The arrival of the “double carbon” era indicates that industrial carbon reduction with high energy consumption and high carbon emission is imperative. From the perspective of carbon emission driving factors, industrial carbon emission is decomposed into five influencing factors: energy intensity, energy structure, industrial structure, economic efficiency, and employee scale. Taking the data of 41 subindustries of industrial industry in Liaoning Province from 2010 to 2019 as the research sample, the carbon emission is calculated. The LMDI model is used to analyze and point out the difference in the driving contribution of carbon emissions of each subindustry. The results show that the total carbon emission of Liaoning province gradually decreases, decreases for the first time in 2014, and gradually turns from flat to upward. Economic efficiency is the only and most important reason to drive the increase of industrial carbon emissions in Liaoning Province, and energy efficiency is the primary factor to curb carbon emissions. High carbon industries play a significant role in promoting the increase of carbon emissions, while the medium and low carbon industries have a better effect on restraining carbon emissions. It provides reference and theoretical basis for the government to adjust the industrial structure, control industrial overcapacity, and realize the “double carbon” goal as soon as possible. It is of great significance for the country to optimize energy layout, ensure energy security, and implement the new energy strategy.

This paper studies the fault prediction of equipment and the decision of joint equipment maintenance in urban traffic system based on Internet of Things and big data as the background of multiple control units, and proposes a joint equipment monitoring model for multi-equipment systems composed of intelligent control units. The network method predicts the fault of the equipment and gives the equipment fault prediction process. Combining the multi-equipment system attributes of intelligent control unit, aim-ing at minimizing the total maintenance cost of control unit system, and taking the reliability of the whole system and the availability of equipment as constraints, a preventive maintenance decision model for joint equipment maintenance is established. Finally, the optimal preventive maintenance interval of the entire system is solved.

Based on the current status of the development of equipment manufacturing enterprises, the main influencing factors and main evaluation indicators of the equipment manufacturing enterprises in the context of dual‐channel marketing are analyzed and determined. According to the organizational layout and transaction form of equipment manufacturing enterprises, the problem of equipment manufacturing enterprise location decision‐making with online channel and offline channel trading mode is divided into two categories. One is the single‐plant location decision problem, and the other is the multiplant location decision problem. In the single‐plant location decision problem, we have established the conceptual model and mathematical model. Based on prospect theory, we use multicriteria decision‐making method to quantify and standardize the evaluation index and determine the reference value, and the priority of the scheme is determined according to the final comprehensive value. In the multiplant location decision problem, we have established the conceptual model and the logical model and established the mathematical model with the goal of minimizing the cost and time. Finally, an example is given to solve the location problem of single plant and multiple plants. The feasibility of the model is proved by the solution and verification.

Based on the two-stage innovation value chain model, incorporating environmental regulation policies and three-waste emissions into the research framework, constructing a green technology innovation efficiency evaluation index system from the perspectives of green inputs and outputs, and utilizing the super-efficiency SBM model and the Malmquist-Luenberger index model, assessing the effectiveness of green technological innovation in two aspects, namely, static, and dynamic, respectively. The results show that: the average value of green technical innovation efficiency of China's hightech industries is 0.5277, with significant differences between provinces, but the overall is constantly improving, showing an upward trend; the green technological innovation efficiency decreases from east to west, and the differences among the three major regions are not only embodied in spatial patterns, but also in the growth rate; during the period of investigation, the inefficient provinces are gradually decreasing, and the level of green technological innovation of China is constantly getting better; China's The average value of ML index of green technical innovation efficiency of high-tech industries in China is greater than 1, and the overall development trend is better; the increase of ML index in the eastern and central regions mainly relies on the positive influence of technological efficiency, while the decrease of ML index in the western part is affected by the recession of technological progress and technological efficiency, and the efficiency of technology research and development stage is generally higher than that of scientific and technical achievements transformation stage, and low efficiency of achievements transformation stage is mainly affected by the inhibiting effect of technological progress – inhibitory effect of technological advancement.

How to maximize customer satisfaction is an important research topic in the service quality evaluation. This paper proposes an evaluation method of comprehensive product quality for customer satisfaction based on the intuitionistic fuzzy number. In this method, we design a questionnaire and investigate the customer’s language evaluation information of product quality evaluation, including product expectations and product perception at first. And then, the product quality evaluation model is obtained by Delphi method; that is, the first-level evaluation indexes and the second-level evaluation indexes are obtained and the weight vector of each evaluation index is determined. Next, language evaluation information translates into corresponding fuzzy numbers using intuitionistic fuzzy numbers. Therefore, the results of the product quality evaluation of the production system are obtained using the weighted mean method. Finally, an example is used to illustrate the feasibility and effectiveness of the proposed method.