Power grid construction auxiliary decision-making method and system based on remote sensing image

Abstract

The invention discloses a power grid construction auxiliary decision-making method and system based on a remote sensing image, and the method comprises the steps: obtaining a remote sensing image of each geographic region, and carrying out the preprocessing of the remote sensing image, and obtaining a preprocessed image; extracting topographic values and geological values of the corresponding geographic areas from the preprocessed image to obtain corresponding feature area values, and selecting a plurality of areas to be analyzed according to the feature area values of the geographic areas; performing construction difficulty analysis on the power transmission lines in the plurality of areas to be analyzed, and performing construction decision analysis according to a construction difficulty analysis result to obtain construction decision information; and outputting the construction decision information. According to the method, large-area geographic information is rapidly obtained by means of remote sensing images, the early-stage investigation time is shortened, the construction conditions of different areas can be evaluated more accurately, a scientific basis is provided for power grid construction site selection and line planning, influence factors such as terrain, geology and environment are comprehensively considered, and safer construction areas and schemes can be selected.

Description

Technical Field

[0001] The present invention relates to the technical field of power grid construction decision-making, and in particular to a power grid construction auxiliary decision-making method and system based on remote sensing images. Background Art

[0002] Remote sensing technology can obtain image data of large geographical areas and has unique advantages in obtaining geographic information. With the continuous advancement of technology, the resolution and data accuracy of remote sensing images are constantly improving, providing a rich source of data for applications in various fields.

[0003] Power grid construction involves multiple decision-making aspects such as line planning and site selection. Traditional survey and decision-making methods are inefficient and have limited accuracy, and are difficult to meet the rapid development of large-scale power grid construction and the requirements for safety, economy, etc. Advanced technical means are needed to assist decision-making in order to improve construction efficiency, reduce costs, and ensure safety.

[0004] Some existing power grid construction decision-making support systems do not combine multiple regional factors for analysis when in use, and thus cannot provide reasonable decision-making solutions for power grid construction. Summary of the invention

[0005] In view of the shortcomings of the prior art, the present invention provides a remote sensing image-based auxiliary decision-making method and system for power grid construction, which solves the problem that the analysis is not well combined with multiple factors in the region, thus failing to give a reasonable decision-making plan for power grid construction.

[0006] To achieve the above objectives, the present invention is implemented through the following technical solutions:

[0007] According to a first aspect of the present invention, a method for assisting decision-making in power grid construction based on remote sensing images is provided. The method comprises the following steps:

[0008] Acquire remote sensing images of various geographical areas and preprocess them to obtain preprocessed images;

[0009] Extracting terrain values ​​and geological values ​​of corresponding geographical areas from the preprocessed image, thereby obtaining corresponding characteristic area values, and selecting a plurality of areas to be analyzed according to the characteristic area values ​​of each geographical area;

[0010] Performing a construction difficulty analysis on the transmission lines in the plurality of areas to be analyzed, and performing a construction decision analysis based on the construction difficulty analysis results to obtain construction decision information;

[0011] The construction decision information is output.

[0012] Preferably, extracting the topographic value and geological value of the corresponding geographical area from the preprocessed image to obtain the characteristic area value includes:

[0013] The average elevation value, terrain moisture index and terrain ruggedness index of the corresponding geographical area are extracted from the preprocessed image through the digital elevation model DEM, and the terrain value of the corresponding geographical area is obtained by weighted summation of the average elevation value, terrain moisture index and terrain ruggedness index;

[0014] Determine the number of all buildings and the total building area of ​​all buildings in the corresponding geographical area according to the preprocessed image, and then obtain the building distribution value in the corresponding geographical area = the number of buildings ÷ the total building area;

[0015] Determine the regional area and the vegetation coverage area of ​​the corresponding geographical area according to the preprocessed image, and then obtain the vegetation coverage value = vegetation coverage area ÷ regional area;

[0016] Substitute the building distribution value and the vegetation coverage value into the following formula to obtain the geological value:

[0017] Geological value = building distribution value × b1 + vegetation coverage value × b2

[0018] Among them, b1 and b2 are the weight coefficients of building distribution value and vegetation coverage value respectively;

[0019] The topographic values ​​and geological values ​​of each geographical area are summed to obtain the characteristic area value corresponding to each geographical area.

[0020] Preferably, multiple areas to be analyzed are selected according to the characteristic area values ​​of each geographical area, including:

[0021] The characteristic area values ​​of all geographic areas are sorted from small to large, and multiple geographic areas with the smallest characteristic area values ​​are selected as the areas to be analyzed.

[0022] Preferably, performing construction difficulty analysis on the transmission lines in the plurality of areas to be analyzed includes:

[0023] Select the current area to be analyzed as the target object, and plan the transmission line for the current area to be analyzed;

[0024] Select the current transmission line based on the planning results;

[0025] Determine the terrain difficulty coefficient corresponding to the current transmission line;

[0026] Determine the geological difficulty coefficient corresponding to the current transmission line;

[0027] Determine the environmental difficulty coefficient corresponding to the current transmission line;

[0028] The construction difficulty value of the current transmission line is calculated according to the terrain difficulty coefficient, geological difficulty coefficient and environmental difficulty coefficient corresponding to the current transmission line, and then the construction difficulty values ​​of all transmission lines in all areas to be analyzed are obtained.

[0029] Preferably, determining the terrain difficulty coefficient corresponding to the current transmission line includes:

[0030] Through DEM, the elevation data of the construction area corresponding to the current transmission line is extracted from the preprocessed image, and the standard deviation or range of the elevation data is calculated to represent the terrain relief, and then the terrain relief index is determined;

[0031] Through DEM, the slope of the construction area corresponding to the current transmission line is extracted from the preprocessed image, and then the slope index is determined;

[0032] The terrain complexity is calculated by comprehensively considering the terrain relief, slope, and landform type, and then the terrain complexity index is determined:

[0033] Terrain complexity = terrain undulation × slope × landform type coefficient,

[0034] Among them, the landform type coefficient is obtained by assigning values ​​to different landform types;

[0035] The terrain difficulty coefficient corresponding to the current transmission line is obtained by taking the weighted sum of the terrain undulation index, slope index and terrain complexity index.

[0036] Preferably, determining the geological difficulty coefficient corresponding to the current transmission line includes:

[0037] Obtain the soil type of the construction area corresponding to the current transmission line, and then obtain the quantitative value of the soil type;

[0038] Evaluate the geological stability of the construction area corresponding to the current transmission line, and then obtain the quantitative value of geological stability;

[0039] Analyze the flow rate and water level of groundwater in the construction area corresponding to the current transmission line, and then obtain the quantitative value of groundwater conditions;

[0040] The quantified values ​​of soil type, geological stability and groundwater conditions are weighted and summed to obtain the geological difficulty coefficient corresponding to the current transmission line.

[0041] Preferably, determining the environmental difficulty coefficient corresponding to the current power transmission line includes:

[0042] Determine the vegetation coverage area in the construction area corresponding to the current transmission line, and determine the quantified value of vegetation coverage of the target object based on the vegetation coverage area;

[0043] Obtain the biological impact index in the construction area corresponding to the current transmission line and obtain the quantitative value of biological impact;

[0044] Obtain the climate condition index in the construction area corresponding to the current transmission line to obtain the quantitative value of the climate condition; the climate condition index is determined based on the predicted values ​​of temperature, precipitation, and wind speed in the construction area corresponding to the current transmission line during the expected construction period; the predicted values ​​of temperature, precipitation, and wind speed are predicted based on the historical data corresponding to the expected construction period;

[0045] The environmental difficulty coefficient is determined by taking the weighted sum of the quantified values ​​of vegetation coverage, biological impact and climate conditions.

[0046] Preferably, the quantified value of vegetation coverage, the quantified value of biological impact and the quantified value of climate condition are weighted and summed to determine the environmental difficulty coefficient, and further includes:

[0047] Establish the judgment matrix A=[a ij ], where A is a 3×3 matrix, i and j represent any one of the following three environmental difficulty factors: vegetation coverage, biological impact and climate conditions; a ij is an element of the judgment matrix, which indicates the importance of environmental difficulty factor i over environmental difficulty factor j during the expected construction period of the construction area corresponding to the current transmission line;

[0048] Determine the maximum eigenvalue λ of the judgment matrix max Based on the maximum eigenvalue, the consistency ratio CR of the judgment matrix is ​​determined according to the following formula, where CR = (λ max -3) / 1.16;

[0049] Determine whether CR is less than 0.1. If CR is less than 0.1, it is considered that the judgment matrix satisfies the consistency test. Otherwise, adjust the element a of the judgment matrix. ij , until the judgment matrix satisfies the consistency test;

[0050] After normalizing each column in the judgment matrix, the normalized matrix is ​​summed row by row to obtain the weight coefficient of each environmental difficulty factor;

[0051] According to the weight coefficient, the quantified value of vegetation coverage, the quantified value of biological impact and the quantified value of climate conditions are weighted and summed to determine the environmental difficulty coefficient.

[0052] Alternatively, the quantified value of vegetation coverage, the quantified value of biological impact and the quantified value of climate condition are weighted and summed to determine the environmental difficulty coefficient, further comprising:

[0053] Obtain environmental assessment reports, construction difficulty records, and operation and maintenance records of previous transmission line construction in different regions, from which quantified values ​​of vegetation coverage, biological impact, and climate conditions are obtained for each region. Count the number of times that transmission line construction in each region is delayed or encounters problems due to vegetation coverage, biological impact, and climate conditions, and obtain the environmental difficulty coefficient value of the corresponding region based on the number of delays or problems.

[0054] A multivariate linear regression model was established with the environmental difficulty coefficient as the dependent variable and the quantified values ​​of vegetation cover, biological impact and climate conditions as the independent variables.

[0055] The loop multivariate linear regression model is trained using the obtained data of each region to obtain normalized regression coefficients corresponding to the quantified values ​​of vegetation coverage, the quantified values ​​of biological impact and the quantified values ​​of climate conditions as corresponding weight coefficients.

[0056] Preferably, the construction difficulty value of the current transmission line is calculated according to the terrain difficulty coefficient, geological difficulty coefficient and environmental difficulty coefficient corresponding to the current transmission line, and then the construction difficulty values ​​of all transmission lines in all areas to be analyzed are obtained, including:

[0057] The terrain difficulty coefficient, geological difficulty coefficient and environmental difficulty coefficient corresponding to the current transmission line are weighted and summed to obtain the construction difficulty value corresponding to the current transmission line.

[0058] Preferably, a construction decision analysis is performed based on the construction difficulty analysis result to obtain construction decision information, including:

[0059] The construction difficulty values ​​of all transmission lines in each area to be analyzed are sorted from small to large, and the average construction difficulty value corresponding to all transmission lines in each area to be analyzed is calculated, and the average construction difficulty value is used as the measurement standard value of the corresponding area to be analyzed;

[0060] The area to be analyzed with the smallest measurement standard value is selected as the standard construction area, and the transmission line with the smallest construction difficulty value in the standard construction area is selected and marked as the standard construction line to generate construction decision information.

[0061] According to a second aspect of the present invention, there is provided a power grid construction decision support system based on remote sensing images using the method according to the first aspect of the present invention. The system comprises:

[0062] A remote sensing image acquisition unit is used to acquire remote sensing images of various geographical areas and perform preprocessing to obtain preprocessed images;

[0063] A regional comprehensive analysis unit is used to extract the terrain value and geological value of the corresponding geographical area from the preprocessed image, thereby obtaining the corresponding characteristic area value, and selecting a plurality of areas to be analyzed according to the characteristic area value of each geographical area;

[0064] A regional construction analysis unit, configured to perform construction difficulty analysis on the transmission lines in the plurality of areas to be analyzed, and perform construction decision analysis based on the construction difficulty analysis results to obtain construction decision information;

[0065] The construction decision output unit is used to output the construction decision information.

[0066] Compared with the prior art, the present invention has the following beneficial effects:

[0067] The present invention uses remote sensing images to quickly acquire geographic information of a large area, shortens the time for preliminary investigation, and can more accurately evaluate the construction conditions of different regions through precise image processing and comprehensive analysis of multiple factors, providing a scientific basis for decisions such as site selection and line planning for power grid construction. At the same time, the present invention comprehensively considers influencing factors such as topography, geology, and environment to evaluate risks in the early stages of construction, which helps to select safer construction areas and plans. In addition, the present invention can better estimate the weight coefficients corresponding to the quantified values ​​of vegetation coverage, the quantified values ​​of biological impact, and the quantified values ​​of climate conditions through the hierarchical analysis method or multiple linear regression, thereby more accurately determining the environmental difficulty coefficient, so that the generated construction decision information is safer and more reliable. BRIEF DESCRIPTION OF THE DRAWINGS

[0068] Figure 1 A flowchart of a remote sensing image-based decision-making auxiliary method for power grid construction according to the present invention;

[0069] Figure 2 The present invention is a structural block diagram of a power grid construction auxiliary decision-making system based on remote sensing images. DETAILED DESCRIPTION

[0070] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in 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 creative work are within the scope of protection of the present invention.

[0071] Example 1

[0072] This embodiment provides a method for assisting decision-making in power grid construction based on remote sensing images. Figure 1 , the method comprises the following steps:

[0073] Step 1: Obtain remote sensing images of various geographical areas and preprocess them to obtain preprocessed images.

[0074] Remote sensing imagery The acquisition of imagery typically involves the use of remote sensing platforms such as satellites, aircraft, drones, or ground-based sensors that carry various types of sensors to capture images and data of the Earth's surface.

[0075] After acquiring the remote sensing image, the pre-processed image can be obtained through geometric correction, radiation correction and other methods. The geometric correction is mainly processed through the collinear equation model, while the radiation correction is mainly processed through atmospheric correction, radiation calibration and terrain correction.

[0076] Step 2: extracting the terrain value and geological value of the corresponding geographical area from the preprocessed image, thereby obtaining the corresponding characteristic area value, and selecting a plurality of areas to be analyzed according to the characteristic area value of each geographical area.

[0077] Specifically, this step may include the following sub-steps:

[0078] Step 21, extracting the average elevation value, terrain moisture index and terrain ruggedness index of the corresponding geographical area from the preprocessed image through the digital elevation model DEM, and performing weighted summation of the average elevation value, terrain moisture index and terrain ruggedness index to obtain the terrain value of the corresponding geographical area.

[0079] Among them, the geographical area corresponding to the obtained preprocessed image is labeled as i, and i=1, 2, ..., j, where j represents the number of geographical areas. At the same time, the comprehensive values ​​corresponding to different geographical areas i are calculated, and the calculation of the comprehensive values ​​includes terrain values ​​and geological values. A geographical area with any label is taken as the analysis object, and the corresponding terrain value and geological value are further calculated.

[0080] The extraction of the average elevation value of the analysis object includes: obtaining the elevation values ​​of all elevation points in the analysis object through the digital elevation model DEM, and calculating the average of the elevation values ​​of all elevation points as the average elevation value, and the specific calculation formula is: average elevation value = Σ elevation value / number of elevation points.

[0081] In a specific example, suppose there is an analysis area, and several elevation point data in the area are obtained from the digital elevation model, which are 10 meters, 12 meters, 15 meters, 13 meters, and 11 meters. Then the number of points is 5, and the total elevation value is 10+12+15+13+11=61 meters. According to the formula, the average elevation = 61÷5 = 12.2 meters.

[0082] The extraction of the terrain moisture index of the analysis object includes: obtaining the upstream catchment area of ​​the analysis object, and obtaining the local slope corresponding to the analysis object, and substituting the obtained parameters into the formula TWI=ln(α / tanβ) to calculate the terrain moisture index of the analysis object, where TWI is the terrain moisture index, α is the upstream catchment area, and tanβ is the local slope.

[0083] Extraction of the terrain moisture index of the analysis object includes: calculating the terrain ruggedness index of the analysis object according to the formula TRI=(Σ|ΔE|) / 2n, where ΔE is the elevation difference between adjacent cells and n is the total number of edges.

[0084] In a specific example, suppose there is a 3×3 cell area (9 cells in total) to represent part of the analysis object area, and the elevation differences ΔE of adjacent cells are: 1 meter, 1.2 meters, 0.8 meters, 0.9 meters, etc. (assuming there are 12 groups of adjacent cells in total, that is, n=12). Then first calculate Σ|ΔE|=1+1.2+0.8+0.9+... (the sum of the absolute values ​​of the elevation differences of other adjacent cells), assuming the result is 10 meters, then TRI=(Σ|ΔE|) / 2n=10 / (2×12)=0.42 (keep two decimal places).

[0085] Substitute the obtained average elevation value, terrain humidity index, and terrain ruggedness index into the following formula to calculate the terrain value corresponding to the analysis object:

[0086] Terrain value = average elevation value × a1 + terrain humidity index × a2 + terrain ruggedness index × a3

[0087] Among them, a1, a2 and a3 are the weight coefficients corresponding to the average elevation value, terrain moisture index and terrain ruggedness index respectively. The specific values ​​are set by the operator or the expert system.

[0088] Step 22, determining the building distribution value and the vegetation coverage value in the corresponding geographical area according to the preprocessed image, and performing weighted summation on the building distribution value and the vegetation coverage value to obtain the geological value of the corresponding geographical area.

[0089] Determination of the building distribution value in the corresponding geographical area includes: obtaining all the buildings in the analysis object, and obtaining the corresponding quantity, and obtaining the building area corresponding to all the buildings, and then substituting the quantity and building area into the formula building distribution value = building quantity ÷ area, and further calculating to obtain the building distribution value corresponding to the analysis.

[0090] Determining the vegetation coverage value in the corresponding geographical area includes: obtaining all vegetation in the analysis object, obtaining the vegetation coverage area, and obtaining the regional area corresponding to the analysis, and then substituting the vegetation coverage area and the regional area into the formula vegetation coverage value = vegetation coverage area ÷ regional area.

[0091] Then substitute the building distribution value and vegetation coverage value into the following formula to get the address value:

[0092] The geological value is calculated by geology value = building distribution value × b1 + × vegetation coverage value b2.

[0093] Among them, b1 and b2 are the weight coefficients of building distribution value and vegetation coverage value respectively, and the specific values ​​are set by the operator or expert system.

[0094] In a specific example, suppose that in a study area, after measurement and calculation, the building distribution value is 0.3, the vegetation coverage value is 0.7, the operator sets the weight coefficient b1 of the building distribution value to 0.4, and the weight coefficient b2 of the vegetation coverage value to 0.6. Substituting the above data into the formula, we can obtain: geological value = 0.3×0.4+0.7×0.6=0.12+0.42=0.54.

[0095] Step 23, summing the terrain value and geological value of each geographical area to obtain the characteristic area value corresponding to each geographical area.

[0096] Specifically, the calculated terrain value and geological value are summed to obtain the regional value of the current analysis object, and the regional values ​​of all different geographical areas are calculated in this way.

[0097] Step 24, sorting the characteristic area values ​​of all geographical areas from small to large, and selecting multiple geographical areas with the smallest characteristic area values ​​as areas to be analyzed.

[0098] In this step, preferably, three geographical areas with the smallest area values ​​are selected and recorded as areas to be analyzed.

[0099] Step 3: Conduct construction difficulty analysis on the transmission lines within the multiple areas to be analyzed, conduct construction decision analysis based on the construction difficulty analysis results, and obtain construction decision information.

[0100] Generally speaking, this step includes analyzing the acquired area to be analyzed, analyzing the construction difficulty corresponding to the transmission lines in the area to be analyzed, comprehensively selecting standard construction areas, further performing construction decision analysis based on the construction difficulty of different transmission lines, and generating construction decision information.

[0101] Specifically, this step includes the following sub-steps:

[0102] Step 31, selecting the current area to be analyzed as the target object, performing power transmission line planning on the current area to be analyzed, and selecting the current power transmission line from the planning results.

[0103] Taking the case of three areas to be analyzed as an example, each area to be analyzed is labeled as o, and o=1, 2, 3. Then, the transmission line planning is performed corresponding to the area to be analyzed o, and the planning result is recorded as Lo.

[0104] Step 32, determining the terrain difficulty coefficient corresponding to the current transmission line.

[0105] This step specifically includes:

[0106] Obtain the terrain undulation of the target object, obtain the elevation data of the construction area through the digital elevation model (DEM), calculate the standard deviation or range of the elevation to represent the terrain undulation, and assign different values ​​according to the terrain undulation and record it as F, such as 0-50 meters for low undulation, 50-100 meters for medium undulation, and above 100 meters for high undulation, and assign different values ​​respectively, such as 1 for low undulation, 2 for medium undulation, and 3 for high undulation. Then, based on the DEM data, calculate the slope of the construction area, and assign the slope as P, such as 0-5 degrees for low slope, 5-15 degrees for medium slope, and above 15 degrees for high slope. Different values ​​are assigned to them respectively, such as 1 for low slope, 2 for medium slope and 3 for high slope. The terrain complexity index is calculated by comprehensively considering the terrain relief, slope and landform type, and the landform type is assigned a value. The landform type coefficient can be set according to different landform types, such as 1 for plain, 2 for hill and 3 for mountain. The terrain complexity index is divided into different intervals and assigned different values ​​respectively. The calculation formula is terrain complexity index = terrain relief × slope × landform type coefficient. At the same time, the obtained quantitative indicators are weighted and summed, and the quantitative indicators here are expressed as the quantified value of terrain relief, the quantified value of slope and the quantified value of terrain complexity. At the same time, the quantitative indicators are substituted into the formula terrain difficulty coefficient = quantified value of terrain relief × weight 1 + quantified value of slope × weight 2 + quantified value of terrain complexity × weight 3 to calculate the terrain difficulty coefficient.

[0107] In a specific example, for example, the terrain undulation of a construction area is medium undulation, with a quantized value of 2; the slope is high slope, with a quantized value of 3; the terrain complexity is medium complexity, with a quantized value of 2. If the weight of the terrain undulation is 0.3, the weight of the slope is 0.4, and the weight of the terrain complexity is 0.3, then the terrain difficulty coefficient of the area is:

[0108] Terrain difficulty coefficient = 2×0.3+3×0.4+2×0.3=0.6+1.2+0.6=2.4.

[0109] Step 33, determining the geological difficulty coefficient corresponding to the current transmission line.

[0110] This step specifically includes:

[0111] The soil type in the target object is obtained, and different soil types are assigned to obtain soil type quantitative values. For example, the clay difficulty value is 2, the sand difficulty value is 3, and the rock difficulty value is 5. At the same time, the geological stability corresponding to the target object is judged, and the geological stability quantitative value is assigned according to the judgment result. For example, it is graded according to the degree of geological disaster risk, low risk is 1, medium risk is 3, and high risk is 5. Then the groundwater situation of the target object is analyzed, and different groundwater flow rates and groundwater levels are assigned to obtain groundwater situation quantitative values. For example, low groundwater level and slow water flow is 1, medium groundwater level is 2, and high groundwater level or fast water flow is 3. At the same time, the obtained quantitative indicators are substituted into the formula geological difficulty coefficient = soil type quantitative value × soil type weight + geological stability quantitative value × geological stability weight + groundwater situation quantitative value × groundwater situation weight, and the geological difficulty coefficient of the target object is calculated.

[0112] In a specific example, for example, the soil type of a certain area is sandy soil, the geological stability is medium risk, and the groundwater situation is medium groundwater level, then the geological difficulty coefficient of the area is: 3×0.3+3×0.4+2×0.3=0.9+1.2+0.6=2.7.

[0113] Step 34, determining the environmental difficulty coefficient corresponding to the current transmission line.

[0114] This step specifically includes:

[0115] Step 341, determining the vegetation coverage area in the construction area corresponding to the current transmission line, and determining the quantized value of the vegetation coverage of the target object based on the vegetation coverage area.

[0116] As an example, the coverage level of the target object can be judged based on the vegetation coverage area, and at the same time, quantified values ​​of vegetation coverage can be obtained by assigning values ​​according to different coverage levels. For example, the vegetation coverage can be divided into different levels, such as low coverage (0%-30%) is assigned a value of 1, medium coverage (30%-60%) is assigned a value of 2, and high coverage (above 60%) is assigned a value of 3.

[0117] Step 342, obtaining the biological impact index in the construction area corresponding to the current transmission line, and obtaining the biological impact quantification value;

[0118] As an example, the biological impact index is determined by judging whether there are rare wildlife habitats, migration channels, etc. within the target object, and the corresponding assignment is performed to obtain a quantitative value of the biological impact. For example, a value of 1 is assigned if there is no wildlife impact, a value of 2 is assigned if there is general wildlife impact, and a value of 3 is assigned if there is rare wildlife impact.

[0119] Step 343, obtain the climate condition index in the construction area corresponding to the current transmission line, and obtain the quantitative value of the climate condition; the climate condition index is determined based on the temperature, precipitation, and wind speed forecast values ​​of the construction area corresponding to the current transmission line during the expected construction period; the temperature, precipitation, and wind speed forecast values ​​are predicted based on the historical data corresponding to the expected construction period.

[0120] As an example, by assigning quantization according to different climate conditions, a quantized value of the climate conditions can be obtained. For example, a mild climate is assigned a value of 1, a relatively severe climate (such as high temperature, heavy rain, strong wind, etc. that occur occasionally) is assigned a value of 2, and a severe climate (such as high temperature, heavy rain, strong wind, etc. that occur frequently) is assigned a value of 3.

[0121] Step 344, performing weighted summation on the quantified value of vegetation coverage, the quantified value of biological impact and the quantified value of climate condition to determine the environmental difficulty coefficient.

[0122] Substitute the obtained quantitative indicators into the formula environmental difficulty coefficient = quantified value of vegetation coverage × vegetation coverage weight + quantified value of biological impact × biological impact weight + quantified value of climate conditions × climate condition weight to calculate the environmental difficulty coefficient, where the weight coefficients of vegetation coverage weight, wildlife impact weight and climate condition weight are set by the operator, and the specific weight coefficients can be determined by the analytic hierarchy process (AHP) or the multiple linear regression method.

[0123] In the hierarchical analysis method, the environmental difficulty coefficient can be used as the target layer, and the vegetation coverage, wildlife impact and climate conditions can be used as the criterion layer. The 1-9 scaling method of AHP can be used to determine the relative importance of the criterion layer factors when compared pairwise. Among them, 1 means that the two factors are equally important; 3 means that one factor is slightly more important than the other; 5 means that one factor is obviously more important than the other; 7 means that one factor is strongly more important than the other; 9 means that one factor is extremely important than the other. 2, 4, 6, and 8 represent the middle values ​​of adjacent judgments. The judgment matrix is ​​constructed by comparing the relative importance of the criterion layer factors pairwise. In this embodiment, since there are three environmental difficulty factors, namely vegetation coverage, biological impact and climate conditions, the judgment matrix A is a 3×3 matrix, which can be expressed as [a ij ], where i and j represent any one of the above three environmental difficulty factors. ij is an element of the judgment matrix, indicating the importance of environmental difficulty factor i over environmental difficulty factor j during the expected construction period of the construction area corresponding to the current transmission line.

[0124] Comparison between vegetation coverage and wildlife impact: A team of relevant experts (including power engineering experts, ecologists, etc.) can be organized for in-depth discussion and evaluation. For example, experts consider that in certain specific areas, the activities of wild animals may cause problems such as damage to construction equipment and affect the construction progress (such as the need to set up special protective measures or wait for the wildlife migration season) to the construction of transmission lines, while vegetation coverage mainly affects the preliminary channel clearing work of construction. After comprehensive consideration, if it is considered that the impact of wildlife is slightly more important than vegetation coverage in terms of the environmental difficulty of transmission line construction, then the corresponding element in the judgment matrix is ​​assigned a value of 3. Conversely, if experts believe that vegetation coverage has a greater impact on construction difficulty in some cases, such as in areas with dense vegetation and difficult to clear, which may lead to a significant increase in construction costs, and local wildlife has little interference with construction, then the element of vegetation coverage relative to wildlife impact is assigned a value of 1 / 3;

[0125] Comparison between wildlife impact and climate conditions: Considering that the activity patterns of wildlife may be affected to a certain extent by climate conditions, and the universal impact of climate conditions on construction. For example, in cold and snowy areas, wildlife may hibernate or migrate, causing relatively little interference to construction, while severe climate conditions seriously hinder construction. In this case, the element comparing wildlife impact and climate conditions can be assigned a value of 1 / 3, indicating that climate conditions are slightly more important than wildlife impact. However, in some ecological protection areas, there are many species of wildlife and they are extremely sensitive to habitats, while climate conditions are relatively mild, so this element can be assigned a value of 3, indicating that wildlife impact is slightly more important than climate conditions;

[0126] Assuming that we have three criterion layer factors: vegetation coverage (A), biological influence (B), and climate conditions (C), the judgment matrix constructed based on the above pairwise comparison results is shown in the numerical part of Table I:

[0127] Table I

[0128]

[0129]

[0130] In order to ensure the rationality of the judgment matrix, a consistency check is required. The consistency index CI (Consistency Index) is calculated and compared with the average random consistency index RI (Random Index). Among them, for the third-order judgment matrix, the common value of RI is about 0.58; the consistency index CI can be obtained by determining the maximum eigenvalue λ of the judgment matrix. max , and based on the maximum eigenvalue λ max Specifically, CI = (λ max-n) / (n-1), n ​​is the order of the judgment matrix (3 in this example). Based on CI and RI, if the consistency ratio CR=CI / RI=(λ max -3) / 1.16 is less than 0.1, the judgment matrix is ​​considered to have satisfactory consistency and the obtained weight coefficient is reasonable; otherwise, the judgment matrix needs to be readjusted until it meets the consistency test standard (that is, CR is less than 0.1).

[0131] According to the judgment matrix that meets the consistency test standard, the weight coefficients corresponding to the quantified values ​​of vegetation coverage, biological impact and climate conditions can be determined. The specific method is: after normalizing each column in the judgment matrix, the normalized matrix is ​​summed up by row to obtain the weight coefficients of each environmental difficulty factor (i.e., vegetation coverage, biological impact and climate conditions).

[0132] In the multivariate linear regression method, the determination of the weight coefficients corresponding to the quantified values ​​of vegetation cover, the quantified values ​​of biological impact and the quantified values ​​of climate conditions may include:

[0133] First, obtain environmental assessment reports, construction difficulty records, and operation and maintenance records for transmission line construction in different regions. Among them, these projects can be distributed in different vegetation coverage areas, near wildlife habitats, and in areas with different climatic conditions. From these previous data, we can obtain the quantitative values ​​of vegetation coverage, biological impact, and climatic conditions in each region, and count the number of times the transmission line construction in each region was delayed or encountered problems due to vegetation coverage factors, biological impact factors, and climatic conditions. Based on these times, the environmental difficulty coefficient value of the corresponding area can be obtained.

[0134] Next, a multivariate linear regression model was established with the environmental difficulty coefficient as the dependent variable and the quantified values ​​of vegetation cover, biological impact and climate conditions as independent variables. The quantified value of biological impact can be determined by considering the wildlife activity intensity index, the number of rare wildlife species, etc.; the quantified value of climate conditions can be determined by considering the annual average temperature, annual precipitation and maximum wind speed.

[0135] Subsequently, the loop multiple linear regression model is trained using the obtained data of each region to obtain normalized regression coefficients corresponding to the quantified values ​​of vegetation coverage, the quantified values ​​of biological impact and the quantified values ​​of climate conditions as corresponding weight coefficients.

[0136] It should be noted that, in addition to the multiple linear regression model, an association model can also be established, for example, by using logistic regression or other suitable regression analysis methods. In addition, during training, an independent validation data set can be used to verify the established association model. For example, the collected data is divided into a training set and a validation set according to a certain ratio (such as 70% for the training set and 30% for the validation set). The model is established by the training set, and then the accuracy, goodness of fit and other indicators of the model are evaluated on the validation set. If the model performance is not ideal, the model is optimized, such as adding or deleting independent variables, using variable transformation methods (such as logarithmic transformation, square transformation, etc.) to improve the linear or nonlinear relationship between variables, and the weight coefficient of each factor is determined according to the optimized association model. For example, in the final multiple linear regression model, the relative importance weight of each factor is determined by standardizing (normalizing) the regression coefficient. Assume that the standardized regression coefficient of vegetation coverage related indicators is 0.25, the standardized regression coefficient of wildlife impact related indicators is 0.35, and the standardized regression coefficient of climate condition related indicators is 0.4, which are used as weight coefficients corresponding to the quantified values ​​of vegetation coverage, biological impact, and climate condition. In this way, for example, the vegetation coverage of a construction area is medium, the wildlife has a general impact, and the climate conditions are relatively severe. Then the environmental difficulty coefficient of the area is: 2×0.25+2×0.35+2×0.4=0.5+0.7+0.8=2.

[0137] Through the hierarchical analysis method or the multiple regression method, the influence of various environmental difficulty factors on the construction of transmission lines can be obtained more scientifically, so as to more accurately determine the environmental difficulty coefficient, so as to make the generated construction decision information safer and more reliable.

[0138] Step 35, calculating the construction difficulty value of the current transmission line according to the terrain difficulty coefficient, geological difficulty coefficient and environmental difficulty coefficient corresponding to the current transmission line, and then obtaining the construction difficulty values ​​of all transmission lines in all areas to be analyzed.

[0139] Specifically, the calculated terrain difficulty coefficient, geological difficulty coefficient and environmental parameter coefficient are substituted into the following formula to obtain the current construction difficulty value of the transmission line:

[0140] Construction difficulty value = terrain difficulty coefficient × 0.3 + geological difficulty coefficient × 0.2 + environmental difficulty coefficient × 0.2 + socio-economic difficulty coefficient × 0.3; Construction difficulty value = terrain difficulty coefficient × 0.4 + geological difficulty coefficient × 0.3 + environmental difficulty coefficient × 0.3.

[0141] By analogy, the construction difficulty values ​​of all transmission lines in all areas to be analyzed can be obtained.

[0142] Step 36, sort the construction difficulty values ​​of all transmission lines in each area to be analyzed from small to large, calculate the average construction difficulty corresponding to all transmission lines in each area to be analyzed, and use the average construction difficulty as the measurement standard value corresponding to the area to be analyzed.

[0143] Step 37, select the area to be analyzed with the smallest measurement standard value as the standard construction area, select the transmission line with the smallest construction difficulty value in the standard construction area as the standard construction line, and generate construction decision information.

[0144] The generated decision information can be sent to corresponding operators and displayed.

[0145] Step 4: Output construction decision information.

[0146] It should be noted that the contents not described in detail in this specification belong to the prior art known to those skilled in the art.

[0147] Example 2

[0148] This embodiment provides a power grid construction decision-making support system based on remote sensing images. Figure 2 The system comprises:

[0149] A remote sensing image acquisition unit is used to acquire remote sensing images of various geographical areas and perform preprocessing to obtain preprocessed images;

[0150] A regional comprehensive analysis unit is used to extract the terrain value and geological value of the corresponding geographical area from the preprocessed image, thereby obtaining the corresponding characteristic area value, and selecting a plurality of areas to be analyzed according to the characteristic area value of each geographical area;

[0151] The regional construction analysis unit is used to perform construction difficulty analysis on the transmission lines in the multiple areas to be analyzed, perform construction decision analysis based on the construction difficulty analysis results, and obtain construction decision information.

[0152] The construction decision output unit is used to output the construction decision information.

[0153] The above embodiments are only used to illustrate the technical method of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical method of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical method of the present invention.

[0154] The beneficial effect of the present invention is that, compared with the prior art, the present invention can quickly obtain large-area geographic information with the help of remote sensing images, shorten the preliminary survey time, and through precise image processing and multi-factor comprehensive analysis, can more accurately evaluate the construction conditions of different regions, and provide a scientific basis for decisions such as site selection and line planning for power grid construction; at the same time, the present invention comprehensively considers influencing factors such as topography, geology, and environment, and evaluates risks in the early stage of construction, which helps to select safer construction areas and plans; in addition, the present invention can better estimate the weight coefficients corresponding to the quantified values ​​of vegetation coverage, the quantified values ​​of biological impact, and the quantified values ​​of climate conditions through hierarchical analysis method or multivariate linear regression, thereby more accurately determining the environmental difficulty coefficient, so that the generated construction decision information is safer and more reliable.

[0155] The present disclosure may be a system, a method and / or a computer program product. The computer program product may include a computer-readable storage medium carrying computer-readable program instructions for causing a processor to implement various aspects of the present disclosure.

[0156] A computer-readable storage medium may be a tangible device that can hold and store instructions used by an instruction execution device. A computer-readable storage medium may be, for example, but not limited to, an electrical storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing. More specific examples of computer-readable storage media (a non-exhaustive list) include: a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a static random access memory (SRAM), a portable compact disk read-only memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a floppy disk, a mechanical encoding device, such as a punch card or a raised structure in a groove on which instructions are stored, and any suitable combination of the foregoing. As used herein, a computer-readable storage medium is not to be interpreted as a transient signal per se, such as a radio wave or other freely propagating electromagnetic wave, an electromagnetic wave propagating through a waveguide or other transmission medium (e.g., a light pulse through a fiber optic cable), or an electrical signal transmitted through a wire.

[0157] The computer-readable program instructions described herein can be downloaded from a computer-readable storage medium to each computing / processing device, or downloaded to an external computer or external storage device via a network, such as the Internet, a local area network, a wide area network, and / or a wireless network. The network can include copper transmission cables, optical fiber transmissions, wireless transmissions, routers, firewalls, switches, gateway computers, and / or edge servers. The network adapter card or network interface in each computing / processing device receives the computer-readable program instructions from the network and forwards the computer-readable program instructions for storage in the computer-readable storage medium in each computing / processing device.

[0158] The computer program instructions for performing the operation of the present disclosure may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-related instructions, microcode, firmware instructions, state setting data, or source code or object code written in any combination of one or more programming languages, including object-oriented programming languages, such as Smalltalk, C++, etc., and conventional procedural programming languages, such as "C" language or similar programming languages. Computer-readable program instructions may be executed completely on a user's computer, partially on a user's computer, as an independent software package, partially on a user's computer, partially on a remote computer, or completely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer via any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., using an Internet service provider to connect via the Internet). In some embodiments, an electronic circuit, such as a programmable logic circuit, a field programmable gate array (FPGA), or a programmable logic array (PLA), may be customized by utilizing the state information of the computer-readable program instructions, and the electronic circuit may execute the computer-readable program instructions, thereby realizing various aspects of the present disclosure.

[0159] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents, and any modifications or equivalent replacements that do not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A power grid construction decision-making assistance method based on remote sensing images, characterized in that: The steps include: Acquire remote sensing images of various geographical areas and preprocess them to obtain preprocessed images; Extracting terrain values ​​and geological values ​​of corresponding geographical areas from the preprocessed image, thereby obtaining corresponding characteristic area values, and selecting a plurality of areas to be analyzed according to the characteristic area values ​​of each geographical area; Performing a construction difficulty analysis on the transmission lines in the plurality of areas to be analyzed, and performing a construction decision analysis based on the construction difficulty analysis results to obtain construction decision information; The construction decision information is output.

2. The method for assisting decision-making in power grid construction based on remote sensing images according to claim 1, characterized in that: Extract the terrain and geological values ​​of the corresponding geographical area from the preprocessed image, and then obtain the characteristic area values, including: The average elevation value, terrain moisture index and terrain ruggedness index of the corresponding geographical area are extracted from the preprocessed image through the digital elevation model DEM, and the terrain value of the corresponding geographical area is obtained by weighted summation of the average elevation value, terrain moisture index and terrain ruggedness index; among which: Average elevation value = Σ elevation value / number of elevation points; Terrain wetness index TWI = ln(α / tanβ), Terrain Roughness Index TRI = (Σ|ΔE|) / 2n, α is the upstream catchment area, tanβ is the local slope, ΔE is the elevation difference between adjacent cells, and n is the total number of edges; According to the preprocessed image, the building distribution value and the vegetation coverage value in the corresponding geographical area are determined, and the building distribution value and the vegetation coverage value are weighted and summed to obtain the geological value of the corresponding geographical area; wherein: Building distribution value = number of buildings ÷ total building area; Vegetation coverage value = vegetation coverage area ÷ regional area; The topographic values ​​and geological values ​​of each geographical area are summed to obtain the characteristic area value corresponding to each geographical area.

3. The method for assisting decision-making in power grid construction based on remote sensing images according to claim 1, characterized in that: Select multiple areas to be analyzed based on the characteristic area values ​​of each geographical area, including: The characteristic area values ​​of all geographic areas are sorted from small to large, and multiple geographic areas with the smallest characteristic area values ​​are selected as the areas to be analyzed.

4. The method for assisting decision-making in power grid construction based on remote sensing images according to claim 1, characterized in that: Conducting a construction difficulty analysis on the transmission lines in the plurality of areas to be analyzed, including: Select the current area to be analyzed as the target object, plan the transmission line for the current area to be analyzed, and select the current transmission line according to the planning results; Determine the terrain difficulty coefficient corresponding to the current transmission line; Determine the geological difficulty coefficient corresponding to the current transmission line; Determine the environmental difficulty coefficient corresponding to the current transmission line; The construction difficulty value of the current transmission line is calculated according to the terrain difficulty coefficient, geological difficulty coefficient and environmental difficulty coefficient corresponding to the current transmission line, and then the construction difficulty values ​​of all transmission lines in all areas to be analyzed are obtained.

5. The method for assisting decision-making in power grid construction based on remote sensing images according to claim 4, characterized in that: Determine the terrain difficulty coefficient corresponding to the current transmission line, including: Through DEM, the elevation data of the construction area corresponding to the current transmission line is extracted from the preprocessed image, and the standard deviation or range of the elevation data is calculated to represent the terrain relief, and then the terrain relief index is determined; Through DEM, the slope of the construction area corresponding to the current transmission line is extracted from the preprocessed image, and then the slope index is determined; The terrain complexity is calculated by comprehensively considering the terrain relief, slope, and landform type, and then the terrain complexity index is determined: Terrain complexity = terrain undulation × slope × landform type coefficient, Among them, the landform type coefficient is obtained by assigning values ​​to different landform types; The terrain difficulty coefficient corresponding to the current transmission line is obtained by taking the weighted sum of the terrain undulation index, slope index and terrain complexity index.

6. The method for assisting decision-making in power grid construction based on remote sensing images according to claim 4, characterized in that: Determine the geological difficulty coefficient corresponding to the current transmission line, including: Obtain the soil type of the construction area corresponding to the current transmission line, and then obtain the quantitative value of the soil type; Evaluate the geological stability of the construction area corresponding to the current transmission line, and then obtain the quantitative value of geological stability; Analyze the flow rate and water level of groundwater in the construction area corresponding to the current transmission line, and then obtain the quantitative value of groundwater conditions; The quantified values ​​of soil type, geological stability and groundwater conditions are weighted and summed to obtain the geological difficulty coefficient corresponding to the current transmission line.

7. The method for assisting decision-making in power grid construction based on remote sensing images according to claim 4, characterized in that: Determine the environmental difficulty coefficient corresponding to the current transmission line, including: Determine the vegetation coverage area in the construction area corresponding to the current transmission line, and determine the quantified value of vegetation coverage of the target object based on the vegetation coverage area; Obtain the biological impact index in the construction area corresponding to the current transmission line and obtain the quantitative value of biological impact; Obtain the climate condition index in the construction area corresponding to the current transmission line to obtain the quantitative value of the climate condition; the climate condition index is determined based on the predicted values ​​of temperature, precipitation, and wind speed in the construction area corresponding to the current transmission line during the expected construction period; the predicted values ​​of temperature, precipitation, and wind speed are predicted based on the historical data corresponding to the expected construction period; The environmental difficulty coefficient is determined by taking the weighted sum of the quantified values ​​of vegetation coverage, biological impact and climate conditions.

8. The method for assisting decision-making in power grid construction based on remote sensing images according to claim 7, characterized in that: The environmental difficulty coefficient is determined by weighted summing of the quantified values ​​of vegetation cover, biological impact and climate conditions, and also includes: Establish the judgment matrix A=[a ij ], where A is a 3×3 matrix, i and j represent any one of the following three environmental difficulty factors: vegetation coverage, biological impact, and climate conditions; a ij is an element of the judgment matrix, which indicates the importance of environmental difficulty factor i over environmental difficulty factor j during the expected construction period of the construction area corresponding to the current transmission line; Determine the maximum eigenvalue λ of the judgment matrix max Based on the maximum eigenvalue, the consistency ratio CR of the judgment matrix is ​​determined according to the following formula, where CR = (λ max -3) / 1.16; Determine whether CR is less than 0.

1. If CR is less than 0.1, it is considered that the judgment matrix satisfies the consistency test. Otherwise, adjust the element a of the judgment matrix. ij , until the judgment matrix satisfies the consistency test; After normalizing each column in the judgment matrix, the normalized matrix is ​​summed row by row to obtain the weight coefficient of each environmental difficulty factor; According to the weight coefficient, the quantified value of vegetation coverage, the quantified value of biological impact and the quantified value of climate conditions are weighted and summed to determine the environmental difficulty coefficient.

9. The method for assisting decision-making in power grid construction based on remote sensing images according to claim 7, characterized in that: The environmental difficulty coefficient is determined by weighted summing of the quantified values ​​of vegetation cover, biological impact and climate conditions, and also includes: Obtain environmental assessment reports, construction difficulty records, and operation and maintenance records of previous transmission line construction in different regions, from which quantified values ​​of vegetation coverage, biological impact, and climate conditions are obtained for each region. Count the number of times that transmission line construction in each region is delayed or encounters problems due to vegetation coverage, biological impact, and climate conditions, and obtain the environmental difficulty coefficient value of the corresponding region based on the number of delays or problems. A multivariate linear regression model was established with the environmental difficulty coefficient as the dependent variable and the quantified values ​​of vegetation cover, biological impact and climate conditions as the independent variables. The loop multivariate linear regression model is trained using the obtained data of each region to obtain normalized regression coefficients corresponding to the quantified values ​​of vegetation coverage, the quantified values ​​of biological impact and the quantified values ​​of climate conditions as corresponding weight coefficients.

10. The method for assisting decision-making in power grid construction based on remote sensing images according to claim 4, characterized in that: The construction difficulty value of the current transmission line is calculated according to the terrain difficulty coefficient, geological difficulty coefficient and environmental difficulty coefficient corresponding to the current transmission line, and then the construction difficulty values ​​of all transmission lines in all areas to be analyzed are obtained, including: The terrain difficulty coefficient, geological difficulty coefficient and environmental difficulty coefficient corresponding to the current transmission line are weighted and summed to obtain the construction difficulty value corresponding to the current transmission line.

11. The method for assisting decision-making in power grid construction based on remote sensing images according to claim 4, characterized in that: According to the construction difficulty analysis results, construction decision analysis is carried out to obtain construction decision information, including: The construction difficulty values ​​of all transmission lines in each area to be analyzed are sorted from small to large, and the average construction difficulty value corresponding to all transmission lines in each area to be analyzed is calculated, and the average construction difficulty value is used as the measurement standard value of the corresponding area to be analyzed; The area to be analyzed with the smallest measurement standard value is selected as the standard construction area, and the transmission line with the smallest construction difficulty value in the standard construction area is selected and marked as the standard construction line to generate construction decision information.

12. A power grid construction decision support system based on remote sensing images using the method according to any one of claims 1 to 11, characterized in that: include: A remote sensing image acquisition unit is used to acquire remote sensing images of various geographical areas and perform preprocessing to obtain preprocessed images; A regional comprehensive analysis unit is used to extract the terrain value and geological value of the corresponding geographical area from the preprocessed image, thereby obtaining the corresponding characteristic area value, and selecting a plurality of areas to be analyzed according to the characteristic area value of each geographical area; A regional construction analysis unit, configured to perform construction difficulty analysis on the transmission lines in the plurality of areas to be analyzed, and perform construction decision analysis based on the construction difficulty analysis results to obtain construction decision information; The construction decision output unit is used to output the construction decision information.

Images