Ice region power transmission line optimization method and device considering icing difference, equipment and storage medium

Abstract

The invention relates to an ice region power transmission line optimization method and device considering icing difference, equipment and a storage medium, and the method comprises the steps: obtaining the line structure data of an ice region power transmission line, obtaining the meteorological data and topographic data of the environment where the ice region power transmission line is located, and carrying out the calculation of the line structure data, the meteorological data and the topographic data, and inputting the line structure data, the meteorological data and the topographic data into a preset prediction model for prediction to obtain icing information of the ice region power transmission line, and finally inputting the icing information, the line structure data, the meteorological data and the topographic data into a preset line optimization model for line parameter optimization to obtain optimized ice region power transmission line parameters. And designing the ice region power transmission line by using the optimized ice region power transmission line parameters. According to the method, the weak part of the power transmission line in the ice region in operation can be accurately identified while line optimization is carried out, and targeted performance improvement is carried out on the weak part, so that the ice disaster resistance of the power transmission line in operation is remarkably enhanced.

Description

Technical Field

[0001] The present application relates to the technical field of power transmission lines, and in particular to an optimization method, device, equipment and storage medium for power transmission lines in ice areas taking into account ice coverage differences. Background Art

[0002] During the operation of power transmission lines, icing is a serious hazard that can severely impact line safety and reliability, particularly in low-temperature regions and high-altitude areas. Icing refers to the phenomenon in which, under specific meteorological conditions, supercooled water droplets or vapor in the air freeze or condense on the surface of transmission lines, forming a layer of ice. Icing can cause flashovers between the conductors and ground wires of transmission lines and alter the internal force distribution of transmission towers, further impacting the safe operation of the power grid. In recent years, the icing problem facing transmission lines in icy areas has become increasingly severe, placing higher demands on line safety and economic efficiency.

[0003] At present, under extreme conditions, how to optimize the parameters of ice area transmission lines so that they can adapt to the icing meteorological environment has become an urgent problem to be solved in the field of power transmission systems. Summary of the Invention

[0004] Based on this, it is necessary to provide an optimization method, device, equipment and storage medium for ice zone power transmission lines that can adapt to ice-covered meteorological environments and take into account ice cover differences in order to address the above technical problems.

[0005] In a first aspect, the present application provides an optimization method for power transmission lines in ice regions taking into account ice coverage differences, the method comprising:

[0006] Obtaining line structure data of ice region transmission lines, as well as meteorological data and topographic data of the environment where the ice region transmission lines are located;

[0007] Inputting line structure data, meteorological data, and terrain data into a preset prediction model for prediction, thereby obtaining ice coverage information of the transmission line in the ice area; the ice coverage information includes at least one of the following: ground wire ice coverage thickness, conductor ice coverage thickness, and ground-conductor ice coverage thickness difference;

[0008] Icing information, line structure data, meteorological data, and terrain data are input into a preset line optimization model to optimize line parameters and obtain optimized ice area transmission line parameters; the optimized ice area transmission line parameters are used to design ice area transmission lines.

[0009] In some embodiments, a preset line optimization model includes a first prediction sub-model, a second prediction sub-model, and an optimization sub-model. Ice cover information, line structure data, meteorological data, and terrain data are input into the preset line optimization model to optimize line parameters, thereby obtaining optimized ice region transmission line parameters, including:

[0010] Inputting icing information and line structure data into the first prediction sub-model for prediction, thereby obtaining line reconstruction cost information and line performance information of the ice area transmission line;

[0011] Inputting line reconstruction cost information, icing information, line structure data, meteorological data, and terrain data into the second prediction sub-model for cost prediction, thus obtaining comprehensive cost information for transmission lines in ice areas;

[0012] With the goal of minimizing comprehensive costs and optimizing line performance, the line structure data is input into the optimization sub-model for optimization and adjustment to obtain the optimized parameters of the ice area transmission line.

[0013] In some embodiments, the second prediction sub-model includes a first prediction module and a second prediction module, and inputs line modification cost information, icing information, line structure data, meteorological data, and terrain data into the second prediction sub-model to perform cost prediction, thereby obtaining comprehensive cost information for transmission lines in icy areas, including:

[0014] Inputting icing information, line structure data, meteorological data, and terrain data into a first prediction module to perform cost prediction to obtain basic cost information; the basic cost information includes at least one of material cost information, construction cost information, design cost information, and maintenance cost information;

[0015] The basic cost information and line reconstruction cost information are input into the second prediction module for cost prediction to obtain comprehensive cost information.

[0016] In some embodiments, with the goal of minimizing comprehensive costs and optimizing line performance, line structure data is input into the optimization sub-model for optimization and adjustment, thereby obtaining optimized ice region transmission line parameters, including:

[0017] Inputting the line structure data into the optimization sub-model for optimization adjustment to obtain the adjusted line structure data;

[0018] The adjusted line structure data is used as the new line structure data, and the step of inputting the icing information and the line structure data into the first prediction sub-model for prediction is returned to execute until the comprehensive cost is minimized and the line performance is optimized, and the line structure data when the comprehensive cost is minimized and the line performance is optimized is determined as the optimized ice area transmission line parameters.

[0019] In some embodiments, the method further comprises:

[0020] Obtaining a preset mapping relationship, icing sample data, and basic cost sample data; the preset mapping relationship is used to characterize the correspondence between icing data, line performance data, and transformation cost data;

[0021] Construct economic cost sample data based on basic cost sample data and transformation cost data;

[0022] Construct line performance sample data based on line performance data;

[0023] Based on the icing sample data, line performance sample data and economic cost sample data, an initial line optimization model is trained to obtain a preset line optimization model.

[0024] In some embodiments, obtaining a preset mapping relationship includes:

[0025] Acquire ice coverage data of multiple groups of sample transmission lines; the ice coverage data includes at least one of ground wire ice coverage thickness, conductor ice coverage thickness, and ground conductor ice coverage thickness difference;

[0026] For any set of sample transmission line icing data, the icing data is input into the simulation model corresponding to the transmission line, and the transmission line parameters in the simulation model are adjusted to obtain the line performance data and transformation cost data under different transmission line parameters;

[0027] A preset mapping relationship is established based on icing data, multiple sets of line performance data under the icing data, and reconstruction cost data.

[0028] In a second aspect, the present application further provides an optimization device for power transmission lines in ice regions taking into account ice coverage differences, the device comprising:

[0029] An acquisition module is used to acquire line structure data of the ice area transmission line, as well as meteorological data and topographic data of the environment where the ice area transmission line is located;

[0030] A prediction module is configured to input line structure data, meteorological data, and terrain data into a preset prediction model for prediction, thereby obtaining ice coverage information of the transmission line in the ice area; the ice coverage information includes at least one of the following: ground wire ice coverage thickness, conductor ice coverage thickness, and ground-conductor ice coverage thickness difference;

[0031] The optimization module is used to input ice cover information, line structure data, meteorological data and terrain data into a preset line optimization model to optimize line parameters and obtain optimized ice area transmission line parameters; the optimized ice area transmission line parameters are used to design ice area transmission lines.

[0032] In a third aspect, the present application further provides a computer device, comprising a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the following steps are implemented:

[0033] Obtaining line structure data of ice region transmission lines, as well as meteorological data and topographic data of the environment where the ice region transmission lines are located;

[0034] Inputting line structure data, meteorological data, and terrain data into a preset prediction model for prediction, thereby obtaining ice coverage information of the transmission line in the ice area; the ice coverage information includes at least one of the following: ground wire ice coverage thickness, conductor ice coverage thickness, and ground-conductor ice coverage thickness difference;

[0035] Icing information, line structure data, meteorological data, and terrain data are input into a preset line optimization model to optimize line parameters and obtain optimized ice area transmission line parameters; the optimized ice area transmission line parameters are used to design ice area transmission lines.

[0036] In a fourth aspect, the present application further provides a computer-readable storage medium having a computer program stored thereon, wherein when the computer program is executed by a processor, the following steps are implemented:

[0037] Obtaining line structure data of ice region transmission lines, as well as meteorological data and topographic data of the environment where the ice region transmission lines are located;

[0038] Inputting line structure data, meteorological data, and terrain data into a preset prediction model for prediction, thereby obtaining ice coverage information of the transmission line in the ice area; the ice coverage information includes at least one of the following: ground wire ice coverage thickness, conductor ice coverage thickness, and ground-conductor ice coverage thickness difference;

[0039] Icing information, line structure data, meteorological data, and terrain data are input into a preset line optimization model to optimize line parameters and obtain optimized ice area transmission line parameters; the optimized ice area transmission line parameters are used to design ice area transmission lines.

[0040] In a fifth aspect, the present application further provides a computer program product, the computer program product comprising a computer program, which, when executed by a processor, implements the following steps:

[0041] Obtaining line structure data of ice region transmission lines, as well as meteorological data and topographic data of the environment where the ice region transmission lines are located;

[0042] Inputting line structure data, meteorological data, and terrain data into a preset prediction model for prediction, thereby obtaining ice coverage information of the transmission line in the ice area; the ice coverage information includes at least one of the following: ground wire ice coverage thickness, conductor ice coverage thickness, and ground-conductor ice coverage thickness difference;

[0043] Icing information, line structure data, meteorological data, and terrain data are input into a preset line optimization model to optimize line parameters and obtain optimized ice area transmission line parameters; the optimized ice area transmission line parameters are used to design ice area transmission lines.

[0044] The above-mentioned method, apparatus, device, and storage medium for optimizing ice-covered transmission lines taking into account ice coverage differences obtains line structure data of the ice-covered transmission line, as well as meteorological and topographical data of the environment in which the ice-covered transmission line is located. The method then inputs the line structure data, meteorological and topographical data into a preset prediction model for prediction, obtaining ice coverage information for the ice-covered transmission line. Finally, the ice coverage information, line structure data, meteorological and topographical data are input into a preset line optimization model for line parameter optimization, obtaining optimized ice-covered transmission line parameters. The optimized ice-covered transmission line parameters are then used to design the ice-covered transmission line. In the above-mentioned method, since the prediction model takes into account line performance and economic costs during training, the safety and economic efficiency of the transmission line structure are comprehensively considered when optimizing the ice-covered transmission line using the trained prediction model. On the one hand, while performing line optimization, the weak points of the operating ice-covered transmission line can be accurately identified and targeted performance improvements can be made, thereby significantly enhancing the ability of the operating transmission line to withstand ice disasters. On the other hand, the above-mentioned line parameter optimization method can carry out customized optimization design based on the actual situation of ice zone transmission lines and provide effective improvement solutions. It can not only save the design cost of future transmission tower line systems, but also improve the reliability of the lines. Its practicality and innovation are more prominent. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] Figure 1 is a diagram of the internal structure of a computer device in some embodiments;

[0046] Figure 2 FIG1 is a flow chart of a method for optimizing power transmission lines in ice regions taking into account ice coverage differences in some embodiments;

[0047] Figure 3 This is a second flow chart of a method for optimizing power transmission lines in ice regions taking into account ice coverage differences in some embodiments;

[0048] Figure 4 FIG3 is a flow chart of a method for optimizing power transmission lines in ice regions taking into account ice coverage differences in some embodiments;

[0049] Figure 5 FIG4 is a flowchart of a method for optimizing power transmission lines in ice regions taking into account ice coverage differences in some embodiments;

[0050] Figure 6 FIG5 is a flowchart of a method for optimizing power transmission lines in ice regions taking into account ice coverage differences in some embodiments;

[0051] Figure 7 FIG6 is a flowchart of a method for optimizing power transmission lines in ice regions taking into account ice cover differences in some embodiments;

[0052] Figure 8 This is a structural block diagram of an optimization device for power transmission lines in ice areas taking into account ice coverage differences in some embodiments. DETAILED DESCRIPTION

[0053] In the embodiments of this application, the term "and / or" describes the association relationship between associated objects, indicating that three relationships can exist. For example, A and / or B can represent three situations: A exists alone, A and B exist simultaneously, and B exists alone. The character " / " generally indicates that the associated objects are in an "or" relationship.

[0054] In the embodiments of the present application, the term "plurality" refers to two or more than two, and other quantifiers are similar.

[0055] In the embodiments of the present application, the term "at least one" means one or more. For example, at least one of A, B and C can mean the following six situations: A exists alone, B exists alone, C exists alone, A and B exist at the same time, A and C exist at the same time, B and C exist at the same time, and A, B and C exist at the same time.

[0056] The following will be combined with the drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0057] During the operation of power transmission lines, icing is a serious hazard that seriously affects the safety and reliability of the lines, especially in low-temperature areas and high-altitude regions. Icing refers to the phenomenon in which supercooled water droplets or water vapor in the air freeze or condense on the surface of the transmission line under specific meteorological conditions, forming a layer of ice. Icing can cause flashover accidents between the conductors and ground wires of the transmission line and change the internal force distribution of the transmission towers, thereby affecting the safe operation of the power grid. In recent years, the icing problem faced by transmission lines in icy areas has become increasingly serious, placing higher demands on the safety and economic efficiency of the lines. Currently, how to optimize the parameters of transmission lines in icy areas under extreme conditions to enable them to adapt to icy meteorological environments has become a pressing issue in the field of power transmission systems.

[0058] In view of this, the embodiments of the present application propose an optimization method, device, equipment and storage medium for ice area transmission lines that take into account ice coverage differences. By optimizing the parameters of the ice area transmission lines, the ice area transmission lines can adapt to the icing meteorological environment.

[0059] It should be noted that the beneficial effects or technical problems solved by the embodiments of the present application are not limited to this one, but may also include other implicit or related problems. For details, please refer to the description of the following embodiments.

[0060] The following specific embodiments describe in detail the technical solution of the present application and how the technical solution of the present application solves the above-mentioned technical problems. The following specific embodiments can be combined with each other, and the same or similar concepts or processes may not be repeated in some embodiments. The embodiments of the present application will be described below in conjunction with the accompanying drawings.

[0061] In some embodiments, the optimization method for ice area transmission lines considering ice coverage differences provided in the embodiments of the present application can be applied to Figure 1 In the computer device shown, the computer device can be a terminal or a server, and its internal structure can be as shown in FIG. Figure 1 As shown, the computer device includes a processor, memory, an input / output interface, a communication interface, a display unit, and an input device. The processor, memory, and input / output interface are connected via a system bus, while the communication interface, display unit, and input device are connected to the system bus via the input / output interface. The processor of the computer device provides computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores an operating system and computer programs. The internal memory provides an environment for the operating system and computer programs stored in the non-volatile storage medium. The input / output interface of the computer device is used to exchange information between the processor and external devices. The communication interface of the computer device is used to communicate with external terminals via wired or wireless communication, which can be achieved via Wi-Fi, mobile cellular networks, NFC (near-field communication), or other technologies. When executed by the processor, the computer program implements a method for optimizing power transmission lines in ice-covered areas that takes into account ice cover differences. The display unit of the computer device is used to produce visual images and can be a display screen, a projection device, or a virtual reality imaging device. The display screen can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device can be a touch layer covering the display screen, or a button, trackball or touchpad set on the computer device casing, or an external keyboard, touchpad or mouse.

[0062] Those skilled in the art will understand that Figure 1 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than shown in the figure, or combine certain components, or have a different component arrangement.

[0063] In some embodiments, as Figure 2 As shown in the figure, an optimization method for ice area transmission lines considering ice cover difference is provided. Figure 1 The computer device in the example is used to illustrate the process, including the following steps:

[0064] S201, obtaining line structure data of the ice region transmission line, and obtaining meteorological data and topographic data of the environment where the ice region transmission line is located.

[0065] Ice-covered transmission lines refer to transmission lines located in areas prone to icing. Line structural data includes at least one of the current ground wire model, ground wire spacing, and suspension point height for the ice-covered transmission line. Meteorological data includes at least one of the current temperature, humidity, wind speed, and precipitation for the ice-covered transmission line. Topographic data includes at least one of the current altitude, terrain relief, and terrain type for the ice-covered transmission line.

[0066] In an embodiment of the present application, the computer device can obtain the line structure data of the ice-region transmission line, as well as the meteorological data and topographic data of the environment where the ice-region transmission line is located by means of web crawling. Optionally, the computer device can obtain the line structure data of the ice-region transmission line from the design drawings, construction records and related technical documents of the transmission line, or use professional measuring equipment, such as a total station, laser rangefinder, etc., to measure the actual structure of the transmission line to obtain the line structure data. The computer device can obtain the meteorological data of the meteorological station near the ice region from the meteorological department where the ice-region transmission line is located, or use the monitoring data of meteorological satellites and radars to obtain real-time meteorological data of the ice region. The computer device can obtain the topographic map of the ice region, use satellite remote sensing technology or conduct field surveys in the ice region to obtain topographic data.

[0067] S202: Inputting the line structure data, meteorological data and terrain data into a preset prediction model for prediction to obtain ice coverage information of the transmission line in the ice area.

[0068] The ground conductor ice thickness difference is the difference between the ground conductor ice thickness and the conductor ice thickness. The preset prediction model can be a neural network model, a machine learning model, or a mathematical relationship model. The ice information includes at least one of the ground conductor ice thickness, the conductor ice thickness, and the ground conductor ice thickness difference.

[0069] In an embodiment of the present application, after the computer device obtains the line structure data, meteorological data, and terrain data based on the above steps, the line structure data, meteorological data, and terrain data can be input into a preset prediction model for prediction to obtain icing information of the transmission line in the icy area. Optionally, after obtaining the line structure data, meteorological data, and terrain data, the computer device can normalize and standardize the line structure data, meteorological data, and terrain data to obtain processed line structure data, processed meteorological data, and processed terrain data, and then input the processed line structure data, processed meteorological data, and processed terrain data into a preset prediction model for prediction to obtain icing information of the transmission line in the icy area.

[0070] It should be noted that the computer device can pre-build an initial prediction model and then train the initial prediction model based on the structural data samples, meteorological data samples, and ice cover data samples of the ice area transmission line to obtain a preset prediction model. The specific training method is as follows:

[0071] First, computer equipment can obtain a large amount of relevant parameters of historical ice-area transmission lines as line structure data samples, such as conductor type, diameter, suspension height, span, line direction, etc.; obtain a large amount of historical meteorological data as meteorological data samples, such as temperature, humidity, wind speed, wind direction, precipitation, atmospheric pressure, etc.; obtain a large amount of historical terrain data as terrain data samples; and obtain a large amount of actual icing information of historical ice-area transmission lines as label data for model training, such as ice thickness, ice weight, ice type, etc.

[0072] After obtaining the route structure data samples, meteorological data samples, and terrain data samples, they can be preprocessed. Specifically, the data can be checked for missing values, outliers, and other issues. Missing values can be filled using interpolation methods (such as linear interpolation or spline interpolation). Outliers can be corrected or removed based on the data distribution. The different types of data can then be normalized to the same scale, for example, using Z-score or Min-Max normalization. The preprocessed data can then be divided into training, validation, and test sets according to a specific ratio (e.g., 70% training set, 15% validation set, and 15% test set). Finally, after obtaining the training set, validation set, and test set, a suitable model architecture can be selected as the initial prediction model based on actual needs or the characteristics of the ice area transmission lines. After determining the initial prediction model, a suitable loss function is selected (such as using a stochastic gradient algorithm or an adaptive moment estimation algorithm to minimize the loss function). Finally, a suitable optimization algorithm (such as a stochastic gradient algorithm) is selected to minimize the loss function. Finally, the initial prediction model is trained using the training set data. Specifically, in each training cycle, the training set data is input into the initial prediction model to obtain the ice thickness of the ground conductor output by the model. The loss between the predicted ice thickness of the ground conductor and the corresponding label data is then calculated, and the optimization algorithm is used to update the model parameters so that the loss function gradually decreases. The model parameters can then be continuously adjusted to minimize the value of the loss function to obtain a trained initial prediction model.

[0073] Furthermore, the trained initial prediction model can be validated using the validation set data using methods such as mean squared error (MSE), mean absolute error (MAE), and coefficient of determination (R²) to evaluate model performance. Based on the validation results, the model's hyperparameters (such as the learning rate, number of hidden layer neurons, and tree depth) can be adjusted to improve performance. Finally, the tuned model can be tested using the test set data to evaluate its generalization ability. If the test results meet the requirements, the trained initial prediction model will be used as the preset prediction model for the aforementioned application.

[0074] For example, in the embodiment of the present application, a long short-term memory neural network is selected as the initial prediction model. The relationship between the input gate, forget gate, and output gate of the long short-term memory neural network is represented by (1)-(3). The relationship for predicting the ice thickness of the ground conductor is represented by (4):

[0075] (1)

[0076] (2)

[0077] (3)

[0078] (4)

[0079] in, is the input vector, is the hidden state at the previous moment, 、 、 is the input weight matrix, 、 、 is the hidden layer weight matrix, 、 、 is the bias term, is the sigmoid function, Represented by LSTM network parameters, The nonlinear mapping function defined is is the input feature vector, is the hidden state at the previous moment, is the predicted ice thickness of the ground conductor.

[0080] S203: Inputting ice cover information, line structure data, meteorological data, and terrain data into a preset line optimization model to optimize line parameters, thereby obtaining optimized ice area transmission line parameters.

[0081] The optimized ice-region transmission line parameters are used to design the ice-region transmission line. The preset line optimization model can be a neural network model, a machine learning model, or a mathematical relational model. The optimized ice-region transmission line parameters include at least one of conductor model parameters, tower spacing parameters, insulator configuration parameters, and anti-icing material parameters.

[0082] In an embodiment of the present application, the computer device can pre-train an initial line optimization model based on ice sample data, line performance sample data, and economic cost sample data to obtain a preset line optimization model. After the computer device obtains the line structure data, meteorological data, and terrain data based on the above steps, the line structure data, meteorological data, and terrain data can be input into a preset prediction model for prediction to obtain icing information of the ice area transmission line. Optionally, after obtaining the line structure data, meteorological data, and terrain data, the computer device can normalize and standardize the line structure data, meteorological data, and terrain data to obtain processed line structure data, processed meteorological data, and processed terrain data, and then input the processed line structure data, processed meteorological data, and processed terrain data into a preset prediction model for prediction to obtain icing information of the ice area transmission line.

[0083] The embodiment of the present application provides an optimization method for ice-covered transmission lines that takes into account ice coverage differences. The method obtains line structure data of the ice-covered transmission line and meteorological data and topographic data of the environment in which the ice-covered transmission line is located. The line structure data, meteorological data, and topographic data are then input into a preset prediction model for prediction to obtain ice coverage information of the ice-covered transmission line. Finally, the ice coverage information, line structure data, meteorological data, and topographic data are input into a preset line optimization model for line parameter optimization to obtain optimized ice-covered transmission line parameters. The optimized ice-covered transmission line parameters are then used to design the ice-covered transmission line. In the above method, since the prediction model takes into account line performance and economic cost during training, the safety and economy of the transmission line structure are comprehensively considered when optimizing the ice-covered transmission line using the trained prediction model. On the one hand, while performing line optimization, the weak points of the operating ice-covered transmission line can be accurately identified and targeted performance improvements can be made, thereby significantly enhancing the ability of the operating transmission line to resist ice disasters. On the other hand, the above-mentioned line parameter optimization method can carry out customized optimization design based on the actual situation of ice zone transmission lines and provide effective improvement solutions. It can not only save the design cost of future transmission tower line systems, but also improve the reliability of the lines. Its practicality and innovation are more prominent.

[0084] In some embodiments, the above-mentioned preset line optimization model includes a first prediction sub-model, a second prediction sub-model and an optimization sub-model. On this basis, a specific implementation method of line parameter optimization is also provided, such as Figure 3 As shown, the above-mentioned step S203 of "inputting ice cover information, line structure data, meteorological data and terrain data into a preset line optimization model to optimize line parameters and obtain optimized ice area transmission line parameters" includes:

[0085] S301: Input icing information and line structure data into a first prediction sub-model for prediction, and obtain line reconstruction cost information and line performance information of the ice area transmission line.

[0086] Among them, the first prediction sub-model is used to predict the line transformation cost information and line performance information of the ice area transmission line. The first prediction sub-model can be a neural network model, a machine learning model, or a mathematical relationship model. The line transformation cost information represents the corresponding transformation cost when the transformation is carried out based on each optimization result during the parameter optimization process. The line performance information includes mechanical performance information and electrical performance information. Mechanical performance mainly focuses on the ability of the line to withstand external forces in the physical structure to ensure the stability and safety of the line under harsh conditions such as icing. Electrical performance mainly involves the conductivity, insulation performance and electromagnetic compatibility of the line to ensure that the line can transmit electric energy safely and stably.

[0087] In an embodiment of the present application, the computer device may pre-construct a first initial prediction sub-model based on a neural network or machine learning algorithm and perform training to obtain a trained first prediction sub-model. After obtaining icing information and line structure data of the ice-region transmission line, the computer device may input the icing information and line structure data into the first prediction sub-model for prediction, thereby obtaining line modification cost information and line performance information for the ice-region transmission line.

[0088] The line reconstruction cost information may include at least one of material replacement costs, labor costs, equipment costs, and transportation costs. Material replacement costs include at least one of conductor replacement costs, tower material costs, and insulator replacement costs. Labor costs include at least one of construction personnel costs and technician costs. Equipment costs include at least one of construction equipment costs and maintenance equipment costs. Transportation costs include at least one of material transportation costs and equipment transportation costs.

[0089] Line performance information may include at least one of conductor mechanical performance information, tower mechanical performance information, and hardware mechanical performance information. Conductor mechanical performance information includes at least one of conductor tensile strength, conductor elastic modulus, and conductor vibration resistance. Conductor tensile strength indicates the conductor's ability to resist tensile damage. In ice-covered conditions, the conductor bears additional weight, and higher tensile strength can prevent conductor breakage. Conductor elastic modulus indicates the conductor's ability to elastically deform under stress. A suitable elastic modulus can maintain appropriate sag in the conductor in the face of ice and temperature fluctuations. Conductor vibration resistance indicates that transmission lines in icy areas are susceptible to vibration and swaying in the breeze. Good vibration resistance can reduce conductor fatigue damage and extend the conductor's service life. The mechanical performance information of a tower includes at least one of its strength, stability, and deformation capacity. Tower strength refers to the tower's ability to withstand loads such as conductors, icing, and wind, and includes its axial compressive strength, bending strength, and torsional strength. Tower stability refers to the tower's ability to maintain balance and stability under various loads, including foundation stability and overall stability. Deformation capacity refers to the allowable deformation of the tower under load; reasonable deformation capacity can prevent damage to the tower due to localized stress concentration. Hardware mechanical performance information includes hardware connection strength and hardware wear resistance. Hardware connection strength refers to the fact that hardware is used to connect components such as conductors, towers, and insulators, and its connection strength directly affects the overall mechanical performance of the line. Hardware wear resistance refers to the friction and wear that hardware will experience during long-term operation. Good wear resistance ensures hardware connection reliability.

[0090] Electrical performance information may include at least one of the conductor's electrical performance, insulation performance, and electromagnetic compatibility. Conductor electrical performance can be reflected by resistivity and current carrying capacity. Resistivity represents the physical quantity of the conductor's resistance to current flow. Lower resistivity can reduce line power loss. Current carrying capacity represents the maximum current a conductor can safely carry under specified environmental conditions. The current carrying capacity is related to factors such as the conductor's material, cross-sectional area, and heat dissipation conditions. Insulation performance can be reflected by insulation resistance, breakdown voltage, and pollution flashover voltage. Insulation resistance represents the resistance of the insulator and line insulation to current flow. Higher insulation resistance can prevent leakage and flashover accidents. Breakdown voltage represents the voltage value when an insulator breaks down under the action of an electric field. The higher the breakdown voltage, the better the insulation performance of the insulator. Pollution flashover voltage indicates that in a dirty environment, dirt easily accumulates on the insulator surface, reducing insulation performance. Pollution flashover voltage is an important indicator for measuring an insulator's ability to resist pollution flashover. Electromagnetic compatibility can be reflected by radio interference and audible noise. Radio interference means that the transmission line will generate radio interference during operation, affecting the normal operation of surrounding radio communications and electronic equipment. Therefore, the radio interference level of the line needs to be controlled. Audible noise means that the line will generate audible noise during corona discharge, which will affect the surrounding environment and residents' lives. Therefore, the audible noise should be controlled within the allowable range.

[0091] S302: Inputting line reconstruction cost information, icing information, line structure data, meteorological data, and terrain data into a second prediction sub-model for cost prediction to obtain comprehensive cost information of the ice area transmission line.

[0092] The second prediction sub-model is used to predict the comprehensive cost information of ice-region transmission lines. The second prediction sub-model can be a neural network model, a machine learning model, or a mathematical relational model. The comprehensive cost information includes material cost information, design cost information, construction cost information, maintenance cost information, and line modification cost information.

[0093] In an embodiment of the present application, the computer device may pre-construct a second initial prediction sub-model based on a neural network or machine learning algorithm and perform training to obtain a trained second prediction sub-model. After obtaining line modification cost information, icing information, line structure data, meteorological data, and terrain data based on the above steps, the computer device may input the line modification cost information, icing information, line structure data, meteorological data, and terrain data into the second prediction sub-model for cost prediction, thereby obtaining comprehensive cost information for transmission lines in icy areas.

[0094] Optionally, the second prediction sub-model includes a first prediction module and a second prediction module. On this basis, Figure 4 As shown, the above S302 includes:

[0095] S3021: Input icing information, line structure data, meteorological data, and terrain data into a first prediction module to perform cost prediction and obtain basic cost information.

[0096] The first prediction module is used to predict basic cost information, which includes at least one of material cost information, construction cost information, design cost information, and maintenance cost information.

[0097] In this embodiment of the present application, the computer device may pre-build a first initial prediction network based on a neural network or machine learning algorithm and perform training to obtain a trained first prediction module. After obtaining icing information, line structure data, meteorological data, and terrain data based on the aforementioned steps, the computer device may input the icing information, line structure data, meteorological data, and terrain data into the first prediction module to perform cost prediction and obtain basic cost information.

[0098] S3022: Input the basic cost information and the line reconstruction cost information into the second prediction module for cost prediction to obtain comprehensive cost information.

[0099] Among them, the second prediction module is used to predict comprehensive cost information.

[0100] In an embodiment of the present application, the computer device may pre-construct a second initial prediction network based on a neural network or machine learning algorithm and perform training to obtain a trained second prediction module. After the computer device obtains the basic cost information and the line modification cost information based on the aforementioned steps, the basic cost information and the line modification cost information may be input into the second prediction module, which then performs a summation or weighted summation calculation on the basic cost information and the line modification cost information to obtain the comprehensive cost information.

[0101] S303, with the goal of minimizing comprehensive costs and optimizing line performance, input the line structure data into the optimization sub-model for optimization and adjustment to obtain optimized ice area transmission line parameters.

[0102] Among them, the optimized parameters of the ice area transmission line include at least one of the conductor model, tower spacing, insulator configuration, and anti-icing material.

[0103] In embodiments of the present application, a computer device can pre-construct an initial optimization sub-model based on a neural network or machine learning algorithm and perform training to obtain a trained optimization sub-model. After obtaining line performance information and comprehensive cost information, the computer device can establish an objective function with minimizing comprehensive cost and maximizing line performance as optimization objectives. For example, a weighted summation approach can be used to comprehensively consider cost and performance indicators. The collected line structure data is then input as initial parameters into the optimization sub-model. Initial parameters such as population size, number of iterations, crossover probability, and mutation probability are also set for the optimization algorithm. In each iteration, the optimization algorithm calculates the objective function value based on the current parameter combination. Then, through operations such as selection, crossover, and mutation, a new parameter combination is generated and the objective function value is calculated again. This process is repeated until a stopping condition is met, such as reaching the maximum number of iterations or the objective function value converges to a stable value. Various constraints must be considered during the optimization process, such as ensuring that the conductor tension cannot exceed its tensile strength and that the height and spacing of towers meet design specifications. Parameter combinations that do not meet the constraints are corrected or eliminated to ensure the feasibility of the optimization results.

[0104] Optional, such as Figure 5 As shown, the above S303 includes:

[0105] S3031: Input the line structure data into the optimization sub-model for optimization adjustment to obtain the adjusted line structure data.

[0106] Among them, the optimization sub-model is used to adjust and optimize the line structure data.

[0107] In the embodiment of the present application, after obtaining the line structure data, the computer device can input the line structure data into the optimization sub-model for optimization and adjustment to obtain the adjusted line structure data.

[0108] S3032, using the adjusted line structure data as new line structure data, returning to the step of inputting the icing information and line structure data into the first prediction sub-model for prediction until the comprehensive cost is minimized and the line performance is optimized, and determining the line structure data when the comprehensive cost is minimized and the line performance is optimal as the optimized ice area transmission line parameters.

[0109] In an embodiment of the present application, after the computer device obtains the adjusted line structure data based on the above steps, it can use the adjusted line structure data as new line structure data and return to execute the step of inputting the icing information and line structure data into the first prediction sub-model for prediction, that is, return to execute S301 until the comprehensive cost is minimized and the line performance is optimized, and determine the line structure data when the comprehensive cost is minimized and the line performance is optimal as the optimized ice area transmission line parameters.

[0110] For example, the above process includes screening key parameters, multi-objective optimization, solution evaluation, and updating design requirements. The mathematical representation of the multi-objective optimization decision algorithm is a minimization problem. , the constraints are , ,…, .in, is the decision variable, is the objective function, is the constraint function. For two solutions x and y, if for all i, there is , and there exists at least one i such that , then y is dominated by x. If within the design space, if no other solution can dominate solution x, then x is a Pareto optimal solution. The set of all Pareto optimal solutions is the Pareto solution set, denoted as P. For minimizing multi-objective optimization problems, the mapping of all Pareto optimal solutions in the objective function space shows the trade-offs of these solutions on various objective functions, which is the Pareto frontier, which can be expressed as:

[0111] (5)

[0112] in is the vector of objective function values for solving x. By applying this principle, we can effectively solve and analyze multi-objective optimization problems, thereby obtaining solutions that maximize line reliability and minimize construction costs.

[0113] In some embodiments, as Figure 6 As shown, the above method also includes:

[0114] S401: Obtain a preset mapping relationship, ice cover sample data, and basic cost sample data.

[0115] The preset mapping relationship is used to represent the correspondence between icing data, line performance data, and reconstruction cost data. The icing sample data includes at least one of ground wire icing thickness sample data, conductor icing thickness sample data, and ground wire icing thickness difference sample data. The basic cost sample data includes at least one of material cost sample data, construction cost sample data, design cost sample data, and maintenance cost sample data.

[0116] In the embodiment of the present application, a correspondence between icing data, line performance data, and retrofit cost data can be pre-established and stored as a preset mapping relationship in a preset path. The computer device can obtain icing sample data and basic cost sample data for transmission lines in ice areas within a preset time period through network crawling or manual collection, and obtain the preset mapping relationship from the preset path.

[0117] Optional, such as Figure 7 As shown, the above S403 includes:

[0118] S4011, obtaining ice coverage data of multiple groups of sample transmission lines.

[0119] The ice coating data includes at least one of the ground wire ice coating thickness, the conductor ice coating thickness and the ground-conductor conductor ice coating thickness difference.

[0120] In the embodiment of the present application, the computer device can obtain ice coverage data of multiple groups of sample transmission lines by web crawling or manual collection.

[0121] S4012: For any set of sample transmission line icing data, the icing data is input into a simulation model corresponding to the transmission line, and the transmission line parameters in the simulation model are adjusted to obtain line performance data and modification cost data under different transmission line parameters.

[0122] In an embodiment of the present application, a computer device may pre-construct a simulation model of a transmission line. After obtaining ice coverage data for multiple sets of sample transmission lines, the computer device may input the ice coverage data for any set of sample transmission line into the simulation model corresponding to the transmission line, adjust the transmission line parameters in the simulation model, and obtain line performance data and retrofit cost data under different transmission line parameters. For example, for ice coverage data for a first set of sample transmission lines, the computer device may input the ice coverage data for the first set of sample transmission lines into the simulation model corresponding to the transmission line, adjust the transmission line parameters in the simulation model multiple times, and obtain line performance data and retrofit cost data corresponding to each adjustment.

[0123] S4013: Construct a preset mapping relationship based on the icing data, multiple groups of line performance data under the icing data, and the transformation cost data.

[0124] In an embodiment of the present application, the computer device obtains icing data based on the above steps, as well as multiple sets of line performance data and multiple sets of transformation cost data for each set of icing data under multiple adjustments, and can construct a preset mapping relationship based on the icing data, the multiple sets of line performance data and the transformation cost data under the icing data.

[0125] S402: Construct economic cost sample data based on basic cost sample data and transformation cost data.

[0126] In an embodiment of the present application, after obtaining the basic cost sample data and the transformation cost data, the computer device can construct the economic cost sample data based on the basic cost sample data and the transformation cost data.

[0127] S403: Construct line performance sample data according to the line performance data.

[0128] In an embodiment of the present application, after obtaining the line performance data, the computer device can construct line performance sample data based on the line performance data.

[0129] S404: Training an initial line optimization model based on the ice cover sample data, the line performance sample data, and the economic cost sample data to obtain a preset line optimization model.

[0130] In an embodiment of the present application, after the computer device obtains the icing sample data, line performance sample data and economic cost sample data based on the above steps, it can train the initial line optimization model based on the icing sample data, line performance sample data and economic cost sample data to obtain a preset line optimization model.

[0131] It should be noted that the computer device can pre-construct a first initial prediction sub-model, a second initial prediction sub-model and an initial optimization sub-model, and construct an initial line optimization model based on these three sub-models. After the first initial prediction sub-model makes a preliminary prediction on the input information, the preliminary prediction result can be input into the second initial prediction sub-model for a secondary prediction to obtain two prediction results, and then the two prediction results are input into the initial optimization sub-model for parameter optimization. Then, based on the two prediction results and the parameter optimization results, the first initial prediction sub-model, the second initial prediction sub-model and the initial optimization sub-model are trained to obtain the preset line optimization model in the aforementioned application.

[0132] In addition, there are three training methods for training the above-mentioned initial line optimization model. The first is that the first initial prediction sub-model and the corresponding second initial prediction sub-model are trained networks. During training, it is only necessary to adjust the parameters of the initial optimization sub-model until the output result of the first initial prediction sub-model and the output result of the second initial prediction sub-model meet the preset conditions, thereby obtaining a trained initial optimization sub-model. After that, the trained initial optimization sub-model is jointly constructed with the trained first initial prediction sub-model to obtain a preset line optimization model. The second is that the second initial prediction sub-model is a trained network. During training, it is necessary to adjust the parameters of the initial optimization sub-model and the parameters of the first initial prediction sub-model until the output result of the first initial prediction sub-model and the output result of the second initial prediction sub-model meet the preset conditions, thereby obtaining a trained initial optimization sub-model. The trained sub-model and the trained first initial prediction sub-model are then combined with the trained initial optimization sub-model and the trained first initial prediction sub-model to obtain a preset line optimization model; the third type is that the initial optimization sub-model, the first initial prediction sub-model and the second initial prediction sub-model are all untrained networks. During training, it is necessary to adjust the parameters of the initial optimization sub-model, the parameters of the first initial prediction sub-model and the parameters of the initial optimization sub-model until the output results of the initial optimization sub-model, the output results of the first initial prediction sub-model and the output results of the second initial prediction sub-model meet the preset conditions, thereby obtaining the trained initial optimization sub-model, the trained first initial prediction sub-model and the trained second initial prediction sub-model, and then combining the trained initial optimization sub-model and the trained first initial prediction sub-model to obtain a preset line optimization model. Similarly, using the same concept, the computer device can construct the first initial prediction network and the second initial prediction network, and construct the initial first prediction sub-model based on these two networks.

[0133] In summary of all the above embodiments, a method for optimizing power transmission lines in ice regions taking into account ice coverage differences is also provided. The method includes:

[0134] S501, obtaining a preset line optimization model, specifically including S1-S7:

[0135] S1, obtaining ice coverage data of multiple groups of sample transmission lines, wherein the ice coverage data includes at least one of ground wire ice coverage thickness, conductor ice coverage thickness, and ground-conductor ice coverage thickness difference.

[0136] S2: For any set of sample transmission line icing data, the icing data is input into a simulation model corresponding to the transmission line, and the transmission line parameters in the simulation model are adjusted to obtain line performance data and transformation cost data under different transmission line parameters.

[0137] S3: Constructing a preset mapping relationship based on the icing data, multiple groups of line performance data under the icing data, and the transformation cost data.

[0138] S4: Obtain ice cover sample data and basic cost sample data, wherein the preset mapping relationship is used to represent the corresponding relationship between ice cover data, line performance data, and reconstruction cost data.

[0139] S5, constructing economic cost sample data based on basic cost sample data and transformation cost data.

[0140] S6, constructing line performance sample data based on the line performance data.

[0141] S7, training an initial line optimization model based on the icing sample data, the line performance sample data, and the economic cost sample data to obtain a preset line optimization model.

[0142] S502: Acquire line structure data of the ice region transmission line, and acquire meteorological data and topographic data of the environment where the ice region transmission line is located.

[0143] S503: Inputting the line structure data, meteorological data, and terrain data into a preset prediction model for prediction to obtain ice coverage information of the transmission line in the ice area. The ice coverage information includes at least one of ground wire ice coverage thickness, conductor ice coverage thickness, and ground-conductor ice coverage thickness difference.

[0144] S504: Input the icing information and line structure data into the first prediction sub-model for prediction, and obtain line reconstruction cost information and line performance information of the ice area transmission line.

[0145] S505: Input the icing information, line structure data, meteorological data, and terrain data into a first prediction module for cost prediction to obtain basic cost information, wherein the basic cost information includes at least one of material cost information, construction cost information, design cost information, and maintenance cost information.

[0146] S506: Input the basic cost information and the line reconstruction cost information into a second prediction module for cost prediction to obtain comprehensive cost information.

[0147] S507: Input the line structure data into the optimization sub-model for optimization and adjustment to obtain adjusted line structure data.

[0148] In step S508, the adjusted line structure data is used as the new line structure data, and the process returns to step S504 until the overall cost is minimized and the line performance is optimized. The line structure data at which the overall cost is minimized and the line performance is optimized is then determined as the optimized ice region transmission line parameters. The optimized ice region transmission line parameters are used to design the ice region transmission line.

[0149] The method described in the embodiments of the present application can bring the following beneficial effects: (1) By regularly updating the economic evaluation results, it can adapt to changes in the operating conditions of the transmission line and be more flexible. Based on the dynamic model, the failure rate and maintenance cost of the line under various meteorological conditions can be predicted, thereby more accurately evaluating the economic efficiency of the line. (2) By combining advanced prediction models and algorithms, such as machine learning and artificial intelligence, the accuracy of future variable predictions is improved, that is, the accuracy is higher. By training historical data, the model can more accurately predict future power demand and cost trends, thereby reducing prediction errors. (3) It can be applied to line economic analysis systems and optimization design software, improving analysis efficiency and reducing computing resource consumption, that is, faster prediction speed and wider application range. The use of efficient algorithms or modular methods makes it possible to quickly complete economic evaluation while ensuring accuracy. (4) By combining the transmission line economic prediction model that takes into account the difference in ice coverage level with the multi-objective optimization decision algorithm, in the future design of ice-covered transmission lines, a low-cost, high-performance optimization design scheme can be adopted based on the local ice coverage level.

[0150] The methods described in the above steps are all described in the above embodiments. Please refer to the above description for details and will not be repeated here.

[0151] It should be understood that, although the various steps in the flowcharts involved in the various embodiments described above are displayed in sequence according to the instructions of the arrows, these steps are not necessarily executed in sequence in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order restriction on the execution of these steps, and these steps can be executed in other orders. Moreover, at least a portion of the steps in the flowcharts involved in the various embodiments described above can include multiple steps or multiple stages, and these steps or stages are not necessarily executed and completed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a portion of steps or stages in other steps.

[0152] Based on the same inventive concept, embodiments of the present application also provide an apparatus for optimizing transmission lines in ice regions taking into account ice coverage differences, which is used to implement the aforementioned method for optimizing transmission lines in ice regions taking into account ice coverage differences. The solution provided by this apparatus is similar to the solution described in the aforementioned method. Therefore, the specific limitations of one or more embodiments of the apparatus for optimizing transmission lines in ice regions taking into account ice coverage differences provided below can be found in the limitations of the method for optimizing transmission lines in ice regions taking into account ice coverage differences, and will not be further elaborated here.

[0153] In some embodiments, as Figure 8As shown, an optimization device for ice region transmission lines taking into account ice coverage differences is provided, comprising:

[0154] The acquisition module 11 is used to acquire the line structure data of the ice area transmission line, and acquire the meteorological data and terrain data of the environment where the ice area transmission line is located.

[0155] The prediction module 12 is used to input the line structure data, meteorological data and terrain data into a preset prediction model for prediction to obtain the icing information of the transmission line in the ice area; the icing information includes at least one of the ground wire ice thickness, the conductor ice thickness and the ground conductor ice thickness difference.

[0156] The optimization module 13 is used to input ice cover information, line structure data, meteorological data and terrain data into a preset line optimization model to optimize line parameters and obtain optimized ice area transmission line parameters; the optimized ice area transmission line parameters are used to design ice area transmission lines.

[0157] In some embodiments, the prediction module includes:

[0158] The first prediction unit is used to input ice coverage information and line structure data into the first prediction sub-model for prediction, so as to obtain line reconstruction cost information and line performance information of the ice area transmission line.

[0159] The second prediction unit is used to input line reconstruction cost information, icing information, line structure data, meteorological data and terrain data into the second prediction sub-model to perform cost prediction and obtain comprehensive cost information of ice area transmission lines.

[0160] The optimization unit is used to input the line structure data into the optimization sub-model for optimization and adjustment with the goal of minimizing the comprehensive cost and optimizing the line performance, and obtain the optimized parameters of the ice area transmission line.

[0161] In some embodiments, the second prediction unit includes:

[0162] The first prediction subunit is used to input icing information, line structure data, meteorological data and terrain data into the first prediction module to perform cost prediction and obtain basic cost information; the basic cost information includes at least one of material cost information, construction cost information, design cost information and maintenance cost information.

[0163] The second prediction subunit is used to input the basic cost information and the line reconstruction cost information into the second prediction module to perform cost prediction and obtain comprehensive cost information.

[0164] In some embodiments, the optimization unit includes:

[0165] The adjustment sub-unit is used to input the line structure data into the optimization sub-model for optimization adjustment to obtain the adjusted line structure data.

[0166] The loop sub-unit is used to use the adjusted line structure data as new line structure data, return to execute the step of inputting the icing information and line structure data into the first prediction sub-model for prediction until the comprehensive cost is minimized and the line performance is optimized, and determine the line structure data when the comprehensive cost is minimized and the line performance is optimal as the optimized ice area transmission line parameters.

[0167] In some embodiments, the above-mentioned device for optimizing power transmission lines in ice regions taking into account ice cover differences further includes: a training module, specifically including:

[0168] The acquisition unit is used to obtain a preset mapping relationship, ice coating sample data and basic cost sample data; the preset mapping relationship is used to characterize the corresponding relationship between ice coating data, line performance data and transformation cost data.

[0169] The first constructing unit is used to construct economic cost sample data according to the basic cost sample data and the transformation cost data.

[0170] The second constructing unit is configured to construct line performance sample data according to the line performance data.

[0171] The training unit is used to train the initial line optimization model based on ice cover sample data, line performance sample data and economic cost sample data to obtain a preset line optimization model.

[0172] In some embodiments, the acquisition unit includes:

[0173] The acquisition subunit is used to obtain ice coating data of multiple groups of sample transmission lines; the ice coating data includes at least one of the ground wire ice coating thickness, the conductor ice coating thickness and the ground conductor ice coating thickness difference.

[0174] The adjustment subunit is used to input the icing data of any group of sample transmission lines into the simulation model corresponding to the transmission line, adjust the transmission line parameters in the simulation model, and obtain the line performance data and transformation cost data under different transmission line parameters.

[0175] A subunit is constructed to construct a preset mapping relationship based on icing data, multiple groups of line performance data under the icing data, and transformation cost data.

[0176] Each module in the aforementioned device for optimizing power transmission lines in iced areas with consideration of ice coverage differences can be implemented in whole or in part through software, hardware, or a combination thereof. Each module can be embedded in or independent of a processor in a computer device in the form of hardware, or can be stored in a computer device memory in the form of software, so that the processor can call and execute the corresponding operations of each module.

[0177] In some embodiments, a computer device is provided, including a memory and a processor, wherein a computer program is stored in the memory, and when the processor executes the computer program, the steps of the method for optimizing ice area transmission lines considering ice cover differences described in any of the above embodiments are implemented.

[0178] In some embodiments, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps of the method for optimizing ice area transmission lines considering ice cover differences described in any of the above embodiments are implemented.

[0179] In some embodiments, a computer program product is provided, comprising a computer program, which, when executed by a processor, implements the steps of the method for optimizing ice region transmission lines considering ice cover differences described in any of the above embodiments.

[0180] Those skilled in the art will appreciate that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the above-mentioned embodiments. In particular, any reference to memory, database, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The databases involved in the various embodiments provided herein may include at least one of a relational database and a non-relational database. Non-relational databases may include, but are not limited to, distributed databases based on blockchains. The processors involved in the various embodiments provided herein may be, but are not limited to, general-purpose processors, central processing units (CPUs), graphics processing units (GPUs), digital signal processors (DSPs), programmable logic devices (PLDs), data processing logic devices based on quantum computing, and the like.

[0181] The technical features of the above embodiments can be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0182] The above-described embodiments merely represent several implementation methods of the present application. While the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the present application. It should be noted that a person of ordinary skill in the art may make various modifications and improvements without departing from the spirit of the present application, and these modifications and improvements fall within the scope of protection of the present application. Therefore, the scope of protection of the present application shall be determined by the appended claims.

Claims

1. An optimization method for power transmission lines in ice areas taking into account ice coverage differences, characterized in that: The method comprises: Acquiring line structure data of an ice region transmission line, and acquiring meteorological data and topographic data of an environment where the ice region transmission line is located; Inputting the line structure data, the meteorological data, and the terrain data into a preset prediction model for prediction to obtain ice coverage information of the transmission line in the ice area; the ice coverage information includes at least one of ground wire ice coverage thickness, conductor ice coverage thickness, and ground-conductor ice coverage thickness difference; The icing information, the line structure data, the meteorological data and the terrain data are input into a preset line optimization model to optimize the line parameters to obtain optimized ice area transmission line parameters; the optimized ice area transmission line parameters are used to design the ice area transmission line.

2. The method according to claim 1, characterized in that The preset line optimization model includes a first prediction sub-model, a second prediction sub-model, and an optimization sub-model. The icing information, the line structure data, the meteorological data, and the terrain data are input into the preset line optimization model to optimize line parameters, thereby obtaining optimized ice area transmission line parameters, including: Inputting the icing information and the line structure data into the first prediction sub-model for prediction, thereby obtaining line reconstruction cost information and line performance information of the ice area transmission line; Inputting the line reconstruction cost information, the icing information, the line structure data, the meteorological data, and the terrain data into the second prediction sub-model for cost prediction to obtain comprehensive cost information of the ice area transmission line; With the goal of minimizing comprehensive costs and optimizing line performance, the line structure data is input into the optimization sub-model for optimization and adjustment to obtain the optimized ice area transmission line parameters.

3. The method according to claim 2, characterized in that The second prediction sub-model includes a first prediction module and a second prediction module. The line reconstruction cost information, the icing information, the line structure data, the meteorological data, and the terrain data are input into the second prediction sub-model to perform cost prediction, thereby obtaining comprehensive cost information of the ice region transmission line, including: Inputting the icing information, the line structure data, the meteorological data, and the terrain data into the first prediction module to perform cost prediction to obtain basic cost information; the basic cost information includes at least one of material cost information, construction cost information, design cost information, and maintenance cost information; The basic cost information and the line reconstruction cost information are input into the second prediction module for cost prediction to obtain the comprehensive cost information.

4. The method according to claim 2, characterized in that The line structure data is input into the optimization sub-model for optimization and adjustment with the goal of minimizing the comprehensive cost and optimizing the line performance to obtain the optimized ice region transmission line parameters, including: Inputting the line structure data into the optimization sub-model for optimization adjustment to obtain adjusted line structure data; The adjusted line structure data is used as new line structure data, and the step of inputting the icing information and the line structure data into the first prediction sub-model for prediction is returned to execute until the comprehensive cost is minimized and the line performance is optimized, and the line structure data when the comprehensive cost is minimized and the line performance is optimized is determined as the optimized ice area transmission line parameters.

5. The method according to any one of claims 1 to 4, characterized in that The method further comprises: Obtaining a preset mapping relationship, icing sample data, and basic cost sample data; the preset mapping relationship is used to characterize the correspondence between icing data, line performance data, and transformation cost data; Constructing economic cost sample data based on the basic cost sample data and the transformation cost data; constructing line performance sample data according to the line performance data; An initial line optimization model is trained based on the ice coating sample data, the line performance sample data, and the economic cost sample data to obtain the preset line optimization model.

6. The method according to claim 5, characterized in that The obtaining of the preset mapping relationship includes: Acquire ice coating data of multiple groups of sample transmission lines; the ice coating data includes at least one of ground wire ice coating thickness, conductor ice coating thickness, and ground conductor ice coating thickness difference; For any set of sample transmission line icing data, the icing data is input into a simulation model corresponding to the transmission line, and the transmission line parameters in the simulation model are adjusted to obtain line performance data and modification cost data under different transmission line parameters; A preset mapping relationship is established according to the icing data, multiple groups of line performance data under the icing data, and transformation cost data.

7. An optimization device for power transmission lines in ice areas taking into account ice coverage differences, characterized in that: The device comprises: An acquisition module, configured to acquire line structure data of an ice region transmission line, and acquire meteorological data and topographic data of an environment where the ice region transmission line is located; a prediction module, configured to input the line structure data, the meteorological data, and the terrain data into a preset prediction model for prediction, thereby obtaining ice coverage information of the transmission line in the ice area; the ice coverage information comprising at least one of ground wire ice coverage thickness, conductor ice coverage thickness, and ground-conductor ice coverage thickness difference; The optimization module is used to input the icing information, the line structure data, the meteorological data and the terrain data into a preset line optimization model to optimize the line parameters and obtain optimized ice area transmission line parameters; the optimized ice area transmission line parameters are used to design the ice area transmission line.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 6 are implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

10. A computer program product comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

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