An autonomously controllable low-code power grid digital twin construction method and system

Abstract

The application provides a kind of self-controllable low code power grid digital twin construction method and system, belongs to the field of power system digitalization, method includes: the three-dimensional model of each basic component in power grid is constructed, and equipment component library is obtained;Equipment component library includes multiple basic component models;Basic component model is composed of triangular facets;Based on the basic component model in equipment component library, the three-dimensional model of power grid equipment is quickly constructed using drag and drop and other low code forms;The three-dimensional model of power grid equipment is bound with the twin data of power grid equipment and simulation and machine learning model, and the power grid digital twin is obtained, and the power grid digital twin is visually presented.The application improves the reuse rate of three-dimensional model data, reduces the construction cost of power grid equipment three-dimensional model, and can help managers intuitively master the operation state of power grid equipment.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of power system digitization, in particular to a self-controllable low-code power grid digital twin rapid construction method. BACKGROUND

[0002] Industrial software is the foundation of intelligent manufacturing, and it plays an important role in improving production efficiency, reducing costs, and improving product quality. Therefore, the technical level and self-controllable degree of industrial software are directly related to the development process of intelligent manufacturing. However, there are still some technical bottlenecks in some key technical fields, such as Autodesk Computer Aided Design (CAD) software. These technical bottlenecks limit the development speed and application range of industrial software, and need to be broken through by technology research and innovation.

[0003] In recent years, power grid companies have actively carried out the application of three-dimensional digitalization of power grids, and have developed a series of three-dimensional digitalization products. These achievements have promoted the improvement of power grid construction and management level to varying degrees. Three-dimensional models can carry more information and provide more realistic visual experience than two-dimensional graphics. However, three-dimensional model construction is complex and costly, and the reusability is very low. Since 2016, the demand for power grid model library construction has increased day by day, and a large number of detailed and valuable three-dimensional power grid models have been constructed. These models are mostly used in specific systems, and once the system development is completed, these data are left in the case and are not used again. Even if they are used again, they are only used for modification and updating of related equipment. The low reusability that does not match the high cost limits the promotion of three-dimensional digitalization products.

[0004] Based on the above problems, there is an urgent need for a method that can improve the reusability of three-dimensional model data and reduce the construction cost of three-dimensional models. SUMMARY

[0005] The purpose of the present application is to provide a self-controllable low-code power grid digital twin construction method and system, which can improve the reusability of three-dimensional model data and reduce the construction cost of three-dimensional models.

[0006] To achieve the above purpose, the present application provides a self-controllable low-code power grid digital twin construction method, which comprises:

[0007] Constructing three-dimensional models of each basic component in the power grid to obtain a device component library; the device component library includes a plurality of basic component models; the basic component models are composed of triangular facets;

[0008] Based on the basic component models in the device component library, a three-dimensional model of a power grid device is constructed in a low-code form;

[0009] Bind the power grid equipment three-dimensional model with the twin data of the power grid equipment and the simulation and machine learning model to obtain a power grid digital twin, and visually present the power grid digital twin.

[0010] Optionally, the basic components in the power grid include a power transformation engineering basic geometric component, a power transformation engineering section steel component, an overhead transmission line component, and a cable line component.

[0011] Optionally, the three-dimensional model of each basic component in the power grid is constructed, specifically including: for any basic component in the power grid, according to the configuration file of the basic component, constructing the three-dimensional model of the basic component according to the library, the primitive group, the primitive, and the primitive attribute.

[0012] Optionally, the three-dimensional model of each basic component in the power grid is constructed, specifically including: reading the geometric parameters of the engineering geometry file and analyzing the geometric parameters according to the corresponding space matrix to convert into a solid model to obtain the three-dimensional model of each basic component; the engineering geometry file is a GIM model, a 3dsMax file, or an FBX file.

[0013] Optionally, the self-controllable low-code power grid digital twin construction method further includes: using a sub-region-based patch reduction method to reduce the patches of each basic component model in the device component library.

[0014] Optionally, the sub-region-based patch reduction method is used to reduce the patches of each basic component model in the device component library, specifically including:

[0015] For any basic component model, in the a-th reduction process, for any sub-region of the basic component model after the a-1-th reduction, the flatness of two adjacent triangular patches is calculated according to the coordinates of the vertices of the two adjacent triangular patches in the sub-region; 0

[0016] For any vertex in the basic component model after the a-1-th reduction, the average distance from the vertex to its adjacent vertices and the sum of the flatness of the triangular patches to which the connecting edges of the vertex belong are calculated.

[0017] The average distance from the vertex to its adjacent vertices and the sum of the flatness of the triangular patches to which the connecting edges of the vertex belong are weighted to obtain the influence radius of the vertex.

[0018] According to the influence radius of each vertex, the vertices in the basic component model after the a-1-th reduction are merged to obtain the basic component model after the a-th reduction.

[0019] Optionally, the flatness of two adjacent triangular patches is calculated according to the following formula:

[0020]

[0021]

[0022]

[0023] N 11 =(y 12 -y 11 )*(z 13 -z 11 )-(z 12 -z 11 )*(y 13 -y 11 );

[0024] N 12 =(z 12 -z 11 )*(x 13 -x 11 )-(x 12 -x 11 )*(z 13 -z 11 );

[0025] N 13 =(x 12 -x 11 )*(y 13 -y 11 )-(y 12 -y 11 )*(x 13 -x 11 );

[0026] N 21 =(y 22 -y 21 )*(z 23 -z 21 )-(z 22 -z 21 )*(y 23 -y 21 );

[0027] N 22 =(z 22 -z 21 )*(x 23 -x 21 )-(x 22 -x 21 )*(z 23 -z 21 );

[0028] N 23 =(x 22-x 21 )*(y 23 -y 21 )-(y 22 -y 21 )*(x 23 -x 21 );

[0029] wherein, θ is the flatness of two adjacent triangular facets, n1 is the unit normal vector of one of the adjacent triangular facets, n2 is the unit normal vector of the other of the adjacent triangular facets, i, j, k are unit vectors along x, y, z axes respectively, i=(1, 0, 0), j=(0, 1, 0), k=(0, 0, 1), N 11 , N 12 , N 13 are normal vectors of one of the adjacent triangular facets respectively, N 21 , N 22 , N 23 are normal vectors of the other of the adjacent triangular facets respectively, (x 11 , y 11 , z 11 ), (x 12 , y 12 , z 12 ), (x 13 , y 13 , z 13 ) are three vertex coordinates of one of the adjacent triangular facets respectively, (x 21 , y 21 , z 21 ), (x 22 , y 22 , z 22 ), (x 23 , y 23 , z 23 ) are three vertex coordinates of the other of the adjacent triangular facets respectively.

[0030] Optionally, the self-controllable low-code power grid digital twin construction method further comprises: adding material and animation effects to the power grid equipment three-dimensional model.

[0031] Optionally, the twin data of the power grid equipment comprises equipment static information and equipment dynamic information; the equipment static information comprises equipment factory parameters, and the equipment dynamic information comprises operation data.

[0032] To achieve the above-mentioned purpose, the application further provides a self-controllable low-code power grid digital twin construction system, comprising:

[0033] An assembly library construction module is configured to construct three-dimensional models of basic assemblies in a power grid to obtain an equipment assembly library, wherein the equipment assembly library includes a plurality of basic assembly models, and each basic assembly model is composed of triangular facets;

[0034] An equipment model construction module is configured to construct a three-dimensional model of a power grid equipment based on the basic assembly models in the equipment assembly library in a low-code form;

[0035] A twin construction module is configured to bind the three-dimensional model of the power grid equipment with twin data of the power grid equipment and simulation and machine learning models to obtain a power grid digital twin, and to visually present the power grid digital twin.

[0036] According to the embodiments of the present application, the following technical effects are achieved: by constructing three-dimensional models of basic assemblies in a power grid, a user can quickly construct a three-dimensional model of a power grid equipment in a drag-and-drop form, thereby improving the reuse rate of three-dimensional model data and reducing the construction cost of the three-dimensional model of the power grid equipment. Further, by binding the three-dimensional model of the power grid equipment with twin data of the power grid equipment to construct a power grid digital twin, the manager can intuitively master the operation state of the power grid equipment. BRIEF DESCRIPTION OF DRAWINGS

[0037] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings needed in the embodiments. Obviously, the drawings described below only illustrate some of the embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0038] Figure 1 A flowchart of the autonomous controllable low-code power grid digital twin construction method provided by the present application;

[0039] Figure 2 A construction process diagram of the low-code power grid digital twin;

[0040] Figure 3 A schematic diagram of the autonomous controllable low-code power grid digital twin construction system provided by the present application. DETAILED DESCRIPTION

[0041] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only some of the embodiments of the present application, but not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0042] The purpose of the present application is to provide an autonomous controllable low-code power grid digital twin construction method and system. Based on parameterized three-dimensional modeling technology, users can quickly build three-dimensional models of power grid equipment using the drag-and-drop form. Further, it can integrate basic data such as main equipment, auxiliary equipment, and monitoring equipment, and bind twin information.

[0043] To make the above-mentioned purposes, features and advantages of the present application more obvious and easy to understand, the present application will be further described in detail below with reference to the drawings and specific embodiments.

[0044] Embodiment one

[0045] As shown in Figure 1 and Figure 2 , the present embodiment provides an autonomous controllable low-code power grid digital twin construction method, which comprises:

[0046] Step 100: Constructing three-dimensional models of each basic component in the power grid to obtain a device component library. The device component library includes a plurality of basic component models. The basic component model is composed of triangular facets.

[0047] The basic component is the basis of model construction, including basic geometric components of power transformation engineering, power transformation engineering steel components, overhead transmission line components, and cable line components.

[0048] The present application can adopt a parameterized construction method and an imported structured component method to construct three-dimensional models of each basic component in the power grid:

[0049] (1) Parameterized construction method: For any basic component in the power grid, according to the configuration file of the basic component, the three-dimensional model of the basic component is constructed according to the library, the primitive group, the primitive, and the primitive attribute. Primitive attribute includes primitive construction callback function name, primitive attribute column name, primitive attribute data type, primitive attribute whether it is an array, primitive attribute whether it is a primary key, primitive attribute whether it is not empty, primitive attribute default value and primitive attribute note description.

[0050] (2) Import structured component method: read the geometric parameters of the engineering geometry file, and analyze the geometric parameters according to the corresponding space matrix to convert into a solid model to obtain the three-dimensional model of each basic component. The engineering geometry file is a GIM model, a 3dsMax file or an FBX file.

[0051] Specifically, according to the modeling specification of the digital design three-dimensional GIM model, the geometric parameters of each level file in the GIM model are read, and the geometric parameters are analyzed according to the corresponding space matrix to convert into a solid model. It has opened up a channel for introducing models edited by mainstream modeling tools in the market into the low-code power grid digital twin rapid construction platform.

[0052] After obtaining the device component library, the self-controllable low-code power grid digital twin construction method further comprises:

[0053] Step 200: adopting a sub-region-based patch pruning method, performing patch pruning on each basic component model in the device component library. That is, performing lightweight processing on the basic component model in the device component library.

[0054] The construction of the basic component model is based on triangular patches, and the number of triangular patches determines the amount of resource occupation and the efficiency of rendering. On the premise of not affecting the appearance effect of the basic component model, the fewer the number of triangular patches, the higher the rendering efficiency, and the less the resource occupation. The application adopts a sub-region-based patch pruning technology to reduce the number of triangular patches of the basic component model to improve the rendering efficiency.

[0055] Specifically, step 200 comprises:

[0056] Step 201: for any basic component model, in the a-th pruning process, for any sub-region of the basic component model after the a-1-th pruning, the flatness of two adjacent triangular patches is calculated according to the coordinates of the vertices of the two adjacent triangular patches in the sub-region. 0

[0057] The angle between the normal vectors of all adjacent triangular patches in a certain sub-region can reflect the size of the curvature change of the model surface in this sub-region, that is, the application uses the angle between the normal vectors to represent the flatness. A threshold angle can be given, when the angle between the normal vectors of adjacent triangular patches is less than the threshold angle, it is considered that the sub-region is flat and can be simplified; when the angle between the normal vectors of adjacent triangular patches is greater than or equal to the threshold angle, the sub-region is retained.

[0058] The unit normal vector of a triangular patch can be determined by the connection order of the edges of the triangular patch according to the right-hand rule. If the coordinates of the three vertices of a triangular patch are (x 11 ,y 11 ,z 11 ), (x 12 ,y 12 ,z 12 ) and (x 13 ,y 13 ,z 13 ), then the unit normal vector of the triangular patch can be calculated as follows:

[0059]

[0060] Where i, j, and k are unit vectors along the x, y, and z axes, respectively, i=(1, 0, 0), j=(0, 1, 0), and k=(0, 0, 1).

[0061] The normal vector N of this triangular facet 21 , N 22 , N 23 The following formula is used for calculation:

[0062] N 11 = (y 12 -y 11 )*(z 13 -z 11 )-(z 12 -z 11 )*(y 13 -y 11 );

[0063] N 12 = (z 12 -z 11 )*(x 13 -x 11 )-(x 12 -x 11 )*(z 13 -z 11 );

[0064] N 13 = (x 12 -x 11 )*(y 13 -y 11 )-(y 12 -y 11 )*(x 13 -x 11 )。

[0065] The normal vector of two adjacent triangular facets is calculated according to the unit normal vectors of the two adjacent triangular facets:

[0066]

[0067] wherein θ is the flatness of the two adjacent triangular facets, n1 is the unit normal vector of one of the adjacent triangular facets, and n2 is the unit normal vector of the other of the adjacent triangular facets.

[0068] The normal vector angle can reflect whether the sub-region is flat. If θ < 5°, the sub-region is marked as flat, and the two triangular facets in the sub-region can be deleted. The common edge of the two triangular facets is marked as 1, and the marks of the two triangular facets are modified to 1.

[0069] Step 202: For any vertex in the base component model after the a-1th deletion, the average distance from the vertex to its adjacent vertices and the sum of the flatness of the triangular facets to which the connecting edges of the vertex belong are calculated.

[0070] Step 203: Weighting the sum of the average distance of the vertex to its adjacent vertices and the flatness of the triangular facets to which the connecting edges of the vertex belong, to obtain the influence radius of the vertex.

[0071] Specifically, calculating the influence radius of a vertex involves using the weighted values of the distances between adjacent vertices and the normal vector changes of the vertex as the influence radius, taking into account the local characteristics of the vertex. First, the average distance of adjacent vertices is calculated, and for each vertex, the average distance of the vertex to adjacent vertices is calculated. At the same time, the flatness of each triangular facet is calculated in step 201, and for each vertex, the sum of the flatness of the triangular facets to which the connecting edges of the vertex belong is calculated, which measures the average value of the normal vector changes of the associated triangular facets. The flatness of the triangular facets associated with the vertex and the average distance of the adjacent vertices are calculated as the influence radius of the vertex.

[0072] Step 204: According to the influence radius of each vertex, the vertices in the base component model after the a-1th reduction are merged to obtain the base component model after the ath reduction.

[0073] Specifically, first, an initial cost is calculated for each vertex in the base component model, representing the impact of removing the vertex. The initial cost of the vertex is the influence radius of the vertex. Then, the vertex pair with the smallest cost is selected and merged into a new vertex. The position of the new vertex is the midpoint of the merged vertices. Other positions can also be selected according to different strategies. At the same time, the adjacent triangular facets need to be updated: remove the triangular facets related to the merged vertices and create new triangular facets with the new vertex, which involves updating the connection relationship of the adjacent triangular facets. At the same time, the adjacent vertices are updated, updating other vertices adjacent to the merged vertices to reflect the position of the new vertex. For different three-dimensional models, including updating the connection relationship, normal, texture coordinates, etc. of the adjacent vertices. Finally, the cost (i.e. influence radius) of each vertex is recalculated to ensure that the next selected vertex pair can maximize the reduction of the complexity of the model, and steps 201 to 204 are repeated until the desired reduction degree is reached or the termination condition is reached.

[0074] Step 300: Based on the base component model in the device component library, a low-code form is used to build a power grid device three-dimensional model. That is, the present application can provide users with a low-code form such as drag-and-drop to quickly build a power grid device three-dimensional model.

[0075] Further, the self-controllable low-code power grid digital twin construction method further comprises adding material and animation effects to the power grid device three-dimensional model.

[0076] Specifically, the model editor lays the foundation for the rapid construction of the power grid digital twin. Users can individually construct components based on the model editor or quickly assemble components into power grid equipment in the form of low-code by dragging and dropping from the device component library.

[0077] Basic components and profiled steel components are displayed in the three-dimensional model editing area of the model editor. Basic components include spheres, cuboids, cylinders, rotating ellipsoids, prisms, circular platforms, circular rings, terminal plates, insulator strings, and stretchers. Profiled steel components include conductors, cables, and mounting round rigid hole plates.

[0078] The process of creating a three-dimensional model of a power grid device using basic components involves the unification of different component coordinate systems, the adjustment of component size and position through the component adjustment interface, and the connection and matching between components. At the same time, the model editor provides a material interface that can add materials to the entire power grid device three-dimensional model, add materials to specified locations of the power grid device three-dimensional model, and add materials to specified surfaces of the power grid device three-dimensional model, making the power grid device three-dimensional model visually closer to the real device. Adding actions to the power grid device three-dimensional model, i.e., adding animation effects, the model editor provides an animation effect interface to add various animations to the power grid device three-dimensional model, including position animations, color material animations, and running parameter change corresponding animations.

[0079] Step 400: Bind the power grid device three-dimensional model with the twin data of the power grid device and the simulation and machine learning model to obtain a power grid digital twin, and visually present the power grid digital twin.

[0080] The twin data of the power grid device includes device static information and device dynamic information. The device static information includes device factory parameters, and the device dynamic information includes running data.

[0081] The power grid device three-dimensional model completes the binding with the twin data through the data interface, establishes the device visual information state, and binds the device static information with the device dynamic data information,

[0082] Specifically, the basic data of the main device, auxiliary device, and monitoring device are integrated, and the single and discrete device state and control signal are integrated to improve the information flow with strong comprehensiveness, good continuity, high transparency, and high digitalization. At the same time, sensor channels can be added, such as temperature, humidity, voltage, and current channels, to display channel values and their states, including minimum value, maximum value, small state, suitable state, and large state. The information obtained from online monitoring and analysis such as load analysis and abnormal analysis is bound with related devices to realize the visualization of twin information.

[0083] The application is based on web-side three-dimensional engine development, visualizes power grid three-dimensional scene data, dynamically loads models in data flow, realizes functions such as twin state visualization, multi-person cooperation, inspection, fault alarm, information layer display, and completely adapts to various power equipment twin operations. The power grid three-dimensional scene data includes the operation data of the equipment.

[0084] In addition, the application displays the appearance and complex mechanical structure of various types of equipment and facilities in the power grid through three-dimensional modeling. It supports the disassembly of power grid equipment three-dimensional models and the display of components and parameters. At the same time, it can integrate video monitoring, equipment operation monitoring, environmental monitoring, and other sensor real-time monitoring data, monitor the equipment location distribution, type, operating environment, and operating state, support real-time alarm of equipment operation anomalies (faults, short-circuit impact, overload, over-temperature, etc.) and equipment detailed information query, and assist managers in intuitively grasping the equipment operating state and timely discovering equipment safety hazards.

[0085] Specifically, the recognition algorithm is used to recognize the values of various types of meters and oil level meters, and the on-off state of the equipment; the fault defect algorithm and the temperature difference difference algorithm are used to predict the equipment failure time and give an early warning. When the monitoring parameters of the equipment and facilities are abnormal or close to the alarm threshold, an alarm is given and the fault area is automatically located, and the operation and maintenance personnel can quickly go to the specified area for verification according to the prompt, timely discover and eliminate the fault, and thus minimize the loss.

[0086] When a 500kV substation is constructed using a traditional three-dimensional modeling method, it is time-consuming and costly. The application is based on parametric three-dimensional modeling technology, integrates different types of equipment and component-level assemblies from different manufacturers in the transmission and transformation project, and constructs a device component library, laying a three-dimensional model foundation for the digital twin application of the transmission and transformation project. The device component library covers 104 basic primitives, more than 20 types of power transformation equipment, and more than 40,000 general components from 1,500 manufacturers, supporting element preview, editing, saving, exporting, etc. Through the device element library, users can quickly construct a three-dimensional model of power grid equipment using the drag-and-drop form, improve the reuse rate of three-dimensional model data, reduce the construction cost of three-dimensional models, and improve the efficiency of three-dimensional modeling of the transmission and transformation project.

[0087] Further, the constructed three-dimensional model of power grid equipment can integrate basic data such as main equipment, auxiliary equipment, and monitoring equipment, and bind twin information. Combined with simulation technology and machine learning models, the digital twin of power grid equipment is quickly constructed. From the rapid construction of the three-dimensional model of power grid equipment to the generation of the digital twin model, fault diagnosis and state prediction are performed to form a closed loop of the analysis process.

[0088] Embodiment two

[0089] In order to implement the method corresponding to the above-mentioned embodiment one, to realize the corresponding functions and technical effects, the following provides an autonomous controllable low-code power grid digital twin construction system.

[0090] As shown in Figure 3 The autonomous controllable low-code power grid digital twin construction system provided by the embodiment includes: a component library construction module 21, a device model construction module 22, and a twin construction module 23.

[0091] The component library construction module 21 is configured to construct three-dimensional models of each basic component in the power grid to obtain a device component library. The device component library includes a plurality of basic component models, and each basic component model is composed of triangular facets.

[0092] The device model construction module 22 is configured to construct a three-dimensional model of a power grid device in a low-code form based on the basic component models in the device component library.

[0093] The twin construction module 23 is configured to bind the three-dimensional model of the power grid device with twin data of the power grid device and simulation and machine learning models to obtain a power grid digital twin, and to visually present the power grid digital twin.

[0094] Compared with the prior art, the autonomous controllable low-code power grid digital twin construction system provided by the embodiment has the same beneficial effects as the autonomous controllable low-code power grid digital twin construction method provided by embodiment one, and will not be described here.

[0095] Embodiment three

[0096] The embodiment provides an electronic device, which includes a memory and a processor. The memory is configured to store a computer program, and the processor is configured to execute the computer program to enable the electronic device to perform the autonomous controllable low-code power grid digital twin construction method of embodiment one.

[0097] Optionally, the electronic device can be a server.

[0098] In addition, the embodiment of the present application also provides a computer readable storage medium, which stores a computer program. When the computer program is executed by a processor, the autonomous controllable low-code power grid digital twin construction method of embodiment one is realized.

[0099] In the present specification, each embodiment is described in a progressive manner, and each embodiment focuses on the difference from other embodiments. The same or similar parts between each embodiment can be referred to each other.

[0100] The principles and implementation manners of the present application are described by using specific examples in the present application, and the above examples are only used to help understand the method of the present application and its core idea; meanwhile, for the general technical personnel in the art, the specific implementation manners and application ranges will be changed according to the idea of the present application. In conclusion, the content of the present specification should not be understood as the limitation of the present application.

Claims

1. An autonomously controllable low-code power grid digital twin construction method, characterized by, The autonomous controllable low-code power grid digital twin construction method comprises the following steps: Constructing a three-dimensional model of each basic component in the power grid to obtain a device component library; the device component library comprises a plurality of basic component models; the basic component models are composed of triangular facets; the basic components in the power grid include basic geometric components of a power transformation project, steel components of the power transformation project, overhead transmission line components, and cable line components; A facet reduction method based on sub-regions is used to reduce the facets of each basic component model in the device component library, specifically comprising the following steps: For any basic component model, in the athreduction process, for any sub-region of the basic component model after the ath-1reduction, the flatness of two adjacent triangular facets is calculated according to the coordinates of the vertices of the two adjacent triangular facets in the sub-region; 0 For any vertex in the basic component model after the ath-1reduction, the average distance from the vertex to its adjacent vertices and the sum of the flatness of the triangular facets to which the connecting edges of the vertex belong are calculated; The average distance from the vertex to its adjacent vertices and the sum of the flatness of the triangular facets to which the connecting edges of the vertex belong are weighted to obtain the influence radius of the vertex; According to the influence radius of each vertex, the vertices in the basic component model after the ath-1reduction are merged to obtain the basic component model after the athreduction; Based on the basic component models in the device component library, a low-code form is used to construct a three-dimensional model of a power grid device; specifically, a three-dimensional model of a power grid device is constructed in a Tola pull form based on a parameterized three-dimensional modeling technology; The three-dimensional model of the power grid device is bound with the twin data of the power grid device and the simulation and machine learning model to obtain a power grid digital twin, and the power grid digital twin is visually presented.

2. The autonomously controllable low-code power grid digital twin building method of claim 1, wherein, Constructing a three-dimensional model of each basic component in the power grid, specifically comprising the following steps: For any basic component in the power grid, a three-dimensional model of the basic component is constructed according to the configuration file of the basic component, in the order of library, primitive group, primitive, and primitive attribute.

3. The autonomously controllable low-code power grid digital twin building method of claim 1, wherein, Constructing a three-dimensional model of each basic component in the power grid, specifically comprising the following steps: The geometric parameters of the engineering geometry file are read and analyzed according to the corresponding spatial matrix to convert them into a solid model, thereby obtaining a three-dimensional model of each basic component; the engineering geometry file is a GIM model, a 3dsMax file, or an FBX file.

4. The autonomously controllable low-code power grid digital twin building method of claim 1, wherein, The flatness of two adjacent triangular facets is calculated using the following formula: N 11 = (y 12 -y 11 )*(z 13 -z 11 )-(z 12 -z 11 )*(y 13 -y 11 ); N 12 = (z 12 - z 11 )*(x 13 - x 11 )-(x 12 - x 11 )*(z 13 - z 11 ); N 13 = (x 12 -x 11 )*(y 13 -y 11 )-(y 12 -y 11 )*(x 13 -x 11 ); N 21 = (y 22 -y 21 )*(z 23 -z 21 )-(z 22 -z 21 )*(y 23 -y 21 ); N 22 = (z 22 -z 21 )*(x 23 -x 21 )-(x 22 -x 21 )*(z 23 -z 21 ); N 23 = (x 22 - x 21 ) * (y 23 - y 21 ) - (y 22 - y 21 ) * (x 23 - x 21 ) ; wherein θ is the flatness of two adjacent triangular facets, n1 is a unit normal vector of one of the adjacent triangular facets, n2 is a unit normal vector of the other of the adjacent triangular facets, i, j, k are unit vectors along x, y, z axes respectively, i=(1, 0, 0), j=(0, 1, 0), k=(0, 0, 1), N 11 , N 12 , N 13 are normal vectors of one of the adjacent triangular facets, N 21 , N 22 , N 23 are normal vectors of the other of the adjacent triangular facets, (x 11 , y 11 , z 11 ), (x 12 , y 12 , z 12 ), (x 13 , y 13 , z 13 ) are three vertex coordinates of one of the adjacent triangular facets, (x 21 , y 21 , z 21 ), (x 22 , y 22 , z 22 ), (x 23 , y 23 , z 23 ) are three vertex coordinates of the other of the adjacent triangular facets. wherein θ is the flatness of two adjacent triangular facets, n1 is a unit normal vector of one of the adjacent triangular facets, n2 is a unit normal vector of the other of the adjacent triangular facets, i, j, k are unit vectors along x, y, z axes respectively, i=(1, 0, 0), j=(0, 1, 0), k=(0, 0, 1), N 11 , N 12 , N 13 are normal vectors of one of the adjacent triangular facets, N 21 , N 22 , N 23 are normal vectors of the other of the adjacent triangular facets, (x 11 , y 11 , z 11 ), (x 12 , y 12 , z 12 ), (x 13 , y 13 , z 13 ) are three vertex coordinates of one of the adjacent triangular facets, (x 21 , y 21 , z 21 ), (x 22 , y 22 , z 22 ), (x 23 , y 23 , z 23 ) are three vertex coordinates of the other of the adjacent triangular facets.

5. The autonomously controllable low-code power grid digital twin building method of claim 1, wherein, The autonomous controllable low-code power grid digital twin construction method further comprises the following steps: Material and animation effects are added to the three-dimensional model of the power grid device.

6. The autonomously controllable low-code power grid digital twin building method of claim 1, wherein, The twin data of the power grid device includes device static information and device dynamic information; the device static information includes device factory parameters, and the device dynamic information includes operation data.

7. An autonomously controllable low-code power grid digital twin construction system applied to the autonomously controllable low-code power grid digital twin construction method of any one of claims 1-6, characterized in that, The autonomous controllable low-code power grid digital twin construction system comprises the following components: A component library construction module is configured to construct a three-dimensional model of each basic component in the power grid to obtain a device component library; the device component library comprises a plurality of basic component models; the basic component models are composed of triangular facets; A device model construction module is configured to construct a power grid device three-dimensional model in a low-code form based on a basic component model in the device component library. A twin construction module is configured to bind the power grid device three-dimensional model with twin data of the power grid device and a simulation and machine learning model to obtain a power grid digital twin, and to visually present the power grid digital twin.

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