Gim-based simulation analysis method for power transmission mechanized construction and related products

Abstract

The application belongs to the technical field of power system construction and mechanized construction technology, and specifically relates to a power transmission mechanized construction simulation analysis method based on GIM and related products, which comprises the following steps: collecting GIM model data, satellite image data and laser radar data of a construction area; collecting equipment parameter data of construction engineering machinery; combining the GIM model data, satellite image data and laser radar data to construct a construction site model; constructing a mechanical equipment model according to the equipment parameter data; determining path planning and equipment scheduling through the construction site model and the mechanical equipment model, and loading them to a simulation platform to simulate the construction process; through three-dimensional visualization and simulation analysis of the construction site, construction managers can intuitively understand the construction progress and potential risks and make timely adjustment decisions. At the same time, automatic data analysis and optimization suggestions reduce manual intervention, improve the accuracy and reliability of the analysis.

Description

Technical Field

[0001] This invention relates to the field of power system construction and mechanized construction technology, specifically to a simulation analysis method and related products for mechanized power transmission construction based on GIM. Background Technology

[0002] With the continuous development of power systems, the scale and complexity of transmission line construction are also gradually increasing. Traditional construction methods mainly rely on manual judgment and on-site experience, which has limitations in handling complex terrain, optimizing construction resources, and ensuring construction safety. Especially when facing changing natural environments and different construction requirements, manual judgment often struggles to provide a comprehensive and accurate analysis.

[0003] Mechanized construction is gaining increasing attention as an effective means to improve construction efficiency and safety. However, existing mechanized construction methods still lack systematic analysis and simulation tools for the construction process. Traditional construction preparation and planning rely on experience, which often fails to effectively cope with complex construction environments, leading to unexpected situations during construction and affecting project progress and safety.

[0004] Currently, industry analysis of the construction process mainly relies on rules of thumb and simple planning tools, making dynamic management and optimization of the construction process particularly difficult. Especially in the integration and processing of multi-source data, there is a lack of efficient and automated solutions. Summary of the Invention

[0005] To address the aforementioned technical problems, this invention provides a simulation analysis method and related products for mechanized power transmission construction based on GIM, which enables refined modeling, dynamic simulation, and automated analysis of the construction site.

[0006] This invention is achieved through the following technical solution:

[0007] A three-dimensional simulation analysis method for mechanized power transmission construction based on GIM model data includes:

[0008] Collect GIM model data, satellite imagery data, and LiDAR data of the construction area; collect equipment parameter data of construction machinery and equipment;

[0009] By combining GIM model data, satellite imagery data, and LiDAR data, a construction site model is constructed.

[0010] Construct a mechanical equipment model based on equipment parameter data;

[0011] The path planning and equipment scheduling are determined by the construction site model and the mechanical equipment model, and then loaded into the simulation platform to simulate the construction process.

[0012] Specifically, tower information of the transmission circuit is obtained from GIM model data. The tower information includes: tower three-dimensional coordinates and structural information.

[0013] Topographic information of the construction area was obtained from satellite imagery and lidar data, including digital elevation models and digital orthophotos.

[0014] Environmental parameter information of the construction area is obtained by integrating it through a GIS geographic information system. The environmental parameter information includes vegetation, buildings and other land features.

[0015] The tower information, topographic information, and environmental parameter information are converted to the Global Geodetic Coordinate System and then fused to obtain a fused dataset.

[0016] Anomalies are detected using a density-based clustering algorithm and removed from the fused dataset to obtain a cleaned dataset.

[0017] Co-kriging interpolation is used to obtain multi-source datasets by identifying missing data points in the clear dataset.

[0018] Wavelet transform is performed on multi-source datasets to obtain features at different scales, and a multi-source data feature set is constructed.

[0019] Specifically, methods for constructing construction site models include:

[0020] A set of ground points is extracted from lidar data using a random sampling consensus algorithm;

[0021] A triangular irregular network is constructed on the set of ground points, and the Laplace equation is used to smooth the terrain of the triangular irregular network.

[0022] Solving partial differential equations using boundary conditions and the finite element method A smooth terrain model is obtained, where H(x, y) is the terrain elevation function. Here, ρ(x, y) is the Laplace operator, and ρ(x, y) is the source term function representing the density of topographic elevation variation.

[0023] Image segmentation is performed on satellite image data to extract environmental parameter information and obtain classification results; then, vegetation, buildings and other land features are modeled separately and fused with the terrain model to obtain a rough construction site model.

[0024] The coarse model was enhanced with multi-scale details using three-dimensional wavelet transform to obtain the final construction site model M(x, y, z). Among them, W l(x, y, z) are the wavelet coefficients at the l-th scale, Ψ l (x, y, z) are the corresponding wavelet basis functions, and L is the number of wavelet decomposition levels.

[0025] Specifically, other land features include: waterways, roads, and bridges; the modeling methods include:

[0026] The extracted building areas are reconstructed using a shape-from-contour algorithm, and building models are constructed based on the building's outline and height information.

[0027] A fractal set algorithm is used to simulate the three-dimensional structure of vegetation, and an iterative function system is used to generate vegetation morphology to construct a plant model.

[0028] The outline of a low-lying area or water basin is determined from the digital elevation model, and the water surface shape is surface-scraped using the water body outline to construct a water body model.

[0029] Binary path information of roads is obtained from image segmentation. Curve fitting is performed on the extracted binary path information. The road path is mapped to the terrain model according to the digital elevation model to construct the road model.

[0030] The bridge outline is extracted from the image segmentation, a shape-to-outline reconstruction algorithm is used, and a bridge model is constructed based on the bridge's outline, design height, and span.

[0031] Specifically, the methods for modeling mechanical equipment include:

[0032] The collected construction machinery and equipment form an equipment set EQ = {EQ1, EQ2, ..., EQ} N}, where N is the number of construction machinery and equipment; and obtain the i-th piece of equipment E i Equipment parameter data: Length L i Width W i Height H i Mass m i Inertial Tensor I i Engine power P i Maximum torque τ max，i Maximum speed v max，i Minimum turning radius R min，i ;

[0033] Using CAD software and based on length L i Width W i and height H i Create the EQ for the i-th device. i Three-dimensional models of construction machinery and equipment;

[0034] By combining kinematic and dynamic equations, a dynamic model of construction machinery and equipment is created.

[0035] EQ of the i-th device is established using engine power. i The energy efficiency model.

[0036] Optionally, the kinematic equations of the construction machinery are determined using the Denavit-Hartenberg parametric method, and the transformation matrix T for each j joint is determined. j , Where, θ j Let d be the rotation angle of the joint. j Let a be the position of the joint along the axial direction. j For the displacement between joints, α j This refers to the torsional angle of the joint;

[0037] The angular velocity of each joint is determined using the joint pose transformation matrix. and angular acceleration

[0038] The dynamic equations of construction machinery and equipment are determined using the Newton-Euler equations, and the linear acceleration 'a' of the equipment is determined. i and the torque M of the equipment i , Among them, F i For the resultant force acting on the device, ω i I is the angular velocity of the device. i For the inertia tensor of the device;

[0039] Methods combining kinematic and dynamic equations include:

[0040] The angular velocity and acceleration of each joint are used as inputs to the Newton-Euler equations, and the force and torque of each joint are calculated using the Newton-Euler equations. The force and torque are calculated step by step from the base to the end using the recursive formula in the Newton-Euler method.

[0041] Determine the final dynamic model Where q is the displacement of the joint. Let M(q) be the acceleration of the joint, and M(q) be the inertia matrix. Let G(q) be the matrix of Coriolis force and centrifugal force, where G(q) is the gravity term and τ is the driving torque of the joint.

[0042] Specifically, the methods for determining equipment scheduling and path planning include:

[0043] Define the set of construction tasks Where M represents the total number of construction tasks; and where τ represents the number of construction tasks. i Includes: Task types Mission Location Required equipment EQ j ;

[0044] Establish dependencies between tasks to form a directed acyclic graph. Where the edge set ε represents the order of the tasks;

[0045] The construction site model is converted into an environmental map required for path planning, including obstacle information and drivable areas, in order to minimize the total construction time T. total Total energy consumption E total To achieve the goal, an optimization model for equipment scheduling and path planning is established, and the optimal equipment scheduling and path planning are obtained.

[0046] Load the construction site model and mechanical equipment model into the simulation platform, and load the equipment scheduling and path planning into the simulation platform;

[0047] Dynamic simulation is performed using a simulation platform.

[0048] Specifically, methods for conducting dynamic simulation include:

[0049] The construction process is divided into discrete time steps Δt, and the total number of simulation steps is determined.

[0050] Calculate each time step t using a dynamic model k The displacement q of the lower device j (t k ) and speed

[0051] The speed obtained through calculation and acceleration Update the device location.

[0052] When the device EQ j Upon arrival at the mission location, the mission commences, and the mission progress is updated in real time based on the equipment's execution efficiency. i Completion level;

[0053] At each time step t k Collision detection is performed using bounding boxes or directed bounding box algorithms; if a collision risk is detected, a velocity-based obstacle avoidance method is used to replan the local path.

[0054] Using the equipment's energy efficiency model, calculate the equipment's energy consumption at time step t. k Energy consumption E j (t k The total energy consumption of all equipment is accumulated to obtain the total energy consumption of the construction process.

[0055] Optionally, methods for establishing optimization models for equipment scheduling and path planning and performing objective optimization include:

[0056] Establish objective function Where, x = [x ij [x] represents the device scheduling matrix. ij =1 indicates the device's EQ j Execute task τ i , γ={γ j (t)} represents the device EQ j The path planning set, where α and β are weighting coefficients for balancing construction time and construction energy consumption;

[0057] Define the constraints, including task allocation constraints: Equipment capacity constraint x ij ≤c ij c ij =1 indicates the device's EQ j Capable of performing tasks τ i c ij =0 indicates the device's EQ j Unable to execute task τ i Task dependency constraints τ k It is τ i The prerequisite tasks. This is the start time of the task. Task completion time; Path feasibility constraints: F safe The safe zone in the construction site model; equipment dynamic constraints: f j EQ for the device j The dynamic equations;

[0058] Encode the device scheduling matrix as part of a chromosome, and the device path γ j (t) is discretized into control points and encoded as another part of the chromosome; a genetic algorithm fitness function is constructed. in, λ1, λ2, and λ3 are the penalty terms for violating the constraints, and λ1, λ2, and λ3 are the weight coefficients for different constraints.

[0059] The chromosomes are subjected to genetic, crossover, and mutation processes, and through multiple iterations, the individual with the highest fitness is selected as the optimal solution to obtain the optimal equipment scheduling and path planning.

[0060] A computer program product includes a computer program / instructions that, when executed by a processor, implement the method described in any of the above.

[0061] Compared with the prior art, the present invention has the following advantages and beneficial effects:

[0062] The method provided by this invention first collects GIM model data, satellite image data, and lidar data of the construction area to construct a comprehensive model of the construction site; secondly, it constructs a mechanical equipment model based on equipment parameter data and performs optimization analysis on path planning and equipment scheduling through a simulation platform.

[0063] This invention enables construction managers to intuitively understand construction progress and potential risks through 3D visualization and simulation analysis of the construction site, allowing them to make timely adjustment decisions. Simultaneously, automated data analysis and optimization suggestions reduce manual intervention and improve the accuracy and reliability of the analysis. Attached Figure Description

[0064] The accompanying drawings illustrate exemplary embodiments of the present invention and, together with the description thereof, serve to explain the principles of the invention. These drawings are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, but do not constitute a limitation on the embodiments of the present invention.

[0065] Figure 1 This is a flowchart illustrating the simulation analysis method for mechanized power transmission construction based on GIM according to the present invention.

[0066] Figure 2 This is a flowchart illustrating the method for creating a construction site model according to the present invention.

[0067] Figure 3 This is a flowchart illustrating the method for determining equipment scheduling and path planning according to the present invention. Detailed Implementation

[0068] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are for illustrative purposes only and are not intended to limit the scope of the invention.

[0069] It should also be noted that, for ease of description, only the parts relevant to the present invention are shown in the accompanying drawings.

[0070] Where there is no conflict, the embodiments and features described herein can be combined with each other. The present invention will now be described in detail with reference to the accompanying drawings and embodiments.

[0071] GLIM stands for Grid Information Model, a technical standard developed by the State Grid Corporation of China to meet the needs of 3D design for power transmission and transformation projects. The State Grid began developing the standard in 2017. In 2018, the State Grid Economic Research Institute independently developed a scalable GIM standard system suitable for power transmission and transformation project construction by establishing a unified data architecture, coding system, interaction method, design depth, and deliverable format. In 2019, the State Grid released seven technical standards, including model interaction specifications, which, together with the "Technical Guidelines for 3D Design of Power Transmission and Transformation Projects Part 1: Substations (Converter Stations)" released in 2018, formed the State Grid's 3D design technical standards.

[0072] Example 1

[0073] like Figure 1 As shown, a three-dimensional simulation analysis method for mechanized power transmission construction based on GIM model data is provided, including:

[0074] The project collects GIM model data, satellite imagery data, and LiDAR data for the construction area. The GIM model includes data on various electrical equipment. Satellite imagery data, taken by satellites, reflects the surface features of a wide area and helps in understanding the environmental conditions of the construction area. LiDAR data contains three-dimensional spatial information obtained using laser ranging technology, enabling precise depiction of the terrain and the height and shape of objects.

[0075] Collect equipment parameter data for construction machinery and equipment; equipment parameter data includes the physical characteristics of the machinery and equipment, such as size, weight, power, maximum torque, etc.

[0076] By combining GIM model data, satellite imagery data, and LiDAR data, a construction site model is constructed. Through data fusion technology, these three types of data are integrated to create a comprehensive construction site model. The model includes not only terrain features but also the surrounding environment and features, ensuring a realistic reflection of the actual situation during construction.

[0077] Mechanical equipment models were constructed based on equipment parameter data. Using previously collected equipment parameters, 3D models of various construction machinery were built using computer-aided design (CAD) software. The shape and motion characteristics of the equipment were simulated, providing a basis for path planning and scheduling.

[0078] Path planning and equipment scheduling are determined using a construction site model and a machinery and equipment model, and then loaded into a simulation platform to simulate the construction process. Path planning refers to determining the optimal travel path for machinery and equipment based on terrain and obstacle information in the construction site model. Equipment scheduling involves the arrangement and time planning of different equipment in the construction task. After completing path planning and scheduling, this information is input into the simulation platform for dynamic simulation of the construction process to evaluate key indicators such as construction efficiency, safety, and resource utilization.

[0079] This embodiment integrates data from different sources to form a comprehensive view of the construction environment, and then combines it with a mechanical equipment model for path planning and scheduling, thereby achieving optimized management and dynamic simulation of the construction process.

[0080] Example 2

[0081] Because the data sources for collection are different, it is necessary to obtain information from different data sources and process the data in order to provide a foundation for subsequent construction simulation and optimization analysis.

[0082] The tower information of the transmission circuit is obtained from the GIM model data. The tower information includes the three-dimensional coordinates and structural information of the tower. The three-dimensional coordinates provide the spatial location of the tower in the construction area, while the structural information includes the tower's height, type, and material properties.

[0083] Topographic information of the construction area is obtained from satellite imagery and lidar data. This information includes a digital elevation model (DEM) and digital orthophotos. The DEM, generated using lidar or other surveying techniques, represents the elevation changes of the earth's surface, providing a topographic basis for construction. The digital orthophotos are geometrically corrected satellite images that accurately reflect the position and shape of ground objects, providing a detailed environmental view for construction.

[0084] Environmental parameter information of the construction area is obtained by integrating data through a GIS (Geographic Information System). This environmental parameter information includes vegetation, buildings, and other geographical features. A GIS is sometimes also called a "geological information system." It is a specific and very important spatial information system. Supported by computer hardware and software systems, it is a technological system for collecting, storing, managing, processing, analyzing, displaying, and describing geographic distribution data across the entire or part of the Earth's surface (including the atmosphere).

[0085] To ensure all data is analyzed within the same coordinate system and avoid data fusion errors caused by coordinate inconsistencies, tower information, topographic information, and environmental parameter information are converted to the Global Geodetic Coordinate System (WGS84) and then fused to obtain a fused dataset. Among them, T k (·) represents a data conversion function. For the dataset of the kth data source, w k The weights of the data source (satisfying that the sum of the weights is 1) satisfy... For the weighted norm, C k Let K be the covariance matrix of the k-th data source, where K is the total number of data sources.

[0086] Anomalies O were detected using a density-based clustering algorithm and removed from the fused dataset to obtain a cleaned dataset. Outlier data points are removed from the fused dataset using a density-based clustering algorithm. This algorithm analyzes the distribution of data points, identifying isolated points with low density as anomalies, thus ensuring the quality and consistency of the remaining data. n ε (x i ) as x i n is the number of data points within a neighborhood centered at a radius of ε. avg δ represents the average number of neighboring data points. δ is the outlier threshold.

[0087] Co-kriging interpolation is used to obtain multi-source datasets by identifying missing data points in the clear dataset. Co-kriging interpolation is a statistical method that uses a spatial correlation model between variables to infer the values ​​of missing data points based on information from recalled data points. By using information from other relevant variables, this method can provide more accurate interpolation results, forming a multi-source dataset. in, V(s) is the estimated value of the target variable at position s0. i ) and U(u j λ represents the observed values ​​of the main variable and the covariate, respectively. i μ j The weighting coefficients are determined by solving the Kriging system equations for the covariance matrix.

[0088] Wavelet transform was applied to a multi-source dataset to extract features at different scales, constructing a multi-source data feature set. Wavelet transform is a signal processing technique that decomposes data into different frequency components to extract important features. The constructed multi-source data feature set provides a foundation for subsequent construction analysis and decision-making.

[0089] Example 3

[0090] like Figure 2 As shown, the methods for constructing a construction site model include:

[0091] A set of ground points is extracted from LiDAR data using the Random Sample Consensus (RANSAC) algorithm, a method for extracting model parameters from noisy data. In this step, the algorithm randomly selects points from the LiDAR data and estimates the parameters of the plane in which they lie. This process is repeated until a set containing ground points is obtained.

[0092] A triangular irregular network (TIN) is constructed on the set of ground points, and the Laplace equation is used to smooth the terrain. The TIN is a modeling method that connects spatial points into triangles, effectively representing terrain undulations. After constructing the TIN, the Laplace equation is applied for smoothing, a process that minimizes the elevation differences between points, resulting in a smoother and more natural terrain model.

[0093] Solving partial differential equations using boundary conditions and the finite element method A smooth terrain model is obtained, where H(x, y) is the terrain elevation function. Let be the Laplace operator, representing the second partial derivative of elevation. ρ(x, y) is the source term function representing the density of topographic elevation variation. Using the finite element method, this equation is numerically solved under given boundary conditions, ultimately yielding a smooth terrain model.

[0094] Fully convolutional neural networks (FCNs) were used to segment satellite imagery data. Image segmentation techniques divide satellite imagery into different regions to extract information about vegetation, buildings, and other features. Other feature regions include waterways, roads, and bridges.

[0095] Environmental parameter information is extracted and classification results are obtained. After modeling vegetation, buildings and other land features separately, the models are merged with the terrain model to obtain a rough construction site model. This model integrates terrain and environmental features and can more accurately reflect the actual situation of the construction area.

[0096] The coarse model was enhanced with multi-scale details using three-dimensional wavelet transform to obtain the final construction site model M(x, y, z). Among them, W l (x, y, z) are the wavelet coefficients at the l-th scale, Ψ l (x,y,z) represents the corresponding wavelet basis function, and L represents the number of wavelet decomposition levels.

[0097] The specific modeling methods are provided below.

[0098] The methods for modeling include:

[0099] The extracted building areas are reconstructed using a shape-to-contour algorithm, and a building model is constructed based on the building's outline and height information. The shape-to-contour algorithm analyzes the geometric features of the outline to transform two-dimensional contour information into a three-dimensional model. During the reconstruction process, the building's height information is also incorporated to ensure that the constructed model is consistent with the actual building in spatial location and scale. The final building model accurately reflects the structural layout of the construction site.

[0100] A fractal set algorithm is employed to simulate the three-dimensional structure of vegetation, and an iterative function system is used to generate vegetation morphology, thus constructing a plant model. The fractal set algorithm is a modeling method based on mathematical fractal theory, capable of generating complex yet natural shapes. In this step, an iterative function system (IFS) is used to generate the three-dimensional morphology of the vegetation. This method defines a set of transformation functions to progressively generate a vegetation model with self-similar characteristics, effectively representing the hierarchy and richness of the vegetation, making the generated model more natural.

[0101] The outlines of low-lying areas or watersheds are determined from the digital elevation model (DEM). The water surface shape is then surface-fitted using these outlines to construct a water body model. By analyzing the DEM, low-lying areas and watersheds in the terrain can be identified. After determining the water body outlines, these outlines are used to generate a surface shape for the water surface. This process typically employs surface fitting techniques to accurately represent the morphological characteristics of the water surface and construct a realistic water body model.

[0102] Binary path information of the road is obtained from image segmentation. Curve fitting is performed on the extracted binary path information. The road path is mapped to the terrain model according to the digital elevation model to construct the road model. The contour is mapped to the actual terrain according to the digital elevation model to ensure that the elevation changes of the road match the surrounding terrain, and finally the road model is constructed.

[0103] The bridge outline is extracted from the image, and a shape-to-outline reconstruction algorithm is used to construct a bridge model based on the outline, design height, and span. After extracting the bridge's outline information through image segmentation, a shape-to-outline reconstruction algorithm is used, combined with parameters such as the bridge's design height and span, to create the model. This modeling process not only ensures that the bridge's shape conforms to the actual design but also considers the bridge's structural characteristics, enabling the final model to accurately reflect the bridge layout at the construction site.

[0104] Example 4

[0105] Methods for modeling mechanical equipment include:

[0106] The collected construction machinery and equipment form an equipment set EQ = {EQ1, EQ2, ..., EQ} N}, where N is the number of construction machinery and equipment; and obtain the i-th piece of equipment E i Equipment parameter data: Length L i Width W i Height H i Mass m i Inertial Tensor I i Engine power P i Maximum torque τ max，i Maximum speed v max，i Minimum turning radius R min，i The parameters define the physical characteristics and performance of each piece of equipment, forming the basis for mechanical equipment modeling.

[0107] Using CAD software and based on length L i Width W i and height H i Create the EQ for the i-th device. i The three-dimensional model of the construction machinery and equipment; based on the equipment's dimensional parameters, designers can draw the three-dimensional structural model of the equipment in computer-aided design (CAD) software. Furthermore, for key components of the equipment, **non-uniform rational B-spline (NURBS)** surfaces can be used for precise description.

[0108] S(u, v) is a parametric surface, where u and v are variables in the parameter domain. Let p and q be B-spline basis functions. ij To control the vertices. w ij This is the weighting factor.

[0109] A dynamic model of construction machinery is created by combining kinematic and dynamic equations. Based on the kinematic and dynamic characteristics of the equipment, a dynamic model is established. Kinematic equations describe the equipment's motion states, such as position, velocity, and acceleration; while dynamic equations consider the forces acting on the equipment during motion, including gravity, friction, and inertial forces. By combining these two types of equations, the behavior of the equipment during construction can be simulated more accurately.

[0110] EQ of the i-th device is established using engine power. i An energy efficiency model. The model can be used to calculate the energy consumption of equipment under different operating conditions and to evaluate its operating efficiency.

[0111] Among them, the kinematic equations of construction machinery are determined using the Denavit-Hartenberg (DH) parametric method. The DH parametric method is a commonly used robotics method for describing the kinematic characteristics of robotic arms and other chain mechanisms. By defining four DH parameters for each joint, the transformation matrix of the joint can be constructed, i.e., the transformation matrix Tjj for each j-th joint is determined. Where, θ j Let d be the rotation angle of the joint. j Let a be the position of the joint along the axial direction. j For the displacement between joints, α j This represents the torsional angle of the joint.

[0112] The angular velocity of each joint is determined using the joint pose transformation matrix. and angular acceleration

[0113] The dynamic equations of construction machinery and equipment are determined using the Newton-Euler equations, and the linear acceleration 'a' of the equipment is determined. i and the torque M of the equipment i , Among them, F i For the resultant force acting on the device, ω i I is the angular velocity of the device. i For the inertia tensor of the device;

[0114] Methods combining kinematic and dynamic equations include:

[0115] The angular velocity and acceleration of each joint are used as inputs to the Newton-Euler equations, and the forces and torques of each joint are calculated using the Newton-Euler equations. The forces and torques are calculated step by step from the base to the end using the recursive formulas in the Newton-Euler method, which ensures the overall consistency of the model.

[0116] Determine the final dynamic model Where q is the displacement of the joint. Let M(q) be the acceleration of the joint, and M(q) be the inertia matrix. Let G(q) be the matrix of Coriolis force and centrifugal force, where G(q) is the gravity term and τ is the driving torque of the joint.

[0117] Example 5

[0118] like Figure 3 As shown, the methods for determining equipment scheduling and path planning include:

[0119] Define the set of construction tasks Where M represents the total number of construction tasks; and where τ represents the number of construction tasks. iIncludes: Task types Mission Location Required equipment EQ j Task type specifies the nature of the task, such as transportation or installation; task location specifies the specific location of the task on the construction site, usually represented by coordinates; required equipment specifies the specific mechanical equipment required to complete the task.

[0120] Establish dependencies between tasks to form a directed acyclic graph. In this graph, the edge set ε represents the order of tasks; the nodes in the graph represent construction tasks, while the edge set represents the dependencies and order between tasks. This representation makes it clear which tasks must be completed before others, thus providing a basis for subsequent scheduling and planning.

[0121] The construction site model is converted into an environmental map required for path planning, including obstacle information and drivable areas, in order to minimize the total construction time T. total Total energy consumption E total To achieve this goal, the construction site model was converted into an environmental map that included obstacle information and drivable areas. This conversion process ensured that path planning took into account the actual conditions of the site, such as terrain undulations and obstacle distribution. Achieving optimization objectives ensured the efficiency and economy of the construction process.

[0122] An optimization model for equipment scheduling and path planning is established to obtain the optimal equipment scheduling and path planning. Based on the defined construction tasks and the transformed environmental map, an optimization model is constructed. This model takes into account task priority, equipment capacity, and resource constraints, and obtains the optimal equipment scheduling scheme and path planning through algorithmic solution, thereby ensuring that the construction tasks can be carried out efficiently and smoothly.

[0123] Load the construction site model and mechanical equipment model into the simulation platform, and load the equipment scheduling and path planning into the simulation platform; the simulation platform can be AnyLogic, MATLAB / Simulink, FlexSim, Arena, etc.

[0124] Dynamic simulation is performed using a simulation platform. Methods for performing dynamic simulation include:

[0125] The construction process is divided into discrete time steps Δt to facilitate step-by-step calculation of the equipment's motion state. The total number of simulation steps is determined. Each time step t k This corresponds to a discrete moment in the time series.

[0126] Calculate each time step t using a dynamic model k The displacement q of the lower device j (t k) and speed

[0127] The speed obtained through calculation and acceleration Update the device location. Based on the current speed and acceleration, and combined with the discrete time step, the device's position information is updated step by step to accurately simulate its motion trajectory.

[0128] When the device EQ j Upon arrival at the mission location, the mission commences, and the mission progress is updated in real time based on the equipment's execution efficiency. i Completion level;

[0129] At each time step t k Collision detection is performed using bounding boxes or directed bounding box algorithms; if a collision risk is detected, a velocity-based obstacle avoidance method is used to replan the local path.

[0130] Using the equipment's energy efficiency model, calculate the equipment's energy consumption at time step t. k Energy consumption E j (t k The total energy consumption of all equipment is accumulated to obtain the total energy consumption of the construction process. Energy consumption data provides an important basis for assessing the energy consumption of construction projects, which helps to optimize resource allocation and reduce construction costs.

[0131] Example 6

[0132] Specific methods are provided for establishing an optimization model for equipment scheduling and path planning and performing target optimization in Example 5:

[0133] Establish objective function Where, x = [x ij [x] represents the device scheduling matrix. ij =1 indicates the device's EQ j Execute task τ i , γ={γ j (t)} represents the device EQ j The path planning set, where α and β are weighting coefficients for balancing construction time and construction energy consumption;

[0134] Determine the constraints.

[0135] Task allocation constraints: Ensure that each task can only be executed by one device.

[0136] Equipment capacity constraint x ij ≤c ij c ij =1 indicates the device's EQ jCapable of performing tasks τ i c ij =0 indicates the device's EQ j Unable to execute task τ i Ensure that the equipment can only perform tasks within its capabilities.

[0137] Task Dependency Constraints τ k It is τ i The prerequisite tasks. This is the start time of the task. Set the completion time for the task; ensure that the order of tasks is followed.

[0138] Path feasibility constraints: F safe This represents the safe zone in the construction site model; ensure the path remains within the safe zone.

[0139] Equipment dynamic constraints: f j EQ for the device j The dynamic equations of the equipment are determined to ensure that the dynamic behavior of the equipment conforms to reality.

[0140] Encode the device scheduling matrix as part of a chromosome, and the device path γ j (t) is discretized into control points and encoded as another part of the chromosome; a genetic algorithm fitness function is constructed. in, λ1, λ2, and λ3 are the penalty terms for violating the constraints, and λ1, λ2, and λ3 are the weight coefficients for different constraints.

[0141] Penalty item P penalty The purpose is to impose additional penalties on solutions that violate constraints in optimization problems, thereby guiding genetic algorithms or other optimization algorithms to prioritize solutions that satisfy the constraints. Penalty terms can be designed based on different constraints, such as task allocation, path feasibility, and equipment capability. In the penalty term formula:

[0142] The first item: Penalty for task assignment constraints. The goal is to ensure that each task is assigned to at most one device, x ij =1 indicates the device's EQ j Execute task τ i , Assign constraints to the task. If the constraint is violated (i.e., ... The penalty term is positive if positive and zero otherwise. λ1 is the penalty weight coefficient of the task assignment constraint, used to control the importance of the constraint.

[0143] The second item: the penalty for task dependency constraints. The goal is to ensure mission τ i Only in the prerequisite task τ k Start once completed. For task τ i The start time, For task τ k Completion time, This is a task dependency constraint. If this constraint is violated (i.e....), The penalty term is positive if it is positive and zero otherwise. λ2 is the penalty weight coefficient for the task dependency constraint.

[0144] The third item: the penalty for path feasibility constraints. The goal is to ensure the equipment's EQ. j The path is within the safe zone in the construction site model. j (t) represents the device EQ j In path planning at time t, F safe This is a safe zone for drivability. If the path feasibility constraint is violated (i.e., ... Indicator functions A return value of 1 indicates that the path is not within the safe zone, thus accumulating a penalty. λ3 is the penalty weight coefficient for the path feasibility constraint.

[0145] The chromosomes are subjected to genetic, crossover, and mutation processes, and through multiple iterations, the individual with the highest fitness is selected as the optimal solution to obtain the optimal equipment scheduling and path planning.

[0146] Example 7

[0147] A simulation analysis device for mechanized power transmission construction based on GIM includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the steps of the antenna interface unit testing method described above.

[0148] Memory is used to store software programs and modules. The processor executes various terminal functions and data processing by running the software programs and modules stored in memory. Memory can mainly consist of a program storage area and a data storage area. The program storage area can store the operating system, at least one executable program required for a given function, etc.

[0149] The storage data area can store data created based on the use of the terminal. Furthermore, the memory can include high-speed random access memory, and may also include non-volatile memory, such as at least one disk storage device, flash memory, or other volatile solid-state storage devices.

[0150] A computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of the method described above.

[0151] Without loss of generality, computer-readable media can include computer storage media and communication media. Computer storage media includes volatile and non-volatile, removable and non-removable media implemented using any method or technology for storing information such as computer-readable instruction data structures, program modules, or other data. Computer storage media includes RAM, ROM, EPROM, EEPROM, flash memory or other solid-state storage technologies, CD-ROM, DVD or other optical storage, magnetic tape cassettes, magnetic tape, disk storage, or other magnetic storage devices. Of course, those skilled in the art will recognize that computer storage media are not limited to the above-mentioned types. The aforementioned system memories and mass storage devices can be collectively referred to as memory.

[0152] A computer program product comprising a computer program / instructions that, when executed by a processor, implement the method described above.

[0153] Computer program products include computer programs or instruction sets used to perform specific tasks or achieve specific functions. These programs or instructions are designed to be executed by a processor to implement a series of predefined steps or operations. The program product may be stored in various forms of computer storage media, such as memory, hard disks, solid-state drives, optical discs, or other forms of digital storage devices. It may exist in the form of compiled binary code or in the form of scripts or bytecode that can be executed by an interpreter. Through carefully designed algorithms and logical instructions, the program product enables the processor to process data in a specific order and manner, performing various functions such as data analysis, user interaction, and device control.

[0154] In the description of this specification, the references to terms such as "one embodiment / mode," "some embodiments / modes," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment / mode or example is included in at least one embodiment / mode or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment / mode or example. Moreover, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments / modes or examples. Furthermore, without contradiction, those skilled in the art can combine and integrate the different embodiments / modes or examples described in this specification, as well as the features of different embodiments / modes or examples.

[0155] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this application, "multiple" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0156] Those skilled in the art should understand that the above embodiments are merely for illustrating the present invention and are not intended to limit the scope of the invention. Those skilled in the art can make other changes or modifications based on the above invention, and these changes or modifications still fall within the scope of the present invention.

Claims

1. A GIM-based simulation analysis method for power transmission mechanized construction, characterized in that, include: Collect GIM model data, satellite imagery data, and LiDAR data of the construction area; collect equipment parameter data of construction machinery and equipment; By combining GIM model data, satellite imagery data, and LiDAR data, a construction site model is constructed. Construct a mechanical equipment model based on equipment parameter data; The path planning and equipment scheduling are determined by the construction site model and the mechanical equipment model, and then loaded into the simulation platform to simulate the construction process; The methods for determining equipment scheduling and path planning include: Defining a construction task set wherein, is the total number of construction tasks; wherein, a construction task includes: a task type , a task location , required equipment ; Establish dependencies between tasks, forming a directed acyclic graph where the edge set is the order of tasks; Converting a construction site model into an environment map required for path planning, including obstacle information and drivable areas, to minimize total construction time and total energy consumption To this end, an optimization model for equipment scheduling and path planning is established, and the optimal equipment scheduling and path planning are obtained; Load the construction site model and mechanical equipment model into the simulation platform, and load the equipment scheduling and path planning into the simulation platform; Dynamic simulation is performed using a simulation platform. Methods for dynamic simulation include: dividing the construction process into discrete time steps , determining a total number of simulation steps ; Calculating each time step with a dynamics model Displacement of the lower device And velocity ; the calculated speed and acceleration , updating the position of the device, ; When the device arrives at the task location, the task is started to be executed, and the completion degree of the task is updated in real time according to the execution efficiency of the device ; At each time step collision detection is performed by bounding volume or oriented bounding volume algorithms; if a collision risk is detected, a local path is replanned using velocity obstacle methods; Using the equipment's energy efficiency model, calculate the equipment's energy consumption at time step. Energy consumption The total energy consumption of all equipment is accumulated to obtain the total energy consumption of the construction process. .

2. The GIM-based power transmission mechanized construction simulation analysis method according to claim 1, characterized in that, Tower information of the transmission circuit is obtained from GIM model data. The tower information includes: tower three-dimensional coordinates and structural information. Topographic information of the construction area was obtained from satellite imagery and lidar data, including digital elevation models and digital orthophotos. Environmental parameter information of the construction area is obtained by integrating it through a GIS geographic information system. The environmental parameter information includes vegetation, buildings and other land features. The tower information, topographic information, and environmental parameter information are converted to the Global Geodetic Coordinate System and then fused to obtain a fused dataset. ; Anomalies are detected using a density-based clustering algorithm and removed from the fused dataset to obtain a cleaned dataset. ; Co-kriging interpolation is used to obtain multi-source datasets by identifying missing data points in the clear dataset. ; Wavelet transform is performed on multi-source datasets to obtain features at different scales, and a multi-source data feature set is constructed.

3. The simulation analysis method for mechanized power transmission construction based on GIM according to claim 1, characterized in that, Methods for constructing construction site models include: A set of ground points is extracted from lidar data using a random sampling consensus algorithm; A triangular irregular network is constructed on the set of ground points, and the Laplace equation is used to smooth the terrain of the triangular irregular network. Solving partial differential equations using boundary conditions and the finite element method To obtain a smooth terrain model, where, It is a topographic elevation function. For the Laplace operator, The source term function representing the density of topographic elevation variation; Image segmentation is performed on satellite image data to extract environmental parameter information and obtain classification results; then, vegetation, buildings and other land features are modeled separately and fused with the terrain model to obtain a rough construction site model. Multi-scale detail enhancement of the coarse model was performed using three-dimensional wavelet transform to obtain the final construction site model. , ,in, For the first Wavelet coefficients at each scale, For the corresponding wavelet basis functions, This represents the number of wavelet decomposition layers.

4. The simulation analysis method for mechanized power transmission construction based on GIM according to claim 3, characterized in that, Other terrain features include: waterways, roads, and bridges; modeling methods include: The extracted building areas are reconstructed using a shape-from-contour algorithm, and building models are constructed based on the building's outline and height information. A fractal set algorithm is used to simulate the three-dimensional structure of vegetation, and an iterative function system is used to generate vegetation morphology to construct a plant model. The outline of a low-lying area or water basin is determined from the digital elevation model, and the water surface shape is surface-scraped using the water body outline to construct a water body model. Binary path information of roads is obtained from image segmentation. Curve fitting is performed on the extracted binary path information. The road path is mapped to the terrain model according to the digital elevation model to construct the road model. The bridge outline is extracted from the image segmentation, a shape-to-outline reconstruction algorithm is used, and a bridge model is constructed based on the bridge's outline, design height, and span.

5. The simulation analysis method for mechanized power transmission construction based on GIM according to claim 1, characterized in that, Methods for modeling mechanical equipment include: The collected construction machinery and equipment form an equipment collection. ,in, The quantity of construction machinery and equipment; and obtain the number of... Taiwanese equipment Device parameter data: length ,width ,high ,quality Inertial tensor Engine power Maximum torque Maximum speed Minimum turning radius ; Using CAD software and based on length ,width and height Create the first Taiwanese equipment Three-dimensional models of construction machinery and equipment; By combining kinematic and dynamic equations, a dynamic model of construction machinery and equipment is created. Establish the first using engine power Taiwanese equipment The energy efficiency model.

6. The simulation analysis method for mechanized power transmission construction based on GIM according to claim 5, characterized in that, The kinematic equations of construction machinery and equipment were determined using the Denavit-Hartenberg parametric method. Transformation matrix of each joint , ,in, Let be the angle of rotation of the joint. This refers to the position of the joint along its axial direction. This refers to the displacement between joints. This refers to the torsional angle of the joint; The angular velocity of each joint is determined using the joint pose transformation matrix. and angular acceleration ; The dynamic equations of construction machinery and equipment are determined using the Newton-Euler equations, and the linear acceleration of the equipment is determined accordingly. and the torque of the equipment , ,in, The resultant force acting on the equipment, The angular velocity of the device, For the inertia tensor of the device; Methods combining kinematic and dynamic equations include: The angular velocity and acceleration of each joint are used as inputs to the Newton-Euler equations, and the force and torque of each joint are calculated using the Newton-Euler equations. The force and torque are calculated step by step from the base to the end using the recursive formula in the Newton-Euler method. Determine the final dynamic model ,in, This represents the displacement of the joint. For the acceleration of the joint, It is the inertia matrix. The matrix of Coriolis force and centrifugal force. It is a gravitational term. This is the driving torque of the joint.

7. The simulation analysis method for mechanized power transmission construction based on GIM according to claim 1, characterized in that, Methods for establishing optimization models for equipment scheduling and path planning and performing objective optimization include: Establish the objective function ,in, For equipment scheduling matrix, Indicates device Execute the task , For equipment The set of path planning, and To balance the weighting coefficients for construction time and construction energy consumption; Define the constraints, including task allocation constraints: Equipment capacity constraints , Indicates device Able to perform tasks , Indicates device Unable to perform task Task dependency constraints , yes The prerequisite tasks. This is the start time of the task. Task completion time; Path feasibility constraints: , The safe zone in the construction site model; equipment dynamic constraints: , For equipment The dynamic equations; Encode the device scheduling matrix as part of a chromosome, and the device path Discretize into control points and encode into another part of the chromosome; construct the fitness function of the genetic algorithm. ,in, Penalties for violating the constraints; The chromosomes are subjected to genetic, crossover, and mutation processes, and through multiple iterations, the individual with the highest fitness is selected as the optimal solution to obtain the optimal equipment scheduling and path planning.

8. A computer program product comprising a computer program / instructions, characterized in that, When the computer program / instructions are executed by the processor, they implement the method as described in any one of claims 1-7.

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