Dense power transmission channel newly-added path planning method fusing physical and geographic information

Abstract

The invention provides a dense power transmission channel newly-added path planning method fusing physical and geographic information, and particularly relates to the technical field of intelligent line selection of power transmission lines. The method specifically comprises the following steps: S1, screening physical information and geographic information factors and unifying dimensions; s2, obtaining the weight of each factor of dense power transmission channel line planning; s3, obtaining a single geographic information layer and endowing the single geographic information layer with a geographic information factor weight; s4, obtaining a single physical information layer and endowing a physical information factor weight; s5, superposing all the geographic information layers and the physical information layers to obtain a dense power transmission channel weighted information layer; and S6, performing two-stage planning of'channel planning-line planning 'on the dense power transmission channel weighted information layer. Compared with a traditional planning method, the method has the advantages that the considered influence factors are more comprehensive, the result is more accurate, and the error between the design and the practical application is effectively reduced.

Description

Technical Field

[0001] The patent of this invention relates to the technical field of intelligent line selection for power transmission lines, and specifically to a new path planning method for dense power transmission channels that integrates physical and geographic information. Background Art

[0002] At present, the factors considered in path planning mainly include natural environment factors, engineering factors, socio-economic factors, prohibited areas, and natural disaster factors. However, in reality, if the path planning area is located in a dense transmission channel, it is not comprehensive to only consider the above factors.

[0003] The weights obtained by the analytic hierarchy process used in the current path planning factor confirmation method are too dependent on the expert's knowledge system and experience, and the evaluation results are greatly affected by subjective factors. The evaluation criteria for the sub-layer indicator system of multiple indicators often have large deviations. The results of the entropy weight method for indicator weights are completely dependent on objective sample data, so the difference in the original data determines the size of the weight. In actual applications, there are often many special data that cause the obtained weights to differ too much from people's common sense. Summary of the invention

[0004] In order to overcome the above-mentioned shortcomings of the prior art, the present invention provides a new path planning method for dense transmission channels that integrates physical and geographical information. The specific technical solution is as follows:

[0005] A method for planning additional paths for dense power transmission channels integrating physical and geographic information specifically comprises the following steps:

[0006] S1. Screen the main factors applicable to the physical information and geographical information of dense transmission channels, and unify the dimensions of all the main factors through the Delphi method;

[0007] S2. Based on principal component analysis (PCA) and combined hierarchical-entropy weight analysis, the weights of geographic information factors and physical information factors in the main factors of dense transmission channel planning are obtained;

[0008] S3. Using the ArcGIS platform, the area to be planned is replicated into several single geographic information layers, and a geographic information factor weight is assigned to each grid in the grid matrix of each single geographic information layer;

[0009] S4. Using the existing integrated environmental monitoring system, the area to be planned is replicated into several single physical information layers, and a physical information factor weight is assigned to each grid in the grid matrix of each single physical information layer;

[0010] S5. Overlay all geographic information layers with physical information layers to obtain a dense transmission channel weighted information layer;

[0011] S6. Perform two-stage planning of “channel planning-line planning” on the weighted information layer model of dense transmission channels, and correct the distortion of the path to obtain the dense transmission channel path planning with the optimal cost.

[0012] Preferably, the main factors in S1 include geographic information factors and physical information factors; the geographic information factors include natural environment factors, engineering factors, socio-economic factors, prohibited passage areas, and natural disaster factors; and the physical information factors include dense transmission channel factors.

[0013] Preferably, the method of unifying dimensions by the Delphi method specifically comprises the following sub-steps:

[0014] S1.1 Classify the sub-factors of each factor contained in the geographic information factor and the physical information factor;

[0015] S1.2 The grading of each sub-factor in S1.1 is evaluated from appropriate to unsuitable according to expert opinions, and is divided into nine grade scales from 1 to 9 to obtain the grade scale of a single sub-factor.

[0016] Preferably, the method of obtaining the weights of each main factor based on principal component analysis (PCA) and combined “hierarchical-entropy weight analysis” specifically includes the following steps:

[0017] The principal component analysis method PCA described in S2 specifically includes the following sub-steps:

[0018] S2.1.1 A total of n experts scored m sub-factors, and the specific scores were based on the grade scale of the individual sub-factors obtained in S1.2;

[0019] Get the sample matrix X with m rows and n columns:

[0020]

[0021] Among them, the elements in the sample matrix X are recorded as x ij , i=1,2,...,m; j=1,2,...,n;

[0022] S2.1.2 Calculate the covariance matrix Σ of the sample matrix X:

[0023] Σ=E(XE(X))(XE(X)) T ;

[0024] Where T is the matrix transpose, and E(X) is the expectation of the sample matrix X;

[0025] S2.1.3 Use Matlab software to calculate the n eigenvalues ​​λ of the covariance matrix Σ in S2.1.2 and the n corresponding eigenvectors Y corresponding to the n eigenvalues;

[0026] Number and sort the n eigenvalues ​​λ from large to small, and record them as λ1≥λ2≥...≥λ n , and their corresponding eigenvectors are recorded as Y1, Y2, ...Y n ;

[0027] S2.1.4 The feature vectors Y1, Y2, ...Y n Considered as the principal component, calculate the i-th principal component Y i Individual contribution rate:

[0028]

[0029] S2.1.5 Screening sub-factors by contribution rate:

[0030] From the n principal components, select the individual contribution rates of the first h principal components for accumulation, h≤n, so that the cumulative contribution rate just satisfies greater than or equal to 85%, that is,

[0031]

[0032] The combined "hierarchical-entropy weight analysis method" described in S2 specifically includes the following sub-steps:

[0033] S2.2.1 Calculate the weight of sub-factors using the analytic hierarchy process o :

[0034] S2.2.1.1 Retain the first h principal components with cumulative contribution rates greater than or equal to 85% and construct a comprehensive evaluation vector F:

[0035] F=η1Y1+η2Y2+...+η h Y h ;

[0036] Among them, F is an m-dimensional column vector, and the element is recorded as f i , i=1,2,...,m;

[0037] S2.2.1.2 Construct judgment matrix B:

[0038]

[0039] Among them, the elements in the judgment matrix B are recorded as b op , o=1,2,3,...,m; p=1,2,3,...,m;

[0040] S2.2.1.3 Construct the subjective judgment matrix B′:

[0041]

[0042] The element in the subjective judgment matrix B′ is denoted as b′ op , b′ op is the relative importance of the oth element and the pth element in the subjective judgment matrix B′; wherein the relative importance between two elements is divided into seventeen scales of 1 / 9, 1 / 8, 1 / 7, 1 / 6, 1 / 5, 1 / 4, 1 / 3, 1 / 2, 1, 2, 3, 4, 5, 6, 7, 8, 9; o = 1, 2, 3, ..., m; p = 1, 2, 3, ..., m;

[0043] S2.2.1.4 Use the method of calculating the geometric mean to calculate the comprehensive judgment matrix B″, where the element is denoted by b″ op :

[0044]

[0045] Using the software MATLAB, find the maximum eigenvalue λ of the comprehensive judgment matrix B″ max The corresponding eigenvector ω of the machine is:

[0046] B″ω=λ″ max ω;

[0047] Normalize ω to get the weight vector ω″ corresponding to the hierarchical analysis method; where ω″ o is the weight of the factor in the weight vector ω″;

[0048] S2.2.2 Calculate the weight coefficient ψ of the sub-factor using the entropy weight method i :

[0049] S2.2.2.1 Calculate the entropy value E corresponding to the i-th sub-factor i ;

[0050]

[0051] Among them, the entropy value E i Should satisfy 0 <E i <1;s ij is the weight of the i-th sub-factor at the j-th expert level scale, i = 1, 2, 3, ..., m; j = 1, 2, 3, ..., n;

[0052] S2.2.2.2 Construct the entropy weight of i sub-factors as:

[0053]

[0054] Where n is the number of experts, i = 1, 2, 3, …, m;

[0055] S2.2.3 Calculate the comprehensive weight according to S2.2.1.4 and S2.2.2.2 to obtain the weight δ of the i-th sub-factori :

[0056]

[0057] Also preferably, S3 specifically includes the following sub-steps:

[0058] S3.1 Sub-factor weights δ obtained from S2.2.3 i The weight value δ of the geographical information factor is selected terrain，ii ; Among them, the total number of geographic information factors is m terrain , ii = 1, 2, ..., m terrain ;

[0059] S3.2 Use the ArcGIS platform to copy the area to be planned into a total of m terrain A single geographic information layer is created, and each single geographic information layer is divided into a geographic raster matrix, where the total number of each geographic raster matrix is ​​recorded as n 栅格 indivual;

[0060] S3.3 Assign a weight value δ of the corresponding geographic information factor to each grid in the geographic grid matrix of all single geographic information layers terrain，ii and area movement cost C terrain，ii .

[0061] Also preferably, S4 specifically includes the following sub-steps:

[0062] S4.1 Sub-factor weights δ obtained from S2.2.4 i The weight value δ of the physical information factor is selected phy，jj , where the total number of physical information sub-factors is m phy , satisfying m phy +m terrain =m; jj = 1, 2, ..., m phy ;

[0063] S4.2 Use the existing integrated environmental monitoring system to copy the area to be planned into a total of m phy The single physical information layer is divided into physical grid matrices, where the total number of each physical grid matrix is ​​recorded as n. 栅格 indivual;

[0064] S4.3 Assign a weight value δ of the corresponding physical information factor to each grid in the physical grid matrix of all single physical information layers phy，jj and area movement cost C phy，jj .

[0065] Further preferably, the m obtained in S3.3 terrainA single geographic information layer and m obtained by S4.3 phy The single physical information layers are superimposed on each other to obtain the dense transmission channel weighted information layer of the final planned area, and the n dense transmission channel weighted information layer is 栅格 Each grid is assigned a comprehensive domain movement cost; among them, the qth grid is assigned a comprehensive domain movement cost

[0066] It is further preferred that the two-stage planning of "channel planning-line planning" in S6 is completed based on the dense transmission channel weighted information layer:

[0067] In the channel planning stage, a threshold o is set; the comprehensive domain mobility cost C in the grid matrix of the dense transmission channel weighted information layer is selected q All unit grids with a value less than the threshold o are selected, and the grid set after eliminating isolated unit grids is selected as the channel; the value of the threshold o is the average value of the comprehensive field mobile cost in the grid matrix of the dense transmission channel weighted information layer 20%

[0068] In the path planning stage, the channels obtained in the channel planning stage are first merged and spliced, and the merged total channels are used as the line selection range in the path planning stage; the Dijkstra algorithm is used to calculate the optimal path for dense transmission channels and correct the path distortion of the planned path to increase the optional directions of the path and reduce the number of corners to obtain the final path.

[0069] More preferably, the channel planning stage eliminates the two factors of crossing and turning angle; the path planning stage eliminates the factor of turning angle greater than 45° to complete path distortion correction.

[0070] The beneficial effects of the present invention are:

[0071] When screening the factors required for path planning, the present invention considers physical information factors, dense transmission channel factors and geographic information factors at the same time, which is more comprehensive; through the principal component analysis method (PCA) and the combined "hierarchical-entropy weight analysis method", the weights of each sub-factor of dense transmission channel line planning are obtained, and then the weights of each sub-factor are cleaned and inserted into the geographic grid matrix and physical grid matrix of the planned area to obtain the corresponding geographic information layer and physical information layer. Finally, the geographic information layer and the physical information layer are superimposed to obtain the final dense transmission channel weighted information layer, and the planning of the path and channel is further completed on the basis of the dense transmission channel weighted information layer. Compared with the traditional planning method, the factors considered are more comprehensive and the results are more accurate, which effectively reduces the error between the design and the actual application. BRIEF DESCRIPTION OF THE DRAWINGS

[0072] The drawings constituting the specification of this application are used to provide further understanding of this application and do not constitute improper limitations on this application.

[0073] Figure 1 is a flow chart of the present invention;

[0074] Figure 2 This is a schematic diagram of the classification of expert opinions;

[0075] Figure 3 This is a geographic information layer generated by the present invention in the embodiment (taking water bodies as an example);

[0076] Figure 4 This is a physical information layer generated by the present invention in the embodiment (taking electromagnetic interference as an example);

[0077] Figure 5 Existing transmission line paths and comprehensive field mobility cost maps for dense transmission corridors;

[0078] Figure 6 A dense power transmission channel weighted information channel diagram generated by the present invention in an embodiment;

[0079] Figure 7 It is the optimal path diagram (dashed line) of the dense transmission channel information line generated by the present invention in the embodiment. DETAILED DESCRIPTION

[0080] The specific implementation of the method for planning additional paths for dense power transmission channels integrating physical and geographic information provided by the present invention is further described in conjunction with the accompanying drawings and embodiments.

[0081] like Figure 1 As shown, a new path planning method for dense transmission channels integrating physical and geographic information specifically includes the following steps:

[0082] S1. Screen the main factors applicable to the physical information and geographical information of dense transmission channels, and unify the dimensions of all the main factors through the Delphi method;

[0083] Preferably, the main factors include geographic information factors and physical information factors; the geographic information factors include natural environment factors, engineering factors, socio-economic factors, prohibited passage areas, and natural disaster factors; the physical information factors include dense transmission channel factors, as shown in Table 1 below.

[0084] Table 1. Main factors of physical and geographical information applicable to dense transmission channels

[0085]

[0086] Preferably, the method of unifying dimensions by the Delphi method specifically comprises the following sub-steps:

[0087] S1.1 The sub-factors of each factor contained in the geographic information factor and the physical information factor are graded, as shown in Table 2 below;

[0088] Table 2. Grading table of sub-factors corresponding to each factor

[0089]

[0090]

[0091] S1.2 The classification of each sub-factor in S1.1 is evaluated from appropriate to inappropriate according to the expert opinions, and is divided into nine grade scales from 1 to 9, and the grade scale of a single sub-factor is obtained, as shown in Table 3 below. Among them, grades 1 to 3 are factors that are suitable for consideration in path planning, grades 4 to 6 are factors that are generally suitable for consideration in path planning, and grades 7 to 9 are factors that are not suitable for consideration in path planning. Figure 2 shown.

[0092] Table 3. Rating scale of individual sub-factors

[0093]

[0094]

[0095] S2. The method of obtaining the weights of each main factor based on the principal component analysis method PCA and the combined "hierarchical-entropy weight analysis method" specifically includes the following steps:

[0096] The principal component analysis method PCA described in S2 specifically includes the following sub-steps:

[0097] S2.1.1 A total of n experts scored m sub-factors, and the specific scores were based on the grade scale of the individual sub-factors obtained in S1.2;

[0098] The sample matrix X with m rows and n columns is obtained (see Table 4 below for details):

[0099]

[0100] Table 4. Sample data

[0101] Expert opinion Expert 1 Expert 2 … Expert n-1 Expert Factor 1 <![CDATA[x 1，1 ]]> <![CDATA[x 1，2 ]]> … <![CDATA[x 1，n-1 ]]> <![CDATA[x 1，n ]]> Factor 2 <![CDATA[x 2，1 ]]> <![CDATA[x 2，2 ]]> … <![CDATA[x 2，n-1 ]]> <![CDATA[x 2，n ]]> … … … … … … Factor m-1 <![CDATA[x m-1，1 ]]> <![CDATA[x m-1，2 ]]> … <![CDATA[x m-1，n-1 ]]> <![CDATA[x m-1，n ]]> Factor m <![CDATA[x m，1 ]]> <![CDATA[x m，2 ]]> … <![CDATA[x m，n-1 ]]> <![CDATA[x m，n ]]>

[0102] Among them, the elements in the sample matrix X are recorded as x ij , i=1,2,…,m; j=1,2,…,n;

[0103] S2.1.2 Calculate the covariance matrix ∑ of the sample matrix X:

[0104] ∑ = E(XE(X))(XE(X)) T ;

[0105] Where T is the matrix transpose, and E(X) is the expectation of the sample matrix X;

[0106] S2.1.3 Use Matlab software to calculate the n eigenvalues ​​λ of the covariance matrix ∑ in S2.1.2 and the n corresponding eigenvectors Y corresponding to the n eigenvalues;

[0107] Number and sort the n eigenvalues ​​λ from large to small, and record them as λ1≥λ2≥...≥λ n , and their corresponding eigenvectors are recorded as Y1, Y2, ...Y n ;

[0108] S2.1.4 The feature vectors Y1, Y2, ...Y n Considered as the main component (Y i The eigenvalues ​​λ corresponding to the principal components i The larger the value, the richer the information it contains. They are independent of each other and can reflect the information of multiple original sub-factors, which makes the principal component better reflect the essence of the problem than the original variable. Calculate the i-th principal component Y i Individual contribution rate:

[0109]

[0110] S2.1.5 Screening sub-factors by contribution rate:

[0111] From the n principal components, select the individual contribution rates of the first h principal components for accumulation, h≤n, so that the cumulative contribution rate just satisfies greater than or equal to 85%, that is,

[0112]

[0113] The combined "hierarchical-entropy weight analysis method" described in S2 specifically includes the following sub-steps:

[0114] S2.2.1 Calculate the weight of sub-factors using the analytic hierarchy process o :

[0115] S2.2.1.1 Retain the first h principal components with cumulative contribution rates greater than or equal to 85% and construct a comprehensive evaluation vector F:

[0116] F=η1Y1+η2Y2+...+η h Y h ;

[0117] Among them, F is an m-dimensional column vector, and the element is recorded as f i, i=1,2,...,m;

[0118] S2.2.1.2 Construct judgment matrix B:

[0119]

[0120] Among them, the elements in the judgment matrix B are recorded as b op , o=1,2,3,...,m; p=1,2,3,...,m;

[0121] S2.2.1.3 Construct the subjective judgment matrix B′:

[0122]

[0123] The element in the subjective judgment matrix B' is denoted as b' op , b′ op is the relative importance of the oth element and the pth element in the subjective judgment matrix B′, o = 1, 2, 3, ..., m; p = 1, 2, 3, ..., m; wherein the relative importance between two elements is divided into seventeen scales of 1 / 9, 1 / 8, 1 / 7, 1 / 6, 1 / 5, 1 / 4, 1 / 3, 1 / 2, 1, 2, 3, 4, 5, 6, 7, 8, 9, as shown in Table 5 below;

[0124] Table 5. Relative importance scale between two elements in the subjective judgment matrix

[0125]

[0126] S2.2.1.4 Use the method of calculating the geometric mean to calculate the comprehensive judgment matrix B″, where the element is denoted by b″ op :

[0127]

[0128] Using the software MATLAB, find the maximum eigenvalue λ of the comprehensive judgment matrix B″ max The corresponding eigenvector ω of the machine is:

[0129] B″ω=λ″ max ω;

[0130] Normalize ω to get the weight vector ω″ corresponding to the hierarchical analysis method; where ω″ o is the weight of the factor in the weight vector ω″;

[0131] S2.2.2 Calculate the weight coefficient ψ of the sub-factor using the entropy weight method i :

[0132] S2.2.2.1 Calculate the entropy value E corresponding to the i-th sub-factori (The entropy value can represent the discrete degree of a sub-factor. The smaller the information entropy value, the greater the discrete degree of the sub-factor):

[0133]

[0134] Among them, the entropy value E i Should satisfy 0<E i <1;s ij is the weight of the i-th sub-factor at the j-th expert level scale, i = 1, 2, 3, ..., m; j = 1, 2, 3, ..., n;

[0135] S2.2.2.2 Construct the entropy weight of i sub-factors as:

[0136]

[0137] Where n is the number of experts, i = 1, 2, 3, …, m;

[0138] S2.2.3 Calculate the comprehensive weight according to S2.2.1.4 and S2.2.2.2 to obtain the weight δ of the i-th sub-factor i :

[0139]

[0140] S3. Using the ArcGIS platform, the area to be planned is copied into several single geographic information layers, and a geographic information factor weight is assigned to each grid in the grid matrix of each single geographic information layer, which specifically includes the following sub-steps:

[0141] S3.1 Sub-factor weights δ obtained from S2.2.3 i The weight value δ of the geographical information factor is selected terrain，ii ; Among them, the total number of geographic information factors is m terrain , ii = 1, 2, …, m terrain ;

[0142] S3.2 Use the ArcGIS platform to copy the area to be planned into a total of m terrain A single geographic information layer is created, and each single geographic information layer is divided into a geographic raster matrix, where the total number of each geographic raster matrix is ​​recorded as n 栅格 indivual;

[0143] S3.3 Assign a weight value δ of the corresponding geographic information factor to each grid in the geographic grid matrix of all single geographic information layers terrain，ii and area movement cost C terrain，ii .

[0144] S4. Using the existing integrated environmental monitoring system, the area to be planned is replicated into several single physical information layers, and a physical information factor weight is assigned to each grid in the grid matrix of each single physical information layer, specifically including the following sub-steps:

[0145] S4.1 Sub-factor weights δ obtained from S2.2.4 i The weight value δ of the physical information factor is selected phy，jj , where the total number of physical information sub-factors is m phy , satisfying m phy +m terrain =m; jj=1,2,...,m phy ;

[0146] S4.2 Use the existing integrated environmental monitoring system to copy the area to be planned into a total of m phy The single physical information layer is divided into physical grid matrices, where the total number of each physical grid matrix is ​​recorded as n. 栅格 indivual;

[0147] S4.3 Assign a weight value δ of the corresponding physical information factor to each grid in the physical grid matrix of all single physical information layers phy，jj and area movement cost C phy，jj .

[0148] S5. Substitute the m obtained in S3.3 terrain A single geographic information layer and m obtained by S4.3 phy The single physical information layers are superimposed on each other to obtain the dense transmission channel weighted information layer of the final planned area, and the n dense transmission channel weighted information layer is 栅格 Each grid is assigned a comprehensive domain movement cost; among them, the qth grid is assigned a comprehensive domain movement cost

[0149] S6. Perform two-stage planning of “channel planning-line planning” on the dense transmission channel weighted information layer model, and correct the path distortion to obtain the dense transmission channel path planning with the optimal cost:

[0150] In the channel planning stage, a threshold o is set; the comprehensive domain mobility cost C in the grid matrix of the dense transmission channel weighted information layer is selected q All unit grids with a value less than the threshold o are selected, and the grid set after eliminating isolated unit grids is selected as the channel; the value of the threshold o is the average value of the comprehensive field mobile cost in the grid matrix of the dense transmission channel weighted information layer 20%

[0151] In the path planning stage, the channels obtained in the channel planning stage are first merged and spliced, and the merged total channels are used as the line selection range in the path planning stage; the Dijkstra algorithm is used to calculate the optimal path for dense transmission channels and correct the path distortion of the planned path to increase the optional directions of the path and reduce the number of corners to obtain the final path.

[0152] It is worth noting that the channel planning stage eliminates the two factors of crossing and turning angle; the path planning stage eliminates the factor of turning angle greater than 45° to complete the path distortion correction.

[0153] Example:

[0154] The area to be planned in this embodiment is 100km×100km, and the main factors are natural environment factors (Nat), dense transmission channel factors (Den), and engineering factors (E). The level scales of individual sub-factors of each factor are detailed in Table 6(a), Table 6(b), and Table 6(c):

[0155] Table 6 (a). Natural environmental factors (Nat)

[0156]

[0157] Table 6(b). Dense transmission channel factors (Den)

[0158]

[0159] Table 6(c). Engineering factors (E)

[0160]

[0161] According to principal component analysis (PCA), we can get:

[0162] Natural environment factors: Take the first two principal components (λ1 = 95.78, λ2 = 3.169 cumulative contribution rate η p =0.98);

[0163] Dense transmission channel factors: Take the first two principal components (λ1=50.57, λ2=6.095 cumulative contribution rate η p =0.99);

[0164] Engineering factors: Take the first three principal components (λ1=31.78,λ2=4.52,λ3=2.92 cumulative contribution rate η p =0.89); and

[0165] By retaining the principal components with cumulative contribution rates greater than or equal to 85%, the following comprehensive evaluation is obtained:

[0166] FNat =[26,23,19,12,5,3];

[0167] F Den =[23,10,13];

[0168] F E =[12, 12, 8, 7, 11, 15, 9, 21, 9, 8];

[0169] The weights of the sub-factors are calculated according to the hierarchical analysis method:

[0170] ω″ Nat =[0.383,0.249,0.18,0.10,0.05,0.03];

[0171] ω″ Den =[0.63,0.15,0.22];

[0172] ω″ E =[0.11,0.08,0.05,0.04,0.07,0.13,0.06,0.28,0.09,0.06];

[0173] According to the entropy weight method, the entropy weight of each sub-factor can be obtained as:

[0174] ψ″ Nat =[0.29,0.26,0.21,0.14,0.06,0.04];

[0175] ψ″ Den =[0.49,0.23,0.28];

[0176] ψ″ E =[0.12,0.10,0.07,0.06,0.1,0.14,0.08,0.18,0.08,0.07];

[0177] Calculate the comprehensive weight to get the weight of the sub-factor:

[0178]

[0179] Using ArcGIS platform, 16 single geographic information layers are generated, each single geographic information layer is divided into a geographic grid matrix, and each geographic grid matrix generates 100×100 grids; among them, the geographic information factors selected in this embodiment include: water bodies, river floodplains, forests, cultivated land, grasslands, ice areas, polluted areas, deserts and bare surfaces, etc. Figure 3 Taking the grid of the water area as an example (black mark), each grid is assigned its corresponding water weight and domain movement cost C terain，ii ;

[0180] Using the existing integrated environmental monitoring system, three single physical information layers are generated, namely tower collapse, voltage fluctuation, and electromagnetic interference. Figure 4 Taking the grid with severe electromagnetic interference as an example (black mark), each grid is assigned its corresponding electromagnetic interference weight and domain movement cost C phy，jj .

[0181] The above 16 single geographic information layers and 3 single physical information layers are superimposed on each other to obtain the dense transmission channel weighted information layer of the final planned area, and the 100×100 grids of the dense transmission channel weighted information layer are all assigned comprehensive field mobility costs;

[0182] Each grid in the weighted information layer is assigned a comprehensive domain movement cost:

[0183]

[0184] Select the threshold value as The grids whose comprehensive domain movement cost is greater than the threshold are marked in black, and we get Figure 5 , where the existing transmission lines in the dense transmission channel are marked as solid lines; the comprehensive field mobility cost C in the grid matrix of the dense transmission channel weighted information layer is selected q All unit grids smaller than the threshold o are selected, and the grid set after eliminating isolated unit grids is selected as the channel, such as Figure 6 As shown;

[0185] The channels obtained in the channel planning stage are merged and spliced, and the merged total channel is used as the line selection range in the path planning stage; the Dijkstra algorithm is used to calculate the optimal path for dense transmission channels and correct the path distortion of the planned path to increase the optional directions of the path and reduce the number of corners, and the final path is obtained, such as Figure 7 shown.

[0186] Compared with traditional planning methods, the present invention considers more comprehensive factors and produces more accurate results, effectively reducing the error between design and actual application.

[0187] In the present invention, the orientation or position relationship indicated by terms such as "upper", "lower", "bottom", "top", etc. is based on the orientation or position relationship shown in the accompanying drawings. They are relational words determined only for the convenience of describing the structural relationship of the various parts or elements of the present invention. They do not specifically refer to any part or element in the present invention and cannot be understood as limitations on the present invention. Terms such as "connected" and "connected" should be understood in a broad sense, indicating that it can be fixedly connected, integrally connected or detachably connected; it can be directly connected or indirectly connected through an intermediate medium. For relevant scientific research or technical personnel in this field, the specific meanings of the above terms in the present invention can be determined according to specific circumstances, and they cannot be understood as limitations on the present invention.

[0188] Of course, the above description is not a limitation of the present invention, and the present invention is not limited to the above examples. Changes, modifications, additions or substitutions made by technicians in this technical field within the essential scope of the present invention should also fall within the protection scope of the present invention.

Claims

1. A method for planning additional paths for dense power transmission channels that integrates physical and geographic information, characterized in that: The specific steps include: S1. Screen the main factors applicable to the physical information and geographical information of dense transmission channels, and unify the dimensions of all the main factors through the Delphi method; S2. Based on principal component analysis (PCA) and combined hierarchical-entropy weight analysis, the weights of geographic information factors and physical information factors in the main factors of dense transmission channel line planning are obtained; S3. Using the ArcGIS platform, the area to be planned is replicated into several single geographic information layers, and a geographic information factor weight is assigned to each grid in the grid matrix of each single geographic information layer; S4. Using the existing integrated environmental monitoring system, the area to be planned is replicated into several single physical information layers, and a physical information factor weight is assigned to each grid in the grid matrix of each single physical information layer; S5. Overlay all geographic information layers with physical information layers to obtain a dense transmission channel weighted information layer; S6. Perform two-stage planning of "channel planning-line planning" on the dense transmission channel weighted information layer model, and correct the path distortion to obtain the dense transmission channel path planning with the optimal cost.

2. The method for planning additional paths for dense power transmission channels integrating physical and geographic information according to claim 1, characterized in that: The main factors described in S1 include geographical information factors and physical information factors; The geographic information factors include natural environment factors, engineering factors, socio-economic factors, prohibited areas, and natural disaster factors; The physical information factor includes a dense transmission channel factor.

3. The method for planning additional paths for dense power transmission channels integrating physical and geographic information according to claim 2, characterized in that: The method of unifying dimensions by the Delphi method specifically comprises the following sub-steps: S1.1 Classify the sub-factors of each factor contained in the geographic information factor and the physical information factor; S1.2 The grading of each sub-factor in S1.1 is evaluated from appropriate to unsuitable according to expert opinions, and is divided into nine grade scales from 1 to 9 to obtain the grade scale of a single sub-factor.

4. The method for planning additional paths for dense power transmission channels integrating physical and geographic information according to claim 3 is characterized in that: The principal component analysis method PCA and the combined "hierarchical-entropy weight analysis method" are used to obtain the weights of each main factor. The following steps are involved: The principal component analysis method PCA described in S2 specifically includes the following sub-steps: S2.1.1 A total of n experts scored m sub-factors, and the specific scores were based on the grade scale of the individual sub-factors obtained in S1.2; Get the sample matrix X with m rows and n columns: Among them, the elements in the sample matrix X are recorded as x ij ,i=1,2,...,m;j=1,2,...,n; S2.1.2 Calculate the covariance matrix ∑ of the sample matrix X: ∑=E(XE(X))(XE(X)) T ; Where T is the matrix transpose, and E(X) is the expectation of the sample matrix X; S2.1.3 Use Matlab software to calculate the n eigenvalues ​​λ of the covariance matrix ∑ in S2.1.2 and the n corresponding eigenvectors Y corresponding to the n eigenvalues ​​λ; Number and sort the n eigenvalues ​​λ from large to small, and record them as λ1≥λ2≥...≥λ n , and their corresponding eigenvectors are recorded as Y1, Y2, ...Y n ; S2.1.4 The feature vectors Y1, Y2, ...Y n Considered as the principal component, calculate the i-th principal component Y i Individual contribution rate: S2.1.5 Screening sub-factors by contribution rate: From the n principal components, select the individual contribution rates of the first h principal components for accumulation, h≤n, so that the cumulative contribution rate just satisfies greater than or equal to 85%, that is, The combined "hierarchical-entropy weight analysis method" described in S2 specifically includes the following sub-steps: S2.2.1 Calculate the weight of sub-factors using the analytic hierarchy process o : S2.2.1.1 Retain the first h principal components with cumulative contribution rates greater than or equal to 85% and construct a comprehensive evaluation vector F: F=η1Y1+η2Y2+…+η h Y h ; Among them, F is an m-dimensional column vector, and the element is recorded as f i , i=1,2,...,m; S2.2.1.2 Construct judgment matrix B: Among them, the elements in the judgment matrix B are recorded as b op , o=1, 2, 3,..., m; p=1, 2, 3,..., m; S2.2.1.3 Construct the subjective judgment matrix B': The element in the subjective judgment matrix E′ is denoted as b′ op , b′ op is the relative importance of the oth element and the pth element in the subjective judgment matrix B′; wherein the relative importance between two elements is divided into seventeen scales of 1 / 9, 1 / 8, 1 / 7, 1 / 6, 1 / 5, 1 / 4, 1 / 3, 1 / 2, 1, 2, 3, 4, 5, 6, 7, 8, 9; o = 1, 2, 3, ..., m; p = 1, 2, 3, ..., m; S2.2.1.4 Use the method of calculating the geometric mean to calculate the comprehensive judgment matrix B″, where the element is denoted by b″ op : Using the software MATLAB, find the maximum eigenvalue λ of the comprehensive judgment matrix B″ max The corresponding eigenvector ω of the machine is: B″ω=λ″ max oh; Normalize ω to get the weight vector ω″ corresponding to the hierarchical analysis method; where ω″ o is the weight of the oth factor in the weight vector ω″; S2.2.2 Calculate the weight coefficient ψ of the sub-factor using the entropy weight method i : S2.2.2.1 Calculate the entropy value E corresponding to the i-th sub-factor i : Among them, the entropy value E i Should satisfy 0 <E i <1;s ij is the weight of the i-th sub-factor at the j-th expert level scale, i = 1, 2, 3, ..., m; j = 1, 2, 3, ..., n; S2.2.2.2 Construct the entropy weight of i sub-factors as: Where n is the number of experts, i = 1, 2, 3, ..., m; S2.2.3 Calculate the comprehensive weight according to S2.2.1.4 and S2.2.2.2 to obtain the weight δ of the i-th sub-factor i :

5. The method for planning additional paths for dense power transmission channels integrating physical and geographic information according to claim 4 is characterized in that: S3 specifically includes the following sub-steps: S3.1 Sub-factor weights δ obtained from S2.2.3 i The weight value δ of the geographical information factor is selected terrain，ii ; Among them, the total number of geographic information factors is m terrain , ii = 1, 2, ..., m terrain ; S3.2 Use the ArcGIS platform to copy the area to be planned into a total of m terrain A single geographic information layer is created, and each single geographic information layer is divided into a geographic raster matrix, where the total number of each geographic raster matrix is ​​recorded as n 栅格 indivual; S3.3 Assign a weight value δ of the corresponding geographic information factor to each grid in the geographic grid matrix of all single geographic information layers terrain，ii and area movement cost C terrain，ii .

6. The method for planning additional paths for dense power transmission channels integrating physical and geographic information according to claim 5, characterized in that: S4 specifically includes the following sub-steps: S4.1 Sub-factor weights δ obtained from S2.2.4 i The weight value δ of the physical information factor is selected phy,jj , where the total number of physical information sub-factors is m phy , satisfying m phy +m terrain =m; jj=1,2,...,m phy ; S4.2 Use the existing integrated environmental monitoring system to copy the area to be planned into a total of m phy The single physical information layer is divided into physical grid matrices, where the total number of each physical grid matrix is ​​recorded as n. 栅格 indivual; S4.3 Assign a weight value δ of the corresponding physical information factor to each grid in the physical grid matrix of all single physical information layers phy,jj and area movement cost C phy,jj .

7. The method for planning additional paths for dense power transmission channels integrating physical and geographic information according to claim 6, characterized in that: The m obtained by S3.3 terrain A single geographic information layer and m obtained by S4.3 phy The single physical information layers are superimposed on each other to obtain the dense transmission channel weighted information layer of the final planned area, and the n dense transmission channel weighted information layer is 栅格 Each grid is assigned a comprehensive domain movement cost; among them, the qth grid is assigned a comprehensive domain movement cost 8. The method for planning additional paths for dense power transmission channels integrating physical and geographic information according to claim 7, characterized in that: The two-stage planning of "channel planning-line planning" in S6 is completed based on the dense transmission channel weighted information layer: In the channel planning stage, a threshold o is set; Select the comprehensive domain mobility cost C in the grid matrix of the dense transmission channel weighted information layer q All unit grids with a value less than the threshold o are selected, and the grid set after eliminating isolated unit grids is selected as the channel; the value of the threshold o is the average value of the comprehensive field mobile cost in the grid matrix of the dense transmission channel weighted information layer 20% In the path planning stage, the channels obtained in the channel planning stage are first merged and spliced, and the merged total channels are used as the line selection range in the path planning stage; the Dijkstra algorithm is used to calculate the optimal path for dense transmission channels and correct the path distortion of the planned path to increase the optional directions of the path and reduce the number of corners to obtain the final path.

9. The method for planning additional paths for dense power transmission channels integrating physical and geographic information according to claim 8, characterized in that: The channel planning stage eliminates the two factors of crossing and cornering; The path planning stage eliminates the factor of turning angles greater than 45° to complete path distortion correction.

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