Power transmission line icing dynamic visualization method, system and device based on three-dimensional GIM model and medium

Abstract

The invention discloses a power transmission line icing dynamic visualization method, system and device based on a three-dimensional GIM model, and a medium, and the method comprises the steps: building a GIM model based on unmanned plane laser point cloud data, carrying out the precise digitalization of the geometric structure of a power transmission line through a multi-level filtering and feature extraction algorithm, and enabling the model precision to reach the centimeter level; a complete icing state evaluation system is formed through integration of multi-source icing data fusion and intelligent feature extraction, and an icing prediction function is realized; according to the three-dimensional dynamic physical simulation technology based on the catenary theory, traditional two-dimensional calculation is creatively expanded to a three-dimensional space, uneven distribution of icing and wind load time-varying characteristics are considered, and real physical expression of the icing state of the wire is achieved; and dynamic presentation and deep data mining of the icing process are realized through time axis dynamic demonstration and a multi-dimensional interactive visualization technology. Through organic combination of the above schemes, the modeling precision, the data processing capability, the physical simulation and the interactive experience are improved compared with a traditional method.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of power equipment modeling and prediction, and particularly relates to a power transmission line icing dynamic visualization method, system, device and medium based on a three-dimensional GIM model. BACKGROUND

[0002] Icing phenomenon not only increases the load of the conductor and causes conductor breakage, but also causes serious consequences such as insulator flashover and tower collapse, which brings great risks to power grid operation. Traditional icing monitoring mainly relies on manual inspection and a small number of sensor monitoring, which cannot meet the requirements of real-time, accuracy and intuitiveness of modern power grids, and there is an urgent need to develop new intelligent icing monitoring and visualization technology.

[0003] The widely used icing monitoring method based on two-dimensional GIS map and simple chart display has many technical defects. This method mainly collects icing thickness, conductor tension and other data through online monitoring terminals, and then displays the icing state of each monitoring point in the form of color markers or numerical labels on the two-dimensional electronic map. The operation and maintenance personnel need to check the data table to understand the specific icing parameters. However, this traditional method cannot accurately reflect the spatial geometric structure of the power transmission line and the three-dimensional characteristics of the icing distribution, lacks the ability to finely display the icing state of specific device components such as towers, conductors and insulators, and it is difficult for operation and maintenance personnel to intuitively understand complex icing conditions and potential risks. At the same time, this method lacks dynamic demonstration function, cannot show the evolution process of icing over time, and cannot predict the dynamic behavior change of the conductor under icing conditions based on physical simulation, which seriously affects the scientificity and timeliness of operation and maintenance decision-making. SUMMARY

[0004] In view of the above existing problems, the present application is proposed.

[0005] Therefore, the present application provides a power transmission line icing dynamic visualization method and system based on a three-dimensional GIM model to solve the problem that the current method cannot accurately reflect the spatial geometric structure of the power transmission line and the three-dimensional characteristics of the icing distribution, lacks the ability to finely display the icing state of specific device components such as towers and conductors, and cannot predict the dynamic behavior change of the conductor under icing conditions based on physical simulation.

[0006] To solve the above technical problems, the present application provides the following technical solutions:

[0007] In a first aspect, the present application provides a power transmission line icing dynamic visualization method based on a three-dimensional GIM model, comprising: acquiring point cloud data of the power transmission line, identifying key structural features of the tower by using a segmentation algorithm based on geometric constraints, and constructing a three-dimensional GIM model through feature extraction and geometric parameter construction.

[0008] Acquire multi-source data of transmission lines, fuse icing-related data from different sources, and extract key features of icing status;

[0009] The key features are input into the icing prediction model, and the predicted icing thickness is output.

[0010] The predicted ice thickness is used as the core input to the ice risk assessment model, and combined with other ice-related data, a risk assessment index is generated.

[0011] Based on catenary theory and modern computer graphics, the predicted ice thickness is transformed into a three-dimensional morphological change in a three-dimensional GIM model, realizing dynamic physical simulation of the conductor under ice and wind load.

[0012] The constructed GIM model, icing data and risk assessment results, as well as the simulated dynamic morphology of the conductor, are integrated and visualized in a unified manner.

[0013] As a preferred embodiment of the dynamic visualization method for transmission line icing based on a 3D GIM model described in this invention, the method for identifying key structural features of the towers using a segmentation algorithm based on geometric constraints includes:

[0014] Based on the lattice structure of the tower, the angles and proportions of the structure, a set of characteristic points of the main structure of the tower is constructed.

[0015] The main geometric structure of the tower is fitted using the RANSAC algorithm, the structural parameter matrix of the tower is calculated, and the geometric parameters are extracted.

[0016] Based on the extracted feature points and geometric parameters, a three-dimensional GIM model of the tower is constructed.

[0017] As a preferred embodiment of the dynamic visualization method for icing of transmission lines based on a 3D GIM model described in this invention, the method involves: acquiring multi-source data of the transmission line, fusing icing-related data from different sources, and extracting key features of the icing state, including:

[0018] The collected multi-source data is bound and associated with the corresponding components in the 3D GIM model through a data interface;

[0019] Suppose that at time t, data from various field sensors at monitoring point i are collected and integrated to form a multi-dimensional state vector, thereby obtaining the key features of the icing state;

[0020] The key features include ice thickness, which is calculated by inverting the changes in conductor tension and suspension angle.

[0021] The advantages of this preferred technical solution are as follows: the catenary theory is chosen as the basis because it can accurately describe the static equilibrium state of flexible conductors under gravity, and is the standard method for conductor design and analysis in power engineering.

[0022] As a preferred embodiment of the dynamic visualization method for transmission line icing based on a three-dimensional GIM model described in this invention, the method involves inputting the key features into the icing prediction model and outputting a predicted icing thickness value, including:

[0023] Key features of the icing state are input into a long short-term memory network model;

[0024] The prediction model takes into account historical icing data, meteorological trends and environmental factors, and outputs the predicted icing thickness for each q-hour period within a preset time frame.

[0025] As a preferred embodiment of the dynamic visualization method for transmission line icing based on a 3D GIM model described in this invention, the predicted icing thickness is used as the core input to the icing risk assessment model, and combined with other icing-related data, a risk assessment index is generated, including:

[0026] The input is a conductor image that combines the predicted icing thickness with the icing conductor parameters;

[0027] A risk assessment index is output through edge detection, texture analysis, and morphological feature verification.

[0028] The risk assessment index includes: icing thickness, meteorological conditions, equipment status, and environmental factors.

[0029] As a preferred embodiment of the dynamic visualization method for transmission line icing based on a 3D GIM model described in this invention, the method involves: based on catenary theory and modern computer graphics, transforming the predicted icing thickness into a 3D morphological change in the 3D GIM model, including:

[0030] Let the weight per unit length of the conductor be q. s (N / m), the weight of the ice covering is q i (N / m), wind load is q w (N / m), then the actual specific load q of the conductor in space t The vector combination of vertical and horizontal loads needs to be considered, expressed as:

[0031]

[0032] The standard equation of a conductor in a catenary coordinate system is derived based on the geometric properties of the catenary and is expressed as:

[0033]

[0034] Where H is the horizontal tension component of the conductor, x and y are the horizontal and vertical coordinates of the catenary coordinate system, respectively, and the origin of the coordinate system is located at the lowest point of the catenary.

[0035] The actual coordinates of any point on the conductor in three-dimensional space are calculated using a coordinate transformation matrix;

[0036] Based on a 3D rendering engine and a geometric level detail algorithm, the model accuracy is dynamically adjusted according to the viewing distance.

[0037] As a preferred embodiment of the dynamic visualization method for transmission line icing based on a three-dimensional GIM model described in this invention, the method involves: unifying and visualizing the constructed GIM model, icing data, risk assessment results, and simulated conductor dynamic morphology, including:

[0038] Dynamic demonstration of time dimension through keyframe animation;

[0039] A density-based color mapping algorithm is used to map the numerical value of ice thickness into a color representation.

[0040] Secondly, the present invention provides a dynamic visualization system for icing of transmission lines based on a three-dimensional GIM model, comprising:

[0041] The acquisition module is used to acquire point cloud data of transmission lines, and uses a segmentation algorithm based on geometric constraints to identify key structural features of the towers. A three-dimensional GIM model is constructed through feature extraction and geometric parameters.

[0042] The feature fusion and extraction module is used to acquire multi-source data of transmission lines, fuse icing-related data from different sources, and extract key features of icing status.

[0043] The prediction module is used to input the key features into the icing prediction model and output the predicted value of icing thickness.

[0044] The risk assessment module is used to take the predicted ice thickness as the core input to the ice risk assessment model and combine it with other ice-related data to generate a risk assessment index.

[0045] The simulation module is used to transform the predicted ice thickness into a three-dimensional morphological change in a three-dimensional GIM model based on catenary theory and modern computer graphics, so as to realize the dynamic physical simulation of the conductor under ice and wind load.

[0046] The rendering and interaction module is used to integrate and visualize the constructed GIM model, icing data and risk assessment results, as well as the simulated dynamic morphology of the conductor.

[0047] Thirdly, the present invention provides a computer device, comprising:

[0048] Memory and processor;

[0049] The memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions. When the computer-executable instructions are executed by the processor, they implement the steps of the dynamic visualization method for icing of transmission lines based on a three-dimensional GIM model.

[0050] Fourthly, the present invention provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, implement the steps of the dynamic visualization method for icing of transmission lines based on a three-dimensional GIM model.

[0051] Compared with existing technologies, the beneficial effects of this invention are as follows: Based on the construction of a refined GIM three-dimensional digital twin model of transmission lines, combined with three-dimensional rendering technology and geographic information systems, it aims to improve the shortcomings of traditional icing monitoring methods in terms of visualization accuracy, real-time performance, and intuitiveness. By integrating UAV laser point cloud data acquisition technology, icing intelligent identification algorithm, and conductor catenary simulation algorithm, it solves key technical problems such as component-level precision three-dimensional modeling, real-time fusion rendering of multi-source data, and dynamic visualization display based on physical simulation. This provides a high-precision, highly real-time, and highly intuitive three-dimensional visualization solution for transmission line icing monitoring, thereby improving the intelligent level of power grid operation and maintenance and emergency response capabilities. Attached Figure Description

[0052] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0053] Figure 1 This is a schematic diagram of the overall process of the dynamic visualization method for icing of transmission lines based on a three-dimensional GIM model according to an embodiment of the present invention.

[0054] Figure 2 This is a schematic diagram illustrating intelligent feature extraction and risk prediction in a dynamic visualization method for icing of transmission lines based on a three-dimensional GIM model, as described in an embodiment of the present invention. Detailed Implementation

[0055] To make the above-mentioned objects, features, and advantages of the present invention more apparent and understandable, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the protection scope of the present invention.

[0056] Example 1, referring to Figure 1 As an embodiment of the present invention, a method for dynamic visualization of icing on transmission lines based on a three-dimensional GIM model is provided, comprising:

[0057] S100: Acquire point cloud data of transmission lines, use a segmentation algorithm based on geometric constraints to identify key structural features of towers, and construct a 3D GIM model through feature extraction and geometric parameters;

[0058] S200: Acquire multi-source data of transmission lines, fuse icing-related data from different sources, and extract key features of icing status;

[0059] S300: Input the key features into the icing prediction model and output the predicted icing thickness;

[0060] S400: The predicted ice thickness is used as the core input to the ice risk assessment model, and combined with other ice-related data, a risk assessment index is generated.

[0061] S500: Based on catenary theory and modern computer graphics, the predicted ice thickness is transformed into a three-dimensional morphological change in a three-dimensional GIM model, realizing dynamic physical simulation of the conductor under ice and wind load.

[0062] S600: Unifies and visualizes the constructed GIM model, icing data and risk assessment results, as well as the simulated dynamic morphology of the conductor.

[0063] It should be noted that the dynamic visualization method for transmission line icing based on a three-dimensional GIM model proposed in this invention achieves a complete technical link from data acquisition to visualization display through four key technical steps in steps S100-S600. That is, it combines the concept of digital twin, three-dimensional modeling technology, physical simulation algorithm and real-time visualization technology to build a complete intelligent monitoring and visualization system for transmission line icing.

[0064] Example 2, refer to Figures 1-2 As an embodiment of the present invention, based on the above embodiment, a dynamic visualization method for icing of transmission lines based on a three-dimensional GIM model is provided.

[0065] In this embodiment of the application, the point cloud data of the transmission line in step S100 can be obtained by using UAV lidar technology to obtain high-precision point cloud data of the transmission line, and then a three-dimensional GIM model with component-level precision can be constructed based on point cloud feature extraction technology.

[0066] It should be noted that UAV laser point cloud was chosen as the basic data source because it can provide centimeter-level measurement accuracy and acquire comprehensive three-dimensional spatial information under complex terrain conditions, making it particularly suitable for the modeling needs of long-span linear infrastructure such as power transmission lines.

[0067] In an optional implementation, S100 may further include a preprocessing step. For example, in the point cloud preprocessing stage, the original point cloud data P0 = {p i |p i =(x i ,y i ,z i The point cloud is filtered and denoised for the number of points i = 1, 2, ..., N, where N is the total number of points in the cloud. i ,y i ,z i Let be the 3D coordinates of the i-th point. Due to environmental interference and sensor errors, the original point cloud contains a large number of noisy points, which must be effectively filtered to ensure the accuracy of subsequent modeling.

[0068] In another alternative implementation, based on the previous implementation, a statistical filtering algorithm is used to remove noise points to obtain clean point cloud data P1:

[0069] P1={p i ∈P0|d i ≤μ+β·σ}

[0070] Where d i For point p i The average distance to its k-neighbors, μ and σ are the mean and standard deviation of the average distance to all points, respectively, and β is the filter strength parameter.

[0071] It should be noted that this filtering method based on local statistical characteristics can effectively preserve the structural feature points of transmission lines while removing discrete noise points, laying the foundation for subsequent feature extraction.

[0072] In this embodiment of the application, the identification of key structural features of the tower using a segmentation algorithm based on geometric constraints in step S100 includes the following steps A1-A3:

[0073] A1: Based on the lattice structure of the tower, the angles and proportions of the structure, construct a set of characteristic points of the main structure of the tower;

[0074] Unlike conventional buildings, transmission towers have unique geometric features, and the features in A1 can be used as the basis for extraction.

[0075] For example, the set of feature points of the main structure of the tower can be represented as:

[0076] F t ={f j |f j =(x j ,y j ,z j ,c j ),j=1,2,...,M}

[0077] Among them, f j x represents the j-th feature point; j The x-coordinate of the feature point in three-dimensional space; y-coordinate j The z-coordinate represents the Y-coordinate of the feature point in three-dimensional space; j c represents the Z-coordinate of the feature point in three-dimensional space; j The semantic category label represents the feature point (e.g., tower body point, crossarm point, hanging point, and tower leg point); M is the total number of feature points for a single tower.

[0078] A2: Fit the main geometric structure of the tower using the RANSAC algorithm, calculate the structural parameter matrix of the tower, and extract the geometric parameters;

[0079] Specifically, the structural parameter matrix S includes parameters such as tower height, width, and key angles.

[0080] It should be noted that the RANSAC algorithm was chosen because it is robust to outliers and can accurately extract geometric features in the presence of noise and partial occlusion, which is crucial for modeling transmission lines in complex environments.

[0081] A3: Based on the extracted feature points and geometric parameters, construct a 3D GIM model of the tower.

[0082] It should also be noted that, compared with traditional mesh modeling, the above-mentioned parametric modeling method can significantly reduce the model size, improve rendering efficiency, and maintain geometric accuracy, which is of great significance for the real-time visualization of large-scale power transmission lines.

[0083] In another alternative implementation, based on the implementation methods of A1-A3, model accuracy verification can also be evaluated by comparing the geometric deviation between the constructed GIM model and the original point cloud data. For example, when the average distance error is less than 5cm, the model accuracy is considered to meet the requirements.

[0084] In this embodiment of the application, step S200 involves acquiring multi-source data of the transmission line, fusing icing-related data from different sources, and extracting key features of the icing state, including the following steps B1-B3:

[0085] B1: The collected multi-source data is bound and associated with the corresponding components in the 3D GIM model through the data interface;

[0086] Specifically, multi-source data can be collected through equipment such as online monitoring terminals, high-definition cameras, and meteorological monitoring stations distributed at key locations along power transmission lines.

[0087] B2: Suppose that at time t, data from various field sensors at monitoring point i are collected and integrated to form a multi-dimensional state vector, and the key features of the icing state are obtained.

[0088] For example, let the icing monitoring data of monitoring terminal i at time t be a multi-dimensional vector D. i (t):

[0089] D i (t)=[h i (t),σ i (t),T i (t),H i (t),V i (t),θ i (t),F i (t),α i (t)] T

[0090] Among them, h i (t) represents the ice thickness, ρ i (t) is the ice density, T i (t) represents the ambient temperature, H i (t) represents humidity, V i (t) represents the wind speed, and θ i (t) is the wind direction angle, F i (t) represents the tension in the conductor, α i (t) represents the tilt angle of the insulator string. Data such as temperature, humidity, wind speed and direction, tension, and tilt angle are obtained directly from field sensors, while the ice thickness and density need to be calculated indirectly.

[0091] It should be noted that traditional icing monitoring methods often rely on a single data source, such as manual inspection or simple online monitoring devices, resulting in scattered data and difficulty in forming a unified assessment of icing status. This step establishes a multimodal data fusion framework. It is important to clarify that the GIM model in this invention serves as a three-dimensional digital twin carrier of all data, rather than the physical source of the data. All raw monitoring data, such as ambient temperature and humidity, wind speed, and conductor tension, are collected by physical equipment deployed at the transmission line site. This multi-dimensional data acquisition method comprehensively captures all aspects of the icing status, providing a sufficient data foundation for subsequent intelligent analysis.

[0092] B3: The key feature includes ice thickness, which is calculated by inverting the changes in conductor tension and suspension angle.

[0093] Specifically, the icing thickness h i It can be calculated using the following formula:

[0094]

[0095] Where, r w Where F is the radius of the conductor, F0 is the design tension, and F ice q is the tension after icing, q0 is the weight per unit length of the conductor, and q w For wind load.

[0096] In another alternative implementation, the ice thickness calculated based on the above implementation can also be used to obtain the ice density.

[0097] It should be noted that the indirect measurement of icing thickness is based on the principle of conductor mechanical equilibrium. Since directly measuring icing thickness is extremely difficult in real-world environments, this invention employs an indirect measurement method based on a physical model. The icing thickness is calculated by monitoring changes in conductor tension and sag angle. The advantage of this method is that it can utilize existing conductor monitoring equipment without requiring additional dedicated sensors, thus reducing system implementation costs.

[0098] In this embodiment of the application, step S300, which inputs the key features into the icing prediction model and outputs the predicted icing thickness value, includes the following steps C1-C2:

[0099] C1: Input the key features of the icing state into the Long Short-Term Memory (LSTM) network model;

[0100] It should be noted that LSTM is used to process long-term dependencies in time series data, and this model is particularly suitable for handling complex time series problems such as icing, which are affected by multiple meteorological factors.

[0101] C2: The prediction model takes into account historical icing data, meteorological trends and environmental factors, and outputs the predicted icing thickness every N hours within a preset time period.

[0102] For example, output the predicted ice thickness every 6 hours over the next 48 hours.

[0103] In another optional implementation, the prediction model in step S300 can also be a Transformer time series prediction model. The attention mechanism directly captures the temporal correlation at any distance, such as the impact of the temperature trend 48 hours before the arrival of a cold wave on the current icing. It supports heterogeneous inputs and can automatically ignore redundant information, such as irrelevant meteorological fluctuations, making it suitable for areas with complex micro-topography.

[0104] It should be noted that traditional icing monitoring systems can only provide the current status and lack predictive capabilities, thus failing to meet the needs of power grid disaster prevention and mitigation. In contrast, the icing prediction algorithm for a future preset time period is based on time series analysis and machine learning techniques, enabling it to predict future icing trends and provide forward-looking support for operation and maintenance decisions.

[0105] In this embodiment of the application, step S400 uses the predicted icing thickness as the core input to the icing risk assessment model, and combines it with other icing-related data to generate a risk assessment index, including the following steps D1-D2:

[0106] D1: Input the traverse image and icing traverse parameters that combine the predicted icing thickness;

[0107] D2: Outputs a risk assessment index through edge detection, texture analysis, and morphological feature verification;

[0108] The risk assessment index includes: icing thickness, meteorological conditions, equipment status, and environmental factors.

[0109] For example, the risk assessment index R(t) can be expressed as:

[0110] R(t)=ω1·R1(t)+ω2·R2(t)+ω3·R3(t)+ω4·R4(t)

[0111] Where R1(t) is the icing thickness risk index, R2(t) is the meteorological risk index, R3(t) is the equipment condition risk index, R4(t) is the environmental risk index, and ω1, ω2, ω3, and v4 are the corresponding weighting coefficients.

[0112] It should be noted that the icing risk assessment employs a multi-factor comprehensive evaluation method to establish a comprehensive risk assessment model. This assessment model and the aforementioned icing prediction algorithm are logically closely linked and progressive. The predicted icing thickness for a future preset time period (e.g., 48 hours) output by the icing prediction algorithm is used as a core parameter input into this risk assessment model to evaluate future icing risks.

[0113] In another alternative implementation, the model can also directly use the currently measured ice thickness value to perform real-time risk assessment.

[0114] In another alternative implementation, such as Figure 2 As shown, in addition to the risk level, the prediction and identification results can also include the icing type, icing thickness, and coverage.

[0115] Compared to traditional single-indicator assessments, multi-factor assessments can more comprehensively reflect the overall severity of icing hazards. At the same time, this comprehensive assessment method can provide a more scientific and accurate risk level judgment, providing a reliable basis for operation and maintenance decisions.

[0116] In this embodiment of the application, step S500, based on catenary theory and modern computer graphics, transforms the predicted ice thickness into a three-dimensional morphological change in a three-dimensional GIM model, including the following steps E1-E4:

[0117] E1: Due to the consideration of icing weight and wind load, the stress state of the conductor becomes complex; therefore, the self-weight per unit length of the conductor is set as q. s (N / m), the weight of the ice covering is q i (N / m), wind load is q w (N / m), then the actual specific load q of the conductor in space t The vector combination of vertical and horizontal loads needs to be considered, expressed as:

[0118]

[0119] Specifically, the weight of the icing is calculated based on the icing thickness and density output in the steps above.

[0120] E2: The standard equation of the conductor in the catenary coordinate system is derived based on the geometric properties of the catenary and is expressed as:

[0121]

[0122] Where H is the horizontal tension component of the conductor, x and y are the horizontal and vertical coordinates of the catenary coordinate system, respectively, and the origin of the coordinate system is located at the lowest point of the catenary.

[0123] It should be noted that the catenary theory was chosen as the basis because it can accurately describe the static equilibrium state of flexible conductors under gravity, and is the standard method for conductor design and analysis in power engineering.

[0124] E3: The actual coordinates of any point on the conductor in three-dimensional space are calculated using the coordinate transformation matrix T;

[0125] Specifically, considering the uneven distribution of icing, the conductor is divided into multiple micro-segments, each with independent icing parameters. This segmentation method can more accurately simulate actual icing conditions, especially for long-distance transmission lines crossing complex terrain, where icing distribution often exhibits significant spatial differences. The actual coordinates (X,Y,Z) of any point P(x,y) on the conductor in three-dimensional space are calculated using the coordinate transformation matrix T:

[0126]

[0127] The transformation matrix T takes into account the spatial orientation of the conductor and the wind deflection angle.

[0128]

[0129] Wind deflection angle The calculation takes into account the ratio of wind load to gravity load:

[0130]

[0131] E4: Based on a 3D rendering engine combined with a geometric level detail algorithm, it dynamically adjusts the model accuracy according to the viewing distance.

[0132] In one alternative implementation, E4, based on a WebGL-based 3D rendering engine, is a key technology for achieving high-performance visualization. Simultaneously, it employs Level of Detail (LOD) technology, adjusting the rendering based on the viewing distance d. v Dynamically adjusting model accuracy is expressed as:

[0133]

[0134] Where L represents the LOD level, and d1 and d2 are the LOD switching distance thresholds.

[0135] It should be noted that traditional 2D rendering techniques struggle to represent complex 3D scenes, while CPU-based 3D rendering performance cannot meet the demands of real-time interaction. This invention employs WebGL technology, fully utilizing the parallel computing capabilities of the GPU to achieve smooth rendering of large-scale 3D scenes. To further improve rendering efficiency, a multi-level detail rendering strategy is combined, which significantly reduces GPU load and improves overall system performance while maintaining visual quality.

[0136] In another alternative implementation, based on the E4 implementation described above, a material rendering system, such as Physically Based Rendering (PBR), can be incorporated. PBR can realistically simulate the optical properties of materials. Compared to traditional empirical shading models, PBR shading models are based on microsurface theory and use parameters such as metallicity, roughness, and albedo to describe material properties. This allows for a consistent and realistic appearance under different lighting conditions, making it particularly suitable for depicting material changes in conductors and towers after icing.

[0137] It should also be noted that the direct purpose of step S500 is to transform the abstract data (ice thickness, density) calculated in step S300 into the specific, physically consistent three-dimensional morphological changes of the conductor in the three-dimensional GIM model. Based on the classical catenary theory and modern computer graphics technology, dynamic physical simulation of the conductor under icing and wind loads is achieved. Traditional icing monitoring systems mostly use static symbols or simple charts to display the icing state, lacking a description of the actual physical behavior of the conductor and failing to intuitively reflect the impact of icing on transmission lines. This invention extends the traditional two-dimensional catenary calculation to three-dimensional space and considers the non-uniformity of icing distribution and the time-varying characteristics of wind loads, making the simulation results closer to reality.

[0138] In this embodiment of the application, step S600 integrates and visualizes the constructed GIM model, icing data, risk assessment results, and simulated conductor dynamics, including:

[0139] Dynamic demonstration of time dimension through keyframe animation;

[0140] For example, the timeline animation control system employs keyframe animation technology, supporting multiple interpolation algorithms to ensure the smoothness and realism of the animation. A sequence of key time points {t1, t2, ..., t...} is set. n}, the corresponding icing state sequence {S(t1), S(t2), ..., S(t)} n )}, intermediate time t∈[t k , t k+1 The state is calculated using the cubic Hermitian interpolation algorithm:

[0141] S(t) = h 00 (u)·S(t k )+h 10 (u)·(t k+1 -t k )·S′(t k )

[0142] +h 0r (u)·S(k t+1 )+h 11 (u)·(tk+1 -t k )·S′(t k+1 )

[0143] Where u = tt k / t k+1 -t k h is the normalized time parameter. 00 (u), h 10 (u), h 01 (u), h 11 (u) is the basis function for cubic Hermitian interpolation. Hermitian interpolation is chosen over nonlinear interpolation because it ensures the smoothness of state changes, avoids unnatural jumps between keyframes, and makes the icing process demonstration more realistic.

[0144] A density-based color mapping algorithm is used to map the numerical value of ice thickness into a color representation;

[0145] For example, the color mapping function uses piecewise linear interpolation and supports various predefined color schemes, such as warm and cool tones, and rainbow tones. The color value C(h) corresponding to the icing thickness h is calculated using the following formula:

[0146]

[0147] Among them, C min and C max These are the color values ​​corresponding to the minimum and maximum icing thicknesses, h. min and h max This represents the range of values ​​for icing thickness. This visualization method allows users to intuitively perceive the spatial distribution and severity of icing, and is more efficient than traditional numerical representations.

[0148] In another alternative implementation, visualization can also support precise mouse picking and touch interaction through a component-level interactive detection system, which is also the foundation for deep data mining. Traditional GIS systems typically only support overall or regional selection, making it difficult to achieve precise interaction with specific components. Therefore, a ray casting algorithm can be used to convert screen coordinates into rays in three-dimensional space, and then calculate the intersection points of the rays with the three-dimensional model to achieve precise selection of specific components such as towers, conductors, and insulators. Screen coordinates (x... s y s The equation of the corresponding ray in the world coordinate system is:

[0149] r(t) = O + t·D

[0150] Where O is the ray origin (camera position), D is the ray direction vector, t is a parameter, and r(t) is the ray vector. The intersection detection of the ray and the 3D model employs an efficient spatial segmentation algorithm to ensure real-time responsiveness in complex scenes. The multi-scale spatial navigation system supports seamless switching from a global perspective to local details, meeting observation needs at different scales. Navigation control uses quaternions to represent rotation, avoiding gimbal lock-up issues. Camera state updates employ an exponential smoothing algorithm to ensure smooth motion and improve user experience.

[0151] p(t+1)=κ·p g +(1-κ)·p(t)

[0152] q(t+1)=slerp(q(t),q g ,κ)

[0153] s(t+1)κ·s g +(1-κ)·s(t)

[0154] Where κ is the smoothing coefficient, slerp is the spherical linear interpolation function, and p g q s and s g These are the target position, rotation, and scaling values, respectively.

[0155] In another optional implementation, meteorological effects simulation can be added, rendering weather phenomena such as rain, snow, and fog in real time to enhance the realism of the visualization. Rendering weather effects not only improves the system's visual appeal but, more importantly, it visually demonstrates the impact of meteorological conditions on icing formation and development, helping users understand the relationship between icing and meteorological factors. Rain and snow effects are achieved using particle system technology, while fog effects are simulated through volumetric rendering technology; the combination of these two techniques creates a highly realistic meteorological environment.

[0156] It should be noted that the S600 is a key component of the user experience. Its function is as an integrated human-computer interaction terminal, unifying and visualizing the aforementioned GIM model, processed icing data and risk assessment results, and simulated conductor dynamics. Through advanced animation technology and interactive design, the complex icing evolution process is presented to the user in an intuitive and smooth manner. Traditional icing monitoring systems often use static data tables or simple charts, making it difficult for users to understand the spatiotemporal evolution of icing and its potential risks. This invention constructs a multi-dimensional interactive system that not only supports dynamic demonstrations in the time dimension but also provides free exploration in the spatial dimension and in-depth data mining, achieving a "what you see is what you get" visualization effect.

[0157] Example 3 illustrates a schematic scheme for a dynamic visualization method of transmission line icing based on a 3D GIM model. It should be noted that the technical solution of this system for dynamic visualization of transmission line icing based on a 3D GIM model is based on the same concept as the aforementioned method for dynamic visualization of transmission line icing based on a 3D GIM model. Details not described in detail in the system for dynamic visualization of transmission line icing based on a 3D GIM model in this embodiment can be found in the description of the aforementioned method for dynamic visualization of transmission line icing based on a 3D GIM model.

[0158] This embodiment also provides another dynamic visualization system for transmission line icing based on a 3D GIM model, including:

[0159] The acquisition module is used to acquire point cloud data of transmission lines, and uses a segmentation algorithm based on geometric constraints to identify key structural features of the towers. A three-dimensional GIM model is constructed through feature extraction and geometric parameters.

[0160] The feature fusion and extraction module is used to acquire multi-source data of transmission lines, fuse icing-related data from different sources, and extract key features of icing status.

[0161] The prediction module is used to input the key features into the icing prediction model and output the predicted value of icing thickness.

[0162] The risk assessment module is used to take the predicted ice thickness as the core input to the ice risk assessment model and combine it with other ice-related data to generate a risk assessment index.

[0163] The simulation module is used to transform the predicted ice thickness into a three-dimensional morphological change in a three-dimensional GIM model based on catenary theory and modern computer graphics, so as to realize the dynamic physical simulation of the conductor under ice and wind load.

[0164] The rendering and interaction module is used to integrate and visualize the constructed GIM model, icing data and risk assessment results, as well as the simulated dynamic morphology of the conductor.

[0165] This embodiment also provides a computer device suitable for dynamic visualization of icing on transmission lines based on a 3D GIM model, comprising: a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to implement the method for dynamic visualization of icing on transmission lines based on a 3D GIM model as proposed in the above embodiment.

[0166] This embodiment also provides a storage medium storing a computer program that, when executed by a processor, implements the method for dynamic visualization of icing on transmission lines based on a three-dimensional GIM model as proposed in the above embodiments.

[0167] The storage medium proposed in this embodiment and the method for dynamic visualization of transmission line icing based on a three-dimensional GIM model proposed in the above embodiments belong to the same inventive concept. Technical details not described in detail in this embodiment can be found in the above embodiments, and this embodiment has the same beneficial effects as the above embodiments.

[0168] Based on the above description of the implementation methods, those skilled in the art will clearly understand that the present invention can be implemented using software and necessary general-purpose hardware, and of course, it can also be implemented using hardware. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as a computer floppy disk, read-only memory (ROM), random access memory (RAM), flash memory, hard disk, or optical disk, etc., including several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods of the various embodiments of the present invention.

[0169] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A dynamic visualization method for icing on transmission lines based on a 3D GIM model, characterized in that, include: Point cloud data of transmission lines are acquired, and a segmentation algorithm based on geometric constraints is used to identify key structural features of the towers. A three-dimensional GIM model is constructed through feature extraction and geometric parameters. Acquire multi-source data of transmission lines, fuse icing-related data from different sources, and extract key features of icing status; The key features are input into the icing prediction model, and the predicted icing thickness is output. The predicted ice thickness is used as the core input to the ice risk assessment model, and combined with other ice-related data, a risk assessment index is generated. Based on catenary theory and modern computer graphics, the predicted ice thickness is transformed into a three-dimensional morphological change in a three-dimensional GIM model, realizing dynamic physical simulation of the conductor under ice and wind load. The constructed GIM model, icing data and risk assessment results, as well as the simulated dynamic morphology of the conductor, are integrated and visualized in a unified manner.

2. The method for dynamic visualization of icing on transmission lines based on a 3D GIM model as described in claim 1, characterized in that, The segmentation algorithm based on geometric constraints is used to identify the key structural features of the tower, including: Based on the lattice structure of the tower, the angles and proportions of the structure, a set of characteristic points of the main structure of the tower is constructed. The main geometric structure of the tower is fitted using the RANSAC algorithm, the structural parameter matrix of the tower is calculated, and the geometric parameters are extracted. Based on the extracted feature points and geometric parameters, a three-dimensional GIM model of the tower is constructed.

3. The method for dynamic visualization of icing on transmission lines based on a three-dimensional GIM model as described in claim 2, characterized in that, Acquire multi-source data of transmission lines, fuse icing-related data from different sources, and extract key features of icing status, including: The collected multi-source data is bound and associated with the corresponding components in the 3D GIM model through a data interface; Suppose that at time t, data from various field sensors at monitoring point i are collected and integrated to form a multi-dimensional state vector, thereby obtaining the key features of the icing state; The key features include ice thickness, which is calculated by inverting the changes in conductor tension and suspension angle.

4. The method for dynamic visualization of icing on transmission lines based on a three-dimensional GIM model as described in claim 3, characterized in that, The key features are input into the icing prediction model, and the predicted icing thickness is output, including: Key features of the icing state are input into a long short-term memory network model; The prediction model takes into account historical icing data, meteorological trends and environmental factors, and outputs the predicted icing thickness for each q-hour period within a preset time frame.

5. The method for dynamic visualization of icing on transmission lines based on a three-dimensional GIM model as described in claim 4, characterized in that, The predicted icing thickness is used as the core input to the icing risk assessment model, and combined with other icing-related data, a risk assessment index is generated, including: The input is a conductor image that combines the predicted icing thickness with the icing conductor parameters; A risk assessment index is output through edge detection, texture analysis, and morphological feature verification. The risk assessment index includes: icing thickness, meteorological conditions, equipment status, and environmental factors.

6. The method for dynamic visualization of icing on transmission lines based on a three-dimensional GIM model as described in claim 5, characterized in that, Based on catenary theory and modern computer graphics, the predicted icing thickness is transformed into three-dimensional morphological changes in a 3D GIM model, including: Let the weight per unit length of the conductor be q. s (N / m), the weight of the ice covering is q i (N / m), wind load is q w (N / m), then the actual specific load q of the conductor in space t The vector combination of vertical and horizontal loads needs to be considered, expressed as: The standard equation of a conductor in a catenary coordinate system is derived based on the geometric properties of the catenary and is expressed as: Where H is the horizontal tension component of the conductor, x and y are the horizontal and vertical coordinates of the catenary coordinate system, respectively, and the origin of the coordinate system is located at the lowest point of the catenary. The actual coordinates of any point on the conductor in three-dimensional space are calculated using a coordinate transformation matrix; Based on a 3D rendering engine and a geometric level detail algorithm, the model accuracy is dynamically adjusted according to the viewing distance.

7. The method for dynamic visualization of icing on transmission lines based on a three-dimensional GIM model as described in claim 6, characterized in that, The constructed GIM model, icing data, risk assessment results, and simulated conductor dynamics are integrated and visualized, including: Dynamic demonstration of time dimension through keyframe animation; A density-based color mapping algorithm is used to map the numerical value of ice thickness into a color representation.

8. A dynamic visualization system for icing of transmission lines based on a three-dimensional GIM model, using the method described in any one of claims 1-7, characterized in that, include: The acquisition module is used to acquire point cloud data of transmission lines, and uses a segmentation algorithm based on geometric constraints to identify key structural features of the towers. A three-dimensional GIM model is constructed through feature extraction and geometric parameters. The feature fusion and extraction module is used to acquire multi-source data of transmission lines, fuse icing-related data from different sources, and extract key features of icing status. The prediction module is used to input the key features into the icing prediction model and output the predicted value of icing thickness. The risk assessment module is used to take the predicted ice thickness as the core input to the ice risk assessment model and combine it with other ice-related data to generate a risk assessment index. The simulation module is used to transform the predicted ice thickness into a three-dimensional morphological change in a three-dimensional GIM model based on catenary theory and modern computer graphics, so as to realize the dynamic physical simulation of the conductor under ice and wind load. The rendering and interaction module is used to integrate and visualize the constructed GIM model, icing data and risk assessment results, as well as the simulated dynamic morphology of the conductor.

9. A computer device, characterized in that, include: Memory and processor; The memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions. When the computer-executable instructions are executed by the processor, they implement the steps of the dynamic visualization method for icing of transmission lines based on a three-dimensional GIM model as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, It stores computer-executable instructions, which, when executed by a processor, implement the steps of the dynamic visualization method for icing of transmission lines based on a three-dimensional GIM model as described in any one of claims 1 to 7.

Images