Investment decision-making method for multiple subjects to participate in electric power infrastructure
By obtaining and processing the attribute variables of the decision plan, calculating the comprehensive evaluation and accurate score values, the problem of inaccuracy of decision results in the existing technology is solved, and the accuracy and information integrity of investment decisions are improved.
Patent Information
- Application Number
- CN202411302324.0
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-09-18
- Publication Date
- 2024-12-20
AI Technical Summary
Existing investment decision-making methods cannot effectively integrate the mutual influence between different attributes, resulting in inaccurate decision-making results and low efficiency in fund use.
By obtaining the attribute variables of the decision scheme, including language variables and function variables, setting the weight vector, converting it into a language decision matrix and processing it, calculating the comprehensive evaluation and accurate score value of the scheme, and determining the best solution based on the sorting rules.
It improves the accuracy of decision-making results, minimizes the lack of information, can describe decision-maker preferences more accurately, and intuitively display the pros and cons of each project.
Smart Images
Figure CN119168422A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the technical field of investment decision-making, and in particular to a method for multi-party participation in investment decision-making for power infrastructure. Background Art
[0002] With the growth of the economy, the total fixed asset investment of the whole society and the investment amount of various industries are steadily increasing. However, in general, although the investment amount is large, the efficiency of fund use is low, and some investments are ineffective. The main reasons are insufficient supervision and unscientific decision-making process, which leads to the inability of existing methods to effectively integrate the mutual influence between different attributes, thus leading to inaccurate decision-making results. In order to improve the accuracy of decision-making results and make investment funds truly play a role, it is necessary to strengthen the management and supervision of investment, especially the management of investment decisions. Summary of the invention
[0003] In order to overcome the above-mentioned defects of the prior art, an embodiment of the present invention provides a method for multiple parties to participate in investment decision-making of power infrastructure, so as to solve the problem of inaccuracy of decision results.
[0004] To achieve the above object, the present invention provides the following technical solutions: A method for multi-party participation in power infrastructure investment decision-making includes the following steps: obtaining decision scheme attribute variables, the decision scheme attribute variables including language variables and function variables; setting a weight vector for each group of uncertain membership language variables to obtain an attribute-independent information integration operator; converting the language variables into a language decision matrix and processing the matrix to obtain a comprehensive evaluation of the scheme; calculating the comprehensive evaluation of the scheme based on the function variables to obtain an accurate score value of the scheme; and sorting the accurate score values of the scheme according to a sorting rule to obtain an optimal scheme.
[0005] In a preferred embodiment, the decision scheme attribute variables include language variables and function variables, specifically: the language variables include the expression form and decision operation rules of uncertain membership language variables; the function variables include score value functions, exact value functions, and distances between variables.
[0006] In a preferred embodiment, the decision operation rule is specifically: according to the preset uncertain membership linguistic variables, logical operations are performed on two uncertain membership linguistic variables respectively to obtain the decision operation rule.
[0007] In a preferred embodiment, the distance between variables is calculated according to the standard Hamming distance formula, specifically: comparing the membership of the uncertain membership linguistic variables for distance calculation; The membership comparison results at all positions of the uncertain membership linguistic variable are added together to obtain the standard Hamming distance.
[0008] In a preferred embodiment, the scheme is comprehensively evaluated and obtained by the following method: an uncertain membership language weighted arithmetic mean operator is selected to integrate the language decision matrix to obtain a first evaluation value; an uncertain membership language weighted geometric mean operator is selected to integrate the language decision matrix to obtain a second evaluation value; the first evaluation value and the second evaluation value are added to obtain a comprehensive evaluation value.
[0009] In a preferred embodiment, the sorting rule is specifically as follows: if the comprehensive evaluation values of the schemes are different, the scheme with a higher comprehensive evaluation value is sorted first; if the comprehensive evaluation values of the schemes are the same, the scheme with a higher precise score is sorted first; if the precise scores of the schemes are the same, the two schemes are ranked in parallel; after the sorting is completed, the scheme at the front is the best scheme.
[0010] One or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages: 1. Through the participation of multiple parties in the investment decision-making method of power infrastructure, the accuracy of decision-making results is improved, and the problem of inaccurate decision-making results caused by the inability to effectively integrate the mutual influence between different attributes in the existing technology is effectively solved.
[0011] 2. By describing the possible linguistic variables of attribute information, the possible membership degree of each linguistic variable is given, thereby avoiding information loss to the greatest extent possible, thereby describing the decision maker's preferences more accurately and completely.
[0012] 3. Through the decision-making method based on uncertain membership linguistic variables, a comprehensive evaluation value is obtained, which makes it possible to intuitively see the pros and cons of each project. BRIEF DESCRIPTION OF THE DRAWINGS
[0013] Figure 1 A flow chart of a method for multiple parties to participate in investment decision-making in power infrastructure provided in an embodiment of the present application; DETAILED DESCRIPTION The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.
[0014] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the statement "include..." do not exclude the presence of other identical elements in the process, method, article or device including the elements.
[0015] Embodiment 1, Figure 1 The present invention provides a method for multiple parties to participate in investment decision-making of power infrastructure, including the following steps: Acquire decision scheme attribute variables, wherein the decision scheme attribute variables include language variables and function variables; A weight vector is set for each group of uncertain membership linguistic variables to obtain an attribute-independent information integration operator; The linguistic variables are converted into a linguistic decision matrix and processed to obtain a comprehensive evaluation of the scheme; Calculate the comprehensive evaluation of the scheme based on the function variables to obtain an accurate score value of the scheme; Sort the precise scores of the solutions according to the sorting rules to get the best solution.
[0016] The linguistic variables include the expression form and decision operation rules of the uncertain membership linguistic variables; The function variables include score value function, exact value function, and distance between variables; The expression form of the uncertain membership linguistic variable is obtained as follows: A language phrase set is obtained, and an uncertain membership language variable set is preset to represent the continuous form of the language phrase set.
[0017] The language phrase set is specifically:
[0018] The uncertain membership linguistic variable set is specifically:
[0019] Where: A is the set of uncertain membership language variables, S is the set of language phrases, Represents the element x to the language variable The membership degree of Represents the element x to the language variable The degree of membership.
[0020] It should be noted that , then a is an uncertain membership linguistic variable.
[0021] The decision operation rules are obtained as follows: According to the preset uncertain membership linguistic variables, logical operations are performed on two uncertain membership linguistic variables respectively to obtain decision operation rules.
[0022] The decision operation rules are as follows:
[0023] The score value function is calculated as follows:
[0024] Where: is the score function, g is the total number of solutions, Represents the element x to the language variable The membership degree of Represents the element x to the language variable The degree of membership.
[0025] The calculation formula of the exact value function is as follows:
[0026] Where: is the score function, g is the total number of solutions, Represents the element x to the language variable The membership degree of Represents the element x to the language variable The degree of membership.
[0027] The distance between variables is calculated according to the standard Hamming distance formula, specifically: Compare the membership of uncertain membership linguistic variables for distance calculation; The membership comparison results at all positions of the uncertain membership linguistic variable are added together to obtain the standard Hamming distance.
[0028] It should be noted that when comparing the membership of uncertain membership linguistic variables, if the two membership values are different, they are recorded as 1, and if they are the same, they are recorded as 0.
[0029] It should be noted that the lengths of the two uncertain membership linguistic variables to be calculated should be the same. If the lengths are different, the length of the shorter uncertain membership linguistic variable should be padded, or the length of the longer uncertain membership linguistic variable should be trimmed to the same length as the shorter variable.
[0030] The standard Hamming distance is calculated as follows:
[0031] Where: is the standard Hamming distance, which indicates the degree of difference between two uncertain membership linguistic variables, g is the total number of solutions, Represents the element x to the language variable The membership degree of Represents the element x to the language variable The degree of membership.
[0032] The uncertain membership linguistic variable is a form of fuzzy linguistic variable used to describe the attribute preference of the decision maker, allowing the decision maker to express the attribute information with a set of memberships, such as {(A, 0.6), (B, 0.4)} to indicate that the probability of a certain attribute belonging to A is 0.6, and the probability of belonging to B is 0.4. This expression can more clearly reflect the decision maker's inclination and provide a more accurate description of fuzzy information, especially in the case of high uncertainty, which helps to understand and integrate the decision maker's preferences.
[0033] The standard Hamming distance is an indicator used to measure the difference between two strings of equal length or binary vectors. It calculates the number of different characters or bits at the same position. If there are two strings with the same length, the Hamming distance is the total number of different characters at the corresponding positions. This concept is widely used in coding theory, information transmission, error detection and other fields.
[0034] The independent information integration operators include an uncertain membership language weighted arithmetic average operator, an uncertain membership language weighted geometric average operator, and an uncertain membership language ordered weighted arithmetic average operator.
[0035] The weight vector is set for each group of uncertain membership linguistic variables, specifically:
[0036] Where: w is the weight vector corresponding to each group of uncertain membership linguistic variables.
[0037] The uncertain membership linguistic weighted arithmetic average operator is defined as follows:
[0038] It should be noted that if Then, the weighted arithmetic mean operator of uncertain membership language can be simplified to the arithmetic mean operator of uncertain membership language.
[0039] The calculation formula of the uncertain membership language arithmetic average operator is as follows:
[0040] It should be noted that the weighted arithmetic mean operator of uncertain membership language has monotonicity, boundedness and idempotence properties.
[0041] The calculation formula of the uncertain membership linguistic weighted geometric mean operator is as follows:
[0042] It should be noted that if Then, the uncertain membership linguistic weighted geometric mean operator can be simplified to the uncertain membership linguistic geometric mean operator.
[0043] The calculation formula of the uncertain membership linguistic geometric mean operator is as follows:
[0044] The uncertain membership language ordered weighted arithmetic mean operator is defined as follows:
[0045] Where: yes Sort by descending order Big elements.
[0046] The initial uncertain membership language decision matrix is converted into a standard uncertain membership language decision matrix. When the uncertain membership language variable given by the decision maker contains only one language phrase and its membership is 1, that is, This special form indicates that it can make a very certain judgment. In order to perform relevant operations, it is necessary to first transform the form into a standard form so that all uncertain membership linguistic variables have the same form.
[0047] The comprehensive evaluation of the scheme is obtained as follows: Selecting the uncertain membership language weighted arithmetic average operator to perform integration processing on the language decision matrix to obtain the first evaluation value; Selecting the uncertain membership language weighted geometric mean operator to perform integration processing on the language decision matrix to obtain the second evaluation value; The first evaluation value and the second evaluation value are added to obtain a comprehensive evaluation value.
[0048] The first evaluation value is obtained in the following way:
[0049] Where: is the language decision matrix.
[0050] The second evaluation value is obtained as follows:
[0051] Where: is the language decision matrix.
[0052] The comprehensive evaluation value is obtained as follows:
[0053] Where: It is a comprehensive evaluation value.
[0054] The information integration operator, when the decision maker uses uncertain membership linguistic variables to describe his preference information for attribute values, in order to improve calculation efficiency, can consider giving information integration operators when the attributes are independent of each other, including weighted arithmetic mean operator, weighted geometric mean operator, ordered weighted arithmetic mean operator and ordered weighted geometric mean operator. By giving these information integration operators, a multi-attribute decision-making method is given.
[0055] The precise score of the scheme is obtained as follows: Compare the comprehensive evaluation values of the schemes. When the comprehensive evaluation values are the same, further calculate the precise values of the schemes respectively. The exact score of the scheme is calculated as follows:
[0056] Where: It is the exact score value of the solution.
[0057] The sorting rules are specifically: If the comprehensive evaluation values of the schemes are different, the scheme with a higher comprehensive evaluation value will be ranked first; If the comprehensive evaluation values of the schemes are the same, the scheme with a higher precision score will be ranked first; If the exact scores of the two solutions are the same, the rankings of the two solutions are tied.
[0058] After the sorting is completed, the first solution is the best solution.
[0059] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.
[0060] The above embodiments may be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented by software, the above embodiments may be implemented in whole or in part in the form of a computer program product.
[0061] Those of ordinary skill in the art will appreciate that the modules and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.
[0062] In addition, each functional module in each embodiment of the present application may be integrated into one processing module, or each module may exist physically separately, or two or more modules may be integrated into one module.
[0063] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.
[0064] Finally: The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.
Claims
1. A method for multi-party participation in power infrastructure investment decision-making, characterized in that: The following steps are involved: Acquire decision scheme attribute variables, wherein the decision scheme attribute variables include language variables and function variables; A weight vector is set for each group of uncertain membership linguistic variables to obtain an attribute-independent information integration operator; The linguistic variables are converted into a linguistic decision matrix and processed to obtain a comprehensive evaluation of the scheme; Calculate the comprehensive evaluation of the scheme based on the function variables to obtain an accurate score value of the scheme; Sort the precise scores of the solutions according to the sorting rules to get the best solution.
2. The method for investment decision-making involving multiple parties in power infrastructure as claimed in claim 1, characterized in that: The decision-making scheme attribute variables include language variables and function variables, specifically: The linguistic variables include the expression form and decision operation rules of the uncertain membership linguistic variables; The function variables include a score value function, an exact value function, and a distance between variables.
3. The method for multi-party participation in power infrastructure investment decision-making as claimed in claim 2, characterized in that: The decision operation rules are specifically: According to the preset uncertain membership linguistic variables, logical operations are performed on two uncertain membership linguistic variables respectively to obtain decision operation rules.
4. The method for investment decision-making involving multiple parties in power infrastructure as claimed in claim 3, characterized in that: The score value function is calculated as follows: Where: is the score function, g is the total number of solutions, Represents the element x to the language variable The membership degree of Represents the element x to the language variable The degree of membership.
5. The method for investment decision-making involving multiple parties in power infrastructure as claimed in claim 4, characterized in that: The calculation formula of the exact value function is as follows: Where: is the score function, g is the total number of solutions, Represents the element x to the language variable The membership degree of Represents the element x to the language variable The degree of membership.
6. The method for investment decision-making involving multiple parties in power infrastructure as claimed in claim 5, characterized in that: The distance between variables is calculated according to the standard Hamming distance formula, specifically: Compare the membership of uncertain membership linguistic variables for distance calculation; The membership comparison results at all positions of the uncertain membership linguistic variable are added together to obtain the standard Hamming distance.
7. The method for investment decision-making involving multiple parties in power infrastructure as claimed in claim 6, characterized in that: The standard Hamming distance is calculated as follows: Where: is the standard Hamming distance, which indicates the degree of difference between two uncertain membership linguistic variables, g is the total number of solutions, Represents the element x to the language variable The membership degree of Represents the element x to the language variable The degree of membership.
8. The method for multi-party participation in power infrastructure investment decision-making as claimed in claim 7, characterized in that: The comprehensive evaluation value is obtained as follows: Where: It is a comprehensive evaluation value.
9. The method for multi-party participation in power infrastructure investment decision-making as claimed in claim 8, characterized in that: The comprehensive evaluation of the scheme is obtained as follows: Selecting the uncertain membership language weighted arithmetic average operator to perform integration processing on the language decision matrix to obtain the first evaluation value; Selecting the uncertain membership language weighted geometric mean operator to perform integration processing on the language decision matrix to obtain the second evaluation value; The first evaluation value and the second evaluation value are added to obtain a comprehensive evaluation value.
10. The method for multi-party participation in power infrastructure investment decision-making as claimed in claim 9, characterized in that: The sorting rules are specifically: If the comprehensive evaluation values of the schemes are different, the scheme with a higher comprehensive evaluation value will be ranked first; If the comprehensive evaluation values of the schemes are the same, the scheme with a higher precision score will be ranked first; If the exact scores of the two solutions are the same, the ranking of the two solutions will be tied; After the sorting is completed, the first solution is the best solution.