Novel power distribution network planning evaluation method and system

Abstract

The invention discloses a novel power distribution network planning evaluation method and system, and relates to the technical field of power system optimization and energy management, and the method comprises the steps: obtaining the original data of each index in a novel power distribution network index system, and carrying out the standardization and non-dimensionalization preprocessing of the index data; subjective empowerment is carried out through a DEMATEL method, objective empowerment is carried out through a CRITIC method, and subjective and objective comprehensive empowerment is carried out based on a game theory optimization weight coefficient; and novel power distribution network planning evaluation sorting based on a VIKOR method. According to the method, the data preprocessing quality is improved, a solid foundation is laid for subsequent weight assignment and evaluation sorting, the beneficial effect of accurately describing the interaction between indexes is achieved, the reasonability and reliability of weight assignment are improved, the difference and conflict of index data are fully considered, the scientificity of objective weight assignment is ensured, and the method is suitable for large-scale popularization and application. The objective weight assignment accuracy is improved, and an important basis is provided for comprehensive weight assignment.

Description

Technical Field

[0001] The present invention relates to the technical field of power system planning and energy management, and in particular to a novel distribution network planning and evaluation method and system. Background Art

[0002] With the rapid development of my country's economy, the economy has increasingly become an important part of the national economy. As the foundation of economic development, the planning and evaluation of distribution networks is particularly important. In the past few decades, the planning and evaluation technology of distribution networks has made great progress. Researchers have proposed a variety of evaluation methods and models, such as hierarchical analysis method, fuzzy comprehensive evaluation method, data envelopment analysis method, etc. These methods have improved the rationality and economy of distribution network planning to a certain extent. However, with the acceleration of the process of intelligence and informatization, the existing technology still has many shortcomings in dealing with the complex and changeable distribution network planning and evaluation needs.

[0003] However, the existing technology has major defects in the preprocessing of indicator data. In the distribution network planning and evaluation process, there are many indicators involved and the data sources are diverse. How to effectively standardize and dimensionlessly process these data is the key to ensuring the accuracy of the evaluation results. The existing technology often ignores this point, resulting in the lack of reliability and comparability of the evaluation results. The choice of weight assignment method has an important impact on the evaluation results. The existing weight assignment methods mainly include subjective weighting and objective weighting, but most studies have failed to effectively combine the two, resulting in certain subjective biases or objective distortions in the evaluation results. The existing evaluation methods are not comprehensive enough in terms of comprehensive weighting strategies, especially the lack of application of game theory to optimize weight coefficients, resulting in low accuracy of the evaluation results. In the sorting process, the existing evaluation methods often fail to fully consider the preferences and actual needs of decision makers, making it difficult for the evaluation results to guide actual operations. Summary of the invention

[0004] In view of the above-mentioned problems, the present invention is proposed.

[0005] Therefore, the technical problem solved by the present invention is: the existing power system optimization and energy management technologies have the problems of insufficient index data preprocessing, loose combination of subjective and objective weight assignment methods, and incomplete consideration of comprehensive weighting strategies, as well as the problem of how to achieve efficient and accurate evaluation and ranking.

[0006] To solve the above technical problems, the present invention provides the following technical solutions: a new distribution network planning and evaluation method, comprising obtaining the original data of each indicator in the new distribution network indicator system, standardizing and dimensionless preprocessing the indicator data; performing subjective weighting through the DEMATEL method, performing objective weighting through the CRITIC method, and performing subjective and objective comprehensive weighting based on game theory optimization weight coefficients; and new distribution network planning evaluation ranking based on the VIKOR method.

[0007] As a preferred solution of a novel distribution network planning and evaluation method described in the present invention, wherein: the standardization and dimensionless preprocessing of the indicator data includes preprocessing the benefit-type indicator, the cost-type indicator, and the intermediate indicator to obtain an n*m-dimensional standardized data matrix, where n is the number of indicators and m is the number of solution samples, and the indicators are compared and analyzed. The benefit-type indicator processing formula is expressed as:

[0008]

[0009] Among them, x ij represents the original value of the ith indicator in the jth solution, min(x i ) represents the minimum value of the ith indicator among all solutions, max(x i ) represents the maximum value of the i-th indicator among all solutions, It represents the standardized value of the i-th benefit indicator and the cost indicator processing formula, which is expressed as:

[0010]

[0011] The center indicator processing formula is expressed as:

[0012]

[0013] m=min(x i ), M = max(x i )

[0014] As a preferred solution of the novel distribution network planning and evaluation method described in the present invention, the subjective weighting by the DEMATEL method includes analyzing the logical relationship and direct influence relationship between the elements in the system through experts, revealing the inherent causal relationship of the system and identifying key factors, determining the degree of direct influence of each indicator on each other, and constructing a normalized influence matrix X, which is expressed as:

[0015]

[0016] Among them, a ij It represents the influence of index i on index j, and its value is {0, 1, 2, 3, 4}. The larger the value, the greater the influence. n represents the number of indicators, and max represents the maximum value of the sum of rows or columns. The normalized influence matrix is ​​realized, and the comprehensive influence matrix Y is calculated by matrix transformation. E is the unit matrix, which is expressed as:

[0017]

[0018] Among them, b ijIndicates the comprehensive influence of the ith indicator on the indicator j. The influence of each indicator D is calculated by the comprehensive influence matrix i Influence C i Centrality M i and the degree of cause R i , expressed as:

[0019]

[0020]

[0021] M i =D i +C i

[0022] R i =D i -C i

[0023] The subjective weight w of each indicator is calculated by the DEMATEL method based on the centrality si , expressed as:

[0024]

[0025] Among them, n represents the number of indicators.

[0026] As a preferred solution of the novel distribution network planning and evaluation method described in the present invention, wherein: the objective weights calculated by the CRITIC method include calculating the standard deviation S of each indicator i To reflect the differences in indicator data, it is expressed as:

[0027]

[0028]

[0029] in, It represents the average value of the standardized scores of each scheme of the i-th indicator. At the same time, the correlation coefficient between indicators is calculated to reflect the conflict between indicators R′i, which is expressed as:

[0030]

[0031]

[0032] Among them, r ij represents the correlation coefficient between indicator i and indicator j, and Represent the standardized scores of the i-th and j-th indicators respectively, and calculate the indicator information C through indicator differences and conflicts. i , expressed as:

[0033]

[0034] Calculate the objective weight of the indicator w oi , expressed as:

[0035]

[0036] in, Represents the standardized value of the i-th benefit indicator.

[0037] As a preferred solution of the novel distribution network planning and evaluation method described in the present invention, the subjective and objective comprehensive weighting based on the optimization weight coefficient of game theory includes combining the subjective double weight and the objective weight by linear combination to obtain the final subjective and objective combined weight, which is expressed as:

[0038] w=λ1w s +λ2w o

[0039] Among them, λ1 and λ2 represent the weight coefficients of subjective weight and objective weight respectively, w is the combined weight, w s is the subjective weight, w o The objective weight is constructed based on the mathematical model of game theory. After derivation, two weight coefficients can be solved and normalized, which is expressed as:

[0040]

[0041]

[0042] Where T represents the transpose operation, is the normalized weight coefficient, which is brought into the optimized weight coefficient to obtain the combined weight w, expressed as:

[0043]

[0044] Among them, w o is the objective weight.

[0045] As a preferred solution of the new distribution network planning and evaluation method described in the present invention, the new distribution network planning and evaluation ranking based on the VIKOR method includes normalizing the original indicator data matrix through a data preprocessing formula to obtain a normalized matrix X, and the optimal value and the worst value of the positive and negative ideal solution indicators in the normalized matrix are the maximum value and the minimum value of the indicator data, which are expressed as:

[0046]

[0047] Calculate the group utility value S′ and individual regret value R″ of the planning scheme j , expressed as:

[0048]

[0049]

[0050] Calculate the compromise decision value Q, where u is the decision mechanism coefficient, ranging from [0-1], weighing the importance of maximizing the group utility value and minimizing the individual regret value, expressed as:

[0051]

[0052] The obtained compromise decision value Q is used to evaluate and prioritize the schemes.

[0053] Another object of the present invention is to provide a new distribution network planning and evaluation system, which can perform subjective weighting through the DEMATEL method, objective weighting through the CRITIC method, and perform subjective and objective comprehensive weighting based on game theory optimization weight coefficients, thereby solving the problems of low accuracy of weight assignment, inaccurate understanding of the evaluation model, and inaccurate identification of key factors in current power system planning and energy management technologies.

[0054] As a preferred solution of the new distribution network planning and evaluation system described in the present invention, it includes: a data preprocessing module, a comprehensive weighting module, and a scheme scoring and selection module; the data preprocessing module is used to obtain the original data of each indicator in the new distribution network indicator system, and standardize and dimensionless preprocess the indicator data; the comprehensive weighting module is used to perform subjective weighting through the DEMATEL method, objective weighting through the CRITIC method, and subjective and objective comprehensive weighting based on the optimization weight coefficient of game theory; the scheme scoring and selection module is used for the new distribution network planning evaluation ranking based on the VIKOR method.

[0055] A computer device comprises a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement a new distribution network planning and evaluation method.

[0056] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of a novel distribution network planning and evaluation method.

[0057] Beneficial effects of the present invention: The new distribution network planning and evaluation method provided by the present invention obtains the original data of each indicator in the new distribution network indicator system, and standardizes and dimensionless preprocesses these data, thereby achieving data consistency and comparability, eliminating the dimensional influence and numerical size differences between different indicators, ensuring the accuracy and scientificity of the subsequent evaluation process, improving the quality of data preprocessing, and laying a solid foundation for subsequent weight assignment and evaluation ranking. Through subjective weighting by the DEMATEL method, an in-depth analysis of the inherent causal relationship of each element in the indicator system is achieved. By constructing a comprehensive influence matrix and calculating the influence, influence, centrality and cause degree, the key points of identification and quantification are achieved. The beneficial effects of indicators, this method not only improves the transparency of weight assignment, but also enhances the practical guiding significance of the evaluation results through the analysis of the expert system, objectively assigns weights through the CRITIC method, calculates the standard deviation and correlation coefficient of each indicator, and thus obtains the information volume of the indicator, fully considers the differences and conflicts of indicator data, ensures the scientific nature of objective weight assignment, improves the accuracy of objective weight assignment, and provides an important basis for comprehensive weighting. By optimizing the weight coefficient based on game theory and coordinating the relationship between subjective and objective, the evaluation results are more accurate and authentic. The present invention achieves better results in terms of the objectivity of the evaluation process, the rationality of weight allocation and the practicability of the evaluation results. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0059] Figure 1 An overall flow chart of a novel distribution network planning and evaluation method provided for the first embodiment of the present invention.

[0060] Figure 2 A specific flow chart of a novel distribution network planning and evaluation system provided for the third embodiment of the present invention. DETAILED DESCRIPTION

[0061] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

[0062] Example 1, reference Figure 1 , is an embodiment of the present invention, and provides a new distribution network planning and evaluation method, comprising:

[0063] S1: Obtain the original data of each indicator in the new distribution network indicator system, and perform standardization and dimensionless preprocessing on the indicator data.

[0064] Furthermore, the standardization and dimensionless preprocessing of the indicator data includes preprocessing the benefit-type indicators, cost-type indicators, and intermediate indicators to obtain an n*m-dimensional standardized data matrix, where n is the number of indicators and m is the number of program samples. The indicators are compared and analyzed, and the benefit-type indicator processing formula is expressed as:

[0065]

[0066] Among them, x ij represents the original value of the ith indicator in the jth solution, min(x i ) represents the minimum value of the ith indicator among all solutions, max(x i ) represents the maximum value of the i-th indicator among all solutions, It represents the standardized value of the i-th benefit indicator and the cost indicator processing formula, which is expressed as:

[0067]

[0068] The center indicator processing formula is expressed as:

[0069]

[0070] m=min(x i ), M = max(x i )

[0071] It should be noted that by obtaining the original data of each indicator in the new distribution network indicator system and standardizing and dimensionless preprocessing these data, the consistency and comparability of the data are achieved, the dimensional influence and numerical size differences between different indicators are eliminated, the accuracy and scientificity of the subsequent evaluation process are ensured, the quality of data preprocessing is improved, and a solid foundation is laid for the subsequent weight assignment and evaluation ranking.

[0072] S2: Subjective weighting is performed through the DEMATEL method, objective weighting is performed through the CRITIC method, and subjective and objective comprehensive weighting is performed based on the optimization of weight coefficients based on game theory.

[0073] Furthermore, subjective weighting through the DEMATEL method includes analyzing the logical relationship and direct influence relationship between the elements in the system through experts, revealing the internal causal relationship of the system and identifying key factors, determining the degree of direct influence of each indicator on each other, and constructing a normalized influence matrix X, which is expressed as:

[0074]

[0075] Among them, a ij It represents the influence of index i on index j, and its value is {0, 1, 2, 3, 4}. The larger the value, the greater the influence. n represents the number of indicators, and max represents the maximum value of the sum of rows or columns. The normalized influence matrix is ​​realized, and the comprehensive influence matrix Y is calculated by matrix transformation. E is the unit matrix, which is expressed as:

[0076]

[0077] Among them, b ij Indicates the comprehensive influence of the ith indicator on the indicator j. The influence of each indicator D is calculated by the comprehensive influence matrix i Influence C i Centrality M i and the degree of cause R i , expressed as:

[0078]

[0079]

[0080] M i =D i +C i

[0081] R i =D i -C i

[0082] The subjective weight W of each indicator is calculated by the DEMATEL method based on the centrality si , expressed as:

[0083]

[0084] Among them, n represents the number of indicators.

[0085] It should be noted that the subjective weighting through the DEMATEL method has achieved the systematic use of expert knowledge, revealed the logical relationship and direct influence relationship between the elements in the system, identified key factors, and constructed a standardized influence matrix to determine the degree of direct influence between the various indicators. Through this step, the beneficial effect of accurately depicting the interaction between indicators has been achieved, and the rationality and reliability of weight assignment have been improved.

[0086] Furthermore, the objective weights calculated by the CRITIC method include calculating the standard deviation S of each indicator. i To reflect the differences in indicator data, it is expressed as:

[0087]

[0088]

[0089] in, It represents the average value of the standardized scores of each scheme of the i-th indicator. At the same time, the correlation coefficient between indicators is calculated to reflect the conflict between indicators R′i, which is expressed as:

[0090]

[0091]

[0092] Among them, r ij represents the correlation coefficient between indicator i and indicator j, and Represent the standardized scores of the i-th and j-th indicators respectively, and calculate the indicator information C through indicator differences and conflicts. i , expressed as:

[0093]

[0094] Calculate the objective weight of the indicator w oi , expressed as:

[0095]

[0096] in, Represents the standardized value of the i-th benefit indicator.

[0097] It should also be noted that through the CRITIC method, objective weighting is carried out, benefit indicators, cost indicators, and intermediate indicators are effectively and reasonably compared and analyzed, and the standard deviation and correlation coefficient of each indicator are calculated to obtain the amount of information of the indicator. This fully considers the differences and conflicts of indicator data, ensures the scientific nature of objective weight assignment, improves the accuracy of objective weight assignment, and provides an important basis for comprehensive weighting.

[0098] Furthermore, the subjective and objective comprehensive weighting based on the optimization weight coefficient of game theory includes combining the subjective double weight and the objective weight by linear combination to obtain the final subjective and objective combined weight, which is expressed as:

[0099] w=λ1w s +λ2w o

[0100] Among them, λ1 and λ2 represent the weight coefficients of subjective weight and objective weight respectively, w is the combined weight, w s is the subjective weight, w o The objective weight is constructed based on the mathematical model of game theory. After derivation, two weight coefficients can be solved and normalized, which is expressed as:

[0101]

[0102]

[0103] Where T represents the transpose operation, is the normalized weight coefficient, which is brought into the optimized weight coefficient to obtain the combined weight w, expressed as:

[0104]

[0105] Among them, w o is the objective weight.

[0106] S3: New distribution network planning evaluation and ranking based on VIKOR method.

[0107] Furthermore, the new distribution network planning evaluation ranking based on the VIKOR method includes normalizing the original indicator data matrix through the data preprocessing formula to obtain a normalized matrix X. In the normalized matrix, the optimal and worst values ​​of the positive and negative ideal solution indicators are the maximum and minimum values ​​of the indicator data, expressed as:

[0108]

[0109] Calculate the group utility value S′ and individual regret value R″ of the planning scheme j , expressed as:

[0110]

[0111]

[0112] Calculate the compromise decision value Q, where u is the decision mechanism coefficient, ranging from [0-1], weighing the importance of maximizing the group utility value and minimizing the individual regret value, expressed as:

[0113]

[0114] The compromise decision value Q is used to evaluate and prioritize the solutions.

[0115] Example 2, an embodiment of the present invention, provides a new distribution network planning and evaluation method. In order to verify the beneficial effects of the present invention, scientific demonstration is carried out through economic benefit calculation and simulation experiments.

[0116] Firstly, in order to verify the beneficial effects of the present invention, a scientific demonstration is carried out through comparative experiments, taking 4 new distribution network planning schemes as examples, and the data of each indicator are shown in Table 1.

[0117] Table 1 Indicators of new distribution network planning scheme

[0118]

[0119] Data preprocessing, that is, standardization and dimensionless processing of the indicator data in Table 1. The normalized data can be found in Table 2.

[0120] Table 2 Indicators of the new distribution network planning scheme after standardization

[0121]

[0122]

[0123] Combined with the established new power system planning evaluation index system, the subjective and objective weighting method is used to evaluate the four planning schemes. The weight results of each indicator are shown in Table 3, and the overall scoring results are shown in Table 4.

[0124] Table 3 Results of weights of indicators

[0125]

[0126]

[0127] Table 4 Overall score table for new power system planning assessment

[0128]

[0129] As can be seen from Table 4, the evaluation and ranking results of the method constructed by the present invention show that the overall scores of Scheme 3 and Scheme 4 are better, while the score ranking results of Scheme 1 and Scheme 2 are worse. When the decision-making experts perform VIKOR ranking, combined with different planning scenarios, the decision-making mechanism coefficient u takes different values, and the ranking results fluctuate slightly. The overall ranking results of Scheme 3 and Scheme 4 vary greatly. From the perspective of indicators, Scheme 3 is slightly lacking in traditional indicators, such as the proportion of overloaded lines and the N-1 pass rate, but the levels of new indicators are very high, while Scheme 4 focuses on the best traditional indicators. In general, Scheme 3 and Scheme 4 are both excellent evaluation schemes that can be promoted, with different focuses. Decision-making experts can select the best and most suitable schemes in different planning scenarios. The conclusion shows that the game theory-VIKOR evaluation model constructed by the present invention can reasonably and accurately evaluate and rank the distribution network.

[0130] To sum up, the new distribution network planning and evaluation method described in the present invention can evaluate different schemes to a certain extent to obtain the overall differences between different schemes, and can evaluate the advantages and disadvantages of various aspects of the planning schemes, thereby providing a basis for the construction of a new distribution network, continuously improving system performance, and achieving the purpose of improving the system architecture, which can provide effective support and guarantee for the planning and development of a new distribution network.

[0131] Example 3, reference Figure 2 , which is an embodiment of the present invention, provides a new distribution network planning and evaluation system, including a data preprocessing module, a comprehensive weighting model module, and a scheme scoring and selection module.

[0132] The data preprocessing is used to obtain the original data of each indicator in the new distribution network indicator system, and to standardize and dimensionlessly preprocess the indicator data; the comprehensive weighting model module is used to perform subjective weighting through the DEMATEL method, objective weighting through the CRITIC method, and subjective and objective comprehensive weighting based on the optimization of weight coefficients based on game theory; the scheme scoring and selection module is used for the evaluation and ranking of the new distribution network planning based on the VIKOR method.

[0133] If the function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the methods of each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc. Various media that can store program codes.

[0134] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in conjunction with such instruction execution systems, devices or apparatuses. For the purposes of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in conjunction with such instruction execution systems, devices or apparatuses.

[0135] More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk case (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be a paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering, or processing in another suitable manner as necessary, and then stored in a computer memory.

[0136] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc. It should be noted that the above embodiments are only used to illustrate the technical solution of the present invention and are not limited. Although the present invention is described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that the technical solution of the present invention can be modified or replaced by equivalents without departing from the spirit and scope of the technical solution of the present invention, which should be included in the scope of the claims of the present invention.

[0137] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A new distribution network planning and evaluation method, characterized in that: include: Obtain the original data of each indicator in the new distribution network planning and evaluation indicator system, and perform standardization and dimensionless preprocessing on the indicator data; Subjective weighting is performed through the DEMATEL method, objective weighting is performed through the CRITIC method, and subjective and objective comprehensive weighting is performed based on the optimization of weight coefficients based on game theory; The VIKOR method is used to evaluate and rank new distribution network planning.

2. A new distribution network planning and evaluation method as claimed in claim 1, characterized in that: The standardization and dimensionless preprocessing of the indicator data includes: By preprocessing the benefit indicators, cost indicators, and intermediate indicators, we get a standardized data matrix of n*m dimensions, where n is the number of indicators and m is the number of program samples. We compare and analyze the indicators, and the benefit indicator processing formula is expressed as: Among them, x ij represents the original value of the ith indicator in the jth solution, min(x i ) represents the minimum value of the ith indicator among all solutions, max(x i ) represents the maximum value of the i-th indicator among all solutions, It represents the standardized value of the i-th benefit indicator and the cost indicator processing formula, which is expressed as: The center indicator processing formula is expressed as: m=min(x i ),M=max(x i ) 3. A new distribution network planning and evaluation method as claimed in claim 1, characterized in that: The subjective empowerment through the DEMATEL method includes: Through expert analysis of the logical relationship and direct influence relationship between the various elements in the system, the inherent causal relationship of the system is revealed and the key factors are identified. The degree of direct influence of each indicator on each other is determined, and the normalized influence matrix X is constructed, which is expressed as: Among them, a ij It represents the influence of index i on index j, and its value is {0, 1, 2, 3, 4}. The larger the value, the greater the influence. n represents the number of indicators, and max represents the maximum value of the sum of rows or columns. The normalized influence matrix is ​​realized, and the comprehensive influence matrix Y is calculated by matrix transformation. E is the unit matrix, which is expressed as: Among them, b ij Indicates the comprehensive influence of the ith indicator on the indicator j. The influence of each indicator D is calculated by the comprehensive influence matrix i Influence C i Centrality M i and the degree of cause R i , expressed as: M i =D i +C i R i =D i -C i The subjective weight w of each indicator is calculated by the DEMATEL method based on the centrality si , expressed as: Among them, n represents the number of indicators.

4. A new distribution network planning and evaluation method as claimed in claim 1, characterized in that: The objective weight is calculated by the CRITIC method, including: Calculate the standard deviation S of each indicator i To reflect the differences in indicator data, it is expressed as: in, It represents the average value of the standardized scores of each scheme of the i-th indicator, and the correlation coefficient between the indicators is calculated to reflect the conflict between the indicators R' i , expressed as: Among them, r ij represents the correlation coefficient between indicator i and indicator j, and Represent the standardized scores of the i-th and j-th indicators respectively, and calculate the indicator information C through indicator differences and conflicts. i , expressed as: Calculate the objective weight of the indicator w oi , expressed as: in, Represents the standardized value of the i-th benefit indicator.

5. A new distribution network planning and evaluation method as claimed in claim 1, characterized in that: The subjective and objective comprehensive weighting based on the optimization weight coefficient of game theory includes: The subjective and objective weights are combined by linear combination to obtain the final subjective and objective combined weights, which are expressed as: w=λ1w s +λ2w o Among them, λ1 and λ2 represent the weight coefficients of subjective weight and objective weight respectively, w is the combined weight, w s is the subjective weight, w o The objective weight is constructed based on the mathematical model of game theory. After derivation, two weight coefficients can be solved and normalized, which is expressed as: Where T represents the transpose operation, is the normalized weight coefficient, which is brought into the optimized weight coefficient to obtain the combined weight w, expressed as: Among them, w o is the objective weight.

6. A new distribution network planning and evaluation method as claimed in claim 1, characterized in that: The new distribution network planning evaluation and ranking based on the VIKOR method includes: The original indicator data matrix is ​​normalized by the data preprocessing formula to obtain the normalized matrix X. In the normalized matrix, the optimal and worst values ​​of the positive and negative ideal solution indicators are the maximum and minimum values ​​of the indicator data, expressed as: Calculate the group utility value S' and individual regret value R" of the planning scheme j , expressed as: Calculate the compromise decision value Q, where u is the decision mechanism coefficient, ranging from [0-1], weighing the importance of maximizing the group utility value and minimizing the individual regret value, expressed as: The compromise decision value Q is used to evaluate and prioritize the solutions.

7. A system using the novel distribution network planning and evaluation method as claimed in any one of claims 1 to 7, characterized in that: It includes data preprocessing module, comprehensive weighting module, and solution scoring and selection module; The data preprocessing module is used to obtain the original data of each indicator in the new distribution network indicator system, and perform standardization and dimensionless preprocessing on the indicator data; The comprehensive weighting module is used to perform subjective weighting through the DEMATEL method, objective weighting through the CRITIC method, and subjective and objective comprehensive weighting based on the optimization of weight coefficients based on game theory; The scheme scoring and selection module is used to evaluate and rank new distribution network planning through the VIKOR method.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the novel distribution network planning and evaluation method described in any one of claims 1 to 6 are implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the novel distribution network planning and evaluation method described in any one of claims 1 to 7 are implemented.

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