A method for evaluating an electric-hydrogen coupling conversion grid sending project and a terminal

Abstract

The application discloses a kind of electric hydrogen coupling conversion power grid sending project post-evaluation method and terminal, the evaluation data of the electric hydrogen coupling conversion power grid sending project obtained is preprocessed, then respectively using objective weighting model and subjective weighting model Objective weight and subjective weight of preprocessed evaluation data are calculated, least square method is used to fuse objective weight and subjective weight, obtain the most weight, using evaluation algorithm based on the most weight and preprocessed evaluation data to the electric hydrogen coupling conversion power grid sending project is evaluated, and evaluation result is obtained, so that multiple weighting algorithm is fused, based on the most weight and preprocessed evaluation data of the project is evaluated, compared with single weight direct current project post-evaluation method, evaluation result is more reasonable, reliable, to improve the reliability and effectiveness of evaluation.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of engineering evaluation, in particular to a method and terminal for post-evaluation of electric-hydrogen coupling conversion power grid sending-out engineering. BACKGROUND

[0002] Electric-hydrogen coupling conversion power grid sending-out engineering involves the interconnection and coordination of power systems and hydrogen energy systems, including conversion technologies, application scenarios, and policy support of electric energy and hydrogen energy. Electric-hydrogen coupling helps improve the flexibility, stability, and sustainability of energy systems, providing support for energy transformation and climate change mitigation.

[0003] Generally, comprehensive evaluation includes evaluation objects, evaluation indexes, weight coefficients, comprehensive evaluation models, and evaluators. The general steps of comprehensive evaluation are: defining the evaluation purpose, determining the evaluation object, establishing the evaluation index system, determining the evaluation weight coefficients corresponding to each evaluation index, selecting or constructing the total evaluation model, calculating the comprehensive evaluation value of the evaluated object and sorting or classifying. Generally, in the post-evaluation analysis of direct current engineering, various evaluation factors are involved, and when establishing the evaluation index system, the evaluation criteria may be expanded to more than nine categories. Ordinary analytic hierarchy process is suitable for index systems with no more than nine single-level indexes, but cannot handle cases where the number of evaluation criteria exceeds nine. At this time, the analytic hierarchy process may have problems such as not passing the consistency check, affecting the reliability of the evaluation results of electric-hydrogen coupling conversion power grid sending-out engineering. SUMMARY

[0004] The technical problem to be solved by the present application is to provide a method and terminal for post-evaluation of electric-hydrogen coupling conversion power grid sending-out engineering, which can improve the reliability and effectiveness of evaluation.

[0005] To solve the above technical problems, the technical scheme adopted by the present application is:

[0006] A method for post-evaluation of electric-hydrogen coupling conversion power grid sending-out engineering, comprising the steps of:

[0007] Obtaining evaluation data of electric-hydrogen coupling conversion power grid sending-out engineering, and preprocessing the evaluation data to obtain preprocessed evaluation data;

[0008] Using an objective weighting model to calculate the objective weight of the preprocessed evaluation data, and using a subjective weighting model to calculate the subjective weight of the preprocessed evaluation data;

[0009] Using the least squares method to fuse the objective weight and the subjective weight to obtain the minimum weight;

[0010] An evaluation algorithm is used to evaluate the electric-hydrogen coupling conversion power grid sending project based on the final weight and the preprocessed evaluation data, and an evaluation result is obtained.

[0011] To solve the above technical problems, another technical solution adopted by the present application is:

[0012] An electric-hydrogen coupling conversion power grid sending project post-evaluation terminal, comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein the processor implements the following steps when executing the computer program:

[0013] Obtain evaluation data of an electric-hydrogen coupling conversion power grid sending project, and preprocess the evaluation data to obtain preprocessed evaluation data;

[0014] An objective weight of the preprocessed evaluation data is calculated using an objective weighting model, and a subjective weight of the preprocessed evaluation data is calculated using a subjective weighting model;

[0015] The objective weight and the subjective weight are fused using a least square method to obtain a final weight;

[0016] An evaluation algorithm is used to evaluate the electric-hydrogen coupling conversion power grid sending project based on the final weight and the preprocessed evaluation data, and an evaluation result is obtained.

[0017] The present application has the following advantages: the obtained evaluation data of the electric-hydrogen coupling conversion power grid sending project is preprocessed, then the objective weight and the subjective weight of the preprocessed evaluation data are calculated using an objective weighting model and a subjective weighting model respectively, the objective weight and the subjective weight are fused using a least square method to obtain a final weight, and an evaluation algorithm is used to evaluate the electric-hydrogen coupling conversion power grid sending project based on the final weight and the preprocessed evaluation data to obtain an evaluation result. In this way, multiple weighting algorithms are fused, the project is evaluated based on the obtained final weight and the preprocessed evaluation data, the evaluation result is more reasonable and reliable compared to the direct current project post-evaluation method of a single weight, and thus the reliability and effectiveness of the evaluation are improved. BRIEF DESCRIPTION OF DRAWINGS

[0018] Figure 1 A step flowchart of an electric-hydrogen coupling conversion power grid sending project post-evaluation method according to an embodiment of the present application;

[0019] Figure 2 A structural schematic diagram of an electric-hydrogen coupling conversion power grid sending project post-evaluation terminal according to an embodiment of the present application;

[0020] Figure 3The evaluation flowchart in the post-evaluation method of the electric-hydrogen coupling conversion power grid sending-out project of the embodiment of the present application. DETAILED DESCRIPTION

[0021] To make the technical content, the achieved purposes and effects of the present application clear, the following will be described in combination with the embodiments and the accompanying drawings.

[0022] Please refer to Figure 1 The post-evaluation method of the electric-hydrogen coupling conversion power grid sending-out project, comprising the steps of:

[0023] Obtaining the evaluation data of the electric-hydrogen coupling conversion power grid sending-out project, and preprocessing the evaluation data to obtain preprocessed evaluation data;

[0024] Using an objective weighting model to calculate the objective weight of the preprocessed evaluation data, and using a subjective weighting model to calculate the subjective weight of the preprocessed evaluation data;

[0025] Using the least square method to fuse the objective weight and the subjective weight to obtain the minimum weight;

[0026] Using an evaluation algorithm to evaluate the electric-hydrogen coupling conversion power grid sending-out project based on the minimum weight and the preprocessed evaluation data to obtain an evaluation result.

[0027] From the above description, the beneficial effects of the present application are that the obtained evaluation data of the electric-hydrogen coupling conversion power grid sending-out project is preprocessed, then the objective weight and the subjective weight of the preprocessed evaluation data are calculated using an objective weighting model and a subjective weighting model respectively, the objective weight and the subjective weight are fused using the least square method to obtain the minimum weight, and the electric-hydrogen coupling conversion power grid sending-out project is evaluated using an evaluation algorithm based on the minimum weight and the preprocessed evaluation data to obtain an evaluation result. In this way, multiple weighting algorithms are fused, the project is evaluated based on the obtained minimum weight and the preprocessed evaluation data, compared with the direct current project post-evaluation method of a single weight, the evaluation result is more reasonable and reliable, thereby improving the reliability and effectiveness of the evaluation.

[0028] Further, the preprocessing of the evaluation data to obtain the preprocessed evaluation data comprises:

[0029] Calculating the common factor variance of the evaluation data;

[0030] Based on the common factor variance, using the decision laboratory method to analyze and screen the evaluation data to obtain the preprocessed evaluation data.

[0031] From the above description, the common factor variance of the evaluation data is calculated first, and then the evaluation data is analyzed and screened based on the common factor variance using the decision laboratory method, so that the factors that can best reflect the situation of the electric-hydrogen coupling conversion power grid sending project can be obtained, thereby ensuring the effectiveness of the final evaluation result.

[0032] Further, the calculating the common factor variance of the evaluation data comprises:

[0033]

[0034] In the formula, r represents the common factor variance of the evaluation data, X represents the evaluation data, and Y represents the common factor data of the evaluation data.

[0035] From the above description, the common factor variance of the evaluation data is calculated, so that the evaluation data with a large amount of information can be screened out subsequently, which is convenient for evaluation and ensures the effectiveness of the evaluation.

[0036] Further, the objective weighting model comprises a grey system method, an information entropy method, an improved principal component analysis method, and an ordered weighted average operator method.

[0037] The calculating the objective weight of the pretreated evaluation data using the objective weighting model comprises:

[0038] The objective weight of the pretreated evaluation data is calculated using the grey system method, the information entropy method, the improved principal component analysis method, and the ordered weighted average operator method, respectively.

[0039] From the above description, the objective weight of the pretreated evaluation data is calculated using the grey system method, the information entropy method, the improved principal component analysis method, and the ordered weighted average operator method, respectively, which can integrate the advantages of each weighting method and ensure the rationality of the final weight.

[0040] Further, the subjective weighting model is an analytic hierarchy process method.

[0041] The calculating the subjective weight of the pretreated evaluation data using the subjective weighting model comprises:

[0042] The subjective weight of the pretreated evaluation data is calculated using the analytic hierarchy process method.

[0043] From the above description, the subjective weight of the pretreated evaluation data is calculated using the analytic hierarchy process method, which is more concise and practical, and the subjective weight is more systematic.

[0044] Further, the calculating the subjective weight of the pretreated evaluation data using the analytic hierarchy process method comprises:

[0045] The evaluation data is preprocessed to obtain preprocessed evaluation data, and the preprocessed evaluation data is subjected to subjective weighting to obtain a subjective weight of the preprocessed evaluation data.

[0046] The correlation coefficient matrix of the n indexes is calculated based on the original data matrix.

[0047] The eigenvalues of the correlation coefficient matrix and the eigenvectors corresponding thereto are calculated.

[0048] A preset number of eigenvalues and the eigenvectors corresponding thereto are selected, and the subjective weight of the preprocessed evaluation data is determined based on the preset number of eigenvalues and the eigenvectors corresponding thereto.

[0049] As can be seen from the above description, the analytic hierarchy process is a multi-factor decision analysis method combining qualitative and quantitative analysis. Using the method to subjectively weight the evaluation data can quantify the experience judgment of the decision maker, and the method is more convenient to use in the case of complex target factor structure and lack of necessary data.

[0050] Further, the evaluation algorithm is used to evaluate the electric-hydrogen coupling conversion power grid sending project based on the maximum weight and the preprocessed evaluation data to obtain an evaluation result, which includes:

[0051] The clustering analysis method, the grey network analysis method and the grey system method are respectively used to evaluate the electric-hydrogen coupling conversion power grid sending project based on the maximum weight and the preprocessed evaluation data to obtain evaluation scores.

[0052] The multi-criteria compromise solution ranking method is used to analyze all the evaluation scores to obtain an optimal evaluation score, which is taken as the evaluation result.

[0053] As can be seen from the above description, the clustering analysis method, the grey network analysis method and the grey system method are respectively used to evaluate the electric-hydrogen coupling conversion power grid sending project based on the maximum weight and the preprocessed evaluation data, and then the multi-criteria compromise solution ranking method is used to analyze all the evaluation scores to obtain an optimal evaluation score, which is taken as the evaluation result. Through multiple analysis methods, the quantization problem of data indexes in the evaluation process is fully considered, which is suitable for evaluation problems of arbitrary dimension, thereby improving the reliability of the evaluation.

[0054] Further, the multi-criteria compromise solution ranking method is used to analyze all the evaluation scores to obtain an optimal evaluation score, which includes:

[0055]

[0056] In the formula, s ij represents the optimal evaluation score, n represents the ordinal number, k represents the ranking, and x ki represents the evaluation score i. represents the mean value i, x kj represents the evaluation score j, represents the mean value j.

[0057] As can be seen from the above description, using the multi-criteria compromise solution ranking method to analyze all the evaluation scores can obtain the most accurate evaluation result, ensuring the rationality and effectiveness of the evaluation.

[0058] Further, the least square method is used to fuse the objective weight and the subjective weight to obtain the final weight, which includes:

[0059]

[0060]

[0061] 0≤ΔL j,k,t ≤L j,t ;

[0062]

[0063] In the formula, G represents the final weight, i∈θ j represents the data ordinal value, G i,j,k,t represents the first objective weight calculated by the grey system method, l∈EX j represents the expectation j, y l,k,t represents the second objective weight calculated by the information entropy method, represents the third objective weight calculated by the improved principal component analysis method, m∈γ j represents the calculation ordinal, represents the fourth objective weight calculated by the ordered weighted averaging operator method, Z l,k,t represents the subjective weight, G i,j x i,j,k,t represents the lower limit value of weight calculation, represents the upper limit value of weight calculation, ΔL j,k,t represents the step distance difference value, L j,t represents the step distance, represents the principal component lower limit, represents the principal component upper limit.

[0064] As can be seen from the above description, the least square method is used to fuse the objective weight and the subjective weight, thereby realizing the effective fusion of multiple weights and improving the reliability of the evaluation result.

[0065] Please refer to Figure 2Another embodiment of the present application provides an evaluation terminal for post-project of electric-hydrogen coupling conversion power grid sending, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements each step of the above-mentioned evaluation method for post-project of electric-hydrogen coupling conversion power grid sending when executing the computer program.

[0066] The above-mentioned evaluation method for post-project of electric-hydrogen coupling conversion power grid sending and the terminal can be applied to the scenario of evaluating the post-project of electric-hydrogen coupling conversion power grid sending, and the following specific embodiments are described:

[0067] Please refer to Figure 1 and Figure 3 Embodiment one of the present application is:

[0068] An evaluation method for post-project of electric-hydrogen coupling conversion power grid sending, comprising the steps of:

[0069] S1, obtaining evaluation data of the post-project of electric-hydrogen coupling conversion power grid sending, and preprocessing the evaluation data to obtain preprocessed evaluation data, specifically comprising S11-S13:

[0070] S11, obtaining evaluation data of the post-project of electric-hydrogen coupling conversion power grid sending.

[0071] In an optional embodiment, the evaluation data comprises line equivalent single loop length, tower base number, strain angle tower base number, tower material quantity, conductor branch number, conductor cross-sectional area, wire quantity, conductor price, complex terrain length, earthwork quantity, foundation steel quantity, and foundation steel price, and the complex terrain comprises river network, marsh, mountain, high mountain, desert, and steep ridge.

[0072] S12, calculating the common factor variance of the evaluation data, specifically:

[0073]

[0074] In the formula, r represents the common factor variance of the evaluation data, X represents the evaluation data, and Y represents the common factor data of the evaluation data, which is obtained through data collection.

[0075] S13, analyzing and screening the evaluation data based on the common factor variance using the decision laboratory method to obtain preprocessed evaluation data, as shown in Figure 3 .

[0076] Specifically, the common factor variance and the evaluation data are taken as inputs of the decision laboratory method to obtain a simplified data input matrix, i.e., preprocessed evaluation data in the form of a matrix.

[0077] S2, calculating objective weights of the preprocessed evaluation data using an objective weighting model and calculating subjective weights of the preprocessed evaluation data using a subjective weighting model, specifically comprising S21-S22:

[0078] As shown in the objective weighting model includes a grey system method, an information entropy method, an improved principal component analysis method and an ordered weighted average operator method; the subjective weighting model is an analytic hierarchy process; Figure 3

[0079] S21, calculating objective weights of the preprocessed evaluation data using the grey system method, the information entropy method, the improved principal component analysis method and the ordered weighted average operator method respectively.

[0080] The grey system method, the information entropy method, the improved principal component analysis method and the ordered weighted average operator method are directly calculated by using existing methods, and the results are used as objective weights.

[0081] S22, calculating subjective weights of the preprocessed evaluation data using the analytic hierarchy process, specifically comprising S221-S224:

[0082] S221, assuming that an evaluation index system is composed of n indexes and m samples, determining an original data matrix of the evaluation index system, specifically:

[0083]

[0084] In the formula, L represents the original data matrix of the evaluation index system, l ij represents an element in the original data matrix.

[0085] S222, calculating an n-index correlation coefficient matrix R=(r ij ) n×n based on the original data matrix, specifically:

[0086]

[0087]

[0088] In the formula, represents an index coefficient of the i-th data matrix, represents an index coefficient of the j-th data matrix, r ij represents an element in the index correlation coefficient matrix, l ki represents an element in the k-th row and i-th column of the original data matrix, l kj represents an element in the k-th row and j-th column of the original data matrix.

[0089] ​S223, calculate the eigenvalues of the correlation coefficient matrix and the eigenvectors corresponding thereto.

[0090] Specifically, the eigenvalues of the correlation coefficient matrix R and the eigenvectors corresponding thereto are calculated, that is, the variance is solved: |R-λI|=0, where I represents the eigenvector, and this formula identifies that the absolute value of the eigenvalue of the variance is zero;

[0091] The equation is solved to obtain n eigenvalues, denoted as λ1, λ2, …, λ n The corresponding n unit eigenvectors are denoted as e1, e2, …, e n .

[0092] S224, a preset number of eigenvalues and the eigenvectors corresponding to the tone are selected, and the subjective weight of the preprocessed evaluation data is determined based on the preset number of eigenvalues and the eigenvectors corresponding to the tone.

[0093] Specifically, the first h (h h h that is,

[0094]

[0095] w is normalized to obtain the subjective weight of the preprocessed evaluation data.

[0096] S3, the objective weight and the subjective weight are fused using the least square method to obtain the optimal weight, as shown in the following formula: Figure 3 Specifically,

[0097]

[0098]

[0099] 0≤ΔL j,k,t ≤L j,t ;

[0100]

[0101] In the formula, G represents the optimal weight, i∈θ j represents the data sequence value, G i,j,k,t represents the first objective weight calculated by the grey system method, l∈EX j represents the expectation j, y l,k,t ​Second objective weight calculated by information entropy method, Third objective weight calculated by improved principal component analysis method, m∈γ j Indicates the calculation of ordinal number, Fourth objective weight calculated by ordered weighted average operator method, Z l,k,t Indicates the subjective weight, G i,j x i,j,k,t Indicates the lower limit value of weight calculation, Indicates the upper limit value of weight calculation, ΔL j,k,t Indicates the step distance difference value, L j,t Indicates the step distance, Indicates the lower limit of principal component, Indicates the upper limit of principal component.

[0102] The criterion of the operator in the calculation process is as follows: the minimum value of the weight and the weighted operator can be obtained by calculating the eigenvalue, which has continuous additivity:

[0103]

[0104]

[0105]

[0106] In the formula, N Z Indicates the base number of eigenvalue, a i,j Indicates the eigenvalue coefficient i, G i,j Indicates the eigenvalue G, a m,j Indicates the eigenvalue coefficient j, P m,j Indicates the eigenvalue Pj, CF m Indicates the minimum value of eigenvalue, ESP represents the expected minimum eigenvalue, ECP represents the expected variance value, P' m Indicates the eigenvalue difference P, NHP represents the nonlinear eigenvalue, Indicates the average value of eigenvalue, P' m,j Indicates the eigenvalue Pm, FOR m,j Indicates the principal component of eigenvalue.

[0107] S4, using evaluation algorithm to evaluate the electric hydrogen coupling conversion power grid sending project based on the maximum weight and the preprocessed evaluation data, and obtaining evaluation result, such as Figure 3 As shown, specifically comprising S41-S42:

[0108] S41, respectively using clustering analysis method, grey network analysis method and grey system method to evaluate the electric hydrogen coupling conversion power grid sending project based on the maximum weight and the preprocessed evaluation data, and obtaining evaluation score.

[0109] The grey system method calculation formula is as follows:

[0110]

[0111] Delta i (k) = |X0(k) - X i (k) |;

[0112] In the formula, xi i (k) represents an evaluation score, Delta i (k) represents an absolute difference value of the index k.

[0113] The cluster analysis method, the grey network analysis method and the grey system method are all existing algorithms.

[0114] S42, using a multi-criteria compromise solution ranking method to analyze all the evaluation scores, obtaining an optimal evaluation score and taking the optimal evaluation score as an evaluation result, specifically:

[0115]

[0116] In the formula, s ij represents the optimal evaluation score, n represents a serial number, k represents ranking, xi ki represents an evaluation score i, represents an average value i, xi kj represents an evaluation score j, represents an average value j.

[0117] Please refer to Figure 2 Embodiment two of the present application is:

[0118] A post-evaluation terminal for an electric-hydrogen coupling conversion power grid sending project, comprising a memory, a processor and a computer program stored in the memory and capable of running on the processor, and the processor implements each step in the post-evaluation method for the electric-hydrogen coupling conversion power grid sending project in embodiment one when executing the computer program.

[0119] In summary, the application provides an electric-hydrogen coupling conversion power grid sending project post-evaluation method and terminal, pre-processes the evaluation data of the obtained electric-hydrogen coupling conversion power grid sending project, then calculates the objective weight and subjective weight of the pre-processed evaluation data using an objective weighting model and a subjective weighting model respectively, fuses the objective weight and the subjective weight using the least square method to obtain the final weight, evaluates the electric-hydrogen coupling conversion power grid sending project based on the final weight and the pre-processed evaluation data using an evaluation algorithm to obtain an evaluation result, thereby fusing multiple weighting algorithms, evaluating the project based on the obtained final weight and the pre-processed evaluation data, compared with the direct current project post-evaluation method of a single weight, the evaluation result is more reasonable and reliable, thereby improving the reliability and effectiveness of the evaluation; in addition, the electric-hydrogen coupling conversion power grid sending project is evaluated based on the final weight and the pre-processed evaluation data using a cluster analysis method, a grey network analysis method and a grey system method respectively, then all the evaluation scores are analyzed using a multi-criteria compromise solution ranking method to obtain the optimal evaluation score as the evaluation result, the quantization problem of the data index in the evaluation process is fully considered through multiple analysis methods, the method is suitable for evaluation problems of arbitrary dimension, and the reliability of the evaluation is further improved.

[0120] The above description is only an embodiment of the application, and does not limit the patent range of the application, and any equivalent transformation or direct or indirect application in the related technical field using the content of the specification and drawings of the application is also included in the patent protection range of the application.

Claims

1. A method for post-evaluation of an electro-hydrogen coupling conversion power grid transmission project, characterized in that, Including the following steps: The evaluation data of the electro-hydrogen coupling conversion power grid transmission project is obtained, and the evaluation data is preprocessed to obtain preprocessed evaluation data. The objective weighting model is used to calculate the objective weights of the preprocessed evaluation data, and the subjective weighting model is used to calculate the subjective weights of the preprocessed evaluation data. The objective weights and subjective weights are fused using the least squares method to obtain the final weights; The evaluation algorithm is used to evaluate the power grid transmission project of the electro-hydrogen coupling conversion based on the final weight and the preprocessed evaluation data, and the evaluation results are obtained. The preprocessing of the evaluation data to obtain preprocessed evaluation data includes: Calculate the common factor variance of the evaluation data; The evaluation data were analyzed and screened using the decision laboratory method based on the common factor variance to obtain preprocessed evaluation data. The calculation of the common factor variance of the evaluation data includes: ； In the formula, r represents the common factor variance of the evaluation data, X represents the evaluation data, and Y represents the common factor data of the evaluation data; The objective weighting model includes the grey system method, the information entropy method, the improved principal component analysis method, and the ordered weighted average operator method. The calculation of the objective weights of the preprocessed evaluation data using the objective weighting model includes: The objective weights of the preprocessed evaluation data are calculated using the grey system method, the information entropy method, the improved principal component analysis method, and the ordered weighted average operator method, respectively. The subjective weighting model is the Analytic Hierarchy Process (AHP). The subjective weighting of the preprocessed evaluation data calculated using the subjective weighting model includes: The subjective weights of the preprocessed evaluation data are calculated using the analytic hierarchy process described above. The evaluation algorithm evaluates the electro-hydrogen coupling conversion power grid transmission project based on the final weights and the preprocessed evaluation data, and the evaluation results include: The electro-hydrogen coupling conversion power grid transmission project was evaluated using cluster analysis, grey network analysis, and grey system method based on the final weights and the preprocessed evaluation data, respectively, to obtain an evaluation score. The multi-criteria compromise solution ranking method is used to analyze all the evaluation scores, obtain the optimal evaluation score, and use it as the evaluation result.

2. The post-evaluation method for an electro-hydrogen coupling conversion power grid transmission project according to claim 1, characterized in that, The calculation of the subjective weights of the preprocessed evaluation data using the analytic hierarchy process includes: Suppose that the rating index system consists of n indicators and m samples, determine the original data matrix of the rating index system; Calculate the correlation coefficient matrix of n indicators based on the original data matrix; Calculate the eigenvalues ​​of the correlation coefficient matrix and their corresponding eigenvectors; Select a preset number of feature roots and feature vectors corresponding to tone, and determine the subjective weights of the preprocessed evaluation data based on the preset number of feature roots and feature vectors corresponding to tone.

3. The post-evaluation method for an electro-hydrogen coupling conversion power grid transmission project according to claim 1, characterized in that, The method of using a multi-criteria compromise solution ranking to analyze all the evaluation scores and obtain the optimal evaluation scores includes: ； In the formula, s ij Let represent the optimal evaluation score, n represent the ordinal number, and k represent the ranking. Indicates the evaluation score i. Let i represent the mean. Let j represent the evaluation score. Let j represent the mean.

4. The post-evaluation method for an electro-hydrogen coupling conversion power grid transmission project according to claim 1, characterized in that, The process of fusing the objective weights and the subjective weights using the least squares method to obtain the final weights includes: ； ； ； ； In the formula, G represents the final weight. Indicates the ordinal value of the data. This represents the first objective weight calculated by the grey system method. Let j represent the expected value. This represents the second objective weight calculated using the information entropy method. This represents the third objective weight calculated by the improved principal component analysis method. Indicates the calculation of ordinal numbers. This represents the fourth objective weight calculated using the ordered weighted average operator method. Indicates subjective weighting. This represents the lower limit for weight calculation. This represents the upper limit of the weight calculation. This represents the difference in step distance. Indicates the step distance. Indicates the lower limit of the principal component. Indicates the upper limit of principal components.

5. A post-evaluation terminal for an electro-hydrogen coupling conversion power grid transmission project, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements each step of the post-evaluation method for an electro-hydrogen coupling conversion power grid transmission project as described in any one of claims 1 to 4.

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