longitude and latitude coordinates and working distance can be collected according to GPS data, such as the longitude and latitude coordinate position is (34.0522, -118.2437), and the working distance is 10 kilometers. The step S1 collects the crop type and historical yield according to the agricultural production records in the big data, such as the crop type is corn, and the historical yield is 8 tons in 2022 and 9 tons in 2023. The soil fertility is collected according to the soil testing instrument, such as the soil fertility is medium. The irrigation conditions are collected according to the monitoring data of the irrigation system, such as the irrigation conditions are drip irrigation. This embodiment does not limit the wireless transmission method, and those skilled in the art can freely set it according to actual conditions, and only need to meet the transmission requirements, such as LoRa can be set. This embodiment does not limit the judgment method of abnormal data, and those skilled in the art can freely set it according to actual conditions, and only need to meet the abnormal data judgment requirements, such as the 3σ principle can be set.

[0079] Specifically, the step S1 collects the land parcel data source in real time, cleans the collected land parcel data source, and judges the abnormal data, so as to obtain an accurate, reliable and clean land parcel data source and ensure the integrity of the land parcel data source.

[0080] Specifically, in step S2, when correcting the abnormal data, the abnormal data and correction method of longitude and latitude coordinates, working distance, crop type, soil fertility, irrigation conditions and historical yield are added to the abnormal detection model, the output result of the abnormal detection model is obtained, and the correction method for the abnormal data is output, including deleting duplicate values, replacing abnormal values ​​with the mean method, unifying the data format, manually correcting the abnormal longitude and latitude coordinates, comparing the satellite image data source of the actual location of the working plot with the abnormal longitude and latitude coordinates, adjusting the abnormal longitude and latitude coordinates, and converting the corrected abnormal data into a binary grid as normal data.

[0081] Specifically, this embodiment does not limit the construction process of the anomaly detection model. Those skilled in the art can freely set it according to actual conditions, as long as the intelligent analysis requirements of abnormal data are met. For example, the parameters of latitude and longitude coordinates, working distance, crop type, soil fertility, irrigation conditions and historical yield can be set as indexes. The correction method for the abnormal data of the plot is to use the data combination of the index corresponding content as the anomaly detection model construction data, divide 70% of the anomaly detection model construction data into a model training set, and divide 30% of the anomaly detection model construction data into a model test set. The convolutional neural network model is trained according to the model training set, and the trained convolutional neural network model is tested according to the model test set. When the test accuracy reaches 98%, the trained convolutional neural network model is output as an anomaly detection model. When the test accuracy does not reach 98%, the trained convolutional neural network model continues to be trained until the test accuracy reaches 98% and then it is output as an anomaly detection model.

[0082] Specifically, the step S2 corrects the abnormal data judgment result according to the land parcel data source, so as to ensure the accuracy and reliability of the data, and provide a solid foundation for subsequent data analysis and positioning of land parcel boundary data.

[0083] Specifically, in step S3, when obtaining the coordinates of the boundary points of the plot, the maximum longitude xmax, the minimum longitude xmin, the maximum latitude ymax, and the minimum latitude ymin are obtained according to the longitude and longitude data in the plot data source. The number of grid cells a1 in the longitude direction and the number of grid cells a2 in the latitude direction are calculated according to the maximum longitude xmax, the minimum longitude xmin, the maximum latitude ymax, and the minimum latitude ymin. A1=(xmax-xmin) / d, a2=(ymax-ymin) / d, d is the grid step size, the initial value of the two-dimensional array is set to 0, the size is a1×a2, the initial value of the two-dimensional array is initialized to 0, the size is a1×a2, and the traversal is performed. Plot data source, obtain longitude and latitude data (x1, y1), set x2 = (x1-xmin) / d, y2 = (y1-ymin) / d, set the value of the two-dimensional array at index x2 and y2 to 1, indicating that the two-dimensional array is covered by the plot data source, set the value of (x2, y2) to initialize the two-dimensional array data information A0, pass the two-dimensional array as a parameter to the opencv2 boundary search function findContours, and obtain the boundary point data cluster c1. According to the index information of the elements in c1, the data information A1 corresponding to the index position of the initialized two-dimensional array data information A0 can be obtained, and the data information A1 can be recorded in the set c2. The set c2 is the plot boundary point coordinate data set.

[0084] Specifically, the plot boundary point coordinates refer to the coordinate values ​​of points located at the edge of the working plot, and the step size refers to the spatial distance between grid units.

[0085] Specifically, step S3 obtains the coordinates of the plot boundary points according to the latitude and longitude data in the plot data source, so as to reduce manual intervention through automation technology, improve surveying and mapping efficiency and reduce costs, and can work stably under complex terrain and various environmental conditions, providing high-precision and high-reliability plot boundary data.

[0086] Specifically, in step S4, the satellite image data source of the actual position of the work plot is input into the GIS software to load the preliminary mapped work plot, obtain the three-dimensional model of the work plot, obtain the coordinates of the plot boundary points of the three-dimensional model of the work plot, set the coordinates of the plot boundary points to (x1, y1, z1), and the actual plot coordinates obtained by drone mapping are (x2, y2, z2). The X-axis error is A, A=x1-x2, the Y-axis error is B, B=y1-y2, the Z-axis error is C, C=z1-z2, and the preset error threshold is D0. The X-axis error A is compared with the preset error threshold D0, and the validity of the coordinates of the plot boundary points is judged according to the comparison result, and output according to the judgment result, wherein:

[0087] When A falls outside D0, the X-axis coordinate of the boundary point of the plot is determined to be invalid, and the output result is that the boundary point of the plot is invalid. x1 is adjusted to x1=x2 to obtain the coordinates of the second boundary point of the plot, and the preliminary mapped operation plot is loaded using GIS software according to the coordinates of the boundary point of the second plot to obtain the three-dimensional model of the second operation plot. When the X-axis coordinate of the boundary point of the plot is determined to be valid, the validity of the Y-axis coordinate of the boundary point of the plot is judged;

[0088] When A falls within D0, the X-axis coordinate of the boundary point of the plot is determined to be valid, and the validity of the Y-axis coordinate of the boundary point of the plot is determined.

[0089] Specifically, in step S4, when judging the validity of the Y-axis coordinate of the boundary point of the plot, the Y-axis error B is compared with the preset error threshold D0, and the validity of the coordinate of the boundary point of the plot is judged according to the comparison result, and output according to the judgment result, wherein:

[0090] When B falls outside D0, the Y-axis coordinate of the boundary point of the plot is determined to be invalid, and the output result is that the boundary point of the plot is invalid. y1 is adjusted to y1=y2 to obtain the coordinates of the boundary point of the third plot, and the preliminary mapped operation plot is loaded using GIS software according to the coordinates of the boundary point of the third plot to obtain the three-dimensional model of the third operation plot. When the Y-axis coordinate of the boundary point of the plot is determined to be valid, the validity of the Z-axis coordinate of the boundary point of the plot is judged;

[0091] When B falls within D0, the Y-axis coordinate of the boundary point of the plot is determined to be valid, and the validity of the Z-axis coordinate of the boundary point of the plot is determined.

[0092] Specifically, in step S4, when judging the validity of the Z-axis coordinates of the boundary point of the plot, the Z-axis error C is compared with the preset error threshold D0, and the validity of the coordinates of the boundary point of the plot is judged according to the comparison result, and output according to the judgment result, wherein:

[0093] When C falls outside D0, the Z-axis coordinate of the boundary point of the plot is determined to be invalid, and the output result is that the boundary point of the plot is invalid. z1 is adjusted to z1=z2 to obtain the coordinates of the fourth boundary point of the plot, and the preliminary mapped operation plot is loaded using GIS software according to the coordinates of the boundary point of the fourth plot to obtain the three-dimensional model of the fourth operation plot. When the Z-axis coordinate of the boundary point of the plot is determined to be valid, the coordinates of the boundary point of the plot are output;

[0094] When C falls within D0, the Z-axis coordinate of the boundary point of the plot is determined to be valid, and the coordinates of the boundary point of the plot are output.

[0095] Specifically, the three-dimensional model of the work plot refers to the three-dimensional space modeling of the work plot. This embodiment does not limit the method for obtaining the three-dimensional model of the work plot. Those skilled in the art can freely set it according to actual conditions, and only need to meet the positioning requirements. For example, GIS software can be set. The preset error threshold refers to a pre-set error range when locating the coordinates of the boundary points of the plot, such as setting the range of D0 to -3≤D0≤3. This embodiment does not limit the method for obtaining the coordinates of the actual plot. Those skilled in the art can freely set it according to actual conditions, and only need to meet the positioning requirements. For example, drone surveying can be set, and the high-precision camera carried by the drone can be used to perform low-altitude flight photography of the plot, and then the image data can be processed through the surveying and mapping software to obtain the precise coordinates of the plot. This embodiment does not limit the type of surveying and mapping software. Those skilled in the art can freely set it according to actual conditions, and only need to meet the surveying and mapping requirements. For example, Pix4Dmapper can be set, and the photos taken by the drone can be imported into Pix4Dmapper. Pix4Dmapper will automatically match, correct and splice the images. After setting the marker points and the coordinate system, the processed plot coordinates can be exported.

[0096] Specifically, step S4 uses GIS software to load the preliminarily mapped work plot, obtains the plot boundary points according to the plot data source, and adjusts the plot boundary points to optimize the spatial representation of the work plot and ensure that the boundary of the work plot is accurate and reflects the actual situation.

[0097] Specifically, in step S5, the outputted plot boundary point coordinates are connected into lines using the "point to line" tool in the GIS software. When obtaining the plot boundary line, the plot boundary line feature line segment Y is compared with the preset non-smooth line segment YO, and the overlap threshold △Y is set. The smoothness of the plot boundary line is judged according to the comparison result, and the plot boundary line is optimized according to the judgment result, wherein:

[0098] When the overlap between Y and Y0 is ≥ △Y, the smoothness of the boundary line of the plot is determined to be abnormal, and the smoothness of the boundary line of the plot is optimized. The output optimized coordinates are (xi,yi,zi) is the coordinate of the i-th original point, Ni is the set of points within 5m of the i-th point and its surroundings;

[0099] When the overlap between Y and Y0 is less than △Y, the smoothness of the boundary line of the plot is determined to be normal.

[0100] Specifically, in step S5, the Euclidean distance d between the starting point (x3, y3, z3) and the end point (x4, y4, z4) is compared with the preset distance d0, and the continuity of the plot boundary line is judged according to the comparison result, and the plot boundary line is optimized according to the judgment result, wherein:

[0101] When d>d0, the land parcel boundary line is determined to be discontinuous, the continuity of the land parcel boundary line is optimized, an intermediate point is generated between the starting point and the end point, the distance between the starting point and the end point and the intermediate point is set to d1≤d0, so that the land parcel boundary line is continuous, and the land parcel boundary line is output;

[0102] When d≤d0, the boundary line of the land parcel is determined to be continuous and the boundary line of the land parcel is output.

[0103] Specifically, the plot boundary line refers to the clear geographical boundary of the working plot, the smoothness refers to the lack of mutation in the geometric shape of the plot boundary line, the original point refers to the three-dimensional coordinates of the i-th point on the plot boundary line, which constitutes the original representation of the plot boundary line. This embodiment does not limit the value of the coincidence threshold, and technicians in this field can freely set it according to actual conditions, as long as it meets the comparison requirements, such as 80%. The continuity refers to the line formed by connecting the coordinates of the plot boundary points in chronological order to maintain an uninterrupted and unbroken state as a whole. The preset distance d0 refers to the preset maximum value of the straight-line distance between the coordinates of two adjacent plot boundary points, such as setting d0=0.5m.

[0104] Specifically, in step S5, the output plot boundary point coordinates are connected into lines using the "point to line" tool in the GIS software to adjust the boundary points, obtain the plot boundary line, and optimize the smoothness and continuity of the plot boundary line to improve the representation accuracy of the plot boundary and reduce errors caused by the collection or processing of the plot data source.

[0105] Specifically, in step S6, when judging the terrain of the work plot, the grid color distribution is obtained according to the plot boundary line and the plot data source to identify the concave and convex surface of the work plot, wherein:

[0106] When the grid color changes from light to dark and then to light again, the work plot is convex;

[0107] When the grid color changes from dark to light and then to dark again, the work plot is concave.

[0108] Specifically, the grid color distribution refers to mapping the plot data onto a regular grid and assigning a color value to each grid unit to represent the specific attributes of the unit. The color value corresponds to the plot data source through a color mapping relationship so as to intuitively display the attribute distribution and characteristics of the plot on the grid map. The color mapping relationship refers to the allocation of grids according to the plot data source (such as longitude and latitude, crop type, and yield).

[0109] Specifically, step S6 obtains the grid color distribution according to the plot boundary line and the plot data source, and judges the terrain of the working plot, so as to display the terrain characteristics of the plot in a visual manner, and provide a basis for intelligent perception of the plot boundary data.

[0110] Specifically, in step S7, when judging the attributes of the operation plot, the crop type, soil fertility, irrigation conditions and historical yield of the operation plot are obtained according to the plot data source, and a comprehensive evaluation is performed on the plot. The comprehensive score V is compared with the preset score V0, and the attributes of the operation plot are judged according to the comparison result, wherein:

[0111] When V>V0, the operation plot is determined to be an important area;

[0112] When V≤V0, the operation plot is determined to be a non-important area.

[0113] The number of grid color divisions is adjusted according to the properties of the operation plot, including:

[0114] When V>V0, the operation plot is an important area, and the number of grid color divisions S=6;

[0115] When V≤V0, the operation area is a non-important area, and the number of grid color divisions S=3.

[0116] Specifically, the comprehensive score V refers to a numerical value calculated based on the comprehensive attributes of the plot. The plot data source is standardized and converted into a percentage form, and weights are introduced to reflect the overall condition or value of the plot. This embodiment does not limit the distribution of weights. Technical personnel in this field can freely set them according to actual conditions as long as the scoring requirements are met. For example, the crop type, soil fertility, irrigation conditions and historical yield can be set to 25%, 25%, 25% and 25% respectively. The important area refers to the operating plot with complex terrain, diverse ecological environment and high resource value. The non-important area refers to the plot with simple terrain, simple ecological environment and low resource value.

[0117] Specifically, step S7 obtains the crop type, soil fertility, irrigation conditions and historical yield of the operating plot based on the plot data source, judges the attributes of the operating plot, and adjusts the number of grid color divisions based on the results of the judgment of the operating plot attributes, so as to analyze the plot attributes in a quantitative manner and provide a scientific basis for subsequent agricultural production, land planning, resource management, etc.

[0118] Specifically, in step S8, when analyzing the frequency of agricultural machinery operation according to the number of operations on the plot, the agricultural machinery operation frequency T is compared with the preset agricultural machinery operation frequency T0, and the operation plot attribute judgment process is adjusted according to the comparison result, wherein:

[0119] When T>T0, it is determined that the frequency of agricultural machinery operation is high and the correction coefficient is set The preset color division number S0 is adjusted according to the correction coefficient, and the adjusted preset color division number is Sa, and Sa=S0×a is set;

[0120] When T≤T0, it is determined that the operation frequency of agricultural machinery is low, and the correction coefficient b=-2lnS-S0 is set.

[0121] The preset color division number S0 is adjusted according to the correction coefficient, and the adjusted preset color division number is Sb, and Sb=S0×b is set.

[0122] Specifically, the number of land operations includes the number of tillage times, fertilization times and irrigation times. The agricultural machinery operation frequency T refers to the number of times and time proportion of actual agricultural machinery operations within three months. The preset agricultural machinery operation frequency T0 refers to the agricultural machinery operation frequency target value pre-set according to agricultural production needs, such as T0 can be set to 5.

[0123] Specifically, step S8 determines the frequency of agricultural machinery operations according to the number of land operations, and adjusts the process of determining the attributes of the operating land to ensure the efficiency and rationality of agricultural machinery operations. At the same time, the judgment of land attributes is adjusted according to actual operating conditions to better guide agricultural production.

[0124] Specifically, in step S9, when the agricultural machinery operation frequency judgment process is adjusted according to the agricultural machinery operation distance, the effective operation distance M is compared with the preset effective operation distance M0, and the effectiveness of the agricultural machinery operation is judged according to the comparison result, and the agricultural machinery operation frequency judgment process is adjusted according to the judgment result, wherein:

[0125] When M>M0, the agricultural machinery operation is judged to be valid and recorded in the agricultural machinery operation frequency statistics;

[0126] When M≤M0, the agricultural machinery operation is judged to be invalid and ignored in the agricultural machinery operation frequency statistics.

[0127] Specifically, the effective working distance M refers to the minimum distance that the agricultural machinery must travel before entering the plot and starting to work, and the preset effective working distance M0 refers to the maximum setting value of the minimum distance that the agricultural machinery must travel within the boundary line of the plot, such as M0 can be set to 5m.

[0128] Specifically, step S9 adjusts the agricultural machinery operation frequency judgment process according to the agricultural machinery operation distance, so as to accurately identify the agricultural machinery operation frequency and avoid the influence of recognition errors on the judgment of the operation plot attributes.

[0129] So far, the technical solutions of the present invention have been described in conjunction with the preferred embodiments shown in the accompanying drawings. However, it is easy for those skilled in the art to understand that the protection scope of the present invention is obviously not limited to these specific embodiments. Without departing from the principle of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will fall within the protection scope of the present invention.

Claims

1. A method for locating land boundary data based on intelligent perception, characterized in that: include: Step S1, real-time collection of land parcel data sources, cleaning of the collected land parcel data sources, and determination of abnormal data in the land parcel data sources; Step S2, correcting the abnormal data in the land data source to obtain corrected abnormal data, and using the corrected abnormal data as normal data; Step S3, obtaining the coordinates of the boundary points of the plot according to the longitude and latitude data in the plot data source; Step S4, using GIS software to load the initially mapped work plot, obtaining the plot boundary points according to the plot data source, and adjusting the plot boundary points; Step S5, using the "point to line" tool in the GIS software to connect the outputted plot boundary point coordinates into a line, obtain the plot boundary line, and optimize the smoothness and continuity of the plot boundary line; Step S6, obtaining the grid color distribution according to the plot boundary line and the plot data source, and judging the terrain of the operation plot; Step S7, according to the data source of the plot, the crop type, soil fertility, irrigation conditions and historical yield of the operation plot are obtained, the attributes of the operation plot are judged, and the number of grid color divisions is adjusted according to the result of the judgment of the attributes of the operation plot; Step S8, judging the frequency of agricultural machinery operation according to the number of operations on the plot, and adjusting the process of judging the attributes of the operation plot; Step S9, adjusting the agricultural machinery operation frequency determination process according to the agricultural machinery operation distance; In the step S4, the satellite image data source of the actual position of the work plot is input into the GIS software to load the preliminary mapped work plot, obtain the three-dimensional model of the work plot, obtain the coordinates of the plot boundary points of the three-dimensional model of the work plot, set the coordinates of the plot boundary points to (x1, y1, z1), and the actual plot coordinates obtained by drone mapping are (x2, y2, z2). The X-axis error is A, A=x1-x2, the Y-axis error is B, B=y1-y2, the Z-axis error is C, C=z1-z2, and the preset error threshold is D0. The X-axis error A is compared with the preset error threshold D0, and the validity of the coordinates of the plot boundary points is judged according to the comparison result, and output according to the judgment result, wherein: When A falls outside D0, the X-axis coordinate of the boundary point of the plot is determined to be invalid, and the output result is that the boundary point of the plot is invalid. x1 is adjusted to x1=x2 to obtain the coordinates of the boundary point of the second plot, and the preliminary mapped operation plot is loaded using GIS software according to the coordinates of the boundary point of the second plot to obtain the three-dimensional model of the second operation plot. When the X-axis coordinate of the boundary point of the plot is determined to be valid, the validity of the Y-axis coordinate of the boundary point of the plot is judged; When A falls within D0, the X-axis coordinate of the boundary point of the plot is determined to be valid, and the validity of the Y-axis coordinate of the boundary point of the plot is determined; In step S4, when judging the validity of the Y-axis coordinates of the boundary point of the plot, the Y-axis error B is compared with the preset error threshold D0, and the validity of the coordinates of the boundary point of the plot is judged according to the comparison result, and output according to the judgment result, wherein: When B falls outside D0, the Y-axis coordinate of the boundary point of the plot is determined to be invalid, and the output result is that the boundary point of the plot is invalid. y1 is adjusted to y1=y2 to obtain the coordinates of the boundary point of the third plot, and the preliminary mapped operation plot is loaded using GIS software according to the coordinates of the boundary point of the third plot to obtain the three-dimensional model of the third operation plot. When the Y-axis coordinate of the boundary point of the plot is determined to be valid, the validity of the Z-axis coordinate of the boundary point of the plot is judged; When B falls within D0, the Y-axis coordinate of the boundary point of the plot is determined to be valid, and the validity of the Z-axis coordinate of the boundary point of the plot is determined; In step S4, when judging the validity of the Z-axis coordinates of the boundary point of the plot, the Z-axis error C is compared with the preset error threshold D0, and the validity of the coordinates of the boundary point of the plot is judged according to the comparison result, and output according to the judgment result, wherein: When C falls outside D0, the Z-axis coordinate of the boundary point of the plot is determined to be invalid, and the output result is that the boundary point of the plot is invalid. z1 is adjusted to z1=z2 to obtain the coordinates of the fourth boundary point of the plot, and the preliminary mapped operation plot is loaded using GIS software according to the coordinates of the boundary point of the fourth plot to obtain the three-dimensional model of the fourth operation plot. When the Z-axis coordinate of the boundary point of the plot is determined to be valid, the coordinates of the boundary point of the plot are output; When C falls within D0, the Z-axis coordinate of the boundary point of the plot is determined to be valid, and the coordinate of the boundary point of the plot is output; In step S5, the outputted plot boundary point coordinates are connected into lines using the "point to line" tool in the GIS software. When obtaining the plot boundary line, the plot boundary line feature line segment Y is compared with the preset non-smooth line segment YO, and the overlap threshold △Y is set. The smoothness of the plot boundary line is judged according to the comparison result, and the plot boundary line is optimized according to the judgment result, wherein: When the overlap between Y and Y0 is ≥ △Y, the smoothness of the boundary line of the plot is determined to be abnormal, and the smoothness of the boundary line of the plot is optimized. The output optimized coordinates are (x i ,y i , z i ) is the coordinate of the i-th original point, N i is a set containing the i-th point and its surrounding points within 5m; When the coincidence degree between Y and Y0 is less than △Y, the smoothness of the boundary line of the plot is judged to be normal; In step S5, the Euclidean distance d between the starting point (x3, y3, z3) and the end point (x4, y4, z4) is compared with the preset distance d0, and the continuity of the plot boundary line is judged according to the comparison result, and the plot boundary line is optimized according to the judgment result, wherein: When d>d0, the land parcel boundary line is determined to be discontinuous, the continuity of the land parcel boundary line is optimized, an intermediate point is generated between the starting point and the end point, the distance between the starting point and the end point and the intermediate point is set to d1≤d0, so that the land parcel boundary line is continuous, and the land parcel boundary line is output; When d≤d0, the boundary line of the land parcel is determined to be continuous and the boundary line of the land parcel is output.

2. The method for locating land boundary data based on intelligent perception according to claim 1, characterized in that: In step S1, when the land data source is collected in real time, the collected land data source is cleaned, duplicate data is removed, missing data is collected again, and the data format is guaranteed to be consistent. The abnormal data is judged by the 3σ principle, n is the number of samples, xi is each sample data, where: When xi≥3σ, the sample data value is determined to be abnormal data and removed; When xi<3σ, the sample data value is determined to be normal data and uploaded to the server via wireless transmission.

3. The method for locating land boundary data based on intelligent perception according to claim 2, characterized in that: In step S2, when correcting the abnormal data, the abnormal data and correction method of the longitude and latitude coordinates, working distance, crop type, soil fertility, irrigation conditions and historical yield are added to the abnormal detection model, the output result of the abnormal detection model is obtained, and the correction method for the abnormal data is output, including deleting duplicate values, replacing abnormal values ​​with the mean method, unifying the data format, manually correcting the abnormal longitude and latitude coordinates, comparing the satellite image data source of the actual location of the working plot with the abnormal longitude and latitude coordinates, adjusting the abnormal longitude and latitude coordinates, and converting the corrected abnormal data into a binary grid as normal data.

4. The method for locating land boundary data based on intelligent perception according to claim 3, characterized in that: In the step S3, when obtaining the coordinates of the boundary points of the plot, the maximum longitude xmax, the minimum longitude xmin, the maximum latitude ymax, and the minimum latitude ymin are obtained according to the longitude and longitude data in the plot data source. The number of grid cells a1 in the longitude direction and the number of grid cells a2 in the latitude direction are calculated according to the maximum longitude xmax, the minimum longitude xmin, the maximum latitude ymax, and the minimum latitude ymin. A1=(xmax-xmin) / d, a2=(ymax-ymin) / d, d is the grid step size, the initial value of the two-dimensional array is set to 0, the size is a1×a2, the initial value of the two-dimensional array is initialized to 0, the size is a1×a2, and the plot is traversed. Data source, obtain longitude and latitude data (x1, y1), set x2 = (x1-xmin) / d, y2 = (y1-ymin) / d, set the value of the two-dimensional array at index x2 and y2 to 1, indicating that the two-dimensional array is covered by the plot data source, set the value of (x2, y2) to initialize the two-dimensional array data information A0, pass the two-dimensional array as a parameter to the opencv2 boundary search function findContours, and obtain the boundary point data cluster c1. According to the index information of the elements in c1, the data information A1 corresponding to the index position of the initialized two-dimensional array data information A0 can be obtained, and the data information A1 can be recorded in the set c2. The set c2 is the plot boundary point coordinate data set.

5. The method for locating land boundary data based on intelligent perception according to claim 4, characterized in that: In step S6, when judging the terrain of the working plot, the grid color distribution is obtained according to the plot boundary line and the plot data source to identify the concave and convex surface of the working plot, wherein: When the grid color changes from light to dark and then to light again, the work plot is convex; When the grid color changes from dark to light and then to dark again, the work plot is concave.

6. The method for locating land boundary data based on intelligent perception according to claim 5, characterized in that: In step S7, when judging the attributes of the operation plot, the crop type, soil fertility, irrigation conditions and historical yield of the operation plot are obtained according to the plot data source, and a comprehensive evaluation is performed on the plot. The comprehensive score V is compared with the preset score V0, and the attributes of the operation plot are judged according to the comparison result, wherein: When V>V0, the operation plot is determined to be an important area; When V≤V0, the operation plot is determined to be a non-important area.

7. The method for locating land boundary data based on intelligent perception according to claim 6, characterized in that: In step S8, when analyzing the frequency of agricultural machinery operation according to the number of operations on the plot, the agricultural machinery operation frequency T is compared with the preset agricultural machinery operation frequency T0, and the operation plot attribute judgment process is adjusted according to the comparison result, wherein: When T>T0, it is determined that the frequency of agricultural machinery operation is high and the correction coefficient is set The preset color division number S0 is adjusted according to the correction coefficient, and the adjusted preset color division number is Sa, and Sa=S0×a is set; When T≤T0, it is determined that the operation frequency of agricultural machinery is low, and the correction coefficient b=-2lnS-S0 is set, and the preset color division number S0 is adjusted according to the correction coefficient. The adjusted preset color division number is Sb, and Sb=S0×b is set.

8. The method for locating land boundary data based on intelligent perception according to claim 7, characterized in that: In step S9, when the agricultural machinery operation frequency judgment process is adjusted according to the agricultural machinery operation distance, the effective operation distance M is compared with the preset effective operation distance M0, and the effectiveness of the agricultural machinery operation is judged according to the comparison result, and the agricultural machinery operation frequency judgment process is adjusted according to the judgment result, wherein: When M>M0, the agricultural machinery operation is judged to be valid and recorded in the agricultural machinery operation frequency statistics; When M≤M0, the agricultural machinery operation is judged to be invalid and ignored in the agricultural machinery operation frequency statistics.

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