Research On The Comprehensive Evaluation Problems With Uncertain Information Based On Cloud Model

Non-deterministic data,such as fuzzy number,interval number,contact number,grey number and uncertain language variable,can express the uncertain information form.In fact,there are many evaluation information in the process of comprehensive evaluation,which takes the form of non-deterministic data as the carrier.For example,language evaluation information in hospital management statistical evaluation,intuitionistic fuzzy information in enterprise credit evaluation,interval number information in science and technology evaluation,grey number information in project evaluation,two-yuan semantic number information in product performance evaluation,a large number of complex fuzzy information on supply chain management and enterprise process management.For the above problems,there are some indexes with uncertain attributes,and the reviewers tend to give the evaluation information similar to the Non-deterministic data form.Therefore,the study of the comprehensive evaluation method of non-deterministic data has important application value to the social economic evaluation activity.Based on the existing research,this paper studies the problem of the comprehensive evaluation of uncertain data based on cloud model,and considers the related issues from the whole and the stage according to the basic steps of comprehensive evaluation technology.Based on this,the research background of uncertain data synthesis evaluation and the relevant research results of domestic and foreign scholars are studied,and the problem of uncertain data integration based on cloud model,the conversion method with mixed Multi-Attribute data and the application and visualization evaluation of uncertain data synthesis evaluation are researched.Therefore,the comprehensive evaluation theory and method based on cloud model are improved and perfected continuously.The full text is divided into seven chapters:Chapter 1 is the introduction.This chapter expounds the research background and significance of this paper,and reviews the current situation of cloud model theory,complex fuzzy numbers and comprehensive evaluation of uncertain language information,and then introduces the main contents,research methods,structural arrangements and possible innovations of this paper.The second chapter is the theoretical basis.This chapter aims to clarify the theoretical basis of cloud model,such as the numerical characteristics of cloud models,distance measure method and cloud model similarity measure method;The main fuzzy number,grey number,contact number,interval number,intuitionistic fuzzy number,Pythagorean fuzzy number and hesitation fuzzy number of the uncertain type data form are defined,the differences and connections between different non-deterministic data forms are expounded in detail,and the theoretical basis of non-deterministic data evaluation is analyzed.This chapter provides a theoretical basis for the synthesis evaluation method of uncertain data based on cloud model.Chapter 3 is based on cloud model of intuitionistic fuzzy number Bonferroni mean operator integration method.In this chapter,the concept of Bonferroni mean is extended to the Bonferroni harmonic average operator,and the concept and operation of the trapezoidal intuitionistic fuzzy number are introduced,and a sort method of the(triangular)trapezoidal intuitionistic fuzzy numbers is α/β.At the same time,weighted trapezoid intuitionistic fuzzy Bonferroni harmonic mean operator and canonical weighting triangular intuitionistic fuzzy Bonferroni harmonic mean operator are put forward.The concept and operation of intuitionistic fuzzy cloud are given,and the algorithm of intuitionistic fuzzy cloud inversion cloud generation is presented.Finally,the optimal supplier selection problem based on weighted trapezoid intuitionistic fuzzy Bonferroni harmonic mean operator.The evaluation of risk investment based on canonical weighting triangular intuitionistic fuzzy Bonferroni harmonic mean operator and the evaluation of information system security based on cloud model intuitionistic fuzzy Bonferroni mean operator are analyzed,and the results show the validity and feasibility of this method.The fourth chapter is the evaluation method of positive and negative ideal solution of Pythagoras fuzzy number based on cloud model.This chapter is devoted to the analysis of the difference and relation between intuitionistic fuzzy number and Pythagoras fuzzy number concept,puts forward the concept of Pythagoras Fuzzy cloud model,analyzes the fine property of Bidagolas model and the measure method of the distance of Pythagoras Fuzzy cloud model,Combined with cloud model generation algorithm,this paper puts forward the integrated method of Pythagoras fuzzy cloud,and then uses positive and negative ideal solution method to solve the impact analysis of buyer’s evaluation information on the potential customers in e-commerce.Chapter 5 is a hybrid Multi-Attribute evaluation method based on cloud model for interval number companion language variables.This chapter aims to introduce the concept of interval number,the algebraic operation property of interval number and the method of interval number energy degree,combine the universality of(normal)cloud model and the Golden Section method of qualitative and quantitative conversion of linguistic variables,and propose a comprehensive evaluation method of cloud model with mixed Multi-Attribute.It is used to solve the problem of risk situation assessment of air attack target,and the loss and distortion of the information is realized,which shows the superiority of the method.The sixth chapter is the statistical data quality evaluation method based on cloud model.In this chapter,a new method of statistical data quality evaluation based on cloud model is presented for the problem of statistical data quality evaluation method.First,the paper determines the evaluation level language granularity of cloud model,and makes a soft division of it.According to the evaluation Index system of statistic data quality,the cloud model of data quality assessment is drawn from eight dimensions of accuracy,timeliness,applicability,consistency,cohesion,hermeneutics,availability and effectiveness,and the weighted arithmetic average integration technique of cloud model is used to construct the comprehensive cloud;Secondly,based on the similarity of cloud model,the classification of the composite cloud is judged according to the similarity of the cloud model.The example shows that the new method can be used as a reference for the quality evaluation and supervision of statistical data.Chapter 7 is concluding remarks.This chapter summarizes the results of the full text study,and points out the deficiencies in this paper,as well as the problems that need to be improved and further studied in the future.Based on the above research content,we strive to innovate in the following aspects:(1)Because of the large number of interrelated situations in the evaluation,a new algorithm based on weighted trapezoid intuitionistic fuzzy Bonferroni harmonic mean operator and canonical weighting weighted triangular intuitionistic fuzzy Bonferroni harmonic mean value is proposed.The operators are of good properties such as Idempotent,exchangeable,monotone and boundedness,and the operators are applied to the comprehensive evaluation method of multiple attributes.This method can be used to mine the importance of attributes and to reflect the relationships among attributes.Therefore,there is innovation in the method of integration of non-deterministic data.(2)Study the effective "synthesis" of cloud model and Pythagoras fuzzy number,put forward a concept of Pythagoras fuzzy cloud,make up the deficiency of randomness and fuzziness in the process of traditional evaluation,make the method more applicable to the practice and application of comprehensive evaluation.In order to overcome the limitation of the traditional multi-criteria group decision method in Pythagoras Fuzzy environment,a new method of the Pythagorean Fuzzy Cloud Multiple index positive and negative ideal solution is proposed,and the evaluation information of the customer is processed by the Pythagorean reverse cloud algorithm.Pythagoras ’ normal cloud can effectively reflect the fuzziness and randomness of evaluation information.The analysis of the example shows that the proposed method can solve the problem of assistant decision in the purchase of potential customers.(3)Using the research method of this paper to evaluate the quality of statistic data,this paper puts forward a new method based on cloud model similarity method,which is used to estimate the uncertainty data synthetically.The application shows that the new method has the comparative advantage in fuzziness and randomness,and can effectively realize the overall visualization and local visualization evaluation under the index system of statistical data quality evaluation.