# Economic Scheduling Problem of Nanomaterial Import and Export Trade Based on Redundant Data Compression Algorithm and Its Parameter Adjustment Method

Authors: Lihong Lu; Daixin Wang
Journal: Advances in Materials Science and Engineering
 (2022)

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## Abstract

In the context of today’s supply-side structural reform, the research on the relationship between foreign trade and economic growth has received extensive attention from researchers. Since China’s economic reform and opening up in the late 1970s, China’s economy has experienced a relatively long period of rapid growth. From 1978 to the end of 2008, China’s economy grew at an average annual rate of about 10%, and its GDP grew from US$9.75 billion in 1978 to US$1,430.69 billion in 2008. The progress is amazing, the total import and export of goods increased from 20.64 billion US dollars in 1978 to 25.6326 billion US dollars in 2008, and the world ranking rose from 32nd in 1978 to 3rd in 2008, second only to the United States and Germany. The analysis of the test results presented in this paper shows that, from 1995 to 2014, China’s average annual exports accounted for 23.72% of GDP. Since China joined the World Trade Organization in 2001, China’s share of export trade has increased significantly. In 2004, the total value of exports reached 4910.33 billion yuan, equivalent to more than 29% of China’s GDP, and continued to rise in the following years. China’s total exports as a share of GDP declined between 2009 and 2014 but remained above 20% in both cases.


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## Body

## 1. Introduce

Since the early 1990s, the Chinese economy has accelerated its integration into the global economy and has become a driving force for the world economy. According to “2012 China Yearbook Statistics,” my country’s total import and export trade in 2011 was 3,641.86 billion US dollars, an increase of 22.5% over 2010. Among them, the total export value was 1,898.38 billion US dollars, an increase of 20.3%, and the total import value was 1,743.8 billion US dollars, an increase of 24.9%. From an economic point of view, an increase in export volume can lead to an increase in income, thereby promoting the social welfare of the Chinese people. The continuous growth of foreign trade volume reflects the continuous enhancement of the international competitiveness of Chinese products and the year-on-year improvement of China’s trade facilitation level.China is now facing new challenges, whether it is internal reform or opening to the outside world. On the one hand, my country’s export-led economic development has slowed down due to the sharp drop in demand in the European and American markets after the financial crisis. On the other hand, the rapid development of China’s economy is gradually accompanied by structural contradictions and excess production capacity, which are the two major challenges China faces to ensure sustainable economic development. In addition, China’s rapid growth has prompted industrialized countries such as Europe and the United States to continuously adjust their trade structures and change trade rules to curb China’s rapid development in international trade. In addition, the EU and TTIP will also affect the current global trade pattern by occupying a large number of European and American markets and bring new challenges to China’s international trade.Therefore, when conducting international trade, we should pay more attention to the quality of products, rather than just pursuing the rapid growth of quantity. To fight the “protracted war,” we must carry out technological innovation so that export products contain higher technical elements, take technology as the core advantage of China’s participation in the international division of labor, and accelerate the transformation of China’s economy from the current extensive growth model to an intensive growth model. This is also an important step in China’s transformation from a trading power to a trading power. Of course, not only will export trade stimulate technological innovation, but also technological innovation can actively promote the economic development of various countries. It can be seen that the level of science and technology has become the core competitiveness of a country. At present, China has entered a new era of relying on technological innovation and technological development to develop the economies of various countries. In addition to encouraging export enterprises to take the initiative to learn and imitate international cutting-edge technologies for their own use, it is more important to strengthen independent innovation, which is also the top priority of the party and state leaders. For two consecutive years, China has mentioned the innovative concept of “mass entrepreneurship and mass innovation” on important occasions. The Chinese President also attaches great importance to the issue of scientific and technological innovation. He talked about the innovation of the Shanghai delegation for five consecutive years, which is of far-reaching significance. Of course, “innovation” is not only the premise of Shanghai but also the “comprehensive innovation” of every region and everyone in China.


## 2. Related Work

In this paper, based on the redundant data compression algorithm, some technical researches are carried out on the economic scheduling problem of the import and export trade of nanomaterials and the method of parameter adjustment, which can be fully applied to the research in this field. Deibe et al. proposed a new multiresolution noncore technique. It is used for web-based visualization and is implemented through a nonredundant method of organizing data points (which they call hierarchical sharding) and a tree-like structure (called a grid tree of shards (TGPT)) [1]. The effectiveness of frequency diversity is studied; that is, three different frequencies are used in a three-module redundant data transmission system to deal with bad electromagnetic interference [2]. In order to reduce the interference of redundant information to GIS query and improve the performance of information query in terms of accuracy, Lu and Wen proposed a GIS query optimization algorithm based on redundant data deletion and filtering technology [3]. Wang et al. proposed a redundant data storage algorithm based on minimum spanning tree and quasirandom matrix, namely, QRNCDS. According to the minimum spanning tree traversal mechanism [4], k source data packets are distributed to n sensor nodes (n> k) in the network. Slastikhin proposed a simulation model of data transmission system, which introduced time-critical query priority and transmission redundancy. Through simulation experiments, the efficiency evaluation of time-critical point priority and redundancy increase is studied [5]. Hema et al. believe that radio frequency identification (RFID) is a method of using radio waves to collect and capture the technology of storing data on RFID tags on items. Its core idea is to eliminate redundant data in RFID systems [6]. Yu proposed a method to identify redundant data by constructing a path tree cleaning method based on the segmentation method. Through the analysis of identifying redundant data in the process of traditional mobile Internet big data cleaning, the median filtering algorithm is used to extract redundant data features [7]. Li and Zhao proposed the concept of sports video redundant data (SRD). Combined with the content-based video analysis method and the time, structure, rules, and other characteristics of sports videos, the main region color (MRC) and multiregion segmentation algorithms are used to realize the analysis and detection of redundant data in fencing videos [8]. Ru and Li pointed out that, in massive data processing, a large number of similar features will bring redundant interference to data classification. This will lead to the shortcomings of multiple inspections and repetitions in the judgment process of the classification center. Therefore, they proposed a method under the interference of massive data, the high-performance redundant data elimination method [9]. Yang et al. studied the stochastic optimization problem of economic dispatch and interruptible load management using the short-term distribution forecast of wind farm power generation [10]. Beigvand et al. proposed a new optimization algorithm, an adaptive learning algorithm for gravity search with variable acceleration coefficients. It is used to solve highly nonlinear, nonconvex, nonsmooth, nondifferential, high-dimensional single-objective, and multiobjective energy hub economic dispatch problems [11].


## 3. A Compression Algorithm Method for Economic Scheduling of Nanomaterials Import and Export Trade

### 3.1. Comprehensive Research on Compression Algorithms

In the information society, the amount of data in various fields is increasing. A database compression method is proposed to efficiently manage large amounts of data. The database compression method not only can improve the efficiency of storing a large amount of data but also is an important way to improve database performance.The prototype model, also known as the rapid prototyping model, refers to a working prototype of the system that should be established before the actual software development is performed. The architecture of the data mining prototype system CMining based on compressed data is divided into three layers: user interface layer, data compression and data mining layer, and database. The CMining prototype system is shown in Figure1.Figure 1
CMining prototype system.The CMining system implements the following functions: (1) the database compression algorithms H_ItCompress and Itcompress and the header compression algorithm for compressing multidimensional datasets; (2) compression database association rules mining; (3) classification mining of compressed databases; (4) cluster mining of compressed cubes [12–14].Source data is related and redundant, and data compression takes advantage of this feature of source data. In order to reduce the signal gap, use the least number of bits to represent the signal to be output, which can accommodate special data samples and datasets. The general process of data compression technology is shown in Figure2.Figure 2
General steps for data compression.This diagram represents the 3 main steps of data compression: (1) Build a mathematical model that “represents” the original data more compactly or efficiently. (2) Try to express more concisely the model parameters obtained by modeling the original data with the model. Since the representation accuracy of these parameters may be too high, they can be subjected to limited quantization, called “secondary quantization,” to distinguish them from the quantization of the original signal during digitization. (3) Assign code words to model parameters or a quantized representation of the information stream to generate the most compact compressed stream possible [15]. The encoding at this time requires that the quantized symbols of the model parameters can be “faithfully” reproduced, so it is called “sequential encoding.”The original data are compressed, and the resulting output is the compressed data. Data compression is achieved when the storage space occupied by these data is smaller than the original data. When the data needs to be used, it only needs to be restored (decompressed).


### 3.2. Comprehensive Study of Import and Export Trade

The traditional view holds that the most important prerequisite for international trade is the difference in production technology and the resulting difference in relative costs. Using technology as an exogenous variable, the researchers assume that there is a comparative advantage in trade between the two countries based on the assumption that there is no technological progress. In real life, however, no country has fully specialized production but is looking for relatively low-quality products to replace imports. Differences in production technology do not result in absolute differences in product cost. Therefore, simply attributing technical differences to exogenous variables is biased [16].Summarizing the relevant theories of trade structure, it is not difficult to see that, in today’s rapid economic and social development, science and technology cannot be simply attributed to exogenous factors to study [17]. In fact, the impact of scientific and technological innovation on the structure of export trade from different angles is not completely direct, and it is jointly determined by the main body of scientific and technological innovation and the influence of the external environment. Therefore, the entry point of its mechanism research should also be multiangle. The mechanism diagram of the impact of technological innovation on the structure of export trade is shown in Figure 3.Figure 3
The influence mechanism of technological innovation on the structure of export trade.Technology gap theory is one of the important foundations for the evolution of international trade theory. The creators of the “Technology Gap Model” believe that technological innovation will have a certain impact on international trade. The model points out that because a country discovers and introduces a certain new technology or process, it can rely on it to gain technological advantages in products [18, 19]. International trade arises because of the technological gap in countries that have not carried out technological innovation, and it is necessary to acquire technology and products from advanced countries [20].This has always been the case until the importing country introduces technology and after continuous imitation and learning gradually produces products with the same technical level, with comparable innovation and imitation capabilities, so that the production of such commodities can be self-sufficient and no longer imported. However, because the exporting country itself is in a leading position in technological innovation, it can continue to innovate and maintain its comparative advantage with the importing country, thereby continuously promoting the development of export trade. It can be seen that technological innovation has strong practical significance for exporting countries: first, only by accelerating the pace of technological innovation and continuously improving product quality can we have export qualifications and potential innovation, increase the technology gap of export products, and show the comparative advantage of products in the form of increasing exports; the role of technology gap is shown in Figure4.Figure 4
The role of technology gaps.It can be seen that countries that prioritize technological innovation will always produce products with market monopoly [21, 22]. In an open economy, due to the role of technological gaps, countries that have not yet made technological innovations in the international market have strong demand for the product, which enables technologically innovative countries to supply and export trade products to the international market and ultimately gain monopoly profits. From this, it is concluded that technological innovation can drive the production of export trade under the influence of the technological gap [23].China’s export trade has great characteristics in regional distribution. Apart from Hong Kong, Macau, and Taiwan, China is geographically divided into three regions: the eastern region, including Beijing, Hebei, Tianjin, Liaoning, Shandong, Jiangsu, Shanghai, Zhejiang, Guangdong, Fujian, and Hainan, the central region, including Heilongjiang, Jilin, Anhui, Shanxi, Henan, Hubei, Hunan, and Jiangxi, and the western region, including Inner Mongolia, Shaanxi, Sichuan, Chongqing, Guangxi, Gansu, Guizhou, Yunnan, Ningxia, Qinghai, Tibet, and Xinjiang. Among them, the eastern cities are mostly coastal provinces and cities with natural location advantages. My country’s major large ports are all located in the eastern region, and the total export volume has always ranked first among the three major regions. The total export value of each region in China (2000–2010) is shown in Table1.Table 1
China’s total exports by region (2000–2010).
20002001200220032004200520062007200820092010Eastern general2258.82429.42978.84013.1_5457.17022.48896.711097.7_12835.910934.614205.2Central total113.9121.1139.3186.8250.2319.6431.5589.7797.6541.2832.2Western total89.380.5107.8152.4195.9247.6331.2460.3643.5510.4710.1As can be seen from Table1, the total export trade volume of eastern China has grown rapidly in recent years, far exceeding that of the central and western regions. The eastern region has an absolute advantage in foreign trade exports, accounting for more than 80% of the country’s total exports. Although my country has vigorously encouraged the development of the central and western regions in recent years and the total export value of the central region has increased, the regional structure of export trade is still seriously unbalanced [24].Export product structure, that is, the composition of various products in export products, is expressed as the share of various products in a country’s total exports.Two general classification and coding standards have been developed internationally. The Standard International Trade Classification (SITC) developed by the United Nations Statistical Commission in 1948 and the Customs Convention on the Classification of Goods signed by the European Customs Union in Brussels in 1950 were revised in 1972 and renamed the Customs Cooperation Council Classification (CCCN). The establishment of SITC and CCCN simplifies international trade procedures and improves efficiency. However, the coexistence of these two different codes also leads to some disadvantages. Since countries using different codes in international trade had to reclassify and rename them accordingly, the 61st session in 1983 adopted the International Convention on the Harmonized Commodity Description and Coding System and its annex, the Harmonized System. The system codes SITC and CCCN were merged into the HS code, and the HS code was implemented on January 1, 1988.China publishes the “China Statistical Yearbook” every year. Import and export commodities are classified and ranked according to the monthly growth rate, cumulative growth rate, and annual growth rate of the “China Commodity Catalogue.” Based on the second edition of the United Nations Standard International Trade Classification (1980–1991), starting from 1992, on the basis of the Harmonized Commodity Description and Coding System formulated by the Customs Cooperation Committee, an 8-digit commodity code has been adopted. The first 6 digits are the “Harmonized System” code, and the last 2 digits are additional subheadings formulated according to my country’s tariff, statistics, and trade management requirements. The entire catalog contains more than 7400 eight-digit commodity codes [25]. The SITC classification is shown in Table 2.Table 2
SITC classification.
(1) Primary productsCategory 0Food and live animalsThe first sortBeverages and tobaccoCategory 2Inedible raw materials (except fuel)Category 3Mineral fuels, lubricants, and related raw materialsCategory 4Animal and vegetable oils, fats, and waxes(2) Industrial productsCategory 5Chemical products and related productsCategory 6Finished products by raw materialsCategory 7Machinery and transport equipmentClass 8Miscellaneous productsClass 9UncategorizedUnder the SITC, textiles, rubber, and minerals (SITC6), miscellaneous manufacturing (SITC8), and other types of unclassified manufacturing (SITC9) are labor-intensive, that is, resource-intensive. Chemicals and related products (SITC5) and machinery, transport, and equipment (SITC7) are classified as capital- and technology-intensive.


### 3.3. Economic Dispatch

From a mathematical point of view, the economic planning problem is a big problem that mixes multidimensional, multivariable, nonconvex, nonlinear, and other problems. Many researchers have conducted extensive research on the economic load distribution scheduling problem [26, 27]. The methods to solve such problems are relatively mature, mainly traditional optimization algorithms and intelligent optimization algorithms [28].Traditional methods include priority method, dynamic programming method, mixed integer programming method, linear programming method, and Lagrangian relaxation method. Intelligent optimization methods include particle swarm algorithm, simulated annealing algorithm, genetic algorithm, immune algorithm, and neural network method. The following is a brief introduction to several of these algorithms:(1)
Priority method: the earliest proposed economic load distribution method is the priority method, and the direct economic parameter of this method is to adjust the controllable dataset in the control area to the highest economic efficiency.(2)
Dynamic programming method: in the early 1950s, American mathematicians proposed the dynamic programming method. It is a combinatorial optimization algorithm that has no specific requirements for the objective function and is a method for solving multistage decision-making process optimization.(3)
Lagrangian relaxation: Lagrangian relaxation is a method for dealing with lower bounds of complex integer, combinatorial optimization problems suitable for solving large systems of constrained optimization problems [29].(4)
Differential evolution algorithm: the differential evolution algorithm proposed in 1995 reduces the complexity of genetic operations by using real number coding and supports a global population search strategy, which is different from some other optimization algorithms that perform simple mutation operations.(5)
Ant Colony Algorithm: Ant Colony Algorithm is a swarm intelligence algorithm, which is a group of nonintelligent or slightly intelligent individuals exhibiting intelligent behavior through mutual cooperation, thus providing a new possibility for solving complex problems. Ant Colony Algorithm has strong robustness, adopts distributed computing method, and is often used in combination with other algorithms to deal with optimization problems.(6)
Genetic algorithm: this algorithm is the natural superiority theory of American professor to simulate the natural elimination of Darwin to achieve global optimization. It is one of the most widely used intelligent optimization algorithms.(7)
Simulated annealing: this is an algorithm that combines thermodynamics from physics and statistics from advanced mathematics. The implementation of the algorithm is a mature algorithm, which can effectively avoid falling into the local optimal solution, so it is a better global search algorithm in theory.


## 3.1. Comprehensive Research on Compression Algorithms

In the information society, the amount of data in various fields is increasing. A database compression method is proposed to efficiently manage large amounts of data. The database compression method not only can improve the efficiency of storing a large amount of data but also is an important way to improve database performance.The prototype model, also known as the rapid prototyping model, refers to a working prototype of the system that should be established before the actual software development is performed. The architecture of the data mining prototype system CMining based on compressed data is divided into three layers: user interface layer, data compression and data mining layer, and database. The CMining prototype system is shown in Figure1.Figure 1
CMining prototype system.The CMining system implements the following functions: (1) the database compression algorithms H_ItCompress and Itcompress and the header compression algorithm for compressing multidimensional datasets; (2) compression database association rules mining; (3) classification mining of compressed databases; (4) cluster mining of compressed cubes [12–14].Source data is related and redundant, and data compression takes advantage of this feature of source data. In order to reduce the signal gap, use the least number of bits to represent the signal to be output, which can accommodate special data samples and datasets. The general process of data compression technology is shown in Figure2.Figure 2
General steps for data compression.This diagram represents the 3 main steps of data compression: (1) Build a mathematical model that “represents” the original data more compactly or efficiently. (2) Try to express more concisely the model parameters obtained by modeling the original data with the model. Since the representation accuracy of these parameters may be too high, they can be subjected to limited quantization, called “secondary quantization,” to distinguish them from the quantization of the original signal during digitization. (3) Assign code words to model parameters or a quantized representation of the information stream to generate the most compact compressed stream possible [15]. The encoding at this time requires that the quantized symbols of the model parameters can be “faithfully” reproduced, so it is called “sequential encoding.”The original data are compressed, and the resulting output is the compressed data. Data compression is achieved when the storage space occupied by these data is smaller than the original data. When the data needs to be used, it only needs to be restored (decompressed).


## 3.2. Comprehensive Study of Import and Export Trade

The traditional view holds that the most important prerequisite for international trade is the difference in production technology and the resulting difference in relative costs. Using technology as an exogenous variable, the researchers assume that there is a comparative advantage in trade between the two countries based on the assumption that there is no technological progress. In real life, however, no country has fully specialized production but is looking for relatively low-quality products to replace imports. Differences in production technology do not result in absolute differences in product cost. Therefore, simply attributing technical differences to exogenous variables is biased [16].Summarizing the relevant theories of trade structure, it is not difficult to see that, in today’s rapid economic and social development, science and technology cannot be simply attributed to exogenous factors to study [17]. In fact, the impact of scientific and technological innovation on the structure of export trade from different angles is not completely direct, and it is jointly determined by the main body of scientific and technological innovation and the influence of the external environment. Therefore, the entry point of its mechanism research should also be multiangle. The mechanism diagram of the impact of technological innovation on the structure of export trade is shown in Figure 3.Figure 3
The influence mechanism of technological innovation on the structure of export trade.Technology gap theory is one of the important foundations for the evolution of international trade theory. The creators of the “Technology Gap Model” believe that technological innovation will have a certain impact on international trade. The model points out that because a country discovers and introduces a certain new technology or process, it can rely on it to gain technological advantages in products [18, 19]. International trade arises because of the technological gap in countries that have not carried out technological innovation, and it is necessary to acquire technology and products from advanced countries [20].This has always been the case until the importing country introduces technology and after continuous imitation and learning gradually produces products with the same technical level, with comparable innovation and imitation capabilities, so that the production of such commodities can be self-sufficient and no longer imported. However, because the exporting country itself is in a leading position in technological innovation, it can continue to innovate and maintain its comparative advantage with the importing country, thereby continuously promoting the development of export trade. It can be seen that technological innovation has strong practical significance for exporting countries: first, only by accelerating the pace of technological innovation and continuously improving product quality can we have export qualifications and potential innovation, increase the technology gap of export products, and show the comparative advantage of products in the form of increasing exports; the role of technology gap is shown in Figure4.Figure 4
The role of technology gaps.It can be seen that countries that prioritize technological innovation will always produce products with market monopoly [21, 22]. In an open economy, due to the role of technological gaps, countries that have not yet made technological innovations in the international market have strong demand for the product, which enables technologically innovative countries to supply and export trade products to the international market and ultimately gain monopoly profits. From this, it is concluded that technological innovation can drive the production of export trade under the influence of the technological gap [23].China’s export trade has great characteristics in regional distribution. Apart from Hong Kong, Macau, and Taiwan, China is geographically divided into three regions: the eastern region, including Beijing, Hebei, Tianjin, Liaoning, Shandong, Jiangsu, Shanghai, Zhejiang, Guangdong, Fujian, and Hainan, the central region, including Heilongjiang, Jilin, Anhui, Shanxi, Henan, Hubei, Hunan, and Jiangxi, and the western region, including Inner Mongolia, Shaanxi, Sichuan, Chongqing, Guangxi, Gansu, Guizhou, Yunnan, Ningxia, Qinghai, Tibet, and Xinjiang. Among them, the eastern cities are mostly coastal provinces and cities with natural location advantages. My country’s major large ports are all located in the eastern region, and the total export volume has always ranked first among the three major regions. The total export value of each region in China (2000–2010) is shown in Table1.Table 1
China’s total exports by region (2000–2010).
20002001200220032004200520062007200820092010Eastern general2258.82429.42978.84013.1_5457.17022.48896.711097.7_12835.910934.614205.2Central total113.9121.1139.3186.8250.2319.6431.5589.7797.6541.2832.2Western total89.380.5107.8152.4195.9247.6331.2460.3643.5510.4710.1As can be seen from Table1, the total export trade volume of eastern China has grown rapidly in recent years, far exceeding that of the central and western regions. The eastern region has an absolute advantage in foreign trade exports, accounting for more than 80% of the country’s total exports. Although my country has vigorously encouraged the development of the central and western regions in recent years and the total export value of the central region has increased, the regional structure of export trade is still seriously unbalanced [24].Export product structure, that is, the composition of various products in export products, is expressed as the share of various products in a country’s total exports.Two general classification and coding standards have been developed internationally. The Standard International Trade Classification (SITC) developed by the United Nations Statistical Commission in 1948 and the Customs Convention on the Classification of Goods signed by the European Customs Union in Brussels in 1950 were revised in 1972 and renamed the Customs Cooperation Council Classification (CCCN). The establishment of SITC and CCCN simplifies international trade procedures and improves efficiency. However, the coexistence of these two different codes also leads to some disadvantages. Since countries using different codes in international trade had to reclassify and rename them accordingly, the 61st session in 1983 adopted the International Convention on the Harmonized Commodity Description and Coding System and its annex, the Harmonized System. The system codes SITC and CCCN were merged into the HS code, and the HS code was implemented on January 1, 1988.China publishes the “China Statistical Yearbook” every year. Import and export commodities are classified and ranked according to the monthly growth rate, cumulative growth rate, and annual growth rate of the “China Commodity Catalogue.” Based on the second edition of the United Nations Standard International Trade Classification (1980–1991), starting from 1992, on the basis of the Harmonized Commodity Description and Coding System formulated by the Customs Cooperation Committee, an 8-digit commodity code has been adopted. The first 6 digits are the “Harmonized System” code, and the last 2 digits are additional subheadings formulated according to my country’s tariff, statistics, and trade management requirements. The entire catalog contains more than 7400 eight-digit commodity codes [25]. The SITC classification is shown in Table 2.Table 2
SITC classification.
(1) Primary productsCategory 0Food and live animalsThe first sortBeverages and tobaccoCategory 2Inedible raw materials (except fuel)Category 3Mineral fuels, lubricants, and related raw materialsCategory 4Animal and vegetable oils, fats, and waxes(2) Industrial productsCategory 5Chemical products and related productsCategory 6Finished products by raw materialsCategory 7Machinery and transport equipmentClass 8Miscellaneous productsClass 9UncategorizedUnder the SITC, textiles, rubber, and minerals (SITC6), miscellaneous manufacturing (SITC8), and other types of unclassified manufacturing (SITC9) are labor-intensive, that is, resource-intensive. Chemicals and related products (SITC5) and machinery, transport, and equipment (SITC7) are classified as capital- and technology-intensive.


## 3.3. Economic Dispatch

From a mathematical point of view, the economic planning problem is a big problem that mixes multidimensional, multivariable, nonconvex, nonlinear, and other problems. Many researchers have conducted extensive research on the economic load distribution scheduling problem [26, 27]. The methods to solve such problems are relatively mature, mainly traditional optimization algorithms and intelligent optimization algorithms [28].Traditional methods include priority method, dynamic programming method, mixed integer programming method, linear programming method, and Lagrangian relaxation method. Intelligent optimization methods include particle swarm algorithm, simulated annealing algorithm, genetic algorithm, immune algorithm, and neural network method. The following is a brief introduction to several of these algorithms:(1)
Priority method: the earliest proposed economic load distribution method is the priority method, and the direct economic parameter of this method is to adjust the controllable dataset in the control area to the highest economic efficiency.(2)
Dynamic programming method: in the early 1950s, American mathematicians proposed the dynamic programming method. It is a combinatorial optimization algorithm that has no specific requirements for the objective function and is a method for solving multistage decision-making process optimization.(3)
Lagrangian relaxation: Lagrangian relaxation is a method for dealing with lower bounds of complex integer, combinatorial optimization problems suitable for solving large systems of constrained optimization problems [29].(4)
Differential evolution algorithm: the differential evolution algorithm proposed in 1995 reduces the complexity of genetic operations by using real number coding and supports a global population search strategy, which is different from some other optimization algorithms that perform simple mutation operations.(5)
Ant Colony Algorithm: Ant Colony Algorithm is a swarm intelligence algorithm, which is a group of nonintelligent or slightly intelligent individuals exhibiting intelligent behavior through mutual cooperation, thus providing a new possibility for solving complex problems. Ant Colony Algorithm has strong robustness, adopts distributed computing method, and is often used in combination with other algorithms to deal with optimization problems.(6)
Genetic algorithm: this algorithm is the natural superiority theory of American professor to simulate the natural elimination of Darwin to achieve global optimization. It is one of the most widely used intelligent optimization algorithms.(7)
Simulated annealing: this is an algorithm that combines thermodynamics from physics and statistics from advanced mathematics. The implementation of the algorithm is a mature algorithm, which can effectively avoid falling into the local optimal solution, so it is a better global search algorithm in theory.


## 4. Experiment of Economic Scheduling of Import and Export Trade of Nanomaterials Based on Compression Algorithm

### 4.1. Results and Analysis of Compression Algorithms

The storage of massive databases requires a lot of hard disk space, and the management of massive databases also requires a lot of system resources. Database compression technology can effectively reduce the data storage space and can also support the random access feature of the database. A class of databases represented by scientific databases and statistical databases is ubiquitous in the real world. Such databases typically consist of demographic data, scientific experimental data, geographic statistics, environmental data, and economic statistics.The 1990 census data and the 2010 census data of the US Census Bureau were selected for the experiment.The 1990 US Census data are stored in the USCensus1990raw dataset, which contains a 1% data sample of data collected in the 1990 US Census. There are 2,458,286 tuple records in this dataset, and the size is 823 MB. Meanwhile, each data has 125 attribute fields.First, verify the change of the compression ratio of the algorithm when the database records change, and draw the following conclusions: (1) The compression ratio of all compression algorithms is very high because there are a large number of 0s in the census data, so the performance of the ATS compression algorithm is very high. (2) Although block compression does not have the highest compression ratio, the block compression algorithm also has good performance. At the same time, the most important contribution of this algorithm is to provide an efficient way of data acquisition for data mining operations.The algorithm was then tested using data from the 2010 US Census. The census counted all the population information under different household registration types in the United States, including family household registration, collective household registration, and business household registration. Finally, the website published the population information of 51 states and regions in the United States and gave 10% of the total population data records. The census data of 28 states are randomly selected to test the compression algorithm, and the final result of the algorithm shows that the block-based compression algorithm has a good compression ratio for the 2010 census data of the United States. It can be seen from the data that the compression rate of the population data of 28 states by this algorithm has reached more than 76%, and the compression rate of the population data of 13 states after the compression algorithm has reached more than 76% and 78%.Data lossless compression algorithms were proposed many years ago and are now very mature. It has been applied to data compression of video, image, text, and other scenes, and the application system of lossless data compression algorithm in these fields has been quite perfect. Unlike these areas, lossless data compression algorithms are just getting started on embedded portable devices. In order to adapt to the special scenarios of embedded portable devices, the lossless data compression algorithm still needs to be adjusted a lot, and there are still many places to be explored and improved. The two lossless data compression algorithms are based on LZO and LZSS, respectively. The LZO-based memory data fast compression algorithm is a faster compression algorithm, while the LZSS-based memory data efficient compression algorithm is an algorithm with relatively high compression efficiency. The flowchart of the compression process of the LZSS-based memory data efficient compression algorithm is shown in Figure5.Figure 5
Flowchart of compression process of LZSS-based memory data efficient compression algorithm.An efficient and lossless memory data compression algorithm is based on LZSS. The algorithm is to preprocess the data and then compress it using an improved LZSS. Compared with the original LZSS, the improved LZSS algorithm improves the compression rate by more than 20%.


### 4.2. Import and Export Trade Results and Analysis

Macroeconomics looks at the three drivers of economic growth: consumption, investment, and net exports. Since 1980, China has regarded foreign trade as the main driver of economic growth. The proportion of China’s total exports to China’s GDP from 1995 to 2014 is shown in Table3.Table 3
1995–2014 China’s total exports as a proportion of China’s GDP.
YearExport/GDP199519.37199616.57199718.09199816.93199916.92200019.68200118.97200221.27200325.57200429.55200532.70200634.65200733.91200830.70200922.73201025.17201124.46201223.22201322.32201421.63As can be seen from Table3, from 1995 to 2014, China’s average annual export volume accounted for 23.72% of GDP. Since China joined the World Trade Organization in 2001, China’s share of export trade has increased significantly. In 2004, the total value of exports reached 4910.33 billion yuan, equivalent to more than 29% of China’s GDP, and continued to rise in the following years. China’s total exports as a share of GDP declined between 2009 and 2014 but remained above 20% in both cases.In recent years, my country’s foreign trade has developed rapidly, and investment projects in China have gradually increased. From such results, it can be seen that the environment provided by China for foreign businessmen should be favorable, and the trade facilitation index should also be high. However, comprehensively comparing the trade facilitation development level of 57 countries in 2011, my country belongs to the middle and low level of trade facilitation. This shows that China still has great opportunities for development in this area.The assessment of trade facilitation comes from the Global Competitiveness Report. China’s efforts in trade facilitation reforms need to be compared with the level of trade facilitation in recent years. However, since the basic parameters shown in the Global Competitiveness Report are frequently updated, one can only compare the level of trade facilitation between China and other countries horizontally. Figure6 shows the 2011 TFI indices by country:Figure 6
2011 TFI index by country.It can be seen that China’s comprehensive trade facilitation index is 0.68, ranking 33rd among the 57 sample countries, which is lower than the average level of 0.71 and is far behind developed countries. According to estimates, China’s trade facilitation index in 2010 was 0.71, slightly higher than the index in 2011. This may be because the intensification of the European debt crisis has affected China's trade facilitation perception data. This shows that China’s trade facilitation level still has a lot of room for development.The design of trade facilitation indicators is more of a subjective process and cannot correctly reflect whether the development level of my country’s trade facilitation is balanced. From the actual development situation, my country’s trade facilitation level is seriously out of balance. The level of trade facilitation in the eastern coastal areas and some areas with relatively well-developed foreign trade business is significantly higher than that in the inland areas.In order to get a glimpse of the structure of the value added of the economy’s export trade in the industrial distribution, it is necessary to calculate the value added of export trade by industry. Especially in terms of economic growth, the difference in the distribution of the added value of the industry’s export trade will have different impacts on economic growth. In order to study the impact of the industrial distribution structure of value added on economic growth, it is necessary to consider the value-added data of export trade at the industry level. According to the idea of calculating the total value added of foreign trade, a model is used to calculate the total value added of foreign trade, and the three-country model is adopted, but each country has two departments. The accounting logic of the added value of export trade at the industry level still needs to be based on the thinking at the total accounting level.In the stage of intraproduct trade, with the deepening of economic globalization, more and more intermediate goods are imported, and the export commodities of the economy contain a large amount of value created by American intermediate goods. Due to the obvious differences in the economic scale of different economies and the degree of their embeddedness in the world division of labor, the proportion of the added value of export trade in the total export trade has heterogeneity in the distribution of various economies. The added value of export trade only reflects the benefits that the economy obtains from export trade. The larger the scale of export trade, the higher the added value content of export trade. Just as the volume of export trade only reflects the scale of a country’s export trade, the added value of export trade can only reflect the scale of the economic benefit brought by export trade. However, the distribution of industries that create added value in export trade determines the quality of an economy’s export trade, which in turn has an impact on economic growth. Some scholars believe that the impact of trade on the economic growth of an economy depends on the export trade structure of the economy itself. Due to the large difference in the added value of export commodities in various industries, the added value of high-tech commodities is relatively high, and the high-tech commodity units exported by the economy will obtain greater benefits. However, the export of traditional industries does not have much added value. The positive impact on economic growth is limited. Therefore, it is necessary to further observe the industrial composition of the added value of each country’s export trade. Figures7 and 8 show the structure of export value added for some economies.Figure 7
Export value-added structure of some Economies1.Figure 8
Export value-added structure of some Economies2.China, Brazil, Germany, and South Korea represent the BRICS countries, economies caught in the “middle-income trap,” advanced economies, and emerging economies, respectively. The research on these four typical economies shows that the sectoral structure of the added value of export trade can reflect the industry composition of the added value of export trade of these four economies to a certain extent. In the value-added composition of Brazil’s export trade, the agricultural sector contributed the most, followed by the low-tech sector. The value added by exports in the agricultural sector and the low-tech sector accounted for half of the value added to Brazil’s export trade. The proportion of the added value of China’s agricultural exports has dropped to the level of developed countries. Due to China’s large population, the output of the agricultural sector is mainly used to meet the needs of the country’s residents. The need for government policies to guide and maintain domestic food security has resulted in low total exports from China’s agricultural sector. The added value of Germany’s export trade is mainly concentrated in the medium and high-tech fields, the high-tech field, and the service industry. The added value created by these three fields accounts for 80% of the added value of Germany’s export trade. The situation in South Korea is similar to that of Germany. The share is slightly higher than that of Germany, and the share of services is slightly lower than that of Germany. Overall, the industry composition of the added value of Germany’s export trade is more scientific and reasonable.


## 4.1. Results and Analysis of Compression Algorithms

The storage of massive databases requires a lot of hard disk space, and the management of massive databases also requires a lot of system resources. Database compression technology can effectively reduce the data storage space and can also support the random access feature of the database. A class of databases represented by scientific databases and statistical databases is ubiquitous in the real world. Such databases typically consist of demographic data, scientific experimental data, geographic statistics, environmental data, and economic statistics.The 1990 census data and the 2010 census data of the US Census Bureau were selected for the experiment.The 1990 US Census data are stored in the USCensus1990raw dataset, which contains a 1% data sample of data collected in the 1990 US Census. There are 2,458,286 tuple records in this dataset, and the size is 823 MB. Meanwhile, each data has 125 attribute fields.First, verify the change of the compression ratio of the algorithm when the database records change, and draw the following conclusions: (1) The compression ratio of all compression algorithms is very high because there are a large number of 0s in the census data, so the performance of the ATS compression algorithm is very high. (2) Although block compression does not have the highest compression ratio, the block compression algorithm also has good performance. At the same time, the most important contribution of this algorithm is to provide an efficient way of data acquisition for data mining operations.The algorithm was then tested using data from the 2010 US Census. The census counted all the population information under different household registration types in the United States, including family household registration, collective household registration, and business household registration. Finally, the website published the population information of 51 states and regions in the United States and gave 10% of the total population data records. The census data of 28 states are randomly selected to test the compression algorithm, and the final result of the algorithm shows that the block-based compression algorithm has a good compression ratio for the 2010 census data of the United States. It can be seen from the data that the compression rate of the population data of 28 states by this algorithm has reached more than 76%, and the compression rate of the population data of 13 states after the compression algorithm has reached more than 76% and 78%.Data lossless compression algorithms were proposed many years ago and are now very mature. It has been applied to data compression of video, image, text, and other scenes, and the application system of lossless data compression algorithm in these fields has been quite perfect. Unlike these areas, lossless data compression algorithms are just getting started on embedded portable devices. In order to adapt to the special scenarios of embedded portable devices, the lossless data compression algorithm still needs to be adjusted a lot, and there are still many places to be explored and improved. The two lossless data compression algorithms are based on LZO and LZSS, respectively. The LZO-based memory data fast compression algorithm is a faster compression algorithm, while the LZSS-based memory data efficient compression algorithm is an algorithm with relatively high compression efficiency. The flowchart of the compression process of the LZSS-based memory data efficient compression algorithm is shown in Figure5.Figure 5
Flowchart of compression process of LZSS-based memory data efficient compression algorithm.An efficient and lossless memory data compression algorithm is based on LZSS. The algorithm is to preprocess the data and then compress it using an improved LZSS. Compared with the original LZSS, the improved LZSS algorithm improves the compression rate by more than 20%.


## 4.2. Import and Export Trade Results and Analysis

Macroeconomics looks at the three drivers of economic growth: consumption, investment, and net exports. Since 1980, China has regarded foreign trade as the main driver of economic growth. The proportion of China’s total exports to China’s GDP from 1995 to 2014 is shown in Table3.Table 3
1995–2014 China’s total exports as a proportion of China’s GDP.
YearExport/GDP199519.37199616.57199718.09199816.93199916.92200019.68200118.97200221.27200325.57200429.55200532.70200634.65200733.91200830.70200922.73201025.17201124.46201223.22201322.32201421.63As can be seen from Table3, from 1995 to 2014, China’s average annual export volume accounted for 23.72% of GDP. Since China joined the World Trade Organization in 2001, China’s share of export trade has increased significantly. In 2004, the total value of exports reached 4910.33 billion yuan, equivalent to more than 29% of China’s GDP, and continued to rise in the following years. China’s total exports as a share of GDP declined between 2009 and 2014 but remained above 20% in both cases.In recent years, my country’s foreign trade has developed rapidly, and investment projects in China have gradually increased. From such results, it can be seen that the environment provided by China for foreign businessmen should be favorable, and the trade facilitation index should also be high. However, comprehensively comparing the trade facilitation development level of 57 countries in 2011, my country belongs to the middle and low level of trade facilitation. This shows that China still has great opportunities for development in this area.The assessment of trade facilitation comes from the Global Competitiveness Report. China’s efforts in trade facilitation reforms need to be compared with the level of trade facilitation in recent years. However, since the basic parameters shown in the Global Competitiveness Report are frequently updated, one can only compare the level of trade facilitation between China and other countries horizontally. Figure6 shows the 2011 TFI indices by country:Figure 6
2011 TFI index by country.It can be seen that China’s comprehensive trade facilitation index is 0.68, ranking 33rd among the 57 sample countries, which is lower than the average level of 0.71 and is far behind developed countries. According to estimates, China’s trade facilitation index in 2010 was 0.71, slightly higher than the index in 2011. This may be because the intensification of the European debt crisis has affected China's trade facilitation perception data. This shows that China’s trade facilitation level still has a lot of room for development.The design of trade facilitation indicators is more of a subjective process and cannot correctly reflect whether the development level of my country’s trade facilitation is balanced. From the actual development situation, my country’s trade facilitation level is seriously out of balance. The level of trade facilitation in the eastern coastal areas and some areas with relatively well-developed foreign trade business is significantly higher than that in the inland areas.In order to get a glimpse of the structure of the value added of the economy’s export trade in the industrial distribution, it is necessary to calculate the value added of export trade by industry. Especially in terms of economic growth, the difference in the distribution of the added value of the industry’s export trade will have different impacts on economic growth. In order to study the impact of the industrial distribution structure of value added on economic growth, it is necessary to consider the value-added data of export trade at the industry level. According to the idea of calculating the total value added of foreign trade, a model is used to calculate the total value added of foreign trade, and the three-country model is adopted, but each country has two departments. The accounting logic of the added value of export trade at the industry level still needs to be based on the thinking at the total accounting level.In the stage of intraproduct trade, with the deepening of economic globalization, more and more intermediate goods are imported, and the export commodities of the economy contain a large amount of value created by American intermediate goods. Due to the obvious differences in the economic scale of different economies and the degree of their embeddedness in the world division of labor, the proportion of the added value of export trade in the total export trade has heterogeneity in the distribution of various economies. The added value of export trade only reflects the benefits that the economy obtains from export trade. The larger the scale of export trade, the higher the added value content of export trade. Just as the volume of export trade only reflects the scale of a country’s export trade, the added value of export trade can only reflect the scale of the economic benefit brought by export trade. However, the distribution of industries that create added value in export trade determines the quality of an economy’s export trade, which in turn has an impact on economic growth. Some scholars believe that the impact of trade on the economic growth of an economy depends on the export trade structure of the economy itself. Due to the large difference in the added value of export commodities in various industries, the added value of high-tech commodities is relatively high, and the high-tech commodity units exported by the economy will obtain greater benefits. However, the export of traditional industries does not have much added value. The positive impact on economic growth is limited. Therefore, it is necessary to further observe the industrial composition of the added value of each country’s export trade. Figures7 and 8 show the structure of export value added for some economies.Figure 7
Export value-added structure of some Economies1.Figure 8
Export value-added structure of some Economies2.China, Brazil, Germany, and South Korea represent the BRICS countries, economies caught in the “middle-income trap,” advanced economies, and emerging economies, respectively. The research on these four typical economies shows that the sectoral structure of the added value of export trade can reflect the industry composition of the added value of export trade of these four economies to a certain extent. In the value-added composition of Brazil’s export trade, the agricultural sector contributed the most, followed by the low-tech sector. The value added by exports in the agricultural sector and the low-tech sector accounted for half of the value added to Brazil’s export trade. The proportion of the added value of China’s agricultural exports has dropped to the level of developed countries. Due to China’s large population, the output of the agricultural sector is mainly used to meet the needs of the country’s residents. The need for government policies to guide and maintain domestic food security has resulted in low total exports from China’s agricultural sector. The added value of Germany’s export trade is mainly concentrated in the medium and high-tech fields, the high-tech field, and the service industry. The added value created by these three fields accounts for 80% of the added value of Germany’s export trade. The situation in South Korea is similar to that of Germany. The share is slightly higher than that of Germany, and the share of services is slightly lower than that of Germany. Overall, the industry composition of the added value of Germany’s export trade is more scientific and reasonable.


## 5. Compression Algorithm Comprehensive Analysis of Nanomaterials Import and Export Trade Economic Scheduling

### 5.1. Algorithm Analysis of Compression Algorithms

Linde, Buzo, and Gray extended the Lloyd-I algorithm of scalar quantization to multidimensional space, often called the LBG algorithm and also called the GLA algorithm or c-means algorithm in some literature. The flowchart of the LBG algorithm is shown in Figure9.Figure 9
LBG algorithm flowchart.The specific steps of the LBG algorithm are as follows:(1)
Initialization conditions: give quantization level (codebook size)Z, distortion control threshold ε, initial code N0, and training sequence:(1)T0=My,y=1,2,…,Y,Y≥Z.(2)
Codebook:(2)Nx=Nix,i=1,2,…,Z.Find the minimum distortion segmentation of the training sequenceT0 starting from the number of iterations x = 0; that is, if it is true dMy,Nix≤dMy,Nzx for all z = 1, 2, ..., Z, then OK My∈Ri. In the formula, the measure of distortion often uses Euclidean distance; namely,(3)dMy,Nix=1K∑k=1Kmyk−nikx2.Nix is the kth component of the codebook codeword obtained by Nx, the xth iteration. nikx and Myk are the kth component My.(3)
Calculate average distortion(4)Dx=1X∑y=1Ymind1≤i≤ZMy,Nix.If(5)Dx−1−DxDx≤ε,(andD−1=−∞) Nx of the allowable average distortion D, then it is Dx≤ output as a codebook and makes the iterative process; otherwise, it goes to the next step.(4)
Find the arithmetic mean or geometric center of each divisionGi, i = 1, 2, ..., Z, take x = x + 1, and make a new codebook:(6)Nx+1=Ni,x+1=Gi,i=1,2,…,Z.Back to Step2, when the vector quantizer is designed using the above LBG algorithm, the determination of the initial codebook is very important, and a random selection method, a splitting method, or a statistical analysis method can be used.It should be emphasized that the LBG algorithm has three main drawbacks: (1) Finding the coded word closest to the training vector in the codebook requires a lot of memory and requires tedious calculations in the optimal division stage of each iteration. (2) The choice of the initial codebook will affect the convergence speed of the codebook training and the performance of the final codebook. (3) The adaptability of the codebook is not strong. The first disadvantage of codebook design can be overcome by various fast code word search algorithms, but these algorithms do not improve the performance of the codebook. The second disadvantage is that since the LBG algorithm is a descent algorithm, the average deviation value decreases (or at least stays the same) at each iteration. However, each iteration usually results in only local changes to the codebook; that is, the new codebook is unlikely to change significantly compared to the old codebook after each iteration. Therefore, after the initial codebook selection, the algorithm only obtains the local optimal codebook; that is, the LBG algorithm usually does not obtain the global optimal codebook.


### 5.2. Comprehensive Analysis of Nanomaterials

One material that has a high solubility in aqueous solutions is PVA (polyvinyl alcohol), which is also a long-chain polymer material. In this synthesis method, PVA materials can be used to control the morphology of nanomaterials. The effect of the amount of PVA on the morphology of copper sulfide nanomaterials was studied. The reaction temperature had no obvious effect on the formation of copper sulfide nanocrystals in this system, so the focus of the study was on the formation of copper sulfide crystals with different amounts of PVA.The process of producing ultrastructured nanomaterials from copper sulfide consists of three steps. In the first step, regular hexagonal nanosheets are created. This step includes the following three reactions:(7)Cu2+2S2O32−→CuS2O322−2H2O+CuS2O322−→2SO42−+Cu2++2H2SH2S+Cu2+→CuS+2H+Step 2.
Grow small nanosheets. In the first stage, nanosphere-like scaffolds were grown, and in the second stage, small hexagonal nanosheets formed dendritic extensions on the large scaffold. In the case of nanofibers, large nanosheets continue to grow in a specific unidirectional direction at this stage, while small nanosheets grow on large scaffolds. At this stage, PVA still plays a role in shape control, as the smaller nanosheets have the same shape as the larger scaffold.Step 3.
In this step, the nanosheets reach the minimum and maximum density, no new nanosheets are formed, and the final products (CuS nanospheres and superstructured nanotubes) are formed.
PVA (polyvinyl alcohol) is a highly water-soluble material. PVP (polyvinylpyrrolidone) is a very common material for controlling nanotopography in general reactions, but the use of PVA in nanomaterial synthesis is very rare.


### 5.3. Comprehensive Analysis of Import and Export Trade

In the real world, oligopolies have few competitors in the international market. For some specific products, there are only a few suppliers in the international market. These firms will compete in oligopoly. For example, the top 10 semiconductor companies and the top 10 chemical companies in the world both control more than their respective industries. The top ten tire manufacturers hold more than one share of the global tire market. Since McDonnell Douglas was acquired by Boeing, the only global aviation manufacturing companies are Airbus and Boeing. Oligopolistic competition in the Chinese market is not the same as competition among international giants. When the markets of various countries are divided, oligarchs will formulate different competition plans for the markets of various countries, and then international trade between countries will occur. Countries with more companies tend to choose exports.The optimal strategy of the oligarchs is to export. Partial equilibrium models are used to analyze how industry output and trade balances change with the number of Chinese firms. For simplicity, assume that the two countries have the same number of firmsm and that each firm is symmetric. And all have the same production technology with constant returns to scale, the transportation cost per unit product is ξ, and the production cost per unit product is c. Then the profits of the representative companies of the two countries are(8)πX=PXQXD+QYFqXD−cqXD+PYQXF+QYDqXF−cqπY=PYQXF+QYDqYD−cqYD+PXQXD+QYFqYF−cqYF−ξqYF−FY.The profit maximization condition for the firm in countryX is(9)PX+PX1QXD+QYFqXD−c=0PY+PY1QXF+QYDqXF−c−ξ=0.The profit maximization condition for the firm in countryY is(10)PY+PY1QXF+QYDqYD−c=0PX+PX1QXD+QYFqYF−c−ξ=0.Since the assumption of constant returns to scale has been made, the optimal sales volume of each company is independent of the optimal sales volume of other countries; that is, the two markets can be studied separately. Assume thatσY the market share of a single firm in country Y in country X is(11)σY=QYFmQXD+QYF=qYFmqXD+qYF.The demand elasticity of the market in countryX is(12)ε=−PXQXD+QYFPX1=−PXmqXD+qYFPX1.It can be obtained by(13)PX1=−PXmqXD+qYFε.According to the assumption, the market share of the representative enterprise of countryX in country Y can be calculated as(14)σX=c+ξ1−mεm2c+ξ.Because the firms of the two countries are symmetrical, the elasticity of demand and the number of consumers are the same, so the market shares of the representative firms of the two countries are also the same. Obviously, the number of enterprises in a country is related to the export volume of that country’s products. If the number of firms in countryY is fixed, then an increase in the number of firms in country X increases country X’s total exports. Likewise, if the number of firms in country X is fixed, an increase in the number of firms in country Y will also increase country Y’s total exports.


## 5.1. Algorithm Analysis of Compression Algorithms

Linde, Buzo, and Gray extended the Lloyd-I algorithm of scalar quantization to multidimensional space, often called the LBG algorithm and also called the GLA algorithm or c-means algorithm in some literature. The flowchart of the LBG algorithm is shown in Figure9.Figure 9
LBG algorithm flowchart.The specific steps of the LBG algorithm are as follows:(1)
Initialization conditions: give quantization level (codebook size)Z, distortion control threshold ε, initial code N0, and training sequence:(1)T0=My,y=1,2,…,Y,Y≥Z.(2)
Codebook:(2)Nx=Nix,i=1,2,…,Z.Find the minimum distortion segmentation of the training sequenceT0 starting from the number of iterations x = 0; that is, if it is true dMy,Nix≤dMy,Nzx for all z = 1, 2, ..., Z, then OK My∈Ri. In the formula, the measure of distortion often uses Euclidean distance; namely,(3)dMy,Nix=1K∑k=1Kmyk−nikx2.Nix is the kth component of the codebook codeword obtained by Nx, the xth iteration. nikx and Myk are the kth component My.(3)
Calculate average distortion(4)Dx=1X∑y=1Ymind1≤i≤ZMy,Nix.If(5)Dx−1−DxDx≤ε,(andD−1=−∞) Nx of the allowable average distortion D, then it is Dx≤ output as a codebook and makes the iterative process; otherwise, it goes to the next step.(4)
Find the arithmetic mean or geometric center of each divisionGi, i = 1, 2, ..., Z, take x = x + 1, and make a new codebook:(6)Nx+1=Ni,x+1=Gi,i=1,2,…,Z.Back to Step2, when the vector quantizer is designed using the above LBG algorithm, the determination of the initial codebook is very important, and a random selection method, a splitting method, or a statistical analysis method can be used.It should be emphasized that the LBG algorithm has three main drawbacks: (1) Finding the coded word closest to the training vector in the codebook requires a lot of memory and requires tedious calculations in the optimal division stage of each iteration. (2) The choice of the initial codebook will affect the convergence speed of the codebook training and the performance of the final codebook. (3) The adaptability of the codebook is not strong. The first disadvantage of codebook design can be overcome by various fast code word search algorithms, but these algorithms do not improve the performance of the codebook. The second disadvantage is that since the LBG algorithm is a descent algorithm, the average deviation value decreases (or at least stays the same) at each iteration. However, each iteration usually results in only local changes to the codebook; that is, the new codebook is unlikely to change significantly compared to the old codebook after each iteration. Therefore, after the initial codebook selection, the algorithm only obtains the local optimal codebook; that is, the LBG algorithm usually does not obtain the global optimal codebook.


## 5.2. Comprehensive Analysis of Nanomaterials

One material that has a high solubility in aqueous solutions is PVA (polyvinyl alcohol), which is also a long-chain polymer material. In this synthesis method, PVA materials can be used to control the morphology of nanomaterials. The effect of the amount of PVA on the morphology of copper sulfide nanomaterials was studied. The reaction temperature had no obvious effect on the formation of copper sulfide nanocrystals in this system, so the focus of the study was on the formation of copper sulfide crystals with different amounts of PVA.The process of producing ultrastructured nanomaterials from copper sulfide consists of three steps. In the first step, regular hexagonal nanosheets are created. This step includes the following three reactions:(7)Cu2+2S2O32−→CuS2O322−2H2O+CuS2O322−→2SO42−+Cu2++2H2SH2S+Cu2+→CuS+2H+Step 2.
Grow small nanosheets. In the first stage, nanosphere-like scaffolds were grown, and in the second stage, small hexagonal nanosheets formed dendritic extensions on the large scaffold. In the case of nanofibers, large nanosheets continue to grow in a specific unidirectional direction at this stage, while small nanosheets grow on large scaffolds. At this stage, PVA still plays a role in shape control, as the smaller nanosheets have the same shape as the larger scaffold.Step 3.
In this step, the nanosheets reach the minimum and maximum density, no new nanosheets are formed, and the final products (CuS nanospheres and superstructured nanotubes) are formed.
PVA (polyvinyl alcohol) is a highly water-soluble material. PVP (polyvinylpyrrolidone) is a very common material for controlling nanotopography in general reactions, but the use of PVA in nanomaterial synthesis is very rare.


## 5.3. Comprehensive Analysis of Import and Export Trade

In the real world, oligopolies have few competitors in the international market. For some specific products, there are only a few suppliers in the international market. These firms will compete in oligopoly. For example, the top 10 semiconductor companies and the top 10 chemical companies in the world both control more than their respective industries. The top ten tire manufacturers hold more than one share of the global tire market. Since McDonnell Douglas was acquired by Boeing, the only global aviation manufacturing companies are Airbus and Boeing. Oligopolistic competition in the Chinese market is not the same as competition among international giants. When the markets of various countries are divided, oligarchs will formulate different competition plans for the markets of various countries, and then international trade between countries will occur. Countries with more companies tend to choose exports.The optimal strategy of the oligarchs is to export. Partial equilibrium models are used to analyze how industry output and trade balances change with the number of Chinese firms. For simplicity, assume that the two countries have the same number of firmsm and that each firm is symmetric. And all have the same production technology with constant returns to scale, the transportation cost per unit product is ξ, and the production cost per unit product is c. Then the profits of the representative companies of the two countries are(8)πX=PXQXD+QYFqXD−cqXD+PYQXF+QYDqXF−cqπY=PYQXF+QYDqYD−cqYD+PXQXD+QYFqYF−cqYF−ξqYF−FY.The profit maximization condition for the firm in countryX is(9)PX+PX1QXD+QYFqXD−c=0PY+PY1QXF+QYDqXF−c−ξ=0.The profit maximization condition for the firm in countryY is(10)PY+PY1QXF+QYDqYD−c=0PX+PX1QXD+QYFqYF−c−ξ=0.Since the assumption of constant returns to scale has been made, the optimal sales volume of each company is independent of the optimal sales volume of other countries; that is, the two markets can be studied separately. Assume thatσY the market share of a single firm in country Y in country X is(11)σY=QYFmQXD+QYF=qYFmqXD+qYF.The demand elasticity of the market in countryX is(12)ε=−PXQXD+QYFPX1=−PXmqXD+qYFPX1.It can be obtained by(13)PX1=−PXmqXD+qYFε.According to the assumption, the market share of the representative enterprise of countryX in country Y can be calculated as(14)σX=c+ξ1−mεm2c+ξ.Because the firms of the two countries are symmetrical, the elasticity of demand and the number of consumers are the same, so the market shares of the representative firms of the two countries are also the same. Obviously, the number of enterprises in a country is related to the export volume of that country’s products. If the number of firms in countryY is fixed, then an increase in the number of firms in country X increases country X’s total exports. Likewise, if the number of firms in country X is fixed, an increase in the number of firms in country Y will also increase country Y’s total exports.


## 6. Conclusion

The relationship between export trade and economic growth is related not only to the survival and development of enterprises but also to the power of regional economic growth. Therefore, it is particularly important to study the relationship between export trade and total factor productivity under the background that the country advocates slowing down the rate of economic growth and attaching importance to the quality of economic growth. If export trade can promote the improvement of total factor productivity, the active integration of countries, enterprises, and regions into the international trade process will become an inexhaustible driving force for sustainable economic development.



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*Source: 8484956-2022-08-11.xml*