Project scheme decision method and equipment based on fuzzy analytic hierarchy process and cloud model

Abstract

The invention provides a project scheme decision method and equipment based on a fuzzy analytic hierarchy process and a cloud model. The method comprises the following steps: constructing an evaluation index system of a plurality of schemes; a fuzzy analytic hierarchy process is adopted, and the weight of each index is determined according to the importance degree comparison information of each decision maker in the plurality of decision makers for any two indexes; determining the weight of each decision maker according to the trust relationship among the plurality of decision makers; determining characteristic parameters corresponding to the evaluation grades; determining an evaluation cloud model of each decision maker for each scheme about each index according to hesitation fuzzy evaluation information of each decision maker for each scheme about each index and characteristic parameters corresponding to each evaluation level; and according to the weight of each index, the weight of each decision maker and the evaluation cloud model of each decision maker for each scheme about each index, determining a comprehensive evaluation cloud model corresponding to each scheme so as to decide an optimal scheme from the plurality of schemes. The decision-making accuracy can be improved, and the decision-making efficiency is improved.

Description

Technical Field

[0001] The present invention belongs to the field of computer technology, and specifically relates to a project solution decision-making method and device based on fuzzy hierarchical analysis method and cloud model. Background Art

[0002] In recent years, the development and popularization of mobile Internet and the iteration and update of smart devices have created a massive amount of data, which is called "big data". The introduction of the concept of big data means the arrival of a new era, namely the era of big data. The characteristics of big data, such as the huge and diverse total amount of data, the rapid growth of data information, and the uncertainty and incompleteness of data, have led to a large increase in the amount of data involved in traditional projects and a significant increase in the complexity of project decision-making. With the advent of the big data era, complex projects have become a normal state in various industries. Big data will inevitably penetrate into many aspects of project decision-making and become a key factor affecting the effectiveness of decision-making. For example, the dispatching and allocation of thousands of satellites in the Beidou system, the real-time prediction and guidance of passenger flow at stations by high-speed rail departments, and the optimization analysis of smart city operation management by Alibaba Cloud City Brain. The decision-making effect of a project directly affects the success of the project. How to manage and use big data and choose a suitable data management solution are of great significance to improving decision-making efficiency, improving decision-making methods, improving decision-making data, and improving decision-making quality.

[0003] The project decision-making process in the field of information management under big data usually involves multiple decision-making subjects, multiple decision-making goals, multiple decision-making indicators and multiple decision-making schemes, and the decision-making problem is highly uncertain, ambiguous and complex. At present, the difficulties of project decision-making problems are mainly reflected in three aspects. First, the amount of data is huge and diverse. In the process of project decision-making, more types of data need to be processed, including structured data, unstructured data, real-time data, etc. For example, in the financial industry, traditional risk assessment only needs to consider a limited number of indicators, while in the era of big data, the indicators to be considered involve dozens or hundreds; second, the data is uncertain and incomplete. For example, in the medical industry, the use of big data for disease prediction and diagnosis requires consideration of a large amount of uncertain, incomplete and ambiguous data such as the patient's genetic data, medical records data and environmental factors. Third, the decision-maker's weight is difficult to determine. In the context of the big data era, due to the limited knowledge and experience of individuals, more and more decision-makers with different knowledge backgrounds are involved in the decision-making process. Considering the influencing factors such as knowledge background, interests and hobbies, and social relationships, how to empower decision-makers in the decision-making group is a difficulty in complex project decision-making problems. Traditional decision-making methods often find it difficult to effectively deal with these problems, resulting in poor decision accuracy and low decision efficiency. Summary of the invention

[0004] The technical problem to be solved by the present invention is to provide a project solution decision-making method and device based on fuzzy hierarchical analysis method and cloud model, which can improve the accuracy of decision-making and improve the efficiency of decision-making.

[0005] The present invention includes a project solution decision method based on fuzzy hierarchical analysis method and cloud model, including:

[0006] Constructing an evaluation index system for multiple schemes, wherein the evaluation index system includes multiple indicators and multiple evaluation levels;

[0007] Using fuzzy hierarchical analysis method, and according to the comparison information of the importance of any two indicators by each decision maker among multiple decision makers, the weight of each indicator is determined;

[0008] Determining the weight of each decision maker according to the degree of trust between the multiple decision makers;

[0009] Determine characteristic parameters corresponding to each evaluation level, wherein the characteristic parameters include expectation, entropy and super entropy;

[0010] Determine the evaluation cloud model of each decision maker on each of the schemes with respect to each of the indicators according to the hesitant fuzzy evaluation information of each of the decision makers on each of the schemes with respect to each of the indicators and the characteristic parameters corresponding to each of the evaluation levels;

[0011] Determine a comprehensive evaluation cloud model corresponding to each of the schemes according to the weight of each of the indicators, the weight of each of the decision makers, and the evaluation cloud model of each of the decision makers on each of the schemes with respect to each of the indicators;

[0012] According to the comprehensive evaluation cloud models corresponding to the multiple solutions, the optimal solution is decided from the multiple solutions.

[0013] According to an embodiment of the present application, determining the weight of each of the indicators according to the comparison information of the importance of any two of the indicators by each of the decision makers among the multiple decision makers includes:

[0014] Determine the triangular fuzzy number of each decision maker for any two indicators according to the comparison information of the importance of each decision maker for any two indicators among the multiple decision makers;

[0015] Determine the fuzzy comparison average of any two of the indicators according to the triangular fuzzy numbers of the decision makers for any two of the indicators;

[0016] Determine the fuzzy average value of each of the indicators according to the fuzzy comparison average values ​​of any two of the indicators;

[0017] The weight of each indicator is determined according to the fuzzy average value of the multiple indicators.

[0018] According to an embodiment of the present application, determining the weight of each indicator according to the fuzzy average value of the multiple indicators includes:

[0019] Determining the fuzzy weight of each of the indicators according to the fuzzy average values ​​of the multiple indicators;

[0020] The fuzzy weights of the indicators are defuzzified and normalized to obtain the weights of the indicators.

[0021] According to an embodiment of the present application, determining the weight of each decision maker according to the trust relationship between the plurality of decision makers includes:

[0022] constructing a trust relationship matrix according to the trust relationships among the plurality of decision makers;

[0023] Calculating the trustworthiness of each of the decision makers according to the trust relationship matrix;

[0024] The weight of each decision maker is determined according to the trust level of each decision maker.

[0025] According to an embodiment of the present application, constructing a trust relationship matrix based on the trust relationships between the multiple decision makers includes:

[0026] Determining the degree of trust and non-trust of each decision maker to each other decision maker according to the trust relationship between the plurality of decision makers;

[0027] A trust relationship matrix is ​​constructed according to the degree of trust and non-trust of each decision maker towards each other decision maker.

[0028] According to an embodiment of the present application, determining the weight of each decision maker according to the trustworthiness of each decision maker includes:

[0029] sorting the plurality of decision makers according to the trustworthiness of each of the decision makers;

[0030] According to the ranking results, calculating the importance weight of each decision maker;

[0031] The importance weights of the decision makers are normalized to obtain the weights of the decision makers.

[0032] According to an embodiment of the present application, determining the evaluation cloud model of each decision maker for each scheme with respect to each indicator according to the hesitant fuzzy evaluation information of each decision maker on each scheme with respect to each indicator and the characteristic parameters corresponding to each evaluation level includes:

[0033] Determining at least one evaluation level of each decision maker on each of the schemes with respect to each of the indicators according to the hesitant fuzzy evaluation information of each of the decision makers on each of the schemes with respect to each of the indicators;

[0034] An evaluation cloud model of each decision maker on each scheme with respect to each indicator is determined based on a characteristic parameter corresponding to at least one of the evaluation levels of each decision maker on each scheme with respect to each indicator.

[0035] According to an embodiment of the present application, the evaluation index system includes multiple levels of indicators, and the multiple indicators are the lowest level indicators among the multiple levels of indicators;

[0036] Determining the comprehensive evaluation cloud model corresponding to each of the schemes according to the weight of each of the indicators, the weight of each of the decision makers, and the evaluation cloud model of each of the decision makers on each of the schemes with respect to each of the indicators includes:

[0037] Determine the evaluation cloud model of each of the schemes with respect to each of the indicators according to the weight of each of the decision makers and the evaluation cloud model of each of the decision makers for each of the schemes with respect to each of the indicators;

[0038] Determine the evaluation cloud model of each scheme for the upper level indicator according to the evaluation cloud model of each scheme for each indicator and the weight of each indicator;

[0039] According to the evaluation cloud model of each scheme regarding the upper-level indicator and the weight of the upper-level indicator, the comprehensive evaluation cloud model corresponding to each scheme is determined.

[0040] The present invention also provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the above-mentioned decision-making method based on fuzzy hierarchical analysis method and cloud model when executing the program.

[0041] The present invention also provides a non-transitory computer-readable storage medium, which stores computer instructions, and the computer instructions are used to enable the computer to execute the above-mentioned decision-making method based on fuzzy hierarchical analysis method and cloud model.

[0042] The beneficial effects of the present invention are as follows: the fuzzy hierarchical analysis method is adopted, and the weight of each indicator is determined according to the comparison information of the importance of any two indicators by each decision maker among multiple decision makers, the weight of each decision maker is determined according to the trust relationship between multiple decision makers, and the characteristic parameters corresponding to each evaluation level are determined, and the characteristic parameters include expectation, entropy and super entropy; according to the hesitant fuzzy evaluation information of each decision maker on each scheme on each indicator and the characteristic parameters corresponding to each evaluation level, the evaluation cloud model of each decision maker on each scheme on each indicator is determined; according to the weight of each indicator, the weight of each decision maker, and the evaluation cloud model of each decision maker on each scheme on each indicator, the comprehensive evaluation cloud model corresponding to each scheme is determined; according to the comprehensive evaluation cloud models corresponding to multiple schemes, the optimal scheme is decided from multiple schemes, so as to take into account the fuzziness and uncertainty of the evaluation information based on the fuzzy hierarchical analysis method and the cloud model, reduce the influence of the experience difference of the decision makers on the evaluation results, thereby improving the accuracy of the decision and improving the efficiency of the decision. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For those skilled in the art, other drawings can be obtained based on these drawings without creative work.

[0044] Figure 1 A flowchart of a project solution decision method based on fuzzy hierarchical analysis method and cloud model provided in an embodiment of the present application.

[0045] Figure 2 A schematic diagram of multiple evaluation level cloud models in a project solution decision method based on fuzzy analytic hierarchy process and cloud model provided in an embodiment of the present application.

[0046] Figure 3 A schematic diagram of the evaluation cloud model for indicator 1 of Scheme 1 in the project scheme decision-making method based on fuzzy hierarchical analysis method and cloud model provided in an embodiment of the present application.

[0047] Figure 4 A schematic diagram of the evaluation cloud model for indicator 2 of Scheme 1 in the project scheme decision-making method based on fuzzy hierarchical analysis method and cloud model provided in an embodiment of the present application.

[0048] Figure 5 A schematic diagram of the evaluation cloud model for indicator 3 of Scheme 1 in the project scheme decision-making method based on fuzzy hierarchical analysis method and cloud model provided in an embodiment of the present application.

[0049] Figure 6A schematic diagram of the evaluation cloud model for indicator 4 of Scheme 1 in the project scheme decision-making method based on fuzzy hierarchical analysis method and cloud model provided in an embodiment of the present application.

[0050] Figure 7 A schematic diagram of the evaluation cloud model for indicator 5 of Scheme 1 in the project scheme decision-making method based on fuzzy hierarchical analysis method and cloud model provided in an embodiment of the present application.

[0051] Figure 8 A schematic diagram of the evaluation cloud model for indicator 6 of Scheme 1 in the project scheme decision-making method based on fuzzy hierarchical analysis method and cloud model provided in an embodiment of the present application.

[0052] Fig. 9 A schematic diagram of a comprehensive evaluation cloud model for Scheme 1 in a project scheme decision method based on fuzzy analytic hierarchy process and cloud model provided in an embodiment of the present application.

[0053] Fig.10 A schematic diagram of the comprehensive evaluation cloud model of Scheme 2 in the project scheme decision-making method based on fuzzy hierarchical analysis method and cloud model provided in an embodiment of the present application.

[0054] Fig.11 A schematic diagram of the comprehensive evaluation cloud model of Scheme 3 in the project scheme decision-making method based on fuzzy hierarchical analysis method and cloud model provided in an embodiment of the present application.

[0055] Fig.12 A schematic diagram of the comprehensive evaluation cloud model of Scheme 4 in the project scheme decision-making method based on fuzzy hierarchical analysis method and cloud model provided in an embodiment of the present application.

[0056] Fig.13 A schematic diagram of a comprehensive evaluation cloud model for four schemes in a project scheme decision method based on fuzzy analytic hierarchy process and cloud model provided in an embodiment of the present application.

[0057] Fig.14 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0058] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of this application.

[0059] The embodiment of the present invention provides a project solution decision method based on fuzzy hierarchical analysis method and cloud model. Figure 1As shown, the method includes steps 101 to 107, which are specifically as follows:

[0060] Step 101: construct an evaluation index system for multiple schemes in a project, wherein the evaluation index system includes multiple indicators and multiple evaluation levels.

[0061] The decision-making process of complex projects involves multiple evaluation indicators and multiple evaluation levels to construct an evaluation indicator system. In some embodiments, the evaluation indicator system includes multi-level indicators, and the multiple indicators in step 101 can be the lowest level indicators in the multi-level indicators. For example, according to the characteristics of the project, three-level indicators are set according to the target layer, the criterion layer and the indicator layer, and the multiple indicators in step 101 can be third-level indicators. Multiple evaluation levels constitute a hesitant fuzzy evaluation set. For example, a 5-granularity hesitant fuzzy evaluation set is set, which includes five evaluation levels of "very poor", "poor", "average", "good" and "very good", that is, T p ={t 1 = Very bad, t 2 = difference, t 3 =General, t 4 =Good, t 5 = Very good}. The multiple solutions in step 101 are alternative solutions in the complex project decision-making process.

[0062] Step 102: using the fuzzy hierarchical analysis method, and according to the comparison information of the importance of any two indicators by each decision maker among the multiple decision makers, determine the weight of each indicator.

[0063] When evaluating indicators, decision makers (such as experts) choose linguistic variables in the hesitant fuzzy language evaluation set for evaluation. Considering the impact of decision makers' knowledge background and experience differences on the judgment of indicator importance, the fuzzy hierarchical analysis method is used to calculate the weights of indicators.

[0064] In some embodiments, the step 102 of determining the weight of each of the indicators according to the comparison information of the importance of any two of the indicators by each of the decision makers among the multiple decision makers includes:

[0065] Determine the triangular fuzzy number of each decision maker for any two indicators according to the comparison information of the importance of each decision maker for any two indicators among the multiple decision makers;

[0066] Determine the fuzzy comparison average of any two of the indicators according to the triangular fuzzy numbers of the decision makers for any two of the indicators;

[0067] Determine the fuzzy average value of each of the indicators according to the fuzzy comparison average values ​​of any two of the indicators;

[0068] The weight of each of the indicators is determined according to the fuzzy average value of the multiple indicators.

[0069] Assume that there are n indicators and K decision makers, and obtain the information that decision maker k (k = 1, 2, ... K) compares the importance of indicators i and j (i, j = 1, 2, ..., N), that is, the importance comparison information. According to the comparison information of the importance of indicators i and j by decision maker k, the fuzzy judgment matrix is ​​constructed through triangular fuzzy numbers. is the triangular fuzzy number of decision maker k for indicators i and j, and are the next value, the median value and the previous value respectively, that is, the fuzzy judgment matrix

[0070]

[0071] According to the triangular fuzzy numbers of K decision makers for indicators i and j, the average value of the triangular fuzzy numbers for indicators i and j is calculated, that is, the fuzzy comparison average value for indicators i and j The fuzzy comparison matrix is

[0072] Calculate the fuzzy average value of index i based on the fuzzy comparison average values ​​of index i and j (Triangular numbers):

[0073]

[0074] After calculating the fuzzy average of each indicator, the weight of each indicator can be determined.

[0075] In some embodiments, determining the weight of each of the indicators according to the fuzzy average of the multiple indicators includes:

[0076] Determining the fuzzy weight of each of the indicators according to the fuzzy average values ​​of the multiple indicators;

[0077] The fuzzy weights of the indicators are defuzzified and normalized to obtain the weights of the indicators.

[0078] First, we calculate the vector sum of the fuzzy average values ​​of all indicators, and then calculate the inverse of the vector sum, replace the fuzzy triangular data, arrange them in ascending order, and then calculate the product of its inverse vector and the fuzzy average value of each indicator to obtain the fuzzy weight of each indicator. Among them, the fuzzy weight of indicator i is

[0079] The fuzzy weight of index i is defuzzified, that is,

[0080] Normalize the weight after defuzzification calculation to get the weight of index i

[0081] Step 103: Determine the weight of each decision maker according to the trust relationship between the multiple decision makers.

[0082] In social organizations, there are complex trust relationships between individuals in the network, forming a trust network. This trust relationship affects individuals' information acquisition, exchange of opinions, and decision-making in the network environment. In the decision-making process, the weight of the decision maker can be calculated with the help of trust relationships.

[0083] In some embodiments, the step 103 of determining the weight of each decision maker according to the trust relationship between the plurality of decision makers includes:

[0084] constructing a trust relationship matrix according to the trust relationships among the plurality of decision makers;

[0085] Calculating the trustworthiness of each of the decision makers according to the trust relationship matrix;

[0086] The weight of each decision maker is determined according to the trust level of each decision maker.

[0087] In some embodiments, constructing a trust relationship matrix according to the trust relationships among the plurality of decision makers includes:

[0088] Determining the degree of trust and non-trust of each decision maker to each other decision maker according to the trust relationship between the plurality of decision makers;

[0089] A trust relationship matrix is ​​constructed according to the degree of trust and non-trust of each decision maker towards each other decision maker.

[0090] Using TD=[(t uv ,d uv )] r×r represents the trust relationship matrix, where t uv represents the degree of trust that decision maker u has in decision maker v, d uv Indicates the degree of non-trust of decision maker u towards decision maker v. (t uu ,d uu )=(1,0)(u,v=1,2,...,r), that is, the degree of trust of each decision maker in himself is 1, and the degree of non-trust is 0. The trust relationship matrix can be recorded as:

[0091]

[0092] According to the trust matrix, the expert's trust score (TS) is calculated, that is,

[0093] In some embodiments, determining the weight of each decision maker according to the trustworthiness of each decision maker includes:

[0094] sorting the plurality of decision makers according to the trustworthiness of each of the decision makers;

[0095] According to the ranking results, calculating the importance weight of each decision maker;

[0096] The importance weights of the decision makers are normalized to obtain the weights of the decision makers.

[0097] According to the trust of each decision maker, multiple decision makers are ranked. Using Yager's Trust Score-Ordered Weighted Averaging Operator (TS-OWA), the importance weight of each decision maker is calculated based on the ranking results. The importance weight of the h-th decision maker is:

[0098]

[0099] in, represents the weight of the h-th decision maker, TS σ(l) Yes {TS 1 ,TS 2 ,...,TS k}, Q is a monotonically increasing function on the interval [0,1], and the mapping interval is [0,1]. And Q(0)=0,Q(1)=1. If x>y, Q(x)≥Q(y).

[0100] Normalize the importance weights of each decision maker to get the weight of each decision maker

[0101] Step 104: Determine characteristic parameters corresponding to each evaluation level, wherein the characteristic parameters include expectation, entropy and super entropy.

[0102] After discussion among decision makers, the value range of each evaluation level is determined. m The upper and lower boundaries of the corresponding value range are The corresponding parameter values ​​are shown in Table 1.

[0103]

[0104] Table 1

[0105] Calculate the characteristic parameters of the corresponding evaluation level cloud model, and generate the corresponding cloud map for each evaluation level cloud model through the forward cloud generator algorithm. The specific steps of drawing the cloud map through the forward cloud generator are:

[0106] Generate normal random numbers

[0107] Generate a normal random number x(x~N(E x ,E n ;

[0108] calculate

[0109] x with μ forms a cloud droplet;

[0110] Repeat the above steps until a number of cloud droplets are generated.

[0111] Step 105: Determine the evaluation cloud model of each decision maker on each scheme with respect to each indicator based on the hesitant fuzzy evaluation information of each decision maker on each scheme with respect to each indicator and the characteristic parameters corresponding to each evaluation level.

[0112] In the decision-making process of complex projects, decision makers may be hesitant about the choice of multiple evaluation levels. In this regard, hesitant fuzzy language evaluation information is used, and decision makers can use descriptions such as "between partial success and basic success" and "basic success or complete success" to make combined evaluations. Based on the hesitant fuzzy evaluation information of each decision maker on each scheme for each indicator and the characteristic parameters corresponding to each evaluation level, the evaluation cloud model of each decision maker on each scheme for each indicator is determined.

[0113] In some embodiments, the step 105 of determining the evaluation cloud model of each decision maker for each scheme with respect to each indicator according to the hesitant fuzzy evaluation information of each decision maker for each scheme with respect to each indicator and the characteristic parameters corresponding to each evaluation level includes:

[0114] Determining at least one evaluation level of each decision maker on each of the schemes with respect to each of the indicators according to the hesitant fuzzy evaluation information of each of the decision makers on each of the schemes with respect to each of the indicators;

[0115] An evaluation cloud model of each decision maker on each scheme with respect to each indicator is determined based on a characteristic parameter corresponding to at least one of the evaluation levels of each decision maker on each scheme with respect to each indicator.

[0116] Remember T p ={t 1 , t 1 ,…,t M} is a set of evaluation levels. The hesitant fuzzy evaluation information of decision maker k on a certain plan regarding a certain indicator includes r+1 evaluation levels, that is, the hesitant fuzzy evaluation information T pThe evaluation cloud model of decision maker k on a certain plan regarding a certain indicator is: in:

[0117]

[0118]

[0119]

[0120] Step 106: Determine a comprehensive evaluation cloud model corresponding to each of the schemes based on the weight of each of the indicators, the weight of each of the decision makers, and the evaluation cloud model of each of the decision makers for each of the schemes with respect to each of the indicators.

[0121] In some embodiments, the evaluation index system includes multiple levels of indicators, and the multiple indicators are the lowest level indicators in the multiple levels of indicators.

[0122] Determining the comprehensive evaluation cloud model corresponding to each of the schemes according to the weight of each of the indicators, the weight of each of the decision makers, and the evaluation cloud model of each of the decision makers on each of the schemes with respect to each of the indicators in step 106 includes:

[0123] Determine the evaluation cloud model of each of the schemes with respect to each of the indicators according to the weight of each of the decision makers and the evaluation cloud model of each of the decision makers for each of the schemes with respect to each of the indicators;

[0124] Determine the evaluation cloud model of each scheme for the upper level indicator according to the evaluation cloud model of each scheme for each indicator and the weight of each indicator;

[0125] According to the evaluation cloud model of each scheme regarding the upper-level indicator and the weight of the upper-level indicator, the comprehensive evaluation cloud model corresponding to each scheme is determined.

[0126] Through the fusion formula, the evaluation cloud models of each decision maker on each plan for each indicator are fused into a comprehensive evaluation cloud model corresponding to each plan. The fusion formula is:

[0127]

[0128] For example, the evaluation index system includes three levels of indicators. For each plan, the evaluation cloud models of multiple decision makers on each third-level indicator and the weights of multiple decision makers are first fused through the above fusion formula to obtain the evaluation cloud model of each third-level indicator. In the fusion formula, T is the evaluation cloud model of a third-level indicator, N is the number of decision makers, is the weight of decision maker i, T i It is the evaluation cloud model of decision maker i on a third-level indicator.

[0129] Then, for each solution, the evaluation cloud models of multiple third-level indicators and the weights of multiple third-level indicators are fused through the above fusion formula to obtain the evaluation cloud models of each second-level indicator. In the fusion formula, T is the evaluation cloud model of a second-level indicator, and N is the number of third-level indicators corresponding to the second-level indicator. is the weight of the third-level indicator i, T i It is the evaluation cloud model of the third level indicator i.

[0130] For each solution, the evaluation cloud model of the first-level indicators is obtained by layer-by-layer fusion calculation in this way, thereby obtaining a comprehensive cloud model.

[0131] Step 107: According to the comprehensive evaluation cloud models corresponding to the multiple solutions, the optimal solution is determined from the multiple solutions.

[0132] Through the forward cloud generator, combined with the digital characteristics of the comprehensive evaluation cloud model corresponding to each scheme, a large number of cloud droplets are generated to achieve the conversion between qualitative and quantitative, and presented in the form of cloud maps to visualize the results. Through the visualization results, the best scheme can be decided from multiple schemes.

[0133] In the era of big data, project decision-making data is large in volume, complex in association, complex in attributes, and numerous in stakeholders, which puts forward higher requirements on the complexity, timeliness, and accuracy of its solution. Although the project decision-making method in the relevant technology retains the original information of most indicators, it is inevitably subjective because the determination of indicator weights is affected by the experience of decision makers. There are problems such as not considering the language granularity between decision makers and lack of consideration of the ambiguity and hesitation of decision makers in evaluating information. The decision-making process basically relies on the experience of on-site decision makers to judge weights and score indicators. In addition, in traditional project decision-making problems, due to the limited field or professional knowledge of a single expert, it is impossible to give a complete opinion, and the decision-making process requires the comprehensive opinions of experts in multiple fields. In traditional methods, the weights of experts are generally given by humans before decision-making, which has subjective biases. Therefore, how to effectively mine and process expert evaluation information to characterize the language preferences of multiple experts, and how to scientifically and effectively solve the expert weights in group decision-making problems, are areas that need to be further supplemented and improved in project decision-making problems.

[0134] Based on this, the embodiment of the present application proposes a hesitant fuzzy project decision information generation method based on the FAHP-cloud model and a decision maker weight confirmation method based on the decision maker trust network. The FAHP method can better handle fuzzy and uncertain factors in decision-making problems, allowing decision makers to consider the importance of each factor more comprehensively. The hesitant fuzzy language set can reflect the state of indecision of the decision maker between two or a finite number of memberships when evaluating an object, and can also retain the original information and preferences of the decision maker; the cloud model has the characteristic of quantitatively representing the qualitative concepts of the decision maker, and the combination of the three can effectively express the hesitation and ambiguity of the decision maker's evaluation information. According to the analysis of the direct trust relationship between decision makers, the decision maker trust network is constructed to calculate the expert weight, which can solve the subjective bias of the decision maker weight given in advance in the traditional method. The project decision information generation method and the decision maker weight determination method proposed in this embodiment can provide new ideas and methods for solving project plan decision problems.

[0135] The following is an example of the project solution decision-making method based on fuzzy hierarchical analysis method and cloud model provided in this embodiment.

[0136] In the era of big data, a large industrial enterprise is facing a project decision-making problem in the field of information management, that is, the project is an information management project. In order to better manage and utilize big data, the enterprise needs to choose a suitable information management solution, that is, multiple solutions in the project are information management solutions. At present, the enterprise has four options: 1 ,…,x 4 , each scheme is different, as shown in Table 2. Four indicators c are formulated according to the scheme attributes 1 , …, c 4 (as shown in Table 3) to measure and compare their advantages and disadvantages. To this end, the company invited five decision makers (i.e. experts) in the field of information management to 1 ,…,e 5 To evaluate each plan.

[0137]

[0138] Table 2

[0139]

[0140] Table 3

[0141] The weight of each indicator for each scheme is determined by fuzzy hierarchical analysis method. Five experts compare the importance of indicators in pairs according to the importance language scale in the table below to obtain the expert judgment matrix A (k) .

[0142]

[0143] Table 4

[0144] Taking the first expert as an example, the importance judgment matrix A of the first expert is (1) for:

[0145]

[0146] Calculate the attribute preference of the first expert, that is, the attribute weight F (1) ={0.307, 0.226, 0.159, 0.135, 0.101, 0.071}, and CR = 0.09 < 0.1, which shows that the constructed importance judgment matrix is ​​feasible and the attribute weight results are reasonable.

[0147] Following the same method, the attribute weights of the remaining four experts are calculated as follows:

[0148] F (2) ={0.418,0.256,0.152,0.089,0.053,0.032}

[0149] F (3) ={0.424,0.254,0.151,0.089,0.052,0.031}

[0150] F (4) ={0.355,0.256,0.166,0.112,0.067,0.044} The five experts use the trust relationship matrix to express their

[0151] F (5) ={0.341,0.240,0.187,0.114,0.072,0.047}

[0152] The trust relationship between them is as follows:

[0153] The trust relationship matrix is

[0154] The calculated expert weights ω are {0.112, 0.130, 0.162, 0.150, 0.446}.

[0155] After expert analysis and discussion, the evaluation levels used to describe the scheme attributes and the corresponding characteristic parameters are shown in Table 5.

[0156]

[0157] Table 5

[0158] With the help of the characteristic parameters of the evaluation level, the evaluation cloud model of each evaluation level is generated by the forward cloud generator to generate the corresponding cloud map, such as Figure 2shown.

[0159] In Scheme 1 (x 1 ) Regarding indicator 1(c 1 ) as an example, the hesitant fuzzy language evaluation values ​​of each expert and the corresponding evaluation cloud model and expert weight are shown in Table 6.

[0160]

[0161] Table 6

[0162] The evaluation cloud models of the five experts are integrated to obtain solution 1 (x 1 ) Regarding indicator 1(c 1 ) corresponds to the evaluation cloud model T 11 =(8.548, 0.545, 0.042), according to the characteristic parameters of the cloud model such as Ex, En, He, etc., the corresponding cloud map is generated using the forward cloud generator algorithm, such as Figure 3 As shown. Using the same method, we get Scheme 1 (x 1 ) The evaluation cloud model for the remaining five indicators (Indicator 2, Indicator 3, Indicator 4, Indicator 5 and Indicator 6) is as follows: Figures 4 to 8 shown.

[0163] After calculating the evaluation cloud model of each expert on each scheme for each indicator, the comprehensive evaluation cloud model of each scheme (Scheme 1, Scheme 2, Scheme 3 and Scheme 4) is obtained by combining the weight of the indicator and the weight of the expert, as shown in the following figure: Figures 9 to 12 In order to compare the advantages and disadvantages of the four schemes intuitively, the comprehensive evaluation cloud models of the four schemes are compared in the same coordinate system, as shown in Fig.13 shown.

[0164] Combining the cloud map analysis of the index evaluation cloud model and the comprehensive evaluation cloud model, it is found that: Scheme 2 (x 2 ) has the best comprehensive effect, which is between "very good" and "good", and tends to be the former, indicating that scheme 2 (x 2 ) The data management efficiency is high and the benefits to the enterprise are the greatest. Solution 3 (x 3 The overall effect of ) was the lowest among the four options, between "average" and "poor", and tended towards the latter. The main reason was that option 3 (x 3 ) is not feasible, so it is not recommended. The comprehensive ranking of the four options is: 2 >x 1 >x 4 >x 3 , which can provide reference for the decision makers of the enterprise.

[0165] This embodiment can also be applied to the selection of logistics suppliers, that is, the project is a logistics supplier selection project, and multiple schemes in the project include multiple logistics suppliers, and multiple decision makers are invited to evaluate the logistics suppliers. An evaluation index system for multiple logistics suppliers in the logistics supplier selection project is constructed, and the evaluation index system includes multiple indicators (such as logistics time and logistics cost, etc.) and multiple evaluation levels (such as very good, good, general, poor and very poor, etc.). The fuzzy hierarchical analysis method is adopted, and the weight of each indicator is determined according to the comparison information of the importance of each decision maker to any two indicators among multiple decision makers. The weight of each decision maker is determined according to the degree of trust between multiple decision makers. The characteristic parameters corresponding to each evaluation level are determined, and the characteristic parameters include expectation, entropy and super entropy. According to the hesitant fuzzy evaluation information of each decision maker on each logistics supplier on each indicator and the characteristic parameters corresponding to each evaluation level, the evaluation cloud model of each decision maker on each logistics supplier on each indicator is determined. According to the weight of each indicator, the weight of each decision maker, and the evaluation cloud model of each decision maker on each logistics supplier on each of the said indicators, the comprehensive evaluation cloud model corresponding to each logistics supplier is determined. According to the comprehensive evaluation cloud model corresponding to multiple logistics suppliers, the optimal logistics supplier in the logistics supplier selection project is determined from multiple logistics suppliers.

[0166] It should be noted that this embodiment can also be applied to other application scenarios, which are not specifically limited here.

[0167] In summary, the embodiment of the present application adopts the fuzzy hierarchical analysis method, and determines the weight of each indicator according to the comparison information of the importance of any two indicators by each decision maker among multiple decision makers, determines the weight of each decision maker according to the degree of trust between multiple decision makers, and determines the characteristic parameters corresponding to each evaluation level, wherein the characteristic parameters include expectation, entropy and super entropy. According to the hesitant fuzzy evaluation information of each decision maker on each scheme regarding each indicator and the characteristic parameters corresponding to each evaluation level, the evaluation cloud model of each decision maker on each scheme regarding each indicator is determined. According to the weight of each indicator, the weight of each decision maker, and the evaluation cloud model of each decision maker on each scheme regarding each indicator, the comprehensive evaluation cloud model corresponding to each scheme is determined. According to the comprehensive evaluation cloud models corresponding to multiple schemes, the optimal scheme is decided from multiple schemes, so as to take into account the fuzziness and uncertainty of the evaluation information based on the fuzzy hierarchical analysis method and the cloud model, reduce the influence of the experience differences of decision makers on the evaluation results, thereby improving the accuracy of the decision and improving the efficiency of the decision.

[0168] Fig.14A schematic diagram of a specific hardware structure of an electronic device provided in this embodiment is shown, and the device may include: a processor 1010, a memory 1020, an input / output interface 1030, a communication interface 1040, and a bus 1050. The processor 1010, the memory 1020, the input / output interface 1030, and the communication interface 1040 are connected to each other in communication within the device through the bus 1050.

[0169] The processor 1010 can be implemented by a general-purpose CPU (Central Processing Unit), a microprocessor, an application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of this specification.

[0170] The memory 1020 may be implemented in the form of ROM (Read Only Memory), RAM (Random Access Memory), static storage device, dynamic storage device, etc. The memory 1020 may store an operating system and other application programs. When the technical solutions provided in the embodiments of this specification are implemented by software or firmware, the relevant program codes are stored in the memory 1020 and are called and executed by the processor 1010.

[0171] The input / output interface 1030 is used to connect the input / output module to realize information input and output. The input / output module can be configured in the device as a component (not shown in the figure), or it can be externally connected to the device to provide corresponding functions. The input device may include a keyboard, a mouse, a touch screen, a microphone, various sensors, etc., and the output device may include a display, a speaker, a vibrator, an indicator light, etc.

[0172] The communication interface 1040 is used to connect a communication module (not shown) to realize communication interaction between the device and other devices. The communication module can realize communication through a wired mode (such as USB, network cable, etc.) or a wireless mode (such as mobile network, WIFI, Bluetooth, etc.).

[0173] The bus 1050 includes a path that transmits information between the various components of the device (eg, the processor 1010, the memory 1020, the input / output interface 1030, and the communication interface 1040).

[0174] It should be noted that, although the above device only shows the processor 1010, the memory 1020, the input / output interface 1030, the communication interface 1040 and the bus 1050, in the specific implementation process, the device may also include other components necessary for normal operation. In addition, it can be understood by those skilled in the art that the above device may also only include the components necessary for implementing the embodiments of the present specification, and does not necessarily include all the components shown in the figure.

[0175] An embodiment of the present invention provides a non-transitory computer-readable storage medium storing a plurality of instructions, which can be loaded by a processor to execute the steps of any one of the project solution decision methods based on fuzzy hierarchical analysis method and cloud model provided by the embodiment of the present invention.

[0176] The non-transitory computer-readable medium of this embodiment includes permanent and non-permanent, removable and non-removable media, and information storage can be implemented by any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, read-only compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device.

[0177] Those skilled in the art should understand that the discussion of any of the above embodiments is merely illustrative and is not intended to imply that the scope of the present disclosure (including the claims) is limited to these examples. Within the scope of the present invention, the above embodiments or technical features in different embodiments may be combined, the steps may be implemented in any order, and there are many other variations of the different aspects of the present invention as described above, which are not provided in detail for the sake of simplicity.

[0178] In addition, to simplify the description and discussion, and in order not to obscure the present invention, known power / ground connections to integrated circuit (IC) chips and other components may or may not be shown in the provided figures. In addition, the devices may be shown in the form of block diagrams to avoid obscuring the present invention, and this also takes into account the fact that the details of the implementation of these block diagram devices are highly dependent on the platform on which the present invention will be implemented (i.e., these details should be fully within the scope of understanding of those skilled in the art). Where specific details (e.g., circuits) are set forth to describe exemplary embodiments of the present invention, it will be apparent to those skilled in the art that the present invention may be implemented without these specific details or with variations in these specific details. Therefore, these descriptions should be considered illustrative rather than restrictive.

[0179] Although the invention has been described in conjunction with specific embodiments of the invention, many replacements, modifications and variations of these embodiments will be apparent to those skilled in the art from the foregoing description. For example, other memory architectures (e.g., dynamic RAM (DRAM)) may use the embodiments discussed.

[0180] The embodiments of the present invention are intended to cover all such substitutions, modifications and variations that fall within the broad scope of the appended claims. Therefore, any omissions, 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 project solution decision method based on fuzzy analytic hierarchy process and cloud model, characterized in that: include: Constructing an evaluation index system for multiple schemes in a project, wherein the evaluation index system includes multiple indicators and multiple evaluation levels; Using fuzzy hierarchical analysis method, and according to the comparison information of the importance of any two indicators by each decision maker among multiple decision makers, the weight of each indicator is determined; Determining the weight of each decision maker according to the trust relationship between the plurality of decision makers; Determine characteristic parameters corresponding to each evaluation level, wherein the characteristic parameters include expectation, entropy and super entropy; Determine the evaluation cloud model of each decision maker on each of the schemes with respect to each of the indicators according to the hesitant fuzzy evaluation information of each of the decision makers on each of the schemes with respect to each of the indicators and the characteristic parameters corresponding to each of the evaluation levels; Determine a comprehensive evaluation cloud model corresponding to each of the schemes according to the weight of each of the indicators, the weight of each of the decision makers, and the evaluation cloud model of each of the decision makers on each of the schemes with respect to each of the indicators; According to the comprehensive evaluation cloud models corresponding to the multiple solutions, the optimal solution in the project is decided from the multiple solutions.

2. The project solution decision-making method based on fuzzy analytic hierarchy process and cloud model as claimed in claim 1, characterized in that: Determining the weight of each of the indicators according to the comparison information of the importance of any two of the indicators by each of the decision makers among the multiple decision makers includes: Determine the triangular fuzzy number of each decision maker for any two indicators according to the comparison information of the importance of each decision maker for any two indicators among the multiple decision makers; Determine the fuzzy comparison average of any two of the indicators according to the triangular fuzzy numbers of the decision makers for any two of the indicators; Determine the fuzzy average value of each of the indicators according to the fuzzy comparison average values ​​of any two of the indicators; The weight of each indicator is determined according to the fuzzy average value of the multiple indicators.

3. The project solution decision-making method based on fuzzy analytic hierarchy process and cloud model as claimed in claim 2 is characterized in that: Determining the weight of each of the indicators according to the fuzzy average value of the multiple indicators includes: Determining the fuzzy weight of each of the indicators according to the fuzzy average values ​​of the multiple indicators; The fuzzy weights of the indicators are defuzzified and normalized to obtain the weights of the indicators.

4. The project solution decision-making method based on fuzzy analytic hierarchy process and cloud model as claimed in claim 1, characterized in that: Determining the weight of each decision maker according to the trust relationship between the plurality of decision makers includes: constructing a trust relationship matrix according to the trust relationships among the plurality of decision makers; Calculating the trustworthiness of each of the decision makers according to the trust relationship matrix; The weight of each decision maker is determined according to the trust level of each decision maker.

5. The project solution decision-making method based on fuzzy analytic hierarchy process and cloud model as claimed in claim 4 is characterized in that: The step of constructing a trust relationship matrix based on the trust relationships among the plurality of decision makers includes: Determining the degree of trust and non-trust of each decision maker to each other decision maker according to the trust relationship between the plurality of decision makers; A trust relationship matrix is ​​constructed according to the degree of trust and non-trust of each decision maker towards each other decision maker.

6. The project solution decision-making method based on fuzzy analytic hierarchy process and cloud model as claimed in claim 4 is characterized in that: Determining the weight of each decision maker according to the trustworthiness of each decision maker includes: sorting the plurality of decision makers according to the trustworthiness of each of the decision makers; According to the ranking results, calculating the importance weight of each decision maker; The importance weights of the decision makers are normalized to obtain the weights of the decision makers.

7. The project solution decision-making method based on fuzzy analytic hierarchy process and cloud model as claimed in claim 1 is characterized in that: Determining the evaluation cloud model of each decision maker on each scheme with respect to each indicator according to the hesitant fuzzy evaluation information of each decision maker on each scheme with respect to each indicator and the characteristic parameters corresponding to each evaluation level includes: Determining at least one evaluation level of each decision maker on each of the schemes with respect to each of the indicators according to the hesitant fuzzy evaluation information of each of the decision makers on each of the schemes with respect to each of the indicators; An evaluation cloud model of each decision maker on each scheme with respect to each indicator is determined based on a characteristic parameter corresponding to at least one of the evaluation levels of each decision maker on each scheme with respect to each indicator.

8. The project solution decision-making method based on fuzzy hierarchical analysis method and cloud model as claimed in claim 1 is characterized in that: The evaluation index system includes multiple levels of indicators, and the multiple indicators are the lowest level indicators among the multiple levels of indicators; Determining the comprehensive evaluation cloud model corresponding to each of the schemes according to the weight of each of the indicators, the weight of each of the decision makers, and the evaluation cloud model of each of the decision makers on each of the schemes with respect to each of the indicators includes: Determine the evaluation cloud model of each of the schemes with respect to each of the indicators according to the weight of each of the decision makers and the evaluation cloud model of each of the decision makers for each of the schemes with respect to each of the indicators; Determine the evaluation cloud model of each scheme for the upper level indicator according to the evaluation cloud model of each scheme for each indicator and the weight of each indicator; According to the evaluation cloud model of each scheme regarding the upper-level indicator and the weight of the upper-level indicator, the comprehensive evaluation cloud model corresponding to each scheme is determined.

9. An electronic device, characterized in that: The method comprises a memory, a processor and a computer program stored in the memory and executable on the processor. When the processor executes the program, the method for project solution decision based on fuzzy hierarchical analysis method and cloud model as described in any one of claims 1 to 8 is implemented.

10. A non-transitory computer-readable storage medium, characterized in that: The non-transitory computer-readable storage medium stores computer instructions, and the computer instructions are used to enable the computer to execute the project solution decision-making method based on fuzzy hierarchical analysis method and cloud model as described in any one of claims 1 to 8.

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