# Reconstruction and Utilization of Energy Economy Information Resources against Humanistic Background

Authors: Lan Du
Journal: International Transactions on Electrical Energy Systems
 (2022)

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## Abstract

In order to explore the problem of reconstruction of energy economic information resources, the author proposes a reconstruction of energy economic information resources based on the background of digital humanities. This method recommends key technical problems and solutions based on information represented by the digital humanities background and explores research on the reconstruction of economic information resources. The research has shown that the reconstruction of energy and economic information resources based on the background of digital humanities is about 30% more efficient than traditional methods. The concept of sustainable energy development is a continuation of the concept of “sustainable development,” emphasizing the improvement of energy efficiency and using it as a way to improve overall economic efficiency and reduce energy-related environmental costs, so that economic development will not harm the environment, and future generations have equal opportunities for development.


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## Body

## 1. Introduction

At the turn of the century, mankind is undergoing an industrial revolution that fundamentally changes the way of production, working, life, and learning [1]. In the future, the focus of competition in the world economy will be on the emerging information industry, because it not only is a new bright spot in the economic development of various countries, but also will become a new driving force for sustainable economic development through integration with traditional industries [2]. The ever-changing information technology has made the economic development of all countries in the world more closely linked. The flow of information resources that breaks the limitations of time and space makes the progress and development of human society closely related [3].Resources are divided into social resources and natural resources [4]. Social resources include manpower, knowledge, information, science, and technology, as well as accumulated capital and social wealth. Their greatest feature is variability. Natural resources include natural environment, land, forests, grasslands, precipitation, rivers, lakes, energy, and minerals. Their essential feature is being limited.In nature, any form of material that can be provided to human life and production needs can be called a natural resource, and it is the basis for human survival [5]. For example, solar radiation, atmosphere, water, organisms, land, various minerals, and energy are natural resources [6]. Those that have economic value to human life and production are called natural resources; for example, at a conference held by the United Nations Environment Programme in Kenya, natural resources were defined as “resources that can generate economic value under certain time and space conditions to improve human beings, elements and conditions of the natural environment for present and future well-being.” With the development of human society and the progress of technology and economy, human beings continue to expand the scope of resource utilization and continue to seek and develop new resources to meet their growing needs.Agile manufacturing is an enterprise that adapts to the rapidly changing market needs, being the enterprise model strategy chosen for maximum competitiveness [7].In modern enterprises, the bridge between the two is the economic information related to the enterprise, so the information becomes an important resource for managing the enterprise [8]. On the other hand, the revolution of information technology has made a huge impact on modern enterprises, and technological innovation will inevitably lead to organizational innovation, including the emergence of a series of new production management systems such as real-time manufacturing systems and flexible production systems, as shown in Figure 1.Figure 1
Economic information flow in modern enterprise management.


## 2. Literature Review

Okafor et al. said that at present all countries in the world attach great importance to the development of advanced manufacturing technology and related production model technologies, and many multinational companies have applied them to achieve the integration of design, manufacturing, marketing, and after-sales service, strengthening the monopoly in the international market [9]. According to Sindi, in the development of Boeing 777 airliner, due to the use of advanced product development and design technology [10], the development cycle was shortened by more than 40% from the past 8 to 9 years to 4.5 years, the cost was reduced by 25%, the error rework rate was reduced by 75%, and the user satisfaction was also greatly improved. Hou stated that, by applying modern integrated manufacturing system technology, the car development cycle has been shortened from the original 48 months to 24 months, the number of crash tests has been reduced from the original hundreds to dozens, and the application of e-commerce technology has reduced the cost of sales by 10% [11]. Fu mentioned that, after the application of advanced comprehensive automation technology, the enterprise benefit was increased by 5% to 8%, and the labor productivity was increased by 10% to 15% [12].The core technology of robotics and automation process equipment has always been valued by countries all over the world. Special robots such as underwater robots, micro robots, medical robots, and human-injuring robots for future services; ultraprecision machining equipment for national defense, aviation, and aerospace; intelligent large-scale construction machinery for infrastructure construction; and high-precision, high-efficiency, low-cost, and high-flexibility basic manufacturing equipment for manufacturing have become the focus of current international manufacturing research and development.Since the 1990s, the process of globalization has accelerated significantly, and it has become the most prominent feature of today’s world. Erdoğan et al. believe that there is currently no unified definition of the concept of “globalization” [13]. Ma et al. believe that the more popular ones come from authoritative institutions such as the International Monetary Fund (IMF) and some well-known Western scholars [14]. In an article published in the Spring 1998 issue of Foreign Policy, Harvard University economist Jeffrey Sachs argued that globalization has the following four levels of meaning: (1) promotion of faster economic growth; (2) impact on macroeconomic stability; (3) impact on income distribution; (4) impact on national and international politics. The above viewpoints are quite representative, reflecting the understanding of the “orthodox” theory of Western scholars on globalization.Today, the globalization of various sectors and activities of human economic society is driven by two forces: one is information technology, and the other is trade liberalization. Informatization and globalization are closely linked, and globalization in the nineteenth century was driven by falling transportation costs, and now it is driven by falling communication costs. With the rapid decline in the cost of communication and data processing and the natural barriers of time and space that divide countries’ markets, inexpensive and efficient communication networks allow companies to locate different parts of their production process in different countries, while keeping the parts together in close contact. As described by Melnyk, exponentially increasing information and communication networks enable the establishment of various international and transnational networks and associations, which often lead to the formation of more substantive organizational structures and the huge flow of information resulting in thousands of global business enterprises, international organizations, and intergovernmental organizations [15].The process of market exchange is the flow of people, money, goods, and information, so people’s commercial exchange activities depend on the means of transportation and information transmission. In a market economy, people make judgments and actions based on market information, and the degree, method, and scope of market information display and transmission directly affect the role of the market mechanism. When the information system is underdeveloped, the market as a whole must be a black box for individual subjects, and the subject’s understanding of the market can only rely on perceptual, superficial, and fragmented and unsystematic understanding. This leads to the spontaneity of market regulation and the blindness and shortsightedness of the behavior of market players. That is, information technology makes the market more transparent and reduces the need for face-to-face contact between producers and consumers. Of course, market players must ultimately allocate resources rationally to ensure sustained and stable economic growth. This means that to realize the free flow of production factors in the market, especially in the international market, on the one hand, various institutional and artificial obstacles must be removed, and on the other hand, there must be technical guarantees. The development of the information industry has continuously opened up the possibility of realizing this kind of mobility in technology, making it adapt to the needs of the development of productive forces in modern society, enabling market entities to better actively adapt to changes in the market environment, and improving the ability to resist economic fluctuation risks.


## 3. Methods

### 3.1. The Game Theory Model of Agile Resource Selection

Based on taking manufacturing resource nodes as isolated individuals, many academic fields combine agent technology with game theory tools and use game models to describe resource selection and reconstruction problems [16]. The basic concepts of game theory include players, actions, information, strategies, payoffs (utilities), outcomes, and equilibria, where players depend on choosing actions (or strategies) to maximize their level of payoffs (utility), the “rational man” hypothesis.The division of game theory can be carried out from two different perspectives. The first perspective is the sequence of actions of the participants. From this point of view, the game can be divided into static game and dynamic game. The second angle of dividing the game is the player’s knowledge about the characteristics, strategic space, and payoff function of other players (opponents). From this perspective, the game can be divided into complete information game and incomplete information game. Combining the divisions of the two angles, we obtain four different types of games and their corresponding equilibrium concepts, as shown in Table1.Table 1
Types of games and their equilibria.
InformationSequence of actionsStaticDynamicFull informationComplete information static gameComplete information dynamic gameIncomplete informationIncomplete information static gameIncomplete information dynamic gameFor the convenience of studying the problem, think of resource reduction as a device or work center. The resource agent logically reconstructs local and remote resources according to the processing needs of subtasks, and the generated feasible strategy set is expressed as follows:(1)Fi=Fi′:δ′≤1−∑i≠1δiki,where k is the resource index (k = 1, …, M), M is the total number of candidate resources, t is the time slot index (t = l, …, T), and T is the number of planning cycles for task completion, as in the following equations:(2)δikt=1,subtaskiis assigned tokat timet,(3)δikt=0,other.The payout function is shown in the following equation:(4)ui=u′:ui′=−∑i=1rλikt+πikt,which is shown in the following equations:(5)λikt=fi,k,(6)λikt=0.The unit transportation cost (T2, T3) from k to resource k+1 satisfy the equation (7)-Equation (10)(7)δdz=0,t≺t1,(8)δdz=1,other,(9)δik+1t=0,t≺t3,(10)δik+1t=1,other.


### 3.2. Evaluation Index System

A specific requirementM must be realized in a certain manufacturing system S [17]. If there is no enterprise alliance, the enterprise undertaking this task must be a specific enterprise in S, Sz, etc. or beyond the legal concept of enterprise; instead, it is a manufacturing subsystem constructed by some selected resource elements, such as the “independent manufacturing island” proposed by Zhang Shu Jiaotou of Tongji University. The ability of the corresponding manufacturing resources E, Ez, etc. to realize the requirement M can be evaluated and calculated from 7 indicators. The hierarchical structure of the elements is shown in Table 2 and Table 3.Table 2
Structural layering.
Put into production/PProduction/STransport/rQuality/QTime/TCost/CTime/TQuality/QTime/TCost/CQuality/QTable 3
Element hierarchy.
Time/TCost/CQuality/QPut into production/PProduction/SQuality/QPut into production/PProduction/STransport/RQuality/QThe evaluation of manufacturing resources covers a wide range, far beyond these seven evaluation indicators; in practice, according to specific evaluation requirements,n evaluation indicators are set. The index system is layered, which is a classification mainly based on the degree of relevance and importance, and establishes a category relationship. The number of hierarchical layers will generally increase with the increase of indicators, and it can also be continuously expanded. In theory, it can also be extended to nondeterministic integer layers, such as n layers.Since the cost index of quality compliance is a possible additional cost of quality compliance, which is different from the normal cost of production, it is taken out as an independent indicator [18]. The vector method establishes the agility evaluation of manufacturing resources; the original data must be accurately and quantitatively obtained from the objective reality of the manufacturing resources themselves; and most of them are reflected on the basis of time, cost, and other values that are easier to quantify and have units of measurement. They are difficult to conceptualize and descriptive indicators of manufacturing resources that require abstract judgment are used as raw data.As can be seen in Table4, the competitiveness of manufacturing subsystems covers a wide range, these seven evaluation indicators can be surpassed, n evaluation indicators are set according to specific evaluation requirements, n importance weights are assigned, and finally a more precise G value can be obtained. For a specific market demand, G is a relative index, reflecting the market competitiveness of each manufacturing subsystem in the manufacturing system. In the absence of network alliances, it reflects the agility and competitiveness of manufacturing enterprises; the closer G to 1, the stronger the ability of the enterprise to meet a certain market demand; if G is closer to 0, it shows that the company's ability to meet a certain market demand is weaker.Table 4
Two-layer weights.
ApAsAyAqAt1Ac1At2Ac2At3Ac3Aq1A1A2A3A4A5A6A7


### 3.3. Energy Forecast

According to the period length of the energy forecast, it can be divided into long-term forecast, medium-term forecast, and short-term forecast [19]. Long-term energy forecasts are forecasts for long-term planning and strategic decision-making. The forecast period generally refers to 10 to 15 years or more. The results of long-term forecasts of supply and demand, while less precise or accurate, can raise extremely important strategic questions. Long-term energy forecasting is of great significance for formulating long-term energy development plans, arranging energy construction projects in advance, and meeting social needs.Medium-term energy forecast refers to the forecast of the development of energy supply and demand in the medium term, which serves for the formulation of medium-term planning and decision-making [20]. The forecast period is generally 5 to 10 years, and the accuracy of the mid-term energy forecast is higher than that of the long-term forecast, but the factors affecting energy supply and demand in the mid-term forecast are difficult to accurately grasp; therefore, energy supply and demand forecasts always come with various assumptions.Short-term forecast of energy refers to the forecast of the development of energy supply and demand in the short term [21]. The forecast period is generally 1 year to 5 years. Such forecasts are the scientific basis for formulating five-year plans and annual plans. Because the forecast time is relatively short, the possible development and changes of the national economy are relatively clear, and the energy structure is unlikely to change greatly, the results of energy supply and demand forecasts need to have high precision or accuracy. This prediction result directly affects the current planning arrangement and has a direct guiding role for the development of energy and national economy.Macro-energy forecast refers to the forecast of the energy supply and demand prospects of a country or region [22]. Macro-energy forecasting is to take the general blueprint of social and economic development as the object of investigation and predict the total demand and supply of energy. Sometimes, for a certain need, a specific energy product can be forecasted, such as forecasting the total demand and total supply of raw coal—for example, the various possibilities of the development of energy supply and demand and their impact, and the policies or decisions currently determined and to be implemented. When conducting qualitative energy prediction, it is mainly through the analysis of historical data and the study of future energy supply and demand conditions, with the forecaster’s rich experience and logical reasoning ability, in order to speculate and judge the nature of energy supply and demand in the future.Quantitative forecasting of energy refers to the determination of the future quantitative performance of energy supply and demand, such as the forecast of total energy production and total demand. Energy supply and demand forecasts are based on historical data, statistics, and other information; using mathematical or other analytical techniques, build a model that can represent the quantitative relationship, and use it to calculate the quantity that energy supply and demand may represent in the future. Commonly used quantitative forecasting techniques include exponential smoothing forecasting method, unit output value and output energy consumption forecasting method, energy elasticity coefficient forecasting method, input-output forecasting method, sectoral analysis comprehensive forecasting method, and some other methods of econometrics [23].Energy timing forecast refers to the determination of the future performance time of energy supply and demand, for example, the time when a new energy source (such as solar energy) can be applied to production, the time for product replacement, and the time for a certain decision to achieve the expected effect. Energy timing prediction generally relies on people's logical reasoning and judgment. Therefore, the methods applied to energy forecasting are mainly various survey analysis methods and analogy methods. The “growth” curve method can be used when making timed forecasts for the replacement of energy enterprises’ products.The target constraints of energy forecasting can be divided into normative forecasting and exploratory forecasting. The principle of the system means that when forecasting energy supply, we should regard energy supply and factors that affect energy supply, such as economic development, energy resources, transportation capacity, and ecological environment, as a system. We regard both the energy supply system and the energy demand system as having specific functions, a whole composed of elements that are organically connected to each other. Through comprehensive investigation and research, we propose alternative solutions to build predictive models. This way, it is possible to analyze the problem comprehensively, overcome one-sidedness, and improve the scientificity of prediction [24].


### 3.4. Theoretical Methods for Comprehensive Energy Forecasting

The grey forecasting method is a loose systematic method, which abandons the link of system structure analysis; by directly accumulating the original data of energy supply and demand, the overall law of the seeking system is generated, and an exponential growth model is constructed. This method can construct different prediction models according to the different characteristics of the original data, such as the grey topology model applied to the data with changing growth rate or noise data and the model containing multiple consumption factors; therefore, the prediction range of this method is very wide, both long-term and short-term predictions can be used, the amount of data required is not large, and it can also be used when data is lacking.Artificial neural network (Ann) is a complex network system which is widely connected by a large number of simple artificial neurons to imitate the human brain neural network. It establishes the nonlinear input and output model of the system on the basis of a given large number of input and output signals and parallel processing of data; in essence, it is to give a large amount of data to an artificial neural network constructed according to a certain structural form and excitation function for learning, and then given a future input, the computer judges the expected output based on past “experience.” This method is actually a black-box simulation of the system, which is suitable for dynamic consumption forecast values and dynamic training systems, and obtains high forecasting accuracy, because its “black-box operation” is more helpful for formulating policies and improving utilization; therefore, this method is suitable for long-term and short-term consumption forecasting [25].The system dynamics method regards the object under study as a dynamic system with a complex feedback structure and changes with time and draws a system flow chart representing the system structure and dynamics through system analysis; then, the relationship between the variables is quantified, and the structural equation of the system is established, so that the computer can be used for simulation experiments to predict the future of the system. The application effect of this method is closely related to the forecaster’s professional knowledge, practical experience, and system analysis and modeling ability. Through analysis and establishment of system model, the system can be whitened, and the hidden law of the system can be found out through computer dynamic simulation. This method can not only predict the long-term forecast objects, but also find out the influencing factors and function relationships of the system, which is beneficial to system optimization. However, the system analysis process is complex, the workload is large, and the ability of analysts is relatively high, so it is not suitable for short-term forecasting. For long-term forecasting, its advantages are very obvious.


## 3.1. The Game Theory Model of Agile Resource Selection

Based on taking manufacturing resource nodes as isolated individuals, many academic fields combine agent technology with game theory tools and use game models to describe resource selection and reconstruction problems [16]. The basic concepts of game theory include players, actions, information, strategies, payoffs (utilities), outcomes, and equilibria, where players depend on choosing actions (or strategies) to maximize their level of payoffs (utility), the “rational man” hypothesis.The division of game theory can be carried out from two different perspectives. The first perspective is the sequence of actions of the participants. From this point of view, the game can be divided into static game and dynamic game. The second angle of dividing the game is the player’s knowledge about the characteristics, strategic space, and payoff function of other players (opponents). From this perspective, the game can be divided into complete information game and incomplete information game. Combining the divisions of the two angles, we obtain four different types of games and their corresponding equilibrium concepts, as shown in Table1.Table 1
Types of games and their equilibria.
InformationSequence of actionsStaticDynamicFull informationComplete information static gameComplete information dynamic gameIncomplete informationIncomplete information static gameIncomplete information dynamic gameFor the convenience of studying the problem, think of resource reduction as a device or work center. The resource agent logically reconstructs local and remote resources according to the processing needs of subtasks, and the generated feasible strategy set is expressed as follows:(1)Fi=Fi′:δ′≤1−∑i≠1δiki,where k is the resource index (k = 1, …, M), M is the total number of candidate resources, t is the time slot index (t = l, …, T), and T is the number of planning cycles for task completion, as in the following equations:(2)δikt=1,subtaskiis assigned tokat timet,(3)δikt=0,other.The payout function is shown in the following equation:(4)ui=u′:ui′=−∑i=1rλikt+πikt,which is shown in the following equations:(5)λikt=fi,k,(6)λikt=0.The unit transportation cost (T2, T3) from k to resource k+1 satisfy the equation (7)-Equation (10)(7)δdz=0,t≺t1,(8)δdz=1,other,(9)δik+1t=0,t≺t3,(10)δik+1t=1,other.


## 3.2. Evaluation Index System

A specific requirementM must be realized in a certain manufacturing system S [17]. If there is no enterprise alliance, the enterprise undertaking this task must be a specific enterprise in S, Sz, etc. or beyond the legal concept of enterprise; instead, it is a manufacturing subsystem constructed by some selected resource elements, such as the “independent manufacturing island” proposed by Zhang Shu Jiaotou of Tongji University. The ability of the corresponding manufacturing resources E, Ez, etc. to realize the requirement M can be evaluated and calculated from 7 indicators. The hierarchical structure of the elements is shown in Table 2 and Table 3.Table 2
Structural layering.
Put into production/PProduction/STransport/rQuality/QTime/TCost/CTime/TQuality/QTime/TCost/CQuality/QTable 3
Element hierarchy.
Time/TCost/CQuality/QPut into production/PProduction/SQuality/QPut into production/PProduction/STransport/RQuality/QThe evaluation of manufacturing resources covers a wide range, far beyond these seven evaluation indicators; in practice, according to specific evaluation requirements,n evaluation indicators are set. The index system is layered, which is a classification mainly based on the degree of relevance and importance, and establishes a category relationship. The number of hierarchical layers will generally increase with the increase of indicators, and it can also be continuously expanded. In theory, it can also be extended to nondeterministic integer layers, such as n layers.Since the cost index of quality compliance is a possible additional cost of quality compliance, which is different from the normal cost of production, it is taken out as an independent indicator [18]. The vector method establishes the agility evaluation of manufacturing resources; the original data must be accurately and quantitatively obtained from the objective reality of the manufacturing resources themselves; and most of them are reflected on the basis of time, cost, and other values that are easier to quantify and have units of measurement. They are difficult to conceptualize and descriptive indicators of manufacturing resources that require abstract judgment are used as raw data.As can be seen in Table4, the competitiveness of manufacturing subsystems covers a wide range, these seven evaluation indicators can be surpassed, n evaluation indicators are set according to specific evaluation requirements, n importance weights are assigned, and finally a more precise G value can be obtained. For a specific market demand, G is a relative index, reflecting the market competitiveness of each manufacturing subsystem in the manufacturing system. In the absence of network alliances, it reflects the agility and competitiveness of manufacturing enterprises; the closer G to 1, the stronger the ability of the enterprise to meet a certain market demand; if G is closer to 0, it shows that the company's ability to meet a certain market demand is weaker.Table 4
Two-layer weights.
ApAsAyAqAt1Ac1At2Ac2At3Ac3Aq1A1A2A3A4A5A6A7


## 3.3. Energy Forecast

According to the period length of the energy forecast, it can be divided into long-term forecast, medium-term forecast, and short-term forecast [19]. Long-term energy forecasts are forecasts for long-term planning and strategic decision-making. The forecast period generally refers to 10 to 15 years or more. The results of long-term forecasts of supply and demand, while less precise or accurate, can raise extremely important strategic questions. Long-term energy forecasting is of great significance for formulating long-term energy development plans, arranging energy construction projects in advance, and meeting social needs.Medium-term energy forecast refers to the forecast of the development of energy supply and demand in the medium term, which serves for the formulation of medium-term planning and decision-making [20]. The forecast period is generally 5 to 10 years, and the accuracy of the mid-term energy forecast is higher than that of the long-term forecast, but the factors affecting energy supply and demand in the mid-term forecast are difficult to accurately grasp; therefore, energy supply and demand forecasts always come with various assumptions.Short-term forecast of energy refers to the forecast of the development of energy supply and demand in the short term [21]. The forecast period is generally 1 year to 5 years. Such forecasts are the scientific basis for formulating five-year plans and annual plans. Because the forecast time is relatively short, the possible development and changes of the national economy are relatively clear, and the energy structure is unlikely to change greatly, the results of energy supply and demand forecasts need to have high precision or accuracy. This prediction result directly affects the current planning arrangement and has a direct guiding role for the development of energy and national economy.Macro-energy forecast refers to the forecast of the energy supply and demand prospects of a country or region [22]. Macro-energy forecasting is to take the general blueprint of social and economic development as the object of investigation and predict the total demand and supply of energy. Sometimes, for a certain need, a specific energy product can be forecasted, such as forecasting the total demand and total supply of raw coal—for example, the various possibilities of the development of energy supply and demand and their impact, and the policies or decisions currently determined and to be implemented. When conducting qualitative energy prediction, it is mainly through the analysis of historical data and the study of future energy supply and demand conditions, with the forecaster’s rich experience and logical reasoning ability, in order to speculate and judge the nature of energy supply and demand in the future.Quantitative forecasting of energy refers to the determination of the future quantitative performance of energy supply and demand, such as the forecast of total energy production and total demand. Energy supply and demand forecasts are based on historical data, statistics, and other information; using mathematical or other analytical techniques, build a model that can represent the quantitative relationship, and use it to calculate the quantity that energy supply and demand may represent in the future. Commonly used quantitative forecasting techniques include exponential smoothing forecasting method, unit output value and output energy consumption forecasting method, energy elasticity coefficient forecasting method, input-output forecasting method, sectoral analysis comprehensive forecasting method, and some other methods of econometrics [23].Energy timing forecast refers to the determination of the future performance time of energy supply and demand, for example, the time when a new energy source (such as solar energy) can be applied to production, the time for product replacement, and the time for a certain decision to achieve the expected effect. Energy timing prediction generally relies on people's logical reasoning and judgment. Therefore, the methods applied to energy forecasting are mainly various survey analysis methods and analogy methods. The “growth” curve method can be used when making timed forecasts for the replacement of energy enterprises’ products.The target constraints of energy forecasting can be divided into normative forecasting and exploratory forecasting. The principle of the system means that when forecasting energy supply, we should regard energy supply and factors that affect energy supply, such as economic development, energy resources, transportation capacity, and ecological environment, as a system. We regard both the energy supply system and the energy demand system as having specific functions, a whole composed of elements that are organically connected to each other. Through comprehensive investigation and research, we propose alternative solutions to build predictive models. This way, it is possible to analyze the problem comprehensively, overcome one-sidedness, and improve the scientificity of prediction [24].


## 3.4. Theoretical Methods for Comprehensive Energy Forecasting

The grey forecasting method is a loose systematic method, which abandons the link of system structure analysis; by directly accumulating the original data of energy supply and demand, the overall law of the seeking system is generated, and an exponential growth model is constructed. This method can construct different prediction models according to the different characteristics of the original data, such as the grey topology model applied to the data with changing growth rate or noise data and the model containing multiple consumption factors; therefore, the prediction range of this method is very wide, both long-term and short-term predictions can be used, the amount of data required is not large, and it can also be used when data is lacking.Artificial neural network (Ann) is a complex network system which is widely connected by a large number of simple artificial neurons to imitate the human brain neural network. It establishes the nonlinear input and output model of the system on the basis of a given large number of input and output signals and parallel processing of data; in essence, it is to give a large amount of data to an artificial neural network constructed according to a certain structural form and excitation function for learning, and then given a future input, the computer judges the expected output based on past “experience.” This method is actually a black-box simulation of the system, which is suitable for dynamic consumption forecast values and dynamic training systems, and obtains high forecasting accuracy, because its “black-box operation” is more helpful for formulating policies and improving utilization; therefore, this method is suitable for long-term and short-term consumption forecasting [25].The system dynamics method regards the object under study as a dynamic system with a complex feedback structure and changes with time and draws a system flow chart representing the system structure and dynamics through system analysis; then, the relationship between the variables is quantified, and the structural equation of the system is established, so that the computer can be used for simulation experiments to predict the future of the system. The application effect of this method is closely related to the forecaster’s professional knowledge, practical experience, and system analysis and modeling ability. Through analysis and establishment of system model, the system can be whitened, and the hidden law of the system can be found out through computer dynamic simulation. This method can not only predict the long-term forecast objects, but also find out the influencing factors and function relationships of the system, which is beneficial to system optimization. However, the system analysis process is complex, the workload is large, and the ability of analysts is relatively high, so it is not suitable for short-term forecasting. For long-term forecasting, its advantages are very obvious.


## 4. Results and Analysis

The evaluation of social welfare adopts the traditional cumulative discounted utility method, and the cumulative discounted utility value is reflected in the discount of the population and the benchmark individual utility value in time. When the time preference ratep>0, both the population L and the benchmark personal utility value U are positively related to the CDU, and the size of ρ at this time reflects whether people prefer the current situation or the future situation. The larger ρ is, the more people are willing to enjoy the present life and ignore the future; the smaller the ρ, the more the attention people pay to the future; that is, the future utility value has a higher discount today, as shown in Figure 2.Figure 2
The effect of time preference rate.As an intermediary link connecting energy production and consumption, energy distribution plays an important role in social reproduction [26]. Scientific and reasonable distribution of energy products is of great significance to the smooth progress of social reproduction and the stable development of national economy. The distribution of energy products is an important part of the distribution of total social products. Through the distribution and redistribution of the total social product, three social funds are finally formed: the compensation fund, the consumption fund, and the accumulation fund. From the perspective of social reproduction, the three major funds are closely related to the two major departments of material production, and they are exchanged in accordance with the principles of material replacement and value compensation, so that products can enter the field of consumption from the production field, and social reproduction can be repeated and continuously carried out.


## 5. Conclusion

In the past ten years, with the wide application of information technology, people’s ability to use information technology to produce and collect data has been greatly improved, and a large number of databases have been used in business management, government offices, and scientific research. People are eager to get more information that can help decision-making by analyzing data. Although the current database system can efficiently realize data entry, query, statistics, and other functions, due to the huge amount of data and the serious lack of analysis methods in the database system, it is impossible to discover the hidden interconnections in the data, and it is also impossible to predict the future development trend based on current and historical data.



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*Source: 2301322-2022-08-25.xml*