# Application of Machine Learning Algorithms in the Development and Consumption Trend of Green and Intelligent Vehicles under the Background of Big Data

Authors: BenShuang Liang; Jing Yang; Yaxin Guo; Xin Guo
Journal: Wireless Communications and Mobile Computing
 (2022)

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## Abstract

In the face of global warming, air pollution, and other difficulties, electric vehicles have become an industry strongly supported by various countries due to their good environmental protection characteristics. In the context of big data, people are exposed to more and more information, and the convenience brought by big data is also increasing. Based on this background, the development of green and intelligent vehicles is getting faster and faster. This paper is aimed at studying the application of machine learning algorithms in the development and consumption trends of green and intelligent vehicles in the context of big data. This paper proposes machine learning algorithms based on big data, as well as support vector machine algorithms and so on. Machine learning algorithms specialize in how computers simulate or implement human learning behaviors to acquire new knowledge or skills and to reorganize existing knowledge structures to continuously improve their performance. The test results of this paper show that, starting from 2014, China has begun to vigorously develop green and intelligent vehicles. In 2014, the production volume of green and intelligent vehicles in China was 3,675, and the sales volume was 2,790. The development of green and intelligent vehicles is not very good and has not been fully accepted by the public. However, since 2017, the production and sales of green and intelligent vehicles have been slowly increasing. By 2020, the production of green and intelligent vehicles will be 24,360 and the sales will be 24,090. It can be seen that with the development of time, green and intelligent vehicles are gradually being recognized.


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## Body

## 1. Introduction

Since the reform and opening up, China’s economy has developed rapidly, but problems such as insufficient energy resources and environmental degradation have become more and more prominent. In order to fundamentally solve these problems, we must start from the economic system, energy system, and environmental system. China proposes a green development plan, proving that China’s green development path is unstoppable. Energy conservation and emission reduction and environmental policies can also help lead the purification of green technology innovation activities. Big data, or huge amount of data, refers to the amount of data involved that is so large that it cannot be acquired, managed, processed, and organized into information that helps companies make more active decisions within a reasonable time through mainstream software tools.In the past, automobiles mainly used nonrenewable resources such as gasoline and diesel to supply electricity. However, China’s per capita oil resources are seriously insufficient and need to be imported in large quantities. The increase in the number of conventional vehicles has led to a sharp increase in the use of gasoline and diesel, which has added to the pressure on China’s energy supply. Therefore, improving the exhaust control technology of vehicles, reducing emissions, and using clean energy are very important for energy conservation and emission reduction, as well as the green transformation of the automotive industry.The innovations of this paper are: (1) This paper introduces the theoretical knowledge of big data and machine learning algorithms and uses machine learning algorithms to analyze how machine learning algorithms play a role in the development of green and intelligent vehicles and the application of consumption trends. (2) This paper expounds the support vector machine and the BP neural network. Through experiments, it is found that the machine learning algorithm can effectively analyze the development and consumption trends of green and intelligent vehicles.


## 2. Related Work

With the development of the times, people’s economic level is constantly improving, and more and more people use smart cars. Rajalakshmi and Rajakumar found that people faced problems when parking their vehicles in parking spaces, and the existing parking systems were not able to accommodate the flexibility of people’s travel. So they came up with a sensor network-based system that can classify cars and track parking spaces based on their length. The images of the cars are compared with the images stored in the database, and according to their length, each car is entered and the information is displayed in the LCD. The system can accurately detect vehicles in both indoor and outdoor areas. In response to the problem of insufficient parking spaces, Rajalakshmi and Rajakumar proposed a sensor network-based system, but it has not been proved by actual experiments [1]. Chang et al. found that the unprecedented growth of wireless traffic not only poses challenges to the design and evolution of wireless network architectures but also brings great opportunities to promote and improve future networks. At the same time, the evolution of communication and computing technology can make the edge of the network become more intelligent, rich in computing and communication capabilities. They suggested exploring big data analytics to improve edge caching capabilities, which are seen as a way to improve network efficiency and alleviate the high demands of future networks. Chang et al. proposed that the opportunity brought by the unprecedented growth of wireless traffic should also be discovered, but did not specify how to seize this opportunity [2]. Shen and Chan found that sharing forecast information helps all aspects of the supply chain to better match demand and supply, and they believe that sharing forecast information can improve supply chain performance. In the era of big data, supply chain managers have the ability to process massive amounts of data through big data technology and analysis. Big data technologies and analytics provide more accurate predictive information and provide opportunities to transform business models. They analyzed the forecast information sharing of supply chain management in the era of big data. Shen and Chan only saw the benefits brought by big data but did not see the challenges [3]. Zhou and Luo found that big data is a dataset that is suitable for capturing, managing, and possessing the ability to process data within a short period of time. In smart cities, available resources can be used safely, sustainably, and efficiently to achieve positive, measurable economic, and social outcomes. Most of the challenges of big data in smart cities are multidimensional and can be addressed from different multidisciplinary perspectives. They combined the fuzzy logic model and entropy weight method to conduct an empirical study of feasible urban public safety evaluation modeling. Zhou and Luo did not mention how to combine the two methods and what is the specific effect of the combination [4]. Li et al. found that the Internet of Things (IoT) and big data are the two most talked about technology topics in recent years, and they are closely related, and these devices will be able to generate big data that people need to analyze. Therefore, IoT and big data have the potential to revolutionize the entire telecom industry. Li et al. saw the advantages of big data in the future and also saw that big data can bring benefits to the telecommunications industry, but they did not make a specific analysis of big data [5]. Epelde et al. found that in recent years, the digitization of human-generated information flows from traditional manual processes leading to the massive availability of heterogeneous data in most areas of life. This is due to lower costs and improved capabilities of information and communication technology (ICT) for storage, processing, and transmission. They realized that there was a lot of information in the information flow that could be exploited but did not see that manual processes would waste a lot of time and cost [6]. Khosravi et al. found that accurate forecasting of renewable energy plays a key role in the grid. They proposed the use of machine learning algorithms to predict hourly solar irradiance, and the predictive models were developed based on two types of input data. To this end, they developed Multilayer Feedforward Neural Network (MLFFNN), Radial Basis Function Neural Network (RBFNN), Support Vector Regression (SVR), Fuzzy Inference System (FIS), and Adaptive Neuro-Fuzzy Inference System (ANFIS). Khosravi et al. knew that predictive models were developed based on two types but did not conduct a comprehensive analysis of both types [7]. Yang et al. found that the photovoltaic-thermoelectric hybrid system can realize the full spectrum utilization of the solar spectrum, but the surface reflection has always been an important reason for inhibiting its power conversion efficiency. Therefore, they proposed a novel composite nanostructure to reduce surface reflections in a certain range by means of the finite difference time domain (FDTD) simulation method. Although Yang et al. proposed a new type of composite nanostructure, no explanation was given for this new type of composite nanostructure [8].


## 3. Machine Learning Algorithms in the Context of Big Data

### 3.1. The Concept of Green and Intelligent Vehicles

With the increase in the number of automobiles in China, the problem of automobile exhaust pollution and its large energy demand is becoming more and more serious. Promoting the green innovation of automobile enterprises and developing the new energy automobile industry are very important for energy conservation and emission reduction [9]. At present, the development of China’s new energy vehicle industry is faced with the characteristics of high technology, high investment, and high risk, which requires policy guidance and strong support from the government. A green smart car is shown in Figure 1.Figure 1
Green smart car.As shown in Figure1, with the further expansion of car ownership in China, the energy crisis and air pollution have become increasingly prominent, becoming the main problems hindering the development of automobiles. Energy-saving, green, and intelligent new vehicles will be the direction of future automobile development. At present, major automakers have invested a lot of manpower and material resources in the research of alternative energy and new power. New energy vehicle technologies represented by hybrid power, fuel cells, advanced diesel, and alcohol gasoline have shown a rapid development trend. In the process of the rapid increase of automobiles, the pollution of automobile exhaust to China’s environment cannot be ignored. Many studies have shown that traffic pollution, especially automobile exhaust, is an important cause of urban smog in China. If the electric energy used by electric vehicles comes entirely from fossil fuels, the emissions of air pollutants produced by electric vehicles will exceed that of ordinary fuel vehicles [10]. Therefore, more and more electric vehicle charging stations will be equipped with certain photovoltaic power generation equipment to supply the electric energy required for electric vehicle charging. The performance of the electric vehicle is shown in Figure 2.Figure 2
Performance of electric vehicles.As shown in Figure2, the worldwide problems of energy depletion and environmental pollution are becoming more and more serious, and the development of new energy vehicles is the top priority for energy conservation and emission reduction in China and the world. Compared with some well-known enterprises, China is relatively backward in new energy vehicle drive technology and power battery material technology and is much behind in the patent strategy construction plan [11].


### 3.2. Relevant Machine Learning Algorithms in the Context of Big Data

Everyone recognizes the importance of big data, but there are different opinions on the definition of big data. Big data is an abstract concept. In addition to a large amount of data, big data has several characteristics that determine the difference between big data and the concepts of “large data” and “very large data.” In a general sense, big data is a key factor of production to promote the development of the digital economy. The development of the digital economy is the only way to achieve high-quality economic development and build a modern economic system. Promoting the digital transformation of the economy and society is actually the transition from the era of industrial economy to the era of digital economy. Big data refers to the identification, acquisition, management, processing, and collection of data that cannot be provided by traditional IT technology, hardware, and software tools within a limited time [12]. The components of big data are shown in Figure 3.Figure 3
Components of big data.As shown in Figure3, the value chain of big data can be divided into four stages: data generation, data collection, data preservation, and data analysis. Data analysis is the last important stage of the big data value chain and the basis for realizing the value and application of big data. Its purpose is to extract useful value, provide judgmental recommendations, or support decision-making analysis, which has the potential to yield different levels of potential value [13]. With the rapid development of the global economy, the demand for energy is also increasing, the reserves of traditional fossil energy are shrinking, and environmental problems such as global warming have become increasingly prominent. Energy issues have become the focus of attention around the world. The new energy power generation technology and application represented by solar energy and wind energy have become the research hotspots in various countries [14].


#### 3.2.1. Support Vector Machine (SVM)

Support vector machines and kernel methods are important methods in the field of machine learning, and their basic theories and practical engineering applications are relatively mature. Initially, support vector machines were used for classification and regression tasks, after which some of these important problems were studied in depth. Together, these vectors support the classification hyperplane and are therefore called support vector machines [15]. The schematic diagram of SVM is shown in Figure 4.Figure 4
SVM schematic.As shown in Figure4, the final decision function of SVM is only determined by a small number of support vectors, and the computational complexity depends on the number of support vectors, not the dimension of the sample space, which avoids the “curse of dimensionality” in a sense. SVM has the strongest robustness to samples in this classification. Robustness is the ability of a system to survive abnormal and dangerous situations. For example, whether the computer software cannot crash or crash under the condition of input error, disk failure, network overload, or intentional attack is the robustness of the software. The classification to be solved can be described using
(1)wTa+b=0,where w=w1,w2,⋯,wd is the normal of the classification hyperplane and the distance from the training sample to the hyperplane can be expressed as
(2)r=wTa+bw.For a linearly classifiable sample set, if hyperplanew,b can correctly classify all samples, then for any sample ai,bi∈D, there is
(3)wTai+b≥+1,bi=+1,wTai+b≺−1,bi=−1.The point closest to the classification surface is called the support vector, and the distance from the support vector to the classification hyperplane is(4)r=1w.To find the so-called “maximum interval” classification hyperplane, it is necessary to findw,b that satisfies formula (3) and maximize r, that is,
(5)s.t.biwTai+b≥1,i=1,2,⋯,m.Formula (5) can be obtained by solving the desired classification hyperplane. This is a quadratic planning problem; note that it can be solved by using the related method of the quadratic planning problem. But S-gradient solution, least squares, etc. can also be used to solve such problems [16]. The least square formula is a mathematical formula, called curve fitting in mathematics, which includes not only linear regression equations but also matrix least squares.


#### 3.2.2. Introduction to the Variant Form of Support Vector Machine

Compared with traditional statistical learning methods, support vector machines have obvious advantages in preventing overfitting and high training accuracy. Therefore, with the in-depth study of SVM by a large number of scholars, many variant algorithms of SVM appear. These methods mainly have certain advantages in a certain aspect by changing coefficients, adding function terms and so on. Compared with previous statistical learning methods, SVMs have obvious advantages to prevent overfitting and achieve high training accuracy [17]. Therefore, according to the detailed study of SVM by most scholars, quadratic relaxation C-SVM, LS-SVM, and other methods have appeared. These methods have specific advantages in specific aspects, mainly by changing the coefficients and adding correlation terms.The quadratic relaxation C-SVM is based on the original C-SVM, replacingζ on the objective function with ζ2, so the original problem is transformed into
(6)minw,b=12w2+C∑i=1nζi2,where ζ2 is also used to characterize the degree of misclassification of the sample by the hyperplane. By solving the Lagrange multiplier method, it is easy to obtain its dual formula as
(7)minα=12∑i=1n∑j=1naiajKaiaj+12C.Since the slack variable in the objective function of the original problem has become a quadratic form, it also considers both the empirical risk and the minimum model complexity, which conforms to the principle of structural risk minimization [18].LS-SVM is the least square support vector machine, which is a kind of SVM (support vector machine), but it is simpler to calculate than SVM. In order to solve the problem that C-SVM needs to use the least square method to calculate the complexity, this paper proposes LS-SVM, the objective function uses the least square method, and the formula constraint is used instead of the inequality constraint, which can effectively solve the linear formula and simplify the calculation. Its original objective function is(8)minα=12w2+12C∑i=1nζi2.Among them,ζi must be nonnegative. When ζi=0, the minimum value is still satisfied; then, ζi≺0 makes the target problem larger, and the solution of formula (8) about ζi is a nonnegative value, which is
(9)0YTYQ+1CIba=0I.Weighted support vector machines believe that various samples play various roles in constructing the optimal classification decision hyperplane. Such problems often occur in practical real-world applications [19].


### 3.3. BP Neural Network

A BP neural network is a multilayer feedforward neural network trained according to the error back propagation algorithm, and it is one of the most widely used neural network models. The BP neural network has a complete theoretical system and learning mechanism and builds a multilayer model by imitating the response process of human brain neurons to external signals. It is learned iteratively through two processes of signal forward propagation and error feedback adjustment [20]. It has good correlation and can effectively solve complex problems such as nonlinear classification, function approximation, and medical detection. The structure diagram of BP neural network is shown in Figure 5.Figure 5
BP neural network structure diagram.As shown in Figure5, the BP neural network is usually a 3-layer neural network consisting of a single input layer, a single output layer, and a single hidden layer. Each layer is composed of multiple neurons with processing functions, and the neurons in each layer are connected to each other, and there is no connection between internal neurons [21].Assuming that the output vector of thei-th layer is Oi=oi1,oi2,⋯,oiNT, the input vector of the i+1-th layer is
(10)neti+1=WiOiT.Thei-th layer output vector is
(11)Oi=fneti+Bi.Among them,f⋅ is the neuron excitation function, and the sigmoid function is generally selected. In a mathematical formula such as
(12)fa=1+e−ax−1,where a is a constant, the shape of the sigmoid function is like an S shape, also known as the S function, and its shape changes as a becomes smaller and becomes flat. The sigmoid function is a common sigmoid function in biology, also known as the sigmoid growth curve. In information science, the sigmoid function is often used as the activation function of neural network because of its monoincrease and inverse function monoincrease properties.OM is the actual output, and D=d1,d2,⋯,dNM is the expected output, so the objective function of the BP algorithm is
(13)E=12Y−DTY−D.The traditional BP algorithm does not consider the gradient descent direction of thet−1-th iteration. If there is a relatively flat area in the error surface, it is easy to fall into the area near the local minimum and cannot jump out and converge to the overall minimum. The momentum term reflects the adjustment experience accumulated before and acts as a damping effect on the adjustment at time t. When the error surface fluctuates suddenly, the oscillation trend can be reduced and the convergence speed can be improved. Therefore, a momentum term needs to be added as in
(14)Δwijkt=Δwijkt−1+λδikoij.In formula (14), Δwijkt−1 is the momentum term, although λδiko⟶0 and Δwijkt−1 is not 0, it can make it jump out of the local minimum faster and speed up the network convergence rate and performance.


### 3.4. VGGNet Model

The VGGNet model is a deep convolutional neural network. It explores the relationship between the depth of convolutional neural networks and their performance. With the widespread use of convolutional neural networks in the field of computer vision, many methods have been tried to improve the network. In terms of datasets, using multiscale maps of the entire image during training and testing can also improve recognition accuracy. In a research work, people try to study the relationship between network depth and recognition accuracy [22]. Experimental studies show that deeper networks can achieve better recognition results, as shown in Table 1.Table 1
Comparison table of different network error rates.
Network codeNumber of network layers1-5 experiments’ error rate6-10 experiments’ error rateA1126.79.5B1225.98.4C1524.98.3D1524.87.2E1824.87.2As shown in Table1, at the same time, without fine-tuning, the training results of VGGNet are used for feature extraction, and then SVM is used as a classifier for other datasets, and very good results have been achieved.Vanishing or exploding gradients are the main reason that makes neural networks harder to train. There are some methods to solve this problem, such as normalizing the initial value and adding normalization layers in the middle of the network; these techniques can make the neural network with dozens of layers converge under the action of stochastic gradient descent and backpropagation. Normalization is a dimensionless processing method, which makes the absolute value of the physical system value into a relative value relationship, simplifies the calculation, and reduces the magnitude of the effective method.When the deeper network starts to converge, a degradation problem is exposed. With the deepening of the network layers, the accuracy of the network reaches the highest and decreases rapidly. Unexpectedly, this reduction in accuracy is not due to overfitting, as shown in Figure6.Figure 6
Two network training set error rates.As shown in Figure6, the degradation of accuracy on the training set shows that not all deep networks are easy to train. Now, suppose there are a shallower neural network and an equivalent deeper neural network. Such a structure proves that a deeper network should not have a higher error rate than a corresponding shallower network.


### 3.5. Scenario Analysis of China’s Energy Consumption Based on Green Development

Green development is a method of social development aimed at improving the happiness of human life. Everyone should play their role in saving resources, reducing the emission of polluting gases, and promoting green development. Green development has become an important trend. Many countries take the development of green industries as an important means to promote economic restructuring.The factors closely related to the energy consumption structure are GDP, population, industrial structure, and environment. If China wants to achieve the goal of green development, it must first make a rational allocation plan for the energy consumption structure. The environmental load model can also be referred to as the IPAT model. This model accurately describes the relationship between the environment, economy, and energy and plays an important role in the study of green development. The environment can be described in terms of the amount of information delivered to the individual, a concept known as the load of the environment. A high-load environment is an environment that transmits a large amount of sensory information; a low-load environment is an environment with less stimulus information. In all other conditions being equal, the high-load environment had a stronger arousal effect than the low-load environment. The general form of the IPAT model is(15)I=P⋅A⋅T=P⋅YP⋅IY.In the formula,I refers to the environmental load; P refers to the total population; Y refers to the gross domestic product (GDP). Using the carbon dioxide emissions generated by energy consumption to reflect the environmental load and replacing I with C, the above formula will become
(16)C=P⋅YP⋅CY.Energy consumption will produce carbon dioxide in the use, but the carbon dioxide produced per unit of energy of different varieties is different. This difference is expressed by the carbon dioxide emission coefficient, and the total carbon dioxide emission can be expressed as(17)C=∑i=14Ei⋅fi.E represents the total energy consumption; Ei represents different energy consumption varieties; that is to say, in the total energy consumption, the ratio of the i-th energy consumption to the total energy consumption. fi is the amount of carbon dioxide gas released by the i-th unit of energy consumption. According to the relationship between energy and GDP, the total energy consumption can be expressed as
(18)E=Y×EY=Y×∑YEi.China attaches equal importance to economic development and environmental protection. With the continuous advancement of technology, China’s economic development has become more and more rapid, but the promotion of China’s industrial structure and energy intensity is relatively small. China still has to take economic development as the first task, so the demand for energy is still very large.


### 3.6. Necessity to Promote the Consumption of Energy-Saving and Environment-Friendly Vehicles

Increasing the consumption of energy-saving and environment-friendly vehicles will help alleviate the contradiction between China’s energy supply and demand. With the increasingly serious environmental pollution and the aggravation of the contradiction between energy supply and demand, the transformation of automobile energy demand is imperative. Countries are accelerating the formulation and implementation of technological innovations for energy-saving and environment-friendly vehicles and supporting the energy-saving and environment-friendly vehicle industry. Harmful gas emissions are shown in Figure7.Figure 7
Hazardous gas emissions.As shown in Figure7, while automobiles consume a lot of resources, the exhaust gas they emit will seriously affect human health. Carbon monoxide in car exhaust binds to hemoglobin in the blood 250 times faster than oxygen. Therefore, even a small amount of carbon monoxide inhalation may cause terrible hypoxic injury. With the rapid increase in automobile consumption, the exhaust gas brought by fuel vehicles includes harmful and toxic gases such as carbon dioxide, lead compounds, and carbon monoxide, which will seriously threaten the ecological environment and human health.
(1)
To reduce harmful gas emissions, it is necessary to increase the proportion of consumption of energy-saving and environment-friendly vehiclesThe exhaust emissions of traditional fuel vehicles mainly include carbon monoxide, carbon oxides, nitrogen oxides, and automobile particulate matter, and the emissions of the four main exhaust emissions of automobiles are increasing year by year. Carbon monoxide has the highest annual emissions of the four major emissions.(2)
Increasing the proportion of energy-saving and environment-friendly automobile consumption will help ease the pressure on China’s crude oil supplyAt present, the power of the automobile industry in various countries is mainly provided by petroleum, and China is no exception. At this stage, the number of new energy vehicles and low-displacement vehicles has not been accepted by the majority of Chinese consumers. Under the circumstance that the number of vehicles in China continues to rise, the amount of gasoline used in vehicles is rising. Oil is the blood of modern economic and social development, and it is also a strategic resource of a country. Excessive dependence on foreign countries will seriously restrict the harmonious and healthy development of economy and society. Therefore, increasing the consumption ratio of energy-saving and environment-friendly vehicles will help curb the rising trend of China’s dependence on foreign oil and change the increasingly serious contradiction between China’s oil supply and demand.(3)
To seize the dominance and development rights of the new generation of automobile technology, it is necessary to adjust the development concept in advanceThe research and development of energy-saving and environment-friendly vehicles are still in its infancy in the world, and the early research and development of new technologies require a lot of financial support. According to the product cycle theory, the funds invested in the product development stage are mainly compensated in the product maturity stage, and the development of the automobile industry has its own particularity. The automobile industry is one of the important pillar industries of the country. It involves a long industrial chain, a long R&D cycle, and a large capital investment. In the global automobile industry, China belongs to the emerging market. Although it develops rapidly, there is still a big gap between it and the developed economies, especially in the energy saving and environmental protection automobile industry.


## 3.1. The Concept of Green and Intelligent Vehicles

With the increase in the number of automobiles in China, the problem of automobile exhaust pollution and its large energy demand is becoming more and more serious. Promoting the green innovation of automobile enterprises and developing the new energy automobile industry are very important for energy conservation and emission reduction [9]. At present, the development of China’s new energy vehicle industry is faced with the characteristics of high technology, high investment, and high risk, which requires policy guidance and strong support from the government. A green smart car is shown in Figure 1.Figure 1
Green smart car.As shown in Figure1, with the further expansion of car ownership in China, the energy crisis and air pollution have become increasingly prominent, becoming the main problems hindering the development of automobiles. Energy-saving, green, and intelligent new vehicles will be the direction of future automobile development. At present, major automakers have invested a lot of manpower and material resources in the research of alternative energy and new power. New energy vehicle technologies represented by hybrid power, fuel cells, advanced diesel, and alcohol gasoline have shown a rapid development trend. In the process of the rapid increase of automobiles, the pollution of automobile exhaust to China’s environment cannot be ignored. Many studies have shown that traffic pollution, especially automobile exhaust, is an important cause of urban smog in China. If the electric energy used by electric vehicles comes entirely from fossil fuels, the emissions of air pollutants produced by electric vehicles will exceed that of ordinary fuel vehicles [10]. Therefore, more and more electric vehicle charging stations will be equipped with certain photovoltaic power generation equipment to supply the electric energy required for electric vehicle charging. The performance of the electric vehicle is shown in Figure 2.Figure 2
Performance of electric vehicles.As shown in Figure2, the worldwide problems of energy depletion and environmental pollution are becoming more and more serious, and the development of new energy vehicles is the top priority for energy conservation and emission reduction in China and the world. Compared with some well-known enterprises, China is relatively backward in new energy vehicle drive technology and power battery material technology and is much behind in the patent strategy construction plan [11].


## 3.2. Relevant Machine Learning Algorithms in the Context of Big Data

Everyone recognizes the importance of big data, but there are different opinions on the definition of big data. Big data is an abstract concept. In addition to a large amount of data, big data has several characteristics that determine the difference between big data and the concepts of “large data” and “very large data.” In a general sense, big data is a key factor of production to promote the development of the digital economy. The development of the digital economy is the only way to achieve high-quality economic development and build a modern economic system. Promoting the digital transformation of the economy and society is actually the transition from the era of industrial economy to the era of digital economy. Big data refers to the identification, acquisition, management, processing, and collection of data that cannot be provided by traditional IT technology, hardware, and software tools within a limited time [12]. The components of big data are shown in Figure 3.Figure 3
Components of big data.As shown in Figure3, the value chain of big data can be divided into four stages: data generation, data collection, data preservation, and data analysis. Data analysis is the last important stage of the big data value chain and the basis for realizing the value and application of big data. Its purpose is to extract useful value, provide judgmental recommendations, or support decision-making analysis, which has the potential to yield different levels of potential value [13]. With the rapid development of the global economy, the demand for energy is also increasing, the reserves of traditional fossil energy are shrinking, and environmental problems such as global warming have become increasingly prominent. Energy issues have become the focus of attention around the world. The new energy power generation technology and application represented by solar energy and wind energy have become the research hotspots in various countries [14].


### 3.2.1. Support Vector Machine (SVM)

Support vector machines and kernel methods are important methods in the field of machine learning, and their basic theories and practical engineering applications are relatively mature. Initially, support vector machines were used for classification and regression tasks, after which some of these important problems were studied in depth. Together, these vectors support the classification hyperplane and are therefore called support vector machines [15]. The schematic diagram of SVM is shown in Figure 4.Figure 4
SVM schematic.As shown in Figure4, the final decision function of SVM is only determined by a small number of support vectors, and the computational complexity depends on the number of support vectors, not the dimension of the sample space, which avoids the “curse of dimensionality” in a sense. SVM has the strongest robustness to samples in this classification. Robustness is the ability of a system to survive abnormal and dangerous situations. For example, whether the computer software cannot crash or crash under the condition of input error, disk failure, network overload, or intentional attack is the robustness of the software. The classification to be solved can be described using
(1)wTa+b=0,where w=w1,w2,⋯,wd is the normal of the classification hyperplane and the distance from the training sample to the hyperplane can be expressed as
(2)r=wTa+bw.For a linearly classifiable sample set, if hyperplanew,b can correctly classify all samples, then for any sample ai,bi∈D, there is
(3)wTai+b≥+1,bi=+1,wTai+b≺−1,bi=−1.The point closest to the classification surface is called the support vector, and the distance from the support vector to the classification hyperplane is(4)r=1w.To find the so-called “maximum interval” classification hyperplane, it is necessary to findw,b that satisfies formula (3) and maximize r, that is,
(5)s.t.biwTai+b≥1,i=1,2,⋯,m.Formula (5) can be obtained by solving the desired classification hyperplane. This is a quadratic planning problem; note that it can be solved by using the related method of the quadratic planning problem. But S-gradient solution, least squares, etc. can also be used to solve such problems [16]. The least square formula is a mathematical formula, called curve fitting in mathematics, which includes not only linear regression equations but also matrix least squares.


### 3.2.2. Introduction to the Variant Form of Support Vector Machine

Compared with traditional statistical learning methods, support vector machines have obvious advantages in preventing overfitting and high training accuracy. Therefore, with the in-depth study of SVM by a large number of scholars, many variant algorithms of SVM appear. These methods mainly have certain advantages in a certain aspect by changing coefficients, adding function terms and so on. Compared with previous statistical learning methods, SVMs have obvious advantages to prevent overfitting and achieve high training accuracy [17]. Therefore, according to the detailed study of SVM by most scholars, quadratic relaxation C-SVM, LS-SVM, and other methods have appeared. These methods have specific advantages in specific aspects, mainly by changing the coefficients and adding correlation terms.The quadratic relaxation C-SVM is based on the original C-SVM, replacingζ on the objective function with ζ2, so the original problem is transformed into
(6)minw,b=12w2+C∑i=1nζi2,where ζ2 is also used to characterize the degree of misclassification of the sample by the hyperplane. By solving the Lagrange multiplier method, it is easy to obtain its dual formula as
(7)minα=12∑i=1n∑j=1naiajKaiaj+12C.Since the slack variable in the objective function of the original problem has become a quadratic form, it also considers both the empirical risk and the minimum model complexity, which conforms to the principle of structural risk minimization [18].LS-SVM is the least square support vector machine, which is a kind of SVM (support vector machine), but it is simpler to calculate than SVM. In order to solve the problem that C-SVM needs to use the least square method to calculate the complexity, this paper proposes LS-SVM, the objective function uses the least square method, and the formula constraint is used instead of the inequality constraint, which can effectively solve the linear formula and simplify the calculation. Its original objective function is(8)minα=12w2+12C∑i=1nζi2.Among them,ζi must be nonnegative. When ζi=0, the minimum value is still satisfied; then, ζi≺0 makes the target problem larger, and the solution of formula (8) about ζi is a nonnegative value, which is
(9)0YTYQ+1CIba=0I.Weighted support vector machines believe that various samples play various roles in constructing the optimal classification decision hyperplane. Such problems often occur in practical real-world applications [19].


## 3.2.1. Support Vector Machine (SVM)

Support vector machines and kernel methods are important methods in the field of machine learning, and their basic theories and practical engineering applications are relatively mature. Initially, support vector machines were used for classification and regression tasks, after which some of these important problems were studied in depth. Together, these vectors support the classification hyperplane and are therefore called support vector machines [15]. The schematic diagram of SVM is shown in Figure 4.Figure 4
SVM schematic.As shown in Figure4, the final decision function of SVM is only determined by a small number of support vectors, and the computational complexity depends on the number of support vectors, not the dimension of the sample space, which avoids the “curse of dimensionality” in a sense. SVM has the strongest robustness to samples in this classification. Robustness is the ability of a system to survive abnormal and dangerous situations. For example, whether the computer software cannot crash or crash under the condition of input error, disk failure, network overload, or intentional attack is the robustness of the software. The classification to be solved can be described using
(1)wTa+b=0,where w=w1,w2,⋯,wd is the normal of the classification hyperplane and the distance from the training sample to the hyperplane can be expressed as
(2)r=wTa+bw.For a linearly classifiable sample set, if hyperplanew,b can correctly classify all samples, then for any sample ai,bi∈D, there is
(3)wTai+b≥+1,bi=+1,wTai+b≺−1,bi=−1.The point closest to the classification surface is called the support vector, and the distance from the support vector to the classification hyperplane is(4)r=1w.To find the so-called “maximum interval” classification hyperplane, it is necessary to findw,b that satisfies formula (3) and maximize r, that is,
(5)s.t.biwTai+b≥1,i=1,2,⋯,m.Formula (5) can be obtained by solving the desired classification hyperplane. This is a quadratic planning problem; note that it can be solved by using the related method of the quadratic planning problem. But S-gradient solution, least squares, etc. can also be used to solve such problems [16]. The least square formula is a mathematical formula, called curve fitting in mathematics, which includes not only linear regression equations but also matrix least squares.


## 3.2.2. Introduction to the Variant Form of Support Vector Machine

Compared with traditional statistical learning methods, support vector machines have obvious advantages in preventing overfitting and high training accuracy. Therefore, with the in-depth study of SVM by a large number of scholars, many variant algorithms of SVM appear. These methods mainly have certain advantages in a certain aspect by changing coefficients, adding function terms and so on. Compared with previous statistical learning methods, SVMs have obvious advantages to prevent overfitting and achieve high training accuracy [17]. Therefore, according to the detailed study of SVM by most scholars, quadratic relaxation C-SVM, LS-SVM, and other methods have appeared. These methods have specific advantages in specific aspects, mainly by changing the coefficients and adding correlation terms.The quadratic relaxation C-SVM is based on the original C-SVM, replacingζ on the objective function with ζ2, so the original problem is transformed into
(6)minw,b=12w2+C∑i=1nζi2,where ζ2 is also used to characterize the degree of misclassification of the sample by the hyperplane. By solving the Lagrange multiplier method, it is easy to obtain its dual formula as
(7)minα=12∑i=1n∑j=1naiajKaiaj+12C.Since the slack variable in the objective function of the original problem has become a quadratic form, it also considers both the empirical risk and the minimum model complexity, which conforms to the principle of structural risk minimization [18].LS-SVM is the least square support vector machine, which is a kind of SVM (support vector machine), but it is simpler to calculate than SVM. In order to solve the problem that C-SVM needs to use the least square method to calculate the complexity, this paper proposes LS-SVM, the objective function uses the least square method, and the formula constraint is used instead of the inequality constraint, which can effectively solve the linear formula and simplify the calculation. Its original objective function is(8)minα=12w2+12C∑i=1nζi2.Among them,ζi must be nonnegative. When ζi=0, the minimum value is still satisfied; then, ζi≺0 makes the target problem larger, and the solution of formula (8) about ζi is a nonnegative value, which is
(9)0YTYQ+1CIba=0I.Weighted support vector machines believe that various samples play various roles in constructing the optimal classification decision hyperplane. Such problems often occur in practical real-world applications [19].


## 3.3. BP Neural Network

A BP neural network is a multilayer feedforward neural network trained according to the error back propagation algorithm, and it is one of the most widely used neural network models. The BP neural network has a complete theoretical system and learning mechanism and builds a multilayer model by imitating the response process of human brain neurons to external signals. It is learned iteratively through two processes of signal forward propagation and error feedback adjustment [20]. It has good correlation and can effectively solve complex problems such as nonlinear classification, function approximation, and medical detection. The structure diagram of BP neural network is shown in Figure 5.Figure 5
BP neural network structure diagram.As shown in Figure5, the BP neural network is usually a 3-layer neural network consisting of a single input layer, a single output layer, and a single hidden layer. Each layer is composed of multiple neurons with processing functions, and the neurons in each layer are connected to each other, and there is no connection between internal neurons [21].Assuming that the output vector of thei-th layer is Oi=oi1,oi2,⋯,oiNT, the input vector of the i+1-th layer is
(10)neti+1=WiOiT.Thei-th layer output vector is
(11)Oi=fneti+Bi.Among them,f⋅ is the neuron excitation function, and the sigmoid function is generally selected. In a mathematical formula such as
(12)fa=1+e−ax−1,where a is a constant, the shape of the sigmoid function is like an S shape, also known as the S function, and its shape changes as a becomes smaller and becomes flat. The sigmoid function is a common sigmoid function in biology, also known as the sigmoid growth curve. In information science, the sigmoid function is often used as the activation function of neural network because of its monoincrease and inverse function monoincrease properties.OM is the actual output, and D=d1,d2,⋯,dNM is the expected output, so the objective function of the BP algorithm is
(13)E=12Y−DTY−D.The traditional BP algorithm does not consider the gradient descent direction of thet−1-th iteration. If there is a relatively flat area in the error surface, it is easy to fall into the area near the local minimum and cannot jump out and converge to the overall minimum. The momentum term reflects the adjustment experience accumulated before and acts as a damping effect on the adjustment at time t. When the error surface fluctuates suddenly, the oscillation trend can be reduced and the convergence speed can be improved. Therefore, a momentum term needs to be added as in
(14)Δwijkt=Δwijkt−1+λδikoij.In formula (14), Δwijkt−1 is the momentum term, although λδiko⟶0 and Δwijkt−1 is not 0, it can make it jump out of the local minimum faster and speed up the network convergence rate and performance.


## 3.4. VGGNet Model

The VGGNet model is a deep convolutional neural network. It explores the relationship between the depth of convolutional neural networks and their performance. With the widespread use of convolutional neural networks in the field of computer vision, many methods have been tried to improve the network. In terms of datasets, using multiscale maps of the entire image during training and testing can also improve recognition accuracy. In a research work, people try to study the relationship between network depth and recognition accuracy [22]. Experimental studies show that deeper networks can achieve better recognition results, as shown in Table 1.Table 1
Comparison table of different network error rates.
Network codeNumber of network layers1-5 experiments’ error rate6-10 experiments’ error rateA1126.79.5B1225.98.4C1524.98.3D1524.87.2E1824.87.2As shown in Table1, at the same time, without fine-tuning, the training results of VGGNet are used for feature extraction, and then SVM is used as a classifier for other datasets, and very good results have been achieved.Vanishing or exploding gradients are the main reason that makes neural networks harder to train. There are some methods to solve this problem, such as normalizing the initial value and adding normalization layers in the middle of the network; these techniques can make the neural network with dozens of layers converge under the action of stochastic gradient descent and backpropagation. Normalization is a dimensionless processing method, which makes the absolute value of the physical system value into a relative value relationship, simplifies the calculation, and reduces the magnitude of the effective method.When the deeper network starts to converge, a degradation problem is exposed. With the deepening of the network layers, the accuracy of the network reaches the highest and decreases rapidly. Unexpectedly, this reduction in accuracy is not due to overfitting, as shown in Figure6.Figure 6
Two network training set error rates.As shown in Figure6, the degradation of accuracy on the training set shows that not all deep networks are easy to train. Now, suppose there are a shallower neural network and an equivalent deeper neural network. Such a structure proves that a deeper network should not have a higher error rate than a corresponding shallower network.


## 3.5. Scenario Analysis of China’s Energy Consumption Based on Green Development

Green development is a method of social development aimed at improving the happiness of human life. Everyone should play their role in saving resources, reducing the emission of polluting gases, and promoting green development. Green development has become an important trend. Many countries take the development of green industries as an important means to promote economic restructuring.The factors closely related to the energy consumption structure are GDP, population, industrial structure, and environment. If China wants to achieve the goal of green development, it must first make a rational allocation plan for the energy consumption structure. The environmental load model can also be referred to as the IPAT model. This model accurately describes the relationship between the environment, economy, and energy and plays an important role in the study of green development. The environment can be described in terms of the amount of information delivered to the individual, a concept known as the load of the environment. A high-load environment is an environment that transmits a large amount of sensory information; a low-load environment is an environment with less stimulus information. In all other conditions being equal, the high-load environment had a stronger arousal effect than the low-load environment. The general form of the IPAT model is(15)I=P⋅A⋅T=P⋅YP⋅IY.In the formula,I refers to the environmental load; P refers to the total population; Y refers to the gross domestic product (GDP). Using the carbon dioxide emissions generated by energy consumption to reflect the environmental load and replacing I with C, the above formula will become
(16)C=P⋅YP⋅CY.Energy consumption will produce carbon dioxide in the use, but the carbon dioxide produced per unit of energy of different varieties is different. This difference is expressed by the carbon dioxide emission coefficient, and the total carbon dioxide emission can be expressed as(17)C=∑i=14Ei⋅fi.E represents the total energy consumption; Ei represents different energy consumption varieties; that is to say, in the total energy consumption, the ratio of the i-th energy consumption to the total energy consumption. fi is the amount of carbon dioxide gas released by the i-th unit of energy consumption. According to the relationship between energy and GDP, the total energy consumption can be expressed as
(18)E=Y×EY=Y×∑YEi.China attaches equal importance to economic development and environmental protection. With the continuous advancement of technology, China’s economic development has become more and more rapid, but the promotion of China’s industrial structure and energy intensity is relatively small. China still has to take economic development as the first task, so the demand for energy is still very large.


## 3.6. Necessity to Promote the Consumption of Energy-Saving and Environment-Friendly Vehicles

Increasing the consumption of energy-saving and environment-friendly vehicles will help alleviate the contradiction between China’s energy supply and demand. With the increasingly serious environmental pollution and the aggravation of the contradiction between energy supply and demand, the transformation of automobile energy demand is imperative. Countries are accelerating the formulation and implementation of technological innovations for energy-saving and environment-friendly vehicles and supporting the energy-saving and environment-friendly vehicle industry. Harmful gas emissions are shown in Figure7.Figure 7
Hazardous gas emissions.As shown in Figure7, while automobiles consume a lot of resources, the exhaust gas they emit will seriously affect human health. Carbon monoxide in car exhaust binds to hemoglobin in the blood 250 times faster than oxygen. Therefore, even a small amount of carbon monoxide inhalation may cause terrible hypoxic injury. With the rapid increase in automobile consumption, the exhaust gas brought by fuel vehicles includes harmful and toxic gases such as carbon dioxide, lead compounds, and carbon monoxide, which will seriously threaten the ecological environment and human health.
(1)
To reduce harmful gas emissions, it is necessary to increase the proportion of consumption of energy-saving and environment-friendly vehiclesThe exhaust emissions of traditional fuel vehicles mainly include carbon monoxide, carbon oxides, nitrogen oxides, and automobile particulate matter, and the emissions of the four main exhaust emissions of automobiles are increasing year by year. Carbon monoxide has the highest annual emissions of the four major emissions.(2)
Increasing the proportion of energy-saving and environment-friendly automobile consumption will help ease the pressure on China’s crude oil supplyAt present, the power of the automobile industry in various countries is mainly provided by petroleum, and China is no exception. At this stage, the number of new energy vehicles and low-displacement vehicles has not been accepted by the majority of Chinese consumers. Under the circumstance that the number of vehicles in China continues to rise, the amount of gasoline used in vehicles is rising. Oil is the blood of modern economic and social development, and it is also a strategic resource of a country. Excessive dependence on foreign countries will seriously restrict the harmonious and healthy development of economy and society. Therefore, increasing the consumption ratio of energy-saving and environment-friendly vehicles will help curb the rising trend of China’s dependence on foreign oil and change the increasingly serious contradiction between China’s oil supply and demand.(3)
To seize the dominance and development rights of the new generation of automobile technology, it is necessary to adjust the development concept in advanceThe research and development of energy-saving and environment-friendly vehicles are still in its infancy in the world, and the early research and development of new technologies require a lot of financial support. According to the product cycle theory, the funds invested in the product development stage are mainly compensated in the product maturity stage, and the development of the automobile industry has its own particularity. The automobile industry is one of the important pillar industries of the country. It involves a long industrial chain, a long R&D cycle, and a large capital investment. In the global automobile industry, China belongs to the emerging market. Although it develops rapidly, there is still a big gap between it and the developed economies, especially in the energy saving and environmental protection automobile industry.


## 4. Experiment and Analysis on the Development and Consumption Trend of Green and Intelligent Vehicles

### 4.1. Characteristics of Green and Intelligent Vehicles

Since the 21st century, with the rapid development of the automobile industry and the rapid growth of car ownership, the problem of environmental pollution caused by motor vehicle exhaust has become increasingly prominent. Motor vehicles will emit various air pollutants with complex composition during driving, as shown in Table2.Table 2
The number of cars and the number of drivers.
YearsMotor vehicles (100 million units)Drivers (100 million)Sewage discharge (100 million tons)20151.992.014.5720162.593.155.7820173.103.856.8920184.905.127.9820195.145.3010.6520206.987.0312.09As shown in Table2, many countries in the world have issued preferential tax and subsidy policies for electric vehicles and related industries to promote the rapid development of electric vehicles. With the support of policies, the number of electric vehicles in China has grown rapidly. This paper compares traditional cars and green smart cars, as shown in Figure 8.Figure 8
Characteristics of conventional cars and green smart cars.
(a)
Traditional car(b)
Green smart carAs shown in Figure8, electric vehicles have high hopes for solving air pollution and global warming. However, studies have shown that if the electricity used by electric vehicles comes entirely from fossil fuels, the emissions of CO2 and air pollutants produced by electric vehicles will exceed that of ordinary fuel vehicles. Therefore, in order to make electric vehicles fully play the role of improving air quality and alleviating global warming, electric vehicles should use the electricity generated by the conversion of renewable energy such as wind energy and solar energy as much as possible.


### 4.2. Overall Trend of Automobile Consumption in China

Since the reform and opening up, China’s economy has grown significantly, and economic growth will continue to grow rapidly for a long time. Economic growth will surely drive the overall income of residents to continue to increase, and the purchasing power of residents for automobiles will also continue to increase. Especially in the successive introduction of national policies to strengthen and benefit farmers, it will change the slow growth of rural residents’ income in the past, and rural residents’ income growth has entered a fast lane. Therefore, the potential market for automobile consumption will be gradually released in rural areas. With the growth of residents’ income, more and more people will cross the threshold of automobile consumption, which will promote the continuous growth of automobile consumption. The car sales from 2013 to 2020 and the annual growth rate are shown in Figure9.Figure 9
2013-2020 car sales and annual growth rates.
(a)
2013-2020 vehicle sales(b)
2013-2020 growth rateAs shown in Figure9, at the same time, in order to cope with climate change, save oil resources, protect the environment, and build an environment-friendly society. The state has increased policy support for the consumption of new energy vehicles year by year, so that the whole society can form a unified understanding of the consumption of new energy vehicles. The sales volume (units) of new energy vehicles and the proportion (%) in total vehicle sales are shown in Table 3.Table 3
Sales volume of new energy vehicles (units) and its proportion in total vehicle sales (%).
YearsTotal car salesSales volumeProportion201455681836750.66%2015137619028900.21%2016136857147900.35%2017127225050890.40%As shown in Table3, the main consumers of these energy-saving and environment-friendly vehicles are government units and urban public transport systems, and the majority of residents choose small-displacement vehicles and conventional hybrid vehicles when purchasing energy-saving and environment-friendly vehicles. The consumption of new energy vehicles in China increased from 3675 in 2014 to 5089 in 2017, but the sales of new energy vehicles in 2017 were very small. Compared with the rapidly increasing total vehicle consumption, the proportion of new energy vehicle sales in total vehicle consumption is still very low. After hovering between 2014 and 2017, its proportion was still at 0.40%, but since 2017, the proportion has maintained a stable upward trend.This paper analyzes the production and sales volume and trends of green and intelligent vehicles from 2014 to 2020, as shown in Figure10.Figure 10
2014-2020 green and intelligent vehicle production and sales and trends.As can be seen from Figure10, the production and sales of new energy vehicles increased rapidly after 2017, breaking through the stagnant state of production and sales in the previous period. This is mainly due to the targeted fiscal and tax policies implemented by the Chinese government in recent years, which has made the situation of the new energy vehicle industry tend to be good. Effective policy support makes new energy vehicles not only recognized by consumers but also help China to accelerate the pace of rapid development of the industry.


## 4.1. Characteristics of Green and Intelligent Vehicles

Since the 21st century, with the rapid development of the automobile industry and the rapid growth of car ownership, the problem of environmental pollution caused by motor vehicle exhaust has become increasingly prominent. Motor vehicles will emit various air pollutants with complex composition during driving, as shown in Table2.Table 2
The number of cars and the number of drivers.
YearsMotor vehicles (100 million units)Drivers (100 million)Sewage discharge (100 million tons)20151.992.014.5720162.593.155.7820173.103.856.8920184.905.127.9820195.145.3010.6520206.987.0312.09As shown in Table2, many countries in the world have issued preferential tax and subsidy policies for electric vehicles and related industries to promote the rapid development of electric vehicles. With the support of policies, the number of electric vehicles in China has grown rapidly. This paper compares traditional cars and green smart cars, as shown in Figure 8.Figure 8
Characteristics of conventional cars and green smart cars.
(a)
Traditional car(b)
Green smart carAs shown in Figure8, electric vehicles have high hopes for solving air pollution and global warming. However, studies have shown that if the electricity used by electric vehicles comes entirely from fossil fuels, the emissions of CO2 and air pollutants produced by electric vehicles will exceed that of ordinary fuel vehicles. Therefore, in order to make electric vehicles fully play the role of improving air quality and alleviating global warming, electric vehicles should use the electricity generated by the conversion of renewable energy such as wind energy and solar energy as much as possible.


## 4.2. Overall Trend of Automobile Consumption in China

Since the reform and opening up, China’s economy has grown significantly, and economic growth will continue to grow rapidly for a long time. Economic growth will surely drive the overall income of residents to continue to increase, and the purchasing power of residents for automobiles will also continue to increase. Especially in the successive introduction of national policies to strengthen and benefit farmers, it will change the slow growth of rural residents’ income in the past, and rural residents’ income growth has entered a fast lane. Therefore, the potential market for automobile consumption will be gradually released in rural areas. With the growth of residents’ income, more and more people will cross the threshold of automobile consumption, which will promote the continuous growth of automobile consumption. The car sales from 2013 to 2020 and the annual growth rate are shown in Figure9.Figure 9
2013-2020 car sales and annual growth rates.
(a)
2013-2020 vehicle sales(b)
2013-2020 growth rateAs shown in Figure9, at the same time, in order to cope with climate change, save oil resources, protect the environment, and build an environment-friendly society. The state has increased policy support for the consumption of new energy vehicles year by year, so that the whole society can form a unified understanding of the consumption of new energy vehicles. The sales volume (units) of new energy vehicles and the proportion (%) in total vehicle sales are shown in Table 3.Table 3
Sales volume of new energy vehicles (units) and its proportion in total vehicle sales (%).
YearsTotal car salesSales volumeProportion201455681836750.66%2015137619028900.21%2016136857147900.35%2017127225050890.40%As shown in Table3, the main consumers of these energy-saving and environment-friendly vehicles are government units and urban public transport systems, and the majority of residents choose small-displacement vehicles and conventional hybrid vehicles when purchasing energy-saving and environment-friendly vehicles. The consumption of new energy vehicles in China increased from 3675 in 2014 to 5089 in 2017, but the sales of new energy vehicles in 2017 were very small. Compared with the rapidly increasing total vehicle consumption, the proportion of new energy vehicle sales in total vehicle consumption is still very low. After hovering between 2014 and 2017, its proportion was still at 0.40%, but since 2017, the proportion has maintained a stable upward trend.This paper analyzes the production and sales volume and trends of green and intelligent vehicles from 2014 to 2020, as shown in Figure10.Figure 10
2014-2020 green and intelligent vehicle production and sales and trends.As can be seen from Figure10, the production and sales of new energy vehicles increased rapidly after 2017, breaking through the stagnant state of production and sales in the previous period. This is mainly due to the targeted fiscal and tax policies implemented by the Chinese government in recent years, which has made the situation of the new energy vehicle industry tend to be good. Effective policy support makes new energy vehicles not only recognized by consumers but also help China to accelerate the pace of rapid development of the industry.


## 5. Conclusions

In this era of faster and faster development, people are not only pursuing material needs but also have more and more needs for the environment. China has always advocated vigorously developing green environmental protection, protecting the ecological environment and saving energy. The use of automobiles brings a lot of harmful gases to the living environment. Therefore, people begin to study green and intelligent automobiles. This paper mainly focuses on the application of machine learning algorithms in the development and consumption trends of green and intelligent vehicles in the context of big data. Therefore, this article describes the machine learning algorithm and the concept of green and intelligent vehicles. In the method part, this paper summarizes the machine learning algorithms. Based on the machine learning algorithms, it also proposes support vector machines and neural network algorithms. According to the analysis, these two methods have better classification and recognition effects. In the experimental part, this paper analyzes the development of traditional vehicles and new energy vehicles in recent years and investigates the characteristics of traditional vehicles and new energy vehicles. In the end, it was found that green and intelligent vehicles not only are environment-friendly but also can reduce costs.



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*Source: 4436016-2022-07-27.xml*