CN116862985B - Land high-precision positioning electronic informatization monitoring method - Google Patents
Land high-precision positioning electronic informatization monitoring method Download PDFInfo
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- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
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- G06T7/70—Determining position or orientation of objects or cameras
- G06T7/73—Determining position or orientation of objects or cameras using feature-based methods
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- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S17/00—Systems using the reflection or reradiation of electromagnetic waves other than radio waves, e.g. lidar systems
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Abstract
The invention relates to the technical field of high-precision positioning, in particular to a land high-precision positioning electronic informatization monitoring method, which comprises the following steps: acquiring a three-dimensional point cloud data set of land information by using a laser radar; window division is carried out on the point cloud data sets of the land information to obtain feature extraction factors of the point cloud data in each window; acquiring an optimal size search window to obtain an abnormal data area of a point cloud data set of land information; and effectively compensating an abnormal data area of the point cloud data of the land information, and further positioning the land information with high precision. The invention effectively reduces the influence of the environment and other factors on the monitoring result, and improves the accuracy of the land positioning informationized monitoring method.
Description
Technical Field
The invention relates to the technical field of high-precision positioning, in particular to a land high-precision positioning electronic informatization monitoring method.
Background
Land resource management is one of important application fields in GIS, and labor resources are sufficient at present due to large national land area of China, but many problems still exist in the agricultural production field of China, such as the problems that agricultural planting is affected by environment, soil quality and the like. The fine agriculture can determine further manpower and material resources investment by changing the management mode of land resources so as to achieve the purpose of efficiently utilizing the resources. The land information is widely applied not only in the aspects of crop estimation, land circulation, real estate and the like, but also in the fields of agricultural machinery operation, land mapping, livestock industry and the like.
The point cloud technology can help us to acquire and process the land information more accurately through three-dimensional display and analysis of land resources, improves land utilization efficiency, and provides reliable support for land utilization decisions. The existing point cloud technology has some problems in the aspect of land positioning, mainly comprising the following steps: data quality problems, due to different sensors and devices, noise exists in the data; because of the problem of data volume, how to effectively process the data is an important challenge faced by the point cloud technology because the acquired point cloud data is larger and larger; some errors can occur in the process of acquiring a large amount of point cloud data, for example, the problem that the point cloud data received by equipment are offset can be caused due to the influence of equipment and environmental factors, the original point cloud data distribution can be abnormal, larger errors can occur to the monitoring result, the area with abnormal distribution in the point cloud data needs to be accurately identified, the abnormal value of the area is corrected, and the accuracy and reliability of the point cloud data are improved while the real-time performance is ensured.
Disclosure of Invention
In order to solve the problems, the invention provides a land high-precision positioning electronic informatization monitoring method, which comprises the following steps:
acquiring a three-dimensional point cloud data set of land information by using a laser radar;
carrying out window division on the point cloud data set of the land information in different sizes to obtain all search windows in each size; obtaining the segmentation diameter of each search window; obtaining characteristic extraction factors of point cloud data in each search window according to the segmentation diameter of each search window;
acquiring an optimal size search window according to the point cloud data distribution information in each search window; obtaining all abnormal distribution windows according to the characteristic extraction factors of the point cloud data in each optimal size search window; obtaining an abnormal data area of the point cloud data of the land information according to all the abnormal distribution windows;
acquiring a first distance of each abnormal distribution window, and acquiring an interpolation weight of each abnormal distribution window according to the first distance of each abnormal distribution window and a characteristic extraction factor of point cloud data in each abnormal distribution window; according to the interpolation weight of each abnormal distribution window and the first distance of each abnormal distribution window, effectively compensating an abnormal data area of the point cloud data of the land information to obtain an effectively compensated data area; acquiring point cloud data of the adjusted land information according to the effectively compensated data area; and positioning the land information through the point cloud data of the adjusted land information.
Preferably, the dividing the windows of the point cloud data sets of the land information with different sizes to obtain all the search windows under each size includes the following specific steps:
taking any one point cloud data point as a circle center and taking preset parametersCircular area with radius, denoted radius +.>A search window of any point cloud is next; radius->Starting to increment with increment step length of 1 to obtain each radius +.>The search window of all the point clouds is down,abbreviated as all search windows at each size.
Preferably, the step of obtaining the segmentation diameter of each search window includes the following specific steps:
and acquiring any diameter of each search window, dividing each search window into two parts by the diameter, marking the two parts as a first part and a second part, counting the number of point cloud data points of each part, and if the diameter maximizes the number of the point cloud data points in the first part and the second part of the search window, selecting the diameter as the dividing diameter of each search window.
Preferably, the feature extraction factor of the point cloud data in each search window is obtained according to the segmentation diameter of each search window, and the specific steps include:
for any one search window, taking the circle center of the search window as the origin of a coordinate axis, taking the segmentation diameter of the search window as an abscissa axis, taking the diameter of the vertical segmentation diameter as an ordinate axis, acquiring the position coordinates of all point cloud data points in the search window, and recording the point cloud data points with the same ordinate in the search window as one row to acquire the point cloud data points in each row in the search window; then the firstSize of->The feature extraction factor calculation expression of the point cloud data in each search window is as follows:
in the method, in the process of the invention,indicate->Size of->Feature extraction factors of the point cloud data within the individual search windows;indicate->Size of->Total number of point cloud data points within a respective search window; />Indicate->Under the size ofCenter point cloud data points within a search window; />Indicate->Size of->The>A point cloud data point; />Indicate->Size of->Total number of rows in the search window; />Indicate->Size of->Within the search window->The number of point cloud data points on a row; />Indicate->Size of->A number average of point cloud data points on each row within the search window; />Indicate->Size of->Standard deviation of the number of point cloud data points on each row within the search window; />Indicate->Size of->The>Point cloud data points and->Size of->The Euclidean distance between the center point cloud data points within the individual search windows; />Representing a hyperbolic tangent function.
Preferably, the obtaining the optimal size search window according to the distribution information of the point cloud data in each search window includes the following specific steps:
for any window in any size, acquiring the information entropy of each window in the size, calculating the information entropy average value of all windows in the size, further acquiring the information entropy average value of all windows in the size, and taking the window in the size corresponding to the maximum information entropy average value as the optimal size search window.
Preferably, the step of obtaining all abnormal distribution windows according to the feature extraction factors of the point cloud data in each optimal size search window includes the following specific steps:
presetting a threshold valueAcquiring feature extraction factors of point cloud data in each optimal size search window, and if the feature extraction factors of the point cloud data in any one optimal size search window are larger than a threshold value +.>Marking the optimal size search window as an abnormal distribution window; all the abnormal distribution windows are obtained.
Preferably, the obtaining the abnormal data area of the point cloud data of the land information according to all abnormal distribution windows includes the following specific steps:
taking Euclidean distances among the center point cloud data points of all the abnormal distribution windows as distance measurement parameters, adopting k-means clustering, acquiring an optimal k value by using an elbow method, and inputting the optimal k value into a clustering algorithm to obtain a clustering result of the abnormal distribution windows; and taking the clustering result as an abnormal data area of the point cloud data of the land information.
Preferably, the obtaining the first distance of each abnormal distribution window, and obtaining the interpolation weight of each abnormal distribution window according to the first distance of each abnormal distribution window and the feature extraction factor of the point cloud data in each abnormal distribution window, includes the following specific steps:
centroid of abnormal data area of point cloud data for acquiring land information, will beEuclidean distance between center point cloud data of each abnormal distribution window and centroid of abnormal data area of point cloud data of land information>Let the first distance be +.>The interpolation weight calculation expression of each abnormal distribution window is as follows:
in the method, in the process of the invention,indicate->Interpolation weights of the abnormal distribution windows; />Indicate->Feature extraction factors of point cloud data in the abnormal distribution windows; />Representing the total number of abnormal distribution windows; />Indicate->A first distance of the anomaly distribution windows.
Preferably, the effectively compensating the abnormal data area of the point cloud data of the land information according to the interpolation weight of each abnormal distribution window and the first distance of each abnormal distribution window to obtain the effectively compensated data area comprises the following specific steps:
for any abnormal distribution window, marking the result of the product of the first distance of the abnormal distribution window and the interpolation weight of the abnormal distribution window as a second distance of the abnormal distribution window, and determining the adjusted position of the abnormal distribution window according to the second distance of the abnormal distribution window to obtain an adjusted abnormal distribution window; obtaining all the adjusted abnormal distribution windows; and marking the area formed by all the adjusted abnormal distribution windows as an effectively compensated data area.
Preferably, the acquiring the adjusted abnormal distribution window includes the following specific steps:
translating all point clouds in the abnormal distribution window, so that Euclidean distance between center point cloud data in the abnormal distribution window and barycenter of an abnormal data area of point cloud data of land information is equal to a second distance, and obtaining an adjusted abnormal distribution window; the translation direction is the centroid of an abnormal data area of point cloud data of which the center point cloud data in the abnormal distribution window points to the land information.
The technical scheme of the invention has the beneficial effects that: aiming at the influence of equipment and environmental factors, the point cloud data received by the equipment is offset, so that the original point cloud data distribution is abnormally distributed, and the problem of larger error of the monitoring result is caused; according to the method, a screening model is constructed according to sparse and dense characteristics of point cloud data and distribution characteristics of abnormal point cloud data, an abnormally distributed search window is screened out, boundary information of a region where missing point cloud data occurs is found out through clustering operation, then the direction in which the point cloud data is shifted is analyzed, the approximate position of the shifted data is determined according to the distribution of data points of the abnormally searched window, data points of the point cloud data all represent the terrain height of the point, missing information of the abnormally sparse region of the point cloud data is restored by weight according to the height value of the points of a part with dense data points in the abnormally window, information of the shifted region of the point cloud data is restored as much as possible, so that the point cloud data of land information is relatively complete, errors generated by a land information monitoring result are reduced within a certain range, influence of environment and other factors on the monitoring result is effectively reduced, and the accuracy of the land positioning informatization monitoring method is improved.
Drawings
In order to more clearly illustrate the embodiments of the invention or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described, it being obvious that the drawings in the following description are only some embodiments of the invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of the steps of a land high-precision positioning electronic informatization monitoring method of the invention.
Detailed Description
In order to further describe the technical means and effects adopted by the invention to achieve the preset aim, the following is a detailed description of specific implementation, structure, characteristics and effects of the land high-precision positioning electronic informatization monitoring method according to the invention with reference to the accompanying drawings and the preferred embodiment. In the following description, different "one embodiment" or "another embodiment" means that the embodiments are not necessarily the same. Furthermore, the particular features, structures, or characteristics of one or more embodiments may be combined in any suitable manner.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.
The specific scheme of the land high-precision positioning electronic informatization monitoring method provided by the invention is specifically described below with reference to the accompanying drawings.
Referring to fig. 1, a flowchart of a land high-precision positioning electronic informatization monitoring method according to an embodiment of the invention is shown, the method includes the following steps:
step S001: and acquiring a three-dimensional point cloud data set of the land information by using a laser radar.
It should be noted that, the point cloud is a set of sampling points with space coordinates acquired by the laser radar, and the data volume in the set is larger and denser, so the point cloud is called as point cloud; the efficient processing of the data under limited resources is an important challenge facing the current point cloud technology, and in addition, when the real-time data is acquired, the original point cloud data is offset due to the influence of equipment or environmental factors, so that the original sparse point cloud data is sparse. Because the point cloud data reflects the real-time information of the land to a certain extent, however, the abnormal distribution of the point cloud data can certainly generate larger errors on the positioning monitoring result of the land, the reliability of the land information monitoring data is affected, and no good data support is provided for planning land resources. Therefore, the method and the device mainly screen and analyze the abnormal area in the land information point cloud data, and repair the point cloud data with offset in the abnormal area, so that the point cloud data obtained by monitoring are complete, effective data support can be provided for informationized monitoring of the land, and the influence of the offset of the point cloud data on the data processing result is effectively reduced.
It should be further noted that the point cloud data is a set of discrete points in a three-dimensional space, which is a set shape of a digitized object surface. Because mountain land information regions are relatively wide, the relief is relatively large, the manual positioning-based method is relatively difficult, the workload is large, and the real-time positioning of the land is not facilitated.
Specifically, carry on laser radar on unmanned aerial vehicle, patrol through unmanned aerial vehicle on certain altitude, laser emitter is to ground emission laser, when laser irradiation to ground, utilizes photoelectric converter to convert the laser signal that the reflection was come back into the electrical signal, obtains the three-dimensional coordinate on ground to obtain the point cloud dataset of three-dimensional land information.
To this end, a three-dimensional point cloud data set of land information is obtained.
Step S002: and carrying out window division on the point cloud data set of the land information in different sizes to obtain feature extraction factors of the point cloud data in each window.
It should be noted that, for land information of different heights, distribution and density of data points in the point cloud data set of the land information may be different, for example, on a flat ground, distribution of data points in the point cloud data set of the land information may be relatively uniform, and a height value of a data point in the point cloud data set of the land information is close to a bottom surface height; whereas in the vicinity of mountainous areas or buildings, the distribution of data points in the point cloud data set of land information may be denser, with more elevation changes and undulations.
It should be further noted that, if a large building, a large number of shielding objects exist, sparse point cloud data may be generated in a region behind the shielding objects, and due to the influence of environment and light, the sparse point cloud data may also be caused, because the original point cloud data is offset in the signal receiving process, the original point cloud data at the original position is sparse or even missing, the same other point cloud data may become abnormally dense, and the reason for abnormality is that the point cloud data at the sparse position is overlapped in a certain region, so that the point cloud data in the region is extremely abnormal, and the point cloud data is missing due to the offset, so that the degree of abnormality of the data points in the region is higher. Therefore, it is necessary to accurately identify the distribution area of the abnormally sparse point cloud data.
Presetting a parameterWherein the present embodiment is +.>Examples are described, the present embodiment is not particularly limited, wherein +.>Depending on the particular implementation.
Specifically, mapping a three-dimensional point cloud dataset of land information toObtaining a point cloud data set of land information on a plane; taking any one point cloud data point in the point cloud data set of the land information as a circle center and taking a preset parameter +.>Circular area with radius, denoted radius +.>A search window of any point cloud is next; radius->Starting to increment with increment step length of 1, until one round window can contain the point cloud data set of the whole land information, and obtaining each radius +.>The search windows of all point clouds are simply marked as all the search windows in each size; and acquiring any diameter for each search window, dividing each search window into two parts by the diameter, marking the two parts as a first part and a second part, counting the number of point cloud data points of each part, and selecting the diameter as the dividing diameter of each search window if the diameter maximizes the number of the point cloud data points in the first part and the second part of the search window. It should be noted that if the search window can be divided into two parts with the same number of point cloud data points regardless of any one diameter, one diameter is selected as the dividing diameter of the search window for the search window.
For any one search window, taking the circle center of the search window as the origin of a coordinate axis, taking the segmentation diameter of the search window as an abscissa axis, taking the diameter of the vertical segmentation diameter as an ordinate axis, acquiring the position coordinates of all the point cloud data points in the search window, and recording the point cloud data points with the same ordinate in the search window as one row, thereby acquiring the point cloud data points on each row in the search window.
Then the firstCategory->The feature extraction factor calculation expression of the point cloud data in each search window is as follows:
in the method, in the process of the invention,indicate->Size of->Feature extraction factors of the point cloud data within the individual search windows;indicate->Size of->Total number of point cloud data points within a respective search window; />Indicate->Under the size ofCenter point cloud data points within a search window; />Indicate->Size of->The>A point cloud data point; />Indicate->Size of->Total number of rows in the search window; />Indicate->Size of->Within the search window->The number of point cloud data points on a row; />Indicate->Size of->A number average of point cloud data points on each row within the search window; />Indicate->Size of->Standard deviation of the number of point cloud data points on each row within the search window; />Indicate->Size of->The>Point cloud data points and->Size of->The Euclidean distance between the center point cloud data points within the individual search windows; />Representing a hyperbolic tangent function.
Thus, feature extraction factors of point cloud data in each window are obtained.
Step S003: and acquiring an optimal size search window to obtain an abnormal data area of the point cloud data set of the land information.
It should be noted that, under the condition of judging that the distribution of the point cloud data is sparse, the bias information of the point cloud data is added, so that the abnormal sparse area compared with the normal sparse area can be effectively distinguished, and various distribution situations of the point cloud data in the point cloud data can be better distinguished. Under the expected condition, the difference between the search windows of different point cloud distribution conditions in the point cloud data is obvious, and the search windows with more obvious difference can provide better data support for identifying abnormal areas.
Specifically, for a search window in any size, obtaining the information entropy of each window in the size, calculating the information entropy average value of all windows in the size, further obtaining the information entropy average value of all windows in the size, and taking the window in the size corresponding to the largest information entropy average value as the optimal size search window.
Presetting a threshold valueWherein the present embodiment is +.>Examples are described, the present embodiment is not particularly limited, wherein +.>Depending on the particular implementation.
Acquiring feature extraction factors of point cloud data in each optimal size search window, and if the feature extraction factors of the point cloud data in any one optimal size search window are larger than a threshold valueMarking the optimal size search window as an abnormal distribution window; similarly, all abnormal distribution windows are obtained; taking Euclidean distances among the center point cloud data points of all the abnormal distribution windows as distance measurement parameters, adopting k-means clustering, acquiring an optimal k value by using an elbow method, and inputting the optimal k value into a clustering algorithm to obtain a clustering result of the abnormal distribution windows; taking the clustering result as an abnormal data area of the point cloud data of the land information; wherein, the center point cloud data of all abnormal distribution windows in the clustering resultIs a boundary point of an abnormal region.
So far, an abnormal data area of the point cloud data of the land information is obtained.
Step S004: and effectively compensating an abnormal data area of the point cloud data of the land information, and further positioning the land information with high precision.
It should be noted that, because the boundary points of the abnormal area are all composed of the central point cloud data points of the abnormal distribution window, the part with the largest density in the abnormal distribution window is located outside the abnormal area, that is, the original point cloud data points in the abnormal area are all offset to the area with the largest distribution density of the point cloud data points in the abnormal distribution window, so that the direction from the abnormal area to the largest distribution density of the abnormal distribution window is the direction in which the point cloud data is offset, and the missing information of the abnormal area in the direction is compensated along the direction in which the point cloud data is offset, so that the information of the abnormal area can be better compensated, and more accurate point cloud data information is obtained.
Specifically, the centroid of the abnormal data area of the point cloud data for acquiring the land information is to beEuclidean distance between center point cloud data of each abnormal distribution window and centroid of abnormal data area of point cloud data of land information>Let the first distance be +.>The interpolation weight calculation expression of each abnormal distribution window is as follows:
in the method, in the process of the invention,indicate->Interpolation weights of the abnormal distribution windows; />Indicate->Feature extraction factors of point cloud data in the abnormal distribution windows; />Representing the total number of abnormal distribution windows; />Indicate->A first distance of the anomaly distribution windows.
The specific method for effectively compensating the abnormal data area of the point cloud data of the land information comprises the following steps:
for any abnormal distribution window, marking the result of the product of the first distance of the abnormal distribution window and the interpolation weight of the abnormal distribution window as a second distance of the abnormal distribution window, and determining the adjusted position of the abnormal distribution window according to the second distance of the abnormal distribution window to obtain an adjusted abnormal distribution window;
the specific method for acquiring the adjusted abnormal distribution window comprises the following steps: translating all point clouds in the abnormal distribution window, so that Euclidean distance between center point cloud data in the abnormal distribution window and barycenter of an abnormal data area of point cloud data of land information is equal to a second distance, and obtaining an adjusted abnormal distribution window; the translation direction is the centroid of an abnormal data area of point cloud data of which the center point cloud data in the abnormal distribution window points to the land information.
Obtaining all the adjusted abnormal distribution windows; and marking the area formed by all the adjusted abnormal distribution windows as an effectively compensated data area.
And forming the point cloud data of the adjusted land information according to the effectively compensated data area and the non-abnormal data area of the point cloud data of the land information.
Specifically, the height and shape information of the land are provided through the point cloud data of the adjusted land information, and the point cloud data are used for land measurement, terrain modeling, land planning and other applications; by restoring the point cloud data of the missing area, the method has a good feedback effect on land information, and improves the accuracy of the electronic informatization monitoring method for land high-precision positioning.
The foregoing description of the preferred embodiments of the invention is not intended to be limiting, but rather is intended to cover all modifications, equivalents, alternatives, and improvements that fall within the spirit and scope of the invention.
Claims (10)
1. The land high-precision positioning electronic informatization monitoring method is characterized by comprising the following steps of:
acquiring a three-dimensional point cloud data set of land information by using a laser radar;
carrying out window division on the point cloud data set of the land information in different sizes to obtain all search windows in each size; obtaining the segmentation diameter of each search window; obtaining characteristic extraction factors of point cloud data in each search window according to the segmentation diameter of each search window;
acquiring an optimal size search window according to the point cloud data distribution information in each search window; obtaining all abnormal distribution windows according to the characteristic extraction factors of the point cloud data in each optimal size search window; obtaining an abnormal data area of the point cloud data of the land information according to all the abnormal distribution windows;
acquiring a first distance of each abnormal distribution window, and acquiring an interpolation weight of each abnormal distribution window according to the first distance of each abnormal distribution window and a characteristic extraction factor of point cloud data in each abnormal distribution window; according to the interpolation weight of each abnormal distribution window and the first distance of each abnormal distribution window, effectively compensating an abnormal data area of the point cloud data of the land information to obtain an effectively compensated data area; acquiring point cloud data of the adjusted land information according to the effectively compensated data area; and positioning the land information through the point cloud data of the adjusted land information.
2. The method for monitoring the land high-precision positioning electronic informatization according to claim 1, wherein the method for dividing windows of different sizes of the point cloud data set of the land information to obtain all search windows under each size comprises the following specific steps:
taking any one point cloud data point as a circle center and taking preset parametersCircular area with radius, denoted radius +.>A search window of any point cloud is next; radius->Starting to increment with increment step length of 1 to obtain each radius +.>The search windows of all point clouds are down, abbreviated as all search windows at each size.
3. The method for monitoring land high-precision positioning electronic informatization according to claim 1, wherein said obtaining the dividing diameter of each search window comprises the following specific steps:
and acquiring any diameter of each search window, dividing each search window into two parts by the diameter, marking the two parts as a first part and a second part, counting the number of point cloud data points of each part, and if the diameter maximizes the number of the point cloud data points in the first part and the second part of the search window, selecting the diameter as the dividing diameter of each search window.
4. The land high-precision positioning electronic informatization monitoring method according to claim 1, wherein the feature extraction factor of the point cloud data in each search window is obtained according to the dividing diameter of each search window, comprising the following specific steps:
for any one search window, taking the circle center of the search window as the origin of a coordinate axis, taking the segmentation diameter of the search window as an abscissa axis, taking the diameter of the vertical segmentation diameter as an ordinate axis, acquiring the position coordinates of all point cloud data points in the search window, and recording the point cloud data points with the same ordinate in the search window as one row to acquire the point cloud data points in each row in the search window; then the firstSize of->The feature extraction factor calculation expression of the point cloud data in each search window is as follows:
in the method, in the process of the invention,indicate->Size of->Feature extraction factors of the point cloud data within the individual search windows; />Indicate->Size of->Total number of point cloud data points within a respective search window; />Indicate->Size of->Center point cloud data points within a search window; />Indicate->Size of->The>A point cloud data point;indicate->Size of->Total number of rows in the search window; />Indicate->Size of->Within the search window->The number of point cloud data points on a row; />Indicate->Size of->A number average of point cloud data points on each row within the search window; />Indicate->Size of->Standard deviation of the number of point cloud data points on each row within the search window; />Indicate->Size of->The>Point cloud data points and->Size of->The Euclidean distance between the center point cloud data points within the individual search windows; />Representing a hyperbolic tangent function.
5. The method for monitoring land high-precision positioning electronic informatization according to claim 1, wherein the obtaining the optimal size search window according to the distribution information of the point cloud data in each search window comprises the following specific steps:
for any window in any size, acquiring the information entropy of each window in the size, calculating the information entropy average value of all windows in the size, further acquiring the information entropy average value of all windows in the size, and taking the window in the size corresponding to the maximum information entropy average value as the optimal size search window.
6. The method for monitoring land by electronic informatization with high precision positioning according to claim 1, wherein the step of obtaining all abnormal distribution windows according to the feature extraction factors of the point cloud data in each optimal size search window comprises the following specific steps:
presetting a threshold valueAcquiring feature extraction factors of point cloud data in each optimal size search window, and if the feature extraction factors of the point cloud data in any one optimal size search window are larger than a threshold valueMarking the optimal size search window as an abnormal distribution window; all the abnormal distribution windows are obtained.
7. The land high-precision positioning electronic informatization monitoring method according to claim 1, wherein the abnormal data area of the point cloud data of the land information is obtained according to all abnormal distribution windows, comprising the following specific steps:
taking Euclidean distances among the center point cloud data points of all the abnormal distribution windows as distance measurement parameters, adopting k-means clustering, acquiring an optimal k value by using an elbow method, and inputting the optimal k value into a clustering algorithm to obtain a clustering result of the abnormal distribution windows; and taking the clustering result as an abnormal data area of the point cloud data of the land information.
8. The method for monitoring land by electronic informatization with high precision positioning according to claim 1, wherein the obtaining the first distance of each abnormal distribution window, obtaining the interpolation weight of each abnormal distribution window according to the first distance of each abnormal distribution window and the feature extraction factor of the point cloud data in each abnormal distribution window, comprises the following specific steps:
centroid of abnormal data area of point cloud data for acquiring land information, will beEuclidean distance between center point cloud data of each abnormal distribution window and centroid of abnormal data area of point cloud data of land information>Recorded as the first distance, the firstThe interpolation weight calculation expression of each abnormal distribution window is as follows:
in the method, in the process of the invention,indicate->Interpolation weights of the abnormal distribution windows; />Indicate->Feature extraction factors of point cloud data in the abnormal distribution windows; />Representing the total number of abnormal distribution windows; />Indicate->A first distance of the anomaly distribution windows.
9. The method for monitoring land by electronic informatization with high precision positioning according to claim 1, wherein the method for effectively compensating the abnormal data area of the point cloud data of the land information according to the interpolation weight of each abnormal distribution window and the first distance of each abnormal distribution window to obtain the effectively compensated data area comprises the following specific steps:
for any abnormal distribution window, marking the result of the product of the first distance of the abnormal distribution window and the interpolation weight of the abnormal distribution window as a second distance of the abnormal distribution window, and determining the adjusted position of the abnormal distribution window according to the second distance of the abnormal distribution window to obtain an adjusted abnormal distribution window; obtaining all the adjusted abnormal distribution windows; and marking the area formed by all the adjusted abnormal distribution windows as an effectively compensated data area.
10. The land high-precision positioning electronic informatization monitoring method according to claim 9, wherein the obtaining the adjusted abnormal distribution window comprises the following specific steps:
translating all point clouds in the abnormal distribution window, so that Euclidean distance between center point cloud data in the abnormal distribution window and barycenter of an abnormal data area of point cloud data of land information is equal to a second distance, and obtaining an adjusted abnormal distribution window; the translation direction is the centroid of an abnormal data area of point cloud data of which the center point cloud data in the abnormal distribution window points to the land information.
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