Power transmission and transformation line selection system based on satellite remote sensing and geospatial data

Abstract

The invention discloses a power transmission and transformation line selection system based on satellite remote sensing and geographic space data, belongs to the field of power grid construction, and is used for solving the problems of how to accurately reflect terrain details by using data acquired by satellite remote sensing and data acquired by geographic space and how to finally ensure data quality. The system comprises a data acquisition module, a data preprocessing module, a line selection model construction module, an intelligent line selection module, a scheme evaluation and optimization module and a result output module. According to the invention, a perfect line selection model, dual-data fusion and a perfect data acquisition module and a line selection model construction module are constructed. More flexible and comprehensive influence factor evaluation is realized, the potential value of data is mined, the intelligence and adaptability of the line selection system are further improved, and the line selection result better meets the actual engineering requirements.

Description

Technical Field

[0001] The invention belongs to the field of power grid construction, and in particular, is a transmission and transformation line selection system based on satellite remote sensing and geographic space data. Background Art

[0002] Transmission line path optimization selection method and device based on multi-source remote sensing data (application number CN202210908054.2) The optimization line selection method includes: obtaining multidimensional data of the area through which the transmission line passes, the multidimensional data including: satellite remote sensing data, oblique photography data and laser point cloud data; based on the satellite remote sensing data and the oblique photography data, determining the initial path of the transmission line and the tower information in the initial path; the tower information includes straight tower information and corner tower information; based on the laser point cloud data and the tower information in the initial path, calculating the path parameters of the initial path; fine-tuning the towers in the initial path, recalculating the path parameters of the fine-tuned path, and determining the final path information based on the initial path information and the path parameters of the fine-tuned path.

[0003] This technical solution can achieve effective use of multi-source remote sensing data, optimize the transmission line path, and improve the refinement of the transmission line path. This method uses multi-source remote sensing data to determine the initial path of the transmission line and the tower information in the initial path, preliminarily determines the optional path, and then fine-tunes the towers in the initial path to achieve adjustment and optimization of the line.

[0004] Transmission tower site selection optimization system and method based on remote sensing and slope instability model (application number CN202211403122.6) The system includes a satellite remote sensing system, which acquires multi-source satellite remote sensing data to generate remote sensing data images of the target area; a communication network system, which transmits satellite images of the target area to the ground data receiving station; a data processing system, which receives and cleans and fuses the data to finally generate a basic database; a primary line selection and site selection system, which reads the primary optimization system database of the target area after data cleaning and fusion.

[0005] This technical solution takes into account the situation where tower construction increases the risk of slope instability, improves the accuracy of identifying the risk of slope instability of transmission lines throughout the entire period, enhances the ability to avoid geological disaster risks during transmission line selection, construction, operation and maintenance, improves the ability of the power grid to resist natural disasters and external damage, and ensures the safe and stable operation of the power grid under extreme conditions.

[0006] However, the above-mentioned existing technologies have not taken into account the fact that in the actual transmission line selection, various factors need to be considered, such as topography, line length, distance to obstacles, etc. For areas with complex terrain, how to use data obtained by satellite remote sensing and data obtained by geographic space to accurately reflect terrain details, and ultimately, how to ensure data quality has become a problem that needs to be solved in a transmission line selection system. Summary of the invention

[0007] The present invention aims to provide a transmission and transformation line selection system based on satellite remote sensing and geographic space data, which improves the accuracy of the model's prediction of line length.

[0008] In order to achieve the above technical objectives, the technical solution adopted by the present invention is as follows: The power transmission and transformation line selection system based on satellite remote sensing and geospatial data includes data acquisition module, data preprocessing module, line selection model building module, intelligent line selection module, scheme evaluation and optimization module, and result output module; The data acquisition module is used to acquire the topographic remote sensing image data of the line selection area through satellite remote sensing technology and collect geographic space data; The data preprocessing module is used to correct, enhance, and classify the acquired satellite remote sensing image data, extract key ground object information, and perform format conversion and coordinate unification preprocessing on the geospatial data; The line selection model building module is used to establish a line selection model and use an algorithm to assign weights and conduct a comprehensive evaluation of various factors; The intelligent line selection module is used to input the pre-processed data into the line selection model, use the computing power of the computer to simulate the line selection, and generate candidate line solutions; The scheme evaluation and optimization module is used to evaluate the candidate line schemes one by one according to the preset evaluation index system, the evaluation indexes include engineering cost, topography, safety and reliability, and further optimize and adjust the better schemes; The result output module is used to output the final optimal transmission and transformation line selection plan in the form of a visual map.

[0009] The present invention constructs a complete line selection model, double data weight determination, and perfect data acquisition module, and line selection model construction module. It realizes more flexible and comprehensive evaluation of influencing factors, mines the potential value of data, further improves the intelligence and adaptability of the line selection system, and makes the line selection results more in line with actual engineering needs.

[0010] Furthermore, the specific working steps of the data acquisition module are as follows: S101, clarify data requirements, obtain topographic data of the line planning area according to the objectives and requirements of the transmission and transformation line selection, and analyze the impact of terrain undulation on the difficulty and cost of line construction; S102, select the data source. First, satellite remote sensing data is to obtain image data of the earth's surface by using optical imaging equipment carried on satellites to receive, record and transmit visible light information of various wavelengths; second, geospatial data is to obtain basic geographic data from the geographic information system database, including transportation routes, water system distribution, and administrative divisions. The data can assist in analyzing the relationship between routes and surrounding geographical elements; S103, basic data collection, first, satellite remote sensing data, using commercial satellite data, obtain image data of the required time period and area through data suppliers; second, geospatial data, download geospatial data from the data platform, organize field measurements for some missing data or data that needs to be updated, and use total station equipment to collect spatial location information; S104, data correction processing, for satellite remote sensing images, corrects the radiation errors caused by sensor sensitivity differences, atmospheric scattering and absorption, etc., so that the image grayscale value can truly reflect the reflection or radiation characteristics, providing an accurate data basis for subsequent analysis; S105, optical image data fusion, after obtaining the optical image, the high-resolution panchromatic image is fused with the low-resolution multispectral image to obtain an image with both high spatial resolution and rich spectral information, thus improving the information content and interpretability of the data; S106, data coverage check, by checking whether the satellite remote sensing image has incomplete puzzles or missing geospatial data layers, so as to check whether the collected data covers the predetermined route planning area.

[0011] The present invention utilizes a data acquisition module to obtain the topographic data of the line planning area according to the objectives and requirements of the power transmission and transformation line selection by clarifying the data requirements, selecting the data source, basic data acquisition, data correction processing, optical image data fusion, data coverage inspection, and analyzing the causes of inaccurate data and making corrections or re-acquisition. Finally, the effect of verifying the reliability of the data is achieved, making the acquired data more refined.

[0012] Furthermore, in the data acquisition module, S101, in the step of clarifying data requirements, the total cost model for analyzing the impact of terrain undulation on the difficulty and cost of line construction is as follows: Total cost of power transmission and transformation line construction ,in, is the basic construction cost, is the wiring cost, is the material transportation and land acquisition cost, of which, The calculation formula is as follows. Considering the influence of terrain undulation on line length, the horizontal straight-line distance is The terrain undulation correction coefficient is K, which is related to the complexity of the terrain. It is obtained through statistical analysis of terrain data. For example, in plain areas, k≈1, in hilly areas, K≈1.1~1.3, and in mountainous areas, K≈13.~1.5. The actual line length ; According to the line length L and the average span d, different voltage levels and terrain conditions have different value ranges, such as The average span of the line in the plain area is about , mountain area about , number of towers ; Assume that the average construction cost of a single tower foundation is Consider the increase in the difficulty of foundation construction due to the undulating terrain, such as m=1 for flat terrain and m=1 for moderately undulating terrain. , rugged terrain , then the foundation construction cost ; in, The calculation formula is as follows: the cost of wiring per unit length is Considering the difficulty of cable construction due to the undulating terrain and the increased cost coefficient P of using special technical equipment, the simple terrain , complex terrain , then the wiring cost .

[0013] The total cost model for analyzing the effect of terrain undulation on the difficulty and cost of line construction of the present invention not only considers basic factors, but also dynamically evaluates various factors, such as the impact of foundation construction cost, line stringing cost, material transportation and land acquisition cost assessment on the line, and considers the effect of terrain undulation on the difficulty of line stringing construction using an increased cost coefficient.

[0014] Furthermore, in the data acquisition module, S106, the data coverage check is performed to check whether the satellite remote sensing image is incomplete. The working steps are as follows: A1, vector data acquisition step, select appropriate remote sensing software to acquire vector data of the monitoring area, which can accurately define the boundaries of the area where the transmission and transformation line selection is required; A2, range overlay step, in the selected software, overlay the monitoring area range vector data with the satellite remote sensing image data. Ensure that the coordinate systems of the two are consistent. If they are inconsistent, coordinate conversion is required so that they can accurately correspond under the same spatial reference; A3, the initial step of judging the completeness, visually inspects the superimposed images to see whether the images completely cover the monitoring area. If the image boundary obviously does not reach the monitoring area boundary, or there are large blank areas, it is initially judged that there is an incomplete puzzle problem; A4, distance measurement judgment step, use the distance measurement tool to measure the distance from the edge of the image to the boundary of the monitoring area to accurately determine whether there is an incomplete puzzle.

[0015] Furthermore, in the data acquisition module, S106, in the data coverage check, the steps of checking the missing geospatial data layer are as follows: B1, data layer list sorting step, according to the needs of the power transmission and transformation line selection system, sort out the list of geospatial data layers that should be available, such as terrain layer, land use type layer, traffic line layer, etc., and clarify the data content and function of each layer; B2, data layer loading step, try to load all expected geospatial data layers. The loading methods of data in different formats may be different, and they need to be loaded according to the software operating specifications; B3, layer comparison step, after loading is completed, the actual loaded and displayed layers are compared with the pre-sorted list. If a layer in the list is not displayed in the software interface, or the corresponding layer cannot be found in the layer list, there is a layer missing problem; B4, data path and connection check step. If you suspect that a layer is missing, check whether the storage path of the layer data file is correct. If the path and connection are normal but the layer still cannot be loaded, further confirm that the layer is missing.

[0016] Furthermore, the line selection model construction module includes a model framework construction submodule, an algorithm programming implementation submodule, a verification data preparation submodule, and a model verification submodule. Among them, the model framework builds a submodule, and the model framework adopts a linear programming model based on cost-benefit analysis. The model framework meets the main goal of minimizing the line construction cost and operation cost under the premise of certain safety and environmental requirements. The algorithm programming implementation submodule converts the selected model framework into a specific algorithm and implements it through programming, and gradually searches for the best or better transmission and transformation line selection scheme by continuously comparing the transmission and transformation line selection schemes; The verification data preparation submodule selects a part of the historical power transmission and transformation line project data as a verification data set, which should contain various factors related to line selection and the actual selected line plan, etc., to verify the accuracy and reliability of the line selection model; The model verification submodule inputs the verification data set into the constructed line selection model, runs the model to generate a predicted line plan, and compares and analyzes it with the actual line plan.

[0017] The invention adopts the above technical solution. First, the present invention starts from six dimensions: data acquisition module, data preprocessing module, line selection model construction module, intelligent line selection module, scheme evaluation and optimization module, and result output module, comprehensively considers more factors, and constructs a more complex and intelligent line selection system model to adjust and optimize the line.

[0018] Second, the present invention improves the data acquisition module to ensure data quality. For inaccurate data, the cause is analyzed and correction or re-collection is performed. Finally, the effect of verifying the reliability of the data is achieved, making the acquired data more precise.

[0019] Third, the total cost model for analyzing the impact of terrain undulation on the difficulty and cost of line construction of the present invention not only considers basic factors, but also dynamically evaluates various factors, such as foundation construction cost, line stringing cost, material transportation and land acquisition cost, to evaluate the impact on the line, and considers the cost coefficient of the increase in the difficulty of line stringing construction and the use of special technical equipment due to terrain undulation.

[0020] Fourth, the line selection model construction module of the present invention uses two major data sources in the early stage, namely, satellite remote sensing data and geospatial data, to improve the scientificity, accuracy and intelligence of line selection, reduce the subjectivity of manual intervention, consider various influencing factors more comprehensively, and improve data quality to support more accurate line selection. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] The present invention can be further illustrated by the non-limiting examples given in the accompanying drawings; Figure 1 It is a schematic diagram of the system framework of the present invention; Figure 2 This is a schematic diagram of specific working steps of the data acquisition module of the present invention; Figure 3 A flowchart of the working steps of checking whether a satellite remote sensing image is incomplete in the present invention; Figure 4 This is a flowchart of the steps of checking for missing geospatial data layers of the present invention; Figure 5 A schematic diagram of a module framework for constructing a line selection model of the present invention; Figure 6 A schematic diagram of the working steps of the line selection model construction module of the present invention; Figure 7 A schematic diagram of the influencing factors of the submodules for building the model framework of the present invention; DETAILED DESCRIPTION

[0022] In order to enable those skilled in the art to better understand the present invention, the technical solution of the present invention is further described below in conjunction with the accompanying drawings and embodiments.

[0023] like Figure 1 , Figure 2 , Figure 3 , Figure 4 , Figure 5 , Figure 6 , Figure 7 As shown, the transmission and transformation line selection system based on satellite remote sensing and geospatial data includes a data acquisition module, a data preprocessing module, a line selection model building module, an intelligent line selection module, a scheme evaluation and optimization module, and a result output module; The data acquisition module is used to obtain the topographic remote sensing image data of the selected line area through satellite remote sensing technology, and collect geographic spatial data, wherein the geographic spatial data includes terrain elevation data, geological data, and administrative division data; The data preprocessing module is used to correct, enhance, and classify the acquired satellite remote sensing image data, extract key ground object information, and perform format conversion and coordinate unification preprocessing on the geospatial data; The line selection model building module is used to establish the line selection model and use algorithms to assign weights and conduct comprehensive evaluation on various factors; The intelligent line selection module is used to input the pre-processed data into the line selection model, use the computing power of the computer to simulate the line selection, and generate candidate line plans; The scheme evaluation and optimization module is used to evaluate candidate line schemes one by one according to the preset evaluation index system. The evaluation indexes include engineering cost, topography, safety and reliability, and further optimize and adjust the better schemes. The result output module is used to output the final optimal transmission and transformation line selection plan in the form of a visual map.

[0024] The specific working steps of the data acquisition module are as follows: S101, clarify data requirements, obtain topographic data of the line planning area according to the objectives and requirements of the transmission and transformation line selection, and analyze the impact of terrain undulations on the difficulty and cost of line construction. For example, for areas with complex terrain, high-resolution satellite remote sensing images and high-precision digital elevation model data are required to ensure accurate reflection of terrain details.

[0025] S102, select the data source. First, satellite remote sensing data uses optical imaging equipment on satellites to obtain image data of the earth's surface by receiving, recording and transmitting visible light information of various wavelengths; second, geospatial data is basic geographic data obtained from the geographic information system database, including transportation routes, water system distribution, and administrative divisions. The data can assist in analyzing the relationship between routes and surrounding geographical elements. Among them, the data source can also obtain high-precision topographic mapping data from professional surveying and mapping departments, or use the global positioning system (GPS) to collect control point data on the spot to improve data accuracy.

[0026] S103, basic data collection, first, satellite remote sensing data, use commercial satellite data, and obtain image data of the required time period and area through data suppliers. During the collection process, pay attention to cloud coverage and try to obtain images with no clouds or less clouds to ensure data quality. Second, geospatial data, download geospatial data from the data platform. For some missing or updated data, field measurements can be organized and spatial location information can be collected using total station equipment.

[0027] S104, data correction processing, for satellite remote sensing images, corrects the radiation errors caused by factors such as sensor sensitivity differences, atmospheric scattering and absorption, so that the image grayscale value can truly reflect the reflection or radiation characteristics, providing an accurate data basis for subsequent analysis.

[0028] S105, optical image data fusion, after obtaining the optical image, the high-resolution panchromatic image and the low-resolution multispectral image are fused to obtain an image with both high spatial resolution and rich spectral information, thereby improving the information content and interpretability of the data.

[0029] S106, data coverage check, by checking whether the satellite remote sensing image has incomplete puzzles or missing geospatial data layers, to check whether the collected data covers the predetermined route planning area. For inaccurate data, analyze the cause and make corrections or re-collect, so as to achieve the effect of verifying the reliability of the data.

[0030] In the data acquisition module, S101, in the step of clarifying data requirements, the total cost model for analyzing the impact of terrain undulation on the difficulty and cost of line construction is as follows: Total cost of power transmission and transformation line construction ,in, is the basic construction cost, is the wiring cost, is the material transportation and land acquisition cost, of which, The calculation formula is as follows. Considering the influence of terrain undulation on line length, the horizontal straight-line distance is The terrain undulation correction coefficient is K, which is related to the complexity of the terrain. It is obtained through statistical analysis of terrain data. For example, in plain areas, k≈1, in hilly areas, K≈1.1~1.3, and in mountainous areas, K≈13.~1.5. The actual line length ; According to the line length L and the average span d, different voltage levels and terrain conditions have different value ranges, such as The average span of the line in the plain area is about , mountain area about , number of towers ; Assume that the average construction cost of a single tower foundation is Consider the increase in the difficulty of foundation construction due to the undulating terrain, such as m=1 for flat terrain and m=1 for moderately undulating terrain. , rugged terrain , then the foundation construction cost ; in, The calculation formula is as follows: the cost of wiring per unit length is Considering the difficulty of cable construction due to the undulating terrain and the increased cost coefficient P of using special technical equipment, the simple terrain , complex terrain , then the wiring cost .

[0031] Data acquisition module, S106, data coverage check, check whether the satellite remote sensing image is incomplete. The working steps are as follows: A1, vector data acquisition step, select appropriate remote sensing software to acquire vector data of the monitoring area, which can accurately define the boundaries of the area where the transmission and transformation line selection is required; A2, range overlay step, in the selected software, overlay the monitoring area range vector data with the satellite remote sensing image data. Ensure that the coordinate systems of the two are consistent. If they are inconsistent, coordinate conversion is required so that they can accurately correspond under the same spatial reference; A3, the initial step of judging the completeness, visually inspects the superimposed images to see whether the images completely cover the monitoring area. If the image boundary obviously does not reach the monitoring area boundary, or there are large blank areas, it is initially judged that there is an incomplete puzzle problem; A4, distance measurement judgment step, use the distance measurement tool to measure the distance from the edge of the image to the boundary of the monitoring area to accurately determine whether there is an incomplete puzzle.

[0032] In the data acquisition module, S106, during the data coverage check, the steps for checking the missing geospatial data layer are as follows: B1, data layer list sorting step, according to the needs of the power transmission and transformation line selection system, sort out the list of geospatial data layers that should be available, such as terrain layer, land use type layer, traffic line layer, etc., and clarify the data content and function of each layer; B2, data layer loading step, try to load all expected geospatial data layers. The loading methods of data in different formats may be different, and they need to be loaded according to the software operating specifications; B3, layer comparison step, after loading is completed, the actual loaded and displayed layers are compared with the pre-sorted list. If a layer in the list is not displayed in the software interface, or the corresponding layer cannot be found in the layer list, there is a layer missing problem; B4, data path and connection check step, if a layer is suspected to be missing, check whether the storage path of the layer data file is correct. If the path and connection are normal, but the layer still cannot be loaded, further confirm that the layer is missing. It should be pointed out that sometimes due to data movement, storage device failure, etc., the software cannot correctly read the layer data. In this case, it is not a real layer missing, but a data connection problem.

[0033] The line selection model construction module includes the model framework construction sub-module, algorithm programming implementation sub-module, verification data preparation sub-module, and model verification sub-module. Among them, the model framework builds the submodule. The model framework adopts a linear programming model based on cost-benefit analysis. The model framework meets the main goal of minimizing the line construction cost and operation cost under the premise of certain safety and environmental requirements. The algorithm programming implementation submodule converts the selected model framework into a specific algorithm and implements it through programming. By continuously comparing the transmission and transformation line selection, the optimal or relatively optimal transmission and transformation line selection scheme is gradually searched out; The verification data preparation submodule selects a part of the historical power transmission and transformation line project data as a verification data set. The verification data set should contain various factors related to line selection and the actual selected line plan, etc., to verify the accuracy and reliability of the line selection model; The model verification submodule inputs the verification data set into the constructed line selection model, runs the model to generate the predicted line plan, and compares and analyzes it with the actual line plan. Among them, the model verification submodule uses the terrain deviation index evaluation index to evaluate the degree of conformity between the model prediction results and the actual situation.

[0034] Model verification submodule, the specific verification formula is as follows: The topography deviation index, used to verify the accuracy of the model's assessment of topography, is calculated by quantifying the degree of topography in different areas.

[0035] Topographic Deviation Index = ; in, The model predicts the quantitative value of the topography of the i-th area through which the route passes. is the quantitative value of the topography of the ith area where the actual line passes. It is the total number of areas that the line passes through; the smaller the index is, the closer the model's prediction of the terrain and geomorphic degree is to the actual situation, and the higher the reliability of the model in assessing the terrain and geomorphic degree.

[0036] The line selection model building module also includes an optimization and adjustment submodule. The optimization and adjustment submodule optimizes and adjusts the line selection model according to the model verification results. If it is found that the weight setting of some influencing factors is unreasonable, resulting in a large deviation in the model prediction results, the weights will be readjusted. Through continuous verification and optimization, the performance and reliability of the line selection model can be improved.

[0037] The specific working steps of the line selection model construction module are as follows: S201, construct a basic model framework, use the model framework to build sub-modules, the model framework selects a linear programming model based on cost-benefit analysis, and builds the basic framework of the line selection model to provide a basis for subsequent calculations and optimization.

[0038] S202, model parameter setting and algorithm implementation, using algorithm programming to implement sub-modules, determine the parameters required for the model according to the selected modeling method, and integrate various influencing factors and their weights into the model, and use programming analysis tools to continuously compare the transmission and transformation line selection to gradually search for the optimal or relatively optimal transmission and transformation line selection plan.

[0039] S203, the model is initially run, using the verification data preparation submodule to select a part of the historical power transmission and transformation line project data as a verification data set, and use the data of the verification data set to run the line selection model to obtain a preliminary line plan. Among them, the results are evaluated from multiple dimensions such as engineering feasibility, economic rationality, and terrain friendliness, to check whether the line meets the preset goals and constraints, and analyze the influence of various factors on the line selection results.

[0040] S204, model verification and result evaluation, model verification submodule, inputs the verification data set into the constructed line selection model, runs the model to generate the predicted line plan, and compares and analyzes it with the actual line plan, verifies the final line selection model using actual case data or by simulating different scenarios, and evaluates the reliability and stability of the model. Among them, the performance of the model under different conditions is analyzed, and possible problems are predicted, providing reference and guarantee for actual engineering applications.

[0041] S205, model optimization and adjustment, optimize and adjust the model according to the evaluation results, such as adjusting factor weights, modifying algorithm parameters, supplementing or correcting data, etc., run the model again and evaluate the results, and repeat this process until a line selection plan is obtained.

[0042] The model framework builds a sub-module. The model framework adopts a linear programming model based on cost-benefit analysis. The model framework includes six major factors, including line length factor, terrain complexity factor, terrain avoidance factor, construction cost factor, operation and maintenance difficulty factor, and line construction difficulty factor.

[0043] The data preprocessing module includes: a data format conversion unit, a coordinate system unification unit, and a data cleaning unit.

[0044] Data format conversion unit. Since different data sources provide data in different formats, this unit is responsible for converting data in these different formats into a format that is uniformly supported within the system to ensure that the data can be processed smoothly in subsequent modules.

[0045] Coordinate system unification unit, because the data may come from different surveying and mapping periods, different surveying and mapping units or different spatial reference standards, their coordinate systems may be different. The task of this unit is to unify all data into the same coordinate system to ensure the consistency and accuracy of the data in spatial position, so as to facilitate subsequent spatial analysis and overlay operations. Specifically, the coordinate conversion algorithm and parameters are used to convert data according to the conversion relationship between different coordinate systems.

[0046] Data cleaning unit, which is responsible for identifying and removing duplicate records in the data set, detecting and processing outliers in the data, and processing missing parts in the data.

[0047] Among them, the data cleaning unit includes: a data deduplication subunit, an outlier processing subunit, and a missing value processing subunit.

[0048] The data deduplication subunit marks and deletes duplicate records by comparing key attribute fields of the data.

[0049] The outlier processing subunit corrects or fills the topography by combining the surrounding data.

[0050] The missing value processing subunit is used to perform interpolation estimation based on the attribute values ​​of adjacent features for missing attributes of geospatial data, such as using the inverse distance weighted interpolation method.

[0051] The above is a detailed introduction to the power transmission and transformation line selection system based on satellite remote sensing and geospatial data provided by the present invention. The description of the specific embodiment is only used to help understand the method and core idea of ​​the present invention. It should be pointed out that for ordinary technicians in this technical field, without departing from the principle of the present invention, several improvements and modifications can be made to the present invention, and these improvements and modifications also fall within the scope of protection of the claims of the present invention.

Claims

1. A power transmission and transformation line selection system based on satellite remote sensing and geospatial data, characterized in that: It includes data acquisition module, data preprocessing module, line selection model building module, intelligent line selection module, scheme evaluation and optimization module, and result output module; The data acquisition module is used to acquire the topographic remote sensing image data of the line selection area through satellite remote sensing technology and collect geographic space data; The data preprocessing module is used to correct, enhance, and classify the acquired satellite remote sensing image data, extract key ground object information, and perform format conversion and coordinate unification preprocessing on the geospatial data; The line selection model building module is used to establish a line selection model and use an algorithm to assign weights and conduct a comprehensive evaluation of various factors; The intelligent line selection module is used to input the pre-processed data into the line selection model, use the computing power of the computer to simulate the line selection, and generate candidate line solutions; The scheme evaluation and optimization module is used to evaluate the candidate line schemes one by one according to the preset evaluation index system, the evaluation indexes include engineering cost, topographic influence, safety and reliability, and further optimize and adjust the better schemes; The result output module is used to output the final optimal transmission and transformation line selection plan in the form of a visual map.

2. The power transmission and transformation line selection system based on satellite remote sensing and geographic space data according to claim 1 is characterized in that: The specific working steps of the data acquisition module are as follows: S101, clarify data requirements, obtain topographic data of the line planning area according to the objectives and requirements of the transmission and transformation line selection, and analyze the impact of terrain undulation on the difficulty and cost of line construction; S102, select the data source. First, satellite remote sensing data is to obtain image data of the earth's surface by using optical imaging equipment carried on satellites to receive, record and transmit visible light information of various wavelengths; second, geospatial data is to obtain basic geographic data from the geographic information system database, including transportation routes, water system distribution, and administrative divisions. The data can assist in analyzing the relationship between routes and surrounding geographical elements; S103, basic data collection, first, satellite remote sensing data, using commercial satellite data, obtain image data of the required time period and area through data suppliers; second, geospatial data, download geospatial data from the data platform, organize field measurements for some missing or updated data, and use total station equipment to collect spatial location information; S104, data correction processing, for satellite remote sensing images, corrects the radiation errors caused by sensor sensitivity differences, atmospheric scattering and absorption factors, so that the image gray value can truly reflect the reflection or radiation characteristics, providing an accurate data basis for subsequent analysis; S105, optical image data fusion, after obtaining the optical image, the high-resolution panchromatic image and the low-resolution multispectral image are fused to obtain an image with both high spatial resolution and rich spectral information, thereby improving the information content and interpretability of the data; S106, data coverage check, by checking whether the satellite remote sensing image has incomplete puzzles or missing geospatial data layers, so as to check whether the collected data covers the predetermined route planning area.

3. The power transmission and transformation line selection system based on satellite remote sensing and geospatial data according to claim 2 is characterized in that: In the data acquisition module, S101, in the step of clarifying data requirements, the total cost model for analyzing the impact of terrain undulation on the difficulty and cost of line construction is as follows: Total cost of power transmission and transformation line construction ,in, is the basic construction cost, is the wiring cost, is the material transportation and land acquisition cost, of which, The calculation formula is as follows. Considering the influence of terrain undulation on line length, the horizontal straight-line distance is The terrain undulation correction coefficient is K, which is related to the complexity of the terrain. It is obtained through statistical analysis of terrain data. For example, in plain areas, k≈1, in hilly areas, K≈1.1~1.3, and in mountainous areas, K≈13.~1.

5. The actual line length ; According to the line length L and the average span d, different voltage levels and terrain conditions have different value ranges, such as The average span of the line in the plain area is about , mountain area about , number of towers ; Assume that the average construction cost of a single tower foundation is Consider the increase in the difficulty of foundation construction due to the undulating terrain, such as m=1 for flat terrain and m=1 for moderately undulating terrain. , rugged terrain , then the foundation construction cost ; in, The calculation formula is as follows: the cost of wiring per unit length is Considering the difficulty of cable construction due to the undulating terrain and the increased cost coefficient P of using special technical equipment, the simple terrain , complex terrain , then the wiring cost .

4. The power transmission and transformation line selection system based on satellite remote sensing and geographic space data according to claim 3 is characterized in that: The data acquisition module, S106, in the data coverage check, checks whether the satellite remote sensing image is incomplete. The working steps are as follows: A1, a step of obtaining vector data, obtaining vector data of the monitoring area, which can accurately define the boundary of the area where the transmission and transformation line selection is required; A2, range overlay step, overlays the monitoring area range vector data with the satellite remote sensing image data to ensure that the coordinate systems of the two are consistent. If they are inconsistent, coordinate conversion is required so that they can accurately correspond under the same spatial reference; A3, the initial step of judging the completeness, visually inspects the superimposed images to see whether the images completely cover the monitoring area. If the image boundary obviously does not reach the monitoring area boundary, or there is a large blank area, it is preliminarily judged that there is an incomplete puzzle problem; A4, distance measurement judgment step, use the distance measurement tool to measure the distance from the edge of the image to the boundary of the monitoring area to accurately determine whether there is an incomplete puzzle.

5. The power transmission and transformation line selection system based on satellite remote sensing and geographic space data according to claim 4 is characterized in that: In the data acquisition module, S106, in the data coverage check, the steps of checking the missing geospatial data layer are as follows: B1, data layer list sorting step, sort out the list of geospatial data layers that should be available according to the requirements of the power transmission and transformation line selection system, and clarify the data content and function of each layer; B2, data layer loading step, attempts to load all expected geospatial data layers. Data in different formats are loaded differently; B3, layer comparison step. After loading, compare the layers actually loaded and displayed with the pre-sorted list. If a layer in the list is not displayed in the software interface, or the corresponding layer cannot be found in the layer list, there is a layer missing problem. B4, data path and connection check step. If you suspect that a layer is missing, check whether the storage path of the layer data file is correct. If the path and connection are normal but the layer still cannot be loaded, further confirm that the layer is missing.

6. The power transmission and transformation line selection system based on satellite remote sensing and geographic space data according to claim 5 is characterized in that: The line selection model construction module includes a model framework construction submodule, an algorithm programming implementation submodule, a verification data preparation submodule, and a model verification submodule. Wherein, the model framework builds a submodule, and the model framework adopts a linear programming model based on cost-benefit analysis; The algorithm programming implementation submodule converts the selected model framework into a specific algorithm and implements it through programming, searching for the best or better transmission and transformation line selection scheme by continuously comparing the transmission and transformation line selection schemes; The verification data preparation submodule selects a part of the historical power transmission and transformation line project data as a verification data set, which should contain information on factors related to line selection and the content of the line plan actually selected, to verify the accuracy and reliability of the line selection model; The model verification submodule inputs the verification data set into the constructed line selection model, runs the model to generate a predicted line plan, and compares and analyzes it with the actual line plan.

7. The power transmission and transformation line selection system based on satellite remote sensing and geographic space data according to claim 6 is characterized in that: The specific verification formula of the model verification submodule is as follows: The topography deviation index is used to verify the accuracy of the model's topography assessment and is calculated by quantifying the degree of topography in different areas; Topographic Deviation Index = ; in, The model predicts the quantitative value of the topography of the i-th area through which the route passes. is the quantitative value of the topography of the ith area where the actual line passes. It is the total number of areas that the line passes through; the smaller the index is, the closer the model's prediction of the terrain and geomorphic degree is to the actual situation, and the higher the reliability of the model in assessing the terrain and geomorphic degree.

8. The power transmission and transformation line selection system based on satellite remote sensing and geographic space data according to claim 7 is characterized in that: The line selection model building module also includes an optimization and adjustment submodule. The optimization and adjustment submodule optimizes and adjusts the line selection model according to the model verification results; if it is found that the weight settings of certain influencing factors are unreasonable, resulting in a large deviation in the model prediction results, the weights are readjusted.

9. The power transmission and transformation line selection system based on satellite remote sensing and geographic space data according to claim 8 is characterized in that: The specific working steps of the line selection model construction module are as follows: S201, construct a basic model framework, use the model framework to build submodules, the model framework selects a linear programming model based on cost-benefit analysis, and builds a basic framework for the line selection model to provide a basis for subsequent calculation and optimization; S202, model parameter setting and algorithm implementation, using algorithm programming to implement the submodule, according to the selected modeling method, determine the parameters required for the model, and integrate various influencing factors and their weights into the model, and use programming analysis tools to continuously compare the transmission and transformation line selection to search for the best or better transmission and transformation line selection plan; S203, the model is initially run, using the verification data preparation submodule to select a part of the historical power transmission and transformation line project data as a verification data set, and the line selection model is run using the data of the verification data set to obtain a preliminary line plan; S204, model verification and result evaluation, the model verification submodule inputs the verification data set into the constructed line selection model, runs the model to generate a predicted line plan, and compares and analyzes it with the actual line plan. The final line selection model is verified using actual case data or by simulating different scenarios to evaluate the reliability and stability of the model; S205, model optimization and adjustment, based on the evaluation results, the model is optimized and adjusted by adjusting factor weights, modifying algorithm parameters, supplementing or correcting data, running the model again and evaluating the results, and repeating this process until a line selection plan is obtained.

10. The power transmission and transformation line selection system based on satellite remote sensing and geographic space data according to claim 9, characterized in that: The model framework constructs sub-modules, and the model framework adopts a linear programming model based on cost-benefit analysis. The model framework includes six major factors, including line length factor, terrain complexity factor, terrain avoidance factor, construction cost factor, operation and maintenance difficulty factor, and line construction difficulty factor.

Images