Method, device and equipment for generating digital twinborn body based on CAD (Computer Aided Design) model and medium

Abstract

The embodiment of the invention discloses a digital twin generation method and device based on a CAD model, equipment and a medium. The method comprises the following steps: acquiring target point cloud data of target electrical equipment in a transformer substation; determining a target CAD model matched with the target point cloud data according to a shape matching result of the target point cloud data and a CAD model of the electrical equipment; and in a digital twin model of the transformer substation, generating a digital twin model of the target electrical equipment based on the target CAD model and the target point cloud data. According to the technical scheme, the target CAD model corresponding to the target electrical equipment is obtained by performing shape matching on the target point cloud data in the CAD model, and the digital twin model of the target electrical equipment is generated according to the target CAD model and the target point cloud data, so that the effect of improving the generation speed of the digital twin model is achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of digital twins, and in particular to a method, device, equipment and medium for generating a digital twin based on a CAD model. Background Art

[0002] In the process of digital construction of power grids, the construction of digital twins is a very important part. During the construction of digital twins, if the digital twin of the substation is built completely from scratch, there will be problems such as high cost and long time consumption. Summary of the invention

[0003] The present invention provides a method, device, equipment and medium for generating a digital twin based on a CAD model, which can quickly build a digital twin of a substation based on an existing CAD model.

[0004] According to one aspect of the present invention, a method for generating a digital twin based on a CAD model is provided, the method comprising:

[0005] Acquire target point cloud data of target electrical equipment in the substation;

[0006] Determining a target CAD model that matches the target point cloud data according to a shape matching result between the target point cloud data and the CAD model of the electrical equipment;

[0007] In the digital twin model of the substation, a digital twin model of the target electrical equipment is generated based on the target CAD model and the target point cloud data.

[0008] According to another aspect of the present invention, there is provided a device for generating a digital twin based on a CAD model, comprising:

[0009] A target point cloud data acquisition module is used to acquire target point cloud data of target electrical equipment in the substation;

[0010] A target CAD model determination module is used to determine a target CAD model that matches the target point cloud data according to a shape matching result between the target point cloud data and a CAD model of the electrical equipment;

[0011] The digital twin model generation module is used to generate a digital twin model of a target electrical device in the digital twin model of the substation based on the target CAD model and the target point cloud data.

[0012] According to another aspect of the present invention, an electronic device is provided, the electronic device comprising:

[0013] at least one processor; and

[0014] a memory communicatively connected to the at least one processor; wherein,

[0015] The memory stores a computer program that can be executed by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the method for generating a digital twin based on a CAD model as described in any embodiment of the present invention.

[0016] According to another aspect of the present invention, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the method for generating a digital twin based on a CAD model as described in any embodiment of the present invention when executed.

[0017] The technical solution of the embodiment of the present application includes: obtaining target point cloud data of the target electrical equipment in the substation; determining the target CAD model that matches the target point cloud data according to the shape matching result between the target point cloud data and the CAD model of the electrical equipment; in the digital twin model of the substation, generating the digital twin model of the target electrical equipment based on the target CAD model and the target point cloud data. The technical solution obtains the target CAD model corresponding to the target electrical equipment by performing shape matching on the target point cloud data in the CAD model, and generates the digital twin model of the target electrical equipment according to the target CAD model and the target point cloud data, which has the effect of improving the generation speed of the digital twin model.

[0018] It should be understood that the contents described in this section are not intended to identify the key or important features of the embodiments of the present invention, nor are they intended to limit the scope of the present invention. Other features of the present invention will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0020] Figure 1 It is a flow chart of a method for generating a digital twin based on a CAD model according to the first embodiment of the present application;

[0021] Figure 2 It is a flow chart of a method for generating a digital twin based on a CAD model according to the second embodiment of the present application;

[0022] Figure 3It is a structural schematic diagram of a device for generating a digital twin based on a CAD model according to Embodiment 3 of the present application;

[0023] Figure 4 It is a structural schematic diagram of an electronic device that implements a method for generating a digital twin based on a CAD model according to an embodiment of the present application. DETAILED DESCRIPTION

[0024] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only embodiments of a part of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0025] It should be noted that the terms "first", "second", "target", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0026] Embodiment 1

[0027] Figure 1 A flowchart of a method for generating a digital twin based on a CAD model is provided for the first embodiment of the present application. The embodiment of the present application can be applied to the case of generating a digital twin of a substation. The method can be executed by a device for generating a digital twin based on a CAD model. The device for generating a digital twin based on a CAD model can be implemented in the form of hardware and / or software. The device for generating a digital twin based on a CAD model can be configured in an electronic device with data processing capabilities. Figure 1 As shown, the method includes:

[0028] S110, acquiring target point cloud data of target electrical equipment in the substation.

[0029] The target electrical equipment is equipment of a substation, which may be a transformer, a lightning arrester, a grounding device, a line, etc. The target point cloud data is a point cloud obtained by scanning the target electrical equipment. The target point cloud data may be point cloud data obtained by scanning the target electrical equipment at multiple different angles and fusing them. The target point cloud data may reflect the external shape of the target electrical equipment.

[0030] In an embodiment of the present application, optionally, target point cloud data of a target electrical equipment in a substation is obtained, including: determining a scanning point for scanning the target electrical equipment in the substation; scanning the target electrical equipment by a laser scanning device located at the scanning point to obtain scanning data, and determining the target point cloud data of the target electrical equipment based on the scanning data.

[0031] Specifically, a scanning point is determined in the surrounding area of ​​the target electrical equipment, and when the laser scanning device is located at the scanning point, the target electrical equipment is scanned to obtain scanning data, and the scanning data corresponding to each scanning point is fused to obtain target point cloud data of the target electrical equipment.

[0032] S120, determining a target CAD model that matches the target point cloud data according to a shape matching result between the target point cloud data and the CAD model of the electrical device.

[0033] The CAD model is a CAD model of each electrical device in the substation. The CAD model may be pre-established, and the CAD model may be a three-dimensional model.

[0034] Specifically, after obtaining the target point cloud data, shape matching is performed on the target point cloud data and the CAD model to obtain a shape matching result, and the CAD model with the most similar shape to the target point cloud data is determined as the target CAD model.

[0035] Exemplarily, the shape of the target point cloud data is determined, and shape matching is performed on the shape of the target point cloud data and the CAD model of the electrical equipment to obtain a shape matching result, and the similarity between the target point cloud data and the CAD model is determined based on the shape matching result, and the CAD model with the highest similarity is determined as the target CAD model.

[0036] S130, in the digital twin model of the substation, generating a digital twin model of the target electrical equipment based on the target CAD model and the target point cloud data.

[0037] Among them, the digital twin model of the substation lacks the model of the target electrical equipment or the model of the target electrical equipment is incorrect, that is, the digital twin model of the target electrical equipment is in a state to be updated.

[0038] Specifically, in the digital twin model of the substation, the location of the digital twin model of the target electrical equipment is determined based on the location information of the target point cloud data, and the target CAD model is simplified in terms of the number of faces to obtain a model that can be placed in the digital twin, and the model is placed at the corresponding position, thereby realizing the generation of the digital twin model of the target electrical equipment in the digital twin model of the substation.

[0039] The technical solution of the embodiment of the present application includes: obtaining target point cloud data of the target electrical equipment in the substation; determining the target CAD model that matches the target point cloud data according to the shape matching result between the target point cloud data and the CAD model of the electrical equipment; in the digital twin model of the substation, generating the digital twin model of the target electrical equipment based on the target CAD model and the target point cloud data. The technical solution obtains the target CAD model corresponding to the target electrical equipment by performing shape matching on the target point cloud data in the CAD model, and generates the digital twin model of the target electrical equipment according to the target CAD model and the target point cloud data, which has the effect of improving the generation speed of the digital twin model.

[0040] Embodiment 2

[0041] Figure 2 A flowchart of a method for generating a digital twin based on a CAD model is provided in Example 2 of the present application. The embodiment of the present application is optimized based on the above embodiment.

[0042] like Figure 2 As shown, the method of the embodiment of the present application specifically includes the following steps:

[0043] S210, obtaining target point cloud data of target electrical equipment in the substation.

[0044] S220: Determine a point cloud edge contour in the target point cloud data.

[0045] Specifically, a filtering algorithm can be used to remove noise points in the target point cloud data. For example, using a statistical filtering method, for each point cloud, the average distance from it to all its neighboring points is calculated. If this average distance exceeds a set threshold, the point is considered a noise point and removed. This can reduce the interference of noise on subsequent edge contour extraction.

[0046] In order to improve processing efficiency and reduce the amount of data without losing important geometric features, the target point cloud data can be downsampled. For example, the voxel grid downsampling method is used to divide the point cloud space into a number of voxels (small cube units), and then the centroid of all points in each voxel is used to represent the point cloud data in the voxel, thereby streamlining the target point cloud data.

[0047] In a feasible scheme, the curvature of each point in the target point cloud data can be calculated. The curvature reflects the degree of curvature of the point cloud surface, and points at the edge usually have a large curvature change. For each point, its curvature value is estimated by the geometric relationship of its neighborhood points. For example, using the principal component analysis (PCA) method, the eigenvalues ​​and eigenvectors of the neighborhood covariance matrix of the point are calculated, and the curvature of the point is determined based on the size relationship of the eigenvalues. Points whose curvature values ​​exceed the set threshold are marked as possible edge points, and then the edge contour of the point cloud is formed by connecting these edge points.

[0048] In another feasible solution, the normal vector of each point in the point cloud can be estimated. The normal vector represents the normal direction of the point cloud surface at that point. For adjacent points, if the angle between their normal vectors exceeds a certain threshold, then these points may be located on the edge. By traversing the entire point cloud, all points that meet the normal vector angle condition are found and connected to form an edge contour. Data structures such as KD trees (K-Dimensional trees) can be used to quickly search and compare the normal vectors of adjacent points.

[0049] In another feasible solution, edge contours are detected from point cloud data using a trained deep learning model, for example, using a deep learning-based point cloud processing architecture such as PointNet or PointNet++.

[0050] S230, using a target shape retrieval algorithm to perform similarity recognition on the edge contour of the point cloud and the CAD model of the electrical equipment, to obtain a CAD model with the highest similarity to the edge contour of the point cloud.

[0051] In an embodiment of the present application, optionally, a target shape retrieval algorithm is used to perform similarity recognition on the point cloud edge contour and the CAD model of the electrical equipment to obtain a CAD model with the highest similarity to the point cloud edge contour, including: extracting features from the point cloud edge contour to obtain a feature vector of the point cloud edge contour; determining the target dimension in the feature vector of the point cloud edge contour as the segmentation axis; sorting the feature vectors of the point cloud edge contour according to the value of the target dimension; using the feature vector located in the middle position after sorting as the segmentation point, dividing the feature vector of the point cloud edge contour to obtain left and right child nodes of a K-dimensional tree; traversing the CAD model of the electrical equipment, using the feature vector of the current CAD model as a query point, searching in the K-dimensional tree to obtain search results; and determining the CAD model with the highest similarity to the point cloud edge contour based on the search results.

[0052] Specifically, the acquired point cloud edge contour is mathematically described. These features can include geometric features (such as the curvature of the point cloud, the change of the normal vector, the edge length, the angle distribution, etc.), statistical features (such as the mean, variance, standard deviation and other statistics based on the point cloud coordinate values), and some features based on local or global shape descriptors (such as shape context, Fourier descriptor, etc.). Through specific algorithms and calculation methods, these features are quantified into a feature vector, which can comprehensively describe the unique properties of the point cloud edge contour.

[0053] For example, for a simple point cloud edge contour of a planar object, the curvature features of each point on the edge and the statistical features of the angle changes between adjacent points may be extracted and combined into a multi-dimensional feature vector.

[0054] In the obtained feature vector, a target dimension is selected as the segmentation axis. The selection of this target dimension is usually based on the following considerations: first, the dimension has a strong expressiveness in distinguishing different shapes or features; second, the numerical distribution of this dimension has good separability, which is convenient for subsequent segmentation operations. For example, if the feature vector of the edge contour of the point cloud contains a dimension describing the curvature change and a dimension describing the length distribution, and after analysis it is found that the curvature change dimension is quite different between the edge contours of the point clouds of different models, then this dimension can be selected as the segmentation axis.

[0055] According to the value of the target dimension corresponding to the determined split axis, the feature vectors of all point cloud edge contours are sorted. Common sorting algorithms such as quick sort and merge sort can be used for sorting. For example, if the split axis is the curvature change dimension, then the feature vectors are sorted in ascending or descending order according to the numerical value of the dimension in each feature vector. The feature vector in the middle position after sorting is determined as the split point. This split point divides all feature vectors into two parts, one part corresponds to the left child node of the K-dimensional tree, and the other part corresponds to the right child node. This division method can ensure that the number of feature vectors in the left and right child nodes is roughly equal, realizing a balanced binary tree structure, which is conducive to subsequent search and comparison operations. For example, assuming there are 100 feature vectors, after sorting, the 50th feature vector is selected as the split point, then the first 50 feature vectors are assigned to the left child node, and the last 50 feature vectors are assigned to the right child node.

[0056] When traversing the CAD models of electrical equipment, similar feature extraction operations are performed on each CAD model to obtain its corresponding feature vector. These feature vectors are then used as query points to search in the constructed K-dimensional tree. During the search process, according to the structure and segmentation rules of the K-dimensional tree, starting from the root node, the feature vectors of the query point and the current node are compared in turn according to the dimension of the segmentation axis to determine whether the query point should enter the left subtree or the right subtree, until it reaches a leaf node or the search stop condition is met.

[0057] In the search results, the CAD model with the highest similarity to the edge contour of the point cloud is determined by comparing the distance or similarity metric between the query point (i.e., the feature vector of the CAD model) and the feature vector of the edge contour of the point cloud represented by the node in the K-dimensional tree. Common similarity measurement methods include Euclidean distance, Manhattan distance, cosine similarity, etc. For example, the Euclidean distance is used to calculate the spatial distance between the query point and the feature vector of each node. The smaller the distance, the higher the similarity. Then, among all the searched nodes, the CAD model corresponding to the node with the smallest distance is selected as the model with the highest similarity to the edge contour of the point cloud.

[0058] In an embodiment of the present application, optionally, a feature vector of the current CAD model is used as a query point to search in a K-dimensional tree to obtain search results, including: determining a comparison result between the query point and the partition axis of the current node of the K-dimensional tree; determining whether to enter the left child node to continue searching or to enter the right child node to continue searching based on the comparison result; during the search process, recording the nearest neighbor node currently found and its corresponding distance; if a leaf node is searched, calculating the distance between the query point and the feature vector in the leaf node, if the distance is less than the currently recorded nearest neighbor distance, updating the nearest neighbor node and its corresponding distance; backtracking along the search path to check whether the nodes on the backtracking path have nearest neighbor nodes, and if so, updating the nearest neighbor node and its corresponding distance; based on the shape corresponding to the nearest neighbor node, using a distance measurement algorithm to determine the similarity between the current CAD model and the edge contour of the point cloud.

[0059] S240: If the square of the Euclidean distance between the CAD model and the edge contour of the point cloud meets a preset condition, the CAD model is determined as a target CAD model.

[0060] The preset condition may be: a square threshold of the Euclidean distance. If the square of the Euclidean distance between the CAD model and the edge contour of the point cloud is less than the square threshold of the Euclidean distance, the CAD model is determined as the target CAD model.

[0061] S250: In the digital twin model of the substation, a digital twin model of a target electrical device is generated based on the target CAD model and the target point cloud data.

[0062] In an embodiment of the present application, optionally, in the digital twin model of the substation, a digital twin model of the target electrical equipment is generated based on the target CAD model and the target point cloud data, including: determining the location of the target electrical equipment in the digital twin model of the substation according to the location information of the target point cloud data; performing model simplification processing on the target CAD model to obtain a simple model of the target electrical equipment, and generating a digital twin model of the target electrical equipment at the location of the target electrical equipment in the digital twin model of the substation based on the simple model.

[0063] Specifically, when scanning the target electrical equipment, its location information can be recorded, and the location information can be determined as the location information of the target point cloud data. The location of the target electrical equipment in the digital twin model of the substation can be determined based on the location information; the target CAD model can be simplified, for example, the number of model faces can be reduced to obtain a simple model of the target electrical equipment, and the simple model can be converted into a format suitable for the digital twin, thereby generating a digital twin model of the target electrical equipment at the location of the target electrical equipment in the digital twin model of the substation.

[0064] In an embodiment of the present application, optionally, a digital twin model of the target electrical equipment is generated at the location of the target electrical equipment in the digital twin model of the substation based on the simple model, including: determining the pose adjustment information of the target CAD model according to the pose information of the target point cloud data and the pose information of the target CAD model; performing pose adjustment on the simple model, and generating a digital twin model of the target electrical equipment at the location of the target electrical equipment in the digital twin model of the substation based on the simple model after pose adjustment.

[0065] The pose information reflects the angle of the model. For example, the model facing sideways up and sideways down are different poses, and the pose information is also angle information. Specifically, since the pose of the target CAD model is not necessarily the same as the pose of the actual electrical equipment, the rotation angle of the target CAD model can be determined based on the pose information of the target point cloud data and the pose information of the target CAD model, and then the simple model can be rotated based on the rotation angle.

[0066] The technical solution of the embodiment of the present application performs similarity recognition on the point cloud edge contour and the CAD model of the electrical equipment through a target shape retrieval algorithm to obtain a CAD model with the highest similarity to the point cloud edge contour, and then determines a simple model based on the target CAD model. After adjusting the posture of the simple model, the digital twin model of the target electrical equipment in the digital twin is quickly obtained.

[0067] Embodiment 3

[0068] Figure 3 This is a schematic diagram of the structure of a device for generating a digital twin based on a CAD model provided in the third embodiment of the present application. The device can execute the method for generating a digital twin based on a CAD model provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the execution method. Figure 3 As shown, the device comprises:

[0069] The target point cloud data acquisition module 310 is used to acquire the target point cloud data of the target electrical equipment in the substation;

[0070] A target CAD model determination module 320 is used to determine a target CAD model that matches the target point cloud data according to a shape matching result between the target point cloud data and the CAD model of the electrical device;

[0071] The digital twin model generation module 330 is used to generate a digital twin model of a target electrical device in the digital twin model of the substation based on the target CAD model and the target point cloud data.

[0072] The technical solution of the embodiment of the present application includes: a target point cloud data acquisition module 310, which is used to acquire the target point cloud data of the target electrical equipment in the substation; a target CAD model determination module 320, which is used to determine the target CAD model that matches the target point cloud data according to the shape matching result between the target point cloud data and the CAD model of the electrical equipment; a digital twin model generation module 330, which is used to generate a digital twin model of the target electrical equipment based on the target CAD model and the target point cloud data in the digital twin model of the substation. This technical solution obtains the target CAD model corresponding to the target electrical equipment by performing shape matching on the target point cloud data in the CAD model, and generates the digital twin model of the target electrical equipment according to the target CAD model and the target point cloud data, which has the effect of improving the generation speed of the digital twin model.

[0073] In the embodiment of the present application, optionally, the target CAD model determination module 320 includes:

[0074] A point cloud edge contour determining unit, used to determine a point cloud edge contour in the target point cloud data;

[0075] A similarity recognition unit is used to perform similarity recognition on the edge contour of the point cloud and the CAD model of the electrical equipment by using a target shape retrieval algorithm to obtain a CAD model with the highest similarity to the edge contour of the point cloud;

[0076] The target CAD model determining unit is used to determine the CAD model as the target CAD model if the square of the Euclidean distance between the CAD model and the edge contour of the point cloud meets a preset condition.

[0077] In the embodiment of the present application, optionally, the similarity recognition unit includes:

[0078] A feature vector extraction subunit is used to extract features of the edge contour of the point cloud to obtain a feature vector of the edge contour of the point cloud;

[0079] A feature vector sorting subunit is used to determine a target dimension in the feature vector of the edge contour of the point cloud as a segmentation axis; and sort the feature vectors of the edge contour of the point cloud according to the value of the target dimension;

[0080] The sub-node determines the sub-unit, which is used to use the feature vector in the middle position after sorting as the segmentation point, divide the feature vector of the edge contour of the point cloud, and obtain the left and right sub-nodes of the K-dimensional tree;

[0081] The search result determination subunit is used to traverse the CAD model of the electrical equipment, use the feature vector of the current CAD model as a query point, search in the K-dimensional tree, and obtain the search result;

[0082] The CAD model determination subunit is used to determine the CAD model with the highest similarity to the edge contour of the point cloud according to the search results.

[0083] In the embodiment of the present application, optionally, the search result determination subunit is specifically used to:

[0084] Determine the comparison result between the query point and the partition axis of the current node of the K-dimensional tree;

[0085] According to the comparison result, it is determined whether to enter the left child node to continue searching or to enter the right child node to continue searching;

[0086] During the search process, record the nearest neighbor node currently found and its corresponding distance;

[0087] If a leaf node is found, the distance between the query point and the feature vector in the leaf node is calculated. If the distance is less than the nearest neighbor distance of the current record, the distance between the nearest neighbor node and its corresponding node is updated.

[0088] Backtrack along the search path and check whether the node on the backtracking path has a nearest neighbor node. If so, update the nearest neighbor node and its corresponding distance;

[0089] Based on the shapes corresponding to the nearest neighbor nodes, a distance measurement algorithm is used to determine the similarity between the current CAD model and the edge contour of the point cloud.

[0090] In the embodiment of the present application, optionally, the digital twin model generation module 330 includes:

[0091] A location determination unit, used to determine the location of the target electrical equipment in the digital twin model of the substation according to the location information of the target point cloud data;

[0092] The digital twin model generation unit is used to simplify the target CAD model to obtain a simple model of the target electrical equipment, and generate a digital twin model of the target electrical equipment at the location of the target electrical equipment in the digital twin model of the substation based on the simple model.

[0093] In an embodiment of the present application, optionally, the digital twin model generation unit includes:

[0094] A posture adjustment information determination subunit, used to determine the posture adjustment information of the target CAD model according to the posture information of the target point cloud data and the posture information of the target CAD model;

[0095] The posture adjustment subunit is used to adjust the posture of the simple model, and generate a digital twin model of the target electrical equipment at the location of the target electrical equipment in the digital twin model of the substation based on the simple model after the posture adjustment.

[0096] In the embodiment of the present application, optionally, the target point cloud data acquisition module 310 includes:

[0097] a scanning point determination unit, used to determine a scanning point for scanning a target electrical device in a substation;

[0098] The target point cloud data determination unit is used to scan the target electrical device through a laser scanning device located at a scanning point to obtain scanning data, and determine the target point cloud data of the target electrical device based on the scanning data.

[0099] A digital twin generation device based on a CAD model provided in an embodiment of the present application can execute a digital twin generation method based on a CAD model provided in any embodiment of the present invention, and has functional modules and beneficial effects corresponding to the execution method.

[0100] Embodiment 4

[0101] Figure 4A schematic diagram of the structure of an electronic device 10 that can be used to implement an embodiment of the present invention is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices (such as helmets, glasses, watches, etc.) and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present invention described and / or required herein.

[0102] like Figure 4 As shown, the electronic device 10 includes at least one processor 11, and a memory connected to the at least one processor 11, such as a read-only memory (ROM) 12, a random access memory (RAM) 13, etc., wherein the memory stores a computer program that can be executed by at least one processor, and the processor 11 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 12 or the computer program loaded from the storage unit 18 to the random access memory (RAM) 13. In the RAM 13, various programs and data required for the operation of the electronic device 10 can also be stored. The processor 11, the ROM 12, and the RAM 13 are connected to each other through a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0103] A number of components in the electronic device 10 are connected to the I / O interface 15, including: an input unit 16, such as a keyboard, a mouse, etc.; an output unit 17, such as various types of displays, speakers, etc.; a storage unit 18, such as a disk, an optical disk, etc.; and a communication unit 19, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0104] The processor 11 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, digital signal processors (DSPs), and any appropriate processors, controllers, microcontrollers, etc. The processor 11 executes the various methods and processes described above, such as a method for generating a digital twin based on a CAD model.

[0105] In some embodiments, the method for generating a digital twin based on a CAD model may be implemented as a computer program, which is tangibly contained in a computer-readable storage medium, such as a storage unit 18. In some embodiments, part or all of the computer program may be loaded and / or installed on the electronic device 10 via the ROM 12 and / or the communication unit 19. When the computer program is loaded into the RAM 13 and executed by the processor 11, one or more steps of the method for generating a digital twin based on a CAD model described above may be performed. Alternatively, in other embodiments, the processor 11 may be configured to execute the method for generating a digital twin based on a CAD model by any other appropriate means (e.g., by means of firmware).

[0106] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), complex programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0107] Computer programs for implementing the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that when the computer program is executed by the processor, the functions / operations specified in the flow chart and / or block diagram are implemented. The computer program may be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

[0108] In the context of the present invention, a computer-readable storage medium may be a tangible medium that may contain or store a computer program for use by or in combination with an instruction execution system, device or equipment. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices or equipment, or any suitable combination of the foregoing. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0109] To provide interaction with a user, the systems and techniques described herein may be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and a pointing device (e.g., a mouse or trackball) through which the user can provide input to the electronic device. Other types of devices may also be used to provide interaction with the user; for example, the feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form (including acoustic input, voice input, or tactile input).

[0110] The systems and techniques described herein may be implemented in a computing system that includes backend components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes frontend components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such backend components, middleware components, or frontend components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0111] A computing system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The client and server relationship is generated by computer programs running on the corresponding computers and having a client-server relationship with each other. The server may be a cloud server, also known as a cloud computing server or cloud host, which is a host product in the cloud computing service system to solve the defects of difficult management and weak business scalability in traditional physical hosts and VPS services.

[0112] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps described in the present invention can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solution of the present invention can be achieved, and this document does not limit this.

[0113] The above specific implementations do not constitute a limitation on the protection scope of the present invention. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for generating a digital twin based on a CAD model, characterized in that: include: Acquire target point cloud data of target electrical equipment in the substation; Determining a target CAD model that matches the target point cloud data according to a shape matching result between the target point cloud data and the CAD model of the electrical equipment; In the digital twin model of the substation, a digital twin model of the target electrical equipment is generated based on the target CAD model and the target point cloud data.

2. The method according to claim 1, characterized in that Determining a target CAD model matching the target point cloud data according to a shape matching result between the target point cloud data and the CAD model of the electrical equipment includes: In the target point cloud data, determining a point cloud edge contour; Using a target shape retrieval algorithm to perform similarity recognition on the edge contour of the point cloud and the CAD model of the electrical equipment, and obtaining a CAD model with the highest similarity to the edge contour of the point cloud; If the square of the Euclidean distance between the CAD model and the edge contour of the point cloud meets a preset condition, the CAD model is determined as the target CAD model.

3. The method according to claim 2, characterized in that The target shape retrieval algorithm is used to perform similarity recognition on the edge contour of the point cloud and the CAD model of the electrical equipment to obtain the CAD model with the highest similarity to the edge contour of the point cloud, including: Extracting features of the edge contour of the point cloud to obtain a feature vector of the edge contour of the point cloud; Determine the target dimension in the feature vector of the edge contour of the point cloud as the segmentation axis; sort the feature vectors of the edge contour of the point cloud according to the value of the target dimension; The feature vector in the middle position after sorting is used as the segmentation point, and the feature vector of the edge contour of the point cloud is divided to obtain the left and right child nodes of the K-dimensional tree; Traverse the CAD model of the electrical equipment, use the feature vector of the current CAD model as a query point, search in the K-dimensional tree, and obtain the search result; The CAD model having the highest similarity to the edge contour of the point cloud is determined according to the search results.

4. The method according to claim 3, characterized in that The feature vector of the current CAD model is used as a query point and searched in the K-dimensional tree to obtain search results, including: Determine the comparison result between the query point and the partition axis of the current node of the K-dimensional tree; According to the comparison result, it is determined whether to enter the left child node to continue searching or to enter the right child node to continue searching; During the search process, record the nearest neighbor node currently found and its corresponding distance; If a leaf node is found, the distance between the query point and the feature vector in the leaf node is calculated. If the distance is less than the nearest neighbor distance of the current record, the distance between the nearest neighbor node and its corresponding node is updated. Backtrack along the search path and check whether the node on the backtracking path has a nearest neighbor node. If so, update the nearest neighbor node and its corresponding distance; Based on the shapes corresponding to the nearest neighbor nodes, a distance measurement algorithm is used to determine the similarity between the current CAD model and the edge contour of the point cloud.

5. The method according to claim 1, characterized in that In the digital twin model of the substation, a digital twin model of a target electrical device is generated based on the target CAD model and the target point cloud data, including: Determine the location of the target electrical equipment in the digital twin model of the substation according to the location information of the target point cloud data; The target CAD model is simplified to obtain a simple model of the target electrical equipment, and a digital twin model of the target electrical equipment is generated at the location of the target electrical equipment in the digital twin model of the substation based on the simple model.

6. The method according to claim 5, characterized in that Generating a digital twin model of a target electrical device at a location of the target electrical device in the digital twin model of the substation based on the simple model, including: Determine the pose adjustment information of the target CAD model according to the pose information of the target point cloud data and the pose information of the target CAD model; The simple model is adjusted in position and posture, and a digital twin model of the target electrical equipment is generated at the location of the target electrical equipment in the digital twin model of the substation based on the simple model after the position and posture adjustment.

7. The method according to claim 1, characterized in that Obtain target point cloud data of target electrical equipment in the substation, including: determining scanning points for scanning target electrical equipment in the substation; The target electrical device is scanned by a laser scanning device located at a scanning point to obtain scanning data, and target point cloud data of the target electrical device is determined based on the scanning data.

8. A device for generating a digital twin based on a CAD model, characterized in that: include: A target point cloud data acquisition module is used to acquire target point cloud data of target electrical equipment in the substation; A target CAD model determination module is used to determine a target CAD model that matches the target point cloud data according to a shape matching result between the target point cloud data and a CAD model of the electrical equipment; The digital twin model generation module is used to generate a digital twin model of a target electrical device in the digital twin model of the substation based on the target CAD model and the target point cloud data.

9. An electronic device, characterized in that: The electronic device comprises: at least one processor; and a memory communicatively connected to the at least one processor; wherein, The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the method for generating a digital twin based on a CAD model as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the method for generating a digital twin based on a CAD model according to any one of claims 1 to 7 when executed.

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