Electric power facility collapse evaluation method based on remote sensing image SAR (Synthetic Aperture Radar) data

Abstract

The invention discloses an electric power facility collapse assessment method based on remote sensing image SAR data, and belongs to the technical field of image processing, and the method comprises the steps: S1, obtaining analysis point data in electric power facilities through remote sensing images, and generating a distribution diagram of each electric power facility; s2, four pieces of adjacent analysis point data are extracted from the electric power facility and are combined and connected to generate a plurality of second combined surfaces; s3, obtaining a second vector of the electric power facility, and calculating an included angle between the first vector and the second vector of each second combination surface in the electric power facility based on the time interval to obtain a corresponding second included angle set; and S4, generating a prediction model based on the change rate of the angle change sequence, outputting a prediction result set, and evaluating the danger index of the electric power facilities and the safety range between the electric power facilities based on the distribution map. According to the invention, the collapse degree of the electric power facilities in different areas can be evaluated and monitored, so that the problem that the electric power facilities are not maintained in time is avoided.

Description

Technical Field

[0001] The invention belongs to the technical field of image processing, and in particular relates to a method for assessing the collapse of electric power facilities based on remote sensing image SAR data. Background Art

[0002] Power facilities are a general term for a series of facilities such as electrical equipment, metal structures and ancillary buildings used for power generation, transmission, transformation and distribution. They are distributed over a wide area and in a diverse environment. Monitoring the safety of each power facility through remote sensing image SAR data not only saves labor costs, but also can accurately identify the degree of collapse and damage and the scope of impact of each power facility, thereby effectively monitoring each power facility.

[0003] For example, the Chinese patent application "CN108594226B" discloses a method for detecting transmission towers in mountainous areas using SAR images that takes terrain into consideration, including the following steps: for the original single-polarization SAR image set, calculate the corresponding average radar power image; at the same time, generate a range slope angle image with the help of external elevation data; estimate the quantitative relationship between the average radar power and the range slope angle, and use the obtained relationship to simulate the terrain-related power image; subtract the simulated power image from the real power image to obtain a radar power image with the terrain effect reduced; perform dual-parameter constant false alarm detection on the target on the radar power image with the terrain effect reduced to obtain a binary image; perform target pixel clustering analysis on the binary image, and extract it based on linear arrangement features. This invention effectively eliminates the influence of terrain in single-polarization SAR images by simulating amplitude images with elevation data, highlights ground objects, and improves the detection accuracy by using linear extraction and clustering algorithms. For another example, the Chinese patent application "CN110488151B" discloses a vegetation hazard warning system and method for power transmission lines based on remote sensing technology. The system includes: a vegetation growth prediction module, a vegetation height extraction module, a power line elevation extraction module, a relative distance calculation module, and a vegetation risk warning module. This invention combines the advantages of satellite-borne remote sensing technology and airborne remote sensing technology, and can achieve safe distance assessment of vegetation risk sections. According to the assessment results, overgrown vegetation can be cut down in a timely manner, and a more reasonable inspection plan can be formulated to reduce the number of manual inspections, thereby ensuring the safety of transmission lines and reducing the consumption of human and material resources.

[0004] However, the above-mentioned prior art only detects power facilities and transmission lines in mountainous areas by using radar remote sensing data. In actual situations, power facilities whose bottoms are covered by plants may collapse due to gravity and geological influences, and when the degree of collapse of power facilities is large, it will affect other connected power facilities. Summary of the invention

[0005] To solve the above problems, the present invention provides a method, system and storage medium for evaluating the collapse of electric power facilities based on remote sensing image SAR data to solve the problems in the prior art.

[0006] In order to achieve the above-mentioned invention object, the present invention proposes a method for assessing the collapse of power facilities based on remote sensing image SAR data, comprising: Step S1: acquiring an image data set including electric power facilities through remote sensing images, identifying each feature point data in the image data set, acquiring feature coordinates of the feature point data, taking a first distance as a target, clustering each feature point data in combination with the feature coordinates to generate a distribution map of each electric power facility, and defining the feature point data located in the same electric power facility as analysis point data; Step S2: extract four adjacent analysis point data in the power facility and combine and connect them to generate a first combination surface, the first combination surface includes multiple sub-planes, obtain the first vectors of the sub-planes in the first combination surface, and judge whether the first vectors are parallel to each other. If yes, merge the sub-planes into the first combination surface, and then combine and connect the first combination surface with the adjacent analysis point data to generate a second combination surface. If no, combine the sub-planes with the adjacent analysis point data to generate the second combination surface, judge each of the first vectors in the second combination surface, and repeat this step until the analysis point data in the same power facility are expanded; Step S3: obtaining the second vector of the power facility, calculating the angle between the first vector and the second vector of each second combination surface in the power facility, and defining it as a first angle set, setting a time interval, executing the step S1 and the step S2 based on the time interval to correspondingly generate the first vector of each second combination surface and define it as an update vector, obtaining the angle between the update vector and the second vector, and defining it as a second angle set; Step S4: Acquire an angle change sequence based on the second angle set and the time interval, generate a prediction model based on the change rate of the angle change sequence, predict the change rate based on the prediction model and output a prediction result set, and evaluate the danger index of the power facilities and the safety range between each of the power facilities based on the prediction result set and the distribution map.

[0007] Furthermore, in step S1, each of the feature point data is clustered based on the following steps to generate a distribution map of each of the power facilities: Calculate the coordinate distance between two feature point data based on the feature coordinates, extract one feature point data and set it as a first-category label point, obtain the feature point data whose coordinate distance with the first-category label point is less than or equal to the first distance and set it as a first-category label point, repeat this step to obtain all the first-category label points in each feature point data, if the coordinate distance between the feature point data and the first-category label point is greater than the first distance, set the feature point data as a second-category label point, obtain the feature point data whose coordinate distance with the second-category label point is less than or equal to the first distance and set it as a second-category label point, repeat this step to cluster all the feature point data in the image data set into multiple label types; The number of label types corresponding to the feature point data of each of the power facilities is counted, and the number of label types is set as the distribution number. Based on the distribution number, one feature point data is extracted from each of the two power facilities and the coordinate distance of the two feature point data is calculated. The minimum value of the coordinate distance is set as the corresponding distribution distance of the two power facilities. Two analysis point data are extracted from the power facilities to calculate the coordinate distance. The maximum value of the coordinate distance is set as the height value corresponding to the power facility. The distribution map of each of the power facilities is generated based on the distribution number, the distribution distance, the height value and the feature coordinates.

[0008] Furthermore, in step S3, the second vector is obtained and the time interval is set based on the following steps: Calculate the second vector based on the first formula , the first formula is: ,in and are the first vectors corresponding to two adjacent second combined surfaces, is the vector outer product operation, The first vector The corresponding variance value of each analysis point data contained in the second combined surface, The first vector The variance value of each of the analysis point data contained in the corresponding second combination surface, m is the number of permutations and combinations of two adjacent second combination surfaces of the power facilities; The time interval is set based on a second formula , the second formula is: ,in is the first angle set, is the second angle set corresponding to the i-th time interval, and They are respectively the first time interval and the second time interval which are randomly preset.

[0009] Furthermore, in step S3 and step S4, evaluating the risk index of the power facility based on the prediction result set and the distribution map includes the following steps: The change rate of the angle change sequence is obtained based on the third formula , the third formula is , based on the fourth formula, obtain the predicted change rate of the angle change sequence , the fourth formula is: ,in, , is the rate of change corresponding to the i-th time interval, The second angle set corresponding to the i-th time interval is obtained based on the fifth formula to obtain the prediction result set , the fifth formula is: , the risk index W is obtained based on the sixth formula, the sixth formula is: ,in, is the standard normal distribution function, is the average value of each of the change rates, is the standard deviation of each of the above mentioned change rates.

[0010] Furthermore, in step S4, the safety range between the power facilities is obtained based on the following steps: The safety range S of each of the power facilities is calculated based on the seventh formula, and the seventh formula is: ,in, is the height value of the power facility, The distribution distance between the two connected power facilities in the distribution map is determined. When the hazard index is greater than or equal to a preset evaluation threshold, the second combined surface corresponding to the subset with the largest change rate in the prediction result set in the power facility is obtained and defined as a collapse surface. The analysis point data contained in the collapse surface is obtained and defined as collapse point data. The collapse point data is mapped based on the second vector to determine whether the collapse point data mapping is within the safety range of each power facility. If so, the power facility is maintained. If not, the power facility is maintained and other connected power facilities are evaluated and monitored.

[0011] The present invention also provides a power facility collapse assessment system based on remote sensing image SAR data, which is used to implement the above-mentioned power facility collapse assessment method based on remote sensing image SAR data. The system mainly includes: An image extraction module is used to obtain an image data set containing electric power facilities in a remote sensing image, identify each feature point data in the image data set, obtain feature coordinates of the feature point data, cluster each feature point data based on the feature coordinates to generate a distribution map of each electric power facility with a first distance as a target, and define the feature point data located in the same electric power facility as analysis point data; A three-dimensional construction module, used for extracting four adjacent analysis point data in the power facility and combining and connecting them to generate a first combination surface, the first combination surface includes multiple sub-planes, obtaining the first vectors of the sub-planes in the first combination surface, and judging whether the first vectors are parallel to each other. If yes, the sub-planes are merged into the first combination surface, and then the first combination surface and the adjacent analysis point data are combined and connected to generate the second combination surface. If no, the sub-planes and the adjacent analysis point data are combined and connected to generate a second combination surface, and the first vectors in the second combination surface are judged respectively. This step is repeated until the expansion of the analysis point data in the same power facility is completed; An evaluation and monitoring module, configured to obtain a second vector of the power facility, calculate an angle between the first vector and the second vector of each second combination surface in the power facility, and define the angle as a first angle set, set a time interval, execute the step S1 and the step S2 based on the time interval to correspondingly generate the first vector of each second combination surface and define the updated vector, obtain an angle between the updated vector and the second vector, and define the updated vector as a second angle set; A prediction and analysis module obtains an angle change sequence based on the second angle set and the time interval, generates a prediction model based on the change rate of the angle change sequence, predicts the change rate based on the prediction model and outputs a prediction result set, and evaluates the danger index of the power facilities and the safety range between each of the power facilities based on the prediction result set and the distribution map.

[0012] Compared with the prior art, the beneficial effects of the present invention are at least as follows: The present invention firstly extracts feature point data from an image data set containing various power facilities by using remote sensing images, classifies and extracts feature point data belonging to various power facilities in the image data set by clustering, constructs a distribution map between various power facilities by the feature coordinates of the analysis point data in various power facilities, and facilitates the analysis of the safety between various power facilities, then combines and connects the analysis point data contained in the power facilities and determines and generates various second combination surfaces and corresponding first vectors, further constructs the power facilities in three dimensions, and finally obtains the angle sets between each first vector and the second vector in the power facilities, and by setting a time interval, repeats the above steps to generate the corresponding second angle set, thereby monitoring the update vectors of each surface in the power facilities.

[0013] The present invention also constructs a prediction model by using an angle change sequence, outputs a prediction result set to obtain a set of angles between the updated vectors of each face of the power facility and the second vector in a future time interval, and calculates a danger index to determine the degree of collapse of the power facility. The safety range of each power facility is calculated through the distribution map of each power facility. Not only can the collapsed power facilities be maintained in a timely manner, but it can also be determined whether other connected power facilities have a safety impact. If there is a safety impact, the same steps of monitoring are performed on other connected power facilities in a timely manner. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] Figure 1 A flowchart of the steps of the power facility collapse assessment method based on remote sensing image SAR data of the present invention; Figure 2 A flow chart of steps for generating each second combination surface in an electric power facility according to the present invention; Figure 3 It is a distribution diagram between various power facilities of the present invention; Figure 4 This is a structural diagram of the power facility collapse assessment system based on remote sensing image SAR data of the present invention. DETAILED DESCRIPTION

[0015] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0016] It is understood that the terms "first", "second", etc. used in this application may be used herein to describe various elements, but unless otherwise specified, these elements are not limited by these terms. These terms are only used to distinguish a first element from another element. For example, without departing from the scope of this application, a first xx script may be referred to as a second xx script, and similarly, a second xx script may be referred to as a first xx script.

[0017] like Figure 1 As shown in the figure, the power facility collapse assessment method based on remote sensing image SAR data includes: Step S1: Acquire an image data set containing power facilities through remote sensing images, identify each feature point data in the image data set, obtain the feature coordinates of the feature point data, take the first distance as the target, cluster each feature point data in combination with the feature coordinates to generate a distribution map of each power facility, and define the feature point data located in the same power facility as analysis point data.

[0018] Specifically, SAR radar remote sensing has strong penetration of clouds and dark light, and can provide rich surface information to obtain clear and accurate SAR image data. Power facilities refer to any power buildings that need to be monitored, including but not limited to power towers, power stations, and house buildings. In this embodiment, the remote sensing image refers to the surface area image containing multiple power facilities generated by SAR remote sensing based on the longitude and latitude coordinates of the power facility as the center point. The surface area image is intercepted according to the set image frame rate, so that each of the two images of each adjacent image frame rate has an overlapping area containing each power facility, and the image data set is composed of each image frame rate. The feature point data in each image data set is extracted by the image processing technology SURF accelerated robust feature algorithm. The feature point data refers to a feature data set composed of local features of each image in the image data set, such as pixel protrusions, corner points, and edge points on each face of the building. The corresponding feature coordinates are obtained from the feature point data contained in each overlapping area in the image data set. The feature coordinates are based on the image coordinates to describe the location information of the feature point data, such as feature point data The image data set contains feature point data of multiple power facilities and other buildings. By setting the first distance, the feature point data of different building facilities can be clustered and distinguished. For example, the shortest actual distance between connected power facilities is 10 meters, which is represented by 5 pixels in the remote sensing image. The first distance is set to 5. Based on the feature point data of the power facilities, clustering is performed at the first distance of 5 to extract all the feature point data contained in each power facility. The specific clustering method will be explained in detail later. The number of power facilities contained in the image data set is determined based on the number of clustered results. The distribution distance between each power facility and the height value corresponding to the power facility are calculated according to the feature coordinates of the feature point data in each power facility. Thus, a distribution map of each power facility in the image data set is constructed, which is convenient for analyzing and determining the safety impact of each adjacent power facility. The analysis point data refers to the feature point data contained in each power facility.

[0019] Step S2: extract four adjacent analysis point data in the power facility and combine and connect them to generate a first combined surface, the first combined surface includes multiple sub-planes, obtain the first vectors of the sub-planes in the first combined surface, and judge whether the first vectors are parallel. If so, merge the sub-planes into the first combined surface, and then combine the first combined surface with the adjacent analysis point data to generate a second combined surface. If not, combine the sub-planes with the adjacent analysis point data to generate a second combined surface, judge each first vector in the second combined surface respectively, and repeat this step until the analysis point data in the same power facility are expanded.

[0020] Specifically, in this embodiment, in order to accurately analyze the collapse degree of each power facility, it is necessary to construct the analysis point data contained in the power facility in three dimensions based on the corresponding characteristic coordinates to restore the structural diagram of the power facility, such as Figure 2 As shown, the combined connection refers to cross-connecting the included analysis point data into lines, and further generating the first combined surface , which includes multiple sub-planes , and , the first vector refers to the normal vector of any plane. The normal vector is perpendicular to the plane and can be used to describe the inner direction of any plane in a three-dimensional space. If the first vectors are parallel, it means that the corresponding planes are parallel or belong to the same plane. For example, the first combined plane The first vectors f1, f2 and f3 of each sub-plane in the first combined plane are all non-parallel. , and After connecting with other adjacent analysis point data, a second composite surface is formed, such as a sub-plane and analysis point data Expand to form the second combination surface , and perform the same judgment steps on it, and perform the above-mentioned combination connection on all the analysis point data in the power facility. Each second combination surface is all the planes of the power facility, and the first vector corresponding to each second combination plane is the connection structure of all the planes in the power facility, thereby completing the three-dimensional construction of the corresponding power facility.

[0021] Step S3: Obtain the second vector of the power facility, calculate the angle between the first vector and the second vector of each second combination surface in the power facility, and define it as a first angle set, set a time interval, execute steps S1 and S2 based on the time interval to generate the first vector of each second combination surface correspondingly and define it as an update vector, obtain the angle between the update vector and the second vector, and define it as a second angle set.

[0022] Specifically, in this embodiment, the second vector refers to the gravity vector of the power facility in the earth coordinate system, and its specific acquisition method will be explained in detail later. The first angle set refers to the angle set generated by the angles between the first vectors corresponding to each second combined surface and the second vector when the above operation is performed on the power facility for the first time. For example, the first angle set A0 is The time interval refers to the monitoring time interval of the power facility. The specific setting method will be explained in detail later. The update vector after the first vector of each surface corresponding to the power facility is updated is obtained through the time interval, and the angle between the update vector of each surface and the second vector is obtained to generate the second angle set. For example, if the first time interval is set to 1 hour, then step S1 and step S2 are executed after 1 hour to generate the update vector of each second combination surface in the power facility M1. The second angle set A1 of each update vector and the second vector of the power facility M1 is .

[0023] Step S4: Acquire an angle change sequence based on the second angle set and the time interval, generate a prediction model based on the change rate of the angle change sequence, predict the change rate based on the prediction model and output a prediction result set, and evaluate the danger index of the power facilities and the safety range between each power facility based on the prediction result set and the distribution map.

[0024] Specifically, in this embodiment, the angle change sequence refers to an angle data set of the second angle set corresponding to the power facility that changes with the time interval. The prediction model uses the change rate between the angle change sequences obtained in each adjacent time interval to analyze and predict the change rate of a future time interval. By obtaining the change rate of a future time interval, a prediction result set can be output, wherein the prediction result set refers to the angle between the update vector of each second combination surface of the power facility in a future time interval and the second vector. The danger index is an intuitive assessment of the degree of collapse of the power facility. If the danger index of the power facility is greater than the assessment threshold, not only the power facility needs to be maintained, but also the safety of other power facilities connected to the power facility will be affected. The impact range refers to the safety range between each connected power facility. The safety range of the power facility is used to determine the location of other power facilities that will be specifically affected by safety in the distribution map, and further monitor other power facilities in real time. For example, Figure 3 As shown, the danger index of the power facility M1 is too large and it collapses. The collapse angle of the power facility M1 is used to determine whether other power facilities M2 and M3 connected to the power facility M1 are affected. If the collapse angle of the power facility M1 is within the safety range of the power facility M2 but outside the safety range of the power facility M3, due to the force on the transmission lines between the power facilities, only the power facility M3 needs to be monitored.

[0025] The present invention firstly extracts feature point data from an image data set containing various power facilities by using remote sensing images, classifies and extracts feature point data belonging to various power facilities in the image data set by clustering, constructs a distribution map between various power facilities by the feature coordinates of the analysis point data in various power facilities, and facilitates the analysis of the safety between various power facilities, then combines and connects the analysis point data contained in the power facilities and determines and generates various second combination surfaces and corresponding first vectors, further constructs the power facilities in three dimensions, and finally obtains the angle sets between each first vector and the second vector in the power facilities, and by setting a time interval, repeats the above steps to generate the corresponding second angle set, thereby monitoring the update vectors of each surface in the power facilities.

[0026] The present invention also constructs a prediction model by using an angle change sequence, outputs a prediction result set to obtain a set of angles between the updated vectors of each face of the power facility and the second vector in a future time interval, and calculates a danger index to determine the degree of collapse of the power facility. The safety range of each power facility is calculated through the distribution map of each power facility. Not only can the collapsed power facilities be maintained in a timely manner, but it can also be determined whether other connected power facilities have a safety impact. If there is a safety impact, the same steps of monitoring are performed on other connected power facilities in a timely manner.

[0027] It is particularly noteworthy that the present invention can be used to conduct collapse assessments on power facilities and to determine the safety of other connected power facilities, thereby resolving the problem of untimely maintenance of power facilities and damage to adjacent power facilities.

[0028] In step S1, the characteristic point data are clustered based on the following steps to generate a distribution map of each power facility: Calculate the coordinate distance between two feature point data based on the feature coordinates, extract a feature point data and set it as a first-class label point, obtain feature point data whose coordinate distance with the first-class label point is less than or equal to the first distance and set it as the first-class label point, repeat this step to obtain all first-class label points in each feature point data, if the coordinate distance between the feature point data and the first-class label point is greater than the first distance, set the feature point data as a second-class label point, obtain feature point data whose coordinate distance with the second-class label point is less than or equal to the first distance and set it as the second-class label point, repeat this step to cluster all feature point data in the image data set into multiple label types; Count the number of label types corresponding to the feature point data of each power facility, and set the number of label types as the distribution number; based on the distribution number, extract one feature point data from each of the two power facilities and calculate the coordinate distance between the two feature point data; set the minimum value of the coordinate distance as the corresponding distribution distance of the two power facilities; extract two analysis point data from the power facilities to calculate the coordinate distance; set the maximum value of the coordinate distance as the height value of the corresponding power facility; generate a distribution map of each power facility based on the distribution number, distribution distance, height value and feature coordinates.

[0029] Specifically, in this embodiment, the coordinate distance between any two feature point data is calculated by a three-dimensional space distance formula, and a feature point data belonging to the power facility is extracted through the pixel point features of the power facility in the image data set and set as a first-class label point. The feature point data belonging to different buildings can be clustered through the labels. For example, a circular area is formed with the first label point as the center and the first distance 5 as the radius, and all feature point data in the circular area are set as first-class label points. Then, this step is performed for each first-class label point with the first distance 5 as the radius to cluster all first-class label points in the image data set, and a feature point data is extracted from the remaining feature point data in the image data set and set as a second-class label point. The above steps are repeated to cluster the feature point data of each building. The number of label types belonging to each power facility is set to the number of power facilities in the image data set, the distribution distance refers to the interval distance between any two connected power facilities, and the height value refers to the straight-line distance between the highest point and the lowest point of the power facility. For example, after clustering, the power facilities include the first type of label points, the second type of label points and the third type of label points, and the image data set has three connected power facilities, where the minimum value of the coordinate distance between each sub-label point in the first type of label point and the second type of label point is 5, and the distribution orientation of the corresponding first type of label point and the second type of label point can be obtained according to the characteristic coordinates of the two sub-label points. The maximum value of the coordinate distance between any two sub-label points in the first type of label point is 100. In the same way, the distribution map of each power facility is constructed by the distribution distance, distribution orientation and height value of each power facility.

[0030] In step S3, the second vector is obtained based on the following steps, and the time interval is set: Calculate the second vector based on the first formula , the first formula is: ,in and is the first vector corresponding to two adjacent second combined surfaces, is the vector outer product operation, The first vector The corresponding variance value of each analysis point data contained in the second combined surface, The first vector The variance value of each analysis point data contained in the corresponding second combination surface, m is the number of permutations and combinations of two adjacent second combination surfaces of power facilities; Set the time interval based on the second formula , the second formula is: ,in is the first angle set, is the second angle set corresponding to the i-th time interval, and They are the first time interval and the second time interval which are randomly preset respectively.

[0031] Specifically, in this embodiment, since each power facility is constructed based on the gravity direction of the earth coordinates, the plane vector formed by the ground feature point data cannot be completely used as a reference coordinate, and the gravity vector of the power facility in the earth coordinates needs to be obtained. In the first formula, the combination vector of the two second combination surfaces of any two adjacent second combination surfaces of the power facility is generated by performing an outer product operation on the first vectors and multiplying them by a set coefficient value. After performing this operation on each adjacent second combination surface, the average vector is obtained to generate the gravity direction of the power facility. For example, the first vectors of each surface of the power facility obtained by the first monitoring are substituted into the first formula to calculate the second vector, which can more accurately describe the gravity direction of the power facility in the three-dimensional construction without a reference object. Setting the time interval can not only effectively monitor the update vectors of each surface in the power facility, but also reduce the equipment operation load during image processing. For example, the first time interval is set to 1 hour, and the subsequent time intervals are set in turn according to the change rate of the second angle set.

[0032] In step S4, evaluating the danger index of the power facility based on the prediction result set and the distribution map includes the following steps: Obtain the rate of change of the angle change sequence based on the third formula , the third formula is , based on the fourth formula, the predicted change rate of the angle change sequence is obtained , the fourth formula is: ,in, , is the rate of change corresponding to the i-th time interval, is the second angle set corresponding to the i-th time interval, and the prediction result set is obtained based on the fifth formula , the fifth formula is: , based on the sixth formula, the risk index W is obtained. The sixth formula is: ,in, is the standard normal distribution function, is the average value of each change rate, is the standard deviation of each rate of change.

[0033] Specifically, in this embodiment, the rate of change refers to the speed of change of the angle between the update vector of each face in the power facility and the second gravity. The rate of change of the previous time interval can be generated by the ratio of the difference between the second angle sets monitored at any two adjacent time intervals. By predicting the rate of change, the values ​​of each subset of the second angle set of the power facility after a future time interval can be obtained. In the sixth formula, a specific value can be output by using a standard normal distribution function. For example, the second angle set A2 obtained by monitoring the second time interval is , which corresponds to the second angle set A1 of the first time interval Calculate the second rate of change is (0, 10, 0, -10), and its danger index is 0.8, which is greater than the assessment threshold of 0.3, indicating that the degree of collapse of the power facility is large. The collapse point data of the power facility is further used to determine whether its collapse angle is within the safety range of the connected power facilities.

[0034] In step S4, the safety range between the various power facilities is obtained based on the following steps: The safety range S of each power facility is calculated based on the seventh formula, which is: ,in, is the height of the power facility, is the distribution distance between two connected power facilities in the distribution map; when the danger index is greater than or equal to a preset assessment threshold, the second combined surface corresponding to the subset with the largest change rate in the prediction result set in the power facility is defined as the collapse surface; the analysis point data contained in the collapse surface is defined as the collapse point data; the collapse point data is mapped based on the second vector to determine whether there is collapse point data mapped within the safety range of each power facility; if so, the power facility is maintained; if not, the power facility is maintained, and other connected power facilities are evaluated and monitored.

[0035] Specifically, in this embodiment, Figure 3As shown, the safety range is dynamically set relative to the connected power facilities, specifically, the safety range is generated with the connected power facilities as the center. In the seventh formula, in order to avoid the power transmission lines connected between the power facilities being pulled and damaged due to the collapse of the power facilities, the distribution distance and the height value of the connected power facilities are substituted into the seventh formula to calculate the safety range of the connected power facilities. Through the safety range, it can be judged whether any connected power facilities will be affected by the connected transmission lines if they collapse, and whether the transmission lines between the power facilities will be broken to cause safety problems. If they are within the safety range, the connected power facilities will not be affected and the lines will not be broken. If they are outside the safety range, the power facilities not only need to be maintained in time, but also other power facilities connected to them need to be monitored in time, and the transmission lines between them need to be repaired in time. For example, if there is a collapse of power facility M1 and the corresponding hazard index is greater than the assessment threshold, then the collapse direction of power facility M1 can be determined based on the prediction result set, which can be determined by the analysis point data contained therein. If the highest analysis point data of power facility M1 is mapped within the safety range of power facility M2, it means that power facility M1 will not cause safety impact on power facility M2. Then continue to determine whether the highest analysis point data of power facility M1 is mapped within the safety range of another connected power facility M3.

[0036] like Figure 4 As shown, the present invention also provides a power facility collapse assessment system based on remote sensing image SAR data, which is used to implement the above-mentioned power facility collapse assessment method based on remote sensing image SAR data. The system mainly includes: An image extraction module is used to obtain an image data set containing power facilities in a remote sensing image, identify each feature point data in the image data set, obtain feature coordinates of the feature point data, cluster each feature point data based on the first distance as a target, and generate a distribution map of each power facility, and define the feature point data located in the same power facility as analysis point data; A three-dimensional construction module is used to extract four adjacent analysis point data in the power facility and combine and connect them to generate a first combination surface, the first combination surface includes multiple sub-planes, obtain the first vectors of the sub-planes in the first combination surface, and judge whether the first vectors are parallel. If yes, merge the sub-planes into the first combination surface, and then combine and connect the first combination surface with the adjacent analysis point data to generate a second combination surface. If no, combine the sub-planes with the adjacent analysis point data to generate a second combination surface, judge each first vector in the second combination surface, and repeat this step until the expansion of the analysis point data in the same power facility is completed; An evaluation and monitoring module is used to obtain a second vector of the power facility, calculate the angle between the first vector and the second vector of each second combination surface in the power facility, and define it as a first angle set, set a time interval, execute steps S1 and S2 based on the time interval to correspondingly generate the first vector of each second combination surface and define it as an update vector, obtain the angle between the update vector and the second vector, and define it as a second angle set; The prediction and analysis module obtains the angle change sequence based on the second angle set and the time interval, generates a prediction model based on the change rate of the angle change sequence, predicts the change rate based on the prediction model and outputs a prediction result set, and evaluates the danger index of the power facilities and the safety range between each power facility based on the prediction result set and the distribution map.

[0037] The present invention also provides a computer storage medium, which stores program instructions. When the program instructions are executed, the device where the computer storage medium is located is controlled to execute the above-mentioned power facility collapse assessment method based on remote sensing image SAR data.

[0038] It should be understood that, although each step in the flow chart of each embodiment of the present invention is shown in sequence according to the indication of the arrow, these steps are not necessarily performed in sequence according to the order indicated by the arrow. Unless there is a clear explanation in this article, the execution of these steps does not have a strict order restriction, and these steps can be performed in other orders. Moreover, at least a portion of the steps in each embodiment may include a plurality of sub-steps or a plurality of stages, and these sub-steps or stages are not necessarily performed at the same time, but can be performed at different times, and the execution order of these sub-steps or stages is not necessarily performed in sequence, but can be performed in turn or alternately with at least a portion of other steps or sub-steps or stages of other steps.

[0039] Those of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the above-mentioned program can be stored in a non-volatile computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0040] The technical features of the above-mentioned embodiments can be combined arbitrarily. In order to make the description concise, not all possible combinations of the technical features in the above-mentioned embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0041] The above embodiments only express several implementation methods of the present invention, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the patent of the present invention. It should be pointed out that, for those of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present invention, and these all belong to the protection scope of the present invention. Therefore, the protection scope of the patent of the present invention shall be subject to the attached claims.

[0042] The above are only preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for assessing the collapse of power facilities based on remote sensing image SAR data, characterized in that: The method comprises the following steps: Step S1: acquiring an image data set including electric power facilities through remote sensing images, identifying each feature point data in the image data set, acquiring feature coordinates of the feature point data, taking a first distance as a target, clustering each feature point data in combination with the feature coordinates to generate a distribution map of each electric power facility, and defining the feature point data located in the same electric power facility as analysis point data; Step S2: extract four adjacent analysis point data in the power facility and combine and connect them to generate a first combination surface, the first combination surface includes multiple sub-planes, obtain the first vectors of the sub-planes in the first combination surface, and judge whether the first vectors are parallel to each other. If yes, merge the sub-planes into the first combination surface, and then combine and connect the first combination surface with the adjacent analysis point data to generate a second combination surface. If no, combine the sub-planes with the adjacent analysis point data to generate the second combination surface, judge each of the first vectors in the second combination surface, and repeat this step until the analysis point data in the same power facility are expanded; Step S3: obtaining the second vector of the power facility, calculating the angle between the first vector and the second vector of each second combination surface in the power facility, and defining it as a first angle set, setting a time interval, executing the step S1 and the step S2 based on the time interval to correspondingly generate the first vector of each second combination surface and define it as an update vector, obtaining the angle between the update vector and the second vector, and defining it as a second angle set; Step S4: Acquire an angle change sequence based on the second angle set and the time interval, generate a prediction model based on the change rate of the angle change sequence, predict the change rate based on the prediction model and output a prediction result set, and evaluate the danger index of the power facilities and the safety range between each of the power facilities based on the prediction result set and the distribution map.

2. The method for assessing the collapse of electric power facilities based on remote sensing image SAR data according to claim 1, characterized in that: In step S1, each of the feature point data is clustered based on the following steps to generate a distribution map of each of the power facilities: Calculate the coordinate distance between two feature point data based on the feature coordinates, extract one feature point data and set it as a first-category label point, obtain the feature point data whose coordinate distance with the first-category label point is less than or equal to the first distance and set it as a first-category label point, repeat this step to obtain all the first-category label points in each feature point data, if the coordinate distance between the feature point data and the first-category label point is greater than the first distance, set the feature point data as a second-category label point, obtain the feature point data whose coordinate distance with the second-category label point is less than or equal to the first distance and set it as a second-category label point, repeat this step to cluster all the feature point data in the image data set into multiple label types; The number of label types corresponding to the feature point data of each of the power facilities is counted, and the number of label types is set as the distribution number. Based on the distribution number, one feature point data is extracted from each of the two power facilities and the coordinate distance of the two feature point data is calculated. The minimum value of the coordinate distance is set as the corresponding distribution distance of the two power facilities. Two analysis point data are extracted from the power facilities to calculate the coordinate distance. The maximum value of the coordinate distance is set as the height value corresponding to the power facility. The distribution map of each of the power facilities is generated based on the distribution number, the distribution distance, the height value and the feature coordinates.

3. The method for assessing the collapse of electric power facilities based on remote sensing image SAR data according to claim 1, characterized in that: In step S3, the second vector is obtained and the time interval is set based on the following steps: Calculate the second vector based on the first formula , the first formula is: ,in and are the first vectors corresponding to two adjacent second combined surfaces, is the vector outer product operation, The first vector The corresponding variance value of each analysis point data contained in the second combined surface, The first vector The variance value of each of the analysis point data contained in the corresponding second combination surface, m is the number of permutations and combinations of two adjacent second combination surfaces of the power facilities; The time interval is set based on a second formula , the second formula is: ,in is the first angle set, is the second angle set corresponding to the i-th time interval, and The first time interval and the second time interval are respectively randomly preset.

4. The method for assessing the collapse of electric power facilities based on remote sensing image SAR data according to claim 1, characterized in that: In step S4, evaluating the danger index of the power facility based on the prediction result set and the distribution map includes the following steps: The change rate of the angle change sequence is obtained based on the third formula , the third formula is , based on the fourth formula, obtain the predicted change rate of the angle change sequence , the fourth formula is: ,in, , is the rate of change corresponding to the i-th time interval, The second angle set corresponding to the i-th time interval is obtained based on the fifth formula to obtain the prediction result set , the fifth formula is: , the risk index W is obtained based on the sixth formula, the sixth formula is: ,in, is the standard normal distribution function, is the average value of each of the change rates, is the standard deviation of each of the above mentioned change rates.

5. The method for assessing the collapse of electric power facilities based on remote sensing image SAR data according to claim 1, characterized in that: In step S4, the safety range between the power facilities is obtained based on the following steps: The safety range S of each of the power facilities is calculated based on the seventh formula, which is: ,in, is the height of the power facility, The distribution distance between the two connected power facilities in the distribution map is determined. When the hazard index is greater than or equal to a preset evaluation threshold, the second combined surface corresponding to the subset with the largest change rate in the prediction result set in the power facility is obtained and defined as a collapse surface. The analysis point data contained in the collapse surface is obtained and defined as collapse point data. The collapse point data is mapped based on the second vector to determine whether the collapse point data mapping is within the safety range of each power facility. If so, the power facility is maintained. If not, the power facility is maintained and other connected power facilities are evaluated and monitored.

6. A power facility collapse assessment system based on remote sensing image SAR data, used to implement the power facility collapse assessment method based on remote sensing image SAR data as described in any one of claims 1 to 5, characterized in that: The system includes the following modules: An image extraction module is used to obtain an image data set containing electric power facilities in a remote sensing image, identify each feature point data in the image data set, obtain feature coordinates of the feature point data, cluster each feature point data based on the feature coordinates to generate a distribution map of each electric power facility with a first distance as a target, and define the feature point data located in the same electric power facility as analysis point data; A three-dimensional construction module, used for extracting four adjacent analysis point data in the power facility and combining and connecting them to generate a first combination surface, the first combination surface includes multiple sub-planes, obtaining the first vectors of the sub-planes in the first combination surface, and judging whether the first vectors are parallel to each other. If yes, the sub-planes are merged into the first combination surface, and then the first combination surface and the adjacent analysis point data are combined and connected to generate the second combination surface. If no, the sub-planes and the adjacent analysis point data are combined and connected to generate a second combination surface, and the first vectors in the second combination surface are judged respectively. This step is repeated until the expansion of the analysis point data in the same power facility is completed; An evaluation and monitoring module, configured to obtain a second vector of the power facility, calculate an angle between the first vector and the second vector of each second combination surface in the power facility, and define the angle as a first angle set, set a time interval, execute the step S1 and the step S2 based on the time interval to correspondingly generate the first vector of each second combination surface and define the updated vector, obtain an angle between the updated vector and the second vector, and define the updated vector as a second angle set; A prediction and analysis module obtains an angle change sequence based on the second angle set and the time interval, generates a prediction model based on the change rate of the angle change sequence, predicts the change rate based on the prediction model and outputs a prediction result set, and evaluates the danger index of the power facilities and the safety range between each of the power facilities based on the prediction result set and the distribution map.

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