A power line ice warning system based on terrain analysis

Abstract

The present invention relates to the technical field of power system safety monitoring, and specifically to a wire icing warning system based on terrain analysis, including a terrain analysis module, a microclimate simulation module, a meteorological data acquisition module, an icing risk assessment module, a real-time monitoring module, and a warning information generation module; wherein: the terrain analysis module is used to obtain terrain feature data of a target area; the microclimate simulation module is used to perform micro-scale climate simulation on the target area and generate local meteorological parameters; the meteorological data acquisition module is used to collect real-time meteorological data of the target area; and the icing risk assessment module is used to construct an icing risk assessment model. The present invention realizes accurate real-time assessment and warning of icing risks of wires in complex terrain areas by integrating terrain features, real-time meteorological data, and historical icing information, and adopting random forest algorithms and microclimate simulation technology.

Description

Technical Field

[0001] This invention relates to the field of power system safety monitoring technology, and in particular to a power line icing early warning system based on terrain analysis. Background Technology

[0002] Power line icing is a common and serious natural disaster in power systems, especially in complex terrain areas such as mountains and hills. Icing significantly increases the weight of power lines, leading to increased sag and even line breakage due to insufficient mechanical strength, seriously threatening the safe and stable operation of the power system. Most existing icing monitoring and early warning technologies rely on single-point sensors or data from a small number of weather stations, which cannot fully reflect the spatial distribution characteristics of icing risk in the target area. In addition, meteorological conditions in complex terrain areas change rapidly, and traditional monitoring methods are unable to respond quickly to real-time environmental changes, resulting in untimely icing risk warnings or inaccurate predictions.

[0003] Currently, existing technologies have shortcomings in the following aspects: First, they do not fully integrate complex terrain features for detailed analysis of icing risk, making it difficult to reflect the impact of terrain on local meteorological conditions. Second, they lack high-precision microclimate simulation methods, and traditional methods are unable to capture the fine distribution of meteorological elements such as wind speed, temperature, and humidity. Third, risk assessment models often neglect the input and adjustment of dynamic real-time meteorological data during construction, resulting in weak generalization ability and insufficient adaptability of model predictions. Therefore, developing an early warning system that can combine terrain features, real-time meteorological data, and historical data for high-precision icing risk prediction has significant practical and technical value. Summary of the Invention

[0004] To achieve the above objectives, the present invention provides a power line icing early warning system based on terrain analysis.

[0005] A power line icing early warning system based on terrain analysis includes a terrain analysis module, a microclimate simulation module, a meteorological data acquisition module, an icing risk assessment module, a real-time monitoring module, and an early warning information generation module; wherein:

[0006] Terrain Analysis Module: Used to acquire digital elevation model data of the target area, and calculate the slope, aspect, elevation and terrain roughness of the target area based on the digital elevation model data to form terrain feature data of the target area;

[0007] Microclimate Simulation Module: Based on topographic feature data, it uses computational fluid dynamics and numerical weather prediction models to perform microscale climate simulation on the target area, generating local meteorological parameters, including refined wind field, temperature field and humidity field;

[0008] Meteorological data acquisition module: used to deploy a distributed array of meteorological sensors in the target area to collect real-time meteorological data of the target area, including temperature, humidity, precipitation and wind speed;

[0009] Icing Risk Assessment Module: Based on terrain feature data, local meteorological parameters, and historical icing data, an icing risk assessment model is constructed using the random forest algorithm;

[0010] Real-time monitoring module: Receives real-time meteorological data provided by the meteorological data acquisition module, inputs the real-time meteorological data into the constructed icing risk assessment model, performs real-time risk prediction, and outputs the predicted icing thickness value;

[0011] Early warning information generation module: Based on the risk prediction results output by the real-time monitoring module, it generates early warning information for the target area.

[0012] Optionally, the terrain analysis module includes a data acquisition unit, a slope calculation unit, an aspect calculation unit, an elevation calculation unit, a terrain roughness calculation unit, and a feature integration unit; wherein:

[0013] Data acquisition unit: used to acquire digital elevation model data of the target area through satellite remote sensing data source, the digital elevation model data including surface elevation information represented in grid form, and to perform format conversion and projection coordinate correction on the data to ensure data accuracy;

[0014] Slope calculation unit: Based on the elevation value changes in the digital elevation model data, the slope value of each grid cell is calculated using differential operations. The slope value represents the ratio of elevation change with horizontal distance.

[0015] Slope aspect calculation unit: Based on digital elevation model data and slope calculation results, the slope aspect value of each grid unit is calculated, and the slope aspect value represents the azimuth angle of the slope surface relative to the north direction;

[0016] Elevation calculation unit: Extract the elevation value of each grid cell in the digital elevation model data to directly obtain the elevation distribution of each grid cell in the target area;

[0017] Terrain roughness calculation unit: By analyzing the degree of local elevation change in the digital elevation model data, the terrain roughness of each grid cell in the target area is calculated. The terrain roughness is expressed as the standard deviation of the elevation change around the grid cell.

[0018] Feature integration unit: Used to integrate the calculation results of slope, aspect, elevation and terrain roughness to generate terrain feature data of the target area.

[0019] Optionally, the microclimate simulation module includes a mesh generation unit, a fluid dynamics solution unit, an energy transfer solution unit, a water vapor transport solution unit, and an output generation unit; wherein:

[0020] Mesh generation unit: Used to divide the target area into meshes based on terrain feature data, generating a three-dimensional mesh model for computational fluid dynamics simulation;

[0021] Fluid dynamics solution unit: Based on the computational fluid dynamics model, the Reynolds-averaged Navier-Stokes equations are used to solve the fluid dynamics problem, simulating the airflow distribution within the target region. The calculation formula is as follows:

[0022] ,in, For gas density, and The airflow is respectively in and The velocity component in the direction, For pressure, For dynamic viscosity, It is a volume force; through iterative calculation, a refined wind field distribution in the target area is obtained;

[0023] Energy transfer solution unit: Based on the energy conservation equation in the numerical weather prediction model, it simulates the temperature field within the target area. The calculation formula is as follows: ,in, For temperature, For specific heat capacity, Thermal conductivity, For heat source items;

[0024] Water vapor transport solution unit: Based on the water vapor transport equation, it simulates the humidity field distribution within the target area. The calculation formula is as follows: ,in, For humidity, The water vapor diffusion coefficient is... For water vapor source items;

[0025] Output generation unit: Integrates the calculation results of the fluid dynamics solution unit, energy transfer solution unit and water vapor transport solution unit to generate local meteorological parameters of the target area and output them in the form of a three-dimensional mesh.

[0026] Optionally, the mesh division unit includes:

[0027] Data preprocessing: The terrain feature data provided by the terrain analysis module is standardized, and the elevation values ​​are normalized to ensure that the input data are within a uniform scale range;

[0028] Terrain complexity analysis: Based on the terrain roughness and slope of the target area, the mesh refinement requirements within the area are calculated;

[0029] Initial mesh generation: Based on the boundary data of the target area, a quadtree partitioning algorithm is used to generate an initial regular mesh to divide the target area into several initial square units;

[0030] Mesh refinement: Based on the terrain complexity analysis results, an adaptive mesh refinement method is used to adjust the initial mesh; for areas with high roughness or high slope, the side length of the mesh cells is refined; for areas with relatively gentle terrain changes, the resolution of the initial mesh cells is kept unchanged.

[0031] Boundary optimization: The boundaries of the mesh cells are optimized by using the Delaunay triangulation method to generate an unstructured mesh, in order to minimize the maximum angle of the triangle;

[0032] Mesh verification and output: Verify the quality of the mesh cells. Once the shape factors of all meshes meet the quality threshold requirements, output the optimized mesh data.

[0033] Optionally, the meteorological data acquisition module includes a sensor selection unit, a sensor layout unit, a data transmission unit, a sensor calibration unit, and a data storage unit; wherein:

[0034] Sensor selection unit: used to deploy distributed meteorological sensor arrays in the target area. Each sensor array includes a temperature sensor, humidity sensor, precipitation meter, and anemometer.

[0035] The sensor placement unit divides the target area based on the terrain feature data provided by the terrain analysis module and determines the sensor placement locations according to the following rules.

[0036] Rule 1: Deploy high-density sensor arrays in areas with steep slopes and high roughness;

[0037] Rule 2: Deploy low-density sensor arrays in areas with gentle terrain;

[0038] Data transmission unit: used to transmit the collected meteorological data to the system's central data unit in real time for standardization;

[0039] Sensor calibration unit: used to periodically calibrate the deployed sensors. Specifically, it compares the collected temperature, humidity, precipitation and wind speed with data from standard weather stations or known reference equipment, adjusts the sensor measurement deviations, and ensures the consistency of the collected data accuracy.

[0040] Data storage unit: used to store the collected meteorological data in different regions, with the meteorological data for each region archived according to timestamps.

[0041] Optionally, the icing risk assessment module includes a sample construction unit, a model training unit, a model validation unit, a parameter optimization unit, and a model output unit; wherein:

[0042] Sample construction unit: Based on historical icing data, the topographic feature data and local meteorological parameters of the target area are matched with the corresponding icing risk level labels to construct a training sample set and a test sample set; the training sample set is used for model training, and the test sample set is used for model evaluation.

[0043] Model training unit: The random forest algorithm is used to train the training sample set. Multiple decision trees are constructed to classify the data. Each decision tree takes a subset of the feature vector as input and the icing risk level as output. The outputs of all decision trees are combined and voted to form an icing risk prediction model.

[0044] The model validation unit uses a test sample set to validate the trained risk assessment model. By comparing the model's prediction results with the actual risk level labels, it calculates the model's performance metrics, including accuracy, error rate, and classification precision.

[0045] Parameter optimization unit: The hyperparameters of the icing risk assessment model are optimized using cross-validation, including the number of decision trees, maximum depth, and number of features used in each tree, to ensure the model's generalization ability on different sample sets; at the same time, the hyperparameters are dynamically adjusted according to the model's performance indicators on the test sample set.

[0046] Model output unit: Used to save the final optimized icing risk assessment model as a standardized model file for use by the real-time monitoring module.

[0047] Optionally, the model training unit specifically includes:

[0048] Training sample initialization: Receive the training sample set provided by the sample construction unit. Each sample consists of an input feature vector and a corresponding ice accretion risk level label.

[0049] Decision tree construction: Random forests generate multiple decision trees by repeating the following steps multiple times:

[0050] Step 1: Randomly select a predetermined number of samples from the training sample set using the bootstrap sampling method to construct a training subset of a decision tree, and use the remaining samples as the validation set;

[0051] Step 2: Randomly select a subset of features from all features as the candidate set of splitting features for the current decision tree. The number of selected features is usually less than the total number of features.

[0052] Step 3: At each node, select the optimal splitting feature and splitting point from the candidate feature set to achieve the optimal purity of the splitting node.

[0053] Decision tree training: Recursively split the training subset of each decision tree until any of the set conditions are met. Specific conditions include all samples in the current node belonging to the same category, the number of samples being less than a preset threshold, and the decision tree reaching a preset maximum depth.

[0054] Decision tree ensemble: All decision trees are combined into a random forest model. The prediction result of the random forest is the majority vote of the classification results of all decision trees, that is, the classification that appears most frequently is selected as the final prediction result.

[0055] Model output: After training, save the random forest model as an icing risk assessment model.

[0056] Optionally, the parameter optimization unit includes:

[0057] Initial parameter settings: Set the initial hyperparameters of the random forest algorithm, including the number of decision trees, maximum depth, number of features used per tree, and minimum number of sample splits per node, as the initial parameter set for cross-validation;

[0058] Cross-validation grouping: Dividing the training sample set into... Each iteration selects one of several equal subsets as the validation set, and the rest... A subset is used as the training set, and the process is repeated. Next, ensure that each subset is used as a validation set once;

[0059] Model training and validation: In each iteration, the model is trained and validated using the current hyperparameter settings. The icing risk assessment model is trained on a training subset, and the model's performance metrics, including accuracy, recall, and F1 score, are calculated using the validation set.

[0060] Performance Indicators Summary and Evaluation: Summary The verification results of the next iteration are used to calculate the average performance index of the icing risk assessment model as a comprehensive evaluation index for the current hyperparameter settings.

[0061] Dynamic hyperparameter tuning: Based on the average performance index of the validation set, the hyperparameter combination is tuned, and the optimal hyperparameter combination is determined by grid search or random search methods.

[0062] Final hyperparameter determination: Select the hyperparameter combination that yields the best results from the validation and record the hyperparameter settings as the final parameters of the random forest model;

[0063] Test sample performance verification: The optimized icing risk assessment model is finally verified using the test sample set. The performance indicators of the icing risk assessment model on the test sample set are calculated, including accuracy, error rate and classification accuracy. If the test results do not meet the preset performance target, the hyperparameters will be adjusted again and the above optimization steps will be repeated until the performance requirements are met.

[0064] Parameter output: Record the optimized hyperparameters and the final verified performance indicators, and save them as the configuration information file of the icing risk assessment model.

[0065] Optionally, the real-time monitoring module includes a data receiving unit, a data preprocessing unit, a feature matching unit, and a model calling unit; wherein:

[0066] Data receiving unit: used to receive real-time meteorological data provided by the meteorological data acquisition module, including temperature, humidity, precipitation and wind speed;

[0067] Data preprocessing unit: used to preprocess the received real-time meteorological data, including data cleaning, missing value imputation and outlier detection, to ensure the integrity of the input data;

[0068] Feature matching unit: Matches the preprocessed real-time meteorological data with the input features of the icing risk assessment model to ensure that the data format and dimensions are consistent with the model requirements;

[0069] Model Invocation Unit: Invokes the icing risk assessment model already built in the icing risk assessment module, inputs the matched real-time meteorological data into the model, performs real-time icing risk prediction, and outputs the risk prediction results.

[0070] Optionally, the early warning information generation module includes a risk classification unit, a thickness analysis unit, a high-risk point location unit, an information integration unit, and an early warning information output unit; wherein:

[0071] Risk grading unit: Used to receive the risk prediction results output by the real-time monitoring module, compare the icing risk value in the risk prediction results with the preset risk level threshold, and classify it into mild risk, moderate risk and severe risk to generate the risk level of the target area;

[0072] Thickness Analysis Unit: Based on the predicted ice thickness value in the risk prediction results, combined with the meteorological conditions and topographic features of the target area, generate ice thickness distribution maps for different areas to clarify the spatial variation of ice thickness.

[0073] High-risk point location unit: By analyzing the risk level distribution and ice thickness prediction results, identify areas in the target area with a medium or higher ice risk level, determine the specific location of high-risk points, generate a list of high-risk points, and mark the corresponding geographic coordinates;

[0074] Information integration unit: Integrates the data generated by the risk classification unit, thickness analysis unit and high-risk point location unit to generate complete early warning information for the target area, including risk level distribution map, icing thickness distribution map and high-risk point list;

[0075] Early warning information output unit: Outputs the integrated early warning information in digital form, which can be queried by power maintenance departments and relevant monitoring personnel. It supports the display of early warning information in the form of maps, text reports or tables.

[0076] The beneficial effects of this invention are:

[0077] This invention combines terrain feature data, real-time meteorological data, and historical icing data, employs an improved random forest algorithm for risk assessment, and utilizes computational fluid dynamics and numerical weather prediction models for microclimate simulation. This effectively solves the problem of insufficient accuracy in predicting icing risk in complex terrain areas. Furthermore, by introducing refined terrain analysis and meteorological simulation techniques, it can capture the fine distribution characteristics of meteorological elements such as wind speed, temperature, and humidity within the target area, thereby achieving accurate assessment of icing risk.

[0078] This invention, by comprehensively integrating historical data, terrain features, and real-time environmental data, effectively improves the adaptability and generalization ability of risk prediction models, providing a scientific basis for real-time monitoring and risk warning of power line icing in target areas, and significantly reducing the risk of power line outages and economic losses caused by icing. Attached Figure Description

[0079] To more clearly illustrate the technical solutions in this invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only for this invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0080] Figure 1 This is a schematic diagram of a power line icing early warning system according to an embodiment of the present invention;

[0081] Figure 2 This is a schematic diagram of the risk assessment module in an embodiment of the present invention. Detailed Implementation

[0082] The present invention will now be described in detail with reference to the accompanying drawings and specific embodiments. It should also be noted that, to make the embodiments more comprehensive, the following embodiments are the best and preferred embodiments, and those skilled in the art can use other alternative methods to implement some well-known technologies; moreover, the accompanying drawings are only for more specific description of the embodiments and are not intended to specifically limit the present invention.

[0083] It should be noted that the use of terms such as "an embodiment," "an embodiment," "an exemplary embodiment," and "some embodiments" in the specification indicates that the described embodiment may include a specific feature, structure, or characteristic, but not every embodiment necessarily includes that specific feature, structure, or characteristic. Furthermore, when a specific feature, structure, or characteristic is described in connection with an embodiment, implementing such a feature, structure, or characteristic in conjunction with other embodiments (whether explicitly described or not) should be within the knowledge of those skilled in the art.

[0084] Generally, terms can be understood at least partly from their use in context. For example, depending at least partly on the context, the term "one or more" as used herein can be used to describe any feature, structure, or characteristic in a singular sense, or a combination of features, structures, or characteristics in a plural sense. Additionally, the term "based on" can be understood not necessarily to convey an exclusive set of factors, but rather, alternatively, depending at least partly on the context, to allow for the presence of other factors that are not necessarily explicitly described.

[0085] like Figures 1-2 As shown, a power line icing early warning system based on terrain analysis includes a terrain analysis module, a microclimate simulation module, a meteorological data acquisition module, an icing risk assessment module, a real-time monitoring module, and an early warning information generation module; wherein:

[0086] Terrain Analysis Module: Used to acquire digital elevation model (DEM) data of the target area, and calculate the slope, aspect, elevation and terrain roughness of the target area based on the DEM data to form terrain feature data of the target area;

[0087] Microclimate Simulation Module: Based on topographic feature data, it uses computational fluid dynamics (CFD) and numerical weather prediction models to perform microscale climate simulations on the target area, generating local meteorological parameters, including refined wind, temperature, and humidity fields; these local meteorological parameters are used to construct an icing risk assessment model.

[0088] Meteorological data acquisition module: used to deploy a distributed array of meteorological sensors in the target area to collect real-time meteorological data of the target area, including temperature, humidity, precipitation and wind speed;

[0089] Icing Risk Assessment Module: Based on terrain feature data, local meteorological parameters generated by the microclimate simulation module, and historical icing data, an icing risk assessment model is constructed using the random forest algorithm;

[0090] Real-time monitoring module: Receives real-time meteorological data provided by the meteorological data acquisition module, inputs the real-time meteorological data into the constructed icing risk assessment model, performs real-time risk prediction, and outputs the predicted icing thickness value;

[0091] Early warning information generation module: Based on the risk prediction results output by the real-time monitoring module, it generates early warning information for the target area.

[0092] The terrain analysis module includes a data acquisition unit, a slope calculation unit, an aspect calculation unit, an elevation calculation unit, a terrain roughness calculation unit, and a feature integration unit; among which:

[0093] Data acquisition unit: Used to acquire digital elevation model (DEM) data of the target area through satellite remote sensing data sources. The digital elevation model data includes surface elevation information represented in grid form, and performs format conversion and projection coordinate correction on the data to ensure data accuracy.

[0094] Slope Calculation Unit: Based on the elevation changes in the digital elevation model data, the slope value of each grid cell is calculated using differential operations. The slope value represents the ratio of elevation change with horizontal distance. The formula for calculating the slope value is:

[0095] ,in, This is the slope value. Indicates digital elevation model data in The formula for calculating the rate of change of elevation in the horizontal direction is: ; Indicates digital elevation model data in The formula for calculating the rate of change of elevation in the horizontal direction is: ; This is the difference in elevation. and These represent the horizontal distances between grid cells;

[0096] Slope Aspect Calculation Unit: Based on digital elevation model data and slope calculation results, the slope aspect value of each grid unit is calculated. The slope aspect value represents the azimuth angle of the slope surface relative to the north direction; the formula for calculating the slope aspect value is: ,in, The value represents the slope aspect; the result is adjusted to the plane coordinate system. to Scope and These represent the digital elevation model data in... and The rate of change of elevation in the horizontal direction;

[0097] Elevation Calculation Unit: Extracts the elevation value of each grid cell from the digital elevation model data. This directly yields the elevation distribution of each grid cell within the target area, eliminating the need for complex calculations. This represents the elevation value of a specific grid cell;

[0098] Terrain roughness calculation unit: By analyzing the degree of local elevation variation in the digital elevation model data, the terrain roughness of each grid cell within the target area is calculated. Terrain roughness is expressed as the standard deviation of the elevation variation around the grid cell; the formula for calculating roughness is: ,in, For terrain roughness, The elevation value of a certain grid cell. The average elevation around this grid cell. The number of surrounding grid cells;

[0099] Feature integration unit: This unit integrates the calculation results of slope, aspect, elevation, and terrain roughness to generate terrain feature data of the target area, providing input for subsequent modules. Through the collaborative work of the above units, the terrain analysis module can accurately and efficiently extract terrain feature data of the target area, providing basic data support for the microclimate simulation module and the icing risk assessment module.

[0100] The microclimate simulation module includes mesh generation units, fluid dynamics solution units, energy transfer solution units, water vapor transport solution units, and output generation units; among which:

[0101] Mesh generation unit: Used to divide the target area into meshes based on terrain feature data, generating a three-dimensional mesh model for computational fluid dynamics simulation. The mesh model is based on the terrain characteristics of the target area, ensuring mesh refinement in complex terrain areas.

[0102] Fluid dynamics solution unit: Based on the computational fluid dynamics model, the Reynolds-averaged Navier-Stokes (RANS) equations are used to solve the fluid dynamics problem, simulating the airflow distribution within the target region. The calculation formula is as follows:

[0103] ,in, For gas density, and The airflow is respectively in and The velocity component in the direction, For pressure, For dynamic viscosity, It is a volume force; through iterative calculation, a refined wind field distribution in the target area is obtained;

[0104] Energy transfer solution unit: Based on the energy conservation equation in the numerical weather prediction model, it simulates the temperature field within the target area. The calculation formula is as follows: ,in, For temperature, For specific heat capacity, Thermal conductivity, As a heat source term, the refined temperature field distribution of the target region is obtained through calculation;

[0105] Water vapor transport solution unit: Based on the water vapor transport equation, it simulates the humidity field distribution within the target area. The calculation formula is as follows: ,in, For humidity, The water vapor diffusion coefficient is... For the water vapor source term, the calculation results are used to describe the spatial variation of humidity;

[0106] Output generation unit: Integrates the calculation results of the fluid dynamics solution unit, energy transfer solution unit, and water vapor transport solution unit to generate local meteorological parameters of the target area, including refined wind field, temperature field, and humidity field, and outputs them in the form of a three-dimensional grid. Through the collaborative work of the above units, the microclimate simulation module can fully combine the topographic feature data and real-time meteorological data of the target area to accurately simulate microclimate changes under complex terrain, generate high-precision local meteorological parameters, and provide reliable data support for icing risk assessment.

[0107] The grid unit includes:

[0108] Data preprocessing: The terrain feature data provided by the terrain analysis module is standardized, and the elevation values ​​are normalized to ensure that the input data are within a uniform scale range;

[0109] Terrain complexity analysis: Based on the terrain roughness and slope of the target area, the mesh refinement requirements of the area are calculated. The mesh density is determined by the weighted sum of terrain roughness and slope, and the scaling factor is used to adjust the mesh resolution of the target area.

[0110] Initial mesh generation: Based on the boundary data of the target area, a quadtree partitioning algorithm is used to generate an initial regular mesh to divide the target area into several initial square units. The side length of the initial mesh unit is determined according to the area of ​​the region and the number of mesh units.

[0111] Mesh refinement: Based on the terrain complexity analysis results, an adaptive mesh refinement method is used to adjust the initial mesh; for areas with high roughness or high slope, the side length of the mesh cells is refined; for areas with relatively gentle terrain changes, the resolution of the initial mesh cells is kept unchanged.

[0112] Boundary optimization: To ensure the smoothness of the mesh boundary and the computational accuracy, the mesh cell boundary is optimized. The Delaunay triangulation method is used to generate an unstructured mesh to minimize the maximum angle of the triangle and ensure that the geometry of the mesh is close to uniform.

[0113] Mesh verification and output: Verify the quality of the mesh elements. After all mesh shape factors meet the quality threshold requirements, output the optimized mesh data and provide it to the fluid dynamics solution unit.

[0114] The specific steps for dividing the target area into grids are as follows:

[0115] First, based on the terrain roughness of the target area and slope The mesh refinement requirements within the region are calculated; the formula for calculating the mesh density is: ,in, For grid density, This is a scaling factor used to adjust the grid resolution of the target area. For terrain roughness, Slope;

[0116] Then, based on the boundary data of the target area The target region is divided into several initial square units, and the side length of each initial square unit is... Based on the area and the number of grid cells Confirmed, the formula is: ;

[0117] Next, based on the terrain complexity analysis results, an adaptive mesh refinement method was used to adjust the initial mesh; regarding roughness... Larger or steeper slope For higher areas, refine the grid cell side lengths. The formula is: ,in, To refine the factors, adjust the refinement intensity as needed;

[0118] Subsequently, to ensure the smoothness of the mesh boundaries and computational accuracy, the mesh cell boundaries are optimized. The Delaunay triangulation method is used to generate an unstructured mesh, minimizing the maximum angle of the triangles to ensure the mesh geometry is nearly uniform. The optimization objective function is: ,in, For each interior angle of a triangle;

[0119] Finally, verify the quality of the mesh cells and ensure the shape factor of all meshes. The following conditions must be met:

[0120] ,in, The mesh quality threshold is typically set to 0.3; and the final optimized mesh data is output and provided to the fluid dynamics solution unit.

[0121] The meteorological data acquisition module includes a sensor selection unit, a sensor layout unit, a data transmission unit, a sensor calibration unit, and a data storage unit; among which:

[0122] Sensor selection unit: used to deploy distributed meteorological sensor arrays in the target area. Each sensor array includes a temperature sensor, humidity sensor, precipitation meter and anemometer. The sensors are weather-resistant and have long-distance data transmission capabilities, and are suitable for complex terrain and extreme climate environments.

[0123] The sensor placement unit divides the target area based on the terrain feature data provided by the terrain analysis module and determines the sensor placement locations according to the following rules.

[0124] Rule 1: Deploy high-density sensor arrays in areas with steep slopes and high roughness to ensure data coverage accuracy;

[0125] Rule 2: Deploy low-density sensor arrays in areas with gentle terrain to optimize the allocation of sensor resources; the spacing between arrays should be determined based on the complexity of the terrain and the area of ​​the target region to ensure coverage without blind spots.

[0126] Data transmission unit: used to transmit the collected meteorological data to the system's central data unit in real time for standardization;

[0127] Sensor calibration unit: used to periodically calibrate the deployed sensors. Specifically, it compares the collected temperature, humidity, precipitation and wind speed with data from standard weather stations or known reference equipment, adjusts the sensor measurement deviations, and ensures the consistency of the collected data accuracy.

[0128] Data storage unit: Used to store the collected meteorological data in different regions. The meteorological data of each region is archived according to the timestamp, which facilitates the subsequent use by the icing risk assessment module and the microclimate simulation module. Through the collaborative work of the above units, the meteorological data acquisition module can scientifically deploy a distributed meteorological sensor array according to the terrain characteristics and monitoring needs of the target area, so as to achieve full coverage and high-precision acquisition of meteorological data in the target area, and provide reliable real-time meteorological data support for the real-time monitoring module and the microclimate simulation module.

[0129] The icing risk assessment module includes a sample construction unit, a model training unit, a model validation unit, a parameter optimization unit, and a model output unit; among which:

[0130] Sample construction unit: Based on historical icing data, the topographic feature data and local meteorological parameters of the target area are matched with the corresponding icing risk level labels to construct a training sample set and a test sample set; the training sample set is used for model training, and the test sample set is used for model evaluation.

[0131] Model training unit: The random forest algorithm is used to train the training sample set. Multiple decision trees are constructed to classify the data. Each decision tree takes a subset of the feature vector as input and the icing risk level as output. The outputs of all decision trees are combined and voted to form an icing risk prediction model.

[0132] The model validation unit uses a test sample set to validate the trained risk assessment model. By comparing the model's prediction results with the actual risk level labels, it calculates the model's performance metrics, including accuracy, error rate, and classification precision.

[0133] Parameter optimization unit: The hyperparameters of the icing risk assessment model are optimized using cross-validation, including the number of decision trees, maximum depth, and number of features used in each tree, to ensure the model's generalization ability on different sample sets; at the same time, the hyperparameters are dynamically adjusted according to the model's performance indicators on the test sample set.

[0134] Model output unit: This unit saves the final optimized icing risk assessment model as a standardized model file for the real-time monitoring module to use, facilitating subsequent real-time risk prediction. Through the collaborative work of the above units, the icing risk assessment module can utilize terrain feature data, local meteorological parameters, and historical icing data to construct an accurate and reliable random forest algorithm model, providing a scientific basis for predicting the icing risk of power lines in the target area.

[0135] The model training unit specifically includes:

[0136] Training sample initialization: Receive the training sample set provided by the sample construction unit. Each sample consists of an input feature vector and a corresponding ice accretion risk level label.

[0137] Decision tree construction: Random forests generate multiple decision trees by repeating the following steps multiple times:

[0138] Step 1: Randomly select a predetermined number of samples (with duplicates allowed) from the training sample set using the bootstrap sampling method to construct a training subset of the decision tree, and use the remaining samples as the validation set;

[0139] Step 2: Randomly select a subset of features from all features as the candidate set of splitting features for the current decision tree. The number of selected features is usually less than the total number of features.

[0140] Step 3: At each node, select the optimal splitting feature and splitting point from the candidate feature set to achieve the optimal purity of the splitting node.

[0141] Decision tree training: Recursively split the training subset of each decision tree until any of the set conditions are met. Specific conditions include all samples in the current node belonging to the same category, the number of samples being less than a preset threshold, and the decision tree reaching a preset maximum depth.

[0142] Decision tree ensemble: All decision trees are combined into a random forest model. The prediction result of the random forest is the majority vote of the classification results of all decision trees, that is, the classification that appears most frequently is selected as the final prediction result.

[0143] Model output: After training, save the random forest model as an icing risk assessment model.

[0144] The specific computational steps for training the training sample set are as follows:

[0145] First, the training sample set provided by the sample construction unit is received, where each sample consists of an input feature vector. and corresponding icing risk level labels Composition, in which It includes topographic feature data of the target area (such as slope, aspect, elevation, and topographic roughness) and local meteorological parameters (such as wind speed, temperature, and humidity). The category label indicates the level of icing risk;

[0146] Then, the random forest generates multiple decision trees by repeating the following steps multiple times. ;

[0147] Next, the training subset of each decision tree is recursively split until one of the following conditions is met: all samples in the current node belong to the same category; the number of samples is lower than a preset threshold; or the decision tree reaches a preset maximum depth.

[0148] Finally, all decision trees A random forest model is constructed, where the prediction result of the random forest is the majority vote of the classification results of all decision trees. The model expression is:

[0149] ,in, The predicted icing risk level, The indicator function takes a value of 1 when the condition is true and 0 otherwise. Through the above steps, the model training unit uses the random forest algorithm to train and generate an icing risk prediction model that comprehensively considers terrain features and local meteorological parameters. This model can accurately classify different icing risk levels in a multi-dimensional feature space, providing a reliable basis for subsequent risk prediction.

[0150] The parameter optimization unit includes:

[0151] Initial parameter settings: Set the initial hyperparameters of the random forest algorithm, including the number of decision trees, maximum depth, number of features used per tree, and minimum number of sample splits per node, as the initial parameter set for cross-validation;

[0152] Cross-validation grouping: Dividing the training sample set into... Each iteration selects one of several equal subsets as the validation set, and the rest... A subset is used as the training set, and the process is repeated. Next, ensure that each subset is used as a validation set once;

[0153] Model training and validation: In each iteration, the model is trained and validated using the current hyperparameter settings. The icing risk assessment model is trained on a training subset, and the model's performance metrics, including accuracy, recall, and F1 score, are calculated using the validation set.

[0154] Performance Indicators Summary and Evaluation: Summary The validation results of each iteration are used to calculate the average performance index of the icing risk assessment model as a comprehensive evaluation index for the current hyperparameter settings; for example, for the calculation of accuracy: ;

[0155] Dynamic hyperparameter tuning: Based on the average performance metrics of the validation set, the hyperparameter combination is adjusted, and the optimal hyperparameter combination is determined by grid search or random search methods. For hyperparameter combinations with poor validation performance, the number of decision trees is adjusted first (increasing the number of decision trees to improve model stability) or the maximum depth of the trees is reduced (to prevent overfitting). For combinations with good validation performance, the feature usage ratio of each tree is gradually refined to optimize model performance.

[0156] Final hyperparameter determination: Select the hyperparameter combination that yields the best results from the validation and record the hyperparameter settings as the final parameters of the random forest model;

[0157] Test sample performance verification: The optimized icing risk assessment model is finally verified using the test sample set. The performance indicators of the icing risk assessment model on the test sample set are calculated, including accuracy, error rate and classification accuracy. If the test results do not meet the preset performance target, the hyperparameters will be adjusted again and the above optimization steps will be repeated until the performance requirements are met.

[0158] Parameter output: Record the optimized hyperparameters and the final verified performance indicators and save them as the configuration information file of the icing risk assessment model, so as to facilitate subsequent model calls and performance tracking.

[0159] The real-time monitoring module includes a data receiving unit, a data preprocessing unit, a feature matching unit, and a model calling unit; among which:

[0160] Data receiving unit: used to receive real-time meteorological data provided by the meteorological data acquisition module, including temperature, humidity, precipitation and wind speed;

[0161] Data preprocessing unit: used to preprocess the received real-time meteorological data, including data cleaning, missing value imputation and outlier detection, to ensure the integrity of the input data;

[0162] Feature matching unit: Matches the preprocessed real-time meteorological data with the input features of the icing risk assessment model to ensure that the data format and dimensions are consistent with the model requirements;

[0163] Model Invocation Unit: Invokes the icing risk assessment model already built in the icing risk assessment module, inputs the matched real-time meteorological data into the model, performs real-time icing risk prediction, and outputs the risk prediction results; through the collaborative work of the above units, the real-time monitoring module can efficiently input real-time meteorological data into the built icing risk assessment model, perform real-time risk prediction, and provide accurate risk assessment results for the early warning information generation module.

[0164] The early warning information generation module includes a risk classification unit, a thickness analysis unit, a high-risk point location unit, an information integration unit, and an early warning information output unit; among which:

[0165] Risk grading unit: Used to receive the risk prediction results output by the real-time monitoring module, compare the icing risk value in the risk prediction results with the preset risk level threshold, and classify it into mild risk, moderate risk and severe risk to generate the risk level of the target area;

[0166] Thickness Analysis Unit: Based on the predicted ice thickness value in the risk prediction results, combined with the meteorological conditions and topographic features of the target area, generate ice thickness distribution maps for different areas to clarify the spatial variation of ice thickness.

[0167] High-risk point location unit: By analyzing the risk level distribution and ice thickness prediction results, identify areas in the target area with a medium or higher ice risk level, determine the specific location of high-risk points, generate a list of high-risk points, and mark the corresponding geographic coordinates;

[0168] Information integration unit: Integrates the data generated by the risk classification unit, thickness analysis unit and high-risk point location unit to generate complete early warning information for the target area, including risk level distribution map, icing thickness distribution map and high-risk point list;

[0169] Early warning information output unit: Outputs the integrated early warning information in digital form, which can be queried by power maintenance departments and relevant monitoring personnel. It supports the display of early warning information in the form of maps, text reports or tables. Through the collaborative work of the above units, the early warning information generation module can generate early warning information including the predicted value of icing thickness, risk level and location of high-risk points based on the risk prediction results output by the real-time monitoring module, providing accurate decision support for the maintenance and emergency response of power lines.

[0170] This invention encompasses any substitutions, modifications, equivalent methods, and solutions made within the spirit and scope of this invention. To provide the public with a thorough understanding of this invention, specific details are described in detail in the following preferred embodiments; however, those skilled in the art will fully understand the invention even without these details. Furthermore, to avoid unnecessary misunderstanding of the essence of this invention, well-known methods, processes, procedures, components, and circuits are not described in detail.

[0171] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A wire ice warning system based on terrain analysis, characterized in that: It includes terrain analysis module, microclimate simulation module, meteorological data acquisition module, icing risk assessment module, real-time monitoring module and warning information generation module; among which: Terrain analysis module: used to obtain digital elevation model data of the target area, and calculate the slope, slope aspect, altitude and terrain roughness of the target area based on the digital elevation model data to form terrain feature data of the target area; Microclimate simulation module: Based on terrain feature data, computational fluid dynamics and numerical weather forecast models are used to simulate the micro-scale climate of the target area and generate local meteorological parameters, including refined wind field, temperature field and humidity field; Meteorological data acquisition module: used to deploy a distributed meteorological sensor array in the target area to collect real-time meteorological data of the target area, including temperature, humidity, precipitation and wind speed; Icing risk assessment module: Based on terrain feature data, local meteorological parameters and historical icing data, an icing risk assessment model is constructed using the random forest algorithm; Real-time monitoring module: receives real-time meteorological data provided by the meteorological data acquisition module, inputs the real-time meteorological data into the constructed icing risk assessment model, performs real-time risk prediction, and outputs the predicted value of ice thickness; Early warning information generation module: Generates early warning information for the target area based on the risk prediction results output by the real-time monitoring module.

2. The wire ice warning system based on terrain analysis according to claim 1 is characterized in that: The terrain analysis module includes a data acquisition unit, a slope calculation unit, a slope aspect calculation unit, an altitude calculation unit, a terrain roughness calculation unit and a feature integration unit; wherein: Data acquisition unit: used to acquire digital elevation model data of the target area through satellite remote sensing data source, the digital elevation model data includes surface elevation information represented in the form of a grid, and perform format conversion and projection coordinate correction on the data to ensure data accuracy; Slope calculation unit: based on the elevation value change of the digital elevation model data, the slope value of each grid unit is calculated by differential operation, and the slope value represents the ratio of the elevation change with the horizontal distance; Aspect calculation unit: based on the digital elevation model data and the slope calculation result, the aspect value of each grid cell is calculated, and the aspect value represents the azimuth of the slope surface relative to the north direction; Elevation calculation unit: extract the elevation value of each grid cell in the digital elevation model data, and directly obtain the elevation distribution of each grid cell in the target area; Terrain roughness calculation unit: by analyzing the degree of change of local elevation in the digital elevation model data, the terrain roughness of each grid cell in the target area is calculated, and the terrain roughness is expressed as the standard deviation of the elevation change around the grid cell; Feature integration unit: used to integrate the calculation results of slope, aspect, altitude and terrain roughness to generate terrain feature data of the target area.

3. The wire ice warning system based on terrain analysis according to claim 1 is characterized in that: The microclimate simulation module includes a grid division unit, a fluid dynamics solving unit, an energy transfer solving unit, a water vapor transport solving unit and an output generation unit; wherein: Meshing unit: used to mesh the target area according to terrain feature data and generate a three-dimensional mesh model for computational fluid dynamics simulation; Fluid dynamics solving unit: Based on the computational fluid dynamics model, the Reynolds-averaged Navier-Stokes equations are used to solve the fluid dynamics and simulate the airflow distribution in the target area. The calculation formula is: ,in, is the gas density, and The airflow is and The velocity component in the direction, For pressure, is the dynamic viscosity, is the volume force; through iterative calculation, the refined wind field distribution of the target area is obtained; Energy transfer solving unit: Based on the energy conservation equation in the numerical weather forecast model, the temperature field in the target area is simulated. The calculation formula is: ,in, is the temperature, is the specific heat capacity, is the thermal conductivity, is the heat source term; Water vapor transport solution unit: Based on the water vapor transport equation, simulate the humidity field distribution in the target area. The calculation formula is: ,in, For humidity, is the water vapor diffusion coefficient, is the water vapor source term; Output generation unit: Integrate the calculation results of the fluid dynamics solution unit, energy transfer solution unit and water vapor transport solution unit to generate local meteorological parameters of the target area and output them in the form of three-dimensional grid.

4. The wire ice warning system based on terrain analysis according to claim 3 is characterized in that: The grid division unit comprises: Data preprocessing: normalize the terrain feature data provided by the terrain analysis module and normalize the elevation values ​​to ensure that the input data is within a uniform scale; Terrain complexity analysis: Calculate the mesh refinement requirements in the target area based on the terrain roughness and slope; Initial grid generation: Based on the boundary data of the target area, the quadtree partitioning algorithm is used to generate an initial regular grid to divide the target area into a number of initial square units; Mesh refinement: Adaptive mesh refinement is used to adjust the initial mesh according to the results of terrain complexity analysis. For areas with greater roughness or higher slopes, the side length of the mesh unit is refined. For areas with gentle terrain changes, the resolution of the initial mesh unit is kept unchanged. Boundary optimization: Optimize the grid cell boundaries and generate unstructured grids using the Delaunay triangulation method to minimize the maximum angle of the triangle; Mesh verification and output: Verify the quality of mesh cells. Once the shape factors of all meshes meet the quality threshold requirements, the optimized mesh data will be output.

5. The wire ice warning system based on terrain analysis according to claim 1 is characterized in that: The meteorological data acquisition module includes a sensor selection unit, a sensor layout unit, a data transmission unit, a sensor calibration unit and a data storage unit; wherein: Sensor selection unit: used to deploy distributed meteorological sensor arrays in the target area, each sensor array includes a temperature sensor, a humidity sensor, a precipitation meter and an anemometer; The sensor layout unit divides the target area based on the terrain feature data provided by the terrain analysis module and determines the layout positions of the sensors according to the following rules; Rule 1: Place high-density sensor arrays in areas with high slopes and high roughness; Rule 2: Deploy low-density sensor arrays in areas with flat terrain; Data transmission unit: used to transmit the collected meteorological data to the system's central data device in real time for standardization; Sensor calibration unit: used to periodically calibrate deployed sensors. Specifically, the collected temperature, humidity, precipitation and wind speed are compared with data from standard weather stations or known reference equipment to adjust the measurement deviation of the sensors to ensure the accuracy of the collected data. Data storage unit: used to store the collected meteorological data in different regions, and the meteorological data of each region is archived according to the timestamp.

6. The wire ice warning system based on terrain analysis according to claim 1 is characterized in that: The icing risk assessment module includes a sample construction unit, a model training unit, a model verification unit, a parameter optimization unit and a model output unit; wherein: Sample construction unit: Based on historical ice cover data, the terrain feature data and local meteorological parameters of the target area are matched with the corresponding ice cover risk level labels to construct training sample sets and test sample sets; the training sample set is used for model training, and the test sample set is used for model evaluation; Model training unit: Use the random forest algorithm to train the training sample set, and classify the data by building multiple decision trees. Each decision tree takes a subset of the feature vector as input and the icing risk level as output. The outputs of all decision trees are voted on comprehensively to form an icing risk prediction model. The model verification unit verifies the trained risk assessment model using the test sample set, and calculates the model's performance indicators, including accuracy, error rate, and classification accuracy, by comparing the model's prediction results with the actual risk level labels; Parameter optimization unit: Use cross-validation method to optimize the hyperparameters of the icing risk assessment model, including the number of decision trees, maximum depth, and number of features used by each tree, to ensure the generalization ability of the model on different sample sets; at the same time, dynamically adjust the hyperparameters according to the performance indicators of the model on the test sample set; Model output unit: used to save the final optimized icing risk assessment model as a standardized model file for real-time monitoring module to call.

7. The wire ice warning system based on terrain analysis according to claim 6 is characterized in that: The model training unit specifically includes: Training sample initialization: receiving the training sample set provided by the sample construction unit, each sample consists of an input feature vector and a corresponding icing risk level label; Decision Tree Construction: Random Forest generates multiple decision trees by repeating the following steps multiple times: Step 1: Randomly select a predetermined number of samples from the training sample set using the bootstrap sampling method to construct a training subset of a decision tree, and the remaining samples are used as the validation set; Step 2: Randomly select some features from all features as the candidate set of split features for the current decision tree. The number of selected features is usually less than the total number of features. Step 3: At each node, select the optimal split feature and split point from the candidate feature set to optimize the purity of the node after splitting; Decision tree training: Recursively split the training subset of each decision tree until any of the set conditions are met, including that all samples in the current node belong to the same category, the number of samples is lower than the preset threshold, and the decision tree reaches the preset maximum depth; Decision tree ensemble: All decision trees are combined into a random forest model. The prediction result of the random forest is the majority vote of the classification results of all decision trees, that is, the classification with the most occurrences is selected as the final prediction result. Model output: After training is completed, the random forest model is saved as an icing risk assessment model.

8. The wire ice warning system based on terrain analysis according to claim 7 is characterized in that: The parameter optimization unit comprises: Initial parameter setting: Set the initial hyperparameters of the random forest algorithm, including the number of decision trees, the maximum depth, the number of features used by each tree, and the minimum number of node sample splits, as the initial parameter set for cross-validation; Cross-validation grouping: Divide the training sample set into In each iteration, one subset is selected as the validation set, and the rest subsets as training sets, and repeat times, making sure each subset is used as a validation set once; Model training and validation: In each iteration, the current hyperparameter settings are used to train The icing risk assessment model was trained using the training subsets, and the performance indicators of the model, including precision, recall, and F1 score, were calculated using the validation set. Performance Indicator Summary and Evaluation: Summary The verification results of the iterations are used to calculate the average performance index of the icing risk assessment model as a comprehensive evaluation index for the current hyperparameter settings; Dynamic adjustment of hyperparameters: Based on the average performance index of the validation set, the hyperparameter combination is adjusted, and the optimal hyperparameter combination is determined by grid search or random search method; Final hyperparameter determination: Select the hyperparameter combination with the best verification results and record the hyperparameter settings as the final parameters of the random forest model; Test sample performance verification: Use the test sample set to conduct final verification of the optimized icing risk assessment model, and calculate the performance indicators of the icing risk assessment model on the test sample set, including accuracy, error rate, and classification accuracy; if the test results do not meet the preset performance targets, the hyperparameters will be adjusted again and the above optimization steps will be repeated until the performance requirements are met; Parameter output: The optimized hyperparameters and the final verified performance indicators are recorded and saved as the configuration information file of the icing risk assessment model.

9. The wire ice warning system based on terrain analysis according to claim 1 is characterized in that: The real-time monitoring module includes a data receiving unit, a data preprocessing unit, a feature matching unit and a model calling unit; wherein: Data receiving unit: used to receive real-time meteorological data provided by the meteorological data acquisition module, including temperature, humidity, precipitation and wind speed; Data preprocessing unit: used to preprocess the received real-time meteorological data, including data cleaning, missing value filling and outlier detection, to ensure the integrity of the input data; Feature matching unit: matches the preprocessed real-time meteorological data with the input features of the icing risk assessment model to ensure that the data format and dimension are consistent with the model requirements; Model calling unit: calls the icing risk assessment model constructed in the icing risk assessment module, inputs the matched real-time meteorological data into the model, performs real-time icing risk prediction, and outputs the risk prediction results.

10. The wire ice warning system based on terrain analysis according to claim 1, characterized in that: The warning information generation module includes a risk classification unit, a thickness analysis unit, a high-risk point positioning unit, an information integration unit and a warning information output unit; wherein: Risk grading unit: used to receive the risk prediction results output by the real-time monitoring module, and compare the ice risk value in the risk prediction results with the preset risk level threshold, and grade them into mild risk, moderate risk and severe risk to generate the risk level of the target area; Thickness analysis unit: Based on the predicted ice thickness value in the risk prediction results, combined with the meteorological conditions and terrain characteristics of the target area, generate ice thickness distribution maps for different areas to clarify the spatial variation of ice thickness; High-risk point positioning unit: By analyzing the risk level distribution and ice thickness prediction results, identify the areas with medium and above ice risk levels in the target area, determine the specific locations of high-risk points, and generate a list of high-risk points with corresponding geographic coordinates marked; Information integration unit: Integrate the data generated by the risk classification unit, thickness analysis unit and high-risk point positioning unit to generate complete warning information for the target area, including risk level distribution map, ice thickness distribution map and high-risk point list; Warning information output unit: outputs the integrated warning information in digital form for query by power maintenance departments and relevant monitoring personnel, and supports displaying warning information in the form of maps, text reports or tables.

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