Local updating method, device and equipment of digital twinborn body and medium

Abstract

The embodiment of the invention discloses a local updating method, device and equipment of a digital twinborn body and a medium. The method comprises the following steps: acquiring target image data and target point cloud data of the same space area of a transformer substation; processing the target image data and the target point cloud data through a feature descriptor generation model to obtain a target feature descriptor, and if a feature descriptor matched with the target feature descriptor is identified in a pre-established feature map, determining a to-be-updated coordinate in the digital twin according to the coordinate of the feature descriptor; the feature map comprises position coordinates in the transformer substation and feature descriptors at the position coordinates; and updating the digital twin at the to-be-updated coordinate based on the target image data and the target point cloud data. According to the technical scheme, the to-be-updated coordinates can be accurately determined in the digital twinborn body based on the feature descriptors, and local updating can be quickly performed on the digital twinborn body at the position of the to-be-updated coordinates.

Description

Technical Field

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

[0002] With the advancement of power grid digitalization, the importance of digital twin construction has become increasingly prominent.

[0003] Currently, if a digital twin is partially updated, there is a problem that the location of the digital twin to be updated cannot be accurately confirmed. Summary of the invention

[0004] The present invention provides a local update method, device, equipment and medium for a digital twin, which can accurately determine the position to be updated of the digital twin and quickly update the digital twin at the position to be updated.

[0005] According to one aspect of the present invention, a local update method of a digital twin is provided, the method comprising:

[0006] Obtain target image data and target point cloud data in the same spatial area of ​​the substation;

[0007] The target image data and the target point cloud data are processed by a feature descriptor generation model to obtain a target feature descriptor; the feature descriptor reflects the image color feature and the spatial shape feature;

[0008] If a feature descriptor matching the target feature descriptor is identified in a pre-established feature map, the coordinates to be updated in the digital twin are determined according to the coordinates of the feature descriptor; the feature map includes the location coordinates in the substation and the feature descriptors at each location coordinate;

[0009] The digital twin at the coordinates to be updated is updated based on the target image data and the target point cloud data.

[0010] According to another aspect of the present invention, a local update device for a digital twin is provided, comprising:

[0011] A data acquisition module, used to acquire target image data and target point cloud data in the same spatial area of ​​the substation;

[0012] A target feature descriptor determination module is used to process the target image data and the target point cloud data through a feature descriptor generation model to obtain a target feature descriptor; the feature descriptor reflects the image color feature and the spatial shape feature;

[0013] A module for determining coordinates to be updated, for determining the coordinates to be updated in the digital twin according to the coordinates of the feature descriptor if a feature descriptor matching the target feature descriptor is identified in a pre-established feature map; the feature map includes the location coordinates in the substation and the feature descriptors at each location coordinate;

[0014] The digital twin update module is used to update the digital twin at the coordinates to be updated based on the target image data and the target point cloud data.

[0015] According to another aspect of the present invention, there is provided an electronic device, the electronic device comprising:

[0016] at least one processor; and

[0017] a memory communicatively connected to the at least one processor; wherein,

[0018] 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 local update method of the digital twin described in any embodiment of the present invention.

[0019] 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 local update method of the digital twin described in any embodiment of the present invention when executed.

[0020] The technical solution of the embodiment of the present application includes: obtaining target image data and target point cloud data of the same spatial area of ​​the substation; processing the target image data and target point cloud data through a feature descriptor generation model to obtain a target feature descriptor; the feature descriptor reflects the image color features and spatial shape features; if a feature descriptor matching the target feature descriptor is identified in a pre-established feature map, the coordinates to be updated in the digital twin are determined according to the coordinates of the feature descriptor; the feature map includes the position coordinates in the substation and the feature descriptors at each position coordinate; based on the target image data and the target point cloud data, the digital twin at the coordinates to be updated is updated. Based on the feature descriptors, this technical solution can accurately determine the coordinates to be updated in the digital twin, and can quickly update the digital twin locally at the coordinates to be updated.

[0021] 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

[0022] 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.

[0023] Figure 1 It is a flowchart of a local update method of a digital twin provided according to the first embodiment of the present application;

[0024] Figure 2 It is a flowchart of a local update method of a digital twin provided according to the second embodiment of the present application;

[0025] Figure 3 It is a structural schematic diagram of a local update device of a digital twin provided according to the third embodiment of the present application;

[0026] Figure 4 It is a structural schematic diagram of an electronic device that implements a local update method of a digital twin according to an embodiment of the present application. DETAILED DESCRIPTION

[0027] 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.

[0028] 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.

[0029] Embodiment 1

[0030] Figure 1A flowchart of a local update method of a digital twin is provided for the first embodiment of the present application. The embodiment of the present application can be applied to the case of local update of a digital twin. The method can be executed by a local update device of a digital twin. The local update device of the digital twin can be implemented in the form of hardware and / or software. The local update device of the digital twin can be configured in an electronic device with data processing capabilities. Figure 1 As shown, the method includes:

[0031] S110, acquiring target image data and target point cloud data of the same spatial area of ​​the substation.

[0032] The target image data is an image collected from a spatial area. Usually, the target image area includes equipment and facilities of the substation, and the digital twin area corresponding to the target image area may be the digital twin area that needs to be updated. The target point cloud data is the point cloud data collected by the point cloud acquisition device on the same spatial area.

[0033] Specifically, collection points and collection angles can be set for the same spatial area, and data can be collected at the collection points and collection angles by image acquisition devices and point cloud acquisition devices to obtain image data and point cloud data. Since the image acquisition devices and point cloud acquisition devices themselves may have parameter differences, the collected image data and point cloud data may have different fields of view. Their fields of view can be adjusted to obtain target image data and target point cloud data corresponding to the same spatial area.

[0034] S120, processing the target image data and the target point cloud data through a feature descriptor generation model to obtain a target feature descriptor; the feature descriptor reflects the image color feature and the spatial shape feature.

[0035] Among them, the feature descriptor reflects the representative feature vectors in the target image data and the target point cloud data. The feature descriptor generation model can extract image color features from the target image data and spatial shape features from the target point cloud data, and finally obtain the target feature descriptor.

[0036] Specifically, the feature descriptor generation model to be trained may be trained in advance according to the training data to obtain the feature descriptor generation model, wherein each sample in the training data includes image data and point cloud data of the same area.

[0037] S130, if a feature descriptor matching the target feature descriptor is identified in a pre-established feature map, the coordinates to be updated in the digital twin are determined according to the coordinates of the feature descriptor; the feature map includes the location coordinates within the substation and the feature descriptors at each location coordinate.

[0038] The feature map reflects the feature descriptors at each position, that is, the feature map includes coordinate information and feature descriptor information, and the feature descriptors at different coordinates can be determined based on the image information and point cloud information at the coordinates.

[0039] Specifically, the coordinates in the feature map correspond to the coordinates in the digital twin, that is, the feature descriptors in the feature map correspond to the models at the corresponding coordinates in the digital twin. The feature descriptors can be traversed in the pre-established feature map, the feature descriptors that match the target feature descriptors can be selected, and the corresponding coordinates to be updated can be determined in the digital twin according to the coordinates of the feature descriptors.

[0040] S140, updating the digital twin at the coordinates to be updated based on the target image data and the target point cloud data.

[0041] Specifically, after determining the coordinates to be updated, a sub-digital twin at the coordinates to be updated can be generated based on the target image data and the target point cloud data, and the model at the coordinates to be updated in the digital twin can be replaced based on the sub-digital twin to achieve local update of the digital twin.

[0042] The technical solution of the embodiment of the present application includes: obtaining target image data and target point cloud data of the same spatial area of ​​the substation; processing the target image data and target point cloud data through a feature descriptor generation model to obtain a target feature descriptor; the feature descriptor reflects the image color features and spatial shape features; if a feature descriptor matching the target feature descriptor is identified in a pre-established feature map, the coordinates to be updated in the digital twin are determined according to the coordinates of the feature descriptor; the feature map includes the position coordinates in the substation and the feature descriptors at each position coordinate; based on the target image data and the target point cloud data, the digital twin at the coordinates to be updated is updated. Based on the feature descriptors, this technical solution can accurately determine the coordinates to be updated in the digital twin, and can quickly update the digital twin locally at the coordinates to be updated.

[0043] Embodiment 2

[0044] Figure 2 This is a flow chart of a local update method of a digital twin provided in Example 2 of the present application. This embodiment of the present application is optimized based on the above embodiment.

[0045] like Figure 2 As shown, the method of the embodiment of the present application specifically includes the following steps:

[0046] S210, acquiring image data and point cloud data of the substation.

[0047] Specifically, use specific data acquisition equipment to conduct all-round data acquisition at the substation site to ensure the integrity and accuracy of image data and point cloud data. The collected data is stored for subsequent processing.

[0048] S220: performing clustering processing on the point cloud data to obtain divided point cloud groups.

[0049] Specifically, the stored point cloud data is imported into the data processing software. The clustering algorithm is run to divide similar point cloud data into the same group according to the spatial distribution characteristics, density and other attributes of the point cloud data. After processing, several clearly divided point cloud groups are obtained.

[0050] S230, dividing the image data of the substation based on the division method of the point cloud group to obtain a segmented image.

[0051] Specifically, according to the division method of the point cloud group, a segmentation line for dividing the point cloud is determined, the segmentation line is projected onto the image data of the substation, the image data is segmented, and a segmented image corresponding to the point cloud group is obtained.

[0052] In an embodiment of the present application, optionally, image data and point cloud data of the substation are obtained, including: at the same data acquisition position, image acquisition and laser scanning of the substation are performed based on the same angle to obtain image data and point cloud data located in the same field of view; correspondingly, the image data of the substation is divided based on the division method of the point cloud group to obtain a segmented image, including: projecting the edge frame of each point cloud group into the image data, and determining the image located in the projection frame as the segmented image.

[0053] Specifically, when acquiring point cloud data and image data, image acquisition and laser scanning can be performed on the substation at the same data acquisition position and based on the same angle to obtain image data and point cloud data located in the same field of view; accordingly, the image data of the substation is divided based on the division method of the point cloud group to obtain a segmented image, including: projecting the edge frame of each point cloud group into the image data, and determining the image located in the projection frame as the segmented image.

[0054] The present solution is configured in this way so that target image data and target point cloud data corresponding to the same spatial area can be accurately obtained in subsequent steps.

[0055] S240, determining the segmented image and point cloud group corresponding to the same spatial area as target image data and target point cloud data of the same spatial area of ​​the substation respectively.

[0056] In an embodiment of the present application, optionally, target image data and target point cloud data of the same spatial area of ​​the substation are obtained, including: performing shape feature extraction on the point cloud data of the substation to obtain a first shape feature, and determining first electrical equipment information that meets the first shape feature; performing color feature extraction on the image data of the substation to obtain a second color feature, and determining second electrical equipment information that meets the second color feature; if the first electrical equipment information is the same as the second electrical equipment information, then determining the image data and point cloud data as the target image data and target point cloud data of the same spatial area of ​​the substation, respectively.

[0057] Specifically, if a local update is performed on the electrical equipment of the substation, the equipment of the substation can be determined based on the shape features and color features respectively. If the two features correspond to the same device, it indicates that the image data and the point cloud data are the target image data and target point cloud data of the same spatial area of ​​the substation respectively.

[0058] S250, extracting features from the target point cloud data using a point cloud feature extraction model in the feature descriptor generation model to obtain a first feature.

[0059] Specifically, the target point cloud data is input into the point cloud feature extraction model in the feature descriptor generation model. The point cloud feature extraction model will analyze and process the input point cloud data. It can calculate the geometric relationship between points in the point cloud, such as distance, angle, etc. The local shape features of the point cloud will also be considered, such as calculating the normal vector and curvature of each point. These geometric and shape features are key information for describing point cloud data. After a series of calculations and processing, the model extracts representative features from the point cloud data, that is, the first feature is obtained.

[0060] S260, extracting features of the target image data through a convolutional neural network in a feature descriptor generation model to obtain a second feature.

[0061] Specifically, the target image data is input into the convolutional neural network in the feature descriptor generation model. The convolutional neural network consists of multiple convolutional layers, pooling layers, and activation functions. The convolutional layer performs convolution operations with the image through the convolution kernel to extract features of different levels in the image, such as edges, textures, etc. The pooling layer downsamples the feature map after convolution to reduce the amount of data while retaining the main features. The activation function introduces nonlinear factors to the network, enabling it to learn more complex feature representations. After processing by these layers, the network extracts rich feature information from the image data and obtains the second feature.

[0062] S270, mapping the first feature and the second feature to a vector space of a target dimension through a fully connected layer to obtain a target feature descriptor.

[0063] Specifically, the first feature (point cloud feature) and the second feature (image feature) obtained above are input into the fully connected layer. The function of the fully connected layer is to fuse and map features from different sources. It will perform linear transformation and combination on the input features according to the preset target dimension. By continuously adjusting the weights and parameters of the fully connected layer, it can effectively map the first feature and the second feature into the vector space of the target dimension. The final vector is the target feature descriptor, which combines the feature information of the point cloud data and the image data, and can more comprehensively describe the features of the target object.

[0064] In an embodiment of the present application, optionally, the feature descriptor reflects the normal vector features of the point cloud, the curvature features of the point cloud and / or the gradient direction and intensity features of the pixels in the image.

[0065] S280, if a feature descriptor matching the target feature descriptor is identified in a pre-established feature map, the coordinates to be updated in the digital twin are determined according to the coordinates of the feature descriptor; the feature map includes the location coordinates within the substation and the feature descriptors at each location coordinate.

[0066] S290, updating the digital twin at the coordinates to be updated based on the target image data and the target point cloud data.

[0067] In an embodiment of the present application, optionally, the digital twin at the coordinates to be updated is updated based on the target image data and the target point cloud data, including: if the geometric data, spatial position, and self-posture of the first target and the second target match, the digital twin at the coordinates to be updated is updated based on the target image data and the target point cloud data; the first target is the target at the coordinates to be updated in the digital twin; the second target is the target reflected by the target feature descriptor.

[0068] Specifically, when the digital twin is partially updated, the essence is to delete the original first target (the sub-target in the digital twin) at the update coordinates and replace it with the second target (the second target is generated based on the target image data and the target point cloud data). In this process, the geometric data, spatial position, and posture of the first target and the second target need to match each other to achieve replacement without leaving any trace.

[0069] The technical solution of the embodiment of the present application quickly obtains target point cloud data and target image data by first dividing the point cloud and then dividing the image, and extracts features of the target point cloud data through the point cloud feature extraction model in the feature descriptor generation model to obtain the first feature; extracts features of the target image data through the convolutional neural network in the feature descriptor generation model to obtain the second feature; maps the first feature and the second feature to the vector space of the target dimension through the fully connected layer to obtain the target feature descriptor, thereby obtaining a target feature descriptor that can accurately reflect color and shape features, providing support for the subsequent determination of coordinates to be updated.

[0070] Embodiment 3

[0071] Figure 3 This is a schematic diagram of the structure of a local update device for a digital twin provided in the third embodiment of the present application. The device can execute the local update method for a digital twin 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:

[0072] The data acquisition module 310 is used to acquire target image data and target point cloud data of the same spatial area of ​​the substation;

[0073] The target feature descriptor determination module 320 is used to process the target image data and the target point cloud data through the feature descriptor generation model to obtain the target feature descriptor; the feature descriptor reflects the image color feature and the spatial shape feature;

[0074] A module 330 for determining coordinates to be updated, for determining the coordinates to be updated in the digital twin according to the coordinates of the feature descriptor if a feature descriptor matching the target feature descriptor is identified in a pre-established feature map; the feature map includes the location coordinates in the substation and the feature descriptors at each location coordinate;

[0075] The digital twin updating module 340 is used to update the digital twin at the coordinates to be updated based on the target image data and the target point cloud data.

[0076] The technical solution of the embodiment of the present application includes: a data acquisition module 310, which is used to acquire target image data and target point cloud data of the same spatial area of ​​the substation; a target feature descriptor determination module 320, which is used to process the target image data and target point cloud data through a feature descriptor generation model to obtain a target feature descriptor; the feature descriptor reflects the image color feature and spatial shape feature; a to-be-updated coordinate determination module 330, which is used to determine the to-be-updated coordinates in the digital twin according to the coordinates of the feature descriptor if a feature descriptor matching the target feature descriptor is identified in a pre-established feature map; the feature map includes the location coordinates in the substation and the feature descriptors at each location coordinate; a digital twin update module 340, which is used to update the digital twin at the to-be-updated coordinates based on the target image data and the target point cloud data. Based on the feature descriptors, the present technical solution can accurately determine the to-be-updated coordinates in the digital twin, and can quickly perform local updates on the digital twin at the to-be-updated coordinates.

[0077] In the embodiment of the present application, optionally, the data acquisition module 310 includes:

[0078] A data acquisition unit, used to acquire image data and point cloud data of the substation;

[0079] A point cloud group determination unit, used for performing clustering processing on the point cloud data to obtain divided point cloud groups;

[0080] An image segmentation unit, used for segmenting the image data of the substation based on the segmentation method of the point cloud group to obtain a segmented image;

[0081] The target image data and target point cloud data determination unit is used to determine the segmented image and point cloud group corresponding to the same spatial area as the target image data and target point cloud data of the same spatial area of ​​the substation respectively.

[0082] In the embodiment of the present application, optionally, the data acquisition unit is specifically used to:

[0083] At the same data collection position, image collection and laser scanning are performed on the substation based on the same angle to obtain image data and point cloud data in the same field of view;

[0084] Accordingly, the image segmentation unit is specifically used for:

[0085] The edge box of each point cloud group is projected into the image data, and the image within the projected box is determined as the segmented image.

[0086] In the embodiment of the present application, optionally, the data acquisition module 310 includes:

[0087] A first electrical equipment information determination unit, configured to extract shape features from point cloud data of the substation to obtain a first shape feature, and determine first electrical equipment information that meets the first shape feature;

[0088] A second electrical equipment information determination unit, configured to extract color features from the image data of the substation to obtain a second color feature, and determine second electrical equipment information that meets the second color feature;

[0089] The target image data and target point cloud data determination unit is used to determine the image data and point cloud data as the target image data and target point cloud data of the same spatial area of ​​the substation respectively if the first electrical equipment information is the same as the second electrical equipment information.

[0090] In an embodiment of the present application, optionally, the feature descriptor reflects the normal vector features of the point cloud, the curvature features of the point cloud and / or the gradient direction and intensity features of the pixels in the image.

[0091] In the embodiment of the present application, optionally, the digital twin update module 340 includes:

[0092] A digital twin updating unit is used to update the digital twin at the coordinates to be updated based on the target image data and the target point cloud data if the geometric data, spatial position, and self-posture of the first target match those of the second target; the first target is the target at the coordinates to be updated in the digital twin; the second target is the target reflected by the target feature descriptor.

[0093] In the embodiment of the present application, optionally, the target feature descriptor determination module 320 includes:

[0094] A first feature determination unit is used to extract features from the target point cloud data through a point cloud feature extraction model in a feature descriptor generation model to obtain a first feature;

[0095] A second feature determination unit is used to extract features of the target image data through a convolutional neural network in a feature descriptor generation model to obtain a second feature;

[0096] The target feature descriptor determination unit is used to map the first feature and the second feature into a vector space of a target dimension through a fully connected layer to obtain a target feature descriptor.

[0097] A local update device for a digital twin provided in an embodiment of the present application can execute a local update method for a digital twin provided in any embodiment of the present invention, and has functional modules and beneficial effects corresponding to the execution method.

[0098] Embodiment 4

[0099] Figure 4 A 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.

[0100] 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.

[0101] 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.

[0102] 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 local update method of a digital twin.

[0103] In some embodiments, the local update method of the digital twin can 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 can 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 local update method of the digital twin described above can be performed. Alternatively, in other embodiments, the processor 11 can be configured to perform the local update method of the digital twin by any other appropriate means (for example, by means of firmware).

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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).

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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 local update method for a digital twin, characterized in that: include: Obtain target image data and target point cloud data in the same spatial area of ​​the substation; The target image data and the target point cloud data are processed by a feature descriptor generation model to obtain a target feature descriptor; The feature descriptor reflects the image color features and spatial shape features; If a feature descriptor matching the target feature descriptor is identified in a pre-established feature map, the coordinates to be updated in the digital twin are determined according to the coordinates of the feature descriptor; the feature map includes the location coordinates in the substation and the feature descriptors at each location coordinate; The digital twin at the coordinates to be updated is updated based on the target image data and the target point cloud data.

2. The method according to claim 1, characterized in that Obtain target image data and target point cloud data in the same spatial area of ​​the substation, including: Obtain image data and point cloud data of the substation; Performing clustering processing on the point cloud data to obtain divided point cloud groups; Dividing the image data of the substation based on the division method of the point cloud group to obtain a segmented image; The segmented image and point cloud group corresponding to the same spatial area are respectively determined as target image data and target point cloud data of the same spatial area of ​​the substation.

3. The method according to claim 2, characterized in that Obtain image data and point cloud data of the substation, including: At the same data collection position, image collection and laser scanning are performed on the substation based on the same angle to obtain image data and point cloud data in the same field of view; Accordingly, the image data of the substation is divided based on the division method of the point cloud group to obtain a segmented image, including: The edge box of each point cloud group is projected into the image data, and the image within the projected box is determined as the segmented image.

4. The method according to claim 1, characterized in that: Obtain target image data and target point cloud data in the same spatial area of ​​the substation, including: Performing shape feature extraction on the point cloud data of the substation to obtain a first shape feature, and determining first electrical equipment information that meets the first shape feature; Extracting color features from the image data of the substation to obtain a second color feature, and determining second electrical equipment information that meets the second color feature; If the first electrical equipment information is the same as the second electrical equipment information, the image data and the point cloud data are respectively determined as target image data and target point cloud data of the same spatial area of ​​the substation.

5. The method according to claim 1, characterized in that The feature descriptor reflects the normal vector feature of the point cloud, the curvature feature of the point cloud and / or the gradient direction and intensity feature of the pixels in the image.

6. The method according to claim 1, characterized in that The digital twin at the coordinates to be updated is updated based on the target image data and the target point cloud data, including: If the geometric data, spatial position, and self-posture of the first target match those of the second target, the digital twin at the coordinates to be updated is updated based on the target image data and the target point cloud data; the first target is the target at the coordinates to be updated in the digital twin; the second target is the target reflected by the target feature descriptor.

7. The method according to claim 1, characterized in that The target image data and the target point cloud data are processed by the feature descriptor generation model to obtain the target feature descriptor, including: Extracting features from the target point cloud data through a point cloud feature extraction model in a feature descriptor generation model to obtain a first feature; Extracting features of the target image data through a convolutional neural network in a feature descriptor generation model to obtain a second feature; The first feature and the second feature are mapped to the vector space of the target dimension through the fully connected layer to obtain the target feature descriptor.

8. A local update device for a digital twin, characterized in that: include: A data acquisition module, used to acquire target image data and target point cloud data in the same spatial area of ​​the substation; A target feature descriptor determination module is used to process target image data and target point cloud data through a feature descriptor generation model to obtain a target feature descriptor; The feature descriptor reflects the image color features and spatial shape features; A module for determining coordinates to be updated, for determining the coordinates to be updated in the digital twin according to the coordinates of the feature descriptor if a feature descriptor matching the target feature descriptor is identified in a pre-established feature map; the feature map includes the location coordinates in the substation and the feature descriptors at each location coordinate; The digital twin update module is used to update the digital twin at the coordinates to be updated based on the target image data 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 local update method of the digital twin according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, which are used to enable a processor to implement the local update method of the digital twin according to any one of claims 1 to 7 when executed.

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