Mountain photovoltaic booster station site selection optimization method based on GIS platform and variable weight TOPSIS model

Abstract

The invention discloses a mountain photovoltaic booster station site selection optimization method based on a GIS platform and a variable weight TOPSIS model. According to the method, firstly, a GIS platform is used for extracting a plurality of evaluation indexes such as a photovoltaic plant boundary and a selection forbidden area, the evaluation indexes are scored, and a multi-attribute decision matrix is constructed; and then, based on the performance of each index under different schemes, dynamically calculating the weight, and adjusting the weight vector to reflect the current actual situation. Thirdly, multiplying the normalized decision matrix by a variable weight vector to obtain a weighted standardized decision matrix, calculating the distance between each site selection scheme and an ideal solution, and determining an optimal scheme through close degree sorting; according to the method, by introducing a dynamic weight mechanism, the limitation of a fixed weight is avoided, the adaptability and rationality of a site selection scheme are improved, various factors can be comprehensively considered, the site selection of the mountain photovoltaic booster station is optimized, and the method has relatively high scientificity, objectivity and practical application value.

Description

Technical Field

[0001] The invention relates to a mountain photovoltaic booster station site selection optimization method based on a GIS platform and a variable weight TOPSIS model, and belongs to the technical field of site selection. Background Art

[0002] With the rapid development of renewable energy, photovoltaic power generation, as one of the important forms of green energy, is increasingly used in industrial parks and remote mountainous areas. In photovoltaic power generation systems, booster stations are key components, and their site selection directly affects the overall efficiency and economy of the system. Traditional booster station site selection methods mainly rely on the experience of engineers and are evaluated through topographic map analysis and other means. This method is not only time-consuming and labor-intensive, but also easily affected by subjective judgment and lacks scientific and consistent evaluation standards.

[0003] In the existing technology, some studies have tried to combine multi-index decision-making methods such as the analytic hierarchy process (AHP) or particle swarm optimization (PSO) to assist site selection, but these methods still have deficiencies such as fixed weights and single evaluation indicators when dealing with complex geographical environments and multi-factor comprehensive evaluations, making it difficult to fully reflect actual engineering needs. In addition, the GIS platform has natural advantages in geospatial data processing and multi-index analysis, but the single use of GIS for site selection often fails to fully consider the dynamic weight relationship between the evaluation indicators, resulting in insufficient scientificity and reliability of the site selection results. Summary of the invention

[0004] The technical problem to be solved by the present invention is to provide a mountain photovoltaic booster station site selection optimization method based on a GIS platform and a variable weight TOPSIS model to overcome the deficiencies of the prior art.

[0005] The technical solution of the present invention is: a mountain photovoltaic booster station site selection optimization method based on a GIS platform and a variable weight TOPSIS model, the method comprising the following steps:

[0006] a. Using the GIS platform, extract the PV plant boundary, prohibited areas, plane dimensions of the booster station, adjacent roads, cut-and-fill balance, flood control capacity, line connection, construction conditions and environmental protection indicators, and obtain the scores of each indicator;

[0007] b. Based on the scores of each indicator, a multi-attribute decision-making matrix containing multiple site selection options and evaluation criteria is constructed;

[0008] c. Normalize the multi-attribute decision matrix;

[0009] d. Based on the dynamic state of each evaluation indicator, calculate the dynamic weight vector of each indicator and construct a variable weight vector;

[0010] e. Multiply the normalized multi-attribute decision matrix and the variable weight vector item by item to obtain a weighted standardized decision matrix;

[0011] f. Based on the weighted standardized decision matrix, determine the positive ideal solution and negative ideal solution of each evaluation index;

[0012] g. Calculate the distance between each site selection plan and the positive ideal solution and the negative ideal solution, and then calculate the closeness of each plan, and sort the alternative plans according to the closeness;

[0013] h. Based on the sorting results, select the plan with the highest degree of closeness as the optimal booster station location.

[0014] Furthermore, the method for constructing a multi-attribute decision matrix including multiple site selection schemes and evaluation criteria based on the extracted indicators is:

[0015]

[0016] Among them, a ij It represents the score of the jth site selection plan under the i-th evaluation criterion.

[0017] Furthermore, the method for normalizing the multi-attribute decision matrix is:

[0018] For benefit-type indicator i, the normalization method of the corresponding elements of the multi-attribute decision-making matrix is ​​as follows:

[0019]

[0020] For cost-type indicator i, the normalization method of the corresponding elements of the multi-attribute decision matrix is ​​as follows:

[0021]

[0022] Wherein, m represents the total number of evaluation criteria.

[0023] Furthermore, the method of calculating the dynamic weight vector of each indicator based on the dynamic state of each evaluation indicator and constructing the variable weight vector is as follows:

[0024] S1. Determine the fixed weight vector W = [ω1ω2,…,ω m ] T ,and

[0025] S2, determine the current state value of each evaluation indicator according to the performance of the indicator under the current scheme S = [s1s2,…,s m ];

[0026] S3. Constructing equilibrium function B i (Si ),

[0027] B i (S i )=k1·S i , k1 is the adjustment coefficient, k1 is used to ensure that the vector after weight adjustment meets the normalization condition; S4, calculate the variable weight vector W ′ , W i ′ =ω i ·B i (S i );

[0028] S5, normalized variable weight vector

[0029] .

[0031] Furthermore, the normalized multi-attribute decision matrix is ​​multiplied item by item with the variable weight vector to obtain a weighted standardized decision matrix:

[0032]

[0033] Where C represents the weighted standardized decision matrix, X represents the ij As the normalized multi-attribute decision matrix composed of elements, represents the Hadamard product.

[0034] Furthermore, the method for determining the positive ideal solution and the negative ideal solution of each evaluation index based on the weighted standardized decision matrix is:

[0035] For each evaluation index i,

[0036] Taking the maximum value of this index among all solutions to obtain the positive ideal solution,

[0037]

[0038] The positive ideal solutions of all indices are combined into a positive ideal solution vector,

[0039]

[0040] Taking the minimum value of this index among all solutions to obtain the negative ideal solution,

[0041]

[0042] The negative ideal solutions of all indicators are combined into a negative ideal solution vector,

[0043]

[0044] Furthermore, the method of calculating the distance between each site selection scheme and the positive ideal solution and the negative ideal solution, and then calculating the closeness of each scheme is:

[0045] For each solution j, calculate the distance to the positive ideal solution and the distance to the negative ideal solution

[0046]

[0047]

[0048] Calculate the closeness of each solution j

[0049]

[0050] The beneficial effect of the present invention is that compared with the prior art, the present invention uses the GIS platform to extract multiple evaluation indicators, construct a multi-attribute decision matrix, and dynamically evaluates and sorts through a variable weight TOPSIS model, thereby selecting the optimal booster station site selection plan. This method comprehensively considers multiple evaluation indicators, dynamically adjusts the indicator weights, and objectively evaluates the pros and cons of each plan, effectively solving the problems of single evaluation indicators, fixed weights, and difficulty in objective evaluation in traditional site selection methods. It has the advantages of comprehensively considering multiple evaluation indicators, dynamically adjusting indicator weights, and objectively evaluating the pros and cons of each plan. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] Figure 1 It is a flow chart of the present invention. DETAILED DESCRIPTION

[0052] In order to make the objectives, technical solutions and advantages of the present invention more clear, the present invention will be further described in detail below with reference to the accompanying drawings of this specification.

[0053] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with embodiments and drawings. The exemplary embodiments of the present invention and their description are only used to explain the present invention and are not intended to limit the present invention.

[0054] Implementation Example 1:

[0055] With the rapid development of renewable energy, photovoltaic power generation, as one of the important forms of green energy, is increasingly used in industrial parks and remote mountainous areas. In photovoltaic power generation systems, booster stations are key components, and their site selection directly affects the overall efficiency and economy of the system. The traditional method of booster station site selection mainly relies on the experience of engineers and is evaluated through topographic map analysis and other means. This method is not only time-consuming and labor-intensive, but also easily affected by subjective judgment and lacks scientific and consistent evaluation standards. In the prior art, some studies have attempted to combine multi-index decision-making methods such as hierarchical analysis or particle swarm optimization algorithms to assist site selection, but these methods still have deficiencies such as fixed weights and single evaluation indicators when dealing with complex geographical environments and multi-factor comprehensive evaluations, making it difficult to fully reflect actual engineering needs. In addition, the GIS platform has natural advantages in geospatial data processing and multi-index analysis, but the single use of GIS for site selection often fails to fully consider the dynamic weight relationship between the evaluation indicators, resulting in insufficient scientificity and reliability of the site selection results.

[0056] In photovoltaic power generation systems, the site selection of booster stations is crucial. In order to overcome the limitations of traditional site selection methods, reference Figure 1 , a site selection optimization method for mountain photovoltaic booster stations based on GIS platform and variable weight TOPSIS model was proposed. By using the GIS platform to extract the scores of multiple indicators such as photovoltaic plant boundaries, prohibited areas, and booster station plane dimensions, these scores constitute a multi-attribute decision matrix. Through normalization processing, the scores of different units and dimensions are standardized. According to the dynamic state of each indicator, its dynamic weight vector is calculated, and a variable weight vector is constructed. The normalized multi-attribute decision matrix is ​​multiplied by the variable weight vector item by item to obtain a weighted standardized decision matrix. Based on the weighted standardized decision matrix, the positive ideal solution and negative ideal solution of each evaluation indicator are determined, and the distance between each site selection scheme and the positive and negative ideal solutions is calculated, and then the closeness of each scheme is calculated. Finally, the optimal booster station site selection scheme is selected according to the closeness ranking.

[0057] In the process of site selection for photovoltaic booster stations in mountainous areas, the main problems that need to be solved include how to scientifically evaluate multiple factors such as the boundaries of photovoltaic plant areas, prohibited areas, plane dimensions of booster stations, adjacent roads, cut-and-fill balance, flood control capacity, line connection, construction conditions and environmental protection indicators, and comprehensively consider the dynamic weight relationship of these factors. Through the GIS platform, the scores of these factors can be effectively extracted and quantified. However, the site selection method that relies solely on the GIS platform fails to fully consider the dynamic weight relationship between the evaluation indicators, resulting in insufficient scientificity and reliability of the site selection results. In order to overcome this problem, the variable weight TOPSIS model is adopted to calculate the dynamic weight vector of each evaluation indicator and construct a variable weight vector, so as to achieve a scientific and objective evaluation.

[0058] In this method, the GIS platform is first used to extract the scores of multiple indicators such as the boundaries of the photovoltaic plant, the prohibited areas, and the plane dimensions of the booster station. These scores constitute a multi-attribute decision matrix. In order to eliminate the influence of different units and dimensions, the multi-attribute decision matrix needs to be normalized. For benefit-type indicators, the normalization method is to divide each element by the sum of all elements in the column; for cost-type indicators, the normalization method is to calculate the difference between each element and the maximum and minimum values ​​of the column. The normalized matrix allows different evaluation criteria to be compared on the same scale.

[0059] Next, according to the dynamic state of each evaluation indicator, its dynamic weight vector is calculated, and a variable weight vector is constructed. First, the fixed weight vector of each evaluation indicator is determined based on expert experience or hierarchical analysis. Then, according to the performance of each evaluation indicator under the current scheme, its current state value is determined, and a balance function is constructed. Through the balance function, the weight of each evaluation indicator can be adjusted to make it more in line with the actual situation. Finally, the variable weight vector is normalized to obtain a variable weight vector.

[0060] The normalized multi-attribute decision matrix is ​​multiplied by the variable weight vector item by item to obtain the weighted standardized decision matrix. On the basis of the weighted standardized decision matrix, the positive ideal solution and negative ideal solution of each evaluation index are determined. The positive ideal solution is the maximum value of each evaluation index among all schemes, and the negative ideal solution is the minimum value of each evaluation index among all schemes. By calculating the distance between each site selection scheme and the positive ideal solution and the negative ideal solution, the closeness of each scheme can be further calculated. The higher the closeness, the closer the scheme is to the ideal scheme.

[0061] In comparison with existing technologies, this method uses a variable weight TOPSIS model, which overcomes the shortcomings of fixed weights and single evaluation indicators in traditional methods, and can more comprehensively reflect actual engineering needs. At the same time, combined with the advantages of geospatial data processing and multi-index analysis of the GIS platform, different site selection schemes can be evaluated more scientifically and objectively, avoiding the drawbacks of subjective judgment in traditional methods and improving the scientificity and reliability of site selection results.

[0062] Through the above steps, the GIS platform is used to extract and quantify the scores of multiple evaluation indicators, and a multi-attribute decision matrix is ​​constructed. Through normalization processing, the influence of different units and dimensions is eliminated. According to the dynamic state of each evaluation indicator, its dynamic weight vector is calculated, and a variable weight vector is constructed. The normalized multi-attribute decision matrix is ​​multiplied by the variable weight vector item by item to obtain a weighted standardized decision matrix. Based on the weighted standardized decision matrix, the positive ideal solution and negative ideal solution of each evaluation indicator are determined, and the distance between each site selection scheme and the positive and negative ideal solutions is calculated, and then the closeness of each scheme is calculated. Finally, the optimal booster station site selection scheme is selected according to the closeness ranking. Through this method, different site selection schemes can be evaluated scientifically and objectively, avoiding the drawbacks of subjective judgment in traditional methods, and improving the scientificity and reliability of site selection results.

[0063] Furthermore, the present application also proposes a method for normalizing a multi-attribute decision matrix:

[0064] The present application includes a normalization method for benefit-type indicators and cost-type indicators. The benefit-type indicator is normalized by dividing each element by the sum of the indicator to ensure that the value of each element is between 0 and 1. The cost-type indicator is normalized by comparing each element with the maximum and minimum values ​​to ensure that the value of each element is between 0 and 1. These technical features work together to solve the problem of how to normalize a multi-attribute decision matrix. Through the above scheme, different types of evaluation indicators can be effectively processed so that they are comparable in the same decision matrix, thereby improving the scientificity and consistency of the decision-making process.

[0065] For benefit-type indicators, the normalization method is to divide each element in the multi-attribute decision matrix by the sum of the indicators. Specifically, for benefit-type indicator i, the normalization method is:

[0066]

[0067] For cost-type indicators, the normalization method is to compare each element in the multi-attribute decision matrix with the maximum and minimum values ​​of the indicator. Specifically, for cost-type indicator i, its normalization method is:

[0068]

[0069] Among them, m represents the total number of evaluation criteria, a ij is the element of the multi-attribute decision matrix A, a i represents the i-th row of the multi-attribute decision matrix A, the multi-attribute decision matrix

[0070] The advantage of the above normalization method is that it can unify different types of evaluation indicators so that they are comparable in the same decision matrix. This processing method not only improves the scientificity and consistency of the decision-making process, but also effectively solves the problem of normalization of different types of indicators in the multi-attribute decision matrix. Through this method, it can be ensured that each evaluation indicator is reasonably considered and processed in the decision-making process, thereby improving the accuracy and reliability of the decision results.

[0071] Furthermore, the present application also proposes to determine the fixed weight vector W = [ω1ω2,…,ω m ] T ,and According to the performance of the indicator under the current scheme, the current state value S of each evaluation indicator is determined as [s1s2,…,s m ]; construct equilibrium function B i (S i ), B i (S i )=k1·S i , k1 is the adjustment coefficient, k1 is used to ensure that the vector after weight adjustment meets the normalization condition and calculate the variable weight vector W ′ , W i ′ =ω i ·B i (S i ), the normalized variable weight vector

[0072] The fixed weight vector of each evaluation indicator is determined through expert experience or hierarchical analysis to ensure the scientificity and rationality of the initial weight allocation. The current state value of each evaluation indicator is determined by the indicator performance under the current scheme to reflect the actual situation. By constructing the equilibrium function B i (S i ), and use the adjustment coefficient k1 to ensure that the weight-adjusted vector meets the normalization condition. This allows the weight vector to be adjusted dynamically. By normalizing the weight vector Ensure that the sum of the components of the final weight vector is 1. These technical features work together to solve the problem of dynamic weight calculation of evaluation indicators, so that in the multi-attribute decision-making process, the weights can be dynamically adjusted to reflect the actual situation, thereby improving the scientificity and reliability of decision-making.

[0073] Expert experience or analytic hierarchy process can be obtained through questionnaire surveys, expert interviews, etc. The current state value can be determined through actual measurement, historical data analysis, etc. Balance function B i (Si ) can be constructed based on a linear or nonlinear model, and the adjustment coefficient k1 can be determined by experimental data or optimization algorithms. The calculation can be achieved through matrix operations, normalizing the variable weight vector The calculation can be achieved by standardization method.

[0074] Furthermore, the present application also proposes to multiply the normalized multi-attribute decision matrix and the variable weight matrix item by item to obtain a weighted standardized decision matrix. Specifically:

[0075] The technical feature of the present application is to multiply the normalized multi-attribute decision matrix by the variable weight matrix item by item, and obtain the weighted standardized decision matrix by the Hadamard product method. The normalized multi-attribute decision matrix X is represented by X ij As a matrix composed of elements, the variable weight vector It is a weight matrix calculated based on the dynamic state of each evaluation index. By multiplying each item and combining the dynamic weights of each evaluation index, it can more scientifically reflect the pros and cons of each site selection scheme.

[0076] Through the above scheme, the problem of multiplying the normalized multi-attribute decision matrix and the variable weight matrix item by item to obtain the weighted standardized decision matrix is ​​solved. This method combines the dynamic weights of each evaluation index with the decision matrix by the Hadamard product method, which can more accurately reflect the comprehensive performance of each site selection scheme under different evaluation indicators, thereby improving the scientificity and reliability of the site selection results.

[0077] In this application, the normalized multi-attribute decision matrix is ​​multiplied item by item with the variable weight vector to obtain the weighted standardized decision matrix:

[0078]

[0079] Where C represents the weighted standardized decision matrix, X represents the ij As the normalized multi-attribute decision matrix composed of elements, represents the Hadamard product.

[0080] Therefore, the present application obtains a weighted standardized decision matrix by multiplying the normalized multi-attribute decision matrix and the variable weight matrix item by item, which can more accurately reflect the comprehensive performance of each site selection scheme under different evaluation indicators.

[0081] Furthermore, the present application also proposes a method for determining the positive ideal solution and negative ideal solution of each evaluation index based on a weighted standardized decision matrix. For each evaluation index, the maximum value of the index among all schemes is taken to obtain the positive ideal solution, and the positive ideal solutions of all the indexes are combined into a positive ideal solution vector; the minimum value of the index among all schemes is taken to obtain the negative ideal solution, and the negative ideal solutions of all the indexes are combined into a negative ideal solution vector.

[0082] The technical features of the technical solution include: determining the maximum value of each evaluation index among all solutions as the positive ideal solution. Combining the positive ideal solutions of all evaluation indicators into a positive ideal solution vector. Determining the minimum value of each evaluation index among all solutions as the negative ideal solution. Combining the negative ideal solutions of all evaluation indicators into a negative ideal solution vector. The role of these technical features is to obtain positive ideal solutions and negative ideal solutions respectively by calculating the extreme value of each evaluation index, thereby providing standards for subsequent site selection scheme evaluation.

[0083] The above technical solution can solve the problem of how to determine the positive ideal solution and negative ideal solution of each evaluation index in the site selection of mountain photovoltaic booster stations. Specifically, by calculating the maximum and minimum values ​​of each evaluation index in all schemes, the positive ideal solution and negative ideal solution are obtained respectively, and they are combined into vectors, thereby providing a unified evaluation benchmark for each site selection scheme, ensuring the scientificity and reliability of the site selection results.

[0084] Furthermore, the method for determining the positive ideal solution and the negative ideal solution can be implemented by the following steps: First, for each evaluation index, the maximum value of the index in all solutions is calculated to obtain the positive ideal solution, and these positive ideal solutions are combined into a positive ideal solution vector. Then, for each evaluation index, the minimum value of the index in all solutions is calculated to obtain the negative ideal solution, and these negative ideal solutions are combined into a negative ideal solution vector. In the specific implementation process, a programming language such as Python or MATLAB can be used for calculation and vector operations, thereby improving calculation efficiency and accuracy.

[0085] This method can ensure the objectivity and consistency of the site selection evaluation. Compared with the prior art, the method of the present application can more scientifically determine the positive ideal solution and negative ideal solution of each evaluation index, avoid the error caused by subjective judgment in the traditional method, and improve the reliability and scientificity of the site selection results.

[0086] Furthermore, the present application also proposes a method for calculating the distance between each site selection scheme and the positive ideal solution and the negative ideal solution, and then calculating the closeness of each scheme as follows:

[0087] For each solution j, calculate the distance to the positive ideal solution and the distance to the negative ideal solution

[0088]

[0089] Calculate the closeness of each solution j

[0090]

[0091] The greater the degree of closeness, the closer the site selection plan is to the ideal state.

[0092] For example, in practical applications, the evaluation index data of each site selection scheme can be obtained through the GIS platform to construct a decision matrix. Then, the distance between the positive ideal solution and the negative ideal solution of each scheme is calculated, and then the closeness is calculated. In this way, multiple site selection schemes can be effectively evaluated and ranked scientifically.

[0093] Therefore, the method of the present application can not only solve the distance calculation problem between each site selection scheme and the positive ideal solution and the negative ideal solution in the site selection scheme of the photovoltaic booster station, but also quantify and compare the advantages and disadvantages of different site selection schemes and provide scientific decision support. Compared with the traditional method that relies on experience, the method of the present application is more scientific and consistent.

[0094] The parts not described in detail in the present invention are all known technologies to those skilled in the art. Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not limiting. Although the present invention is described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention can be modified or replaced by equivalents without departing from the purpose and scope of the technical solutions of the present invention, which should be included in the scope of the claims of the present invention.

Claims

1. A mountain photovoltaic booster station site selection optimization method based on GIS platform and variable weight TOPSIS model, characterized in that: The method comprises the following steps: a. Using the GIS platform, extract the PV plant boundary, prohibited areas, plane dimensions of the booster station, adjacent roads, cut-and-fill balance, flood control capacity, line connection, construction conditions and environmental protection indicators, and obtain the scores of each indicator; b. Based on the scores of each indicator, a multi-attribute decision-making matrix containing multiple site selection options and evaluation criteria is constructed; c. Normalize the multi-attribute decision matrix; d. Based on the dynamic state of each evaluation indicator, calculate the dynamic weight vector of each indicator and construct a variable weight vector; e. Multiply the normalized multi-attribute decision matrix and the variable weight vector item by item to obtain a weighted standardized decision matrix; f. Based on the weighted standardized decision matrix, determine the positive ideal solution and negative ideal solution of each evaluation index; g. Calculate the distance between each site selection plan and the positive ideal solution and the negative ideal solution, and then calculate the closeness of each plan, and sort the alternative plans according to the closeness; h. Based on the sorting results, select the plan with the highest degree of closeness as the optimal booster station location.

2. The method for optimizing the site selection of mountain photovoltaic booster stations based on GIS platform and variable weight TOPSIS model according to claim 1 is characterized in that: The method of constructing a multi-attribute decision matrix including multiple site selection schemes and evaluation criteria based on the extracted indicators is as follows: Among them, a ij It represents the score of the jth site selection plan under the i-th evaluation criterion.

3. The method for optimizing the site selection of mountain photovoltaic booster stations based on GIS platform and variable weight TOPSIS model according to claim 2 is characterized in that: The method for normalizing the multi-attribute decision matrix is ​​as follows: for the benefit-type indicator i, the normalization method of the corresponding element of the multi-attribute decision matrix is ​​as follows: For cost-type indicator i, the normalization method of the corresponding elements of the multi-attribute decision matrix is ​​as follows: Wherein, m represents the total number of evaluation criteria.

4. The method for optimizing the site selection of mountain photovoltaic booster stations based on GIS platform and variable weight TOPSIS model according to claim 3 is characterized in that: The method of calculating the dynamic weight vector of each indicator based on the dynamic state of each evaluation indicator and constructing the variable weight vector is as follows: S1. Determine the fixed weight vector W = [ω1ω2,Ω,ω m ] T ,and S2, determine the current state value of each evaluation indicator according to the performance of the indicator under the current scheme S = [s1 s2,…,s m ]; S3. Constructing equilibrium function B i (S i ), B i (S i )=k1·S i , k1 is the adjustment coefficient, k1 is used to ensure that the vector after weight adjustment meets the normalization condition; S4. Calculate the variable weight vector W′, W′ i =ω i ·B i (S i ); S5, normalized variable weight vector 5. The method for optimizing the site selection of mountain photovoltaic booster stations based on GIS platform and variable weight TOPSIS model according to claim 4 is characterized in that: The normalized multi-attribute decision matrix is ​​multiplied item by item with the variable weight vector to obtain the weighted standardized decision matrix: Where C represents the weighted standardized decision matrix, X represents the ij As the normalized multi-attribute decision matrix composed of elements, Denotes the Hadamard product.

6. The method for optimizing the site selection of mountain photovoltaic booster stations based on GIS platform and variable weight TOPSIS model according to claim 5 is characterized in that: The method for determining the positive ideal solution and the negative ideal solution of each evaluation index based on the weighted standardized decision matrix is: For each evaluation index i, Taking the maximum value of this index among all solutions to obtain the positive ideal solution, The positive ideal solutions of all indices are combined into a positive ideal solution vector, Taking the minimum value of this index among all solutions to obtain the negative ideal solution, The negative ideal solutions of all indicators are combined into a negative ideal solution vector, 7. The method for optimizing the site selection of mountain photovoltaic booster stations based on GIS platform and variable weight TOPSIS model according to claim 6 is characterized in that: The method for calculating the distance between each site selection scheme and the positive ideal solution and the negative ideal solution, and then calculating the closeness of each scheme is: For each solution j, calculate the distance to the positive ideal solution and the distance to the negative ideal solution Calculate the closeness of each solution j 。

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