Electrical equipment level difference positioning and quantifying method based on digital twinborn model

Abstract

The invention discloses an electrical equipment level difference positioning and quantification method based on a digital twinborn model, relates to the technical field of digital twinborn, constructs a multi-source data fusion and three-dimensional electrical equipment level difference state analysis mechanism, improves the accuracy of level difference positioning and the dimension coverage of quantitative evaluation, and improves the accuracy of level difference positioning. According to the method, the level difference transmission influence relation and mutual coupling characteristics among functional parts of equipment can be comprehensively captured, more accurate and comprehensive level difference state characteristics are formed, a positioning mechanism for comprehensively considering multi-dimensional hazard characteristics is realized, the accuracy of level difference abnormal region positioning and the effectiveness of fault early warning can be improved, and the fault early warning efficiency is improved. According to the method, false detection areas can be effectively eliminated, the reliability of level difference abnormal area screening is improved, and differential analysis and accurate early warning of electrical equipment level difference hazard assessment are realized based on a fault risk coefficient calculation mechanism of a level difference transfer incidence matrix.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of digital twinning, in particular to an electrical equipment level difference positioning and quantification method based on a digital twinning model. BACKGROUND

[0002] With the continuous expansion of the scale of the power system and the continuous improvement of the intelligent operation and maintenance demand, the installation precision and operation state monitoring of electrical equipment have gradually become an important link of power operation and management. The level difference of electrical equipment, as a key factor affecting the stability and service life of the equipment, directly determines the effectiveness of equipment fault warning and maintenance decision. Therefore, it is necessary to construct an electrical equipment level difference detection system with precise positioning and quantitative evaluation capability.

[0003] In the prior art, most electrical equipment level difference detection systems rely on manual inspection and fixed cycle measurement methods, which cannot dynamically monitor the real-time state of the equipment, resulting in a lagging and one-sided nature of the level difference detection. Although some existing technologies introduce tilt sensors or laser range finders for detection, due to the single dimension of data collection, it is difficult to achieve comprehensive judgment of the level difference state of the complex structure of multiple components of the equipment. In the variable operating conditions, using only single measurement data for level difference evaluation has a large error. When facing subtle changes in the structure of the equipment, if only a simple threshold judgment is used, it is easy to miss detection or misjudge the results.

[0004] The prior art lacks joint collection, fusion analysis and collaborative verification capability of multi-source data such as visible light images, infrared thermal imaging and vibration signals of the equipment, lacks recognition mechanism of the level difference transmission correlation between functional components, cannot realize level difference transmission influence analysis based on mechanical coupling and electrical connection between components, and the existing system often uses a method of detecting the level state of a single component in isolation, which has defects such as inability to evaluate the mutual influence between components, one-sided fault risk evaluation, and single quantitative index. In terms of sensing the level difference hazard degree of the equipment, the existing technology mainly relies on simple comparison of fixed thresholds, which cannot differentiate the level difference standards of different functional components, resulting in a significant gap between the detection results and the actual fault risk of the equipment. At the same time, it lacks adaptive evaluation strategies for different component types, and cannot dynamically calculate the fault risk coefficient according to the structural characteristics and transmission correlation matrix of the equipment. SUMMARY

[0005] The present application provides an electrical equipment level difference positioning and quantification method based on a digital twinning model, which solves the problems in the background art.

[0006] To solve the above technical problems, the application adopts the following technical solutions: The application provides an electrical equipment horizontal difference positioning and quantification method based on a digital twin model, comprising: step 1: obtaining the equipment model, structural parameters and three-dimensional structural model of each electrical equipment, and constructing a digital twin model according to the same, thereby analyzing the structural feature reference line and horizontal difference standard comparison coefficient of each electrical equipment, and collecting multi-source data of each electrical equipment, thereby obtaining visible light image data, infrared thermal imaging data and local vibration data of each electrical equipment.

[0007] Step 2: According to the visible light image data of each electrical equipment, the visible light image data is compared and analyzed with the structural feature reference line of each electrical equipment, thereby obtaining the horizontal offset angle value of the structural feature reference line of each electrical equipment.

[0008] Step 3: According to the horizontal difference standard comparison coefficient and the horizontal offset angle value of the structural feature reference line of each electrical equipment, and combining the infrared thermal imaging data and the local vibration data of each electrical equipment, the horizontal difference abnormal area is positioned, thereby determining each effective horizontal difference abnormal area of each electrical equipment.

[0009] Step 4: According to each effective horizontal difference abnormal area of each electrical equipment, the hazard quantification analysis is performed, thereby evaluating the horizontal offset quantification index and the fault risk coefficient of each effective horizontal difference abnormal area of each electrical equipment.

[0010] Step 5: According to the horizontal offset quantification index and the fault risk coefficient of each effective horizontal difference abnormal area of each electrical equipment, the horizontal difference hazard evaluation is performed, and the evaluation result is sent to the equipment operation and maintenance personnel.

[0011] The beneficial effects of the present application are that the present application constructs a multi-source data fusion and three-dimensional electrical equipment level difference state analysis mechanism, improves the accuracy of level difference positioning and the dimension coverage of quantitative evaluation, constructs a level difference transmission correlation matrix through the mechanical coupling strength coefficient, deformation transmission rate and electrical traction coupling coefficient between each functional component, analyzes the level difference standard comparison coefficient of each structure characteristic baseline, can comprehensively capture the level difference transmission influence relationship and mutual coupling characteristics between each functional component of the equipment, form more accurate and comprehensive level difference state characteristics, facilitate subsequent level difference abnormal area positioning and hazard quantitative analysis, and through calculating the level difference hazard coefficient of each structure characteristic baseline and comprehensive evaluation, obtain effective level difference abnormal area, realize a positioning mechanism of multi-dimensional hazard feature comprehensive consideration, the present application can improve the accuracy of level difference abnormal area positioning and the effectiveness of fault early warning, meanwhile, through the multi-source verification confidence calculation mechanism, the present application combines the temperature gradient abnormal coefficient and the vibration abnormal coefficient to cross verify the level difference abnormal area, can effectively exclude the false detection area, improve the reliability of level difference abnormal area screening, and based on the fault risk coefficient calculation mechanism of the level difference transmission correlation matrix, can dynamically evaluate the fault risk according to the transmission influence degree of each effective level difference abnormal area on other affected functional components, realize the differential analysis and accurate early warning of electrical equipment level difference hazard evaluation. BRIEF DESCRIPTION OF DRAWINGS

[0012] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings needed to be used in the embodiments or prior art description. Obviously, the drawings in the following description only constitute some embodiments of the present application, and for those skilled in the art, other drawings can be obtained without creative labor.

[0013] Figure 1 The present application is a method flowchart. DETAILED DESCRIPTION

[0014] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments only constitute some embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0015] REFERENCE Figure 1As shown, the present application provides an electrical equipment horizontal difference positioning and quantification method based on a digital twin model, which comprises the following steps: Step 1: obtaining the equipment model, structural parameters and three-dimensional structure model of each electrical equipment, and constructing a digital twin model based on the same, thereby analyzing the structural feature reference line and the horizontal difference standard comparison coefficient of each electrical equipment, and collecting multi-source data of each electrical equipment, thereby obtaining visible light image data, infrared thermal imaging data and local vibration data of each electrical equipment.

[0016] In one specific embodiment, the equipment model, structural parameters and three-dimensional structure model of each electrical equipment are obtained, and the specific method is as follows: obtaining the equipment model, structural parameters and three-dimensional structure model of each electrical equipment from a local database.

[0017] It should be noted that the local database is used to store the equipment model, structural parameters and three-dimensional structure model of each electrical equipment, the standard three-dimensional structure model corresponding to the equipment model, the basic weight coefficient of each functional component type, the unit area coupling coefficient corresponding to each contact type, the material stiffness parameter and the installation and fixing mode of each functional component, the constraint coefficient corresponding to each installation and fixing mode, the electrical connection path and the conductor tension distribution between each functional component of each electrical equipment, the minimum length threshold of a straight line segment, the camera intrinsic matrix and the extrinsic matrix of the image acquisition device, the position matching distance threshold and the direction matching angle threshold, the horizontal difference hazard coefficient threshold, the multi-source verification confidence threshold, the temperature gradient anomaly threshold, the vibration anomaly threshold, the temperature verification weight coefficient and the vibration verification weight coefficient, the mechanical coupling strength coefficient threshold, the deformation transmission rate threshold and the electrical traction coupling coefficient threshold, and the fault risk coefficient threshold.

[0018] In the specific embodiment of the present application, the digital twin model is constructed, thereby analyzing the structural feature reference line and the horizontal difference standard comparison coefficient of each electrical equipment, and the specific method is as follows: constructing the digital twin model of each electrical equipment according to the equipment model, structural parameters and three-dimensional structure model of each electrical equipment, identifying each functional component of each electrical equipment, extracting the design horizontal reference line of each functional component of each electrical equipment, and taking the same as the structural feature reference line of each electrical equipment.

[0019] In one specific embodiment, the digital twin model of each electrical equipment is constructed, and the specific method is as follows: obtaining the standard three-dimensional structure model corresponding to the equipment model from the local database according to the equipment model of each electrical equipment, parameterizing and adjusting the size parameters and spatial position relationship of each functional component in the standard three-dimensional structure model according to the structural parameters of each electrical equipment, mapping and associating the adjusted three-dimensional structure model with each electrical equipment one by one, thereby constructing the digital twin model of each electrical equipment.

[0020] In one specific embodiment, the functional components of each electrical equipment are identified, and the design level reference lines of the functional components of each electrical equipment are extracted, specifically by: performing semantic segmentation on the three-dimensional structure model according to the digital twin model of each electrical equipment, identifying independent structure units and labeling their functional attributes, thereby identifying the functional components of each electrical equipment, and extracting the intersection line of the edge contour line or installation reference plane with horizontal installation constraints in each functional component as the design level reference line of each functional component of each electrical equipment.

[0021] According to the digital twin model of each electrical equipment, a horizontal difference transmission correlation matrix between the functional components of each electrical equipment is constructed.

[0022] According to the horizontal difference transmission correlation matrix between the functional components of each electrical equipment, and in combination with the design level reference lines of the functional components of each electrical equipment, the horizontal difference standard comparison coefficient of the structural feature reference line of each electrical equipment is calculated.

[0023] In one specific embodiment, the horizontal difference standard comparison coefficient of the structural feature reference line of each electrical equipment is calculated, specifically by: extracting all matrix element values of the row in which the corresponding functional component of the structural feature reference line of each electrical equipment is located according to the horizontal difference transmission correlation matrix between the functional components of each electrical equipment, calculating the cumulative sum of the matrix element values in the row as the total transmission influence value of the corresponding functional component of the structural feature reference line of each electrical equipment, obtaining the basic weight coefficient of each functional component type from the local database according to the spatial position of the design level reference line of each functional component in the digital twin model, multiplying the total transmission influence value by the basic weight coefficient, and thereby obtaining the horizontal difference standard comparison coefficient of the structural feature reference line of each electrical equipment.

[0024] In specific embodiments of the present application, a horizontal difference transmission correlation matrix between the functional components of each electrical equipment is constructed, specifically by: extracting the physical contact surface information and contact area value between the functional components of each electrical equipment according to the digital twin model of each electrical equipment, and calculating the mechanical coupling strength coefficient between the functional components of each electrical equipment.

[0025] In one specific embodiment, the mechanical coupling strength coefficient between the functional components of each electrical equipment is calculated, specifically by: identifying the contact type between the functional components of each electrical equipment according to the physical contact surface information between the functional components of each electrical equipment, obtaining the unit area coupling coefficient corresponding to each contact type from the local database, mapping to obtain the unit area coupling coefficient between the functional components of each electrical equipment, multiplying the contact area value by the unit area coupling coefficient according to the contact area value between the functional components, and thereby obtaining the mechanical coupling strength coefficient between the functional components of each electrical equipment.

[0026] Obtain the material stiffness parameters and mounting and fixing modes of each functional component from the local database, and calculate the deformation transmission rate of each functional component of each electrical equipment when affected by the horizontal offset of the adjacent component.

[0027] In one specific embodiment, the deformation transmission rate of each functional component of each electrical equipment when affected by the horizontal offset of the adjacent component is calculated by: mapping the material elastic modulus value of each functional component according to the material stiffness parameters of each functional component, obtaining the constraint coefficient corresponding to each mounting and fixing mode from the local database according to the mounting and fixing mode of each functional component, mapping to obtain the constraint coefficient of each functional component, performing ratio operation on the elastic modulus value and the constraint coefficient of each functional component, and performing normalization processing, thereby obtaining the deformation transmission rate of each functional component of each electrical equipment when affected by the horizontal offset of the adjacent component.

[0028] Obtain the electrical connection path and wire tension distribution between each functional component of each electrical equipment from the local database, and calculate the electrical traction coupling coefficient between each functional component of each electrical equipment.

[0029] In one specific embodiment, the electrical traction coupling coefficient between each functional component of each electrical equipment is calculated by: according to the electrical connection path between each functional component of each electrical equipment, counting the number of wire connections between each functional component of each electrical equipment , wherein x represents the number of each electrical equipment, , y is a positive integer greater than 2, n represents the number of combinations between each functional component, , m is a positive integer greater than 2, according to the wire tension distribution between each functional component of each electrical equipment, the average of the wire tension mean value between each functional component of each electrical equipment is calculated after addition , thereby obtaining the electrical traction coupling coefficient between each functional component of each electrical equipment .

[0030] According to the mechanical coupling strength coefficient and the electrical traction coupling coefficient between each functional component of each electrical equipment, and combining the deformation transmission rate of each functional component of each electrical equipment when affected by the horizontal offset of the adjacent component, a horizontal difference transmission correlation matrix between each functional component of each electrical equipment is constructed.

[0031] In one specific embodiment, a horizontal difference transmission correlation matrix between the functional components of each electrical equipment is constructed, and the specific method is as follows: taking each functional component of each electrical equipment as the row index and column index of the matrix, for any two functional components, the mechanical coupling strength coefficient, the electrical traction coupling coefficient and the deformation transmission rate of the receiving functional component are weighted and summed, the calculation result is filled in the corresponding row and column position of the matrix, and the matrix filling is completed by traversing all functional component combinations, so as to construct the horizontal difference transmission correlation matrix between the functional components of each electrical equipment.

[0032] In one specific embodiment, visible light image data, infrared thermal imaging data and local vibration data of each electrical equipment are obtained, and the specific method is as follows: each electrical equipment is photographed at multiple angles by an industrial camera to obtain visible light image data of each electrical equipment, each electrical equipment is scanned by an infrared thermal imager to obtain infrared thermal imaging data of each electrical equipment, and a vibration sensor is installed on the surface of a key functional component of each electrical equipment to collect vibration signals within a preset time period to obtain local vibration data of each electrical equipment.

[0033] Step 2: According to the visible light image data of each electrical equipment, the visible light image data is compared and analyzed with each structural feature reference line of each electrical equipment, so as to obtain the horizontal offset angle value of each structural feature reference line of each electrical equipment.

[0034] In the specific embodiment of the present application, the comparison and analysis are performed to obtain the horizontal offset angle value of each structural feature reference line of each electrical equipment, and the specific method is as follows: according to the visible light image data of each electrical equipment, edge detection and straight line segment extraction are performed on the image, so as to obtain each actual structural feature line in the image of each electrical equipment.

[0035] In one specific embodiment, each actual structural feature line in the image of each electrical equipment is obtained, and the specific method is as follows: according to the visible light image data of each electrical equipment, edge detection is performed on the image by using a Canny edge detection algorithm, so as to obtain an edge binary image of the image, straight line segment extraction is performed on the edge binary image by using a Hough transform, a minimum length threshold of the straight line segment is obtained from a local database, straight line segments with a length less than the minimum length threshold of the straight line segment are removed, and the remaining each straight line segment is taken as each actual structural feature line in the image of each electrical equipment.

[0036] According to the projection position and projection length of each structural feature reference line of each electrical equipment on the image plane, the structural feature reference line corresponding to each actual structural feature line of each electrical equipment is screened.

[0037] According to the structure feature reference line corresponding to each actual structure feature line of each electrical equipment, the angle difference between each actual structure feature line of each electrical equipment and the corresponding structure feature reference line is calculated, so as to obtain the horizontal offset angle value of each structure feature reference line of each electrical equipment.

[0038] In a specific embodiment, the angle difference between each actual structure feature line of each electrical equipment and the corresponding structure feature reference line is calculated, so as to obtain the horizontal offset angle value of each structure feature reference line of each electrical equipment. The specific method is as follows: according to the end point coordinates of each actual structure feature line of each electrical equipment, the inclination angle of each actual structure feature line of each electrical equipment relative to the horizontal direction of the image is calculated; according to the structure feature reference line corresponding to each actual structure feature line of each electrical equipment, the ideal horizontal angle of the structure feature reference line is extracted from the digital twin model; the inclination angle of each actual structure feature line of each electrical equipment is subtracted from the ideal horizontal angle of the corresponding structure feature reference line, so as to obtain the horizontal offset angle value of each structure feature reference line of each electrical equipment, wherein the sign of the horizontal offset angle value represents the offset direction of the difference.

[0039] In a specific embodiment of the present application, the structure feature reference line corresponding to each actual structure feature line of each electrical equipment is screened. The specific method is as follows: the camera intrinsic matrix and the camera extrinsic matrix of the image acquisition device are obtained from the local database, and according to each structure feature reference line of each electrical equipment, the three-dimensional space coordinates of each structure feature reference line of each electrical equipment are extracted, so as to calculate the projection center point coordinates and the projection direction vector of each structure feature reference line of each electrical equipment on the image plane. Similarly, the projection center point coordinates and the projection direction vector of each actual structure feature line of each electrical equipment on the image plane are calculated.

[0040] In a specific embodiment, the projection center point coordinates and the projection direction vector of each structure feature reference line of each electrical equipment on the image plane are calculated. The specific method is as follows: according to the three-dimensional space coordinates of each structure feature reference line of each electrical equipment, the start point coordinates and the end point coordinates of each structure feature reference line of each electrical equipment are extracted; according to the camera intrinsic matrix and the camera extrinsic matrix, the start point coordinates and the end point coordinates are respectively subjected to perspective projection transformation, so as to obtain the projection start point coordinates and the projection end point coordinates of each structure feature reference line of each electrical equipment on the image plane; the midpoint of the projection start point coordinates and the projection end point coordinates is taken as the projection center point coordinates, and the unit vector from the projection start point to the projection end point is taken as the projection direction vector.

[0041] The position matching distance threshold and the direction matching angle threshold are obtained from the local database, the distance value between the center point coordinate of each actual structure feature line of each electrical equipment and the projection center point coordinate of each structure feature reference line is calculated, and the included angle value between the direction vector of each actual structure feature line of each electrical equipment and the projection direction vector of each structure feature reference line is calculated, so that the structure feature reference line corresponding to each actual structure feature line of each electrical equipment is analyzed and obtained.

[0042] In one specific embodiment, the structure feature reference line corresponding to each actual structure feature line of each electrical equipment is analyzed and obtained, and the specific method is as follows: according to the distance value between the center point coordinate of each actual structure feature line of each electrical equipment and the projection center point coordinate of each structure feature reference line, each structure feature reference line with a distance value less than the position matching distance threshold is selected as a candidate matching reference line, according to the included angle value between the direction vector of each actual structure feature line and the projection direction vector of each candidate matching reference line, the candidate matching reference line with an included angle value less than the direction matching angle threshold is selected, and if there are multiple candidate matching reference lines meeting the condition, the candidate matching reference line with the smallest distance value is selected as the structure feature reference line corresponding to the actual structure feature line.

[0043] Step 3: According to the horizontal difference standard comparison coefficient of each electrical equipment and the horizontal offset angle value of each structure feature reference line, and combining the infrared thermal imaging data and the local vibration data of each electrical equipment, the horizontal difference abnormal region positioning is performed, so as to determine each effective horizontal difference abnormal region of each electrical equipment.

[0044] In the specific embodiment of the present application, the horizontal difference abnormal region positioning is performed, so as to determine each effective horizontal difference abnormal region of each electrical equipment, and the specific method is as follows: according to the horizontal offset angle value and the horizontal difference standard comparison coefficient of each structure feature reference line of each electrical equipment, the horizontal difference hazard coefficient of each structure feature reference line of each electrical equipment is calculated.

[0045] In one specific embodiment, the horizontal difference hazard coefficient of each structure feature reference line of each electrical equipment is calculated, and the specific method is as follows: according to the horizontal offset angle value of each structure feature reference line of each electrical equipment and the horizontal difference standard comparison coefficient , t represents the number of each structure feature reference line, , s is a positive integer greater than 2, the difference between the horizontal offset angle value of each structural feature reference line and the horizontal difference standard comparison coefficient is calculated, if the difference is positive, it indicates that the horizontal offset angle value exceeds the standard comparison coefficient, and the difference is taken as the over-standard offset, if the difference is negative or zero, the over-standard offset is zero. The ratio between the horizontal offset angle value of each structural feature reference line and the horizontal difference standard comparison coefficient is calculated, the over-standard offset and the ratio are weighted and summed to calculate the horizontal difference hazard coefficient of each structural feature reference line of each electrical equipment .

[0046] The horizontal difference hazard coefficient threshold is obtained from the local database, and each abnormal structural feature reference line of each electrical equipment is screened according to the horizontal difference hazard coefficient threshold, so as to determine each horizontal difference abnormal area of each electrical equipment.

[0047] In one specific embodiment, each abnormal structural feature reference line of each electrical equipment is screened, and the specific method is as follows: if the horizontal difference hazard coefficient of a structural feature reference line of an electrical equipment is greater than the horizontal difference hazard coefficient threshold, the structural feature reference line is marked as an abnormal structural feature reference line, so as to screen each abnormal structural feature reference line of each electrical equipment.

[0048] In one specific embodiment, each horizontal difference abnormal area of each electrical equipment is determined, and the specific method is as follows: according to each abnormal structural feature reference line of each electrical equipment, the functional components corresponding to each abnormal structural feature reference line are identified, and the space bounding box range of each functional component in the digital twin model is extracted, all abnormal structural feature reference lines corresponding to the functional components are traversed to complete clustering and merging, and the space bounding box union of each clustering result is taken as each horizontal difference abnormal area of each electrical equipment.

[0049] According to the infrared thermal imaging data and the local vibration data of each electrical equipment, the multi-source verification confidence of each horizontal difference abnormal area of each electrical equipment is calculated.

[0050] The multi-source verification confidence threshold is obtained from the local database, and each effective horizontal difference abnormal area of each electrical equipment is screened according to the multi-source verification confidence threshold.

[0051] In one specific embodiment, each effective horizontal difference abnormal area of each electrical equipment is screened, and the specific method is as follows: if the multi-source verification confidence of a horizontal difference abnormal area of an electrical equipment is greater than the multi-source verification confidence threshold, the horizontal difference abnormal area is marked as an effective horizontal difference abnormal area, so as to screen each effective horizontal difference abnormal area of each electrical equipment.

[0052] In a specific embodiment of the present application, the multi-source verification confidence of each horizontal difference abnormal area of each electrical equipment is calculated, and the specific method is: according to the temperature distribution characteristics of the corresponding position of each horizontal difference abnormal area of each electrical equipment, the temperature gradient abnormal coefficient of each horizontal difference abnormal area of each electrical equipment is calculated.

[0053] In a specific embodiment, the temperature gradient abnormal coefficient of each horizontal difference abnormal area of each electrical equipment is calculated, and the specific method is: according to the temperature distribution characteristics of the corresponding position of each horizontal difference abnormal area of each electrical equipment, the highest temperature value and the lowest temperature value in each horizontal difference abnormal area of each electrical equipment are extracted, and the difference between the two is calculated as the temperature range value of each horizontal difference abnormal area of each electrical equipment. , wherein i represents the number of each horizontal difference abnormal area, , j is a positive integer greater than 2, the average temperature value of the surrounding normal area of each horizontal difference abnormal area of each electrical equipment is extracted, and the difference between the average temperature value in each horizontal difference abnormal area of each electrical equipment and the average temperature value of the surrounding normal area is calculated. , thereby obtaining the temperature gradient abnormal coefficient of each horizontal difference abnormal area of each electrical equipment , wherein e is a natural constant.

[0054] According to the local vibration data of each electrical equipment, the vibration frequency spectrum characteristics of the corresponding position of each horizontal difference abnormal area of each electrical equipment are extracted, and the vibration abnormal coefficient of each horizontal difference abnormal area of each electrical equipment is calculated.

[0055] In a specific embodiment, the vibration abnormal coefficient of each horizontal difference abnormal area of each electrical equipment is calculated, and the specific method is: according to the vibration frequency spectrum characteristics of the corresponding position of each horizontal difference abnormal area of each electrical equipment, the Fourier transform of the vibration signal is carried out, thereby extracting the main frequency amplitude value and the vibration acceleration root mean square value of each horizontal difference abnormal area of each electrical equipment, extracting the vibration frequency spectrum characteristics of the corresponding position of the surrounding normal area of each horizontal difference abnormal area of each electrical equipment, calculating the main frequency amplitude mean value and the vibration acceleration root mean square mean value of the surrounding normal area of each horizontal difference abnormal area of each electrical equipment, calculating the ratio between the main frequency amplitude value of each horizontal difference abnormal area and the main frequency amplitude mean value of the surrounding normal area, calculating the ratio between the vibration acceleration root mean square value of each horizontal difference abnormal area and the vibration acceleration root mean square mean value of the surrounding normal area, and weighting and summing the two ratios, thereby obtaining the vibration abnormal coefficient of each horizontal difference abnormal area of each electrical equipment.

[0056] obtain the temperature gradient anomaly threshold value, the vibration anomaly threshold value, the temperature verification weight coefficient and the vibration verification weight coefficient from the local database, if the temperature gradient anomaly coefficient of a certain horizontal difference anomaly area of an electrical equipment is greater than the temperature gradient anomaly threshold value, then the temperature verification weight coefficient is accumulated to the multi-source verification confidence of the horizontal difference anomaly area, if the vibration anomaly coefficient of a certain horizontal difference anomaly area of an electrical equipment is greater than the vibration anomaly threshold value, then the vibration verification weight coefficient is accumulated to the multi-source verification confidence of the horizontal difference anomaly area, so as to obtain the multi-source verification confidence of each horizontal difference anomaly area of each electrical equipment.

[0057] Step 4: According to each effective horizontal difference anomaly area of each electrical equipment, the hazard quantification analysis is carried out, so as to evaluate the horizontal offset quantification index and the fault risk coefficient of each effective horizontal difference anomaly area of each electrical equipment.

[0058] In a specific embodiment of the present application, the hazard quantification analysis is carried out, so as to evaluate the horizontal offset quantification index and the fault risk coefficient of each effective horizontal difference anomaly area of each electrical equipment, and the specific method is: according to the horizontal difference hazard coefficient of each structural feature reference line of each electrical equipment, and according to each effective horizontal difference anomaly area of each electrical equipment, the horizontal difference hazard coefficient of each structural feature reference line in each effective horizontal difference anomaly area of each electrical equipment is extracted, and the horizontal offset quantification index of each effective horizontal difference anomaly area of each electrical equipment is calculated accordingly.

[0059] In a specific embodiment, the horizontal offset quantification index of each effective horizontal difference anomaly area of each electrical equipment is calculated, and the specific method is: according to the horizontal difference hazard coefficient of each structural feature reference line in each effective horizontal difference anomaly area of each electrical equipment , wherein p represents the number of each effective horizontal difference anomaly area, q is a positive integer greater than 2, the horizontal offset quantification index of each effective horizontal difference anomaly area of each electrical equipment is calculated , wherein s is the number of structural feature reference lines.

[0060] According to the horizontal difference transmission correlation matrix between each functional component of each electrical equipment, and combining the horizontal offset quantification index of each effective horizontal difference anomaly area of each electrical equipment, the fault risk coefficient of each effective horizontal difference anomaly area of each electrical equipment is analyzed.

[0061] In a specific embodiment of the present application, the fault risk coefficient of each effective horizontal difference anomaly area of each electrical equipment is analyzed, and the specific method is: according to each effective horizontal difference anomaly area of each electrical equipment, each functional component corresponding to each effective horizontal difference anomaly area of each electrical equipment is identified.

[0062] According to the horizontal difference transmission correlation matrix between the functional components of each electrical equipment, and in combination with each functional component corresponding to each effective horizontal difference abnormal area of each electrical equipment, each other area affected functional component of each functional component corresponding to each effective horizontal difference abnormal area of each electrical equipment is screened, and in combination with the horizontal offset quantification index of each effective horizontal difference abnormal area of each electrical equipment, the failure risk coefficient of each effective horizontal difference abnormal area of each electrical equipment is calculated.

[0063] In one specific embodiment, each other area affected functional component of each functional component corresponding to each effective horizontal difference abnormal area of each electrical equipment is screened, and in combination with the horizontal offset quantification index of each effective horizontal difference abnormal area of each electrical equipment, the failure risk coefficient of each effective horizontal difference abnormal area of each electrical equipment is calculated, and the specific method is as follows: according to the horizontal difference transmission correlation matrix between the functional components of each electrical equipment, the mechanical coupling strength coefficient, the deformation transmission rate and the electrical traction coupling coefficient between each functional component corresponding to each effective horizontal difference abnormal area and other functional components are extracted, the mechanical coupling strength coefficient threshold value, the deformation transmission rate threshold value and the electrical traction coupling coefficient threshold value are obtained from the local database, each functional component with the mechanical coupling strength coefficient greater than the mechanical coupling strength coefficient threshold value, or the deformation transmission rate greater than the deformation transmission rate threshold value, or the electrical traction coupling coefficient greater than the electrical traction coupling coefficient threshold value is screened, and the functional components belonging to the current effective horizontal difference abnormal area are removed, the remaining functional components are taken as each other area affected functional component of each functional component corresponding to each effective horizontal difference abnormal area of each electrical equipment, and the mechanical coupling strength coefficient , the deformation transmission rate and the electrical traction coupling coefficient between each functional component corresponding to each effective horizontal difference abnormal area of each electrical equipment and each other area affected functional component are extracted, wherein u represents the number of each functional component, r represents each other area affected functional component, k is a positive integer greater than 2, and in combination with the horizontal offset quantification index of each effective horizontal difference abnormal area of each electrical equipment , thereby obtaining the failure risk coefficient of each effective horizontal difference abnormal area of each electrical equipment , wherein w is the number of functional components, and v is the number of other area affected functional components.

[0064] Step 5: According to the horizontal offset quantification index and the failure risk coefficient of each effective horizontal difference abnormal area of each electrical equipment, the horizontal difference hazard assessment is carried out, and the assessment result is sent to the equipment operation and maintenance personnel.

[0065] In specific embodiments of the present application, the horizontal difference hazard assessment is performed, and the assessment result is sent to the equipment operation and maintenance personnel. The specific method is as follows: the fault risk coefficient threshold is obtained from the local database, the fault risk coefficients of each effective horizontal difference abnormal area of each electrical equipment are compared, if the fault risk coefficient of an effective horizontal difference abnormal area of an electrical equipment is greater than the fault risk coefficient threshold, the effective horizontal difference abnormal area is marked as a pre-warning horizontal difference abnormal area, thereby screening each pre-warning horizontal difference abnormal area of each electrical equipment, and sending it to the equipment operation and maintenance personnel.

[0066] The formulas in the present specification are dimensionless values calculated, the formulas are obtained by software simulation of a large amount of data to obtain a formula of the most recent real situation, and the preset parameters and threshold values in the formulas are set by a person skilled in the art according to the actual situation.

[0067] The above is only an example and description of the concept of the present application. Those skilled in the art can make various modifications or supplements to the described specific embodiments or use similar ways to replace them, as long as they do not deviate from the concept of the present application or exceed the scope defined by the present application, which shall be within the protection scope of the present application.

Claims

1. A method for locating and quantifying the horizontal difference of electrical equipment based on a digital twin model, characterized in that, include: Step 1: Obtain the equipment model, structural parameters and three-dimensional structural model of each electrical device, and construct a digital twin model based on this. Then, analyze the baseline of each structural feature of each electrical device and its horizontal difference standard comparison coefficient, and collect multi-source data for each electrical device to obtain visible light image data, infrared thermal imaging data and local vibration data of each electrical device. Step 2: Based on the visible light image data of each electrical device, compare and analyze it with the baseline of each structural feature of each electrical device to obtain the horizontal offset angle value of each structural feature baseline of each electrical device; Step 3: Based on the horizontal difference standard comparison coefficient of each electrical device and the horizontal offset angle value of each structural feature baseline, and combined with the infrared thermal imaging data and local vibration data of each electrical device, locate the horizontal difference abnormal area, thereby determining the effective horizontal difference abnormal area of ​​each electrical device. Step 4: Conduct hazard quantification analysis based on the abnormal areas of each effective level of each electrical device, thereby assessing the horizontal offset quantification index and failure risk coefficient of each abnormal area of ​​each effective level of each electrical device; Step 5: Based on the horizontal offset quantification index and fault risk coefficient of each effective horizontal difference abnormal area of ​​each electrical equipment, conduct a horizontal difference hazard assessment and send the assessment results to the equipment operation and maintenance management personnel.

2. The method for locating and quantifying the horizontal difference of electrical equipment based on a digital twin model according to claim 1, characterized in that, The specific method for constructing a digital twin model to analyze the baseline of each structural feature of each electrical device and its horizontal difference standard comparison coefficient is as follows: Based on the equipment model, structural parameters and three-dimensional structural model of each electrical equipment, a digital twin model of each electrical equipment is constructed, and each functional component of each electrical equipment is identified. The design horizontal baseline of each functional component of each electrical equipment is extracted and used as the baseline of each structural feature of each electrical equipment. Based on the digital twin models of each electrical device, a horizontal difference transmission correlation matrix is ​​constructed between the functional components of each electrical device; Based on the horizontal difference transmission correlation matrix between the functional components of each electrical equipment, and combined with the design horizontal baseline of each functional component of each electrical equipment, the horizontal difference standard comparison coefficient of each structural feature baseline of each electrical equipment is calculated.

3. The method for locating and quantifying the horizontal difference of electrical equipment based on a digital twin model according to claim 2, characterized in that, The specific method for constructing the horizontal difference transmission correlation matrix between the functional components of each electrical device is as follows: Based on the digital twin models of each electrical device, the physical contact surface information and contact area value between each functional component of each electrical device are extracted, and the mechanical coupling strength coefficient between each functional component of each electrical device is calculated. The material stiffness parameters and installation and fixing methods of each functional component are obtained from the local database, and the deformation transfer rate of each functional component of each electrical equipment is calculated when it is affected by the horizontal offset of adjacent components. Obtain the electrical connection paths and conductor tension distributions between the functional components of each electrical device from the local database, and calculate the electrical traction coupling coefficients between the functional components of each electrical device. Based on the mechanical coupling strength coefficient and electrical traction coupling coefficient between the functional components of each electrical equipment, and combined with the deformation transfer rate of each functional component of each electrical equipment when affected by the horizontal offset of adjacent components, a horizontal difference transfer correlation matrix between the functional components of each electrical equipment is constructed.

4. The method for locating and quantifying the horizontal difference of electrical equipment based on a digital twin model according to claim 1, characterized in that, The comparative analysis is performed to obtain the horizontal offset angle value of the baseline of each structural feature of each electrical device. The specific method is as follows: Based on the visible light image data of each electrical device, edge detection and straight line segment extraction are performed on the image to obtain the actual structural feature lines in the image of each electrical device; Based on the projection position and projection length of each structural feature baseline of each electrical device on the image plane, the structural feature baseline corresponding to each actual structural feature line of each electrical device is selected. Based on the structural feature baselines corresponding to the actual structural feature lines of each electrical device, the angle difference between the actual structural feature lines of each electrical device and the corresponding structural feature baselines is calculated, thereby obtaining the horizontal offset angle value of the structural feature baselines of each electrical device.

5. The method for locating and quantifying the horizontal difference of electrical equipment based on a digital twin model according to claim 4, characterized in that, The specific method for selecting the structural feature baseline corresponding to each actual structural feature line of each electrical device is as follows: The camera intrinsic and extrinsic parameter matrices of the image acquisition device are obtained from the local database. Based on the structural feature baselines of each electrical device, the three-dimensional spatial coordinates of each structural feature baseline of each electrical device are extracted. Thus, the coordinates of the projection center point and projection direction vector of each structural feature baseline of each electrical device on the image plane are calculated. Similarly, the coordinates of the projection center point and projection direction vector of each actual structural feature line of each electrical device on the image plane are calculated. The system retrieves the position matching distance threshold and direction matching angle threshold from the local database, calculates the distance between the center point coordinates of each actual structural feature line of each electrical device and the projection center point coordinates of each structural feature baseline, and calculates the angle between the direction vector of each actual structural feature line of each electrical device and the projection direction vector of each structural feature baseline. This allows the system to analyze and obtain the structural feature baseline corresponding to each actual structural feature line of each electrical device.

6. The method for locating and quantifying the horizontal difference of electrical equipment based on a digital twin model according to claim 3, characterized in that, The specific method for locating horizontal difference aberration areas to determine the effective horizontal difference aberration areas of each electrical device is as follows: Based on the horizontal offset angle value and the standard comparison coefficient of the horizontal difference of each structural feature baseline of each electrical equipment, calculate the horizontal difference hazard coefficient of each structural feature baseline of each electrical equipment; The threshold of the horizontal difference hazard coefficient is obtained from the local database, and the baseline of each abnormal structural feature of each electrical equipment is screened accordingly to determine the abnormal area of ​​each horizontal difference of each electrical equipment. Based on the infrared thermal imaging data and local vibration data of each electrical device, the multi-source verification confidence of each electrical device in each horizontal abnormal region is calculated. The multi-source validation confidence threshold is obtained from the local database, and the abnormal areas of each effective level difference of each electrical device are screened accordingly.

7. The method for locating and quantifying the horizontal difference of electrical equipment based on a digital twin model according to claim 6, characterized in that, The specific method for calculating the multi-source verification confidence of each level difference abnormality region of each electrical device is as follows: Based on the infrared thermal imaging data of each electrical device, the temperature distribution characteristics of the corresponding locations of each horizontal abnormality area of ​​each electrical device are extracted, and the temperature gradient anomaly coefficient of each horizontal abnormality area of ​​each electrical device is calculated accordingly. Based on the local vibration data of each electrical device, the vibration spectrum characteristics of each horizontal abnormal area of ​​each electrical device are extracted, and the vibration anomaly coefficient of each horizontal abnormal area of ​​each electrical device is calculated accordingly. The temperature gradient anomaly threshold, vibration anomaly threshold, temperature verification weight coefficient, and vibration verification weight coefficient are obtained from the local database. If the temperature gradient anomaly coefficient of a certain horizontal anomaly region of an electrical device is greater than the temperature gradient anomaly threshold, the temperature verification weight coefficient is added to the multi-source verification confidence of that horizontal anomaly region. If the vibration anomaly coefficient of a certain horizontal anomaly region of an electrical device is greater than the vibration anomaly threshold, the vibration verification weight coefficient is added to the multi-source verification confidence of that horizontal anomaly region, thereby obtaining the multi-source verification confidence of each horizontal anomaly region of each electrical device.

8. The method for locating and quantifying the horizontal difference of electrical equipment based on a digital twin model according to claim 6, characterized in that, The method for conducting hazard quantification analysis to assess the horizontal offset quantification index and failure risk coefficient of abnormal areas in each effective level difference of each electrical device is as follows: Based on the horizontal difference hazard coefficient of each structural feature baseline of each electrical equipment, and based on each effective horizontal difference abnormal area of ​​each electrical equipment, the horizontal difference hazard coefficient of each structural feature baseline in each effective horizontal difference abnormal area of ​​each electrical equipment is extracted, and the horizontal offset quantitative index of each effective horizontal difference abnormal area of ​​each electrical equipment is calculated accordingly. Based on the horizontal difference transmission correlation matrix between the functional components of each electrical device, and combined with the horizontal offset quantification index of each effective horizontal difference abnormal region of each electrical device, the failure risk coefficient of each effective horizontal difference abnormal region of each electrical device is analyzed.

9. The method for locating and quantifying the horizontal difference of electrical equipment based on a digital twin model according to claim 8, characterized in that, The specific method for analyzing the fault risk coefficients of abnormal regions with varying effective levels for each electrical device is as follows: Based on the abnormal regions of each effective level of each electrical device, identify the functional components corresponding to each abnormal region of each effective level of each electrical device. Based on the horizontal difference transmission correlation matrix between the functional components of each electrical equipment, and combined with the functional components corresponding to the effective horizontal difference abnormal areas of each electrical equipment, the affected functional components in other areas of each functional component corresponding to the effective horizontal difference abnormal areas of each electrical equipment are screened. Combined with the horizontal offset quantification index of each effective horizontal difference abnormal area of ​​each electrical equipment, the fault risk coefficient of each effective horizontal difference abnormal area of ​​each electrical equipment is calculated.

10. The method for locating and quantifying the horizontal difference of electrical equipment based on a digital twin model according to claim 1, characterized in that, The specific method for conducting a horizontal difference hazard assessment and sending the assessment results to equipment operation and maintenance management personnel is as follows: The fault risk coefficient threshold is obtained from the local database. The fault risk coefficients of each effective level difference abnormal area of ​​each electrical equipment are compared. If the fault risk coefficient of a certain effective level difference abnormal area of ​​an electrical equipment is greater than the fault risk coefficient threshold, the effective level difference abnormal area is marked as a warning level difference abnormal area. In this way, the warning level difference abnormal areas of each electrical equipment are screened and sent to the equipment operation and maintenance management personnel.

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