# Big Data Integration Method of Mathematical Modeling and Manufacturing System Based on Fog Calculation

Authors: Xin Chen
Journal: Mathematical Problems in Engineering
 (2021)

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## Abstract

Using big data to promote economic development, improve social governance, and improve service and regulatory capabilities is becoming a trend. However, the current cloud computing for data processing has been difficult to meet the demand, and the server pressure has increased dramatically, so people pay special attention to the big data integration of fog computing. In order to make the application of big data meet people’s needs, we have established relevant mathematical models based on fog calculation, made system big data integration, collected relevant data, designed experiments, and obtained relevant research data by reviewing relevant literature and interviewing professionals. The research shows that big data integration using fog computing modeling has the characteristics of fast response and stable function. Compared with cloud computing and previous computer algorithms, big data integration has obvious advantages, and the computing speed is nearly 20% faster than cloud computing and about 35% higher than other computing methods. This shows that big data integration built by fog computing can have a huge impact on people’s lives.


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## Body

## 1. Introduction

Fog computing processes data at the edge of the network, responds to user requests, and meets the requirements of low latency and high bandwidth in the Internet of things environment. Fog computing provides services for users locally. On the one hand, it can reduce business processing delay and improve work efficiency. On the other hand, it can reduce the demand for network and bandwidth and save system overhead. Compared with cloud computing, fog computing has great advantages in response time and quality of service and meets the requirements of low latency, high reliability, and security [1]. In addition, as a supplement to cloud computing, fog computing can reduce the pressure of cloud data center, reduce bandwidth requirements, balance data processing capacity, and improve the overall efficiency of the system. In recent years, with the rapid development of the Internet of things, fog computing has been widely used in various fields, such as Internet of vehicles, wireless sensors and actuators, smart home, and software defined network. In the future development, fog computing and cloud computing will complement each other and organically and will be widely used in more industries and fields, providing an ideal software and hardware support platform for information processing in the era of Internet of things [2].Nowadays, big data has been involved in many fields, such as medical treatment, agriculture, geological survey, astronomy, and Internet of things, and even developed into the fields of news and e-government [3]. The huge value of massive data brings about new development opportunities for every field. However, the generation of massive data also brings about great challenges to data processing. It not only requires strong computing and analysis capabilities but also takes up a large amount of storage space for data storage, which will undoubtedly lead to excessive pressure and resource waste in cloud computing center [4].Fog calculation provides a new way to solve the problem of data processing. It deploys the virtual machine originally deployed in the cloud data center at the edge of the network. Through the wireless access network, it puts the data with special requirements such as high real-time requirements and sensitive position sensing in the fog server for calculation and analysis or puts some data in the fog server for temporary storage and forwards the remaining non-time-sensitive data to the cloud data center for processing [5]. This can reduce the computing pressure and waste of resources, reduce transmission delay, save energy consumption, and improve the service efficiency of users and the overall performance of the system. Therefore, it is very important to study the data processing in fog calculation. For this, experts at home and abroad have a lot of research [6].Li Zhi analyzed the current difficulties of data solution, fog computing, solved the disadvantages of cloud computing and other previous computing, analyzed the shortcomings of fog computing itself, such as security and stability, through the complex network theory as a mathematical calculation tool, built the fog computing structure model, and proposed the operation framework and solution method of fog computing. However, they are all based on theory, and there is no practical research, which has certain theoretical reference value [7].Tang Linyu thinks that fog computing can provide more services for people. Starting from the allocation of computing resources, it uses fog computing to make models and allocate resources. In the research, it is found that the distribution of fog computing is stable and the demand is matched. The results show that fog computing can make the allocation time relatively stable, and the delay and accuracy of fog calculation are better than those in the original calculation method [8].Fang Wei introduces the difference between cloud computing and fog computing, analyzes the advantages and disadvantages between them, deeply studies the concept, characteristics, and structure of fog computing, discusses and anticipates the application of fog computing in real life, introduces the calculation method of fog computing, thinks that fog computing is sublimation and diffusion above cloud computing, and makes network computing from network center. It expands to the edge of the network, solves the last kilometer of people’s network cognition, and can be used for more research and services [9].The innovation and characteristics of this study are mainly reflected in the following three aspects: first, the definition and connotation of big data industry are theoretically defined and discussed from the perspective of business ecology. Firstly, the definition of big data ecosystem is given. Secondly, the structure model of big data ecosystem is proposed, and its constituent elements are analyzed in detail, which opens up a new theoretical perspective for deepening scholars’ understanding and understanding of big data industry. Thirdly, from the perspective of network governance, this paper discusses the governance mechanism of big data ecosystem and proposes three governance mechanisms, namely, constraint mechanism, incentive mechanism, and coordination and integration mechanism, further expanding the research on big data ecosystem governance. Fourthly, based on the big data ecosystem structure model and governance model, this paper deeply analyzes the status quo of big data industry and puts forward relevant countermeasures and suggestions according to the analysis conclusion, which has certain reference significance for the development of big data industry in other cities in China.


## 2. Big Data Integration Method Based on Fog Computing Production System

### 2.1. Fog Calculation

Fog computing is a system level architecture that provides computing, storage, control, and networking functions near the data generation source along the cloud to the integration of things. Fog computing architecture is mainly divided into three layers: cloud computing layer, fog computing layer, and mobile terminal layer. Fog computing architecture makes services closer to end users, reduces latency, saves energy consumption, and enhances user experience [10].The bottom layer of the architecture is the mobile terminal layer, which contains a large number of intelligent devices and sensors. Data collection, service request, and so on are all from the bottom terminal equipment. Intelligent terminal equipment can preprocess and compress the data and filter out some useless data. At the same time, terminal devices can also communicate with each other through base station or routing equipment to realize data sharing. Fog computing layer is located between cloud computing layer and mobile terminal equipment, which is the bridge between cloud server and terminal equipment. In this layer, simple events and emergency events are detected so as to make quick response to users [11].The delay of fog computing layer includes the communication delay between fog devices and the calculation delay of fog devices. For communication delay, in the undirected graph composed of fog devices, the communication delay between fog nodes is taken as the weight. With the increase of calculation amount, the calculation delay of fog device increases correspondingly; the more the calculation amount increases, the faster the calculation delay of fog device increases [12]. Therefore, the following function is used to describe the calculation delay of fog equipment.(1)T=1wxaibi2.In the above equation,wx is the computing capacity of fog device X, bi is the workload of fog equipment, and ai is the real number set in advance. Therefore, the delay of the fog calculation node is expressed as follows:(2)Tn=min∑i=1x1wxaibi2.The fog computing layer is composed of fog nodes deployed around Internet of things devices. Fog nodes are connected with base stations or routers to reduce the transmission delay between devices. In addition, a large number of fog nodes are deployed at the edge of the network, and even the same service is deployed on multiple fog nodes. This can not only reduce the risk of service interruption caused by the failure of one fog node but also enable one fog node to process data for multiple base stations or routers and other devices [13]. The fog node in the network can also be connected with the cloud data center. When the Internet of things equipment generates a large amount of data to be processed and the computing power of a single fog node can not meet its needs, the fog node will forward the data to the cloud for processing, which will undoubtedly produce a large communication delay and reduce the service efficiency [14].The top layer is the cloud computing layer, including cloud data center and server, which is responsible for the storage, analysis, and centralized control of a large amount of data. In addition, the cloud connects with the fog server through the Internet, thus giving full play to its powerful computing power and providing rich service resources for fog. Therefore, the future development of fog computing will not replace cloud computing but the extension and supplement of cloud computing [15], as shown in Figure 1.Figure 1
Characteristics of fog calculation.With the rapid development of the Internet of things and intelligent sensors, the Internet of things mobile devices are widely used in people’s lives. The current cloud computing model can hardly meet the requirements of mobility, location awareness, and low latency in many scenarios. Fog computing inherits the advantages of cloud computing in many aspects and also has the unique advantages of edge computing. It can provide local services for terminal devices nearby, respond to local users’ service requests in time, and create new opportunities for the development of various fields [16].The energy consumption of data processing in fog calculation is studied by immune optimization algorithm. In the traditional three-layer network architecture, considering the problem that the distance between cloud server and fog node is long and the energy consumption is large, a four-layer network architecture model is proposed; that is, a proxy fog server layer is added between the cloud computing layer and the fog computing layer, so that the cloud server can cache the data resources in advance and provide local services. The specific process of addressing the proxy fog server through the optimal immune algorithm is described in detail, so as to reduce the energy consumption of the fog node to obtain data resources from the proxy fog server and also reduce the number of precache resources from the cloud server to the fog node. Through theoretical analysis and simulation experiments, the effectiveness of the four-layer network architecture model in data processing energy consumption is proved.


### 2.2. Characteristics of Fog Calculation

Fog calculation is a new paradigm. Cisco defines fog computing as a highly virtualized platform between end users and traditional cloud data centers, which can provide computing, storage, and network services [17]. The distributed deployment feature of fog computing at the network edge enables the fog devices on the network edge to directly calculate and store data and applications without having to deliver them to the cloud. Fog computing can be understood as the local cloud, which can be used locally to alleviate the pressure of bandwidth, reduce the delay, and provide real-time services to users.Fog computing is not composed of powerful servers but some scattered devices, including routers, switches, other traditional network devices, and some specially deployed devices, such as local servers. These devices have computing, processing, and storage capabilities and can forward data to cloud data centers [18]. In the application of Internet of things, data processing mainly relies on local server and provides users with low latency and fast response services by using local resources. It needs to be clear that although fog computing makes up for the deficiency of cloud computing to a certain extent, fog computing is not a substitute for cloud computing, but it cooperates with cloud computing to better meet the needs of users [19].Fog computing and cloud computing have some similarities to a certain extent: fog computing and cloud computing are based on virtualization technology, encapsulating physical resources and hardware resources into virtual resources, and providing resources for multiple users through virtualization from shared resource pool. However, fog computing is different from cloud computing in that it has unique characteristics and plays an advantage different from cloud computing [20]. The main characteristics of fog calculation are as follows:(1)
Located at the edge of the network, location aware. In terms of network topology location, the fog end device is closer to the user than the cloud and uses the edge network for communication. Geographically distributed fog nodes can infer their own location and track the end-user equipment, sense the information of nearby devices, and timely transmit messages, so as to improve the real-time response.(2)
Wide geographical distribution. Fog computing devices are distributed and deployed on the edge of the network. A large number of sensor nodes are distributed in the user environment to sense the surrounding environment. Different from the centralized processing of cloud computing, in fog computing, fog devices will sense the data of the devices nearby and use local network for data preprocessing. Due to the large number of divisions of fog area, the amount of data transmission is also reduced, which relieves the pressure of bandwidth congestion.(3)
Support high mobility. Fog computing mainly supports the requests of mobile devices. When mobile devices move from one fog area to another, the user’s requests will also be transferred. In the whole process, there is no need to transmit back and forth through the cloud. Mobile devices and fog devices can communicate directly, so it supports high mobility.(4)
Low latency. The fog device is close to the end-user and communicates in the LAN environment through the fog gateway. It does not need to go through the cloud, which reduces the transmission distance and delay, and can provide real-time services.(5)
Heterogeneity. Fog devices include different types of devices, deployed in different environments, and fog computing can support a variety of heterogeneous hardware and software devices and provide a variety of resources. From the above characteristics, fog computing and cloud computing are different in many aspects. It can be simply understood that the user’s request does not need to be sent to the cloud data center for processing and then to the use, but is directly processed by the fog computing equipment near the user, which not only reduces the transmission distance but also reduces the delay.


### 2.3. Big Data

Unloading part of cloud computing data to fog server for processing can not only reduce data transmission delay and save energy consumption but also reduce the pressure of cloud data center and improve service efficiency. Mobile IOT devices are responsible for receiving data and processing the data in the Intranet in advance to filter out the useless or redundant data. Then, the data are transmitted to the edge fog device, which transforms them into a unified representation framework and fuses them into the feature layer for easy storage. In addition, the fog device will also detect false data and missing data [21]. In order to reduce the risk of being attacked in the process of data transmission, the data will be encoded and encrypted. Finally, the fog device connects the encrypted data to the cloud server. When the data user needs data, it first sends a request to the nearest fog device. If there is data requested by the user in the fog device, it will respond immediately. Otherwise, the fog device will forward the end user’s request to the cloud server. The cloud server searches the encrypted data through the index structure and forwards the data to the user. Users decrypt the data through the key, obtain the data information, and mine the useful value in the data information [22].The development of big data industry is closely related to big data technology and its application. Although it originates from industry practice, the academic research on “big data industry” lags far behind the practical development. From the domestic point of view, the current research on big data industry mainly focuses on government industrial policies and planning, industrial development suggestions, big data industry comparison at home and abroad, and influencing factors of industrial development. However, there is a lack of research on the internal components and governance mechanism of big data industry based on appropriate theoretical perspective. From the perspective of foreign countries, although there are not many related studies, some scholars have begun to discuss the big data industry from the perspective of business ecology [23].The big data ecosystem is divided into three levels, namely, the core value chain at the micro level, the extended value chain at the meso level, and the big data ecosystem at the macro level. Among them, the core value chain takes the data value chain as the core, including direct data suppliers and data value distribution channels; the expanded value chain takes the core value chain as the center, which is composed of technology providers, data markets, suppliers of data suppliers, suppliers of complementary data products and services, and direct data end users [24]; the macro level big data ecosystem mainly refers to some related organizations in the periphery of the system, such as government agencies, regulatory agencies, investors, venture capital and incubators, industry associations, academic and research institutions, standardization organizations, and start-ups and entrepreneur groups, as well as other competitors, stakeholders, and peripheral members [25], as shown in Figure 2.Figure 2
Core owners of big data ecosystem.For big data ecosystem, the diversity of system members is very important. Diversity is an ecological concept. All kinds of organisms in the ecosystem play different important roles in the environment. Many complete food chains and complex food webs have been formed among species and between organisms and the environment. A virtuous cycle of material and energy flow has been formed in the ecosystem. Once the food chain breaks, the system will not function normally. Similar to the natural ecosystem, diversity is also indispensable to the big data business ecosystem: first, the diversity of its members serves as a buffer to deal with the uncertainty of the environment; second, diversity is of great benefit to the value creation of the big data business ecosystem. For example, Alibaba is building a business ecosystem with data as its core. It has successively invested or acquired many Internet companies with a large amount of high-quality data, such as Sina Weibo and Didi travel, playing a huge role in the value creation of the ecosystem; thirdly, diversity is the prerequisite for the self-organization of big data business ecosystem [26].New data sources increase the type of data. If the decline of data cost only boosts the growth of data volume, then the emergence of new data sources and data acquisition technologies will greatly increase the types of future data. The increase of data types will directly lead to the increase of existing data spatial dimensions, which will greatly increase the complexity of future big data. At the beginning of its birth, the computer was only designed for high-speed calculation, and the calculated data was basically limited to the digital field.


### 2.4. Data Integration Method

The user request module is responsible for parameterizing the user’s service request and then transmitting it to the fog gateway. The fog computing processing module receives the user’s requirements, finds suitable resources through resource evaluation, executes tasks, and completes user requests. Cloud computing processing module mainly deals with tasks that cannot be completed by fog computing [27].Fog computing processing module includes fog gateway, fog server, monitoring equipment, and virtual resource pool. Among them, the fog gateway receives the request from the user and transmits it to the neighboring fog server in the LAN. Monitoring devices track the resource utilization and availability of sensors, applications, and services, generate statistical logs, and transmit data to the fog server [28]. The fog server receives the user requests from the mobile terminal, analyzes the user request information according to the monitored resource information, divides the service into several tasks, and processes the calculation locally as much as possible. According to the evaluation results, each task selects the best matching resource from the resource pool, schedules and allocates the resources, and provides services for users to meet their needs.Considering the resources with the same service function, the attributes of the resources are set according to the user preferences, which have certain scalability. The dynamic calculation formula of fog computing resources is as follows:(3)Ua=p∗bji∗c.In order to calculate the weight of fog calculation attributes and ensure the objectivity of evaluation results, we use entropy weight method to determine the entropy value and entropy weight of each resource attribute.(4)t=1lnx∑n=11fnm∗lnfnm,(5)r=1−ty−∑m=1mt.We have(6)fmn=znm∑n=1xznm,(7)∑m=1xwm=1.Based on the attribute value of user request resource, each resource is regarded as a point in multidimensional space, and Euclidean distance is used to measure the proximity between resource and user demand. Due to the user’s preference for a certain attribute or the fact that each attribute of the resource has different influence on the measurement result, set objective weights for each user attribute:(8)d=∑m=1xwn∗rnm−uqm2,(9)dn=11+drn,uq.According to the proximity between the available resources and the user’s requested resources obtained by formulas (8) and (9), the proximity threshold is set between [0,1], and the matching value Q can be obtained.(10)Qδ=rndcosrn,uq≥δ.The similarity between the resources in the matching resource setQ and the resources requested by users is calculated:(11)cosmr,uq=α∗cosr,uq+1−α∗1m∑n=1mδij,where a represents the weight, ranging from [0,1]. The simulation parameters are calculated as follows:(12)F=∑qrp,rpn−qrpmquq,uqn−quq∑n=1nqr−quq2∑quq,uqm−quq2.


## 2.1. Fog Calculation

Fog computing is a system level architecture that provides computing, storage, control, and networking functions near the data generation source along the cloud to the integration of things. Fog computing architecture is mainly divided into three layers: cloud computing layer, fog computing layer, and mobile terminal layer. Fog computing architecture makes services closer to end users, reduces latency, saves energy consumption, and enhances user experience [10].The bottom layer of the architecture is the mobile terminal layer, which contains a large number of intelligent devices and sensors. Data collection, service request, and so on are all from the bottom terminal equipment. Intelligent terminal equipment can preprocess and compress the data and filter out some useless data. At the same time, terminal devices can also communicate with each other through base station or routing equipment to realize data sharing. Fog computing layer is located between cloud computing layer and mobile terminal equipment, which is the bridge between cloud server and terminal equipment. In this layer, simple events and emergency events are detected so as to make quick response to users [11].The delay of fog computing layer includes the communication delay between fog devices and the calculation delay of fog devices. For communication delay, in the undirected graph composed of fog devices, the communication delay between fog nodes is taken as the weight. With the increase of calculation amount, the calculation delay of fog device increases correspondingly; the more the calculation amount increases, the faster the calculation delay of fog device increases [12]. Therefore, the following function is used to describe the calculation delay of fog equipment.(1)T=1wxaibi2.In the above equation,wx is the computing capacity of fog device X, bi is the workload of fog equipment, and ai is the real number set in advance. Therefore, the delay of the fog calculation node is expressed as follows:(2)Tn=min∑i=1x1wxaibi2.The fog computing layer is composed of fog nodes deployed around Internet of things devices. Fog nodes are connected with base stations or routers to reduce the transmission delay between devices. In addition, a large number of fog nodes are deployed at the edge of the network, and even the same service is deployed on multiple fog nodes. This can not only reduce the risk of service interruption caused by the failure of one fog node but also enable one fog node to process data for multiple base stations or routers and other devices [13]. The fog node in the network can also be connected with the cloud data center. When the Internet of things equipment generates a large amount of data to be processed and the computing power of a single fog node can not meet its needs, the fog node will forward the data to the cloud for processing, which will undoubtedly produce a large communication delay and reduce the service efficiency [14].The top layer is the cloud computing layer, including cloud data center and server, which is responsible for the storage, analysis, and centralized control of a large amount of data. In addition, the cloud connects with the fog server through the Internet, thus giving full play to its powerful computing power and providing rich service resources for fog. Therefore, the future development of fog computing will not replace cloud computing but the extension and supplement of cloud computing [15], as shown in Figure 1.Figure 1
Characteristics of fog calculation.With the rapid development of the Internet of things and intelligent sensors, the Internet of things mobile devices are widely used in people’s lives. The current cloud computing model can hardly meet the requirements of mobility, location awareness, and low latency in many scenarios. Fog computing inherits the advantages of cloud computing in many aspects and also has the unique advantages of edge computing. It can provide local services for terminal devices nearby, respond to local users’ service requests in time, and create new opportunities for the development of various fields [16].The energy consumption of data processing in fog calculation is studied by immune optimization algorithm. In the traditional three-layer network architecture, considering the problem that the distance between cloud server and fog node is long and the energy consumption is large, a four-layer network architecture model is proposed; that is, a proxy fog server layer is added between the cloud computing layer and the fog computing layer, so that the cloud server can cache the data resources in advance and provide local services. The specific process of addressing the proxy fog server through the optimal immune algorithm is described in detail, so as to reduce the energy consumption of the fog node to obtain data resources from the proxy fog server and also reduce the number of precache resources from the cloud server to the fog node. Through theoretical analysis and simulation experiments, the effectiveness of the four-layer network architecture model in data processing energy consumption is proved.


## 2.2. Characteristics of Fog Calculation

Fog calculation is a new paradigm. Cisco defines fog computing as a highly virtualized platform between end users and traditional cloud data centers, which can provide computing, storage, and network services [17]. The distributed deployment feature of fog computing at the network edge enables the fog devices on the network edge to directly calculate and store data and applications without having to deliver them to the cloud. Fog computing can be understood as the local cloud, which can be used locally to alleviate the pressure of bandwidth, reduce the delay, and provide real-time services to users.Fog computing is not composed of powerful servers but some scattered devices, including routers, switches, other traditional network devices, and some specially deployed devices, such as local servers. These devices have computing, processing, and storage capabilities and can forward data to cloud data centers [18]. In the application of Internet of things, data processing mainly relies on local server and provides users with low latency and fast response services by using local resources. It needs to be clear that although fog computing makes up for the deficiency of cloud computing to a certain extent, fog computing is not a substitute for cloud computing, but it cooperates with cloud computing to better meet the needs of users [19].Fog computing and cloud computing have some similarities to a certain extent: fog computing and cloud computing are based on virtualization technology, encapsulating physical resources and hardware resources into virtual resources, and providing resources for multiple users through virtualization from shared resource pool. However, fog computing is different from cloud computing in that it has unique characteristics and plays an advantage different from cloud computing [20]. The main characteristics of fog calculation are as follows:(1)
Located at the edge of the network, location aware. In terms of network topology location, the fog end device is closer to the user than the cloud and uses the edge network for communication. Geographically distributed fog nodes can infer their own location and track the end-user equipment, sense the information of nearby devices, and timely transmit messages, so as to improve the real-time response.(2)
Wide geographical distribution. Fog computing devices are distributed and deployed on the edge of the network. A large number of sensor nodes are distributed in the user environment to sense the surrounding environment. Different from the centralized processing of cloud computing, in fog computing, fog devices will sense the data of the devices nearby and use local network for data preprocessing. Due to the large number of divisions of fog area, the amount of data transmission is also reduced, which relieves the pressure of bandwidth congestion.(3)
Support high mobility. Fog computing mainly supports the requests of mobile devices. When mobile devices move from one fog area to another, the user’s requests will also be transferred. In the whole process, there is no need to transmit back and forth through the cloud. Mobile devices and fog devices can communicate directly, so it supports high mobility.(4)
Low latency. The fog device is close to the end-user and communicates in the LAN environment through the fog gateway. It does not need to go through the cloud, which reduces the transmission distance and delay, and can provide real-time services.(5)
Heterogeneity. Fog devices include different types of devices, deployed in different environments, and fog computing can support a variety of heterogeneous hardware and software devices and provide a variety of resources. From the above characteristics, fog computing and cloud computing are different in many aspects. It can be simply understood that the user’s request does not need to be sent to the cloud data center for processing and then to the use, but is directly processed by the fog computing equipment near the user, which not only reduces the transmission distance but also reduces the delay.


## 2.3. Big Data

Unloading part of cloud computing data to fog server for processing can not only reduce data transmission delay and save energy consumption but also reduce the pressure of cloud data center and improve service efficiency. Mobile IOT devices are responsible for receiving data and processing the data in the Intranet in advance to filter out the useless or redundant data. Then, the data are transmitted to the edge fog device, which transforms them into a unified representation framework and fuses them into the feature layer for easy storage. In addition, the fog device will also detect false data and missing data [21]. In order to reduce the risk of being attacked in the process of data transmission, the data will be encoded and encrypted. Finally, the fog device connects the encrypted data to the cloud server. When the data user needs data, it first sends a request to the nearest fog device. If there is data requested by the user in the fog device, it will respond immediately. Otherwise, the fog device will forward the end user’s request to the cloud server. The cloud server searches the encrypted data through the index structure and forwards the data to the user. Users decrypt the data through the key, obtain the data information, and mine the useful value in the data information [22].The development of big data industry is closely related to big data technology and its application. Although it originates from industry practice, the academic research on “big data industry” lags far behind the practical development. From the domestic point of view, the current research on big data industry mainly focuses on government industrial policies and planning, industrial development suggestions, big data industry comparison at home and abroad, and influencing factors of industrial development. However, there is a lack of research on the internal components and governance mechanism of big data industry based on appropriate theoretical perspective. From the perspective of foreign countries, although there are not many related studies, some scholars have begun to discuss the big data industry from the perspective of business ecology [23].The big data ecosystem is divided into three levels, namely, the core value chain at the micro level, the extended value chain at the meso level, and the big data ecosystem at the macro level. Among them, the core value chain takes the data value chain as the core, including direct data suppliers and data value distribution channels; the expanded value chain takes the core value chain as the center, which is composed of technology providers, data markets, suppliers of data suppliers, suppliers of complementary data products and services, and direct data end users [24]; the macro level big data ecosystem mainly refers to some related organizations in the periphery of the system, such as government agencies, regulatory agencies, investors, venture capital and incubators, industry associations, academic and research institutions, standardization organizations, and start-ups and entrepreneur groups, as well as other competitors, stakeholders, and peripheral members [25], as shown in Figure 2.Figure 2
Core owners of big data ecosystem.For big data ecosystem, the diversity of system members is very important. Diversity is an ecological concept. All kinds of organisms in the ecosystem play different important roles in the environment. Many complete food chains and complex food webs have been formed among species and between organisms and the environment. A virtuous cycle of material and energy flow has been formed in the ecosystem. Once the food chain breaks, the system will not function normally. Similar to the natural ecosystem, diversity is also indispensable to the big data business ecosystem: first, the diversity of its members serves as a buffer to deal with the uncertainty of the environment; second, diversity is of great benefit to the value creation of the big data business ecosystem. For example, Alibaba is building a business ecosystem with data as its core. It has successively invested or acquired many Internet companies with a large amount of high-quality data, such as Sina Weibo and Didi travel, playing a huge role in the value creation of the ecosystem; thirdly, diversity is the prerequisite for the self-organization of big data business ecosystem [26].New data sources increase the type of data. If the decline of data cost only boosts the growth of data volume, then the emergence of new data sources and data acquisition technologies will greatly increase the types of future data. The increase of data types will directly lead to the increase of existing data spatial dimensions, which will greatly increase the complexity of future big data. At the beginning of its birth, the computer was only designed for high-speed calculation, and the calculated data was basically limited to the digital field.


## 2.4. Data Integration Method

The user request module is responsible for parameterizing the user’s service request and then transmitting it to the fog gateway. The fog computing processing module receives the user’s requirements, finds suitable resources through resource evaluation, executes tasks, and completes user requests. Cloud computing processing module mainly deals with tasks that cannot be completed by fog computing [27].Fog computing processing module includes fog gateway, fog server, monitoring equipment, and virtual resource pool. Among them, the fog gateway receives the request from the user and transmits it to the neighboring fog server in the LAN. Monitoring devices track the resource utilization and availability of sensors, applications, and services, generate statistical logs, and transmit data to the fog server [28]. The fog server receives the user requests from the mobile terminal, analyzes the user request information according to the monitored resource information, divides the service into several tasks, and processes the calculation locally as much as possible. According to the evaluation results, each task selects the best matching resource from the resource pool, schedules and allocates the resources, and provides services for users to meet their needs.Considering the resources with the same service function, the attributes of the resources are set according to the user preferences, which have certain scalability. The dynamic calculation formula of fog computing resources is as follows:(3)Ua=p∗bji∗c.In order to calculate the weight of fog calculation attributes and ensure the objectivity of evaluation results, we use entropy weight method to determine the entropy value and entropy weight of each resource attribute.(4)t=1lnx∑n=11fnm∗lnfnm,(5)r=1−ty−∑m=1mt.We have(6)fmn=znm∑n=1xznm,(7)∑m=1xwm=1.Based on the attribute value of user request resource, each resource is regarded as a point in multidimensional space, and Euclidean distance is used to measure the proximity between resource and user demand. Due to the user’s preference for a certain attribute or the fact that each attribute of the resource has different influence on the measurement result, set objective weights for each user attribute:(8)d=∑m=1xwn∗rnm−uqm2,(9)dn=11+drn,uq.According to the proximity between the available resources and the user’s requested resources obtained by formulas (8) and (9), the proximity threshold is set between [0,1], and the matching value Q can be obtained.(10)Qδ=rndcosrn,uq≥δ.The similarity between the resources in the matching resource setQ and the resources requested by users is calculated:(11)cosmr,uq=α∗cosr,uq+1−α∗1m∑n=1mδij,where a represents the weight, ranging from [0,1]. The simulation parameters are calculated as follows:(12)F=∑qrp,rpn−qrpmquq,uqn−quq∑n=1nqr−quq2∑quq,uqm−quq2.


## 3. Three Big Data Integration Experiments

### 3.1. Purpose of the Experiment

Based on the theoretical achievements of cloud computing and intelligent research, this paper uses the methods of literature, comparative research, mathematical statistics, and logical analysis to deeply analyze the application of big data integration and intelligent city and study its application mode and characteristics.


### 3.2. Experimental Evaluation Criteria

Entropy method is a relatively objective evaluation index weight assignment method, which can effectively avoid the subjectivity of artificial scoring, and has high accuracy. But, at the same time, this study also realized that the entropy method can not directly reflect the knowledge, opinions, and experience judgment of experts and scholars, and the weight results may be contrary to the actual situation. Therefore, this paper uses AHP and entropy method to determine the weight coefficient of regional higher education evaluation index.


### 3.3. Data Sources

The data in this paper mainly come from 2015-2020 China Statistical Yearbook, regional statistical yearbook, National Bureau of statistics, big data statistical platform, and smart city comprehensive statistical information management platform.


## 3.1. Purpose of the Experiment

Based on the theoretical achievements of cloud computing and intelligent research, this paper uses the methods of literature, comparative research, mathematical statistics, and logical analysis to deeply analyze the application of big data integration and intelligent city and study its application mode and characteristics.


## 3.2. Experimental Evaluation Criteria

Entropy method is a relatively objective evaluation index weight assignment method, which can effectively avoid the subjectivity of artificial scoring, and has high accuracy. But, at the same time, this study also realized that the entropy method can not directly reflect the knowledge, opinions, and experience judgment of experts and scholars, and the weight results may be contrary to the actual situation. Therefore, this paper uses AHP and entropy method to determine the weight coefficient of regional higher education evaluation index.


## 3.3. Data Sources

The data in this paper mainly come from 2015-2020 China Statistical Yearbook, regional statistical yearbook, National Bureau of statistics, big data statistical platform, and smart city comprehensive statistical information management platform.


## 4. Big Data Integration Method Based on Fog Computing

### 4.1. Server Data Calculation Delay

We simulate the verbal delay time of general computing, communication, and cloud computing through the established data model. Without considering the data loss, we set up simulation data to obtain the communication delay and data calculation delay. The details are shown in Table1 and Figures 3 and 4 .Table 1
Server delay data.
Data volume (GB)246810General calculation delay2.13.54.75.66.2Cloud computing latency0.91.31.92.22.7Communication delay1.22.43.13.84.1Figure 3
Server computing latency.Figure 4
Server calculation delay.


### 4.2. Data Delay Performance Analysis of Fog Calculation

In order to verify the effectiveness of fog computing layer in reducing data processing delay, it is compared with the delay of data processing in single fog node and cloud computing, as shown in Table2 and Figures 5 and 6 .Table 2
Fog calculation data delay.
Single fog calculation nodeFog calculation layerCloud computing layer531.33.71093.48.615176.712.3203914.915.1Figure 5
Compute delay performance.Figure 6
Performance calculation of fog calculation layer.


### 4.3. Effect of Data Processing Percentage on Data Processing Delay

We set up a value of X, where x is the percentage of the total data processed in the fog computing layer. In order to verify the performance of the calculation, the effect of X on the data processing delay is studied. The details are shown in Table3 and Figure 7.Table 3
Data percentage impact.
Data volume (gb)/percentage (%)020508010022.11.71.41.21.143.33.12.62.41.964.23.93.02.72.185.44.73.73.63.1107.96.64.95.15.4128.67.36.17.28.9Figure 7
Data processing delay impact.The experimental results show that whenx ≤ 50%, that is, when the amount of data processed in the fog calculation layer is less than half, the larger X is, the smaller the delay is. When x > 50% and the amount of data is small, the larger X is, the smaller the delay is. However, with the increase of data volume, the delay will increase correspondingly, and the greater X is, the faster the corresponding delay increases, even more than the delay caused by the traditional cloud computing layer.


### 4.4. Impact of the Number of Data Nodes on Data Processing

In order to study the influence of the number of fog nodes on the data processing delay in fog computing layer, the data processing delay values were calculated when the total data X was 2 GB, 5 GB, 8 GB, 10 GB, and 14 GB, respectively. The specific statistical results are shown in Table4 and Figure 8.Table 4
Impact of node number delay.
Data volume (gb)/number of nodes258101410.44.310182720.43.77.5132130.43.56.4111840.33.36.18.71250.33.14.77.69.860.23.14.16.68.570.22.73.55.97.3Figure 8
The influence of the number of nodes on data operation.The experimental results show that, with the increasing number of fog nodes, the delay caused by data processing has a downward trend. When the amount of data is small, the increase of fog nodes has little effect on the data processing delay, which is basically in a stable state. When the amount of data is large, the data processing delay decreases obviously with the increase of fog nodes.


## 4.1. Server Data Calculation Delay

We simulate the verbal delay time of general computing, communication, and cloud computing through the established data model. Without considering the data loss, we set up simulation data to obtain the communication delay and data calculation delay. The details are shown in Table1 and Figures 3 and 4 .Table 1
Server delay data.
Data volume (GB)246810General calculation delay2.13.54.75.66.2Cloud computing latency0.91.31.92.22.7Communication delay1.22.43.13.84.1Figure 3
Server computing latency.Figure 4
Server calculation delay.


## 4.2. Data Delay Performance Analysis of Fog Calculation

In order to verify the effectiveness of fog computing layer in reducing data processing delay, it is compared with the delay of data processing in single fog node and cloud computing, as shown in Table2 and Figures 5 and 6 .Table 2
Fog calculation data delay.
Single fog calculation nodeFog calculation layerCloud computing layer531.33.71093.48.615176.712.3203914.915.1Figure 5
Compute delay performance.Figure 6
Performance calculation of fog calculation layer.


## 4.3. Effect of Data Processing Percentage on Data Processing Delay

We set up a value of X, where x is the percentage of the total data processed in the fog computing layer. In order to verify the performance of the calculation, the effect of X on the data processing delay is studied. The details are shown in Table3 and Figure 7.Table 3
Data percentage impact.
Data volume (gb)/percentage (%)020508010022.11.71.41.21.143.33.12.62.41.964.23.93.02.72.185.44.73.73.63.1107.96.64.95.15.4128.67.36.17.28.9Figure 7
Data processing delay impact.The experimental results show that whenx ≤ 50%, that is, when the amount of data processed in the fog calculation layer is less than half, the larger X is, the smaller the delay is. When x > 50% and the amount of data is small, the larger X is, the smaller the delay is. However, with the increase of data volume, the delay will increase correspondingly, and the greater X is, the faster the corresponding delay increases, even more than the delay caused by the traditional cloud computing layer.


## 4.4. Impact of the Number of Data Nodes on Data Processing

In order to study the influence of the number of fog nodes on the data processing delay in fog computing layer, the data processing delay values were calculated when the total data X was 2 GB, 5 GB, 8 GB, 10 GB, and 14 GB, respectively. The specific statistical results are shown in Table4 and Figure 8.Table 4
Impact of node number delay.
Data volume (gb)/number of nodes258101410.44.310182720.43.77.5132130.43.56.4111840.33.36.18.71250.33.14.77.69.860.23.14.16.68.570.22.73.55.97.3Figure 8
The influence of the number of nodes on data operation.The experimental results show that, with the increasing number of fog nodes, the delay caused by data processing has a downward trend. When the amount of data is small, the increase of fog nodes has little effect on the data processing delay, which is basically in a stable state. When the amount of data is large, the data processing delay decreases obviously with the increase of fog nodes.


## 5. Conclusions

This paper describes the research background of fog computing, domestic and international development status, architecture, application scenarios, comparison with cloud computing, and the importance and necessity of data processing research in fog computing and gives a comprehensive understanding of fog computing from a macro perspective. The data processing delay optimization algorithm in fog calculation is designed. In order to solve the problems of high data processing delay and high pressure in cloud data center, the data processing delay problem is studied with the new cloud fog three-layer network architecture model. The delay of each layer of network architecture is defined mathematically, the data processing delay optimization algorithm is proposed, and the algorithm is described in detail. Theoretical analysis and simulation show that the proposed method is better than the traditional cloud computing architecture in reducing data processing delay.With the continuous development of Internet technology, data itself is an asset, which has formed a consensus in the industry. If cloud computing provides a place and channel for the storage and access of data assets, then how to systematize data assets to serve national governance, enterprise decision-making, and even personal life is the core issue of big data, as well as the inherent soul and inevitable upgrading direction of cloud computing.In recent years, informatization has developed rapidly in various industries in China. The high permeability and high integration ability of information technology provide sufficient technical support for information construction and effectively transform and enhance the traditional industry. Big data systematization aims to improve decision-making, supervision, service, and emergency support capabilities, focuses on the integration and development and utilization of information resources, adopts the latest technology, takes intelligent life as the development direction, comprehensively improves the management and service level, and provides comprehensive information services for the public.



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*Source: 9987714-2021-08-12.xml*