Geographic information surveying and mapping data anomaly detection method and system based on data analysis

Abstract

The invention relates to the technical field of data processing, in particular to a geographic information surveying and mapping data anomaly detection method and system based on data analysis, and the method comprises the steps: obtaining a deformation data sequence of a target monitoring point, calculating the edge degree of each piece of data, and screening edge points, selecting a point closest to the target edge point as a target point, screening an edge point of which the optimization degree meets a preset condition from the remaining edge points, connecting the edge point with the target point, taking a connection point as a new target point, repeating the connection process based on the new target point until a termination condition is met, and obtaining an edge; and taking the mean value of the deformation data of all the edge points in each edge as a clustering center of the corresponding edge, and clustering all the data according to the distance from each data to the clustering center, so as to monitor the deformation abnormity of the target monitoring point based on the abnormal degree of the obtained clustering cluster. According to the invention, accurate monitoring of abnormal deformation of the monitoring point can be realized.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing. More specifically, the present invention relates to a method and system for detecting anomalies in geospatial mapping data based on data analysis. Background Art

[0002] In the field of geospatial mapping, deformation monitoring technology can, through high-precision dynamic monitoring means, obtain in real time the change information of potential geological risks such as ground settlement, building deformation, and ground fissures, greatly enhancing the state perception and safety assessment capabilities of infrastructure. Different from traditional geographic information systems (GIS) that rely on static data, deformation monitoring can provide accurate change process data through dynamic monitoring by combining sensor technology, remote sensing technology, and big data analysis, better reflecting the actual state of infrastructure. Therefore, deformation monitoring is an important part of geospatial mapping.

[0003] In related technologies, for example, in the patent application document with the publication number CN113700053A, a method and system for monitoring and warning the deformation of a foundation pit during the construction process based on BIM are disclosed. The method includes: constructing a basic three-dimensional model of the target foundation pit based on BIM and the on-site measured data of the target foundation pit; arranging a plurality of measuring points on the surface of the target foundation pit in the basic three-dimensional model, establishing a spatial coordinate axis with any one of the measuring points as the origin, and obtaining the initial spatial coordinates of all the measuring points; screening out abnormal measuring points according to the initial spatial coordinates and the change of the spatial coordinates of the measuring points; predicting the movement change of the abnormal measuring points; and predicting the abnormal deformation state of the target foundation pit based on the movement change.

[0004] However, in the above solution, during the process of identifying abnormal measuring points, it depends on the change amount of the spatial coordinates of the measuring points at different times, and does not fully consider the spatial distribution characteristics and similarities of the measuring points at different times. And only based on a single deformation amount for abnormal determination, it may lead to a low accuracy in identifying abnormal measuring points, thus affecting the screening effect of abnormal measuring points, and further unable to effectively reflect the true abnormal deformation state of the target monitoring object. Summary of the Invention

[0005] In order to solve the problem of being unable to accurately identify the abnormal deformation of the target monitoring object, the present invention provides a method and system for detecting anomalies in geospatial mapping data based on data analysis.

[0006] According to the first aspect of the present invention, there is provided a method for detecting anomalies in geospatial mapping data based on data analysis, including: Obtaining a sequence of deformation data when performing deformation monitoring on a target monitoring point; For any data in the deformed data sequence, preset the first neighbor set of this data, calculate the edge degree of this data, where the edge degree represents the difference between the local density of this data and the local densities of the data in the first neighbor set, so as to screen edge points according to the edge degrees of each data; Select target edge points, take the edge point closest to the target edge point as the target point, use the direction from the target edge point to the target point as the polar axis, and use the preset direction as the positive direction of the angle to construct a polar coordinate system with the target point as the origin, calculate the preference degree of the remaining edge points, where the preference degree is negatively correlated with both the polar axis and the polar radius of the corresponding edge point in the polar coordinate system, so as to screen connection points according to the preference degree, connect the connection points with the target point, take the connection point as the new target point, construct a polar coordinate system with the new target point as the coordinate origin and repeat the connection process until the preset termination condition is met to obtain an edge, so as to obtain all edges; Take the average value of the deformed data of all edge points in any edge as the clustering center of this edge, and cluster all data according to the distance from each data to the clustering center, so as to monitor the deformation anomaly of the target monitoring point based on the anomaly degree of the clustering cluster, where the anomaly degree represents the dispersion degree of the data inside the corresponding clustering cluster.

[0007] In the present invention, the deformed data similar to the target monitoring point is divided into one category by clustering, so that the anomaly in the deformed data sequence can be determined according to the distribution of the similar data, realizing accurate monitoring of the deformation anomaly of the target monitoring point; and when determining the clustering center, multiple aspects of data are combined, so that the accuracy of the determined clustering center can be guaranteed, and further the accuracy of the clustering result can be guaranteed.

[0008] Preferably, the difference between the local density of any data and the local densities of the data in the first neighbor set is a comprehensive difference, and the method for obtaining the comprehensive difference includes: Preset the second neighbor set of any data, and take the reciprocal of the average distance between this data and the data in the second neighbor set as the local density of this data; the comprehensive difference satisfies the following relational expression: ; In the formula, is the comprehensive difference between the local density of the -th data and the local densities of the data in the first neighbor set; is the local density of the -th data; is the local density of the -th data in the first neighbor set of the -th data; is the number of data in the first neighbor set; is the normalization function.

[0009] The present invention helps to capture the distribution characteristics of data points in a local area by calculating the local density of data, so that the edge points can be screened out by utilizing the feature that the distribution characteristics of edge points and the remaining data are quite different.

[0010] Preferably, the method for obtaining the edge degree of any data includes: Taking the normalized value of the range of the local densities of all data in the first nearest neighbor set of any data as the credibility of the comprehensive difference, and weighting the comprehensive difference with the credibility as the weight to obtain the edge degree of this data.

[0011] Preferably, each data in the deformed data sequence is a binary group data in a two-dimensional space coordinate system; wherein, the abscissa of the two-dimensional space coordinate system is the vibration frequency of the target monitoring point at the corresponding moment, and the ordinate is the distance between the spatial coordinates of the target monitoring point at the corresponding moment and the previous moment.

[0012] The vibration frequency collected by the present invention can reflect the change of the dynamic characteristics of the target monitoring point, and the change of the spatial coordinates can reflect the actual deformation of the target monitoring point, so as to provide a comprehensive, accurate and dynamic analysis perspective for the abnormal deformation monitoring of the target monitoring point.

[0013] Preferably, when screening the connection points of the target point or the new target point, the positive directions of the angles of all the constructed polar coordinate systems are the same, and the method for obtaining the positive direction of the angle includes: Judging the size of the ordinate of the target edge point and the target point. If the ordinate of the target point is greater than or equal to the ordinate of the target edge point, then the clockwise direction is taken as the positive direction of the angle when constructing the polar coordinate system with the target point or the new target point as the coordinate origin; If it is less than the ordinate of the target edge point, then the counterclockwise direction is taken as the positive direction of the angle when constructing the polar coordinate system with the target point or the new target point as the coordinate origin.

[0014] Preferably, when screening the connection points of the current target point, the preference degree of any remaining edge point satisfies the following relational expression: ; In the formula, is the preference degree of the th remaining edge point; , are respectively the polar angle and the polar radius of the th remaining edge point in the current polar coordinate system; is the natural exponential function.

[0015] Preferably, clustering all data according to the distances between each data and the cluster centers includes: Calculate the similarity degree between any data and any clustering center according to the distance between them: ; where is the similarity degree between any data and any clustering center; is the absolute value of the difference in local density between the data and the clustering center, where the local density of the clustering center is the average of the local densities of all data within the minimum circumscribed rectangle of the corresponding edge; is the natural exponential function; is the distance between the data and the clustering center; Classify each data into the cluster of the clustering center with the highest similarity degree to cluster all data.

[0016] Preferably, when clustering all data, the method for obtaining the clustering range of any clustering center includes: Obtain the maximum value of the distances between any two clustering centers, construct a square region with a side length equal to half of the maximum value centered on each clustering center, and use the square region as the clustering range when clustering with the corresponding clustering center.

[0017] The way of determining the clustering range in the present invention can ensure that all data in the deformed data sequence are clustered.

[0018] Preferably, monitoring the deformation anomaly of the target monitoring point based on the anomaly degree of the cluster includes: Calculate the anomaly degree of any cluster in the clustering result. The anomaly degree satisfies the following relational expression: ; where is the anomaly degree of the th cluster; is the standard deviation of the distances between all data points and the clustering center in the th cluster; is the standard deviation of the sequence numbers of the sampling times corresponding to all data points in the th cluster; is the normalization function; If the anomaly degrees of all clusters are greater than the preset anomaly threshold, it is determined that the deformation amount of the target monitoring point is abnormal.

[0019] According to the second aspect of the present invention, there is provided a geographic information mapping data anomaly detection system based on data analysis. The system includes a memory and a processor. A computer program is stored on the memory, and the processor executes the computer program to implement the steps of the first aspect of the present invention.

[0020] The present invention has the following effects: 1. When the present invention monitors the deformation anomaly of the target monitoring point, it can divide the deformation data similar to the monitoring point into one category. When calculating the anomaly degree of each clustering cluster by using the dispersion situation of the data inside each clustering cluster, it can combine the distribution characteristics of the similar deformation data, so as to accurately identify the abnormal data in the deformation data sequence and realize the accurate monitoring of the deformation anomaly of the target monitoring point.

[0021] 2. The present invention obtains the edge by using the feature that the edge of the clustering cluster is in a curve shape, so as to ensure that the extracted edge corresponds to the real boundary of the clustering cluster, so that an accurate clustering center can be obtained, ensuring the accuracy of the division of each data in the deformation data sequence, and thus the anomaly degree of each clustering cluster can be accurately measured. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] By referring to the following detailed description with reference to the accompanying drawings, the above and other objects, features and advantages of the exemplary embodiments of the present invention will become easy to understand. In the drawings, several embodiments of the present invention are shown in an exemplary rather than restrictive manner, and the same or corresponding reference numerals represent the same or corresponding parts, wherein: Figure 1 is a schematic flow chart of the steps of the method for detecting abnormal geographic information mapping data based on data analysis according to an embodiment of the present invention; Figure 2 is a schematic diagram of a polar coordinate system constructed with the target point as the coordinate origin according to an embodiment of the present invention; Figure 3 is another schematic diagram of a polar coordinate system constructed with the target point as the coordinate origin according to an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0023] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative efforts shall fall within the protection scope of the present invention.

[0024] Next, the specific embodiments of the present invention will be described in detail with reference to the drawings.

[0025] Refer to Figure 1 , the method for detecting abnormal geographic information mapping data based on data analysis includes steps S1 - S4, specifically as follows: S1: Obtain a deformation data sequence for deformation monitoring of a target monitoring point.

[0026] It should be noted that deformation monitoring can grasp deformation information in real time through regular measurements of buildings, the ground, and engineering facilities. During the construction of large-scale projects, such as bridges and high-rise buildings, it is necessary to monitor the deformation of the structure to ensure the stability and safety of the project. Its core purpose is to detect and warn in advance of potential safety risks caused by geological or structural deformation. Deformation monitoring mainly includes two categories: horizontal monitoring and vertical monitoring. Specific means such as leveling, satellite positioning technology, and total station surveying are used to conduct regular or continuous observations at the set monitoring points.

[0027] In an exemplary embodiment of the present invention, each data in the deformation data sequence is a binary group data in a two-dimensional space coordinate system; wherein, the abscissa of the two-dimensional space coordinate system is the vibration frequency of the target monitoring point at the corresponding moment, and the ordinate is the distance between the spatial coordinates of the target monitoring point at the corresponding moment and the previous moment.

[0028] Specifically, monitoring points (such as embedded RFID chips) can be arranged at key parts of the building to be monitored (such as load-bearing walls or columns), and then deformation monitoring of all monitoring points can be carried out using surveying technology. The specific process is as follows: Select any monitoring point as the target monitoring point, use measuring equipment such as a total station to measure the horizontal and vertical directions of the target monitoring point, and collect the vibration frequency and spatial coordinate information of the target monitoring point at a predetermined frequency (for example, 0.2 Hz). Then, based on the data measured each time, the corresponding binary group data (vibration frequency, distance between the spatial coordinates at the current moment and the previous moment) is constructed, so as to generate a deformation data sequence for deformation monitoring of the target monitoring point, effectively reflecting the deformation trend and vibration characteristics of the target monitoring point.

[0029] In another embodiment, a deformation data sequence for deformation monitoring of the target monitoring point can also be constructed according to other deformation data of the target monitoring point, such as displacement data, tilt angle data, and temperature data, etc. The type of deformation data in this embodiment is not particularly limited.

[0030] S2: For any data in the deformation data sequence, preset the first neighbor set of this data, calculate the edge degree of this data, and the edge degree represents the difference between the local density of this data and the local densities of the data in the first neighbor set, so as to screen edge points according to the edge degrees of each data.

[0031] It should be noted that when performing deformation monitoring on the target monitoring point, by comparing the spatial coordinate differences at different times, the deformation degree of the target monitoring point can be reflected. When the spatial coordinate differences of the target monitoring point at different time points are small, it indicates that its deformation is small; otherwise, it indicates a large deformation degree. Therefore, by monitoring the anomalies in the deformation data sequence composed of several binary data (vibration frequency, distance between the spatial coordinates at the current moment and the previous moment), the abnormal changes in the deformation data sequence can be effectively identified, thereby helping to judge the deformation degree of the target monitoring point. The clustering algorithm can cluster the data into multiple clusters according to the similarity of the data, so that the anomalies in the deformation data sequence can be effectively identified based on the anomalies of the clusters, realizing the precise monitoring of the deformation anomalies of the target monitoring point.

[0032] It should be further noted that when the deformation degree of the target monitoring point is small, the spatial positions of the target monitoring points obtained during the deformation monitoring of the target monitoring point are relatively close. Therefore, the present invention utilizes this feature to calculate the local density of each data in the deformation data sequence and uses the difference between the local density of each data and the local density of its neighboring data to screen the data at the edge when clustering the deformation data sequence, that is, the edge points.

[0033] Optionally, the first data can be selected in ascending order of the distance from any data in the deformation data sequence to obtain the first nearest neighbor set of this data. The size of in this embodiment is not particularly limited.

[0034] In an exemplary embodiment of the present invention, the difference between the local density of any data and the local density of each data in the first nearest neighbor set is a comprehensive difference, and the determination of the comprehensive difference between the local density of any data and the local density of each data in the first nearest neighbor set can be achieved through the following steps: Step 1: Preset the second nearest neighbor set of any data, and take the reciprocal of the average distance between this data and each data in the second nearest neighbor set as the local density of this data.

[0035] Optionally, the first data can be selected in ascending order of the distance from any data in the deformation data sequence to obtain the second nearest neighbor set of this data. The size of in this embodiment is not particularly limited.

[0036] Specifically, the local density of any data in the deformation data sequence satisfies the following relational expression: ; In the formula, is the local density of the th data; is the The distance between a piece of data and the piece of data in the second nearest neighbor set; is the number of data in the second nearest neighbor set. In this embodiment, .

[0037] Among them, the smaller the average value of the distances between any piece of data in the deformed data sequence and each piece of data in the second nearest neighbor set, the greater the local density of this piece of data.

[0038] Optionally, the Euclidean distance can be used to measure the distance between any two pieces of data, or the Manhattan distance can be used to measure the distance between any two pieces of data. This embodiment does not make a special limitation on the selected distance type.

[0039] It should be noted that when the present invention involves measuring the distance between data in each step, the Euclidean distance is adopted. Therefore, when the distance between data is involved in the subsequent steps, the type of the selected distance will not be elaborated further.

[0040] Step 2: Calculate the comprehensive difference of this piece of data.

[0041] Specifically, the comprehensive difference between the local density of any piece of data and the local densities of each piece of data in the first nearest neighbor set satisfies the following relational expression: ; In the formula, is the comprehensive difference between the local density of the th piece of data and the local densities of each piece of data in the first nearest neighbor set; is the local density of the th piece of data; is the local density of the th piece of data in the first nearest neighbor set of the th piece of data; is the number of data in the first nearest neighbor set. In this embodiment, ; is a normalization function.

[0042] Among them, reflects the difference between the local density of the th piece of data and the local densities of each piece of data in the first nearest neighbor set. The larger this value is, the greater the difference between the local density of the th piece of data and the local densities of its neighboring data, which further indicates that this piece of data is more likely to be an edge point, and the corresponding edge degree of this piece of data is relatively large.

[0043] Optionally, when calculating the local density of data, selecting a larger number of neighboring data can accurately measure the density of the data distribution around the corresponding data; while when calculating the marginal degree of each data, selecting neighboring data with a closer distance as a reference can reduce the amount of calculation while ensuring the accuracy of the calculation result.

[0044] Further, after determining the difference between the local density of any data and the local densities of the data in the first nearest neighbor set, the marginal degree of this data can be calculated.

[0045] In an exemplary embodiment of the present invention, the determination of the marginal degree of any data can be achieved through the following steps: Take the normalized value of the range of the local densities of all data in the first nearest neighbor set of any data as the credibility of the comprehensive difference, and use the credibility as a weight to weight the comprehensive difference to obtain the marginal degree of this data.

[0046] Specifically, the marginal degree of any data satisfies the following relational expression: ; In the formula, is the marginal degree of the th data; is the comprehensive difference between the local density of the th data and the local densities of the data in the first nearest neighbor set; is the maximum value of the local densities of all data in the first nearest neighbor set of the th data; is the minimum value of the local densities of all data in the first nearest neighbor set of the th data; is the normalization function.

[0047] Among them, reflects the range of the local densities of all data in the first nearest neighbor set of the th data. The larger this value is, the larger the value range of the local densities of the data in the first nearest neighbor set of this data. If is larger, it means that has a higher credibility, so that the difference between this data and the local densities of the data in the first nearest neighbor set can be accurately measured, ensuring the accuracy of the determined marginal degree.

[0048] Further, after determining the marginal degrees of the data in the deformed data sequence, the marginal degrees of the data can be compared with a preset threshold (such as 0.8) to determine all the marginal points. The size of the preset threshold in this embodiment is not particularly limited.

[0049] S3: Select a target edge point, take the edge point closest to the target edge point as the target point, use the direction from the target edge point to the target point as the polar axis, and use the preset direction as the positive direction of the angle to construct a polar coordinate system with the target point as the origin. Calculate the preference degree of the remaining edge points. The preference degree is negatively correlated with both the polar axis and the polar radius of the corresponding edge point in the polar coordinate system. Then, filter and connect the connection points with the target point according to the preference degree, and take the connection points as new target points. Construct a polar coordinate system with the new target point as the coordinate origin and repeat the connection process until the preset termination condition is met to obtain an edge, so as to obtain all edges.

[0050] It should be noted that before clustering the deformation data sequence, it is necessary to first determine the clustering center, and the selection of the clustering center directly affects the quality and accuracy of the clustering result. Therefore, in the present invention, by first determining the edge points and then obtaining all edges based on the positional relationship between the edge points, the obtained edges can be used as the basis for further selecting the clustering center to ensure the accuracy of the clustering center.

[0051] Among them, the target edge point refers to a randomly selected edge point; the preference degree refers to the possibility of taking any edge point as the next connection point of the current target point. For example, when the preference degree of any remaining edge point is relatively high, the possibility of taking this edge point as the next connection point of the current target point is relatively large.

[0052] In an exemplary embodiment of the present invention, when screening the connection points of the target point or the new target point, the positive directions of the angles of all constructed polar coordinate systems are the same. The determination of the positive direction of the angle when constructing the polar coordinate system with the target point or the new target point as the coordinate origin can be achieved through the following steps: Judge the magnitudes of the ordinates of the target edge point and the target point. If the ordinate of the target point is greater than or equal to the ordinate of the target edge point, then use the clockwise direction as the positive direction of the angle when constructing the polar coordinate system with the target point or the new target point as the coordinate origin; if it is less than the ordinate of the target edge point, then use the counterclockwise direction as the positive direction of the angle when constructing the polar coordinate system with the target point or the new target point as the coordinate origin.

[0053] Exemplarily, if the ordinate of the target point is greater than or equal to the ordinate of the target edge point, the polar coordinate system constructed with the target point as the coordinate origin is as Figure 2 shown; if the ordinate of the target point is less than the ordinate of the target edge point, the polar coordinate system constructed with the target point as the coordinate origin is as Figure 3 shown.

[0054] Optionally, when constructing the polar coordinate system with the target point or the new target point as the coordinate origin, the determination methods of the positive direction of the angle and the polar axis can ensure the connected domain of the edge points, that is, the edge is a curve, so as to ensure that the obtained edge is the edge of the clustering cluster.

[0055] Further, after determining the polar coordinate system constructed with the current target point as the coordinate origin, connection points of the current target point can be screened according to the preference degrees of the remaining edge points, so as to obtain new target points and continue the connection process until the termination condition is met, and an edge is obtained.

[0056] In an exemplary embodiment of the present invention, when screening the connection points of the current target point, the preference degree of any remaining edge point satisfies the following relational expression: ; In the formula, is the preference degree of the th remaining edge point; , are respectively the polar angle and the polar radius of the th remaining edge point in the current polar coordinate system; is the natural exponential function, where the natural exponential function refers to the exponential function with the natural constant as the base.

[0057] Among them, is the distance from the th remaining edge point to the coordinate origin in the current polar coordinate system; when is smaller, and is relatively small, then the preference degree of taking the th remaining edge point as the next connection point of the current target point is relatively large.

[0058] In another embodiment, the preference degree of any remaining edge point can also be calculated by other calculation formulas. For example, the normalized value of the reciprocal of the product of the polar angle and the polar radius of any remaining edge point can be used as the preference degree of the remaining edge point.

[0059] Further, after determining the preference degrees of all remaining edge points, the edge point with the largest preference degree can be selected from all remaining edge points as the connection point of the target point, and then the connection process is repeated until the maximum value of the preference degrees of all edge points in the current remaining edge points is less than the preference degree threshold, such as 0.8, and an edge is obtained. Then all edges can be obtained. The size of the preference degree threshold in this embodiment is not particularly limited.

[0060] Next, the process of obtaining the edge will be described in detail: Step 1: Randomly select a target edge point and determine the target point of the target edge point; Step 2: Take the ray starting from the target point in the direction from the target edge point to the target point as the polar axis, obtain the positive direction of the angle determined according to the ordinate magnitudes of the target edge point and the target point, construct a polar coordinate system with the target point as the coordinate origin. Then, calculate the preference degree of each remaining edge point according to its polar angle and polar radius in this polar coordinate system, so as to determine the connection point of the target point and make the connection; Step 3: Take the connection point as the new target point, take the ray starting from the new target point in the direction from the target point to the new target point as the polar axis, use the positive direction of the angle obtained in Step 2 to construct a polar coordinate system with the new target point as the coordinate origin, and obtain a new connection point according to the preference degrees of each remaining edge point for connection; if the maximum value of the preference degrees of all the calculated remaining edge points is less than 0.8, terminate the connection process and take the current connected domain as an edge; if it is greater than 0.8, continue to repeat Step 3 until the maximum value of the preference degrees of the remaining edge points is less than 0.8, so as to obtain an edge, and then all edges can be obtained.

[0061] S4: Take the mean value of the deformation data of all edge points in any edge as the clustering center of this edge, and cluster all the data according to the distances from each data to the clustering center, so as to monitor the deformation anomaly of the target monitoring point based on the anomaly degree of the clustering clusters, where the anomaly degree represents the dispersion degree of the data inside the corresponding clustering cluster.

[0062] It should be noted that since the obtained deformation data sequence in the present invention is a number of binary group (vibration frequency, distance between the spatial coordinates at the current moment and the previous moment) data, the clustering center corresponding to each edge is a binary group data composed of the mean values of the values of all edge points in each dimension of the corresponding edge.

[0063] In an exemplary embodiment of the present invention, the clustering of all the data can be achieved through the following steps: Calculate the similarity degree between any data and any clustering center, and divide each data into the clustering cluster of the clustering center with the maximum similarity degree respectively to cluster all the data.

[0064] Specifically, the similarity degree between any data and any clustering center satisfies the following relational expression: ; In the formula, is the similarity degree between any data and any clustering center; is the absolute value of the difference in local density between this data and this clustering center, where the local density of the clustering center is the average value of the local densities of all the data within the minimum circumscribed rectangle corresponding to the edge; is the distance between this data and this clustering center; is the natural exponential function.

[0065] Furthermore, after determining the similarity degree between each data in the deformation data sequence and each cluster center, each data can be respectively divided into the cluster of the corresponding cluster center with the greatest similarity degree, so as to realize the clustering of the deformation data sequence.

[0066] In another embodiment, the K-means algorithm can also be directly used to cluster all data based on the distance between each data and each cluster center, so as to realize the clustering of the deformation data sequence.

[0067] In an exemplary embodiment of the present invention, when clustering all data, the determination of the clustering range of any cluster center can be realized through the following steps: Obtain the maximum value of the distance between any two cluster centers, construct a square region with a side length equal to half of the maximum value centered on each cluster center, and use the square region as the clustering range when clustering with the corresponding cluster center.

[0068] It should be noted that the clustering range determined by the present invention can ensure the clustering of all data, thus further ensuring the accuracy of the clustering result.

[0069] In an exemplary embodiment of the present invention, the monitoring of the deformation anomaly of the target monitoring point can be realized through the following steps: Calculate the anomaly degree of any cluster in the clustering result. If the anomaly degrees of all clusters are greater than the preset anomaly threshold, it is determined that the deformation amount of the target monitoring point is abnormal.

[0070] Specifically, the anomaly degree of any cluster satisfies the following relational expression: ; In the formula, is the anomaly degree of the th cluster; is the standard deviation of the distances between all data points in the th cluster and the cluster center; is the standard deviation of the sequence numbers of the sampling moments corresponding to all data points in the th cluster; is the normalization function.

[0071] Among them, when is relatively large and is relatively large, it indicates that the distribution of the data in the th cluster is relatively dispersed and the sampling time interval is relatively large, then the anomaly degree of this cluster is relatively large.

[0072] It should be noted that, in order to avoid the influence of the collected noise data, the abnormal threshold is preset to 0.8. When the abnormal degree of all clustering clusters is greater than 0.8, it is determined that the deformation amount of the target monitoring point is abnormal, and an alarm is issued to timely remind relevant personnel that the deformation amount at the target monitoring point exceeds the preset safety threshold, so that corresponding measures can be taken for processing in a timely manner.

[0073] The present invention also provides a geographical information mapping data anomaly detection system based on data analysis. The system includes a memory and a processor, and a computer program is stored on the memory. The computer program integrates the functions of the geographical information mapping data anomaly detection method based on data analysis. When the computer program is executed, the present invention can achieve precise monitoring of the deformation anomaly of the monitoring point through the geographical information mapping data anomaly detection method based on data analysis.

[0074] In the description of this specification, the meanings of "a plurality of" and "several" are at least two, such as two, three or more, etc., unless otherwise clearly and specifically defined.

[0075] Although this specification has shown and described multiple embodiments of the present invention, it is obvious to those skilled in the art that such embodiments are provided only by way of example. Those skilled in the art will think of many changes, alterations and alternative ways without departing from the spirit and idea of the present invention. It should be understood that various alternative solutions to the embodiments of the present invention described herein can be adopted in the process of practicing the present invention.

Claims

1. A method for detecting anomalies in geographic information surveying and mapping data based on data analysis, characterized in that: include: Acquire a deformation data sequence when deformation monitoring is performed on a target monitoring point; For any data in the deformed data sequence, a first neighbor set of the data is preset, and the edge degree of the data is calculated, where the edge degree represents the difference between the local density of the data and the local density of each data in the first neighbor set, so as to filter edge points according to the edge degree of each data; Select the target edge point, take the edge point closest to the target edge point as the target point, take the direction from the target edge point to the target point as the polar axis, take the preset direction as the positive direction of the angle, construct a polar coordinate system with the target point as the coordinate origin, calculate the preference degree of the remaining edge points, the preference degree is negatively correlated with the polar axis and polar diameter of the corresponding edge point in the polar coordinate system, select the connection point and connect it with the target point according to the preference degree, take the connection point as the new target point, construct a polar coordinate system with the new target point as the coordinate origin and repeat the connection process until the preset termination condition is met to obtain an edge, so as to obtain all edges; The mean of the deformation data of all edge points in any edge is taken as the cluster center of the edge. All data are clustered according to the distance from each data to the cluster center, so as to monitor the deformation anomaly of the target monitoring point based on the anomaly degree of the cluster cluster. The anomaly degree represents the discrete degree of the data within the corresponding cluster cluster.

2. The method for detecting anomalies in geographic information surveying and mapping data based on data analysis according to claim 1, characterized in that: The difference between the local density of any data and the local density of each data in the first nearest neighbor set is a comprehensive difference. The method for obtaining the comprehensive difference includes: A second nearest neighbor set of any data is preset, and the reciprocal of the average distance between the data and each data in the second nearest neighbor set is taken as the local density of the data; the comprehensive difference satisfies the following relationship: ； In the formula, For the The comprehensive difference between the local density of each data and the local density of each data in the first nearest neighbor set; For the The local density of the data; For the The first neighbor set of the data The local density of the data; is the number of data in the first nearest neighbor set; is the normalization function.

3. The method for detecting anomalies in geographic information surveying and mapping data based on data analysis according to claim 2, characterized in that: The method for obtaining the edge degree of any data includes: The normalized value of the extreme difference of the local density of all data in the first nearest neighbor set of any data is used as the credibility of the comprehensive difference, and the comprehensive difference is weighted by using the credibility as a weight to obtain the marginal degree of the data.

4. The method for detecting anomalies in geographic information surveying and mapping data based on data analysis according to claim 1, characterized in that: Each data in the deformation data sequence is a binary data in a two-dimensional space coordinate system; wherein the horizontal coordinate of the two-dimensional space coordinate system is the vibration frequency of the target monitoring point at the corresponding moment, and the vertical coordinate is the distance between the spatial coordinates of the target monitoring point at the corresponding moment and the previous moment.

5. The method for detecting anomalies in geographic information surveying and mapping data based on data analysis according to claim 4, characterized in that: When selecting the connection points of the target point or the new target point, the positive directions of the angles of all the polar coordinate systems constructed are the same. The method for obtaining the positive directions of the angles includes: Determine the size of the ordinates of the target edge point and the target point. If the ordinate of the target point is greater than or equal to the ordinate of the target edge point, the clockwise direction is used as the positive direction of the angle when constructing a polar coordinate system with the target point or the new target point as the coordinate origin. If it is less than the ordinate of the target edge point, the counterclockwise direction is used as the positive direction of the angle when constructing a polar coordinate system with the target point or the new target point as the coordinate origin.

6. The method for detecting anomalies in geographic information surveying and mapping data based on data analysis according to claim 5, characterized in that: When screening the connection points of the current target point, the preference degree of any remaining edge point satisfies the following relationship: ； In the formula, For the The degree of preference of the remaining edge points; , Respectively The polar angle and polar diameter of the remaining edge points in the current polar coordinate system; is a natural exponential function.

7. The method for detecting anomalies in geographic information surveying and mapping data based on data analysis according to claim 1, characterized in that: The clustering of all data according to the distance between each data and the cluster center includes: According to the distance between any data and any cluster center, calculate the similarity between the data and the cluster center: ; In the formula, is the similarity between any data and any cluster center; is the absolute value of the difference between the local density of the data and the cluster center, where the local density of the cluster center is the average value of the local density of all data within the minimum circumscribed rectangle of the corresponding edge; is the natural exponential function; is the distance between the data and the cluster center; Each data is divided into the corresponding clusters with the largest similarity of the cluster center to cluster all the data.

8. The method for detecting anomalies in geographic information surveying and mapping data based on data analysis according to claim 1, characterized in that: When clustering all data, the method for obtaining the clustering range of any cluster center includes: The maximum value of the distance between any two cluster centers is obtained, a square area with a side length of half the maximum value is constructed with each cluster center as the center, and the square area is used as the clustering range when clustering is performed with the corresponding cluster center.

9. The method for detecting anomalies in geographic information surveying and mapping data based on data analysis according to claim 1, characterized in that: The abnormal degree monitoring of the deformation abnormality of the target monitoring point based on the clustering cluster includes: Calculate the abnormality degree of any cluster in the clustering result, and the abnormality degree satisfies the following relationship: ； In the formula, For the The abnormality of each cluster; For the The standard deviation of the distance between all data points in a cluster and the cluster center; For the The standard deviation of the sampling time sequence numbers corresponding to all data points in the cluster; is the normalization function; If the abnormal degree of all clusters is greater than the preset abnormal threshold, it is determined that the deformation of the target monitoring point is abnormal.

10. The geographic information surveying and mapping data anomaly detection system based on data analysis is characterized by: The geographic information surveying and mapping data anomaly detection system based on data analysis includes a memory and a processor, the memory stores a computer program, and the processor executes the computer program to implement the steps of the geographic information surveying and mapping data anomaly detection method based on data analysis as described in any one of claims 1 to 9.

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