Internet based rural economic entrepreneurship based on mobile edge computing and resource allocation

Currently, the rural economy is developing better and better with the advantages of equality and low cost of Internet platforms. However, inevitably, the rural Internet economy also faces some challenges in its actual development. Entrepreneurship is the main driving force for promoting the development of the national economy and the main method for improving people's living standards and quality. China highly recognizes the positive role of entrepreneurship in regional and even national economic development, and has introduced a number of policies and measures to provide practical support such as financial subsidies for entrepreneurial activities. Mobile edge computing technology not only provides a new economic environment for the information age, but also plays a key role in developing innovation and entrepreneurship and improving business efficiency due to its cross platform and multi-level characteristics. Mobile edge computing technology can process the data generated by resource allocation and provide technical support for resource allocation at the network edge. This paper studies the calculation of big data resource allocation in mobile edge computing systems. Through an analysis of the operation of rural economy based on the Internet, we will deeply explore the advantages that the Internet platform brings to the operation of rural economy. Based on local conditions, the use of Internet resources has strengthened the entrepreneurial capacity of the region, achieved rapid development of the rural economy, strengthened the construction of rural infrastructure, improved the construction of assistance systems, established a business service platform, and introduced relevant incentive mechanisms to ensure the implementation of rural entrepreneurship assistance policies.


Related work
In the era of "Internet plus", business model innovation has gradually been accepted by scholars, and business model innovation has gradually been accepted by the general public. In the literature, the concept of "Internet plus" has been deeply and systematically studied, but on the question of what is the internal logic of business model innovation, the academic community has produced many different voices and never reached a consensus [9]. Reviewing relevant research, it can be found that the understanding of the connotation of business model innovation is mostly carried out from different perspectives such as profitability, value chain, and activity system. The literature shows that the level of business activity mainly depends on the external environment and the group ecology theory discusses the impact of the external environment on the survival and development of startups from a macro level [10]. Different market capacities, regional differences, institutional norms, and political environments can all have an impact on the survival of enterprises. From the perspective of regional ecosystems, the external environment can also have an impact on enterprise productivity [11]. Only by adapting to external environments such as institutional reforms and systems can a company successfully complete its establishment and reduce the risk of entrepreneurial failure. The literature takes returning entrepreneurs as the survey target, and finds that a favorable external macroeconomic environment can have a positive impact on entrepreneurial productivity [12]. The strategic adaptation theory based on company and individual analysis emphasizes the impact of entrepreneurial strategy choices on entrepreneurial productivity. The survival and development of entrepreneurship depends on the effective implementation of entrepreneurial strategies. The literature indicates that in the strategic context of revitalizing rural economic development, the impact of entrepreneurial orientation on entrepreneurial productivity and strategic adaptation to entrepreneurial efficiency have been further developed [13]. The literature considers the relationship between business development and economic growth from a regional perspective. In empirical research, a new indicator related to the birth rate of start-up companies was collected and studied. The data shows that there is a strong positive correlation between business development and economic growth [14]. In order to study the latency and power consumption issues caused by increased multimedia traffic in automotive networks, this paper designs a new resource allocation mechanism based on research on MEC systems, optimizes cache and computer resource planning, and reduces task processing time. Simulation results confirm the effectiveness of this mechanism. A planning algorithm for vehicle data transmission has been proposed in the literature, and experiments have shown that this algorithm can significantly improve the performance of vehicle networks [15]. The literature proposes a resource allocation scheme to solve the problem that vehicle terminals in VEC networks cannot handle large amounts of task data due to their limited computing resources [16]. In order to optimize system energy consumption, MEC computing power allocation and task offloading in high-density network environments have been studied in the literature. Simulation results show that this scheme can not only reduce system energy consumption, but also achieve performance improvement [17]. The MPOHS task offloading algorithm has been proposed in the literature to reduce the task time delay caused by vehicle movement in VEC systems. Simulation experiments have been conducted on this issue, and the experimental results show that this task offloading algorithm can achieve the expected functions. The literature has studied the issue of unloading and installing on-board applications in MEC environments [18]. By using unmanned aerial vehicles to reduce the impact of obstacles on them, an algorithm for assigning tasks in minimal increments has been developed to achieve improved resource utilization while reducing system costs [19].

Mobile edge computing
MEN consists of several base stations, as shown in Figure 1. Each BTS has a MEC server that is used to receive, execute, and transmit offload signals from users within the BTS signal area. The base stations transmit information to each other through a central base station.  If n is used to represent an edge server and m is used to represent a mobile device, when the communication rate between n and m is greater than 0, the power consumption cost is the power consumption cost of the edge server; When the communication rate between n and m is equal, that is, when the two cannot communicate, the power consumption cost will be infinite. The specific revenue received by the edge server is calculated by the initial reward Rm and the penalty function punishmn for mobile device tasks. The calculation method of punishmn is shown in Formula (4): For mobile devices, any mobile device that needs to delete a task wants to complete the task. Based on this, the overall objective function of the mobile device can be represented by Equation (6): Due to latency constraints, it is necessary to calculate and verify the latency caused by downloading, submitting, and executing tasks during each period. The algorithm flow is shown in Figure 2.

Fig. 2. Algorithm Flow Chart
When GBJOS converges, not all edge servers actively request policy changes. For mobile devices, the classic EWA algorithm can be used to help mobile devices complete online learning to achieve the purpose of mobile device side. Figure 3 shows the impact of the number of tasks under different resource allocation policies on the DHR task completion rate.

Fig. 3. Effect of calculated quantity on completion rate
The purpose of this article is to reduce the total runtime of the application, that is, the total runtime of all subtasks in the DAG. Deleting and sorting each subtask can reduce the overall latency of the system, as shown in: In order to minimize the running time and execution time of all subtasks, and in combination with the limitations of energy consumption on equipment, the following optimization problem is established: Based on a specific subtask deletion priority and key deletion path, the earliest node replication based deletion algorithm was developed. This algorithm implements the emission calculation of M subtasks, and determines whether the currently executing subtask vi meets the emission conditions for replicating previous subtasks, that is, whether the inequality is satisfied: TEFT(i * ) > TEFT(i * ) ̅̅̅̅̅̅̅̅̅̅̅̅ (9) As shown in Figure 4. Due to the incomplete channel state, the information model can generate channel estimation errors , and the instantaneous rate achieved based on this can be represented by the following formula: By converting C1 to an objective function, the failure probability problem can be converted to a non probability problem, and the minimum transmission that can be achieved in this way can be represented by the following formula: If pn is used to represent the user's transmission power, the energy consumed when deleting a task can be expressed as:

Resource allocation strategy
To simplify the research, this article assumes that the macro base station has a fixed transmit power for each user, that the macro base station has a fixed resource block for each user, and that the transmit power of the macro base station user is set at the pm level of the resource module. If RBk is assigned to MUEm, the signal-to-interference noise ratio of MUEm over RBk can be expressed as: Therefore, the rates of MUEm and nu can be determined by the Shannon formula.
The sum of the small base station user rates associated with Bn can be represented by the following formula.
The total bandwidth occupied by all users is the amount of bandwidth consumed by the network base station. The total bandwidth consumed by the network base station can be expressed as: Allocate resource blocks and transmit power to achieve joint optimization of system energy efficiency and spectrum efficiency. The multi-objective optimization problem can be expressed as: In order to avoid network congestion and other issues caused by the continuous unloading of tasks, this article stipulates that the joint unloading of APs is only allowed for the unloading of adjacent APs. In combination with the Shannon formula, the maximum data transfer rate generated by CTm, n when deleting tasks can be expressed as follows: After STAm, n deletes a task, the deleted task will be transmitted to the MEC server, and the MEC server will provide a calculation delay, as shown in the following formula: The AP communication resources and remaining computing resources of the MEC server are relatively limited. The main issue studied in this article is how to reasonably allocate resources and minimize the power consumption of STA terminals while respecting task delay constraints: The adaptive function is usually constructed from the objective function of an optimization problem. The optimization objective in this article is to minimize the total energy consumption of STA. The adaptive function can be expressed by the following formula: The optimization goal in this article is to solve the RU allocation problem to minimize the energy consumption caused by deleting tasks. Figure 5 shows how the total energy consumption of STA varies according to the number of iterations. The optimization decision variables include the allocation vector of information resources and computing resources, as well as the unloading decision vector.   Figure 7 shows the basic architecture of the Internet rural economic entrepreneurship platform. Before making a formal entrepreneurial decision, managers must define appropriate variables for the simulation, such as the entrepreneur, entrepreneurial complexity, difficulty, and the possible objective impact of the environment on entrepreneurship. The impact of environmental parameters on the entrepreneurial team cannot be ignored. In order to make the entrepreneurship platform more suitable for the real operating environment of modern commerce, cultivate the entrepreneurial spirit of entrepreneurs, and cultivate their ability to adapt to the rapidly changing market economy, basic environmental parameters have been set based on the information obtained from the rural entrepreneurship internet platform. Participants should actively participate in the analysis of various economic situations in order to improve their ability to independently analyze and solve problems under complex market conditions. After each cycle is initialized, a situation report for the current cycle will be formed to report the status of the current cycle. The report will provide information about the market and current cycle costs, which is crucial for startups, and entrepreneurs need to use this information to guide them in the next cycle.

Construction of Internet rural economic entrepreneurship platform
Cyclic competition market situation: If the average competitive market capacity of each company in the business cycle (the first cycle) is Q1, and an is the market capacity change rate in the nth cycle, then the market capacity in the nth cycle is Qn=an * Qn-1. In terms of the overall social demand for products, the larger the market capacity becomes, the easier it is for companies to make profits.
Labor Cost: In the nth cycle, if the range of changes in some personnel costs and historical average costs is expressed as n, then i represents the personnel cost in the cycle.
After filling in relevant data for startups, you can use the budget tools provided by the Rural Entrepreneurship Assistance Internet Platform to prepare a budget for entrepreneurial costs and initial benefits, and obtain relevant market data reports and various revenue and expenditure reports through the Internet platform and simulation experiments. How to use JMP and other data collation software to summarize and analyze the collected data, analyze it from production, procurement, marketing, and other aspects, and provide a simplified decision-making plan.

Multiple Regression Analysis of Internet Rural Economic Entrepreneurship
In the era of "Internet plus", entrepreneurship in rural areas is increasingly prosperous, but it will still face many serious challenges. In addition, entrepreneurs who return home to start businesses are mostly college students or farmers, most of whom have not been subjected to social beatings and lack social experience; Most of the latter have a low level of education and professional knowledge. The impact of these problems in the early stages of entrepreneurship is basically not very obvious, but the more problems emerge in the later stages, the more obvious they will become.
Entrepreneurs should be familiar with the Internet and have their own profound understanding, but their understanding of "Internet plus" may not be so comprehensive and clear. Using the Internet technology platform to launch online entrepreneurship in rural areas, entrepreneurs should not only have network knowledge, but also have a long-term perspective and a holistic view. Firstly, it is necessary to assess whether the environment in which the region is located has entrepreneurial advantages. Not all regions are suitable for developing entrepreneurial projects. Secondly, it is necessary to estimate the potential and potential future benefits of rural areas. Finally, it is necessary to evaluate the sustainability of rural entrepreneurship. By completing the above three points, the path of entrepreneurship can go further and further, driving regional economic development.
The state supports and encourages migrant workers to return to their hometown to start businesses, and has introduced many entrepreneurship subsidies and local assistance policies. The national financial department has provided a good economic foundation for rural entrepreneurship. However, entrepreneurs must do a good job in the early stages of their work, planning in advance in product development, production channels (or sources of goods), sales channels, marketing, and other aspects, which requires a large amount of funds and costs at this stage.
Using regression analysis, this paper empirically studies the impact of the Internet on the productivity of rural enterprises. Table 1 shows the results of multiple regression analysis of rural enterprises using the Internet as an independent variable. It can be seen that the initial model R2 is 0.381, and after adjustment, R2 becomes 0.362. When the F value is 0.01, it is significant, indicating that the general matching optimization of the regression model is very good. The results show that the regression coefficient of Internet standardization for rural enterprises is 0.553, p<0.01 (sig=0000), indicating that the Internet has a positive and significant impact on the entrepreneurial productivity of the rural economy. The Internet has created a good atmosphere for rural entrepreneurship, and entrepreneurs have provided a broad platform. Using Internet big data analysis technology, you can achieve real-time access to market information. The first step in starting a business is to understand the market situation and clarify market demand. At the same time, short videos can be used to promote rural entrepreneurship, and online marketing can be done well in advance to promote rural economic development. Utilize Internet resources and local policies to improve the rural logistics platform, achieve unified transportation of different products through the construction of third-party platforms, and improve the transportation efficiency and service level of rural logistics. To start a business in rural areas, it is necessary to have a true understanding of the real situation in rural areas, in order to maximize the utilization of local resources. For example, some rural areas may be more suitable for undertaking entrepreneurial activities in agriculture, some rural areas may be more suitable for developing tourism, and some rural areas may be more suitable for undertaking entrepreneurship in handicrafts. At this stage, big data technology has become very practical. Through e-commerce platforms, "Internet plus" entrepreneurial activities can be carried out, urban and rural resources can be integrated, rural industrialization can be promoted, and local economic development can be driven.
The coverage of network base stations in China is relatively extensive, but they have not yet been fully popularized, and the infrastructure in some remote villages has not yet been improved. In the era of Internet plus, the poverty alleviation plan of remote villages cannot be separated from these infrastructure facilities, because online sales plans cannot be separated from the support of the Internet and e-commerce platforms. Based on this, the popularization and improvement of Internet related facilities are indispensable, and it is necessary to accelerate the improvement of these network infrastructure. In addition, the development of online economy cannot be separated from logistics and transportation, and the backwardness of logistics to some extent limits sales and production. If you want to quickly deliver a product to consumers, you must improve the logistics system.

Fitting of Internet rural economic entrepreneurship indicators
According to the comparison results in Table 2, the chi-square value analyzed by the Internet and enterprise productivity structural equation model is 1.657, which is greater than 4. The above fitting indicators meet the requirements, indicating that the models of the Internet and performance model are generally compatible. See Table 2 for specific simulation results. The development of rural economy is relatively backward compared to that of cities, and cross-border e-commerce on imported resources has little impact on rural imports. On the contrary, rural areas have diverse resources, distinctive characteristics, and certain overseas market demands. Therefore, cross-border e-commerce exports have certain significance. Entrepreneurs can use local resources to conduct cross-border export transactions. The entrepreneurial team must first conduct research on foreign demand to determine the export market. The research objects of overseas research are mostly foreigners, and it will be difficult for researchers to complete the research without excellent foreign language skills and perfect planning skills. Therefore, it is necessary to hand over the research to a professional company to provide more accurate results. In addition, entrepreneurs must accurately capture high-quality products for sale, such as some popular handicrafts related to traditional culture, such as Paper Cuttings, purse, etc., and other non handicrafts can be obtained through B2B e-commerce platform. Most rural entrepreneurs are unable to build their own e-commerce platforms, so the main ecommerce platforms used are B2B and B2C models. The B2B business model is to expand sales channels by developing agents to achieve multi-channel product sales, while B2C uses existing third-party platforms for distribution.

Conclusion
With the rapid development of the field of communication, people are gradually entering an era of interconnected everything. With the emergence of new applications and Internet smart devices, more and more smart devices are used around people. Intelligent devices based on mobile edge computing technology are increasingly popular. Combining users with them will help reduce the energy consumption generated by users when performing tasks. Due to insufficient deployment, it often leads to uneven distribution of computing resources. If the MEC server needs to complete multiple deletion tasks at the same time, it needs technical support from a multi user computing algorithm. This paper analyzes the mobile edge computing algorithm of the system from the perspective of users. This algorithm mainly uses genetic algorithm to determine the decision information of user resource allocation. The proportion of the system executing delayed tasks under delay constraints increases significantly, which effectively reduces the total energy consumption of STA in the system. In the following research, we will deeply understand the integration of Internet computing algorithms and communication tasks. Based on theoretical and empirical analysis, this article discusses in detail the reasons why the Internet has successfully driven the development of rural economy. The rapid development of rural economy cannot be separated from the trading platform provided by the Internet, nor can it be separated from the innovation of business models. On this basis, this article also discusses the impact mechanism of the Internet on rural economic enterprises and individual entrepreneurship, and confirms it through empirical analysis.

Conflict of interest
The authors declare that they have no conflict of interests

Ethical approval
This article does not contain any studies with human participants performed by any of the authors.

Data Availability
Data will be made available on request.