Thermal Energy-Kinetic Energy Conversion Efficiency Analysis Method Based on Grey Cluster Analysis

At this stage, although thermal power generation is still the main method of power generation in my country’s power plants, due to more and more energy consumption, the reserves of resources are gradually beginning to be stretched. Application in the power generation and production process of thermal power plants has become a road that more and more power plants must explore for development. In this paper, through the grey cluster analysis, the power plant can fully improve the power generation efficiency of the power plant in the process of utilizing the existing energy, so as to achieve the purpose of energy saving and environmental protection. Combined with the connotation of thermal energy-kinetic energy conversion, this paper establishes a theoretical framework for the analysis method of thermal energy-kinetic energy conversion efficiency based on grey cluster analysis. On the basis of theoretical research, using descriptive statistical tools, the main performance of thermal energy-kinetic energy conversion to promote economic growth is summarized. The realization method of gray cluster analysis theory is to identify and process the external features of gray information, capture effective information, and quantify the features (ie, whitening process), so as to complete the evaluation or decision-making, and verify the effect of these five mechanisms on economic growth. The final results of the study show that when K=1, 2, 3 and 4, the proportion of heat transfer is 41.63%, 42.19%, 49.63% and 52.13%, and the proportion of heat convection is 37.56% and 41.33%, respectively., 29.69% and 26.78%, the heat transfer and heat convection have the greatest influence on the thermal energy-kinetic energy conversion efficiency.


Introduction
With the popularization of domestic electricity consumption, electricity resources have become an indispensable product.Electricity is mainly realized through energy conversion in power plants.Therefore, the efficiency of energy conversion is directly related to the efficiency of electricity output.Therefore, the development of power plants at this stage is It is also more and more concerned by people, and power plants are also paying more and more attention to the research on the conversion efficiency of thermal energy to kinetic energy, so as to make the related industries have better development [1] .Therefore, it has certain practical significance to study the thermal energy-kinetic energy conversion efficiency analysis according to the grey cluster analysis theory.In recent years, many researchers have studied the thermal energy-kinetic energy conversion efficiency analysis method based on gray cluster analysis, and achieved good results.For example, ES Beckjord believes that colleges and universities, as carriers of entrepreneurship and innovation education, must define a clear educational connotation in the process of entrepreneurship and innovation education [2] .Willie Henderso believes that colleges and universities, as a place for cultivating entrepreneurial spirit and an important place for cultivating high-quality innovative talents, adjust higher education policies in a timely manner in line with the tide of the knowledge economy era [3]   .At present, scholars at home and abroad have carried out a lot of research on the comprehensive evaluation method of innovation and entrepreneurship performance, and these previous theoretical and experimental results provide the theoretical basis for the research of this paper.Based on the theoretical basis of gray cluster analysis, this paper analyzes the thermal energy-kinetic energy conversion efficiency, and proves the feasibility of gray cluster analysis in thermal energykinetic energy conversion efficiency through a series of data.The method has a certain effect on improving the thermal energy-kinetic energy conversion efficiency, and provides a feasible solution for improving the conversion efficiency and saving energy resources.

Introduction to Grey System Theory and Application
(1) Gray system The so-called gray system is relative to the white system and the black system.If all the information of a system, such as architecture, influencing factors, and the interaction between influencing factors, is unknown, the system is called a black system; on the contrary, when a system can show enough and perfect The amount of information, the internal and external structures are obvious, the influencing factors and mechanism of action in the system are clear, and there is an obvious development and change law, which is easy to qualitatively analyze and quantitatively describe the system, it is called a white system [4][5] .The gray system is a kind of "half black and half white" system, that is, the system has some characteristics or the information is exposed and known to people, but there are still some important information hidden in the system.
(2) Defects of the grey system Such systems are often difficult to study due to the unclear organizational structure of the system, unclear relationships between modules and modules within the system, and not all of the internal and external factors that affect the system are known.There are many gray systems in real life, such as agricultural ecosystems, meteorological systems, population systems and social systems [6] .When studying such systems, it is difficult to identify the influencing factors and associated information within the system, the relationship between the influencing factors is hidden, and the operating principle of the system is not clear, which makes it impossible to quantitatively describe the system.In that way, objective and clear physical model prototypes are established to analyze, describe and deduce the system.
(3) Classical system analysis theory The classic system analysis theory regards the various behaviors of the system as a process of random change, and then uses various mathematical and statistical methods such as probability, regression analysis, and principal component analysis to start from a large amount of historical data and find historical data.Statistical distribution law [7][8] .When the sample data is sufficient, sufficient information can be obtained by analyzing the historical data.At this time, this kind of system analysis theory can generally achieve good results, but when the system can provide less data for research and the data is not representative Such methods often perform poorly when the sex or data itself contains no other information.
(4) Main viewpoints of grey system theory One of the main points of gray system theory is that although the data, information and resources exposed by the gray system are limited, there must be some changing laws behind the various behavioral data displayed.All stochastic processes can be regarded as a time-series change process that integrates the combined effects of all the characteristics inside and outside the system [9] .Thus, the task of analyzing the gray system can be simplified to this implicit time sequence change process, and the information that the system can provide is the breakthrough of the analysis.In the process of using the gray information provided by the system to model the gray system theory, the first is to weaken the dispersion and randomness of the original data through some data generation methods, and the generated data shows the regularity corresponding to the generation method.Common data generation methods include accumulation generation, accumulation generation, and mean generation.The specific process of each generation mode will be described in detail below.However, although data generation can reduce the dispersion and randomness of the original data, it is helpful for analysis and research, but after all, it is the data obtained after processing, and the original information carried by the data has been missing, which is also modeled by the grey system theory.It is a pity [10] .Therefore, in practical applications, data generation should be performed after making full use of the original data and mining useful information, so as to reduce the information debuff effect caused by data data generation.The advantage of the gray system theory is that it can generate and process the original data to discover the hidden laws in the data when only part of the original data is clear, and use the generated data to construct and solve the first-order whitening differential equation of the gray system.The data response equation can be used to predict the changing trend of the data.

Model Construction Based on Grey Cluster Analysis
(1) Information integrity description In the related research of system cybernetics, "white", "black" and "gray" are often used to describe the completeness of information.Among them, "white" refers to all the details of known information, "black" refers to the lack of all known information, and "gray" is in between, "some known information, unknown information".
(2) Definition and classification of grey cluster analysis The gray system theory was founded by Chinese scholars in the last century.The realization of the theory is to identify and process the external characteristics of gray information, capture effective information, and quantify the characteristics (ie, whitening process), so as to complete the evaluation or decision-making [11] .Gray clustering can be divided into two types: one is gray correlation clustering, which is used to classify the same factors; the other is gray whitening weight clustering, which is used to classify the research objects and determine which sub-objects belong to.
(3) Whitening weight clustering Whitening weight clustering is a method of constructing a whitening weight function to calculate the whitening weights that each cluster object has for different indicators, so as to determine the category of the cluster object.The advantage of this method lies in the universality of sample size and index regularity, the steps are clear, computer-aided calculation can be used, and it is suitable for evaluating systems where only part of the information is completely available and conforms to the gray typicality of cybernetics [12] .In this paper, the grey whitening weight clustering method is used to rate the construction efficiency level of each level index of thermal energy-kinetic energy conversion and sort the improvement priority of the index at the same level, and put forward improvement countermeasures and suggestions in a targeted manner.

Experimental Method
The gray whitening weight function is a dimensionless evaluation of the weight of physiological indicators on each eigenvalue.The higher the whitening weight function value, the closer the kinetic energy conversion efficiency is to the eigenvalue.The calculation formula is: Among them, assuming that the clustering weight of each index is wj (j=1,2,...,m), calculate the weight wj of each clustering index rj.According to the weight formula of the clustering index calculated by the square root method, the clustering coefficient of the clustering object i belonging to the gray class k is calculated, f is the whitening weight function value, and wj is the relative weight of the evaluation index.

Experimental Requirements
In this experiment, based on the grey cluster analysis method, the analysis of thermal energy-kinetic energy conversion efficiency is studied.By calculating the grey whitening weight functions of thermal radiation, heat transfer and thermal convection indicators, the grey whitening of these three thermal energy indicators is calculated.In order to further improve the efficiency of thermal energy-kinetic energy conversion of gray clustering, before constructing the transformation model, the gray clustering analysis method is used to classify the capacity transformation sample set.Systematic analysis of the collected data to verify the feasibility of the grey cluster analysis method.

Heat Energy-Kinetic Energy Conversion Gray Cluster Eigenvalue Proportion Analysis
By calculating the gray-based whitening weight functions of the thermal radiation, heat transfer and thermal convection indicators, the gray-based whitening weights of these three thermal energy indicators are then calculated.The experimental data are shown in the figure below.  1 and Figure 1, it can be seen from the analysis of the proportions of the three eigenvalues of heat radiation, heat transfer and heat convection, the gray cluster eigenvalues of heat transfer have the largest proportion, and the proportions of the eigenvalues in K=1, 2, 3 and 4 are the largest., the proportion of heat transfer is 41.63%, 42.19%, 49.63% and 52.13%, and the proportion of heat convection is 37.56%, 41.33%, 29.69% and 26.78%, respectively.Transfer Mode Conduction Convective Radiation Introduction Heat transfers from a high-temperature object to a low-temperature object, or from the high-temperature part of the object to the low-temperature part, so heat transfer and heat convection have the greatest impact on the thermal energy-kinetic energy conversion efficiency.

Analysis of Thermal Energy-Kinetic Energy Conversion Efficiency Before and After Gray Clustering Method Improvement
Through the analysis of the proportion of the eigenvalues of the gray clustering of thermal energykinetic energy conversion, heat transfer and heat convection have the greatest impact on the thermal energy-kinetic energy conversion efficiency.This experiment continues to analyze the thermal energykinetic energy conversion efficiency before and after the gray clustering method is improved.Show.

Figure 2. Analysis of heat energy-kinetic energy conversion efficiency before and after the gray clustering method is improved
As shown in Figure 2, the thermal energy-kinetic energy conversion process is improved by the gray clustering method, and the thermal energy-kinetic energy conversion efficiency has been improved to a certain extent.The effect of transformation efficiency, the transformation efficiency has increased from 78.13%, 60.25%, 66.19% and 71.25% before cluster analysis to 81.68%, 88.89%, 87.77% and 91.18%, which has a certain improvement effect.

Conclusions
Based on the theoretical basis of gray cluster analysis, this paper analyzes the thermal energy-kinetic energy conversion efficiency, and proves the feasibility of gray cluster analysis in thermal energykinetic energy conversion efficiency through a series of data.From the experimental data of the thermal energy-kinetic energy conversion efficiency analysis before and after the improvement of the specific gravity analysis and the gray clustering method, it can be seen that heat transfer and thermal convection have the greatest influence on the thermal energy-kinetic energy conversion efficiency, and the gray clustering analysis method can improve the thermal energy-kinetic energy conversion efficiency.There is a certain effect.On the basis of analyzing and comparing modeling methods, AHP (Analytical Hierarchy Process) and grey clustering analysis method are selected as the modeling methods for the priority model of safety improvement.

Table 1 .
Heat energy-kinetic energy conversion gray cluster eigenvalue proportion analysis table Figure 1.Heat energy-kinetic energy conversion gray cluster eigenvalue proportion analysis diagram 5 From Table