An improved interval-valued intuitionistic fuzzy multi-attribute group decision-making method considering the risk preference of decision-makers is proposed to solve the multi-attribute group decision-making problem with interval-valued intuitionistic fuzzy numbers and the condition that the attribute weight information is completely unknown. Firstly, the decision-maker weight of each attribute is determined by combining similarity and proximity. In order to consider the influence of the decision-maker's risk preference on the decision result and avoid the asymptotic behavior of interval-valued intuitionistic fuzzy matrix, the risk aversion coefficient of the decision-maker is introduced and combined with the determined decision-maker's weight aggregation to form a group decision matrix. Then, the information of group decision matrix is mined, and the interval-valued intuitionistic fuzzy entropy is used to determine the attribute weight and relative weight. Based on the interval-valued intuitionistic fuzzy distance measure formula and the TODIM method, the overall superiority of each scheme relative to other schemes is obtained by calculating the superiority between schemes, and the optimal scheme is determined by comparing and sequencing. Finally, the rationality and effectiveness of the proposed method are verified by an example of mechanical assembly supplier selection decision.

A multi-attribute group decision-making (MAGDM) method based on intuitionistic fuzzy preference information is proposed for the multi-attribute intuitionistic fuzzy group decision-making problem where the decision-makers weight and attribute weight are completely unknown and the decision-maker has preference information for the scheme. Firstly, an intuitionistic fuzzy interval judgment matrix is established to describe the original data of the key decision indicators for multiple network public opinion emergencies that erupt simultaneously. Secondly, the attribute weights are determined based on the improved intuitionistic fuzzy entropy construction method, and the expert weights are determined by using objective decision information, taking into account the intuitionistic fuzzy entropy of decision matrix. MAGDM can not only synthesize experts' professional experience in different aspects, but also avoid experts' decision-making errors caused by insufficient domain knowledge. It is necessary to continuously adjust the expert weight based on decision-making information to make the comprehensive decision-making information more accurate. Thirdly, a scheme preference model and an attribute weight optimization model are established to determine the ranking method of intuitionistic fuzzy interval values. Then, a modified distance measure of intuitionistic fuzzy sets (IFSs) is introduced to make the evaluation result more accurate and reasonable when it comes to solving the deviation between the evaluation value and ideal solution of each scheme. Finally, the effectiveness and practicability of the proposed decision-making method are verified by an example of emergency crisis severity, It assists decision makers in selecting and implementing the optimal emergency response plan in a timely and efficient manner, which improves the emergency treatment efficiency of network public opinion crisis, helps emergency departments to better deal with the network public opinion crisis, improves the ability of public opinion guidance and control, and provides a new method and idea for multi-attribute intuitionistic fuzzy group decision-making problem.

In this study, aiming at the multi-attribute decision-making problem with incomplete and uncertain attribute weight information and attribute value of interval numbers, a grey target decision-making model of interval numbers based on positive and negative clouts is proposed. Firstly, in this model, the linear transformation operator of interval number is used to normalize the original decision information, and the positive and negative clouts of interval number are designed. Secondly, after the space projection distance between each scheme and the positive and negative clouts is considered comprehensively, the off-target distance is taken as the basis of vector analysis in space to obtain a new comprehensive off-target distance. The existing interval number grey target decision-making model ignores the important influence of interval distribution and the correlation between the attributes in scheme evaluation, and there are some fuzzy errors when setting the weight of attributes. In order to solve the above problems, this paper combined with the uncertainty analysis of the attribute weights, a goal programming is constructed for the objective function based on the comprehensive off-target distance minimization to solve the attribute weight vector, and finally determine the order of the scheme. Finally, the feasibility and effectiveness of the proposed grey target decision model are verified by an example of venture capital projects. Compared with traditional models, the improved model fully considers the characteristics of interval data and the correlation between the attributes.

Energy user’s choice of energy service company affects the smooth implementation of energy performance contracting. Energy performance contracting is a new mechanism for energy saving. In the energy performance contracting, the energy user and energy service company cooperate to save energy and share the benefit. One difficulty in actualizing the energy performance contracting is to select the optimal energy service company. However, there are few literatures to analyze the efficient selection mechanism. A government is used as the energy user and the energy service company selection based on intuitionistic fuzzy entropy and VIKOR framework is studied in this paper. A group decision-making method based on intuitionistic fuzzy entropy and VIKOR framework is proposed to solve the multi-attribute group decisionmaking problem with the unknown decision-makers’ weight and attribute weight. First, the attribute synthetic weight is determined by intuitionistic fuzzy entropy weight method and weight coordination coefficient, and expert weight is determined by objective decision information after considering the intuitionistic fuzzy entropy of decision matrix; then, the intuitionistic fuzzy group decision matrix aggregated by experts is obtained through the aggregation of intuitionistic fuzzy weighted average (IFWA) operator, and the improved intuitionistic fuzzy number distance measure is introduced into the solutions of group utility value and individual regret value to make the evaluation results more accurate and reasonable. Finally, the validity and practicability of decision-making method is analyzed and verified with an example, so as to provide a new method and idea for multi-attribute intuitionistic fuzzy group decision-making.

This paper first designed the overall scheme of a wireless-based intelligent environment monitoring system; this system is comprised of the following four parts: server part, client part, slave computer part, and master computer part. The master computer is equipped with a wireless transmission module and an LCD module. Slave computers are equipped with temperature-humidity sensing modules and wireless transmission modules. The server side has two major interfaces: data display and server establishment. An Android phone as the client can realize the communication between the mobile APP and server through network communication to obtain environmental data in real time. The hardware part of the system consists of wireless transmission module, temperature and humidity module, smoke module, lighting module, flame module, and LCD module. The software implementation part mainly includes the design and implementation of serial communication, the design and implementation of wireless communication, the design and implementation of collection of data acquisition in each sensor module, and the design and implementation of network communication as well as the design and implementation of display unit. With the cooperation of these four parts, we can automatically sense and control the state of the environment to realize human-computer interaction.