Abstract
The paper measures the income distribution effects and welfare effects of VAT itself based on Chip2013 household income and expenditure microdata using the Gini coefficient, Suits index, and Theil index from the perspective of annual income and lifetime income, respectively, and the effective VAT rate. Taking provinces as the basic research unit, we use the first-right method, LISA time path, and Spatiotemporal leap to evaluate China’s well-being level from 2010 to 2020 and analyze its Spatiotemporal dynamic characteristics. At present, most countries adopt a tax rate model with 1∼3 bands. The current VAT rate structure is “13% + 9% + 6%,” which is in line with the international development trend. In the data preprocessing module, the HTTP(S) responses of non-IoT devices are first filtered out and then the text that may contain device information is extracted from the remaining HTTP(S) responses by integrating multidimensional features, and finally, the irrelevant strings in the text are filtered out. At the end of the paper, simulation experiments are designed to verify the function and performance of the identification information extraction system for this heritage connected device, and the results verify that the model identification method in this paper can achieve better identification results compared with existing methods. The results verify that the model recognition method in this paper can achieve better recognition results compared with existing methods.
1. Introduction
The concept of the Internet of Things (Internet of Things, IoT) was originally proposed in 1999 by Kevin Ashton, a British technologist at MIT’s Auto-ID Center. At the time the concept of IoT was introduced, it was only for IoT devices based on RFID technology for data identification and exchange. Later, with the development of IoT, IoT has a broader definition and incorporates more advanced technologies, including sensor technology, nanotechnology, smart embedding technology, etc. Meanwhile, various IoT devices have penetrated all aspects of people’s production and life, from small smart wearable devices, web cameras, and routers to large connected cars, smart factories, and smart buildings, and IoT devices can be seen in various fields such as home, transportation, medical, coordination, agriculture, and industry [1]. It can be said that IoT technology is profoundly affecting people’s production and life all the time, and it is also called the third wave of the information industry revolution and the core support of the fourth industrial revolution. Internet of things technology is a new concept after computer technology, Internet technology, and mobile communication network technology, which is the new wave of the information technology revolution in recent years. IoT technology is based on the Internet through various wireless communications, wired networks, and various end devices to achieve interconnection. In recent years, various industries have been increasing their efforts to study the application of IoT technology in this industry [2]. The development of IoT technology has a very important role in promoting economic development, which can facilitate the transformation of the economic development situation and social progress [3].
The large income gap is a major livelihood issue facing today. The Gini coefficient is an accepted indicator of a country’s income distribution pattern, and the World Bank and other institutions have calculated that the Gini coefficient was 0.32, based on the data from the Urban and Rural Household Survey of the National Bureau of Statistics, and the income distribution was relatively even. The Gini coefficient dropped further to 0.26 but has continued to rise since then [4]. With the progress and development of the times, the main social contradictions have changed, and the new era should focus on the reform of income distribution and pay attention to the fairness of the principle of initial distribution efficiency and the efficiency of the principle of redistribution equity. Among them, the efficiency of the principle of redistribution fairness emphasizes more the scientific and effective government regulation based on the principle of fairness and then enhances the overall fairness of distribution. The government regulation in the field of redistribution mainly relies on two lines of revenue and expenditure, with revenue as the main line and should be based on the fairness of tax burden through the reform of the tax system, setting tax types and tax rates more scientifically, and then building a more efficient and fair income distribution system. VAT is the number one tax, and the study on the income distribution effect and welfare effect of VAT is of great significance in promoting income distribution regulation and welfare improvement [5]. It is necessary to achieve green development in the new era, and it also faces good policy opportunities. Under the wave and background of the global digital economy, the development of information and communication technology provides new momentum for economic growth, promotes industrial structure upgrading, gives birth to new products, new business models, and new modes, and has a profound impact on economic and social development, which may bring new opportunities for green development [6].
At present, China has established several IoT industry test bases in different regions. The remarkable development of national industry test bases successfully confirms the role of IoT in promoting the digital economy. Therefore, increasing the support for the IoT industry is an important measure to further promote the vigorous development of China’s digital economy [7]. The advent of the information age has both provided opportunities for the optimization and upgrading of the IoT industry and made it meet the challenges brought by the different economic structures in the new era. Based on this, the article will start from the traction role of IoT on the digital economy, deeply elaborate on the background of the current situation faced by the industry, and propose corresponding solutions to the problems existing in its development strategy. This paper explores the role of digital information technology on social welfare accounting in the context of the Internet of Things (IoT) so that the relevant coefficients of social welfare can be accounted for more easily and scientifically and provide a scientific and powerful basis for the distribution of social welfare.
2. Related Works
With the increase in the base of mobile devices in the modern information society and the increasing variety of communication forms, byte transmission, audio transmission, picture transmission, video transmission, multidimensional simultaneous transmission, etc., the network requirements put higher demands on the network carrying capacity, and 5G technology was born. 5G networks have the main advantage of significantly higher data transmission rates, faster than the current wired Internet, and another advantage is the lower network latency [8]. Research on human alienation under the influence of information technology: People in the digital society driven by instrumental rationality hold a profit-oriented and efficiency-oriented value, and the value of subjectivity is weakened, and they are in the overall situation of being dominated by technology, which is more serious in the information technology era. In the face of increasingly updated information technology, scholars have proposed different solutions. Some scholars believe that the government’s ability to regulate and control technology should be strengthened, others believe that technology regulation theory should be applied to solve this alienation problem, and others believe that the algorithmic platform itself should be regulated and restrained accordingly [9]. Mohanty et al. believe that the alienation of Marxism is manifested in the contemporary digital era as the technological alienation of artificial intelligence, so that people living in this era have been depleted of their subjectivity, along with the alienation of their interactions with others, and therefore the technological roots of alienation should be analyzed in depth [10]. He et al. argue that with the advent of 5G and IoT artificial intelligence era, technologies such as IoT and big data analysis have received more and more attention [11]. In addition, OPNET is used for modeling and simulation of wireless network architecture in SDN smart campuses to analyze the feasibility and advantages of various campus network topologies. Then, reasonable solutions are proposed for different types of topologies, which are conducive to the better construction of the smart campus.
Foreign scholars began their research on well-being, starting with the concept of well-being, proposing the geography of well-being, and using positivist and welfarist approaches to study social well-being, measuring the level of well-being from different perspectives, both subjective and objective, and conducting multifaceted research on the factors affecting the level of well-being [12]. Well-being is a complex concept involving economics, sociology, philosophy, and psychology, with similar concepts such as welfare, happiness, and quality of life, which have great similarities but also differences. It includes the perception of life satisfaction and positive and negative emotions. There are many different methods for measuring well-being, including subjective well-being, objective well-being, and a combination of subjective and objective well-being. Objective well-being measures tend to construct well-being indices [13]. In the early studies, a single GDP dimension was mostly used as the indicator of well-being measurement, but the level of well-being is a multidimensional concept, and a single economic indicator does not reflect the true situation of the well-being of residents. Under the dominant view of economic development, Western economic welfare science tends to regard income or wealth as the main factor influencing the level of well-being, believing that the increase in absolute income and wealth means a higher consumption level and happier life [14]. With the gradual expansion of the development view from the economic field to the social field, Western academics began to study the relationship between social and cultural factors and well-being. Horváth and Sudár believe that literacy is an important factor affecting the level of well-being and that cultural education can greatly improve people’s well-being [15].
Through the study of the concept of well-being at home and abroad, the article concludes that well-being is a multidimensional concept reflecting the good life of people, which can be understood as the standard of living and the quality of life of people, and contains both subjective and objective well-being. Subjective well-being focuses on individuals’ subjective feelings about good living conditions, while objective well-being focuses on objective possessions, such as wealth, resources, and other objective material conditions [16]. Objective well-being focuses on the spatial comparison of well-being levels in each region, and it is more reasonable to focus on objective well-being when making a spatial assessment of well-being on a large scale. Since the reform and opening, China has experienced rapid economic development, and the average growth rate of GDP from 1996 to 2012 reached 10%, making it one of the fastest-growing countries in the world [17]. However, the rapid economic growth has been accompanied by serious environmental pollution, unfair income distribution, and a much worse social climate. According to the studies of domestic and foreign scholars on the level of national happiness, it has not shown a great improvement corresponding to the high economic growth. Measuring the level of well-being of residents in Shanxi Province based on the human development index and analyzing the spatial differentiation characteristics of the well-being level of residents in Shanxi Province and the influencing factors of its spatial differences are both major events for the development of the geography discipline and an important livelihood issue of concern to the government and the society.
3. Analysis of the Role of Digital Information Technology for Social Welfare Computing in the Context of the Internet of Things
3.1. Digital Information Technology Model Design Based on IoT Background
Various IoT devices permeate all aspects of production and life, ranging from smart wearable devices, web cameras, and routers to connected cars, smart factories, and smart buildings, home furnishing, transportation, medical care, logistics, agriculture, industry, etc. The shadow of IoT devices can be seen in all fields. The Internet of Things is a network that extends the Internet outward through a series of science and technology, using sensor devices to connect objects and computer terminals to automate and intellectualize objects through computer control and to realize the transmission and expression of objects and object information by the Internet, and a network that exchanges and communicates information between objects and objects from the user side. The architecture of IoT can be divided into three parts: sensing layer, network layer, and application layer. The development of IoT cannot be separated from the development of related technologies, and the development of technology is an important premise and guarantee for the development of IoT technology [18]. The five key technologies of IoT technology are network and communication technology, RFID technology, wireless sensing technology, M2M technology, and cloud computing technology. The network is the basis of IoT information transfer and support services, and the perception digital of IoT mainly relies on network and communication technology. A wireless network can be used for IoT underlying data sensing. While point-to-point transmission generally uses Bluetooth or infrared transmission, wireless fidelity is a short-range wireless transmission that can support Internet access within a hundred meters. Wireless sensing technology is the key technology of IoT technology and the key technology in computer applications. It is a kind of fully distributed system without a central point. Through the random position, many sensor nodes are densely placed in the sensing area, which can feel the state of the measured state and convert it into a usable output signal according to certain rules. Wireless sensors integrate logical information with the actual objective world, changing the way people interact with nature.
To analyze the role of digital information technology in social welfare calculation, by sorting out the research contents and progress of artificial intelligence, big data, inter/Internet of things, geographic information systems, and information visualization technology applied in this area, the main purpose is to play the role of accurate prediction and accounting in the acquisition and calculation of raw data for social welfare calculation. In the early stage of early warning, we mainly study the use of artificial intelligence technology to build a data management platform symptom monitoring system to monitor and analyze the raw data of social welfare [19]. As the computing power of machine learning and neural networks improved, it was then carried out to conduct data monitoring based on hidden Markov models of machine learning, research on automatic screening by machine learning methods, and the use of neural network algorithms in combination with available electronic information record data for predicting social well-being. Most of the digital information technologies are applied based on the Internet environment, such as the construction of data prevention and control monitoring and early warning systems, direct data information reporting through mobile terminals, and timely access to data dynamics to provide accurate raw data and precise accounting methods for social well-being accounting.
Intel SGX technology, a new trusted computing technology introduced by Intel in 2013 adds a new set of instruction sets and memory access mechanisms to the original architecture; these extensions allow applications to implement a container called Enclave, which carves out a protected area of the application’s address space, a nonaddressable paged memory area reserved from the system’s physical memory and is encrypted to provide confidentiality and integrity protection for code and data within the container from malware with special privileges [19]. These extensions allow applications to implement a protected area in the application address space called Enclave, which is a nonaddressable paged memory reserved from the system’s physical memory and encrypted to provide confidentiality and integrity protection for code and data inside the container from malware with special privileges, thus enabling isolated operation between different programs and safeguarding the confidentiality and integrity of critical user code and data from malware. The application developed based on SGX technology is divided into trusted and untrusted parts, and the code or data in the trusted part will be protected by the Enclave security container, making it able to resist malware attacks (including applications, operating systems, and BIOS), as shown in Figure 1.
Since the IoT data collection program needs to be in a long and stable operation state, constantly collecting and sending data, the trustworthy platform that guarantees the safe operation of the data collection program and realizes the function of secure data uploading also needs to have high reliability that can run stably for a long time, and each module has high internal robustness that can resist all kinds of abnormal situations that occur during operation. It can guarantee that the IoT data collection program will not be interrupted for a long time due to abnormal conditions in the process of operation [20]. Therefore, repeated testing and tuning of the whole trusted platform to increase its reliability is one of the core objectives of the research realization of this platform. Since the IoT data collection program will continuously collect data from the IoT terminal devices, the data will be sent to the blockchain only after signature and other processing via the trusted platform in this paper, and if the data uploading process of the trusted platform does not reach a certain speed, it will cause a large amount of data accumulation and cannot be uploaded in time. Therefore, when facing a large amount of data, it has high requirements on the performance of the trusted platform, such as data processing speed and data uploading speed.
Digital information technology mainly includes mobile big data analysis, intelligent information processing, Internet, Internet of things, geographic information system (GIS), information visualization, and other technologies, which have the functions of collecting, managing, processing, analyzing, identifying, and applying services to information, which bring new models for research and application work. Based on the analysis and summary of the previous studies and technologies related to IoT device identification information extraction, this paper proposes a rule-based IoT device identification information extraction system, which can automatically extract the brand, type, and model information of devices from the HTTP(S) response data of IoT devices. The main functional module of the system is shown in Figure 2, and its general workflow is as follows: firstly, the HTTP(S) response data in cyberspace needs to be collected; for the collected HTTP(S) responses, the HTTP(S) responses of non-IoT devices need to be filtered out as much as possible; after the filtering is completed, the remaining HTTP(S) response data are extracted and cleaned with text, and then extract the list of candidate device brand, type, and model keywords, and determine the final device identification information with the aid of search engines. For the HTTP(S) responses of IoT devices with client-side redirection or dynamic HTML phenomenon, this paper proposes the use of JavaScript rendering to handle them, and more device keywords can be extracted from the JavaScript rendered web pages.
Due to the multisource heterogeneity of IoT device data and the transmission mode of traditional IoT data single device, this system uses the topic mode of message middleware combined with information encryption to realize the same device transmission mode of IoT data. The business layer is responsible for completing the functions of the system. It completes the processing of various data requests based on various APIs of Java and completes the interaction with the database based on Mybatis, including the functional modules of the system board module, announcement module, and user management module. The presentation layer completes the direct interaction with the user based on the front-end technology. The user’s request is transmitted to the presentation layer through the browser according to HTTP, and the presentation layer receives the request and transfers it to the business layer [21].
3.2. Social Well-Being Computational Model Construction
The Human Development Index (HDI) is calculated by simply weighting three indices: health, education, and economy, and the level of HDI directly reflects the level of well-being of residents in Shanxi Province. Health, education, and income indices are weighted by simple averages to form the HDI, and each index plays a crucial role in the process of human development. The specific analysis of each index can provide insight into the distribution and development trend of each prefecture-level city in terms of health, education, and economic level.
From a global equilibrium perspective, the output of a manufacturer is the sum of the demand for it by all consumers in the model. In the basic model with N consumers and K producers (each producing only one product), single consumer i, who will determine the consumption of each consumer good k by optimizing its utility, has the following:
By summing the demand of all consumers for consumer good k, we obtain the demand for production of vendor k in global equilibrium. In equilibrium, the output of the vendor is equal to its demand, and then the output of the vendor at this point is influenced not only by the prices of all products in the market but also by the respective demands of all consumers. If vendor k happens to be a typical vendor of our choice, then in equilibrium, the demand function is as follows:
Replacing the consumer demand vector with the nominal aggregate demand , i.e., assuming that the distributional role is not considered and only the role of aggregate demand is of interest, is an assumption condition often used in macroeconomic analysis. With the adoption of this assumption, the output of a typical manufacturer in the heterogeneous economic theory model is influenced only by the price of the good itself, the average price of the overall market, the nominal aggregate demand, and the number of manufacturers.
The development trend of the well-being level of the residents of N prefecture from 2000 to 2020 is analyzed by Scatter Matrix Plot, as shown in Figure 3, it can be clearly seen that the human development index of N prefecture is rising, people’s living standard is significantly improved, and the well-being level of the residents is increasing continuously; From 2005 to 2010, the human development index of N province showed a qualitative improvement, and the lowest value exceeded 0.550, and the province broke through the stage of low residents’ well-being level; from 2010 to 2015, the human development index had the largest ring spacing, and the residents’ well-being level reached a new level, and all of them moved to the stage of high residents’ well-being level; from 2015 to 2020, the development of residents’ well-being level of N province slowed down, and this A series of changes are similar to the economic development course of N province, the difference is that the change of human development index is much smaller than the income index. If the value-added tax makes the relative prices perceived by producers and consumers different, and the marginal rate of substitution of goods is not equal to the marginal rate of substitution of production technology, it cannot change the existence of efficiency losses.
The Gini coefficient is the most common measure of income distribution effects. The Gini coefficient is the most common measure of the effect of income distribution and is mainly used to measure the degree of inequality in the distribution of income or wealth of a country’s population. The Italian statistician and sociologist Corrado Gini first developed it. The Gini coefficient was first introduced by the Italian statistician and sociologist Corrado Gini and published in his 1912 paper “Variability and Mutability.” The Gini coefficient is defined mathematically by the Lorenz Curve, which depicts the proportion of the income of the population from the lowest to the highest income to the total income of the population. The area between the Lorenz Curve and the line OM represents the difference between the actual income distribution and the perfectly equal distribution, denoted by A. The area between the Lorenz Curve and the line ONM represents the difference between the actual income distribution and the perfectly equal distribution, denoted by A. The area between the Lorenz Curve and the line ONM represents the difference between the actual income distribution and the perfectly equal distribution, denoted by A. The area between the Lorenz Curve and the line ONM represents the difference between the actual income distribution and the perfectly equal distribution, denoted by A. The area between the Lorentz curve and the line OM represents the gap between the actual income distribution and the perfectly equal distribution, denoted by B. The Gini coefficient is equal to the area between the actual income distribution and the perfectly equal distribution as a percentage of the area between the perfectly equal distribution and the perfectly unequal distribution. The modified design can protect the confidentiality and integrity of the code and data in the container from being damaged by malicious software with special privileges, so it can realize the isolated operation of different programs and ensure the confidentiality and integrity of the user’s critical code and data. Integrity is not compromised by malware.
In general, the Gini coefficient takes a value between 0 and 1 when the sample population has a positive income. When the Gini coefficient is 0, it indicates a perfectly equal distribution of income. When the Gini coefficient is 1, it indicates a perfectly unequal distribution of income. There are many formulas for calculating the Gini coefficient, and the Gini coefficient can be divided into Gini coefficient with discrete probability distribution function and Gini coefficient with continuous probability distribution function based on the characteristics of the probability distribution function. In this chapter, based on the characteristics of the selected sample data, the following Gini coefficient calculation formula is adopted:where denotes the Gini coefficient; denotes the share of per capita income of group, i.e., the share of per capita income of group i in total income; denotes the frequency of population of group i, i.e., the share of the population of group i in total population; denotes the cumulative per capita income of group 1 to group i in total income, and . If the pretax Gini coefficient is greater than the posttax Gini coefficient, it indicates that VAT has a positive income distribution effect and is conducive to reducing income disparity in a country. Conversely, it indicates that VAT has a negative income distribution effect and is not conducive to reducing income disparity in a country. The environmental burden of disease, the impact of air pollution on the ecosystem, the impact of water on the ecosystem, forestry development, climate change, etc., will all have such effects on human welfare. For example, normal production is a necessary condition for healthy and sustainable economic growth. The morbid environment makes life and production unable to proceed normally, and economic malaise directly leads to a decline in the total base of social well-being, and social well-being naturally decreases.
With the development of the economy and society, people gradually recognize the impact of economic decisions on the environment, and the share of the environment in the calculation of national profit and loss has begun to receive attention and research. The environmental burden of disease, the impact of air pollution on ecosystems, the impact of water on ecosystems, forestry development, climate change, etc., all have one effect or another on human welfare. As a result, the analysis and measurement of such effects have gradually gained attention, and many systems of measuring environmental welfare have emerged. The understanding of welfare thinking started with the idea that any result defined as a production activity could be considered beneficial to human welfare, but it was gradually recognized that not all production results would have an impact on human welfare in the present and that those goods or services that are not related to current personal consumption should be rethought since some elements are needed in the future and should be viewed separately from current welfare. A variety of different ideas and perceptions influence the measurement of welfare. Economic welfare is considered to be that part of social welfare that can be measured in monetary terms, and as economic welfare has become better understood, it has been recognized that the factors that affect people’s welfare are not just within the scope of economics.
4. Analysis of Results
4.1. IoT Background Digital Information Technology Model Performance Testing
As mentioned in the requirement analysis, the IoT data collection program needs to be in a permanent and stable running state and cannot be interrupted for a long time due to abnormal conditions. During the testing process of this thesis, the IoT data collection program in the virtual machine is interrupted manually to test whether the monitoring module can successfully detect the abnormality and automatically start the data collection program. It is configured to expect the data collection program to be in the start state and to automatically execute the policy of opening the process when the monitoring result does not meet the expectation. Process information is obtained in real-time during monitoring [22]. When the data collection program in the virtual machine is manually closed, the monitoring module monitors that the target process does not exist and automatically executes the policy of opening the process to successfully start the IoT data collection program and put it in the running state to continuously collect data from the sub-station. As a trusted platform to ensure the safe operation of data collection programs and realize the function of data security on-chain, to cooperate with the long-term operation of the Internet of Things, it needs to have high reliability for long-term stable operation, and each module has high internal robustness, and it can withstand all kinds of abnormal conditions in operation.
The performance test part is mainly tested in 3 aspects: the speed and completeness of data interception and uploading, the performance of the SGX authentication server interface, and whether it has high reliability for long and stable operation. The IoT data collection simulation program is used to control the amount of data sent and the time interval. To determine whether the interception conditions are effective for a long time and not affected by the amount of data, the simulation program is set to generate 1 out of every 10 data that do not meet the interception conditions, and then observe and record the intercepted data and the efficiency of data uploading. The trusted platform studied in this thesis will also keep intercepting IoT data to send its signature to the blockchain side, and after testing and observing, the trusted platform can run stably for at least up to one month. In this paper, the interfaces provided by the SGX authentication server are pressure tested by the Jmeter pressure testing tool. 120 accesses are completed within 20 seconds for both interfaces using 20 threads, and the test results show that the access exception rate is 0%, and the interfaces function normally and stably, which is in line with the expected results. The results of the verification interface test and the authentication interface test are shown in Figure 4.
In addition to testing the verification interface and authentication interface of the system, the accuracy rate and coverage rate of system identification are also important performance indicators. This experiment defines the accuracy rate of system identification as the percentage of correct device identification information among all data identification information extracted by the system and the coverage rate of system identification as the percentage of device information correctly identified by the system among all devices. In this paper, 2,000 different HTTP(S) responses were randomly selected from about 200,000 or so HTTP(S) responses as the test dataset, including 1,500 different IoT only enden n responses for the different non-IoT device HTTP(S) IA responses that were not filtered out according to the filtering step in the test dataset. For the penal letter, some of the non-IoT device HTTP(S) responses ensure that their text information contains at least the device model information. In addition, the HTTP(S) responses of non-IoT devices are introduced in the test dataset because these noninitial network attack months may be misidentified as IoT devices, and the HTTP(S) 4 of these non-IoT devices is an important metric for measuring the system performance of the spin-link hermeneutic non-IoT HTTP(S) responses as a percentage of all non-IoT HTTP(S) responses. The performance metrics of each version of the system rule base were tested on the above test dataset with a threshold parameter of 3 for the number of occurrences in the search results, and the results are shown in Figure 5. It can be seen from the figure that with the continuous improvement of the version library, the accuracy and coverage rate of the system identification is gradually increasing, and the false identification rate of misidentifying non-IoT device HTTP(S) as IoT devices is gradually decreasing.
4.2. Analysis of Social Well-Being Calculation Results
The two most used indicators to measure changes in the welfare level of the population are the equivalence transformation and the compensation transformation, while the excess burden is generally used to measure changes in the social welfare level [22]. The equivalence transformation is a measure of how much a price increase corresponds to a decrease in consumer income, and the compensation transformation is a measure of the amount of compensation given to consumer income after a price change so that consumer welfare remains unchanged before and after the price change. The excess burden is a measure of the impact of the relative price change on the overall loss of social efficiency. Usually, the specific formulas of the equivalence transformation and the compensation transformation differ according to the specific form of the consumer utility function. Consumers must pay a certain amount of necessary living expenses (which is fixed) regardless of whether they have income or not, and other expenses besides the necessary living expenses depend on the marginal propensity to consume and the level of income. Digital information technology mainly includes mobile big data analysis, intelligent information processing, Internet, Internet of Things, geographic information system (GIS), information visualization, and other technologies, with functions such as information collection, management, processing, analysis, identification, and application services.
Due to the complexity and universality of natural and social phenomena, it is necessary to collect as much relevant data as possible to accurately describe and explain various complex phenomena, which leads to bigger and bigger data and thus big data. However, bigger data is not the ultimate purpose of big data but only an intermediate product, because too large data cannot be used directly by human beings and cannot be helpful to human decision-making [23]. Therefore, as data gets bigger, it is also important to consider ways to make big data smaller so that decision-makers can use it. The well-being of individuals is influenced by a variety of factors, such as personal consumption, personal leisure, relative income, environmental quality, and the supply of public goods. In this section, we assume that individual well-being is influenced by three main aspects: personal consumption, public goods consumption, and environmental quality. Since this chapter focuses on the mechanism of macroeconomic policies on well-being, we assume that the variable environmental quality is exogenously given. Based on the analysis of individual well-being, we will further explore the mechanisms by which exogenous demand shocks affect aggregate social well-being. In this economic model, there are employed individuals and unemployed individuals, and the individual well-being of the type employed individual is and that of the typical unemployed individual is . The traditional utility social well-being function treats total social well-being as a simple sum of the well-being or utility of all members of society, and the well-being of any member of society is treated equally.
In terms of the welfare level of urban households, in 2015, 2016, 2017, 2018, 2019, and 2020, all of them have the largest EV/m (CV/m) value for low-income households, with a mean value of 0.0607 (0.0626), and the smallest EV/m (CV/m) value for high-income households, with a mean value of 0.0303 (0.0310). This indicates that VAT has the greatest impact on the welfare of low-income urban households, i.e., low-income urban households suffer the greatest welfare loss [24]. Second, the ratio of the minimum to the maximum value of EV/m (CV/m) is calculated for each year, and its mean value is 0.4992 (0.4952), and the welfare gap between urban households is large each year, and the welfare gap between high- and low-income households remains around two times. Regarding the welfare level of rural households, the EV/m (CV/m) value is the largest for low-income households in 2015, 2016, 2017, 2018, 2019, and 2020, with its mean value of 0.0612 (0.0632); the EV/m (CV/m) value is the smallest for high-income households, with its mean value of 0.0330 (0.0338). This indicates that VAT has the greatest impact on the welfare of low-income households in rural households, i.e., low-income households in rural households suffer the greatest welfare loss. Second, the ratio of the minimum value of EV/m (CV/m) to the maximum value is calculated for each year, and its mean value is 0.5392 (0.5348), and the welfare gap between rural households is large each year, and the welfare gap between high- and low-income households is maintained at about 1.85 times, as shown in Figure 6 for the detailed comparison results.
The smaller the Gini coefficient value, the more equal the income distribution. The value range of the Gini coefficient is . When , it means that income is completely equally distributed; when , it means that income is completely unequally distributed. The study on the development of the well-being level of residents in N province found that the health index, education index, income index, and human development index in N province from 2010 to 2020 are all on an increasing trend, and the level of health index and education index is higher than that of income index; from the perspective of each prefecture-level city, A city has obvious advantages and ranks first in the development of each index, A1 and A2 have poor development of health index, A3 and A4 have poor development of education index, income index, and human development index. The development level of the education index, income index, and human development index lags behind that of other regions in N province. The center of gravity of the well-being structure of residents in each prefecture-level city in N province moves from quadrant 1 to quadrant 2 in the “three-axis diagram,” and gradually moves closer to the origin along the negative half-axis of the income index. The points are more compact, indicating that the well-being structure of residents in N province is accompanied by. It shows that the structure of the well-being level of the residents of N province has changed from the health index to the education index as the income index continues to increase, and the three indices will gradually become balanced by 2020, and the structure of the well-being level of the residents tends to be optimized.
In 1980, the Gini coefficient value was 0.32, indicating that the income distribution was relatively even. Later, due to the rural reform, the gap between urban and rural areas was narrowed. Since the HDI is the average weighted result of the three indices of health, education, and income, the percentage of each index constituting the HDI can be calculated, and the trend of the change in the structure of residents’ well-being level can be obtained according to the proportion of each index in the HDI of each year, as shown in Figure 7. For example, the health index had the highest proportion in 2010 and 2014, accounting for 47.12% and 44.31% of each year, respectively, and the proportion in 2018 is lower than that of the education index, accounting for only 36.43% in 2020, which is a significant decrease. The proportion of the education index has a decreasing trend, but the degree of decrease is much smaller than that of the education index, and the share is the highest after 2000, indicating that the development of education plays an important role in the well-being of the residents of Shanxi Province; the proportion of income index has been rising rapidly since 2010, from 10.89% in 2010 to 29.13% in 2020, greatly narrowing the gap with the other two indices, with health, education and income indices accounting for 34.45%, 35.18%, and 27.63%, respectively, by 2020. By 2020, the three indices of health, education, and income will account for 34.45%, 35.18%, and 27.63%, respectively, and the three indices tend to be in balance, indicating that the shortcomings of the income index have played a key role in rationalizing the well-being of the residents of N.
5. Conclusion
This paper talks about the role of digital information technology in social well-being calculation based on the background of the Internet of Things. The types of well-being levels of residents in N province are evaluated, and standard deviation, spatial autocorrelation, and spatial directional trend graph analysis are applied to study the differences in the spatial distribution of well-being levels of residents in N province in-depth and to explore the influencing factors affecting the differences in well-being levels of residents in N province. The VAT CGE model constructed in the paper, which uses elasticity coefficients to simulate the collinearity between goods and factors, combined with micro data on residents’ household income and expenditure, can measure the income distribution effect and welfare effect of VAT reform in China in a more scientific way. This paper performs device identification information extraction based on HTTP(S) responses of IoT devices. The model identification information extraction method designed in this paper is also applicable to extract identification information from response data of IoT devices of other application layer protocols and can be extended with only minor modifications. However, once the data collection procedure is optimized and the data volume is increased, the current uploading speed will not be able to upload all the collected data in time, so the data processing speed needs to be further optimized later to meet the demand of processing more data volume.
Data Availability
The data used to support the findings of this study are available from the author upon request.
Conflicts of Interest
The author declares no conflicts of interest.
Acknowledgments
This work was supported by Cheongju University.