Intelligent power transmission line path selection method and device and related product
By using PostGIS analysis and path building algorithms to plan transmission line paths in a line simulation environment, the problems of low efficiency and many errors caused by reliance on manual experience in existing technologies are solved, and efficient and accurate path selection is achieved.
Patent Information
- Application Number
- CN202510812971.4
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-06-18
- Publication Date
- 2025-09-26
AI Technical Summary
Existing transmission line path planning mainly relies on manual experience, which is inefficient and prone to errors, making it difficult to achieve efficient and accurate path selection.
PostGIS is used to analyze transmission line path information data, generate a path constraint model, and plan the path in a line simulation environment through a path building algorithm, which is then visualized using an interactive interface.
It improves the efficiency and accuracy of path planning, reduces the dependence on designer experience, and ensures the scientificity and safety of path planning.
Smart Images
Figure CN120706074A_ABST
Abstract
Description
Technical Field
[0001] The present invention belongs to the field of engineering design, and specifically relates to a method and device for intelligent transmission line path selection and related products. Background Art
[0002] Electricity is an energy source powered by electrical energy. It is a power production and consumption system consisting of power generation, transmission, transformation, distribution, and consumption. It converts natural primary energy into electricity through mechanical energy devices, and then supplies electricity to users through transmission, transformation, and distribution. With the development of society, my country's electricity demand is steadily increasing. Transmission projects, as the "power arteries" connecting power sources and load centers, fulfill the core function of long-distance, large-scale, and low-loss transmission of electricity. Within the power grid architecture, transmission line routing is crucial for project economic efficiency, safety, and environmental friendliness. Currently, transmission line routing is typically performed by designers using electronic map software to empirically identify various constraints along the route and avoid obstacles. Therefore, manual route selection is not only inefficient but also relies heavily on the designer's expertise and is prone to errors. Summary of the Invention
[0003] Based on the above problems, the present application provides a method, device and related products for intelligent transmission line path selection to solve the problems existing in the above-mentioned prior art.
[0004] The present application discloses an intelligent transmission line path selection method, comprising: collecting transmission line path information data; analyzing the transmission line path information data based on PostGIS to generate a path constraint model; mapping the path constraint model to a virtual model to construct a line simulation environment; planning the transmission line path in the line simulation environment using a path building algorithm to obtain path planning information; and visualizing the simulation environment and path planning information based on an interactive interface.
[0005] Optionally, the analysis of the transmission line path information data based on PostGIS to generate a path constraint model includes: analyzing the transmission line path information data based on PostGIS to obtain geographic information data; integrating the linear model data in the geographic information data through a linear function to construct a linear feature model; integrating the nonlinear model data in the geographic information data through a surface function to obtain a constraint surface feature model; and combining the linear feature data and the constraint surface feature data to generate a path constraint model.
[0006] Optionally, the linear model data in the geographic information data is integrated through a linear function to construct a linear feature model, including: preprocessing the linear model data in the geographic information data to obtain preprocessed linear model data; integrating the preprocessed linear model data to generate an integrated linear model; verifying the compliance of the integrated linear model to obtain a compliant linear model, and constructing a linear feature model based on the compliant linear model.
[0007] Optionally, the nonlinear model data in the geographic information data is integrated through a surface function to obtain a constrained surface feature model, including: preprocessing the nonlinear model data in the geographic information data to obtain preprocessed nonlinear model data; integrating the preprocessed nonlinear model data to generate the integrated nonlinear model data; verifying the compliance of the integrated nonlinear model to obtain a compliant nonlinear model, and constructing a nonlinear feature model based on the compliant linear model.
[0008] Optionally, the method of planning a transmission line path in the line simulation environment through a path building algorithm and obtaining path planning information includes: obtaining N constraint condition data; based on the N constraint condition data, planning a transmission line path in the line simulation environment through the path building algorithm to obtain M transmission line paths; and obtaining an optimal transmission line path from the M transmission line paths based on an evaluation function as the path planning information.
[0009] Optionally, obtaining the optimal transmission line path from the M transmission line paths based on the evaluation function as path planning information includes: selecting the transmission line path with the largest evaluation function result value from the M transmission line paths as the optimal transmission line path as the path planning information based on the evaluation function.
[0010] The present application also provides an intelligent transmission line path selection method and device, including: a data acquisition module: used to collect transmission line path information data; a condition constraint module: used to analyze the transmission line path information data based on PostGIS and generate a path constraint model; an environment construction module: used to map the path constraint model to a virtual model and construct a line simulation environment; a path optimization module: used to plan the transmission line path in the line simulation environment through a path construction algorithm and obtain path planning information; a visualization module: used to visualize the simulation environment and path planning information based on an interactive interface.
[0011] The present application also provides an electronic device, comprising: a memory and a processor, wherein the memory stores a computer executable program, and the processor is configured to execute the computer executable program to implement any one of the methods described in the present application.
[0012] The present application also provides a computer storage medium, on which a computer executable program is stored. When the computer executable program is executed, it implements any one of the methods described in the present application.
[0013] The present application also provides a computer program product, which includes a computer program. When the computer program is executed by a processor, it implements any of the methods described in the present application.
[0014] The present application provides a method, device and related products for selecting an intelligent transmission line path, wherein the method described includes: collecting transmission line path information data; analyzing the transmission line path information data based on PostGIS to generate a path constraint model; mapping the path constraint model to a virtual model to construct a line simulation environment; planning the transmission line path in the line simulation environment through a path building algorithm to obtain path planning information; and visualizing the simulation environment and path planning information based on an interactive interface. Based on PostGIS spatial database extension technology, the system can efficiently construct a dynamic constraint model. And through three-dimensional panoramic visualization technology, the topography of the line corridor can be simulated. In addition, the path building algorithm can automatically plan the optimal path, saving labor costs. In summary, the present application can improve the efficiency of path planning, reduce dependence on the experience of designers, and improve the accuracy of the design. BRIEF DESCRIPTION OF THE DRAWINGS
[0015] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.
[0016] Figure 1 This is a flow chart of a method for selecting an intelligent transmission line path according to an embodiment of the present application; Figure 2 This is a structural diagram of an intelligent transmission line path selection device according to an embodiment of the present application; Figure 3 This is a schematic diagram of the structure of an electronic device according to an embodiment of the present application; Figure 4 This is a schematic diagram of the hardware structure of the electronic device according to an embodiment of the present application. DETAILED DESCRIPTION
[0017] The implementation of any technical solution in the embodiments of the present application does not necessarily require achieving all of the above advantages at the same time.
[0018] In order to enable those skilled in the art to better understand the solutions of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts shall fall within the scope of protection of the present invention.
[0019] Figure 1 FIG. 1 is a flow chart of a method for selecting a path for an intelligent transmission line according to an embodiment of the present application; FIG. Figure 1 The method for selecting an intelligent transmission line path is shown, comprising: collecting transmission line path information data; analyzing the transmission line path information data based on PostGIS to generate a path constraint model; mapping the path constraint model to a virtual model to construct a line simulation environment; planning the transmission line path in the line simulation environment through a path building algorithm to obtain path planning information; and visualizing the simulation environment and path planning information based on an interactive interface. Based on PostGIS spatial database extension technology, the system can efficiently construct a dynamic constraint model. And through three-dimensional panoramic visualization technology, the topography of the line corridor can be simulated. In addition, the optimal path can be automatically planned through the path building algorithm, saving labor costs. In summary, the present application can improve the efficiency of path planning, reduce dependence on the experience of designers, and improve the accuracy of the design.
[0020] Optionally, the transmission line path information data includes: model terrain data and environmental data. The model terrain data includes: terrain, landform, altitude, land use type and other data; the environmental data includes wind speed, temperature, ice area, dancing area, pollution area and the like. There is no restriction on the method of obtaining the transmission line path information data of the present invention. The model terrain data around the transmission line can be obtained by surveyors based on GPS, or the model terrain data can be obtained in a preset manner. In the embodiment of the present application, a three-dimensional path evaluation system is constructed by integrating model terrain data and environmental data. The scientificity and safety of line design can be significantly improved. In addition, a flexible acquisition method of "GPS measurement + preset data" is provided: for complex areas, GPS measurement by surveyors is used to ensure data accuracy, and for conventional areas, a preset model terrain database can be called to quickly generate a path plan. This layered acquisition strategy can not only control the survey cost, but also focus on key nodes to implement refined surveys, achieving a balanced optimization of efficiency and cost.
[0021] Optionally, the method of analyzing the transmission line path information data based on PostGIS to generate a path constraint model includes: analyzing the transmission line path information data based on PostGIS to obtain geographic information data; integrating the linear model data in the geographic information data through a linear function to construct a linear feature model; integrating the nonlinear model data in the geographic information data through a surface function to obtain a constraint surface feature model; combining the linear feature model and the constraint surface feature model to generate a path constraint model. In this embodiment, first, linear geographic features are topologically merged using linear functions to effectively eliminate redundant nodes and small line segments, significantly improving the continuity and computational efficiency of the path geometry model; second, surface functions are used to spatially aggregate nonlinear constraint features (such as protected areas and prohibited construction areas) to generate complete constraint surface data, ensuring that path planning strictly meets environmental compliance requirements; finally, through the joint modeling of line and surface features, a composite constraint system containing topological relationships and spatial attributes is constructed, which not only ensures the geometric continuity of the path direction, but also realizes the coordinated constraints of multiple environmental factors, greatly improving the scientific nature and engineering feasibility of transmission line path planning.
[0022] Optionally, integrating the linear model data in the geographic information data using a linear function to construct a linear feature model includes: preprocessing the linear model data in the geographic information data to obtain preprocessed linear model data; and preprocessing the linear model data in the geographic information data to obtain preprocessed linear model data. Specifically, to ensure that the linear model data can form a linear feature model, the continuity of the linear model data needs to be checked, discontinuous linear feature model data is removed, and the remaining continuous linear feature model data is retained as the preprocessed linear model data. For example, the linear model data may represent an existing power line. After field measurement using GPS, surveyors collect the location coordinates of S existing power lines. The location coordinates of these S existing power lines can be input as geographic information data. Subsequently, by preprocessing the location coordinates of the S existing power lines, P pieces of linear feature model data that are connected and continuous among the S existing power lines are retained as the preprocessed linear model data. It can be determined that the linear model data is valid. If the linear feature model data of Q of the S established routes cannot be connected, it means that the positioning of the Q established routes is not a continuous line, and the linear feature model cannot be established. In this embodiment, by detecting continuity and eliminating discontinuous linear feature model data, the quality of the preprocessed linear model data is ensured to be higher. Moreover, discontinuous but close linear model data is retained within a preset error range, taking into account possible errors in actual measurement. Therefore, the applicability of the data is enhanced. Even if there are slight deviations in the measurement data, it can be effectively utilized, thereby retaining more useful information and helping to build a more complete path constraint model. In addition, by integrating the preprocessed linear model data to generate an integrated linear model, the data is made more concise and orderly.
[0023] Optionally, detecting the continuity of the linear model data and eliminating discontinuous linear feature models further includes detecting the continuity of the linear model data, retaining discontinuous linear model data within a preset error range, and obtaining the preprocessed linear model data. In actual measurement, due to errors in positioning coordinates caused by varying surveying and mapping accuracies, the inputted linear model data may not completely overlap at both end points. Therefore, if the linear model data is within the preset error range, it can be considered that a continuous linear feature model can be constructed and should be retained as preprocessed linear model data.
[0024] Optionally, the preprocessed linear model data is integrated to generate an integrated linear model. Specifically, the integration of the preprocessed linear model data to generate an integrated linear model includes: identifying the endpoint coordinates of the preprocessed linear model data, connecting the preprocessed linear model data with identical endpoint coordinates at one end into a single continuous line segment, and constructing a linear feature model. For example, coordinate point a (x1, y1, z1) and coordinate point b (x2, y2, z1) form a line segment ab; coordinate point c (x2, y2, z1) and coordinate point d (x4, y4, z1) form a line segment cd. Line segment ab coincides with line segment cd at coordinate point (x2, y2, z1), and therefore line segment ab and line segment cd are connected into a single continuous line segment.
[0025] Optionally, the integration of the pre-processed linear model data to generate an integrated linear model further includes: identifying the endpoint coordinates of the pre-processed linear model data, folding and merging the pre-processed linear model data with exactly the same endpoint coordinates at both ends to construct a linear feature model. For example, coordinate point a (x1, y1, z1) and coordinate point b (x2, y2, z1), the line segment constructed by points a and b is ab; coordinate point c (x1, y1, z1) and coordinate point d (x2, y2, z1), the line segment constructed by points cd is cd, and line segment ab coincides with line segment cd at coordinate points (x1, y1, z1) and (x2, y2, z1), so line segment ab is connected and folded and merged with line segment cd to form a single line segment.
[0026] Optionally, the integration of the pre-processed linear model data to generate an integrated linear model further includes: identifying the endpoint coordinates of the pre-processed linear model data, connecting the pre-processed linear model data with one end point located within the line segment of another pre-processed linear model data with the other pre-processed linear model data to construct a linear feature model. For example, coordinate point a (x1, y1, z1) and coordinate point b (x2, y2, z1), the line segment constructed by points a and b is ab; coordinate point c (x 2- The line segment constructed by the two points x1, y1, z1) and d(x3, y2, z1) is cd, and the coordinate point c(x 2- x1, y1, z1) are collinear with line segment ab, so line segment ab is connected with line segment cd to form a "T" connection.
[0027] Optionally, the compliance of the integrated linear model is verified, a compliant linear model is obtained, and a linear feature model is constructed based on the compliant linear model. Specifically, the compliance of the integrated linear model can be verified by determining the data type. The preprocessed linear model data is stored as coordinate points, while the successfully merged integrated linear model is stored as a line. The compliance of the integrated linear model is verified by determining the data storage type.
[0028] The method of integrating the nonlinear model data in the geographic information data through a surface function to obtain a constrained surface feature model includes: preprocessing the nonlinear model data in the geographic information data to obtain preprocessed nonlinear model data. Specifically, in order to ensure that the nonlinear model data can form a nonlinear feature, it is necessary to detect the compliance of the nonlinear model data, eliminate the nonlinear model data that does not comply, and retain the remaining compliant nonlinear model data as preprocessed nonlinear model data. Specifically, the method of detecting the compliance of the nonlinear model data includes: detecting the consistency of the coordinate system of the nonlinear model data, the topological validity of the nonlinear model data, and the semantic consistency. The nonlinear model data that meets the consistency of the coordinate system, the topological validity of the nonlinear model data, and the semantic consistency is retained as the preprocessed nonlinear model data.
[0029] Optionally, the preprocessed nonlinear model data is integrated to generate the integrated nonlinear model data. Specifically, the integration of the preprocessed nonlinear model data to generate the integrated nonlinear model data includes: identifying two surface features of the preprocessed nonlinear model data that share a boundary, merging them into a single surface feature, and generating the integrated nonlinear model. For example, if plane e and plane f share an edge, and if plane e is marked as an ecological protection zone boundary, then plane f is also an ecological protection zone boundary. Planes e and f are merged into plane g to generate the constrained surface feature model.
[0030] Optionally, the data integration of the preprocessed nonlinear model data to generate an integrated nonlinear model and obtain a constrained surface feature model also includes: identifying two surface elements overlapping with the preprocessed nonlinear model data to generate the integrated nonlinear model; for example, if plane g overlaps with plane h, plane g is marked as a planning area and plane h is marked as a flood inundation area, then plane i is generated to mark the planning area & flooding area, which is the constrained surface feature model.
[0031] Optionally, the integration of the preprocessed nonlinear model data to generate an integrated nonlinear model and obtain a constrained surface feature model also includes: identifying two unrelated surface elements of the preprocessed nonlinear model data and generating the integrated nonlinear models respectively; for example, plane j is unrelated to plane k, plane j is an ecological protection area, and plane k is a planning area, and plane j and plane k are generated as constrained surface feature models respectively.
[0032] Optionally, the compliance of the integrated nonlinear model is verified, a compliant nonlinear model is obtained, and a nonlinear terrain model is constructed based on the compliant linear model. Specifically, the verification of the compliance of the integrated nonlinear model, obtaining a compliant nonlinear model, and constructing a nonlinear terrain model based on the compliant linear model includes: checking whether the integrated nonlinear model has gaps or overlapping areas, marking the integrated nonlinear model without gaps or overlapping areas as compliant, and obtaining a compliant nonlinear model; and marking the integrated nonlinear model with gaps or overlapping areas as non-compliant.
[0033] Optionally, verifying the compliance of the integrated nonlinear model, obtaining a compliant nonlinear model, and constructing a nonlinear feature model based on the compliant linear model further includes: checking whether the original attributes of the integrated nonlinear model and the composite attributes of the integrated nonlinear model after overlap are consistent, comparing the composite attributes with a preset attribute table, marking the integrated nonlinear model as compliant if the original attributes are consistent with the composite attributes and the composite attributes are within the preset attribute table, and obtaining a compliant nonlinear model; and marking the integrated nonlinear model as noncompliant if the attributes are inconsistent or the composite attributes are not within the preset attribute table. For example, if plane m overlaps with plane n, plane m is marked as a tourist scenic area, and plane n is marked as a residential area, then plane o is generated with the labels tourist scenic area & residential area as the integrated nonlinear model. First, the original attributes of planes m and n, i.e., tourist scenic area and residential area, are checked to see whether they are consistent with the composite attributes of plane o, i.e., tourist scenic area & residential area. The tourist scenic area and residential area are then compared with the preset attribute combinations in the preset attribute table. If the preset attribute table contains the preset attribute combination of tourist scenic area and residential area, the area is compliant; if not, the area is non-compliant. This step is added because, in reality, the superposition of two different land use attributes may conflict. Since residential areas cannot exist in tourist scenic areas, such attribute superposition is invalid.
[0034] Optionally, mapping the path constraint model to a virtual model to construct a circuit simulation environment includes: mapping the path constraint model to a virtual model based on digital twin technology to construct a circuit simulation environment.
[0035] Optionally, planning a transmission line path within the line simulation environment using a path-building algorithm to obtain path planning information includes: obtaining N constraint condition data; planning a transmission line path within the line simulation environment using the path-building algorithm based on the N constraint condition data to obtain M transmission line paths; and determining the optimal transmission line path from the M transmission line paths based on an evaluation function as the path planning information. By integrating N constraints, the algorithm can achieve collaborative optimization of multi-dimensional constraints, including geographical, environmental, economic, and regulatory constraints. This integrated design avoids the limitations of traditional planning, where a single factor dominates, significantly improving the engineering feasibility and social compliance of the path, and reducing the risk of later approvals and rerouting costs. After the algorithm generates M candidate paths, it performs multi-objective optimization using an evaluation function. This "generation-screening" model overcomes the local optimality trap, enabling the exploration of more optimal topologies within a complex constraint space, achieving a dynamic balance between economic benefits and engineering safety, and is particularly suitable for long-distance, cross-regional transmission scenarios. In this embodiment, the algorithm generates multiple candidate paths and uses an evaluation function to optimize them based on multiple objectives. This helps overcome local optimality, explore more optimal topologies within complex constraint spaces, and improve the flexibility and efficiency of path planning. The evaluation function comprehensively considers multiple objectives, calculating the result through methods such as weighted geometric averaging to select the optimal path and achieve a dynamic balance between economic benefits and project safety.
[0036] Optionally, the N constraint condition data are obtained, including but not limited to: tortuosity coefficient, safety distance, and number of corners. Specifically, the tortuosity coefficient refers to the value obtained by dividing the actual length of the path by the aviation line. The tortuosity coefficient can reflect the rationality of the path planning of a transmission line, and is usually taken as less than 1.2. The safety distance refers to the vertical distance between the center line of the planned transmission line path and the path constraint model. According to the requirements recorded in the design specifications, the transmission line is required to avoid a certain distance from the existing terrain. The number of corners refers to the number of tension towers in the planned transmission line. Because the structure of the tension tower is more complex than that of the suspension tower, the cost of the tension tower is much higher than that of the straight tower. Usually, the proportion of the number of towers in the entire transmission line project is an important parameter for measuring the cost.
[0037] Optionally, planning a transmission line path in the line simulation environment based on the N constraint data using the path building algorithm to obtain M transmission line paths includes: inputting the N constraint data into a target construction model to obtain the transmission line paths. Repeating the above steps, changing the input parameters of the N constraint data, to obtain the M transmission line paths. Specifically, in the process of planning a transmission line path, not only one primary option is planned, but also at least one alternative option is planned. In addition, during the project review, each path planning option needs to be evaluated. Therefore, by changing the input constraint data, M transmission line paths are obtained for designers to choose from.
[0038] Optionally, inputting the N constraint data into the target construction model to obtain the transmission line path includes inputting the N constraint data into the target construction model, obtaining N constraint combinations with a minimum-cost solution based on a cost function, and then, based on the minimum-cost solution, searching using an A* algorithm starting from the coordinates of the transmission line's starting substation and stopping at the coordinates of the end substation to obtain a preliminary transmission line path. Adjusting the preliminary transmission line path using a correction model to obtain the transmission line path. In this embodiment, the cost function can quantitatively evaluate the costs of different paths, and the A* algorithm is an efficient heuristic search algorithm that can quickly find the optimal path in complex environments, significantly improving path search efficiency and reducing computational time and resource consumption. Furthermore, the correction model can fine-tune the preliminary path to ensure that the path maintains a reasonable deviation distance from terrain features in the simulation environment and avoid conflicts with obstacles. Through operations such as path translation, rotation, and scaling, the system can further optimize the path to better meet actual engineering requirements and improve the feasibility and safety of the path.
[0039] Optionally, the N constraint data are input into a target construction model, and based on a cost function, N constraint combinations are obtained to obtain a minimum-cost solution, including: calculating the minimum-cost solution based on the following formula: f(n) = g(n) + h(n); where g(n) is the actual cost from the starting point to the current node n. In path planning, it is typically calculated based on the distance, time, or resources consumed. For example, in a grid map, if each grid cell represents a certain distance, then g(n) is the number of grid cells traversed from the starting point to node n. h(n) is a heuristic estimate of the cost from node n to the end point. The heuristic function h(n) is an estimate that should be easy to calculate and reflect a lower bound on the actual cost from node n to the end point. Heuristic functions include Euclidean distance, Manhattan distance, etc. f(n) is the sum of g(n) and h(n), representing the total estimated cost from the starting point through node n to the end point. The A* algorithm compares the f(n) values of different nodes and selects the node with the smallest f(n) value for expansion, thereby gradually finding the optimal transmission line path.
[0040] Specifically, in the cost function, a higher cost may be assigned to areas with a greater terrain slope to encourage the target construction model to select a path with a smaller slope. The terrain slope may be extracted from the route simulation environment.
[0041] Adjusting the preliminary transmission line path using a correction model to obtain the transmission line path includes: segmenting the line simulation environment according to set parameters to obtain segmented image blocks; extracting feature parameters from the segmented image blocks to obtain a feature image; aligning the preliminary transmission line path with the feature image to identify the deviation distance between the preliminary transmission line path and features in the simulation environment; and outputting the preliminary transmission line path that meets the deviation distance as the transmission line path. In this embodiment, the correction model uses refinement to segment the line simulation environment into image blocks, extract feature parameters, and align them with the preliminary path, thereby identifying and correcting deviations and ensuring that the final path is highly consistent with the actual terrain. Furthermore, in complex terrain conditions, the correction model can adjust the preliminary path to adapt it to the actual terrain, avoid conflicts with features, and enhance the feasibility of the path. Furthermore, by early detecting and correcting deviations in the preliminary path, the correction model avoids rerouting during the construction phase, significantly reducing the cost of later rerouting.
[0042] Optionally, the adjusting the preliminary transmission line path through the correction model to obtain the transmission line path also includes correcting the preliminary transmission line path that does not conform to the deviation distance, and outputting the corrected preliminary transmission line path that conforms to the deviation distance as the transmission line path.
[0043] Optionally, the correcting the preliminary transmission line path that does not conform to the deviation distance and outputting the corrected preliminary transmission line path that conforms to the deviation distance as the transmission line path includes: performing path translation, rotation, and scaling operations on the preliminary transmission line path that does not conform to the deviation distance, and outputting the preliminary transmission line path that conforms to the deviation distance after the operation as the transmission line path.
[0044] Optionally, obtaining an optimal transmission line path from the M transmission line paths based on an evaluation function as path planning information includes: obtaining the optimal transmission line path from the M transmission line paths based on the following formula: Among them, Q is the evaluation function; L norm is the standard value of path length; C norm is the standard value of construction cost; E norm is the standard value of environmental impact; wL is the weighted value of path length; wC is the weighted value of construction cost; and wE is the weighted value of environmental factors.
[0045] Optionally, you can calculate based on the following formula Among them, L norm is the standard value of path length; L max is the maximum design value of the path length under this voltage level; L min is the minimum design path length for that voltage level; L is the actual length of the transmission line path. For example, the aerial line distance of a 220kV transmission project in a certain location is 20km. According to the design requirements, the tortuosity coefficient is set to 1.2, so the maximum path length is 24km and the minimum path length is 20km.
[0046] Optionally, you can calculate based on the following formula Among them, C norm is the standard value of construction cost; C max is the maximum investment value of the project; C min is the minimum investment for the project; C is the actual investment for the transmission line path. For example, the typical cost of a 220kV transmission project in a certain location is 1.2 million yuan per kilometer. According to design requirements, the investment must not exceed 20% of the typical cost. Therefore, the maximum investment for this project is 1.44 million yuan per kilometer, and the minimum path length is 1.2 million yuan per kilometer, or a lower minimum investment value can be customized based on actual needs.
[0047] Optionally, you can calculate based on the following formula Among them, E norm is the environmental impact standard value; E max is the maximum environmental impact value of the project; E minis the minimum environmental impact value of the project; E is the actual environmental impact value of the transmission line path. For example, the environmental impact score range is 1-10, with 1 being the least environmental impact and 10 being the most severe environmental impact, and the values are reverse normalized.
[0048] Optionally, the path length weighted value wL, the construction cost weighted value wC, and the environmental factor weighted value wE can be constrained based on the following formula: wL + wC + wE = 1. For example, if the project is primarily influenced by cost, followed by environmental factors, and finally by path length, the construction cost weighted value wC can be 0.5; the environmental factor weighted value wE can be 0.3; and the path length weighted value wL can be 0.2. The size of each weighted value represents the importance of each influencing factor.
[0049] Example: Assume that the parameters of a certain line are as follows: the actual path length L of this project is 120 km, the minimum design value of the path length is 50 km, and the maximum design value of the path length is 150 km; the construction cost C is 8.5 million yuan, the maximum investment value is 5 million yuan, and the maximum investment value is 10 million yuan; the environmental impact E is 7, the score range is 1-10, the path length weighted value wL is 0.3, the construction cost weighted value wC is 0.4, and the environmental factor weighted value wE is 0.3. Standardized calculation process: Weighted geometric mean: The evaluation function result of this project can be obtained as 0.31.
[0050] Optionally, obtaining the optimal transmission line path from the M transmission line paths based on the evaluation function as path planning information includes: selecting the transmission line path with the largest evaluation function result value from the M transmission line paths based on the evaluation function as the optimal transmission line path as the path planning information.
[0051] Optionally, visualizing the simulation environment and path planning information based on an interactive interface includes: building an interactive interface based on Unity or WebGL, and visualizing the simulation environment and path planning information based on the interactive interface.
[0052] Figure 2 This is a structural diagram of an intelligent transmission line path selection device according to an embodiment of the present application; Figure 2As shown: The present application also provides a line path selection method device, including: a data acquisition module: used to collect transmission line path information data; a condition constraint module: used to analyze the transmission line path information data based on PostGIS and generate a path constraint model; an environment construction module: used to map the path constraint model to a virtual model and construct a line simulation environment; a path optimization module: used to plan the transmission line path in the line simulation environment through a path construction algorithm and obtain path planning information; a visualization module: used to visualize the simulation environment and path planning information based on an interactive interface.
[0053] Figure 3 This is a schematic diagram of the electronic device structure according to an embodiment of the present application. Figure 3 As shown: The present application also provides an electronic device, comprising: a memory and a processor, wherein the memory stores a computer executable program, and the processor is used to execute the computer executable program to implement any one of the methods described in the present application.
[0054] Figure 4 Schematic diagram of the hardware structure of the electronic device according to the embodiment of the present application; Figure 4 As shown, the hardware structure of the electronic device may include: a processor, a communication interface, a computer-readable medium and a communication bus; wherein the processor, the communication interface and the computer-readable medium communicate with each other via the communication bus.
[0055] Optionally, the communication interface can be an interface of a communication module, such as an interface of a GSM module; wherein the processor can be specifically configured to run an executable program stored in the memory, thereby executing all or part of the processing steps of any of the above method embodiments.
[0056] The processor may be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it may also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA) or other programmable logic device, a discrete gate or transistor logic device, or a discrete hardware component. The various methods, steps, and logic block diagrams disclosed in the embodiments of the present application may be implemented or executed. The general-purpose processor may be a microprocessor or the processor may be any conventional processor, etc. The electronic devices of the embodiments of the present application exist in various forms, including but not limited to: (1) Mobile communication devices: These devices are characterized by their mobile communication capabilities and are primarily designed to provide voice and data communications. These terminals include smartphones (e.g., iPhones), multimedia phones, feature phones, and low-end phones.
[0057] (2) Ultra-mobile personal computer devices: These devices fall under the category of personal computers, have computing and processing capabilities, and generally also have mobile Internet access. These terminals include PDAs, MIDs, and UMPCs, such as the iPad.
[0058] (3) Portable entertainment devices: These devices can display and play multimedia content. These devices include audio and video players (such as iPods), handheld game consoles, e-books, smart toys, and portable car navigation devices.
[0059] (4) Server: A device that provides computing services. The server consists of a processor, hard disk, memory, system bus, etc. The server is similar to a general computer architecture, but because it needs to provide highly reliable services, it has higher requirements in terms of processing power, stability, reliability, security, scalability, and manageability.
[0060] (5) Other electronic devices with data interaction functions.
[0061] It should be pointed out that, according to the needs of implementation, the various components / steps described in the embodiments of the present application can be split into more components / steps, or two or more components / steps or partial operations of components / steps can be combined into new components / steps to achieve the purpose of the embodiments of the present application.
[0062] The above-mentioned method according to the embodiment of the present application can be implemented in hardware, firmware, or be implemented as software or computer code that can be stored in a recording medium (such as CD ROM, RAM, floppy disk, hard disk or magneto-optical disk), or be implemented as a computer code originally stored in a remote recording medium or a non-temporary machine-readable medium downloaded through a network and stored in a local recording medium, so that the method described herein can be stored in such software processing on a recording medium using a general-purpose computer, a special-purpose processor or programmable or special-purpose hardware (such as ASIC or FPGA). It can be understood that a computer, a processor, a microprocessor controller or programmable hardware includes a storage component (for example, RAM, ROM, flash memory, etc.) that can store or receive software or computer code, and when the software or computer code is accessed and executed by a computer, a processor or hardware, the verification code generation method described herein is implemented. In addition, when a general-purpose computer accesses the code for implementing the verification code generation method shown here, the execution of the code converts the general-purpose computer into a special-purpose computer for executing the verification code generation method shown here.
[0063] The present application also provides a computer storage medium, on which a computer executable program is stored. When the computer executable program is executed, it implements any one of the methods described in the present application.
[0064] The present application also provides a computer program product, which includes a computer program. When the computer program is executed by a processor, it implements any method described in the present application.
[0065] It should be noted that the same or similar parts between the various embodiments in this specification can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the device and system embodiments, since they are basically similar to the method embodiments, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiments. The device and system embodiments described above are merely schematic, wherein the modules described as separate components may or may not be physically separated, and the components indicated as modules may or may not be physical modules, that is, they may be located in one place, or they may be distributed on multiple network modules. Some or all of the modules can be selected according to actual needs to achieve the purpose of the scheme of this embodiment. A person of ordinary skill in the art can understand and implement it without paying any creative work.
[0066] The above is merely one specific embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of the present application. Therefore, the scope of protection of the present application should be based on the scope of protection of the claims.
Claims
1. A method for selecting an intelligent transmission line path, characterized in that: include: Collect transmission line path information data; Analyze the transmission line path information data based on PostGIS to generate a path constraint model; Mapping the path constraint model to a virtual model to construct a circuit simulation environment; Planning a transmission line path in the line simulation environment using a path building algorithm to obtain path planning information; The simulation environment and path planning information are visualized based on an interactive interface.
2. The intelligent transmission line path selection method according to claim 1, characterized in that: The method of analyzing the transmission line path information data based on PostGIS to generate a path constraint model includes: analyzing the transmission line path information data by PostGIS to obtain geographic information data; integrating linear model data in the geographic information data by using a linear function to construct a linear feature model; integrating nonlinear model data in the geographic information data by using a surface function to obtain a constraint surface feature model; and combining the linear feature data and the constraint surface feature data to generate a path constraint model.
3. The intelligent transmission line path selection method according to claim 2, characterized in that: The method of integrating the linear model data in the geographic information data through a linear function to construct a linear feature model includes: preprocessing the linear model data in the geographic information data to obtain preprocessed linear model data; integrating the preprocessed linear model data to generate an integrated linear model; verifying the compliance of the integrated linear model to obtain a compliant linear model, and constructing a linear feature model based on the compliant linear model.
4. The intelligent transmission line path selection method according to claim 2, characterized in that: The method of integrating the nonlinear model data in the geographic information data through a surface function to obtain a constrained surface feature model includes: preprocessing the nonlinear model data in the geographic information data to obtain preprocessed nonlinear model data; integrating the preprocessed nonlinear model data to generate the integrated nonlinear model data; verifying the compliance of the integrated nonlinear model to obtain a compliant nonlinear model, and constructing a nonlinear feature model based on the compliant linear model.
5. The intelligent transmission line path selection method according to claim 1, characterized in that: The method of planning a transmission line path in the line simulation environment using a path building algorithm to obtain path planning information includes: obtaining N constraint condition data; planning a transmission line path in the line simulation environment using the path building algorithm based on the N constraint condition data to obtain M transmission line paths; and obtaining an optimal transmission line path from the M transmission line paths based on an evaluation function as the path planning information.
6. The intelligent transmission line path selection method according to claim 5, characterized in that: The obtaining the optimal transmission line path from the M transmission line paths based on the evaluation function as the path planning information includes: selecting, based on the evaluation function, a transmission line path with the largest evaluation function result value from the M transmission line paths as the optimal transmission line path as the path planning information.
7. A method and device for selecting an intelligent transmission line path, characterized in that: include: Data acquisition module: used to collect transmission line path information data; Condition constraint module: used to analyze the transmission line path information data based on PostGIS and generate a path constraint model; environment construction module: used to map the path constraint model to a virtual model and build a line simulation environment; path optimization module: used to plan the transmission line path in the line simulation environment through a path construction algorithm and obtain path planning information; Visualization module: used to visualize the simulation environment and path planning information based on an interactive interface.
8. An electronic device, characterized in that: include: A memory and a processor, wherein the memory stores a computer executable program, and the processor is configured to execute the computer executable program to implement the method according to any one of claims 1 to 6.
9. A computer storage medium, characterized in that The computer storage medium stores a computer executable program, and the computer executable program implements the method according to any one of claims 1 to 6 when executed.
10. A computer program product, characterized in that The computer program product includes a computer program, and when the computer program is executed by a processor, it implements the method according to any one of claims 1 to 6.