Power transmission line optimization method and device

Abstract

The invention relates to the field of power transmission line planning and design, and discloses a power transmission line optimization method which comprises the following steps: performing ground feature recognition and constructing a multi-rule constraint model by obtaining a digital elevation model and remote sensing image data so as to optimize line path planning; calculating and comparing the comprehensive cost of different line schemes by using an ant colony optimization algorithm under the constraint condition of considering a plurality of factors such as a voltage grade, ground feature crossing, a tower type, construction cost and the like; and finally, realizing visual display of the optimal power transmission line path in combination with a GIS tool, and providing visual line planning and decision support. According to the method, an economical and efficient route planning scheme can be generated in a complex ground feature environment.

Description

Technical Field

[0001] The present invention relates to the technical field of power transmission line planning and design, and particularly to a method and device for optimizing transmission lines. Background Art

[0002] Currently, in the process of power transmission line planning and design, the selection of line paths usually relies on traditional manual experience and rules. However, when dealing with complex terrain, ground features, and various constraint factors, traditional methods often face problems of low efficiency and insufficient accuracy. Especially when facing large-scale transmission line construction, manual design not only requires a large amount of time and human resources, but also often leads to inaccurate path planning due to inaccurate identification of ground features, which may even cause unexpected obstacles or additional costs during the later construction process.

[0003] Existing line planning methods lack comprehensive identification and in-depth analysis of terrain and ground features. Although some methods attempt to perform terrain analysis through digital elevation models (DEMs) and remote sensing images, such methods usually fail to fully consider the impact of ground feature types, especially the cost calculation of line planning in complex ground feature environments. The lack of accurate ground feature information results in inflexible line planning and difficulty in adapting to complex and changing construction environments.

[0004] In addition, existing technologies mainly rely on traditional optimization algorithms, such as genetic algorithms and simulated annealing, for path optimization. Although they can find better solutions in some cases, in the face of complex multi-constraint conditions, the efficiency and optimization results of the algorithms are often not satisfactory. Especially after considering multiple factors such as voltage levels, tower types, and ground feature crossings, traditional algorithms often cannot give the optimal solution in a short time and respond slowly to dynamic external environments (such as weather and construction obstacles), lacking flexibility.

[0005] Therefore, in practical applications, especially in the transmission line planning of large ranges and complex environments, existing technologies have problems such as low path selection efficiency, inaccurate cost estimation, and lack of flexibility, and there is an urgent need for a more efficient, accurate, and dynamically adjustable line planning method. Summary of the Invention

[0006] Aiming at the deficiencies of the existing technology, the present invention provides a method and device for optimizing transmission lines, which can generate an economic and efficient line planning scheme in a complex ground feature environment.

[0007] To achieve the above objectives, the present invention is realized through the following technical solutions:

[0008] The present invention provides a method for optimizing transmission lines, including the following steps:

[0009] Obtain digital elevation model data and remote sensing image data, and perform data preprocessing;

[0010] Based on the preprocessed digital elevation model data and remote sensing image data, conduct ground object recognition to obtain the ground object information required for line path planning;

[0011] Combined with the ground object information, construct line planning constraint conditions based on the multi-rule constraint model;

[0012] Under the constraint conditions, use an optimization algorithm to optimize the line path to obtain the optimal transmission line path that meets the constraint conditions;

[0013] Calculate and compare the comprehensive costs of the optimal transmission line paths under different tower configurations;

[0014] Based on the comparison results of the comprehensive costs, select the tower configuration with the lowest comprehensive cost and the optimal transmission line path to form the optimal transmission line scheme.

[0015] This scheme optimizes the transmission line based on image recognition, multi-rule constraints, and comprehensive cost comparison, and can generate an economical and efficient line planning scheme in a complex ground object environment.

[0016] Preferably, based on the preprocessed digital elevation model data and remote sensing image data, conduct ground object recognition, and the ground object information required for line path planning includes:

[0017] Extract terrain elevation features, gradient information, and image texture features, and form a ground object feature vector;

[0018] Based on the ground object feature vector, use a clustering algorithm to conduct a preliminary classification of the ground objects to obtain the ground object classification result;

[0019] Use a deep neural network to optimize the ground object classification result, and remove noise through morphological processing to obtain the final ground object classification result.

[0020] Preferably, the constraint conditions include:

[0021] Cost calculation rules for crossing different ground objects, including water areas, roads, railways, and houses;

[0022] Tower type selection rules, including the applicable terrains of different towers;

[0023] Angle requirements for the line to cross obstacles;

[0024] Restrictions on tower spacing and conductor type due to voltage levels.

[0025] Preferably, the optimization algorithm is the ant colony optimization algorithm; using the ant colony optimization algorithm to optimize the line path includes:

[0026] Initialize the path planning parameters, including pheromone distribution, the number of ants, and the number of iterations;

[0027] Calculate the transition probability of ants when choosing a path, and adjust the path selection weight based on the ground feature crossing cost;

[0028] Update the pheromone according to the path cost and constraints, and gradually converge to the optimal transmission line path.

[0029] Preferably, the calculating and comparing the comprehensive costs of the optimal transmission line paths under different tower configurations includes:

[0030] Calculate the comprehensive costs of the optimal transmission line paths under each tower configuration respectively:

[0031] Calculate the unit cost according to the tower type;

[0032] Calculate the construction cost by combining the tower height and the foundation construction requirements;

[0033] Calculate the line construction cost according to the path length and the types of ground features crossed;

[0034] Calculate the comprehensive cost based on the unit cost, the construction cost, and the line construction cost;

[0035] Compare the comprehensive costs of the optimal transmission line paths under different tower configurations.

[0036] Preferably, the calculating the line construction cost according to the path length and the types of ground features crossed includes: setting corresponding weighting coefficients based on different types of ground features to optimize the line construction cost function; based on the line construction cost function, combining with the path length, calculate the line construction cost. Based on different types of ground features, set corresponding weighting coefficients to optimize the line construction cost function; based on the line construction cost function, combine with the path length, calculate the line construction cost.

[0037] Preferably, the visual output of the optimal transmission line path includes:

[0038] Visualize the terrain, path, and tower distribution through GIS tools;

[0039] Overlay the information of the ground features crossed by the line to provide a comprehensive analysis map.

[0040] Preferably, the method further includes dynamically adjusting the path planning constraints according to changes in external conditions, and updating the optimal transmission line path in real time.

[0041] Preferably, the method further includes historical path scheme management, recording and storing the optimal transmission line paths under different conditions for subsequent adjustment and optimization.

[0042] The present invention also provides a transmission line optimization device, including:

[0043] A data processing module, configured to obtain digital elevation model data and remote sensing image data, and perform data preprocessing;

[0044] A ground object recognition module, configured to perform ground object recognition based on the preprocessed digital elevation model data and remote sensing image data to obtain ground object information required for line path planning;

[0045] A rule constraint module, configured to combine the ground object information and construct constraint conditions for line path planning based on a multi-rule constraint model;

[0046] A path optimization module, configured to perform line path optimization using an optimization algorithm under the constraint conditions to obtain an optimal transmission line path that meets the constraint conditions;

[0047] A cost calculation module, configured to calculate and compare the comprehensive costs of the optimal transmission line paths under different tower configurations;

[0048] A scheme output module, configured to select the tower configuration with the lowest comprehensive cost and the optimal transmission line path based on the comprehensive cost comparison result to form an optimal transmission line scheme.

[0049] The present invention provides a transmission line optimization method. It has the following beneficial effects:

[0050] 1. By comprehensively using digital elevation model and remote sensing image data for ground object recognition and combining the ant colony optimization algorithm for path planning, the present invention can realize the optimization of line paths under complex terrain and ground object conditions. Through these technical means, the system can, on the basis of ensuring technical feasibility, minimize construction costs and improve the economy of line construction.

[0051] 2. The present invention can dynamically adjust the constraint conditions in path planning to adapt to changes in the external environment. With changes in weather, terrain or other external conditions, the system can monitor in real time and automatically adjust path planning parameters to ensure that the line always maintains an optimal scheme in various environments. This flexible dynamic adjustment mechanism significantly improves the adaptability and stability of the path planning scheme, ensuring that the line design can continue to be optimized under the influence of uncertain factors.

[0052] 3. The historical path scheme management function of the present invention helps to accumulate and utilize historical data for path optimization. The storage and recording of each optimal transmission line path enable the system to draw on past experience and data in subsequent path adjustments, avoid repeated design, and save time and resources. The management and comparison of historical data further enhance the intelligence and automation level of the system and improve the efficiency and accuracy of line planning.

[0053] 4. The present invention realizes the visual display of the optimal transmission line path through GIS tools, facilitating decision-makers to intuitively understand and evaluate the line design. By overlaying and displaying path, tower distribution, and crossing feature information, users can clearly see various parameters and limiting conditions of the line planning, thus making more scientific and reasonable decisions. This visualization function improves the user experience of the system and provides more detailed reference data for subsequent line adjustment and construction. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] Figure 1 It is a schematic flowchart of the method of the present invention;

[0055] Figure 2 It is a schematic structural diagram of the device of the present invention.

[0056] Among them, 10. Data processing module; 20. Feature recognition module; 30. Rule constraint module; 40. Path optimization module; 50. Cost calculation module; 60. Scheme output module. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0057] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments in the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0058] Please refer to the appended Figure 1 , the present invention provides a transmission line optimization method, which can efficiently and accurately optimize the path of the transmission line and compare the comprehensive costs, so as to provide intelligent decision-making support for the economy and feasibility of the line.

[0059] As Figure 1 shown, the transmission line optimization method may include the following steps:

[0060] S1. Obtain digital elevation model data and remote sensing image data and perform preprocessing;

[0061] S2. Perform feature recognition to obtain the feature information required for the line path planning;

[0062] S3. Combine the feature information to construct the line planning constraint conditions;

[0063] S4. Under the constraint conditions, use an optimization algorithm to optimize the line path;

[0064] S5. Calculate and compare the comprehensive costs of the optimal transmission line paths under different tower configurations and make comparisons;

[0065] S6. Select the tower configuration with the lowest comprehensive cost and the optimal transmission line path to form the optimal transmission line plan.

[0066] The following will elaborate on this method in combination with specific implementation steps.

[0067] For step S1, in this embodiment, it mainly includes obtaining digital elevation model data and remote sensing image data, and performing data preprocessing.

[0068] First, the digital elevation model (DEM) data is obtained through technical means such as satellite remote sensing, aerial imagery, or light detection and ranging (LiDAR). The DEM data records the elevation information of the ground, usually presented in a grid form, and each grid cell contains the elevation value at that location. Since the measurement accuracies of sensors or devices with different acquisition methods vary, the DEM data needs to be preprocessed. Specifically, data interpolation methods are used to fill in missing data or reduce data noise to ensure the integrity and continuity of terrain information.

[0069] The remote sensing image data is usually obtained by remote sensing satellites or unmanned aerial vehicle platforms and contains multi-dimensional image information of the earth's surface. Common remote sensing images include RGB images, multi-spectral images, and radar imaging, etc. Among them, the RGB image provides the color information of ground objects, and the multi-spectral image can provide reflectance data in different bands, facilitating the classification of different types of ground objects. In the data preprocessing stage, first, the remote sensing image needs to be registered and coordinate-transformed to ensure that the image data is aligned with the DEM data in the same coordinate system. Since the DEM data and the remote sensing image may come from different sensors or acquisition methods, precise matching needs to be carried out through coordinate system transformation and data alignment methods so that the data of both can accurately correspond to the same spatial position.

[0070] In addition, remote sensing images are often interfered by the atmosphere, clouds, or other objects, resulting in the degradation of image quality. Therefore, to eliminate these effects, the image data needs to be denoised. Common denoising methods include median filtering, Gaussian filtering, etc., which can effectively remove the noise in the image, making the image smoother and suitable for subsequent image processing and ground object recognition.

[0071] After completing the data alignment and denoising processing, some other processing can also be performed on the DEM data and remote sensing images according to specific requirements. For example, the slope (i.e., the steepness of the ground surface at a certain point) and aspect (i.e., the orientation of a certain point on the ground surface) of the elevation data can be calculated, and this information is very helpful for subsequent ground object classification and path planning. The calculation of the slope usually uses a gradient operator, such as the Sobel operator, for local differential processing to obtain the change rate of the terrain. The formula is as follows:

[0072]

[0073] Among them, represents the slope, represents the ground elevation, and respectively represent the gradients of the elevation in the X-axis and Y-axis directions.

[0074] The formula for calculating the slope direction is:

[0075]

[0076] Among them, represents the slope direction, which is used to describe the direction of the ground orientation. Through the calculation of these elevation-related features, more accurate terrain information can be provided for subsequent feature recognition and path planning.

[0077] In short, the core of step S1 lies in obtaining and processing digital elevation model data and remote sensing image data to ensure accurate and reliable data input for subsequent steps such as feature recognition, path planning, and cost calculation.

[0078] For step S2, in this embodiment, features are identified based on the preprocessed digital elevation model data and remote sensing image data to obtain the feature information required for route path planning. In specific implementation, feature recognition relies on the fusion processing of digital elevation model (DEM) data and remote sensing image data, and extracts the category and spatial distribution information of features through various algorithms and technical means.

[0079] First, spectral feature analysis needs to be performed on the input remote sensing image data. The remote sensing image contains information in multiple bands, and there are significant differences in the reflectance of different features in the spectral bands. For example, vegetation has a higher reflectance in the near-infrared band, while water has a lower reflectance in this band. Therefore, the method of calculating spectral indices can be used to extract features from the image data. Commonly used spectral indices include the Normalized Difference Vegetation Index (NDVI) and the Normalized Difference Water Index (NDWI). Taking NDVI as an example, its calculation formula is:

[0080]

[0081] Among them, represents the reflectance in the near-infrared band, represents the reflectance in the red band. The NDVI value can effectively distinguish vegetation areas from non-vegetation areas.

[0082] To further improve the accuracy of ground object recognition, this embodiment also combines the terrain feature information in elevation data, such as slope and elevation value range. Different ground objects are usually distributed within specific elevation ranges. For example, forests are generally located in mid - to - high - altitude areas, while cultivated land is mostly distributed in flat areas at low altitudes. Utilizing these elevation features can further optimize the ground object classification results based on spectral features.

[0083] In actual operation, ground object classification based on images and elevation data can adopt supervised classification algorithms or unsupervised classification algorithms. Supervised classification algorithms require input of training samples. For example, the model is trained with ground object category data of known areas (such as topographic maps or field survey results). Common methods include Support Vector Machine (SVM), Random Forest (RF), and Convolutional Neural Network (CNN), etc. Unsupervised classification relies on the inherent distribution characteristics of data for clustering analysis. Common methods include K - means clustering and ISODATA algorithm.

[0084] In specific implementation, this embodiment preferably adopts deep learning technology for ground object recognition. Through Convolutional Neural Network (CNN), multi - level feature information can be automatically extracted from remote sensing image data to achieve precise discrimination of complex ground object categories. Exemplarily, the convolutional neural network model can include multiple convolutional layers, pooling layers, and fully - connected layers. In the model training stage, the network is supervised and learned using labeled ground object data. After training is completed, the model is applied to new remote sensing image data to achieve automatic ground object classification.

[0085] In addition, to improve the stability and accuracy of recognition, multi - source data fusion technology can be introduced. For example, optical remote sensing images and radar images are combined for use. Optical images can provide rich texture and spectral information under sunny conditions, while radar images have the ability to penetrate clouds and observe all - weather. By fusing the two types of data, the robustness of ground object recognition can be improved under complex weather conditions.

[0086] After ground object recognition is completed, post - processing of the classification results is required, including removing classification noise and refining the ground object boundaries. Denoising processing can adopt morphological operations, such as dilation and erosion, to remove isolated pixel points and fill small holes; boundary refinement can be achieved through edge detection algorithms, such as Canny edge detection.

[0087] The finally output ground object information includes the ground object types (such as water bodies, vegetation, cultivated land, buildings, etc.) and their spatial distribution positions. This information will serve as an important input for constructing multiple - rule constraints in subsequent route path planning, providing support for route optimization.

[0088] For step S3, in this embodiment, the constraint conditions for route planning are constructed in combination with ground object information, thereby providing an input basis for subsequent path optimization. The multi-rule constraint model analyzes various restrictive factors and their interaction relationships to establish a multi-dimensional set of constraint conditions, which cover multiple aspects such as technology, economy, and environment involved in the route planning process.

[0089] First, based on the ground object information obtained in step S2, the relationship constraints between ground objects and route path planning need to be defined. For the cost calculation rules for crossing different ground objects, in this embodiment, a detailed cost calculation model is established by analyzing the construction complexity and corresponding unit costs of the interaction between the route path and ground objects such as water areas, roads, railways, and houses.

[0090] Exemplarily, for water areas, since waterproof facilities or floating bridge supports need to be added during construction, the unit cost is relatively high, set as For the intersection construction of roads and railways, traffic diversion and safety construction costs need to be considered, and the unit costs are set as and For the housing area, the relocation compensation cost needs to be calculated, and the unit cost is set as Taking these factors into account, the total construction cost of the route can be expressed as:

[0091]

[0092] Among them, represents the length of the path on the th type of ground object. By adjusting the value of , the priorities and construction impacts of different ground objects can be reflected in path optimization.

[0093] Regarding the selection rules for tower types, in this embodiment, a tower adaptability model is constructed in combination with different terrain conditions. Exemplarily, straight towers are suitable for flat terrain areas, while tension towers are suitable for terrains with steep slopes or corners to ensure the stability and safety of the line. During the tower selection process, terrain slope, elevation change, and construction difficulty need to be comprehensively considered. For example, when the slope θ > 15°, tension towers should be preferentially selected. At the same time, different tower types need to meet the requirements of conductor sag and load intensity, and their selection rules can be formalized as:

[0094]

[0095] Among them, is the maximum bearing capacity of the tower, , , respectively represent the self-weight, wind load, and ice load of the conductor.

[0096] Regarding the angle requirements for the line to cross obstacles, this embodiment sets the crossing angle limits for different obstacles. For example, when the line crosses a railway, the angle needs to be controlled within 60° ≤ α ≤ 90° to ensure that the sag of the conductor does not affect the safe operation of the railway. For road crossings, the recommended angle range is 45° ≤ α ≤ 90°. The implementation of this rule is based on the calculation of the direction vectors of the path and the obstacle, and its mathematical expression is:

[0097]

[0098] where and are the direction vectors of the line path and the obstacle respectively.

[0099] Regarding the restrictions of voltage level on the tower spacing and conductor type, this embodiment fully considers the requirements of voltage level for the physical properties of the line. For example, high-voltage level (such as 500 kV) lines require a larger tower spacing to reduce the electrical interference between conductors, and at the same time, conductors need to use materials with a larger cross-sectional area to reduce resistance. The specific restrictions on the tower spacing can be expressed as:

[0100]

[0101] where is the voltage level, and vary linearly with the voltage level. In addition, the conductor material needs to meet the requirements of tensile strength and electrical performance. For example, the conductor cross-sectional area , is positively correlated with the voltage level.

[0102] The constraint conditions in environmental protection need to consider ecological sensitive areas and protected areas. Exemplarily, for areas such as national forest parks, line crossing should be completely prohibited; for ordinary ecological sensitive areas, a constraint model allowing crossing but requiring payment of ecological compensation fees can be set up to achieve environmental friendliness in the planning.

[0103] After all the rules are constructed, the above-mentioned constraint conditions are comprehensively formed into a constraint condition set and associated through logical expressions. For example, based on the linear programming method, all the constraints can be formalized into a system of linear inequalities to ensure the efficient operation of subsequent optimization algorithms.

[0104] By constructing these multi-rule constraint models, this embodiment provides a systematic theoretical basis and norms for the line path planning, ensuring the feasibility and rationality of the planning results in multiple aspects such as technology, economy, and environment.

[0105] For step S4, in this embodiment, the ant colony optimization algorithm is adopted for the line path optimization process to generate the optimal transmission line path that meets the constraint conditions.

[0106] First, the path planning parameters need to be initialized. Exemplarily, the initial value of the pheromone distribution is set to , where is the preset initial pheromone value, and the path edges and represent adjacent tower positions or path nodes. The number of ants and the number of algorithm iterations need to be selected considering the complexity of the line planning and computing resources. For example, when the line complexity is high, the number of ants can be appropriately increased to improve the diversity of solutions.

[0107] Next, in each iteration, the path selection transition probability of the ants needs to be calculated. The probability that the ant transfers from node to node is given by the following formula:

[0108]

[0109] where is the pheromone concentration of the path edge at the th iteration; is the heuristic information (usually the reciprocal of the edge length); and

[0110] respectively represent the weights of the pheromone and the heuristic information for path selection. To adapt to the ground feature crossing cost constraint in the line planning, the heuristic information is adjusted in this embodiment. Specifically, considering the influence of the crossing costs of different ground feature types on path selection, the correction formula is:

[0111]

[0112] where is the length of the path edge

[0113] The pheromone update process is the key link to achieve algorithm convergence. This embodiment adopts a global and local pheromone update strategy. The local update rule is:

[0114]

[0115] where is the pheromone evaporation coefficient, which controls the attenuation rate of the pheromone concentration; is the initial value of the pheromone.

[0116] The global update rule is executed after one iteration, and only the path edges forming the current optimal path are strengthened:

[0117]

[0118] where, is the pheromone increment, is the length of the optimal path in the current iteration.

[0119] Through the above dynamic update of the pheromone, the ant colony gradually tends to select a better path, thus converging to the optimal transmission line path that meets all constraint conditions. Combining the comprehensive optimization of factors such as path cost, tower selection, and crossing rules, this embodiment can generate an economic and efficient line planning scheme under complex terrain environments.

[0120] For step S5, in this embodiment, the comprehensive cost of the optimal transmission line path under different tower configurations is calculated and compared, and the cost of different line schemes is compared to select the most cost-effective scheme.

[0121] First, calculate its unit cost according to different tower types. The type of tower will be selected according to factors such as terrain conditions, line voltage level, and climate factors. For different types of towers, there are significant differences in their manufacturing materials, design requirements, and construction difficulties, so their unit costs are also different. Generally speaking, the unit cost of straight towers is relatively low, while tension towers, angle towers, etc. require higher material and construction costs. Therefore, the unit cost can be expressed as:

[0122]

[0123] where, is the cost of the materials required for the tower, is the labor cost of construction, is the transportation cost.

[0124] Then, combined with the height of the tower and the requirements for foundation construction, calculate its construction cost. In actual construction, the height of the tower directly affects the requirements for its foundation construction. Tall towers require stronger foundations and more construction time, so the construction cost can be calculated by the following formula:

[0125]

[0126] where, is the height of the tower, is the construction coefficient proportional to the height, is the fixed cost of foundation construction. The difficulty of foundation construction usually varies according to geological conditions.

[0127] Subsequently, the line construction cost is calculated based on the path length and the types of obstacles crossed. The crossing requirements for different types of obstacles will significantly affect the line construction cost. Exemplarily, a line crossing a water area may require the construction of a floating bridge or special support structure design, while a line crossing a railway, road or urban building needs to consider traffic control and special construction conditions. The line construction costs for these types of obstacles crossed can be estimated in the following way:

[0128]

[0129] where is the length of the path on the th type of obstacle, is the unit cost of crossing this obstacle. For example, the crossing cost for a water area may be relatively high, while the crossing cost for a railway will also be relatively expensive.

[0130] Finally, the total construction costs of different schemes are calculated and a comparative analysis is carried out. The total construction cost includes the costs of pole installation, line construction, obstacle crossing, etc. Its calculation formula is:

[0131]

[0132] where is the number of the th type of pole, is the total number of worker days required for construction.

[0133] After calculating the comprehensive construction costs of different line schemes, a comparative analysis is carried out to select the line scheme with the lowest cost and meeting all planning constraints. In this way, while ensuring technical requirements, the overall economic cost of line construction can be minimized as much as possible.

[0134] By implementing this step, this embodiment can comprehensively consider various cost factors in line planning, from pole selection to obstacle crossing, and finally obtain the most economically viable line scheme, thus providing a more reasonable decision-making basis for line planning.

[0135] For step S6, in this embodiment, the optimal transmission line path is visually displayed to facilitate relevant decision-makers to intuitively understand the line planning results in practical applications.

[0136] First, the visualization of the optimal transmission line path is achieved through GIS (Geographic Information System) tools. In this process, the system integrates different types of geographical data to draw the specific path of the line, the distribution of towers, and the information of the ground features crossed by the path. Specifically, GIS tools can overlay and display the line path, tower positions, and ground feature boundaries (such as water areas, forests, roads, railways, etc.). The path of each line is distinguished on the map with different colors and markings according to the types of ground features it passes through and the distribution of towers, making the planning content of different parts clear at a glance.

[0137] The visualization display of the path and tower distribution is mainly achieved through layer control. Each layer represents a type of information, such as terrain, tower distribution, or ground feature type. Exemplarily, the terrain layer can display Digital Elevation Model (DEM) data to help analyze the slope and terrain features of the line; while the tower distribution layer helps analyze the required infrastructure of the line by marking the positions of different types of towers. For the display of the path, different colored lines can represent different line planning schemes, making the differences between different line choices more prominent.

[0138] Secondly, during the visualization process of the optimal transmission line path, the system will also perform overlay processing on the ground feature information crossed by the line to generate a comprehensive analysis map. These maps not only show the line path itself but also display the interaction between the path and various ground features (such as water areas, railways, roads, etc.) through different markings, symbols, and graphics. For example, water areas may be represented by blue shadows, and railways and roads are identified by different styles of lines. This part of the visualization display enables users to clearly see the relationship between the path planning and the ground features, facilitating further analysis and decision-making.

[0139] For the interaction between each path segment and the ground features, the GIS system can also provide additional information through data annotation, such as the type of ground feature crossed, the crossing length, the crossing cost, etc. In this way, users can comprehensively evaluate the advantages and disadvantages of different line schemes. For example, the system may mark that a certain path has a relatively long crossing length over a water area and its construction cost is relatively high, prompting decision-makers to re-evaluate whether to choose this scheme.

[0140] In addition, through the GIS system, users can also provide real-time feedback and adjustment on the optimization effect of the line. The system supports interactive operations. Users can modify the path in certain specific areas according to the displayed visualization results, recalculate and display new line schemes. Through continuous interaction and adjustment, the optimal transmission line path is further optimized to ensure that it meets the constraints in multiple aspects such as technology, economy, and environment.

[0141] Through the above method, the visual output of the optimal transmission line path provided in this embodiment can provide intuitive and easy-to-understand information support for users during the decision-making process, helping users comprehensively understand the influencing factors of different line selection, and then making reasonable decisions.

[0142] Generally speaking, the present invention combines digital elevation model and remote sensing image data for ground object recognition, and uses the ant colony optimization algorithm to realize the optimal selection of the line path. During the path planning process, the ground object crossing cost, tower type selection, terrain slope, crossing angle between the line and obstacles, and the influence of voltage level on tower spacing and conductor type are considered. By calculating and comparing the comprehensive costs of different schemes, finally, the visual display of the optimal transmission line path is realized through GIS tools, providing intuitive path, tower distribution and ground object crossing information to assist decision-makers in making the most economical and efficient line planning scheme.

[0143] As a preferred embodiment of the present invention, the method further includes dynamically adjusting the constraint conditions of path planning according to the changes of external conditions, and updating the optimal transmission line path in real time. Specifically, with the influence of external environments (such as weather changes, terrain changes, ground object condition changes, etc.), the path planning system can automatically monitor these changes and accordingly adjust the constraint conditions in path planning. For example, in case of bad weather or special geological conditions, the system can adjust parameters such as tower spacing, selection of ground object crossing, or construction cost according to the new environmental data, so as to optimize the path planning in real time and ensure that the line planning can always maintain optimality in a dynamically changing environment. This dynamic adjustment mechanism makes the line path planning not just a one-time optimization, but a flexible and intelligent decision-making process that can adapt to different external condition changes.

[0144] As a preferred embodiment of the present invention, the method further includes historical path scheme management, recording and storing the optimal transmission line paths under different conditions for subsequent adjustment and optimization. Specifically, the system can automatically save the optimal transmission line path during each path planning optimization process, and store it together with the constraint conditions, environmental factors and other relevant data at that time. These historical path schemes can provide valuable reference for subsequent line adjustment and optimization. For example, in case of similar environmental changes or constraint conditions, the system can accelerate the optimization process of the current path planning by comparing historical schemes, or quickly find suitable adjustment strategies according to historical data. This historical scheme management function not only improves the efficiency of the system, but also ensures that the previous experience and data can be fully utilized in future path planning, thus further improving the accuracy and flexibility of line design.

[0145] A transmission line optimization device described below can be correspondingly referred to the transmission line optimization method described above.

[0146] Please refer to the appendix Figure 2 , the present invention also provides a transmission line optimization device, including:

[0147] A data processing module 10, configured to obtain digital elevation model data and remote sensing image data, and perform data preprocessing;

[0148] A ground feature recognition module 20, configured to perform ground feature recognition based on the preprocessed digital elevation model data and remote sensing image data to obtain ground feature information required for line path planning;

[0149] A rule constraint module 30, configured to combine the ground feature information and construct constraint conditions for line planning based on a multi-rule constraint model;

[0150] A path optimization module 40, configured to perform line path optimization using an optimization algorithm under the constraint conditions to obtain an optimal transmission line path that meets the constraint conditions;

[0151] A cost calculation module 50, configured to calculate and compare the comprehensive costs of the optimal transmission line paths under different tower configurations;

[0152] A scheme output module 60, configured to select the tower configuration with the lowest comprehensive cost and the optimal transmission line path based on the comprehensive cost comparison result to form an optimal transmission line scheme.

[0153] The device in this embodiment can be used to execute the above method embodiment, and its principle and technical effects are similar, which will not be elaborated here.

[0154] Although the embodiments of the present invention have been shown and described, for those of ordinary skill in the art, it can be understood that various changes, modifications, substitutions, and variations can be made to these embodiments without departing from the principle and spirit of the present invention. The scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for optimizing a power transmission line, characterized in that: The following steps are involved: Obtain digital elevation model data and remote sensing image data, and perform data preprocessing; Based on the pre-processed digital elevation model data and remote sensing image data, ground object recognition is performed to obtain the ground object information required for line path planning; Combined with the terrain information, the constraints of route planning are constructed based on the multi-rule constraint model; Under the constraints, the optimization algorithm is used to optimize the line path to obtain the optimal transmission line path that meets the constraints; Calculate and compare the comprehensive cost of the optimal transmission line path under different tower configurations; Based on the comprehensive cost comparison results, the tower configuration with the lowest comprehensive cost and the optimal transmission line path are selected to form the optimal transmission line plan.

2. The power transmission line optimization method according to claim 1, characterized in that: Based on the pre-processed digital elevation model data and remote sensing image data, ground object recognition is performed to obtain the ground object information required for line path planning, including: Extract terrain elevation features, gradient information and image texture features, and form a terrain feature vector; Based on the feature vectors of the objects, clustering algorithm is used to preliminarily classify the objects and obtain the classification results. A deep neural network is used to optimize the object classification results, and morphological processing is used to remove noise to obtain the final object classification results; The final ground feature classification result is used as the ground feature information required for route planning.

3. The power transmission line optimization method according to claim 1, characterized in that: The constraints include: Cost calculation rules across different features, including water bodies, roads, railways, and houses; Rules for selecting tower types, including the terrain suitability of different towers; The angle requirements for the intersection of the line and the obstacle; Voltage level limits the spacing between towers and conductor types.

4. The method for optimizing a power transmission line according to claim 1, characterized in that: The optimization algorithm is an ant colony optimization algorithm; Ant colony optimization algorithm is used to optimize the line path, including: Initialize path planning parameters, including pheromone distribution, number of ants, and number of iterations; Calculate the transfer probability of ants when choosing a path, and adjust the path selection weight based on the cost of crossing the terrain; The pheromone is updated according to the path cost and constraints, and gradually converges to the optimal transmission line path.

5. The power transmission line optimization method according to claim 1, characterized in that: The calculation and comparison of the comprehensive cost of the optimal transmission line path under different tower configurations includes: Calculate the comprehensive cost of the optimal transmission line path for each tower configuration: Calculate unit cost based on tower type; Calculate construction costs based on tower height and foundation construction requirements; Calculate the line construction cost based on the path length and the type of terrain crossed; Calculate the comprehensive cost based on the unit cost, construction cost and line construction cost; Compare the comprehensive costs of optimal transmission line paths under different tower configurations.

6. The method for optimizing a power transmission line according to claim 5, characterized in that: The method of calculating the line construction cost based on the path length and the type of land feature crossed includes: setting corresponding weighting coefficients based on different land feature types to optimize the line construction cost function; and calculating the line construction cost based on the line construction cost function and in combination with the path length.

7. The power transmission line optimization method according to claim 1, characterized in that: The method further includes: visually outputting the optimal transmission line solution; Visual output of the optimal transmission line solution, including: Use GIS tools to visualize terrain, paths, and tower distribution; Overlay the information of terrain features crossed by the route to provide a comprehensive analysis diagram.

8. The method for optimizing a power transmission line according to claim 1, characterized in that: The method further includes dynamically adjusting the constraint conditions of the path planning according to the changes in external conditions, and updating the optimal transmission line path in real time.

9. The power transmission line optimization method according to claim 1, characterized in that: The method further includes historical path solution management, recording and storing the optimal transmission line paths under different conditions for subsequent adjustment and optimization.

10. A transmission line optimization device, used to execute the transmission line optimization method according to any one of claims 1 to 9, characterized in that: include: Data processing module, used to obtain digital elevation model data and remote sensing image data and perform data preprocessing; The ground object recognition module is used to perform ground object recognition based on the pre-processed digital elevation model data and remote sensing image data to obtain the ground object information required for line path planning; The rule constraint module is used to combine the ground feature information and build the constraint conditions of route planning based on the multi-rule constraint model; The path optimization module is used to optimize the line path using an optimization algorithm under constraint conditions to obtain the optimal transmission line path that meets the constraint conditions; Cost calculation module, used to calculate and compare the comprehensive cost of the optimal transmission line path under different tower configurations; The scheme output module is used to select the tower configuration with the lowest comprehensive cost and the optimal transmission line path to form the optimal transmission line scheme based on the comprehensive cost comparison results.

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