Transformer polymerization degree distribution visualization method, system, equipment and medium

Abstract

The invention belongs to the technical field of data processing, and discloses a transformer polymerization degree distribution visualization method, system and device and a medium, and the method comprises the steps: generating an internal temperature field distribution simulation result of a transformer model through simulation software, and outputting transformer polymerization degree distribution data under different temperature changes; performing mesh generation on the transformer simulation model to obtain a triangular mesh model, and mapping the triangular mesh model to a plane parameter domain; selecting parameter points in the plane parameter domain, and generating corresponding data sampling points of the parameter points in the triangular mesh model by using an inverse mapping technology; and performing curved surface interpolation fitting on the data sampling points to obtain a curved surface fitting result, and performing visualization processing on the curved surface fitting result. According to the method, the sensor simulation model is subdivided to obtain the triangular mesh model, a complex model can be converted into a model with a small number of vertexes and a single boundary, the operation complexity of the fitting process is simplified, and the curved surface fitting efficiency is improved.

Description

Technical Field

[0001] The present invention relates to the field of data processing technology, and in particular to a method, system, device and medium for visualizing the distribution of degree of aggregation of a transformer. Background Art

[0002] The life of the insulation paper is the core factor that determines the life of the transformer, and the degree of polymerization of the insulation paper is an important characteristic parameter that reflects the internal operating status of the transformer. With the digital transformation of the power grid, higher requirements are placed on the internal polymerization of the digital twin transformer, which requires not only accurate deduction of the distribution of the polymerization degree, but also visual reconstruction. The dynamic deduction calculation model of the polymerization degree of insulation paper has been deeply studied and is relatively mature, but it is still difficult to achieve the visualization display of the later digital twin.

[0003] Due to the complex internal structure of the transformer, and the fact that both the simulation calculation results and the dynamic deduction results are discrete point data, the images generated by direct fitting are relatively fuzzy in visualization, and it is difficult to accurately identify specific local locations. At present, although there are a variety of surface reconstruction methods, most of them are applied to 3D modeling and imaging of appearance scenes. For the simulation calculation parameter data of the complex internal structure of the transformer and the indirect deduction parameter data such as the deduced internal aggregation distribution, it is still difficult to perform visual reconstruction due to the discreteness of the data.

[0004] Therefore, how to provide a method, system, equipment and medium for visualizing the distribution of transformer aggregation degree is an urgent problem to be solved. Summary of the invention

[0005] The embodiments of the present invention provide a method, system, device and medium for visualizing the distribution of aggregation degree of transformers, so as to solve the problems existing in the prior art that the image visualization effect is fuzzy and the specific local position cannot be accurately identified.

[0006] In order to have a basic understanding of some aspects of the disclosed embodiments, a brief summary is given below. This summary is not intended to be a general review, nor is it intended to identify key / important components or to delineate the scope of protection of these embodiments. Its only purpose is to present some concepts in a simple form as a preface to the detailed description that follows.

[0007] According to a first aspect of an embodiment of the present invention, a method for visualizing transformer aggregation degree distribution is provided.

[0008] In one embodiment, the transformer aggregation distribution visualization method includes:

[0009] The simulation software is used to generate the simulation results of the internal temperature field distribution of the transformer model, and the simulation results of the internal temperature field distribution are input into the transformer aggregation degree dynamic deduction calculation model to output the transformer aggregation degree distribution data under different temperature changes;

[0010] A transformer simulation model is constructed based on the transformer aggregation distribution data, the transformer simulation model is meshed to obtain a triangular mesh model, and the triangular mesh model is mapped to a plane parameter domain;

[0011] Select parameter points in the plane parameter domain, and use the inverse mapping technology to generate data sampling points corresponding to the parameter points in the triangular mesh model;

[0012] Surface interpolation fitting is performed on the data sampling points to obtain the surface fitting results, and the surface fitting results are visualized to realize the visualization of the transformer aggregation degree distribution.

[0013] In one embodiment, the simulation software is used to generate the internal temperature field distribution simulation results of the transformer model, and the internal temperature field distribution simulation results are input into the transformer aggregation degree dynamic deduction calculation model, and the transformer aggregation degree distribution data under different temperature changes are output, including:

[0014] The internal temperature field distribution of the transformer model is simulated by using simulation software to obtain the simulation result of the internal temperature field distribution of the transformer model;

[0015] The simulation results of the internal temperature field distribution are input into the pre-built transformer aggregation degree dynamic deduction calculation model to deduce the changes in the transformer aggregation degree under different temperature conditions and obtain the transformer aggregation degree distribution data;

[0016] The coordinate data of the internal components of the transformer are classified, and the boundaries of the internal components of the transformer are fitted using boundary curve fitting technology. Based on the boundary fitting results, the aggregation degree distribution data are mapped to the corresponding internal components of the transformer.

[0017] In one embodiment, the classifying the coordinate data of the internal components of the transformer and fitting the boundaries of the internal components of the transformer using boundary curve fitting technology includes:

[0018] Classifying the coordinate data of the internal components of the transformer, and dividing the internal components of the transformer into regular components and irregular components based on the classified coordinate data;

[0019] Extracting vertex coordinates of the rule component, and obtaining an internal point set of the rule component based on the vertex coordinates, so as to mark the coordinate data of the rule component;

[0020] The internal point set of regular shape in the irregular component is extracted, and the surface in the irregular component is fitted by using curve fitting technology to realize the marking of the coordinate data of the irregular component.

[0021] In one embodiment, the expression of the curve fitting technique is:

[0022]

[0023] Where P(t) represents the general form of the parameter vector equation; P i represents the i-th discrete point; B i represents the i-th curve; n represents the total number of discrete points on the plane; t represents the coordinate point on the curve.

[0024] In one embodiment, mapping the triangular mesh model to the plane parameter domain comprises:

[0025] Obtain the boundary point set of the triangular mesh model, and determine whether there is a minimum inner corner point in the boundary point set;

[0026] If there is a minimum inner corner point in the boundary point set, the minimum inner corner point is mapped to the corner point of the plane parameter domain; if there is no minimum inner corner point in the boundary point set, a minimum inner corner point in the boundary point set is extracted as the first corner point of the plane parameter domain;

[0027] Starting from the extracted minimum inner corner point, search for a point that meets the preset conditions in the boundary point set in a counterclockwise direction, and use the point that meets the preset conditions as the second corner point of the plane parameter domain. Continue the traversal process of the corner points until all the corner points in the plane parameter domain are found.

[0028] The remaining boundary points of the boundary point set are mapped to the edge of the plane parameter domain to complete the plane mapping of the triangular mesh model.

[0029] In one embodiment, the selecting of parameter points in the plane parameter domain and the use of an inverse mapping technique to generate data sampling points corresponding to the parameter points in the triangular mesh model include:

[0030] Selecting a parameter point in the plane parameter domain, determining a triangle piece of the parameter point according to the location of the parameter point, and obtaining a triangle piece corresponding to the parameter point in the original triangular mesh model based on the triangle piece of the parameter point;

[0031] Based on the triangle pieces corresponding to the parameter points in the original triangular mesh model, the parameter points are mapped to the data sampling points on the three-dimensional mesh using the inverse mapping formula, and the coordinate values ​​of the data sampling points are generated.

[0032] In one embodiment, the inverse mapping formula is:

[0033] V=fVm +g′V n +(1-fg′)V k ;

[0034] Where V represents the location of the data sampling point; f represents the first side length ratio; g′ represents the second side length ratio; (V k , V m , V n ) represents a triangle patch in a triangular mesh model.

[0035] In one embodiment, performing surface interpolation fitting on the data sampling points to obtain a surface fitting result, and visualizing the surface fitting result to realize visualization of the transformer aggregation distribution includes:

[0036] The boundary tangent vector of the data sampling point is obtained by using a parabola fitting algorithm, and based on the boundary tangent vector conditions of the data sampling point, the control vertex is determined based on the continuous data sampling points by using a two-stage curve inverse algorithm;

[0037] The boundary tangent vectors of the sampling points are fitted with the control vertices to obtain the surface fitting results of the data sampling points, and the surface fitting results are visualized to obtain the visualization results of the transformer aggregation degree distribution.

[0038] According to a second aspect of an embodiment of the present invention, a transformer aggregation degree distribution visualization system is provided.

[0039] In one embodiment, the transformer aggregation distribution visualization system includes:

[0040] The aggregation degree distribution data analysis module is used to generate the internal temperature field distribution simulation results of the transformer model by using the simulation software, and input the internal temperature field distribution simulation results into the transformer aggregation degree dynamic deduction calculation model, and output the transformer aggregation degree distribution data under different temperature changes;

[0041] A triangular mesh model mapping module is used to construct a transformer simulation model based on the transformer aggregation distribution data, mesh the transformer simulation model to obtain a triangular mesh model, and map the triangular mesh model to a plane parameter domain;

[0042] A data sampling point generation module is used to select parameter points in the plane parameter domain and generate data sampling points corresponding to the parameter points in the triangular mesh model using an inverse mapping technique;

[0043] The visualization processing module is used to perform surface interpolation fitting on the data sampling points to obtain the surface fitting results, and to visualize the surface fitting results to realize the visualization of the transformer aggregation degree distribution.

[0044] In one embodiment, the polymerization degree distribution data analysis module includes a temperature field simulation result generation module, a polymerization degree distribution data generation module and a coordinate data classification marking module, wherein:

[0045] The temperature field simulation result generation module is used to simulate the internal temperature field distribution of the transformer model using simulation software to obtain the internal temperature field distribution simulation result of the transformer model;

[0046] The polymerization degree distribution data generation module is used to input the internal temperature field distribution simulation results into a pre-built transformer polymerization degree dynamic deduction calculation model, deduce the polymerization degree change of the transformer under different temperature conditions, and obtain the transformer polymerization degree distribution data;

[0047] The coordinate data classification and marking module is used to classify the coordinate data of the internal components of the transformer, and fit the boundaries of the internal components of the transformer using boundary curve fitting technology, and based on the boundary fitting results, map the polymerization degree distribution data to the corresponding internal components of the transformer.

[0048] In one embodiment, the coordinate data classification and marking module includes a coordinate data classification module, a regular component data marking module and an irregular component data marking module, wherein:

[0049] The coordinate data classification module is used to classify the coordinate data of the internal components of the transformer, and divide the internal components of the transformer into regular components and irregular components based on the classified coordinate data;

[0050] The rule component data marking module is used to extract the vertex coordinates of the rule component and obtain the internal point set of the rule component based on the vertex coordinates to achieve marking of the coordinate data of the rule component;

[0051] The irregular component data marking module is used to extract the internal point set of the regular shape in the irregular component, and use the curve fitting technology to fit the surface in the irregular component to achieve the marking of the coordinate data of the irregular component.

[0052] In one embodiment, the expression of the curve fitting technique is:

[0053]

[0054] Where P(t) represents the general form of the parameter vector equation; P i represents the i-th discrete point; B i represents the i-th curve; n represents the total number of discrete points on the plane; t represents the coordinate point on the curve.

[0055] In one embodiment, the triangular mesh model mapping module includes a minimum inner corner point judgment module, a corner point generation module, a corner point traversal module and a remaining boundary point mapping module, wherein:

[0056] A minimum inner corner point determination module is used to obtain a boundary point set of a triangular mesh model and determine whether there is a minimum inner corner point in the boundary point set;

[0057] A corner point generation module is used to map the minimum inner corner points to the corner points of the plane parameter domain if there are minimum inner corner points in the boundary point set; if there are no minimum inner corner points in the boundary point set, extract a minimum inner corner point in the boundary point set as the first corner point of the plane parameter domain;

[0058] Corner point traversal module, which is used to start from the extracted minimum inner corner point, search for a point that meets the preset conditions in the boundary point set in a counterclockwise direction, and use the point that meets the preset conditions as the second corner point of the plane parameter domain, and continue the corner point traversal process until all corner points in the plane parameter domain are found;

[0059] The remaining boundary point mapping module is used to map the remaining boundary points of the boundary point set to the edge of the plane parameter domain to complete the plane mapping of the triangular mesh model.

[0060] In one embodiment, the data sampling point generation module includes a parameter point determination module and a data sampling point determination module, wherein:

[0061] A parameter point determination module is used to select a parameter point in the plane parameter domain, determine the triangle piece of the parameter point according to the location of the parameter point, and obtain the triangle piece corresponding to the parameter point in the original triangular mesh model based on the triangle piece of the parameter point;

[0062] The data sampling point determination module is used to map the parameter points to the data sampling points on the three-dimensional grid based on the triangle pieces corresponding to the parameter points in the original triangular mesh model using the inverse mapping formula and generate the coordinate values ​​of the data sampling points.

[0063] According to a third aspect of an embodiment of the present invention, a computer device is provided.

[0064] In one embodiment, the computer device includes a memory and a processor, the memory stores a computer program, and the processor implements the steps of the above method when executing the computer program.

[0065] According to a fourth aspect of embodiments of the present invention, a computer-readable storage medium is provided.

[0066] In one embodiment, the computer-readable storage medium stores a computer program, and the computer program implements the steps of the above method when executed by a processor.

[0067] The technical solution provided by the embodiment of the present invention may have the following beneficial effects:

[0068] 1. The present invention divides the sensor simulation model into a triangular mesh model, which can convert a complex model into a model with a small number of vertices and a single boundary, simplify the operation complexity of the fitting process, and improve the surface fitting efficiency.

[0069] 2. Based on surface reconstruction, the present invention maps the triangular mesh model to a plane parameter domain, reselects continuous parameter points in the plane parameter domain, and then uses the inverse mapping method to obtain continuous data sampling points in the three-dimensional space. These continuous data sampling points are used for surface interpolation fitting and visualization processing, which can remove the blocking and splicing operations. The operation is simple, which improves the efficiency of surface reconstruction, and then realizes the visualization of the internal aggregation distribution of the transformer, meeting the demand for visualization of the digital twin transformer.

[0070] It is to be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0071] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the invention and, together with the description, serve to explain the principles of the invention.

[0072] Figure 1 is a flow chart of a method for visualizing transformer aggregation distribution according to an exemplary embodiment;

[0073] Figure 2 is a principle block diagram of a transformer aggregation distribution visualization system according to an exemplary embodiment;

[0074] Figure 3 is a structural schematic diagram of a computer device according to an exemplary embodiment;

[0075] Figure 4 is a schematic diagram of coordinates of four vertices of a rectangular component of a method for visualizing distribution of degree of aggregation of a transformer according to an exemplary embodiment;

[0076] Figure 5 is a schematic diagram of an irregular insulating paperboard showing a method for visualizing distribution of polymerization degree of a transformer according to an exemplary embodiment;

[0077] Figure 6 It is a schematic diagram of mapping a triangular mesh model to a plane parameter domain of a transformer aggregation distribution visualization method according to an exemplary embodiment;

[0078] Figure 7is a schematic diagram of a triangular mesh model with four minimum interior corner points showing a method for visualizing transformer aggregation distribution according to an exemplary embodiment;

[0079] Figure 8 is a schematic diagram of a triangular mesh model with a smoothly changing boundary of a transformer aggregation distribution visualization method according to an exemplary embodiment;

[0080] Fig. 9 is a schematic diagram of boundary point parameterization results of a transformer aggregation distribution visualization method according to an exemplary embodiment;

[0081] Fig.10 is a schematic diagram of planarization results of a space mesh in a triangular mesh model of a transformer aggregation distribution visualization method according to an exemplary embodiment;

[0082] Fig.11 is a schematic diagram of an inverse mapping of a transformer aggregation degree distribution visualization method according to an exemplary embodiment;

[0083] Fig.12 is a schematic diagram of visualization processing results after surface fitting of a transformer aggregation degree distribution visualization method according to an exemplary embodiment;

[0084] Fig.13 It is a schematic diagram of existing data visualization results of a transformer aggregation degree distribution visualization method according to an exemplary embodiment. DETAILED DESCRIPTION

[0085] The following description and accompanying drawings fully illustrate the specific embodiments of this article so that those skilled in the art can practice them. Parts and features of some embodiments may be included in or replace parts and features of other embodiments. The scope of the embodiments of this article includes the entire scope of the claims, as well as all available equivalents of the claims. Herein, the terms "first", "second", etc. are only used to distinguish one element from another, without requiring or implying any actual relationship or order between these elements. In fact, the first element can also be called the second element, and vice versa. Moreover, the terms "include", "comprise" or any other variant thereof are intended to cover non-exclusive inclusion, so that the structure, device or equipment including a series of elements includes not only those elements, but also other elements that are not explicitly listed, or also include elements inherent to such structure, device or equipment. In the absence of more restrictions, the elements defined by the sentence "including one..." do not exclude the existence of other identical elements in the structure, device or equipment including the elements. Each embodiment is described in a progressive manner herein, and each embodiment focuses on the differences from other embodiments, and the same and similar parts between the embodiments can be referred to each other.

[0086] Each module in the device or system of the present application can be implemented in whole or in part by software, hardware, or a combination thereof. The above modules can be embedded in or independent of a processor in a computer device in the form of hardware, or can be stored in a memory in a computer device in the form of software, so that the processor can call and execute operations corresponding to the above modules.

[0087] In the absence of conflict, the embodiments of the present invention and the features of the embodiments may be combined with each other.

[0088] Figure 1 An embodiment of the transformer aggregation degree distribution visualization method of the present invention is shown.

[0089] In this optional embodiment, the transformer aggregation distribution visualization method includes:

[0090] Step S101, using simulation software to generate internal temperature field distribution simulation results of the transformer model, and inputting the internal temperature field distribution simulation results into a transformer aggregation degree dynamic deduction calculation model, and outputting transformer aggregation degree distribution data under different temperature changes;

[0091] Step S103, constructing a transformer simulation model based on the transformer aggregation distribution data, meshing the transformer simulation model to obtain a triangular mesh model, and mapping the triangular mesh model to a plane parameter domain;

[0092] Step S105, selecting parameter points in the plane parameter domain, and using inverse mapping technology to generate data sampling points corresponding to the parameter points in the triangular mesh model;

[0093] Step S107, performing surface interpolation fitting on the data sampling points to obtain a surface fitting result, and performing visualization processing on the surface fitting result to realize visualization of the transformer aggregation degree distribution.

[0094] In this optional embodiment, the simulation software is used to generate the internal temperature field distribution simulation results of the transformer model, and the internal temperature field distribution simulation results are input into the transformer aggregation degree dynamic deduction calculation model, and the transformer aggregation degree distribution data under different temperature changes are output, including:

[0095] The internal temperature field distribution of the transformer model is simulated by simulation software to obtain the simulation results of the internal temperature field distribution of the transformer model; the internal temperature field distribution simulation results are input into the pre-built transformer aggregation dynamic deduction calculation model to deduce the aggregation change of the transformer under different temperature conditions to obtain the transformer aggregation distribution data; the coordinate data of the internal components of the transformer are classified, and the boundaries of the internal components of the transformer are fitted by boundary curve fitting technology; based on the boundary fitting results, the aggregation distribution data are mapped to the corresponding internal components of the transformer.

[0096] In this optional embodiment, the classifying the coordinate data of the internal components of the transformer and fitting the boundaries of the internal components of the transformer using the boundary curve fitting technology includes:

[0097] The coordinate data of the internal components of the transformer are classified, and the internal components of the transformer are divided into regular components and irregular components based on the classified coordinate data; the vertex coordinates of the regular components are extracted, and the internal point set of the regular components is obtained based on the vertex coordinates to realize the marking of the coordinate data of the regular components; the internal point set of the regular shape in the irregular component is extracted, and the surface in the irregular component is fitted using curve fitting technology to realize the marking of the coordinate data of the irregular component.

[0098] In this optional embodiment, the expression of the curve fitting technique is:

[0099]

[0100] Where P(t) represents the general form of the parameter vector equation; P i represents the i-th discrete point; B i represents the i-th curve; n represents the total number of discrete points on the plane; t represents the coordinate point on the curve.

[0101] In this optional embodiment, mapping the triangular mesh model to the plane parameter domain includes:

[0102] Get the boundary point set of the triangular mesh model, and determine whether there is a minimum inner corner point in the boundary point set; if there is a minimum inner corner point in the boundary point set, map the minimum inner corner points to the corner points of the plane parameter domain; if there is no minimum inner corner point in the boundary point set, extract a minimum inner corner point in the boundary point set as the first corner point of the plane parameter domain; starting from the extracted minimum inner corner point, search for a point that meets the preset conditions in the boundary point set in a counterclockwise direction, and use the point that meets the preset conditions as the second corner point of the plane parameter domain, and continue the traversal process of the corner points until all corner points in the plane parameter domain are found; map the remaining boundary points of the boundary point set to the edge of the plane parameter domain to complete the plane mapping of the triangular mesh model.

[0103] In this optional embodiment, the selecting of parameter points in the plane parameter domain and the use of an inverse mapping technique to generate data sampling points corresponding to the parameter points in the triangular mesh model include:

[0104] A parameter point is selected in the plane parameter domain, the triangle piece of the parameter point is determined according to the location of the parameter point, and the corresponding triangle piece of the parameter point in the original triangular mesh model is obtained based on the triangle piece of the parameter point; based on the corresponding triangle piece of the parameter point in the original triangular mesh model, the parameter point is mapped to the data sampling point on the three-dimensional grid using the inverse mapping formula, and the coordinate value of the data sampling point is generated.

[0105] In this optional embodiment, the inverse mapping formula is:

[0106] V=fV m +g′V n +(1-fg′)V k ;

[0107] Where V represents the location of the data sampling point; f represents the first side length ratio; g′ represents the second side length ratio; (V k , V m , V n ) represents a triangle patch in a triangular mesh model.

[0108] In this optional embodiment, performing surface interpolation fitting on the data sampling points to obtain a surface fitting result, and visualizing the surface fitting result to achieve visualization of the transformer aggregation distribution includes:

[0109] The parabola fitting algorithm is used to obtain the boundary tangent vector of the data sampling point, and based on the boundary tangent vector conditions of the data sampling point, the control vertex is determined based on the continuous data sampling points using a two-stage curve inverse algorithm; the boundary tangent vector of the sampling point is fitted with the control vertex to obtain the surface fitting result of the data sampling point, and the surface fitting result is visualized to obtain the visualization result of the transformer aggregation degree distribution.

[0110] Figure 2 An embodiment of the transformer aggregation degree distribution visualization system of the present invention is shown.

[0111] In this optional embodiment, the transformer aggregation distribution visualization system includes:

[0112] The polymerization degree distribution data analysis module 201 is used to generate the internal temperature field distribution simulation results of the transformer model by using the simulation software, and input the internal temperature field distribution simulation results into the transformer polymerization degree dynamic deduction calculation model, and output the transformer polymerization degree distribution data under different temperature changes;

[0113] A triangular mesh model mapping module 203 is used to construct a transformer simulation model based on the transformer aggregation distribution data, mesh the transformer simulation model to obtain a triangular mesh model, and map the triangular mesh model to a plane parameter domain;

[0114] The data sampling point generation module 205 is used to select parameter points in the plane parameter domain and generate data sampling points corresponding to the parameter points in the triangular mesh model using an inverse mapping technique;

[0115] The visualization processing module 207 is used to perform surface interpolation fitting on the data sampling points to obtain a surface fitting result, and to perform visualization processing on the surface fitting result to realize the visualization of the transformer aggregation degree distribution.

[0116] In this optional embodiment, the polymerization degree distribution data analysis module 201 includes a temperature field simulation result generation module (not shown in the figure), a polymerization degree distribution data generation module (not shown in the figure) and a coordinate data classification marking module (not shown in the figure), wherein:

[0117] The temperature field simulation result generation module is used to simulate the internal temperature field distribution of the transformer model using simulation software to obtain the internal temperature field distribution simulation result of the transformer model; the aggregation degree distribution data generation module is used to input the internal temperature field distribution simulation result into a pre-built transformer aggregation degree dynamic deduction calculation model, deduce the aggregation degree change of the transformer under different temperature conditions, and obtain the transformer aggregation degree distribution data; the coordinate data classification and marking module is used to classify the coordinate data of the internal components of the transformer, and use the boundary curve fitting technology to fit the boundaries of the internal components of the transformer, and based on the boundary fitting results, map the aggregation degree distribution data to the corresponding internal components of the transformer.

[0118] In this optional embodiment, the coordinate data classification and marking module includes a coordinate data classification module (not shown in the figure), a regular component data marking module (not shown in the figure) and an irregular component data marking module (not shown in the figure), wherein:

[0119] The coordinate data classification module is used to classify the coordinate data of the internal components of the transformer, and divide the internal components of the transformer into regular components and irregular components based on the classified coordinate data; the regular component data marking module is used to extract the vertex coordinates of the regular components, and obtain the internal point set of the regular components based on the vertex coordinates to realize the marking of the coordinate data of the regular components; the irregular component data marking module is used to extract the internal point set of regular shapes in the irregular components, and use curve fitting technology to fit the curved surface in the irregular components to realize the marking of the coordinate data of the irregular components.

[0120] In this optional embodiment, the expression of the curve fitting technique is:

[0121]

[0122] Where P(t) represents the general form of the parameter vector equation; P i represents the i-th discrete point; B i represents the i-th curve; n represents the total number of discrete points on the plane; t represents the coordinate point on the curve.

[0123] In this optional embodiment, the triangular mesh model mapping module 203 includes a minimum inner corner point judgment module (not shown in the figure), a corner point generation module (not shown in the figure), a corner point traversal module (not shown in the figure) and a remaining boundary point mapping module (not shown in the figure), wherein:

[0124] The minimum inner corner point judgment module is used to obtain the boundary point set of the triangular mesh model and determine whether there is a minimum inner corner point in the boundary point set; the corner point generation module is used to map the minimum inner corner points to the corner points of the plane parameter domain if there is a minimum inner corner point in the boundary point set; if there is no minimum inner corner point in the boundary point set, extract a minimum inner corner point in the boundary point set as the first corner point of the plane parameter domain; the corner point traversal module is used to start with the extracted minimum inner corner point, search for points that meet the preset conditions in the boundary point set in a counterclockwise direction, and use the points that meet the preset conditions as the second corner point of the plane parameter domain, and continue the corner point traversal process until all corner points in the plane parameter domain are found; the remaining boundary point mapping module is used to map the remaining boundary points of the boundary point set to the edge of the plane parameter domain to complete the plane mapping of the triangular mesh model.

[0125] In this optional embodiment, the data sampling point generation module 205 includes a parameter point determination module (not shown in the figure) and a data sampling point determination module (not shown in the figure), wherein:

[0126] The parameter point determination module is used to select parameter points in the plane parameter domain, determine the triangle pieces of the parameter points according to the locations of the parameter points, and obtain the triangle pieces corresponding to the parameter points in the original triangular mesh model based on the triangle pieces of the parameter points; the data sampling point determination module is used to map the parameter points to the data sampling points on the three-dimensional grid based on the triangle pieces corresponding to the parameter points in the original triangular mesh model using the inverse mapping formula, and generate the coordinate values ​​of the data sampling points.

[0127] The transformer aggregation degree distribution visualization method provided by the present invention is described in detail below.

[0128] The simulation results of the internal temperature field distribution of the transformer model are obtained, and the aggregation degree distribution data of any years under different temperature changes are obtained according to the dynamic deduction calculation model of the transformer aggregation degree.

[0129] In the actual application process, after exporting the simulation results of the internal temperature field distribution of the transformer model using simulation software, the distribution data of the degree of aggregation of any age under different temperature changes can be obtained according to the dynamic deduction calculation model of the degree of aggregation of the transformer. However, since the data is discrete point data, it is impossible to visualize and realize accurate digital twins. Therefore, it is necessary to classify the internal coordinate data of the transformer (winding, insulating cardboard, iron core, insulating oil) and mark them. The marking implementation method adopts the method of fitting the boundary curves of each component. Here, the transformer grid temperature field distribution data T=(x, y, T xy ), the specific implementation steps include:

[0130] 1) Windings, cores and rectangular insulating paperboard: For components with rectangular structures, such as Figure 4 As shown, extract the four vertex coordinates of all rectangular components (equivalent to regular components): (x1, y1), (x2, y2), (x3, y3), (x4, y4), where x1 = x3, x2 = x4, y1 = y2, y3 = y4, then the internal point of the rectangular component is: A 长方形 {(x,y)x1≤x≤x2,y3≤y≤y2}.

[0131] 2) The insulating paperboard has an irregular shape (equivalent to an irregular component). In addition to the rectangle, there are other shapes combined to form it, but it is still composed of rectangle A, rectangle C and surface B. Figure 5 The data coordinates of the rectangle A and rectangle C shown in FIG. 1 are shown in step 1), and the internal point set is:

[0132] A={(x,y)|x1≤x≤x2,y3≤y≤y2};

[0133] C={(x,y)|x5≤x≤x7,y7≤y≤y8};

[0134] The B surface is composed of four curves: B1, B2, B3, and B4. Figure 5 As shown, curves B1 and B3 are straight lines, i.e., B1: y=y1, B3: x=x6. Curves B2 and B4 need to be fitted by curve coordinates derived from CAD software. The present invention adopts Bezier curve fitting (Bezier curve fitting refers to generating an optimal Bezier curve through a given set of points so that the curve is as close to these points as possible) method.

[0135] The general form of the parameter vector equation is:

[0136]

[0137] in:

[0138]

[0139] Its component form is:

[0140]

[0141] Where P i (i=0,1,...,n) is the i-th plane discrete point, is the formula for the number of combinations in factorial operation, n is the total number of discrete points on the plane, and t is the coordinate point on the curve; corresponding to the two curves B2 and B4, we can get Then the point set inside surface B is:

[0142]

[0143] In the formula, f B *() represents the curve equation of B*.

[0144] Repeat the above operation to mark the coordinate data of the insulating paperboard, winding and iron core, and the remaining coordinate data will be automatically marked as insulating oil.

[0145] The transformer simulation model is meshed to obtain a triangular mesh model. The transformer simulation model is a transformer model including aggregation degree distribution data, and the triangular mesh model is mapped to a plane parameter domain.

[0146] In practical applications, mapping a triangular mesh model to a plane parameter domain is a plane parameterization process. Plane parameterization is to find a linear mapping function ψ to link the three-dimensional mesh model with the topological homeomorphic model in the two-dimensional plane. j (x j ,y j , z j ), determine a corresponding parameter point P on the two-dimensional plane (i.e., the plane parameter domain) j (u j , v j ),like Figure 6 The plane parameterization result has an important influence on the quality of the fitted surface. Therefore, the basic requirement for the plane parameterization result is that the set of triangles in the plane parameter domain is valid. In addition, it is also required that the difference in geometric characteristics between the triangles in the triangular mesh model (i.e., spatial triangles) and the corresponding triangles in the plane parameter domain is minimal.

[0147] For example, using V b Represents the boundary point set of the triangular mesh model that needs to be parameterized. Suppose there are N boundary points in the triangular mesh model, recorded as V b = {N}. To map the boundary points to the boundary of the plane unit square, we must first determine the V corresponding to the four vertices of the square. b There are two cases for the points in the table:

[0148] ①If V b There are four minimum interior angle points in the system. The so-called minimum interior angle point is the point where the angle between the two adjacent boundary edges and the edge is the smallest. Figure 7 v a 、v b 、v c 、v d are the four smallest inner corner points. If the cumulative chord lengths between the four inner corner points are not much different (for example, less than 1 mm), then the four points are mapped to the four corner points of the plane square, that is:

[0149]

[0150] ②Cannot meet the above conditions (ie V b There are no four minimum interior corner points in , such as Figure 8 As shown, the boundary change of the triangular mesh model is relatively gentle, so take V b The smallest interior corner point v in a As the first corner point of the square (plane parameter domain), that is, And order Where L is the sum of the lengths of all the lines connecting the boundary points.b By v a Start to find a point V in the counterclockwise direction. b ,make minimum, then ψ(v b ) is the second corner point of the square, Find some more v c ,make minimum, then ψ(v c ) is the third corner point of the square, Similarly, find vd, so minimum, then ψ(v d ) is the fourth corner point of the square, For triangular mesh models with smooth boundary changes, this method divides the boundary evenly to obtain a satisfactory parameterized structure, where ||v j+1 -v j || represents the j+1th point v j+1 and the jth point v j The length of the connection between.

[0151] After completing the above steps, you can determine the four corner points, and then b The remaining boundary points in are mapped to the corresponding square edges, Figure 8 Medium a 、V b The v between i Point, using the following formula to map to Fig. 9 The edge ψ(v a )ψ(v b )superior:

[0152]

[0153] In the formula, t i Indicates v i The horizontal coordinate of the point, t i+1 Indicates v i+1 The horizontal coordinate of the point, D i Indicates v i+1 With the i-th point v i σ represents the total length of all lines between two adjacent points.

[0154] Figure 8 Medium V b 、v c The v between s Point, using the following formula to map to Fig. 9 The edge ψ(v b )ψ(v c )superior:

[0155]

[0156] In the formula, t s Indicates v s The vertical coordinate of the point, D s Indicates v s+1 and the sth point v s The length of the line between two adjacent points, σ ​​represents the sum of the lengths of all lines between two adjacent points, v * Indicates point*, and the subscript * is used to distinguish different corner points.

[0157] Figure 8 Medium c 、v d The v between j Point, using the following formula to map to Fig. 9 The edge ψ(v c )ψ(v d )superior:

[0158]

[0159] In the formula, t j Indicates v j The horizontal coordinate of the point, t j+1 Indicates v j+1 The horizontal coordinate of the point, D j Indicates v j+1 and the jth point v j σ represents the total length of all lines between two adjacent points.

[0160] Figure 8 Medium d 、v a The v between r Point, using the following formula to map to Fig. 9 The edge ψ(v d )ψ(v a )superior:

[0161]

[0162] In the formula, t r Indicates v r The vertical coordinate of the point, t r+1 Indicates v r+1 The vertical coordinate of the point, D r Indicates v r+1 and the rth point v r σ represents the total length of all lines between two adjacent points.

[0163] Try to make the point with the smaller inner corner point the corner point of the plane parameter domain. The parameter value of the parameter point between the two corner points in the plane parameter domain is determined by its position in the original triangular mesh model, such as Fig.10 As shown, the triangular mesh model vertex v and its neighborhood Nv Planarize the points in .

[0164] Select continuous parameter points in the plane parameter domain, and combine the inverse mapping method to obtain continuous data sampling points in the triangular mesh model. The parameterization of the triangular mesh model completes the homeomorphic mapping of the mesh model from three-dimensional space to two-dimensional space. Because the mapping is a one-to-one mapping, an inverse mapping can be determined. Conversely, the inverse mapping can be used to determine the position of a parameter point in the original space triangular mesh model according to the position of the parameter point in the plane parameter domain. Then, in the actual application process, if Fig.11 As shown, Fig.11 (a) is a schematic diagram of a triangle patch in a plane parameter domain. Fig.11 (b) is a schematic diagram of a triangle patch in a triangular mesh model. Given a point P in the parameter domain, determine the triangle patch where the point is located (P k , P m , P n ), we can find the triangle piece corresponding to the triangle piece in the spatial triangular mesh model (V k , V m , V n ), point P can be mapped to point V on the three-dimensional grid according to the following formula:

[0165] V=sV k +gV m +(1-sg)V n ;

[0166] Where V represents the location of the data sampling point, and s represents the first area ratio, i.e. △PP m P n Area / △P k P m P n Area, g represents the second area ratio, namely △PP k P n Area / △P k P m P n Area, (V k , V m , V n ) represents a triangle patch in a triangular mesh model.

[0167] Due to the parameterization relationship, when the triangle (P k , P m , P n ) approaches zero, point P belongs to triangle (P k , P m , P n ), and use the following formula to calculate the coordinate value of point V:

[0168] V=fV m +g′V n +(1-fg′)V k ;

[0169] Where V represents the location of the data sampling point; f represents the first side length ratio; g′ represents the second side length ratio; (V k , V m , V n ) represents a triangle in a triangular mesh model, where the side length of the triangle and l are expressed as:

[0170] l=||pp m ||+||pp n ||+||pp k ||;

[0171]

[0172] In the formula, l represents the side length of the triangle, (P k , P m , P n ) represents the triangle where the parameter point is located, f represents the first side length ratio, and g represents the second area ratio.

[0173] The surface interpolation fitting is performed according to the continuous data sampling points to obtain the surface fitting result, and the surface fitting result is visualized. The visualization result is as follows: Fig.12 shown.

[0174] Based on the above content, the parameter points selected in the plane parameter domain are described, and an orderly topological structure is generated in the space along the u and v directions through the inverse mapping method. Therefore, B-spline surface reconstruction is to construct a k×l' B-spline surface interpolation re-sampled spatial data points. Surface reconstruction is a process of solving unknown control vertices i=0,1,…,m+k-1,j=0,1,…,n+k-1. Here, a two-stage curve inversion algorithm is used to obtain the control vertices. Take the quasi-uniform node vector (that is, the set node vector) as:

[0175] U=(0,0,0…i / n…,1,1,1), V=(0,0,0…j / n…,1,1,1);

[0176] Among them, U is the node vector set in the u direction, V is the node vector set in the v direction, i, j = 0, 1, ..., n, the parabola fitting method is used to obtain the boundary tangent vector, and the mixed partial derivative vectors at the four corner points of the data lattice (selected when the data is marked) are taken as zero vectors. When finding the control vertices, the first stage calculates the B-spline control vertices of each cross-section curve in the u direction in:

[0177]

[0178] Add the tangent boundary condition:

[0179]

[0180] Where, d 1,j is the first control vertex in the u direction, d 0,j is the 0th control vertex in the u direction, d n+2,j is the n+2th control vertex in the u direction, d n+1,j is the n+1th control vertex in the u direction. Similarly, d r,j For all control vertices in the u direction, r ranges from 0 to n+2, P n,j is the B-spline formed from the u direction to the nth control vertex, P 0,j is the origin in the u direction, p(u i+3 ) is the B-spline parameter vector equation of each section curve in the u direction.

[0181] Combining the above two equations and fixing a value of j, we can solve the control vertex of a cross-section curve. Until all the

[0182] In the second stage, is the interpolation point, but The corresponding cross-boundary tangent vectors need to be calculated. On the node vector U, the two cross-boundary tangent vectors in the v direction are used as "data points", and the mixed partial derivative is used as the "endpoint v-direction tangent vector". The curve is inversely calculated to find the control vertex t of the cross-boundary tangent vector curve of the first and last u-direction parameter boundary. r,0 ,r=0,1,…,n+2 and t r,1 ,r=0,1,…,n+2, we have:

[0183]

[0184] By fixing a value of r, we can solve a row of control vertices Until all the Once the control vertices are obtained, the B-spline surface can be fitted according to the node vectors and the control vertices.

[0185] In the actual application process, since the simulated temperature field distribution data and the polymerization degree distribution data further dynamically deduced with the help of temperature distribution are three-dimensional data, the coordinates corresponding to the x and y coordinates in the polymerization degree distribution data are used as the z axis, and the problem is converted into a process of smooth surface fitting. In the transformer simulation model, it is often very difficult to fit the overall smooth spline surface of the triangular mesh model with a huge number of vertices and complex structure. It is necessary to first divide the triangular mesh model into four boundary domains, and then perform B-spline surface fitting on each four boundary domain separately, and splice the fitted surfaces. However, for triangular mesh models with a small number of vertices, the block fitting process is complex and inefficient, and the smoothing effect at the surface splicing is also poor. For this reason, for triangular mesh models with a small number of vertices and a single boundary, B-spline surface reconstruction based on plane parameterization is adopted here. Among them, the transformer simulation model is obtained by manually drawing the transformer structural parameter dimensions and importing them into the simulation software.

[0186] The present invention fits and renders the transformer dynamic deduction results. Since the data is discrete point data, the data visualization effect is poor (such as Fig.13 After the surface is reconstructed, the rendering color is continuous, which is better than the original data and can meet the visualization requirements of digital twin transformer evaluation.

[0187] Based on the above description, the method provided by the present invention first maps the triangular mesh model to a plane parameter domain, then reselects continuous parameter points in the plane parameter domain, combines the inverse mapping method to obtain continuous sampling points, and finally performs surface interpolation fitting based on these continuous sampling points. In addition to the limitation of the four-boundary domain, this method can also fit the triangular mesh of the non-four-boundary topological structure once to generate a B-spline surface, while also removing the block and splicing operations, thereby improving efficiency. This method can obtain more sampling points for triangular mesh models with a small number of vertices and a single boundary, and fit them into a surface. The operation is simple, thereby improving efficiency.

[0188] In one embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as follows: Figure 3As shown. The computer device includes a processor, a memory and a network interface connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store static information and dynamic information data. The network interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, the steps in the above method embodiment are implemented.

[0189] Those skilled in the art will understand that Figure 3 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present invention, and does not constitute a limitation on the computer device to which the solution of the present invention is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0190] In addition, the present invention also provides a computer device, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the steps in the above method embodiment when executing the computer program.

[0191] In addition, the present invention further provides a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the steps in the above method embodiment are implemented.

[0192] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided by the present invention can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory or optical memory, etc. Volatile memory can include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM).

[0193] The present invention is not limited to the structures which have been described above and shown in the drawings, and various modifications and changes may be made without departing from the scope thereof. The scope of the present invention is limited only by the appended claims.

Claims

1. A method for visualizing the distribution of transformer aggregation degree, characterized in that: The method includes: The simulation software is used to generate the simulation results of the internal temperature field distribution of the transformer model, and the simulation results of the internal temperature field distribution are input into the transformer aggregation degree dynamic deduction calculation model to output the transformer aggregation degree distribution data under different temperature changes; A transformer simulation model is constructed based on the transformer aggregation distribution data, the transformer simulation model is meshed to obtain a triangular mesh model, and the triangular mesh model is mapped to a plane parameter domain; Select parameter points in the plane parameter domain, and use the inverse mapping technology to generate data sampling points corresponding to the parameter points in the triangular mesh model; Surface interpolation fitting is performed on the data sampling points to obtain the surface fitting results, and the surface fitting results are visualized to realize the visualization of the transformer aggregation degree distribution.

2. The transformer aggregation distribution visualization method according to claim 1, characterized in that: The simulation software is used to generate the internal temperature field distribution simulation results of the transformer model, and the internal temperature field distribution simulation results are input into the transformer aggregation degree dynamic deduction calculation model, and the transformer aggregation degree distribution data under different temperature changes are output, including: The internal temperature field distribution of the transformer model is simulated by using simulation software to obtain the simulation result of the internal temperature field distribution of the transformer model; The simulation results of the internal temperature field distribution are input into the pre-built transformer aggregation degree dynamic deduction calculation model to deduce the changes in the transformer aggregation degree under different temperature conditions and obtain the transformer aggregation degree distribution data; The coordinate data of the internal components of the transformer are classified, and the boundaries of the internal components of the transformer are fitted using boundary curve fitting technology. Based on the boundary fitting results, the aggregation degree distribution data are mapped to the corresponding internal components of the transformer.

3. The transformer aggregation distribution visualization method according to claim 2, characterized in that: The classifying of the coordinate data of the internal components of the transformer and fitting the boundaries of the internal components of the transformer using the boundary curve fitting technology includes: Classifying the coordinate data of the internal components of the transformer, and dividing the internal components of the transformer into regular components and irregular components based on the classified coordinate data; Extracting vertex coordinates of the rule component, and obtaining an internal point set of the rule component based on the vertex coordinates, so as to mark the coordinate data of the rule component; The internal point set of regular shape in the irregular component is extracted, and the surface in the irregular component is fitted by using curve fitting technology to realize the marking of the coordinate data of the irregular component.

4. The transformer aggregation distribution visualization method according to claim 3 is characterized in that: The expression of the curve fitting technique is: Where P(t) represents the general form of the parameter vector equation; P i represents the i-th discrete point; B i represents the i-th curve; n represents the total number of discrete points on the plane; t represents the coordinate point on the curve.

5. The transformer aggregation distribution visualization method according to claim 4, characterized in that: Mapping the triangular mesh model to the plane parameter domain includes: Obtain the boundary point set of the triangular mesh model, and determine whether there is a minimum inner corner point in the boundary point set; If there is a minimum inner corner point in the boundary point set, the minimum inner corner point is mapped to the corner point of the plane parameter domain; if there is no minimum inner corner point in the boundary point set, a minimum inner corner point in the boundary point set is extracted as the first corner point of the plane parameter domain; Starting from the extracted minimum inner corner point, search for a point that meets the preset conditions in the boundary point set in a counterclockwise direction, and use the point that meets the preset conditions as the second corner point of the plane parameter domain. Continue the traversal process of the corner points until all the corner points in the plane parameter domain are found. The remaining boundary points of the boundary point set are mapped to the edge of the plane parameter domain to complete the plane mapping of the triangular mesh model.

6. The transformer aggregation distribution visualization method according to claim 5, characterized in that: The step of selecting parameter points in the plane parameter domain and generating data sampling points corresponding to the parameter points in the triangular mesh model by using an inverse mapping technique comprises: Selecting a parameter point in the plane parameter domain, determining a triangle piece of the parameter point according to the location of the parameter point, and obtaining a triangle piece corresponding to the parameter point in the original triangular mesh model based on the triangle piece of the parameter point; Based on the triangle pieces corresponding to the parameter points in the original triangular mesh model, the parameter points are mapped to the data sampling points on the three-dimensional mesh using the inverse mapping formula, and the coordinate values ​​of the data sampling points are generated.

7. The transformer aggregation distribution visualization method according to claim 6, characterized in that: The inverse mapping formula is: V=fVm+g′Vn+(1-fg′)V k ; Where V represents the location of the data sampling point; f represents the first side length ratio; g′ represents the second side length ratio; (V k , V m , V n ) represents a triangle patch in a triangular mesh model.

8. The transformer aggregation distribution visualization method according to claim 7, characterized in that: The performing surface interpolation fitting on the data sampling points to obtain the surface fitting result, and visualizing the surface fitting result to realize the visualization of the transformer aggregation degree distribution includes: The boundary tangent vector of the data sampling point is obtained by using a parabola fitting algorithm, and based on the boundary tangent vector conditions of the data sampling point, the control vertex is determined based on the continuous data sampling points by using a two-stage curve inverse algorithm; The boundary tangent vectors of the sampling points are fitted with the control vertices to obtain the surface fitting results of the data sampling points, and the surface fitting results are visualized to obtain the visualization results of the transformer aggregation degree distribution.

9. A transformer aggregation distribution visualization system, characterized in that: The system includes: The aggregation degree distribution data analysis module is used to generate the internal temperature field distribution simulation results of the transformer model by using the simulation software, and input the internal temperature field distribution simulation results into the transformer aggregation degree dynamic deduction calculation model, and output the transformer aggregation degree distribution data under different temperature changes; A triangular mesh model mapping module is used to construct a transformer simulation model based on the transformer aggregation distribution data, mesh the transformer simulation model to obtain a triangular mesh model, and map the triangular mesh model to a plane parameter domain; A data sampling point generation module is used to select parameter points in the plane parameter domain and generate data sampling points corresponding to the parameter points in the triangular mesh model using an inverse mapping technique; The visualization processing module is used to perform surface interpolation fitting on the data sampling points to obtain the surface fitting results, and to visualize the surface fitting results to realize the visualization of the transformer aggregation degree distribution.

10. The transformer aggregation distribution visualization system according to claim 9, characterized in that: The polymerization degree distribution data analysis module includes a temperature field simulation result generation module, a polymerization degree distribution data generation module and a coordinate data classification marking module, wherein: The temperature field simulation result generation module is used to simulate the internal temperature field distribution of the transformer model using simulation software to obtain the internal temperature field distribution simulation result of the transformer model; The polymerization degree distribution data generation module is used to input the internal temperature field distribution simulation results into a pre-built transformer polymerization degree dynamic deduction calculation model, deduce the polymerization degree change of the transformer under different temperature conditions, and obtain the transformer polymerization degree distribution data; The coordinate data classification and marking module is used to classify the coordinate data of the internal components of the transformer, and fit the boundaries of the internal components of the transformer using boundary curve fitting technology, and based on the boundary fitting results, map the polymerization degree distribution data to the corresponding internal components of the transformer.

11. The transformer aggregation distribution visualization system according to claim 10, characterized in that: The coordinate data classification and marking module includes a coordinate data classification module, a regular component data marking module and an irregular component data marking module, wherein: The coordinate data classification module is used to classify the coordinate data of the internal components of the transformer, and divide the internal components of the transformer into regular components and irregular components based on the classified coordinate data; The rule component data marking module is used to extract the vertex coordinates of the rule component and obtain the internal point set of the rule component based on the vertex coordinates to achieve marking of the coordinate data of the rule component; The irregular component data marking module is used to extract the internal point set of the regular shape in the irregular component, and use the curve fitting technology to fit the surface in the irregular component to achieve the marking of the coordinate data of the irregular component.

12. The transformer aggregation distribution visualization system according to claim 11, characterized in that: The expression of the curve fitting technique is: Where P(t) represents the general form of the parameter vector equation; P i represents the i-th discrete point; B i represents the i-th curve; n represents the total number of discrete points on the plane; t represents the coordinate point on the curve.

13. The transformer aggregation distribution visualization system according to claim 12, characterized in that: The triangular mesh model mapping module includes a minimum inner corner point judgment module, a corner point generation module, a corner point traversal module and a remaining boundary point mapping module, wherein: A minimum inner corner point determination module is used to obtain a boundary point set of a triangular mesh model and determine whether there is a minimum inner corner point in the boundary point set; A corner point generation module is used to map the minimum inner corner points to the corner points of the plane parameter domain if there are minimum inner corner points in the boundary point set; if there are no minimum inner corner points in the boundary point set, extract a minimum inner corner point in the boundary point set as the first corner point of the plane parameter domain; Corner point traversal module, which is used to start from the extracted minimum inner corner point, search for a point that meets the preset conditions in the boundary point set in a counterclockwise direction, and use the point that meets the preset conditions as the second corner point of the plane parameter domain, and continue the corner point traversal process until all corner points in the plane parameter domain are found; The remaining boundary point mapping module is used to map the remaining boundary points of the boundary point set to the edge of the plane parameter domain to complete the plane mapping of the triangular mesh model.

14. The transformer aggregation distribution visualization system according to claim 13, characterized in that: The data sampling point generation module includes a parameter point determination module and a data sampling point determination module, wherein: A parameter point determination module is used to select a parameter point in the plane parameter domain, determine the triangle piece of the parameter point according to the location of the parameter point, and obtain the triangle piece corresponding to the parameter point in the original triangular mesh model based on the triangle piece of the parameter point; The data sampling point determination module is used to map the parameter points to the data sampling points on the three-dimensional grid based on the triangle pieces corresponding to the parameter points in the original triangular mesh model using the inverse mapping formula and generate the coordinate values ​​of the data sampling points.

15. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 8 are implemented.

16. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 8 are implemented.

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