# Innovative Methods for the Integration of Marketing Concepts and Art Design in Product Design in the Era of Internet of Things

Authors: Hongsu Wang; Na An; Shiyu Ma
Journal: Wireless Communications and Mobile Computing
 (2022)

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## Abstract

Due to the popularity and rapid development of the Internet, it has profoundly affected people’s way of life, covering all aspects of people’s lives, and the communication methods and marketing concepts of all walks of life have also undergone great changes due to its influence. Internet technology simplifies and compresses the marketing process, making consumers sovereign, and the monopoly of traditional media is difficult to reproduce on the Internet. The development of the times has also affected people’s pursuit of “beauty,” and their willingness to consume for design has gradually increased. Based on this, this paper proposes a design method that integrates the marketing concept based on the product design concept and the art design. The Internet of Things is based on the development of the Internet, expands on the basis of the Internet, and extends to people’s daily life with physical devices. It is widely welcomed by various research fields at present. This paper studies the changes brought by the Internet, the integration and innovation of art design and marketing concepts in the Internet age, and the development advantages and changes of traditional product design in information dissemination. Based on this, this paper proposes a user-centered, user-participated design. The experimental results show that the user-participative design method based on the integration of marketing concepts and artistic design and the innovation of functions can be effectively improved by paying attention to user feedback, which can bring good user experience effects. The conversion rate of visits on each platform is above 40%, and it also has a “communication medium” with its own traffic, which can even become a “spiritual sustenance” for direction.


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## Body

## 1. Introduction

In recent decades, developments in the field of information technology have transformed the world, and the marketing department has undergone similar changes. At present, the Internet, as a breakthrough result of the development of information technology, is being combined with marketing activities. Because of this, we can witness a new generation of activity known as the Marketing Age. Today’s customer needs are not limited to meeting their basic needs but require products that satisfy their desires and ease their anxiety. Customers are now looking for products that allow them to be creative and find marketing-defining prices, but they are more likely to be part of the product, contributing to and interacting with it and then using information technology to share their experiences and verify that the product really does what it does. It is also the reason why marketing is no longer focused on the product, just like the internet is no longer data-centric, but it is also an alternative fusion of marketing ideas and artistic design.The continuous rise of Internet technology has led people into a rapidly developing Internet of Things era. Connecting physical devices in reality through the Internet, the information technology of the Internet of Things has played a huge role in various fields of intelligence research. It effectively saves production costs, improves efficiency, creates a more intelligent network structure with minimal cost, transmits data resources through the network structure, and realizes the field collision of data interaction.The innovation of this paper is in two aspects: first, based on the background environment of the Internet of Things, it explores the product design method under the development of the Internet and explores its characteristics in the current era; second, based on the rapid development of the Internet and the integration of people’s ideas and concepts through the Internet, a user-centered design method that promotes user feedback is proposed, which is more in line with user needs and has more media hotspot traffic in marketing.


## 2. Related Work

In order to develop a product design method more suitable for the current era, many scholars at home and abroad have conducted relevant research on the integration method of marketing concept and art design in the context of the Internet of Things. Seppo is basing on a systematic literature review of IoT ecosystems and business models. He builds a conceptual framework for IoT business models and analyzes seven industry cases using qualitative research methods. As a result, the study identified four types of IoT business models: value chain efficiency, industry collaboration, horizontal markets, and platforms. In addition, it discusses three evolutionary paths for the emergence of new business models: opening industry collaboration ecosystems, duplicating solutions across multiple services, and returning to closed ecosystems as the technology matures [1]. Currently, both marketing and the Internet are customer-centric, enhancing the interaction between customers and products. While doing so, they base their value on the user and provide them with more data. W Łukowski introduces the key elements of Marketing 4.0, discusses how it relates to Marketing 3.0 concepts, and explains to what extent Next Generation Marketing is an extrapolation of Marketing 3.0 concepts [2]. Matthyssens aims to understand the key drivers and barriers, both within and outside the company, and gain insight into proven capabilities, emphasizing the ability to create streams of new value initiatives. These ideas then face the current challenges of Industry 4.0 and the Industrial Internet of Things (IIoT). Matthyssens presents a “viewpoint” presenting an empirically based exposition of the new research findings. Based on expert judgment and with existing literature, he suggests that the prospect of five key competencies requires further empirical confirmation [3]. The joint development of cultural heritage protection and tourism has shortcomings in heritage protection and heritage tourism marketing. In response to this problem, Zhenrao uses digital technologies such as oblique aerial photography, 3D laser scanning technology, and 360-degree panoramic technology to digitize cultural landscape heritage sites. He integrates various elements to create a virtual tourism subsystem [4]. Licite-Kurbe analyzes the benefits and risks of the Internet of Things in entrepreneurship and uses the descriptive method, the analytical synthesis method, and the inductive deduction method to achieve the goal. Research shows that IoT can provide multiple opportunities for businesses in all areas of operations, including marketing, logistics, accounting, and human resource management. However, enterprises may face some challenges related to privacy and security, data processing, analysis and management, and monitoring and perception [5]. For a unique contribution, Decker focuses on customer relationship management, product lifecycle management, and business model development and discusses the impact of IoT product enhancements in these areas. He systematically derives ten research propositions through an extensive analysis of current theoretical and practical developments. Finally, he summarizes the research results and gives an outlook on promising directions for further research in IoT-oriented marketing management [6]. The relevant research content of the above scholars provides a certain reference for the research of this paper, but most of the research content focuses on marketing methods and research on the Internet of Things platform; there is a lack of relevant research on the integration of marketing concepts and art design.


## 3. Product Design Methods in the Internet of Things Era

The main manifestation of the Internet of Things (IoT) is based on the development of the Internet. It is transmitted through the connection of the Internet, connecting the actual object equipment in daily life with the network. It is also able to recognize its own thoughts to other devices it transmits, the structure of the Internet of Things to connect things and people more intelligently with minimal cost, and is increasingly popular in daily life and entrepreneurship [7]. IoT covers a wide range of fields, including manufacturing, health sector, agriculture, smart cities, security, and emergencies. [8]. For example, in the street light system of smart cities, traditional street lights are checked and turned on and off by humans, but the application of IoT technology can control the on-off and inspection of street lights by collecting data from sensors distributed on the street lights. The Industrial IoT market will exceed EUR 107 billion by 2021, growing at a CAGR of 7.3% by 2020. The application areas of the Internet of Things are shown in Figure 1.Figure 1
Application areas of IoT.


### 3.1. The Structure of IoT Data Acquisition System

#### 3.1.1. The Structure of the Acquisition System

Figure2 shows the structure of a data acquisition system based on the Internet of Things. The structure is mainly divided into three layers, which can be divided into device layer, network layer, and application layer in terms of process [9]. The device layer is the data collection terminal; the network layer is used to ensure that the data collected by the terminal can be correctly stored in the back-end server center; and the application layer presents specific business applications and solutions to users. According to different application scenarios, it uses the corresponding equipment, network connection method, and application platform [10]. Among them, the equipment layer is the bottom layer of the data acquisition system, which collects environmental data from the outside through analog sensors.Figure 2
Data acquisition system structure.


#### 3.1.2. Key Technologies of Data Acquisition System

The main working principle of the data acquisition system based on the Internet of Things is to convert the collected external environmental data through sensor technology. It converts the analog signal of the sensor into an electrical signal, then converts the electrical signal into a digital signal in a specific message format, and then transmits it to the back-end data center [11]. The acquisition of external signals is usually a conversion process.When the analog signalVn is continuous in time, the relationship between the frequency spectrum and the expression Vm can be expressed as
(1)Vn=∫−+Vmre2πnmbm,(2)Vm=∫−+Vnr−e2πnmbm.When the pulse signalVtFa is in a discrete free state when it is sampled, the free pulse signal can be further determined by the Nyquist sampling theorem [12]. Then the selected sampling interval should satisfy the condition of Fa<=1/2m, so that the sampling frequency ma is higher than twice the maximum frequency of the sampled signal.
(3)VtFa=∫−12na12naVmie2πmtnabm,(4)Vm=∫−12na12naVtnai−e2πmtna.VtFa can be completely determined, and Vn is further determined Vm.
(5)VtFa=na∑−∞+∞VtFa∫−12na12naie2πm1−tnabm.To further reduce the error during acquisition, there are certain equipment requirements for the converter of the data acquisition terminal, which requires excellent resolution, conversion accuracy, and conversion speed [13].


### 3.2. Network Model and Problem Description

#### 3.2.1. Network Model

It is assumed that there are A nodes in a connected sensor network, and they all work under the synchronous low duty cycle working mechanism, and each node has two states of wake-up and sleep [14]. When the node is in the awake state, the main function of the node is to sense or send and receive data [15]. When the node is in sleep state, all functional modules will be turned off except the timer and low-energy monitoring module for waking up the node [16]. In a wireless network environment, data will be interfered during transmission.(1) Interference Model. There are mainly graph-based protocol interference models and SINR physical interference models [17]. Under the protocol interference model, when the link detects that there is a signal of a certain link in the channel, it considers that it has received interference and cannot communicate normally. Different from the protocol interference model, under the physical interference model, whether the communication link can use the channel for transmission does not depend on whether other links already exist in the area using the channel for transmission but depends on whether the sum of the interference caused by all the transmitting links in the area to the link reaches the limit that the link can tolerate. In the protocol, in order to successfully transmit data between node A and node B, it is necessary to make the distance L(R, B) between node R and node B in the data sending state at the same time satisfy the conditions of the following equation.
(6)LR,B≥1+ΔLA,B,Δ>0.In equation (6), L(A,B) represents the distance between nodes A and B. Δ is a parameter set so that the sending node can have a certain distance from the receiving node and not be too close.Considering the cumulative effect of multiple simultaneous transmission signals in the physical model, in order to successfully transmit data between nodes A and B, the ratio of the received signal of node B to other transmission signals and background noise must be higher than the thresholdθ. The equation is expressed as follows:
(7)SinrAB=QA∗GLAB∑R≠AQR∗GLRB+β≥θ.Among them,QA is the transmit power of node A, SinrAB is the signal-to-noise-interference ratio of data transmitted by nodes A and B, β is the representation of background noise, and G⋅ is the signal attenuation function in the above equation.


#### 3.2.2. Problem Description

To define the minimum delay data collection (MDCD) problem, in a given sensor network with X nodes, the first X-1 nodes represent the source node, and the Xth node represents the sink node. The definition of delayed data collection is the time period during which the data packets generated by the source node of X-1 are transmitted to the sink node. The optimization purpose of this problem is to be able to find the node transmission path that minimizes the data collection delay [18].EED(Ha) represents the final delay of one or more hops for the source node Ha to transmit its perceived data to the aggregation node, so that the expression of the optimized objective function is(8)C=maxa=1x‐1EEDHa,∀Ha∈H.Among them,C table minimizes the data.Whether the node Hb at timeS can accept the data transmitted by the Ha node can be represented by the logical variable N(a,b,S) of 0-1, and Fb represents the wake-up time of the node Hb. And it is also a logical variable of 0-1, whether the node Ha can send the data packet Qa at the time of S and can be expressed by t(Qa,b,s). Therefore, the minimum delay data collection (MDCD) problem can be transformed into the following model:Limitation factor:(9)∑a=1,a≠bx‐1Na,b,s1+∑m=1,m≠bx‐1Na,m,s2≤1,whereS1∈Fb,S2∈Fm,(10)∑s=1Sta,b,s≤1,wheres∈0,S,(11)Na,b,s3=0,wheres3∈0,S,s3∉Fb.In the above equations, the functions of inequality (9) are to ensure that the node can only receive data from adjacent nodes and send its own data when it is awake, and the time slots for receiving and sending data cannot be performed at the same time. Therefore, the two links of Figure 3 cannot be scheduled simultaneously [19]. The inequality (10) is to ensure that the same data packet can only be transmitted once by one node. Inequality (11) is to ensure that the node cannot accept data when it is in sleep state [20].Figure 3
Conditional constraints on links.This part first analyzes the strict upper and lower bounds of the data collection delay and gives the proof process. The lower bound of the data acquisition delay isEsinF+K−1Rπ in which the working cycle of the node is Rπ the wake-up time slot of the sink node in the working cycle is EsinF and K represents the number of source nodes.The most reasonable case for data receipts is when the sink node is able to receive data in all wake-up states. Since the sink node can only receive one data packet in a wake-up time slot, the number of wake-up time slots of the sink node is at least equal to the number of source nodes. Therefore, it is the minimum time required by the sink node to receive all packets.Next, it proved that the lower bound is a strict lower bound. As shown in Figure4, it is a best case. The data transfer process can be seen in Table 1. In the list of timeslots with packet transmission, the number in parentheses indicates that the current transmission packet is generated. A lower bound can be obtained for the data collection delay of this example, so lower bound EsinF+K−1Rπ is a strict lower bound.Figure 4
Network topology with a given wake-up time slot for each node.Table 1
Received data nodes and corresponding sending nodes in different time slots.
Node SS slot36710121—3———2———3—34—4(1)—n(2)This upper bound is then proved to be a strict upper bound, as shown in Figure5.Figure 5
The network topology of a node’s wake-up slot in a given situation.Table2 and the figure above represent one of the worst-case scenarios for this case. From the representation in Figure 5 and Table 2, the time required for all data packets to be sent to the sink node is 18, and Ymin1=6, Ymin2=10, Ymin3=2, and ∑n=1iYminn=18; this example can reach the upper bound, so the upper bound ∑n=1iYminn is strictly the upper bound.Table 2
Received data nodes and corresponding sending nodes in different time slots.
Node SS slot261014181—3———2———3—34—4(1)—4(2)


#### 3.2.3. The Maximum Flow Problem under the Limitation of Sending Conditions

Under a given connection number (VGN), that is, when the mobile terminal sends a call to establish, how to convert the MDCD problem into a maximum flow problem with constraints for a given VGN is an important step. The first requirement is the maximum flow transmitted between the source node A and the target node B, which is also the maximum number of data packets that the sink node can collect from the source node within a certain time s. The sink node is a sink node, which is a special node that can accept the transmission packets of all nodes. The binary logical variableK(*, *) is used to indicate whether there is a data flow between two nodes in the VGN, and max is used to indicate all data flows from the source node to the destination node. The main goals are
(12)MaxKA,B.The data collection latency is defined as the time it takes for all data to be delivered to the sink node, which is mainly determined by the maximum end-to-end delay (EED) in packet transmission. The problem of minimum delay data collection is transformed into the problem of minimizing the maximum EED, which is equivalent to the problem of finding the maximum flow with constraints in VGN. Due to the influence of half-duplex communication mode and interference of sensor nodes, among them, half-duplex communication allows signals to be transmitted in two directions but only allows signals to be transmitted in one direction on one channel at a certain time. Therefore, half-duplex communication is actually a simplex communication with switchable directions. The specific constraints are expressed as follows:(1) Nonnegative Flow Restriction. Each edge flow must be greater than or equal to 0, and at the same time, for any point in the VGN, the sum of inflow and outflow must be equal. Corresponding to the VGN network, the following constraints can be drawn:
(13)∑d∈fayfd,sd,fa,s=∑d∈fayfa,s,fd,sd,,wheresd<s<sd∈0,S.Among them,fa is the set of adjacent nodes of node a in the original network. In the half-duplex transceiver limitation, the half-duplex feature is the function that a node cannot receive and transmit at the same time. As shown in Figure 3, when node B receives data from node A, node A cannot send data anymore; otherwise, a data conflict will occur. Corresponding to the VGN network, the following constraints can be drawn:
(14)yfa,s1,fb,s2+yfi,s3,fb,s2≤1,wheres1,s2,s3∈0,S.Among them,y(*,*) is a binary function. When it is 1, it indicates that there is an edge flow, otherwise it is zero.As shown in Figure3, nodes 2 and 3 cannot send data to node 1 at the same time. Based on this constraint, the node capacity in VGN is set to 1, namely Cfa,s=1, so the node capacity in VGN needs to meet the following constraints:
(15)∑d∈fayfd,sd,fa,s≤Cfa,s=1,wheresd≤s∈0.S,(16)∑d∈fayfa,s,fd,sd≤Cfa,s=1,wheres≤sd∈0.S.Since the interfering link has been eliminated by other means, the maximum flow problem with constraints is defined.


### 3.3. Implications of Product Design Methods

#### 3.3.1. The Meaning of Product Design Method

In general, the method is a purposeful behavioral complex, which is the law that human beings themselves summarize through various activities and behaviors and has a certain reference value. The act of art design is a process of time. In this process, the designer must find the best solution that satisfies the functional requirements; that is, there is more than one way to satisfy the condition.The design content expressed in Figure6 shows that the design method is different. The content can be divided into two parts: one is the category of technical methods; the other is the category of engineering methods. People’s design activities are rich in content. Different historical periods and different development stages have different technology status and different design methods.Figure 6
Evolution of design methods over time.


### 3.4. Dilemmas Faced by Traditional Product Design

(1)
The biggest disadvantage of traditional retail to consumers is information asymmetry. Psychological experiments show that amateurs have no confidence in pricing a certain industrial product. As long as the anchor price is thrown, consumers will obediently be led by the nose(2)
Traditional manufacturing is a closed production model, where producers decide what commodities to produce. The roles of producer and consumer are separated(3)
Traditional news in the media industry is somewhat indifferent and objective. As an oligopoly, traditional news media face the challenge of self-promotion, and its voice is gradually weakened


## 3.1. The Structure of IoT Data Acquisition System

### 3.1.1. The Structure of the Acquisition System

Figure2 shows the structure of a data acquisition system based on the Internet of Things. The structure is mainly divided into three layers, which can be divided into device layer, network layer, and application layer in terms of process [9]. The device layer is the data collection terminal; the network layer is used to ensure that the data collected by the terminal can be correctly stored in the back-end server center; and the application layer presents specific business applications and solutions to users. According to different application scenarios, it uses the corresponding equipment, network connection method, and application platform [10]. Among them, the equipment layer is the bottom layer of the data acquisition system, which collects environmental data from the outside through analog sensors.Figure 2
Data acquisition system structure.


### 3.1.2. Key Technologies of Data Acquisition System

The main working principle of the data acquisition system based on the Internet of Things is to convert the collected external environmental data through sensor technology. It converts the analog signal of the sensor into an electrical signal, then converts the electrical signal into a digital signal in a specific message format, and then transmits it to the back-end data center [11]. The acquisition of external signals is usually a conversion process.When the analog signalVn is continuous in time, the relationship between the frequency spectrum and the expression Vm can be expressed as
(1)Vn=∫−+Vmre2πnmbm,(2)Vm=∫−+Vnr−e2πnmbm.When the pulse signalVtFa is in a discrete free state when it is sampled, the free pulse signal can be further determined by the Nyquist sampling theorem [12]. Then the selected sampling interval should satisfy the condition of Fa<=1/2m, so that the sampling frequency ma is higher than twice the maximum frequency of the sampled signal.
(3)VtFa=∫−12na12naVmie2πmtnabm,(4)Vm=∫−12na12naVtnai−e2πmtna.VtFa can be completely determined, and Vn is further determined Vm.
(5)VtFa=na∑−∞+∞VtFa∫−12na12naie2πm1−tnabm.To further reduce the error during acquisition, there are certain equipment requirements for the converter of the data acquisition terminal, which requires excellent resolution, conversion accuracy, and conversion speed [13].


## 3.1.1. The Structure of the Acquisition System

Figure2 shows the structure of a data acquisition system based on the Internet of Things. The structure is mainly divided into three layers, which can be divided into device layer, network layer, and application layer in terms of process [9]. The device layer is the data collection terminal; the network layer is used to ensure that the data collected by the terminal can be correctly stored in the back-end server center; and the application layer presents specific business applications and solutions to users. According to different application scenarios, it uses the corresponding equipment, network connection method, and application platform [10]. Among them, the equipment layer is the bottom layer of the data acquisition system, which collects environmental data from the outside through analog sensors.Figure 2
Data acquisition system structure.


## 3.1.2. Key Technologies of Data Acquisition System

The main working principle of the data acquisition system based on the Internet of Things is to convert the collected external environmental data through sensor technology. It converts the analog signal of the sensor into an electrical signal, then converts the electrical signal into a digital signal in a specific message format, and then transmits it to the back-end data center [11]. The acquisition of external signals is usually a conversion process.When the analog signalVn is continuous in time, the relationship between the frequency spectrum and the expression Vm can be expressed as
(1)Vn=∫−+Vmre2πnmbm,(2)Vm=∫−+Vnr−e2πnmbm.When the pulse signalVtFa is in a discrete free state when it is sampled, the free pulse signal can be further determined by the Nyquist sampling theorem [12]. Then the selected sampling interval should satisfy the condition of Fa<=1/2m, so that the sampling frequency ma is higher than twice the maximum frequency of the sampled signal.
(3)VtFa=∫−12na12naVmie2πmtnabm,(4)Vm=∫−12na12naVtnai−e2πmtna.VtFa can be completely determined, and Vn is further determined Vm.
(5)VtFa=na∑−∞+∞VtFa∫−12na12naie2πm1−tnabm.To further reduce the error during acquisition, there are certain equipment requirements for the converter of the data acquisition terminal, which requires excellent resolution, conversion accuracy, and conversion speed [13].


## 3.2. Network Model and Problem Description

### 3.2.1. Network Model

It is assumed that there are A nodes in a connected sensor network, and they all work under the synchronous low duty cycle working mechanism, and each node has two states of wake-up and sleep [14]. When the node is in the awake state, the main function of the node is to sense or send and receive data [15]. When the node is in sleep state, all functional modules will be turned off except the timer and low-energy monitoring module for waking up the node [16]. In a wireless network environment, data will be interfered during transmission.(1) Interference Model. There are mainly graph-based protocol interference models and SINR physical interference models [17]. Under the protocol interference model, when the link detects that there is a signal of a certain link in the channel, it considers that it has received interference and cannot communicate normally. Different from the protocol interference model, under the physical interference model, whether the communication link can use the channel for transmission does not depend on whether other links already exist in the area using the channel for transmission but depends on whether the sum of the interference caused by all the transmitting links in the area to the link reaches the limit that the link can tolerate. In the protocol, in order to successfully transmit data between node A and node B, it is necessary to make the distance L(R, B) between node R and node B in the data sending state at the same time satisfy the conditions of the following equation.
(6)LR,B≥1+ΔLA,B,Δ>0.In equation (6), L(A,B) represents the distance between nodes A and B. Δ is a parameter set so that the sending node can have a certain distance from the receiving node and not be too close.Considering the cumulative effect of multiple simultaneous transmission signals in the physical model, in order to successfully transmit data between nodes A and B, the ratio of the received signal of node B to other transmission signals and background noise must be higher than the thresholdθ. The equation is expressed as follows:
(7)SinrAB=QA∗GLAB∑R≠AQR∗GLRB+β≥θ.Among them,QA is the transmit power of node A, SinrAB is the signal-to-noise-interference ratio of data transmitted by nodes A and B, β is the representation of background noise, and G⋅ is the signal attenuation function in the above equation.


### 3.2.2. Problem Description

To define the minimum delay data collection (MDCD) problem, in a given sensor network with X nodes, the first X-1 nodes represent the source node, and the Xth node represents the sink node. The definition of delayed data collection is the time period during which the data packets generated by the source node of X-1 are transmitted to the sink node. The optimization purpose of this problem is to be able to find the node transmission path that minimizes the data collection delay [18].EED(Ha) represents the final delay of one or more hops for the source node Ha to transmit its perceived data to the aggregation node, so that the expression of the optimized objective function is(8)C=maxa=1x‐1EEDHa,∀Ha∈H.Among them,C table minimizes the data.Whether the node Hb at timeS can accept the data transmitted by the Ha node can be represented by the logical variable N(a,b,S) of 0-1, and Fb represents the wake-up time of the node Hb. And it is also a logical variable of 0-1, whether the node Ha can send the data packet Qa at the time of S and can be expressed by t(Qa,b,s). Therefore, the minimum delay data collection (MDCD) problem can be transformed into the following model:Limitation factor:(9)∑a=1,a≠bx‐1Na,b,s1+∑m=1,m≠bx‐1Na,m,s2≤1,whereS1∈Fb,S2∈Fm,(10)∑s=1Sta,b,s≤1,wheres∈0,S,(11)Na,b,s3=0,wheres3∈0,S,s3∉Fb.In the above equations, the functions of inequality (9) are to ensure that the node can only receive data from adjacent nodes and send its own data when it is awake, and the time slots for receiving and sending data cannot be performed at the same time. Therefore, the two links of Figure 3 cannot be scheduled simultaneously [19]. The inequality (10) is to ensure that the same data packet can only be transmitted once by one node. Inequality (11) is to ensure that the node cannot accept data when it is in sleep state [20].Figure 3
Conditional constraints on links.This part first analyzes the strict upper and lower bounds of the data collection delay and gives the proof process. The lower bound of the data acquisition delay isEsinF+K−1Rπ in which the working cycle of the node is Rπ the wake-up time slot of the sink node in the working cycle is EsinF and K represents the number of source nodes.The most reasonable case for data receipts is when the sink node is able to receive data in all wake-up states. Since the sink node can only receive one data packet in a wake-up time slot, the number of wake-up time slots of the sink node is at least equal to the number of source nodes. Therefore, it is the minimum time required by the sink node to receive all packets.Next, it proved that the lower bound is a strict lower bound. As shown in Figure4, it is a best case. The data transfer process can be seen in Table 1. In the list of timeslots with packet transmission, the number in parentheses indicates that the current transmission packet is generated. A lower bound can be obtained for the data collection delay of this example, so lower bound EsinF+K−1Rπ is a strict lower bound.Figure 4
Network topology with a given wake-up time slot for each node.Table 1
Received data nodes and corresponding sending nodes in different time slots.
Node SS slot36710121—3———2———3—34—4(1)—n(2)This upper bound is then proved to be a strict upper bound, as shown in Figure5.Figure 5
The network topology of a node’s wake-up slot in a given situation.Table2 and the figure above represent one of the worst-case scenarios for this case. From the representation in Figure 5 and Table 2, the time required for all data packets to be sent to the sink node is 18, and Ymin1=6, Ymin2=10, Ymin3=2, and ∑n=1iYminn=18; this example can reach the upper bound, so the upper bound ∑n=1iYminn is strictly the upper bound.Table 2
Received data nodes and corresponding sending nodes in different time slots.
Node SS slot261014181—3———2———3—34—4(1)—4(2)


### 3.2.3. The Maximum Flow Problem under the Limitation of Sending Conditions

Under a given connection number (VGN), that is, when the mobile terminal sends a call to establish, how to convert the MDCD problem into a maximum flow problem with constraints for a given VGN is an important step. The first requirement is the maximum flow transmitted between the source node A and the target node B, which is also the maximum number of data packets that the sink node can collect from the source node within a certain time s. The sink node is a sink node, which is a special node that can accept the transmission packets of all nodes. The binary logical variableK(*, *) is used to indicate whether there is a data flow between two nodes in the VGN, and max is used to indicate all data flows from the source node to the destination node. The main goals are
(12)MaxKA,B.The data collection latency is defined as the time it takes for all data to be delivered to the sink node, which is mainly determined by the maximum end-to-end delay (EED) in packet transmission. The problem of minimum delay data collection is transformed into the problem of minimizing the maximum EED, which is equivalent to the problem of finding the maximum flow with constraints in VGN. Due to the influence of half-duplex communication mode and interference of sensor nodes, among them, half-duplex communication allows signals to be transmitted in two directions but only allows signals to be transmitted in one direction on one channel at a certain time. Therefore, half-duplex communication is actually a simplex communication with switchable directions. The specific constraints are expressed as follows:(1) Nonnegative Flow Restriction. Each edge flow must be greater than or equal to 0, and at the same time, for any point in the VGN, the sum of inflow and outflow must be equal. Corresponding to the VGN network, the following constraints can be drawn:
(13)∑d∈fayfd,sd,fa,s=∑d∈fayfa,s,fd,sd,,wheresd<s<sd∈0,S.Among them,fa is the set of adjacent nodes of node a in the original network. In the half-duplex transceiver limitation, the half-duplex feature is the function that a node cannot receive and transmit at the same time. As shown in Figure 3, when node B receives data from node A, node A cannot send data anymore; otherwise, a data conflict will occur. Corresponding to the VGN network, the following constraints can be drawn:
(14)yfa,s1,fb,s2+yfi,s3,fb,s2≤1,wheres1,s2,s3∈0,S.Among them,y(*,*) is a binary function. When it is 1, it indicates that there is an edge flow, otherwise it is zero.As shown in Figure3, nodes 2 and 3 cannot send data to node 1 at the same time. Based on this constraint, the node capacity in VGN is set to 1, namely Cfa,s=1, so the node capacity in VGN needs to meet the following constraints:
(15)∑d∈fayfd,sd,fa,s≤Cfa,s=1,wheresd≤s∈0.S,(16)∑d∈fayfa,s,fd,sd≤Cfa,s=1,wheres≤sd∈0.S.Since the interfering link has been eliminated by other means, the maximum flow problem with constraints is defined.


## 3.2.1. Network Model

It is assumed that there are A nodes in a connected sensor network, and they all work under the synchronous low duty cycle working mechanism, and each node has two states of wake-up and sleep [14]. When the node is in the awake state, the main function of the node is to sense or send and receive data [15]. When the node is in sleep state, all functional modules will be turned off except the timer and low-energy monitoring module for waking up the node [16]. In a wireless network environment, data will be interfered during transmission.(1) Interference Model. There are mainly graph-based protocol interference models and SINR physical interference models [17]. Under the protocol interference model, when the link detects that there is a signal of a certain link in the channel, it considers that it has received interference and cannot communicate normally. Different from the protocol interference model, under the physical interference model, whether the communication link can use the channel for transmission does not depend on whether other links already exist in the area using the channel for transmission but depends on whether the sum of the interference caused by all the transmitting links in the area to the link reaches the limit that the link can tolerate. In the protocol, in order to successfully transmit data between node A and node B, it is necessary to make the distance L(R, B) between node R and node B in the data sending state at the same time satisfy the conditions of the following equation.
(6)LR,B≥1+ΔLA,B,Δ>0.In equation (6), L(A,B) represents the distance between nodes A and B. Δ is a parameter set so that the sending node can have a certain distance from the receiving node and not be too close.Considering the cumulative effect of multiple simultaneous transmission signals in the physical model, in order to successfully transmit data between nodes A and B, the ratio of the received signal of node B to other transmission signals and background noise must be higher than the thresholdθ. The equation is expressed as follows:
(7)SinrAB=QA∗GLAB∑R≠AQR∗GLRB+β≥θ.Among them,QA is the transmit power of node A, SinrAB is the signal-to-noise-interference ratio of data transmitted by nodes A and B, β is the representation of background noise, and G⋅ is the signal attenuation function in the above equation.


## 3.2.2. Problem Description

To define the minimum delay data collection (MDCD) problem, in a given sensor network with X nodes, the first X-1 nodes represent the source node, and the Xth node represents the sink node. The definition of delayed data collection is the time period during which the data packets generated by the source node of X-1 are transmitted to the sink node. The optimization purpose of this problem is to be able to find the node transmission path that minimizes the data collection delay [18].EED(Ha) represents the final delay of one or more hops for the source node Ha to transmit its perceived data to the aggregation node, so that the expression of the optimized objective function is(8)C=maxa=1x‐1EEDHa,∀Ha∈H.Among them,C table minimizes the data.Whether the node Hb at timeS can accept the data transmitted by the Ha node can be represented by the logical variable N(a,b,S) of 0-1, and Fb represents the wake-up time of the node Hb. And it is also a logical variable of 0-1, whether the node Ha can send the data packet Qa at the time of S and can be expressed by t(Qa,b,s). Therefore, the minimum delay data collection (MDCD) problem can be transformed into the following model:Limitation factor:(9)∑a=1,a≠bx‐1Na,b,s1+∑m=1,m≠bx‐1Na,m,s2≤1,whereS1∈Fb,S2∈Fm,(10)∑s=1Sta,b,s≤1,wheres∈0,S,(11)Na,b,s3=0,wheres3∈0,S,s3∉Fb.In the above equations, the functions of inequality (9) are to ensure that the node can only receive data from adjacent nodes and send its own data when it is awake, and the time slots for receiving and sending data cannot be performed at the same time. Therefore, the two links of Figure 3 cannot be scheduled simultaneously [19]. The inequality (10) is to ensure that the same data packet can only be transmitted once by one node. Inequality (11) is to ensure that the node cannot accept data when it is in sleep state [20].Figure 3
Conditional constraints on links.This part first analyzes the strict upper and lower bounds of the data collection delay and gives the proof process. The lower bound of the data acquisition delay isEsinF+K−1Rπ in which the working cycle of the node is Rπ the wake-up time slot of the sink node in the working cycle is EsinF and K represents the number of source nodes.The most reasonable case for data receipts is when the sink node is able to receive data in all wake-up states. Since the sink node can only receive one data packet in a wake-up time slot, the number of wake-up time slots of the sink node is at least equal to the number of source nodes. Therefore, it is the minimum time required by the sink node to receive all packets.Next, it proved that the lower bound is a strict lower bound. As shown in Figure4, it is a best case. The data transfer process can be seen in Table 1. In the list of timeslots with packet transmission, the number in parentheses indicates that the current transmission packet is generated. A lower bound can be obtained for the data collection delay of this example, so lower bound EsinF+K−1Rπ is a strict lower bound.Figure 4
Network topology with a given wake-up time slot for each node.Table 1
Received data nodes and corresponding sending nodes in different time slots.
Node SS slot36710121—3———2———3—34—4(1)—n(2)This upper bound is then proved to be a strict upper bound, as shown in Figure5.Figure 5
The network topology of a node’s wake-up slot in a given situation.Table2 and the figure above represent one of the worst-case scenarios for this case. From the representation in Figure 5 and Table 2, the time required for all data packets to be sent to the sink node is 18, and Ymin1=6, Ymin2=10, Ymin3=2, and ∑n=1iYminn=18; this example can reach the upper bound, so the upper bound ∑n=1iYminn is strictly the upper bound.Table 2
Received data nodes and corresponding sending nodes in different time slots.
Node SS slot261014181—3———2———3—34—4(1)—4(2)


## 3.2.3. The Maximum Flow Problem under the Limitation of Sending Conditions

Under a given connection number (VGN), that is, when the mobile terminal sends a call to establish, how to convert the MDCD problem into a maximum flow problem with constraints for a given VGN is an important step. The first requirement is the maximum flow transmitted between the source node A and the target node B, which is also the maximum number of data packets that the sink node can collect from the source node within a certain time s. The sink node is a sink node, which is a special node that can accept the transmission packets of all nodes. The binary logical variableK(*, *) is used to indicate whether there is a data flow between two nodes in the VGN, and max is used to indicate all data flows from the source node to the destination node. The main goals are
(12)MaxKA,B.The data collection latency is defined as the time it takes for all data to be delivered to the sink node, which is mainly determined by the maximum end-to-end delay (EED) in packet transmission. The problem of minimum delay data collection is transformed into the problem of minimizing the maximum EED, which is equivalent to the problem of finding the maximum flow with constraints in VGN. Due to the influence of half-duplex communication mode and interference of sensor nodes, among them, half-duplex communication allows signals to be transmitted in two directions but only allows signals to be transmitted in one direction on one channel at a certain time. Therefore, half-duplex communication is actually a simplex communication with switchable directions. The specific constraints are expressed as follows:(1) Nonnegative Flow Restriction. Each edge flow must be greater than or equal to 0, and at the same time, for any point in the VGN, the sum of inflow and outflow must be equal. Corresponding to the VGN network, the following constraints can be drawn:
(13)∑d∈fayfd,sd,fa,s=∑d∈fayfa,s,fd,sd,,wheresd<s<sd∈0,S.Among them,fa is the set of adjacent nodes of node a in the original network. In the half-duplex transceiver limitation, the half-duplex feature is the function that a node cannot receive and transmit at the same time. As shown in Figure 3, when node B receives data from node A, node A cannot send data anymore; otherwise, a data conflict will occur. Corresponding to the VGN network, the following constraints can be drawn:
(14)yfa,s1,fb,s2+yfi,s3,fb,s2≤1,wheres1,s2,s3∈0,S.Among them,y(*,*) is a binary function. When it is 1, it indicates that there is an edge flow, otherwise it is zero.As shown in Figure3, nodes 2 and 3 cannot send data to node 1 at the same time. Based on this constraint, the node capacity in VGN is set to 1, namely Cfa,s=1, so the node capacity in VGN needs to meet the following constraints:
(15)∑d∈fayfd,sd,fa,s≤Cfa,s=1,wheresd≤s∈0.S,(16)∑d∈fayfa,s,fd,sd≤Cfa,s=1,wheres≤sd∈0.S.Since the interfering link has been eliminated by other means, the maximum flow problem with constraints is defined.


## 3.3. Implications of Product Design Methods

### 3.3.1. The Meaning of Product Design Method

In general, the method is a purposeful behavioral complex, which is the law that human beings themselves summarize through various activities and behaviors and has a certain reference value. The act of art design is a process of time. In this process, the designer must find the best solution that satisfies the functional requirements; that is, there is more than one way to satisfy the condition.The design content expressed in Figure6 shows that the design method is different. The content can be divided into two parts: one is the category of technical methods; the other is the category of engineering methods. People’s design activities are rich in content. Different historical periods and different development stages have different technology status and different design methods.Figure 6
Evolution of design methods over time.


## 3.3.1. The Meaning of Product Design Method

In general, the method is a purposeful behavioral complex, which is the law that human beings themselves summarize through various activities and behaviors and has a certain reference value. The act of art design is a process of time. In this process, the designer must find the best solution that satisfies the functional requirements; that is, there is more than one way to satisfy the condition.The design content expressed in Figure6 shows that the design method is different. The content can be divided into two parts: one is the category of technical methods; the other is the category of engineering methods. People’s design activities are rich in content. Different historical periods and different development stages have different technology status and different design methods.Figure 6
Evolution of design methods over time.


## 3.4. Dilemmas Faced by Traditional Product Design

(1)
The biggest disadvantage of traditional retail to consumers is information asymmetry. Psychological experiments show that amateurs have no confidence in pricing a certain industrial product. As long as the anchor price is thrown, consumers will obediently be led by the nose(2)
Traditional manufacturing is a closed production model, where producers decide what commodities to produce. The roles of producer and consumer are separated(3)
Traditional news in the media industry is somewhat indifferent and objective. As an oligopoly, traditional news media face the challenge of self-promotion, and its voice is gradually weakened


## 4. The Integration Trend of Design and New Media Marketing Concepts in the Internet of Things Era

The integration of product design and marketing concepts, the point is to transform “user needs” into “product needs.”


### 4.1. User Needs

For the definition of user, in the literal sense, the user mainly refers to the user of the product. This paper makes a more detailed division of the concept of users, as shown in Figure7.Figure 7
Consumers contain users.As can be seen from the division in Figure7, the concept of “user” has two meanings.
(1)
Macro perspective: as a user who constitutes a part of human beings, when he uses a certain product, the user will have individual feelings, and individual feelings are only internal factors. In addition, users will also have a common feeling. The problems felt by the commonality and individuality of users converge together to form a rich resource database, which is welcomed by the majority of product design researchers(2)
Micro perspective: users, target audience of the product, contacts. Users have the closest relationship with the product. It can be said that they interact with the product every day, including the user’s use behavior, use scenarios, use habits, interests, and hobbies before using the product. The user himself has built a system, based on this, the design researcher’s understanding of the user cannot be limited to the exploration of his identity level, but also needs to communicate with the user and discuss the factors used during the use. Therefore, it can also be divided at the microlevel. It can be subdivided into interest users, behavior users, scene users, etc.With the advancement of science and technology and the development of the information society, people’s lives have been impacted by the network, and their lifestyles and behaviors have changed to some extent. The development and popularization of mobile terminals make people’s lives more inseparable from IoT products. The popularity of the Internet allows people to obtain more information through the Internet, and various information exchange platforms emerge as the times require, such as Weibo, WeChat, forums, and Tieba. Today’s researchers have learned to use big data to find correlations between users, so they can quickly collect their real feedback and conduct targeted research.As shown by the data in Figure8, QQ still has a relatively fixed mass of users in the network, but the number of users of Weibo tops the list. From this, it can be seen that users’ demand opinions can be better collected through the network, and better user feedback results can also be obtained. For product design, there are more abundant resources.Figure 8
Daily traffic trends and page views per capita in the past 1 month: (a) traffic trends; (b) page views per capita.
(a)(b)


### 4.2. The Core of Internet Thinking: “User Thinking”

At each stage of product development and launch, it mainly includes user thinking, platform thinking, and data thinking. This article takes the strategic and business aspects of the enterprise as the starting point of thinking, combined with “user thinking” to discuss the main line of the product design process.(1)
Internet thinking is far more than that; it exists in every stage of the product, from development to listing. In this article, we take the strategic and business aspects of the enterprise as the starting point and connect the core of Internet thinking in the product design process—“user thinking” with the main line of discussion(2)
User thinking is an in-depth analysis of the product’s business philosophy and consumer cognition, which runs through each stage of the product reaching the user. With the outbreak of the experience economy, products continue to grow under the “user-centered” design concept. As a part of product design, users really participate in the design stage of products, and consumers have more and more voice and feedback through effective channels(3)
Platform thinking emerged in the period of vigorous development of the Internet, and the concept of platform is the driving force of the Internet era. Therefore, the specific direction of platform thinking is a new business form and a new organizational model(4)
Iterative thinking is an improvement on the traditional design process. Among them are micro-innovation, rapid iteration, and other development methods, which are especially suitable for Internet products, because prototypes can be created quickly and evaluated quickly. Changes can be implemented quickly, and release cycles are loosely structured. To achieve rapid response, the search engine can fully iterate on a weekly basis


### 4.3. Product Design of User Participation Mode in the Background of IoT

The origin of a product is demand. The demand analysis is shown in Figure9.Figure 9
Requirements analysis.The needs of users are the needs of products. For the demand analysis of product design, in addition to considering the needs of users, it is also necessary to consider psychological factors and deeper influencing factors, and it also needs to conform to the positioning of the product. Filter out requirements that do not conform to the product strategy and that are less cost-effective when analysis is required.Judging from the data on the conversion rate of network visits in Figure10, the results of user visits to products through the network platform show that the conversion rate of App visits is 19%, and the visit conversion rate of WeChat is 28%. The conversion rate of WeChat platform traffic is higher than that of APP.Figure 10
Network visit conversion rate: (a) APP conversion rate; (b) WeChat conversion rate.
(a)(b)The design of a product is not completed after it is designed according to the collected requirements, and further confirmation is required. First, the designed product should be confirmed with the product operation to confirm whether it can meet the daily data monitoring and analysis needs; the second is to go through various review links, and after passing the requirements review link, it is considered completed and can be entered into development.


## 4.1. User Needs

For the definition of user, in the literal sense, the user mainly refers to the user of the product. This paper makes a more detailed division of the concept of users, as shown in Figure7.Figure 7
Consumers contain users.As can be seen from the division in Figure7, the concept of “user” has two meanings.
(1)
Macro perspective: as a user who constitutes a part of human beings, when he uses a certain product, the user will have individual feelings, and individual feelings are only internal factors. In addition, users will also have a common feeling. The problems felt by the commonality and individuality of users converge together to form a rich resource database, which is welcomed by the majority of product design researchers(2)
Micro perspective: users, target audience of the product, contacts. Users have the closest relationship with the product. It can be said that they interact with the product every day, including the user’s use behavior, use scenarios, use habits, interests, and hobbies before using the product. The user himself has built a system, based on this, the design researcher’s understanding of the user cannot be limited to the exploration of his identity level, but also needs to communicate with the user and discuss the factors used during the use. Therefore, it can also be divided at the microlevel. It can be subdivided into interest users, behavior users, scene users, etc.With the advancement of science and technology and the development of the information society, people’s lives have been impacted by the network, and their lifestyles and behaviors have changed to some extent. The development and popularization of mobile terminals make people’s lives more inseparable from IoT products. The popularity of the Internet allows people to obtain more information through the Internet, and various information exchange platforms emerge as the times require, such as Weibo, WeChat, forums, and Tieba. Today’s researchers have learned to use big data to find correlations between users, so they can quickly collect their real feedback and conduct targeted research.As shown by the data in Figure8, QQ still has a relatively fixed mass of users in the network, but the number of users of Weibo tops the list. From this, it can be seen that users’ demand opinions can be better collected through the network, and better user feedback results can also be obtained. For product design, there are more abundant resources.Figure 8
Daily traffic trends and page views per capita in the past 1 month: (a) traffic trends; (b) page views per capita.
(a)(b)


## 4.2. The Core of Internet Thinking: “User Thinking”

At each stage of product development and launch, it mainly includes user thinking, platform thinking, and data thinking. This article takes the strategic and business aspects of the enterprise as the starting point of thinking, combined with “user thinking” to discuss the main line of the product design process.(1)
Internet thinking is far more than that; it exists in every stage of the product, from development to listing. In this article, we take the strategic and business aspects of the enterprise as the starting point and connect the core of Internet thinking in the product design process—“user thinking” with the main line of discussion(2)
User thinking is an in-depth analysis of the product’s business philosophy and consumer cognition, which runs through each stage of the product reaching the user. With the outbreak of the experience economy, products continue to grow under the “user-centered” design concept. As a part of product design, users really participate in the design stage of products, and consumers have more and more voice and feedback through effective channels(3)
Platform thinking emerged in the period of vigorous development of the Internet, and the concept of platform is the driving force of the Internet era. Therefore, the specific direction of platform thinking is a new business form and a new organizational model(4)
Iterative thinking is an improvement on the traditional design process. Among them are micro-innovation, rapid iteration, and other development methods, which are especially suitable for Internet products, because prototypes can be created quickly and evaluated quickly. Changes can be implemented quickly, and release cycles are loosely structured. To achieve rapid response, the search engine can fully iterate on a weekly basis


## 4.3. Product Design of User Participation Mode in the Background of IoT

The origin of a product is demand. The demand analysis is shown in Figure9.Figure 9
Requirements analysis.The needs of users are the needs of products. For the demand analysis of product design, in addition to considering the needs of users, it is also necessary to consider psychological factors and deeper influencing factors, and it also needs to conform to the positioning of the product. Filter out requirements that do not conform to the product strategy and that are less cost-effective when analysis is required.Judging from the data on the conversion rate of network visits in Figure10, the results of user visits to products through the network platform show that the conversion rate of App visits is 19%, and the visit conversion rate of WeChat is 28%. The conversion rate of WeChat platform traffic is higher than that of APP.Figure 10
Network visit conversion rate: (a) APP conversion rate; (b) WeChat conversion rate.
(a)(b)The design of a product is not completed after it is designed according to the collected requirements, and further confirmation is required. First, the designed product should be confirmed with the product operation to confirm whether it can meet the daily data monitoring and analysis needs; the second is to go through various review links, and after passing the requirements review link, it is considered completed and can be entered into development.


## 5. The Integration of Product Design and New Media in the Internet Era

In the Internet era, product design methods and processes need to adapt to the times. In the process of product development, it is necessary for designers to use Internet thinking to think about new design processes and methods. Under the premise of ensuring the temperament and shape of the enterprise’s products, the specific content of “Internet thinking” can be integrated to encourage users to participate in the design process, which strives to quickly and flexibly exit the product and create a wider product ecosystem.


### 5.1. Mining Users’ “Behavioral Data”

Figure11 makes statistics on the turnover rate of the network platform and the visit time of the website within one month. Staff turnover rate and website visit time are two indicators that reflect user stickiness. In terms of the number and quality of existing users, as well as the platform construction of Weibo, Sina Weibo is far ahead of the four major Weibo.Figure 11
Bounce rate and website visit time within a month; (a) bounce rate; (b) website visit time.
(a)(b)In the Internet era, users have the following characteristics:(1)
The classification of users is automatic. Social platforms such as Weibo and WeChat are set according to the interests of users and form certain circles, which are very useful for design research(2)
The user’s lifestyle is reproduced. Data monitoring technology is used to record how users use products on the Internet. By mining some of the information and data obtained, researchers can replicate user usage scenarios to understand how users use and live(3)
Willingness to share. Users have the habit of sharing, and this active sharing is very beneficial to the design of the study, because the researcher can verify the characteristics of the study through the content provided by the user, which improves the accuracy of the study(4)
The amount of data and information is large, and the reference ability is strong. Products in the era of big data have commonalities; that is, how users use products can be recorded through cameras, test equipment, etc., and qualitative and quantitative analysis can be carried out to find rules(5)
The delivery form is different. Different from the traditional way of going to the street or finding people to send questionnaires, using the online platform, it can directly push information such as survey questionnaires to the products used by users


### 5.2. Build a User’s “Sense of Participation”

Why is the word “participation” so important today? In addition to being related to the consumption habits of young, middle-aged, and elderly people, it is also related to the characteristics of the experience economy era. These people form a relationship chain through the product, gather together with the product as a link, exchange purchasing experience with each other during the purchase process, and share the specific experience of using the product. They draw on the platform of social networking to form communities related to interests, gender, and age. In this community, users’ sense of dignity and self-awareness are gradually enlarged. The level of products is no longer limited to their own functions. Users hope to express their emotions through this platform. It will form a sense of competition, form a virtuous circle, and guide product designers to optimize and improve functions. The visit conversion rate of the platform is shown in Table3.Table 3
Platform conversion rate table.
All platformsAll categoriesVisit conversion rateUVSuccessful paymentAll platformsAll categories43.5%23.331879All platformsAll categories42.5%23.131962All platformsAll categories44.6%22.331873All platformsAll categories45.1%21.41679All platformsAll categories43.5%231859


### 5.3. Pay Attention to User “Feedback”

First of all, do not miss every opportunity for improvement; user feedback is definitely not to be ignored. Many product designers are immersed in the product, and what they can see and hear is one-sided. For product designers, this is the biggest drawback. During the product release period, it is necessary to read the user’s thoughts and understand the user’s evaluation of the product. If the user evaluation channels of a product cannot be opened, then there are two possibilities: one is that users have no feeling for this product at all; the other is that they feel, but there is no convenient and fast feedback channel. For a product, every user request is every opportunity for improvement. The second is to accommodate the majority of users. The answers given by users often confuse product designers, because when the product audience grows, the functional improvements of many products will cause greater shocks: many people like new features, many people do not like new features, and some people have a moderate attitude. The survey data is shown in Figure12.Figure 12
Data comparison: (a) change trend; (b) user segmentation.
(a)(b)As shown in Figure12, the data results for 5 days show that the overall trend of user participation is on the rise. Most of the current user opinions are collected from fixed members, and new visitors account for the least.Finally, designers need to make changes to the product feel refreshed. When users see the improvement of the product, if they have a new feeling of the whole product, it means that the product is really successful; if the user’s feedback on the change is still bad, it means that the user’s needs have not been understood, or there is a problem with the solution.


## 5.1. Mining Users’ “Behavioral Data”

Figure11 makes statistics on the turnover rate of the network platform and the visit time of the website within one month. Staff turnover rate and website visit time are two indicators that reflect user stickiness. In terms of the number and quality of existing users, as well as the platform construction of Weibo, Sina Weibo is far ahead of the four major Weibo.Figure 11
Bounce rate and website visit time within a month; (a) bounce rate; (b) website visit time.
(a)(b)In the Internet era, users have the following characteristics:(1)
The classification of users is automatic. Social platforms such as Weibo and WeChat are set according to the interests of users and form certain circles, which are very useful for design research(2)
The user’s lifestyle is reproduced. Data monitoring technology is used to record how users use products on the Internet. By mining some of the information and data obtained, researchers can replicate user usage scenarios to understand how users use and live(3)
Willingness to share. Users have the habit of sharing, and this active sharing is very beneficial to the design of the study, because the researcher can verify the characteristics of the study through the content provided by the user, which improves the accuracy of the study(4)
The amount of data and information is large, and the reference ability is strong. Products in the era of big data have commonalities; that is, how users use products can be recorded through cameras, test equipment, etc., and qualitative and quantitative analysis can be carried out to find rules(5)
The delivery form is different. Different from the traditional way of going to the street or finding people to send questionnaires, using the online platform, it can directly push information such as survey questionnaires to the products used by users


## 5.2. Build a User’s “Sense of Participation”

Why is the word “participation” so important today? In addition to being related to the consumption habits of young, middle-aged, and elderly people, it is also related to the characteristics of the experience economy era. These people form a relationship chain through the product, gather together with the product as a link, exchange purchasing experience with each other during the purchase process, and share the specific experience of using the product. They draw on the platform of social networking to form communities related to interests, gender, and age. In this community, users’ sense of dignity and self-awareness are gradually enlarged. The level of products is no longer limited to their own functions. Users hope to express their emotions through this platform. It will form a sense of competition, form a virtuous circle, and guide product designers to optimize and improve functions. The visit conversion rate of the platform is shown in Table3.Table 3
Platform conversion rate table.
All platformsAll categoriesVisit conversion rateUVSuccessful paymentAll platformsAll categories43.5%23.331879All platformsAll categories42.5%23.131962All platformsAll categories44.6%22.331873All platformsAll categories45.1%21.41679All platformsAll categories43.5%231859


## 5.3. Pay Attention to User “Feedback”

First of all, do not miss every opportunity for improvement; user feedback is definitely not to be ignored. Many product designers are immersed in the product, and what they can see and hear is one-sided. For product designers, this is the biggest drawback. During the product release period, it is necessary to read the user’s thoughts and understand the user’s evaluation of the product. If the user evaluation channels of a product cannot be opened, then there are two possibilities: one is that users have no feeling for this product at all; the other is that they feel, but there is no convenient and fast feedback channel. For a product, every user request is every opportunity for improvement. The second is to accommodate the majority of users. The answers given by users often confuse product designers, because when the product audience grows, the functional improvements of many products will cause greater shocks: many people like new features, many people do not like new features, and some people have a moderate attitude. The survey data is shown in Figure12.Figure 12
Data comparison: (a) change trend; (b) user segmentation.
(a)(b)As shown in Figure12, the data results for 5 days show that the overall trend of user participation is on the rise. Most of the current user opinions are collected from fixed members, and new visitors account for the least.Finally, designers need to make changes to the product feel refreshed. When users see the improvement of the product, if they have a new feeling of the whole product, it means that the product is really successful; if the user’s feedback on the change is still bad, it means that the user’s needs have not been understood, or there is a problem with the solution.


## 6. Conclusions

This paper firstly introduces the data acquisition system based on the Internet of Things. Then it further expounds the user needs of product design in the era of Internet prevalence and the user characteristics in the context of the Internet of Things era. In the experimental analysis, this paper compares the number of visits of some information exchange platforms, emphasizes the importance of users’ self-contained system in the Internet age, and the importance of user feedback, and affirms the mode of user participation in the design. The research results show that in the network platform, the user’s participation and the number of visits are on the rise as a whole, which shows that this model is in line with the current development. User-participatory design has not been around for a long time in China. There may have been such an idea before, but it is still in the wait-and-see stage. Or it has unconsciously attempted to appeal to and design with users in previous projects but has yet to develop a mature approach. The input-output relationship of participatory design lies in how to mobilize the enthusiasm of users and how to extract basic needs and expectations from many different ideas. It is more practical than strategies on paper. Only by bravely trying, experiencing, and summarizing in practice can we find a design method suitable for our own products.



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*Source: 8435815-2022-04-01.xml*