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Research and implementation of smart city public information mining analysis system based on mobile edge model of game theory

Publié en ligne: 15 Jul 2022
Volume & Edition: AHEAD OF PRINT
Pages: -
Reçu: 21 Feb 2022
Accepté: 29 Apr 2022
Détails du magazine
License
Format
Magazine
eISSN
2444-8656
Première parution
01 Jan 2016
Périodicité
2 fois par an
Langues
Anglais
Introduction

With the vigorous development of the global communication industry and the continuous improvement of my country's economic level, the development of cities and network operators is like a broken bamboo, the amount of urban public information data continues to expand, and the communication tariffs across the country are also rapidly falling, resulting in continuous access to the mobile communication industry. Increase [1,2]. The use of brand-new mobile network technology to realize the rational mining and analysis of smart city public information data has important practical significance.

At present, in the context of the rapid development of mobile Internet services, mobile edge computing (MEC), as one of the core technologies of 5G, has broad development prospects. MEC migrates computing storage capabilities and business service capabilities to the edge of the network, enabling applications, services, and content to achieve localized, short-distance and distributed deployment [3]. The construction of a smart city is in a process from concept to gradual implementation. In a variety of smart city application scenarios, from intelligent transportation and autonomous driving to real-time monitoring and real-time monitoring, various types of data need to be processed at the edge of the network, not in the cloud. Therefore, edge computing plays an important role in a variety of smart city application scenarios. With reference to the Internet of Things and cloud computing industries, the potential market size of edge computing is expected to reach more than tens of billions. Based on the above market prospects and challenges, build a smart city video network service platform based on mobile edge computing technology, support the video network platform wireless camera access, process and distribute video data nearby, and improve the processing efficiency and service quality of the platform. The urban operation ecology provides a platform for co-creation and sharing, supports the sustainable development of smart cities, and provides support for public safety, comprehensive governance, urban management, and people's livelihood services.

After years of development, the GSM global mobile network has been relatively mature. Compared with the traditional network, its biggest advantage is the open interface, which is not limited to the air interface [4,5]. The GSM network has the advantage that the number of users with existing resources is as high as the high network coverage rate, and can well realize the long-distance transmission of data. The GSM network has more prominent advantages in short messages, including the obvious advantages of no dialing, permanent online, and wide coverage. This advantage is especially suitable for frequent transmission of small-flow data. This advantage can be used to set up communication lines in remote and economically underdeveloped areas, and through the use of GSM networks, unlimited charging and remote target monitoring can be achieved [4]. The GSM network can also be used in automatic receivers and bank ATM machines. In addition, because the GSM network can realize the advantages of automatic identification of network parameters, it is often used to detect the position signal strength of certain working base stations. There are also significant advantages in positioning, and it is often used to measure the temperature of the positioning point.

At the advanced academic exchange meeting in November 2013, the concept of “smart city public information integrated platform” was proposed for the first time. This concept specifically clarified the technical system of the smart city public information integrated platform. Hot discussion and unanimous praise. This article will use the GSM network to realize the technical system and system architecture of the “Integrated Platform for Mining and Analysis of Public Information in Smart Cities”. According to the plan of the Ministry of Industry and Information Technology, a comprehensive platform for the mining and analysis of smart city public information has been proposed. Since 2015, the role of e-government in promoting the transformation of government functions and the construction of service-oriented government has become more significant, and has formed a national e-government transmission backbone network. The basic unified e-government network. The platform covers more than 85 percent of the important information of the central ministries and provincial government departments, and the coverage rate of the government affairs departments in prefecture-level cities and counties reaches 70%. And more than fifty percent. With the expansion of e-government services to the grassroots level, the role of mobile networks has become more popular, and a framework for public information platforms and business collaboration has basically taken shape. This framework includes a number of major businesses such as social credit, comprehensive taxation, market supervision, and social security.. According to the aforementioned national policy plans, the task of comprehensively deepening the construction of a smart city public information mining and analysis platform is put on the agenda. The goal is to systematically plan and improve urban public information, optimize information processing capabilities, and improve public service quality.

Continuing to promote the integration of information technology and city management is the guarantee and requirement for realizing the mining and analysis of the smart city public information platform. The smart city public information platform should include the construction of education, employment, medical and housing systems. The construction of smart cities needs to continue to deepen the application of business systems such as finance, industry and commerce, quality inspection, customs, financial supervision, price, energy, and industrial economic operation, and integrate the above business system information into the entire smart city public information platform to achieve better Service and management, and improve the city's market supervision and public service capabilities. Continuing to strengthen the construction of public information platforms for smart cities is conducive to ensuring the construction of information systems for the country's audit supervision, public safety, land and resources, food and drug safety, safe production, national defense technology and industry, and improving the government's ability to execute. At the same time, accelerating the construction of smart city public information platforms can improve the efficiency of government work at all levels. The construction of the smart city public information platform should migrate to the cloud computing model, make full use of the GSM mobile communication network, continue to enrich the public service methods of e-government, make full use of the existing e-government infrastructure, and continue to carry out the intensive construction and application services of e-government. Continue to strengthen the informatization construction of smart cities, provide comprehensive information services for public training, employment, social security, medical treatment, family planning, housekeeping, travel, etc., and provide special information services for the disabled, the elderly, and low-income families. Encourage grassroots e-government application model innovation, and support grassroots governments to carry out pilot demonstrations of enterprise and public-centric e-government service model innovation.

CSM network optimization system related technology
GSM network structure diagram

The GSM global digital mobile communication system is mainly composed of four parts, namely the network subsystem (NSS), the wireless base station subsystem (BSS), the mobile station (MS) and the operation maintenance support system (OSS). The specific structure is shown in Figure 1.

Figure 1

GSM network structure diagram

Among them, Network Subsystem (NSS): This subsystem mainly implements the database functions required for data exchange, user authentication, MS mobility management and wireless network security management in the GSM network. The second part of the mobile station (MS): the mobile phone, it is the part of the mobile terminal responsible for the GSM network. The composition of the mobile station is composed of two parts including the mobile terminal MS and the SIM card for user identification. What the mobile terminal implements in a voice call is voice coding, channel coding, information encryption, demodulation, and information transmission and reception. The third part is the operation and maintenance support system (OSS): this part is responsible for the operation and maintenance of the system, and realizes the monitoring of some equipment inside the GSM mobile communication network, alarm monitoring, and the activation of the equipment in the network, the adjustment of parameters, and the failure of the equipment Diagnosis and troubleshooting, etc[6].

Derivation of the game-theory mobile edge model

With the rapid development of wireless communication technology, mobile devices and computation-intensive applications show a continuous upward trend, and the massive data and information processing has a certain impact on the traditional cloud computing architecture. As one of the key technologies of 5G, mobile edge computing can effectively handle mobile device migration tasks in the network edge deployment server, improve the battery usage time of application devices and reduce the probability of system delay. This paper studies the model calculation process of intelligent city public information mining system based on game theory optimization framework.

Definition 1

The actual system model is a three-layer mobile edge computing network, where N represents the computing service provider, M represents the number of mobile users, and K edge computing nodes are randomly distributed in the network. v = {v1, v2,…, vN} represents the set of computing service providers, u = {u1, u2,…, uM} represents the MTH computing service provider, u = {u1, u2,…, uM} represents the set of mobile users, um, m ∈ {1, 2,…, M} represents the MTH mobile user, E = {E1, E2,…, EK} represents the set of edge computing nodes.

Proposition 2

According to the task representation of mobile users Ωm = (Qm, Dm), on the basis of the simplified model, the calculation formula for the uplink transmission rate between users and edge computing nodes is as follows: Rm,k=Wlog2(1+Pmgm,kN0) {R_{m,k}} = W{\log_2}\left({1 + {{{P_m}{g_{m,k}}} \over {{N_0}}}} \right)

In the above formula, W represents the bandwidth of the uplink, P0 represents the transmission power of the user, gm,k represents the channel gain between user um and node EK, and N represents the noise power. The actual upload delay can be calculated as follows: tm,kup=QmRm,k t_{m,k}^{up} = {{{Q_m}} \over {{R_{m,k}}}}

Lemma 3

Based on the above formula analysis, the calculation formula of energy transfer consumption between user um and node EK is as follows: Gm,kup=tm,kupPm=QmRm,kPm G_{m,k}^{up} = t_{m,k}^{up}{P_m} = {{{Q_m}} \over {{R_{m,k}}}}{P_m}

The next delay is the processing delay of edge computing nodes, and the specific calculation formula is as follows: tm,kcom=Dmfk t_{m,k}^{com} = {{{D_m}} \over {{f_k}}}

Conjecture 5 In the above formula, fk represents the calculation level of the edge computing node, and the energy consumption that can be generated can be calculated by: Gm,kcom=Pktm,kcom=PkDmfk G_{m,k}^{com} = {P_k}t_{m,k}^{com} = {P_k}{{{D_m}} \over {{f_k}}}

In the above formula, P represents the actual power of the node. The delay consumption formula generated by the amount of tasks processed by nodes is as follows: tktotal=tm,kup+tm,kcom=QmRm,k+Dmfk t_k^{total} = t_{m,k}^{up} + t_{m,k}^{com} = {{{Q_m}} \over {{R_{m,k}}}} + {{{D_m}} \over {{f_k}}}

The calculation formula of the total energy consumption generated by the processing tasks of edge computing nodes is as follows: Gktotal=Gm,kup+Gm,kcom=QmRm,kPm+PkDmfk G_k^{total} = G_{m,k}^{up} + G_{m,k}^{com} = {{{Q_m}} \over {{R_{m,k}}}}{P_m} + {P_k}{{{D_m}} \over {{f_k}}}

The overall calculation formula of total delay consumption and energy consumption is as follows: Cktotal=Ikttktotal+IkeGktotal C_k^{total} = I_k^tt_k^{total} + I_k^eG_k^{total}

In the above formula, Iik represents time delay, Iek represents energy consumption factor, and the calculation formula for the total loss of the system is as follows: Call=k=1kCktotal {C_{all}} = \sum\limits_{k = 1}^k {C_k^{total}}

Example 6. At the same time, the rental and computing consumption of service providers should be calculated comprehensively so that the benefits can be maximized. Firstly, adjacency matrix ΨN×K is defined to represent the association relation between service provider Vn and compute node Ek, and the {n, k} element of ΨN×K is: ψn,k={1,IfVnRentsEk0,IfVnDoes'tRentsEk {\psi_{n,k}} = \left\{{\matrix{{1,\,If\,{V_n}\,{Rents} \,\,{E_k}} \hfill \cr {0,\,If\,{V_n}\,Does't \, Rents \,{E_k}} \hfill \cr}} \right.

The utility function of the service provider is defined as follows: Wnv=WngainWnrent=k=1OnWn,kgaink=1OnWn,krent W_n^v = W_n^{gain} - W_n^{rent} = \sum\limits_{k = 1}^{{O_n}} {W_{n,k}^{gain} - \sum\limits_{k = 1}^{{O_n}} {W_{n,k}^{rent}}}

The utility function of the node is defined as follows: WkE=Wk,ngWkcost W_k^E = W_{k,n}^g - W_k^{\cos t}

Grasp the definition of auction model as shown below: bm,n(t)=bm.n(t1)+Δbm,n(t) {b_{m,n}}\left(t \right) = {b_{m,n}}\left({t - 1} \right) + \Delta {b_{m,n}}\left(t \right)

Where Δbm,n(t) represents the change of bidding, which is actually defined as: Δbm,n(t)={1,ThebidattimeTisreducedby10,ThebiddingattimeTstaysthesame1,ThebidattimeTincreasesby1 \Delta {b_{m,n}}\left(t \right) = \left\{{\matrix{{- 1,\,\,The\,bid\,at\,time\,T\,is\,reduced\,by\,1} \hfill \cr {0,\,The\,bidding\,at\,time\,T\,stays\,the\,same} \hfill \cr {1,\,The\,bid\,at\,time\,T\,increases\,by\,1} \hfill \cr}} \right.

Meanwhile, the computing level formula of edge nodes allocated by computing service providers to users is as follows: Lm,n(t)=bm.n(t)m=1Mbm,n(t)k=1Onfk {L_{m,n}}\left(t \right) = {{{b_{m.n}}\left(t \right)} \over {\sum\limits_{m = 1}^M {{b_{m,n}}\left(t \right)}}}\sum\limits_{k = 1}^{{O_n}} {{f_k}}

The overall computing level of bidding allocation provided by users to computing service providers is: Fm(t)=n1NLm,n(t) {F_m}\left(t \right) = \sum\limits_{n \ge 1}^N {{L_{m,n}}\left(t \right)}

And when the task quantity arrival rate λ obeys the Poisson distribution, the probability calculation formula of lmt l_m^t task arrival to users is as follows: Pr(lmt)=(λΔt)lmt|lmt!|eλΔt {P_r}\left({l_m^t} \right) = {{{{\left({\lambda \Delta t} \right)}^{l_m^t}}} \over {\left| {l_m^t!} \right|}}{e^{- \lambda \Delta t}}

The iterative calculation formula of the actual period is as follows: lm(t+1)=lm(t)+lmtlCm(t) {l_m}\left({t + 1} \right) = {l_m}\left(t \right) + l_m^t - l_C^m\left(t \right)

In the above formula, lCm(t)=Fm(t)ΔtDm l_C^m\left(t \right) = {{{F_m}\left(t \right)\Delta t} \over {{D_m}}} represents the number of tasks migrated by users at time t.

Secondly, master the optimization of computing node rental. The actual definition is as follows: WS=n=1Nk=1KΨn,k(WnV+WnV+WkE) {W^S} = \sum\limits_{n = 1}^N {\sum\limits_{k = 1}^K {{\Psi_{n,k}}\left({W_n^V + W_n^V + W_k^E} \right)}}

In this problem, edge computing nodes and service providers need to build matched and balanced states. In order to maximize system benefits, the following formula should be used to analyze the problem: maxΨn=1Nk=1Kψn,k(WnV+WkE)s.t.C1:ψn,k{0,1}C2:k=1Kψn,kOn,n=1,,NC3:n=1Nψn,k1,k=1,,K \matrix{{\mathop {\max}\limits_\Psi \sum\limits_{n = 1}^N {\sum\limits_{k = 1}^K {{\psi_{n,k}}\left({W_n^V + W_k^E} \right)}}} \hfill \cr {s.t.\,C1:{\psi_{n,k}} \in \left\{{0,1} \right\}} \hfill \cr {\,\,\,\,\,\,\,\,\,\,C2:\sum\limits_{k = 1}^K {{\psi_{n,k}} \le {O_n},n = 1,\, \ldots ,N}} \hfill \cr {\,\,\,\,\,\,\,\,\,\,C3:\sum\limits_{n = 1}^N {{\psi_{n,k}} \le 1,k = 1,\, \ldots ,K}} \hfill \cr}

Finally, the auction optimization problem of edge computing nodes is studied. In this paper, all state transition probabilities in the problem are regarded as 1, and four cases of defining the system are mainly considered:

First, state set, the specific form is as follows: s={st|st=s(t)},t=0,1,,Ts(t)=[s1(t),s2(t),.sM(t)]sm(t)={0,1,2,,J} \matrix{{s = \left\{{{s_t}\left| {{s_t} = s\left(t \right)} \right.} \right\},\,t = 0,1, \ldots ,T} \hfill \cr {s\left(t \right) = \left[{{s_1}\left(t \right),\,{s_2}\left(t \right), \ldots .{s_M}\left(t \right)} \right]} \hfill \cr {{s_m}\left(t \right) = \left\{{0,1,2, \ldots ,J} \right\}} \hfill \cr}

Second, dynamic set, the specific form is as follows: A={at|at=a(t)},t=0,1,2,,Ta(t)=[a1(t),a2(t),.aM(t)]am(t)=[Δbm,1(t),Δbm,2(t),.Δbm,N(t)] \matrix{{A = \left\{{{a_t}\left| {{a_t} = a\left(t \right)} \right.} \right\},\,t = 0,1,2, \ldots ,T} \hfill \cr {a\left(t \right) = \left[{{a_1}\left(t \right),\,{a_2}\left(t \right), \ldots .{a_M}\left(t \right)} \right]} \hfill \cr {{a_m}\left(t \right) = \left[{\Delta {b_{m,1}}\left(t \right),\,\Delta {b_{m,2}}\left(t \right), \ldots .\Delta {b_{m,N}}\left(t \right)} \right]} \hfill \cr}

Third, the reward function, the specific definition formula is as follows: rt+1(m)=γ1log(1+Jsm(t))γ2n=1Nbm,n(t)bmax(t) {r_{t + 1}}\left(m \right) = {\gamma_1}\log \left({1 + J - {s_m}\left(t \right)} \right) - {\gamma_2}\sum\limits_{n = 1}^N {{{{b_{m,n}}\left(t \right)} \over {{b_{\max}}\left(t \right)}}}

Fourth, the target equation, the specific form is as follows: maxπt=0Trt+1(m)Ts.t.C1:tlCm(t)θ,mC2:mFm(t)K=1Kfk \matrix{{\mathop {\max}\limits_\pi {{\sum\limits_{t = 0}^T {{r_{t + 1}}\left(m \right)}} \over T}} \hfill \cr {s.t.\,C1:\sum\limits_t {l_C^m\left(t \right) \ge \theta ,\forall m}} \hfill \cr {\,\,\,\,\,\,\,\,\,\,C2:\sum\limits_m {{F_m}\left(t \right) \le \sum\limits_{K = 1}^K {{f_k}}}} \hfill \cr}

In order to ensure the maximum benefit of the system, this paper proposes a dynamic resource allocation algorithm with joint game as the core. The lease and allocation of edge computing nodes are discussed based on the above research matching theory.

On the one hand, the comprehensive definition formula of edge computing node action algorithm based on matching theory is shown as follows: wn,k=β11tktotal+β21Gktotal {w_{n,k}} = {\beta_1}{1 \over {t_k^{total}}} + {\beta_2}{1 \over {G_k^{total}}}

The updating formula of the actual utility function is: WnV=WngainWnrent=k=1On(wn,kWn,krent) W_n^V = W_n^{gain} - W_n^{rent} = \sum\limits_{k = 1}^{{O_n}} {\left({{w_{n,k}} - W_{n,k}^{rent}} \right)}

Then the system benefit function update calculation formula is: WS=n=1Nk=1Kψn,k(k=1Onwn,kWkcost) {W^S} = \sum\limits_{n = 1}^N {\sum\limits_{k = 1}^K {{\psi_{n,k}}\left({\sum\limits_{k = 1}^{{O_n}} {{w_{n,k}} - W_k^{\cos t}}} \right)}}

On the other hand, the auction algorithm is calculated by combining the optimal Behrman equation as shown below: Q*(st,at)=E(rt+1+γmaxat+1Q*(st+1,at+1)|st=s,at=a)=st+1p(st+1|st.at)[rt+1+γmaxat+1Q*(st+1,at+1)] \matrix{{{Q^*}\left({{s_t},{a_t}} \right) = E\left({{r_{t + 1}} + \gamma \mathop {\max}\limits_{{a_{t + 1}}} {Q^*}\left({{s_{t + 1}},{a_{t + 1}}} \right)\left| {{s_t} = s,\,{a_t} = a} \right.} \right)} \hfill \cr {= \sum\limits_{{s_{t + 1}}} {p\left({{s_{t + 1}}\left| {{s_t}.{a_t}} \right.} \right)\left[{{r_{t + 1}} + \gamma \mathop {\max}\limits_{{a_{t + 1}}} {Q^*}\left({{s_{t + 1}},\,{a_{t + 1}}} \right)} \right]}} \hfill \cr}

The actual update formula is: Q(st,at)Q(st,at)+αrt+1+γmaxaQ(st+1.a)Q(st,at) Q\left({{s_t},{a_t}} \right) \leftarrow Q\left({{s_t},{a_t}} \right) + \alpha \left\lfloor {{r_{t + 1}} + \gamma \mathop {\max}\limits_{{a^{'}}} \,Q\left({{s_{t + 1}}.{a^{'}}} \right) - Q\left({{s_t},{a_t}} \right)} \right\rfloor

Final study found that mobile edge based on game theory model of intelligent city public information mining analysis system, can guarantee the compute nodes and service providers to achieve the best matching optimization, such not only can effectively improve the system running efficiency, also can ensure more efficient allocation of resources, quickly adapt to different types of user requirements.

The optimization of the GSM network specifically refers to the use of various index thresholds and past experience to adjust the hardware equipment and parameters of the GSM network. Use the best technical capabilities to make full use of network resources to achieve optimal system performance, seek a balance between network resources and network quality, and optimize the relevant performance index parameters to maximize the network's service capabilities. Traditional networks are unreasonably designed in the construction of some smart cities, failing to achieve full coverage, causing weak signals at the edge of some areas to be dropped; in addition, unreasonable network parameter settings in areas will affect user switching and reselection. These are all It needs to be adjusted gradually in the process of network optimization[7]. Wireless network optimization is the advanced maintenance work after network planning and project construction. From the preliminary planning of the base station to the completion of construction, the parameter configuration is set according to the preliminary planning or default settings. In this way, there will inevitably exist uncertain factors that require network optimization after the device enters the network. As an important work process of wireless network maintenance, wireless network optimization is different from network planning and construction, but it is inseparable from it. Therefore, the best way to improve network performance and service quality is to perform continuous technical optimization of the network on a regular basis[8].

Smart City Public Information Mining Based on Edge Computing GSM Network

The smart city video network service platform based on mobile edge computing has introduced the MEC shunt gateway and MEC platform in its deployment. Using 5G edge computing capabilities, video computing and storage are forwarded to the edge computer room for processing, thereby improving the processing efficiency and service capabilities of the video network service platform. The functional architecture of the Smart City Video Networking Service Platform includes “one cloud, one end and four platforms”. It is a video “convergence, management, empowerment, and application” support platform based on the Inspur cloud architecture. “One cloud” refers to the Inspur cloud platform, including hardware resource pool, Yunhai OS, Yunhai IOP, and the cloud supporting the video network service platform Architecture operation service. “One side” refers to Inspur, deploying MEC offload gateways in mobile edge computer rooms, building MEC platforms, and providing edge-side computing services and storage services. “One end” includes end devices and edge devices, including various video, picture, data collection terminals and edge computing devices, including cameras, smart cameras, gateways, and terminal servers.

The smart city public information mining and analysis comprehensive platform proposed in this paper combines technical research in the field of e-government and an in-depth understanding of the overall framework of smart cities. In order to eliminate the one-sided isolation of information and better realize the interconnection of resources and collaborative office in the information construction of the public platform of smart city construction, the main task of this article is to build the city's resource exchange platform. The exchange subsystem includes: resource catalog subsystem, The resource exchange subsystem, the audit log subsystem, and the pre-resource exchange system set up in front of different government departments, business entities and social organizations.

The function of the smart city public information integrated platform is mainly defined as: through the construction of a city resource exchange platform, the two core engine technologies of resource positioning and resource exchange based on cloud computing, cloud storage and big data technology are used to realize the interconnection of urban information resources. Solve the problem of no connection between various parts of information, and realize the traceability and accountability of urban public information platform data through authorized use under the premise of ensuring that the information is safe and reliable. The databases distributed in different regions of the city are connected to the city resource sharing platform through the pre-exchange system, shared library and bridge system to ensure the authority, authenticity and fairness of the data.

According to Figure 2, the AT89S52 single-chip microcomputer in this system is the core of the overall operation of the system and is responsible for the work control of each system submodule. In addition, DS18B20 is responsible for sending city information data to the single-chip microcomputer, and the city information data processed by the single-chip microcomputer is displayed by the LCD1602 module in the above figure [9]. This system name realizes a single-chip function by using the TC35 module to send urban public information data to the terminal or directly to the PC.

Figure 2

System overall architecture diagram

The composition of the smart city public information integrated platform mainly includes subsystems: big data management system and distributed storage system, basic database system, platform database system and thematic database system to be constructed. The system operation process of the smart city public information platform is shown in Figure 3. Among them, the main work content of GSM network optimization includes hardware operating status, network parameter adjustment, traffic statistics, testing and other data collection and arrangement. Through the above process, we can find out the drawbacks that affect network quality, so as to further find the optimization method of network quality., Use experiments for further verification. Finally, perform data collection, integration, analysis and mining again, and then test the platform to improve network quality and user perception in a continuous cycle.

Figure 3

System operation process

experimental design

The GSM-based smart city public information mining and analysis system of this system requires 3 servers, one of which is used as the collection server HP RP4440, the second is used as the database server HP RP8440, and the last is used as the application server HP RX6600. The other two are required. PC server, as GIS server HP DL380G5 and CORBA respectively Interface server HPDL380G5P. The specific experimental environment deployment is shown in Figure 4.

Figure 4

Server deployment structure

In this paper, a smart city public information mining and analysis system based on GSM network is designed by combining GSM network with other wireless communication technologies [10]. Its features are as follows: The system can upload the city's public information data values to mobile phones or PCs, When new department data is added, the system can make corresponding analysis and processing in time, can categorize corresponding information in time, can be used in a wide range, low operating cost, and long transmission distance.

The design difficulties of this system are as follows.

First of all, the most prominent design difficulty is focused on wireless transmission. This article uses an advanced GSM network to realize the remote transmission of city information.

Secondly, this system uses a single-chip microcomputer to control the work of each module in the system. For example, the civic management and corporate management control the GSM module through the serial port.

This system uses relatively simple TEXT programming language in programming to realize the software programming and debugging of the system. The operating efficiency of the system is relatively high when used for testing, and the operating cost is low. Consider these two factors. The system selects the GSM module TC35 as the wireless transmission element.

In this system, the transmission of SMS (Short Message Service), the information processing service of the GSM mobile network, is completed through the signaling channel, which belongs to the GSM communication network, and only needs to complete the encoding of the city data information and the destination through the GSM network protocol. Then it can be sent to the mobile provider service center. The information of the urban public information service center will not be sent directly to the terminal, but will choose to store the information. Therefore, when the system is not turned on, the message will not be lost. The smart city public information mining and analysis platform implemented in conjunction with the GSM network is shown in Figure 5.

Figure 5

Smart City Information Platform

In the process of building a smart city public information mining and analysis system, it is necessary to integrate the existing data of various departments. These data and resources include two parts: new and historical. While analyzing and mining historical data, new data is added as the best entry point for building a smart city public information mining and analysis system. The core idea of smart city construction is to focus on the smart city resource exchange platform, build a front-end exchange system and shared database to connect the information and service desks of each subsystem, and combine the new platform with integration and upgrade to speed up the smart city. The construction and application of a comprehensive public information platform.

As shown in Figure 6, the system integrates data distributed on different functional platforms such as medical and health, smart transportation, labor and employment, environmental protection, etc., to realize resource sharing, avoid information isolation, and further realize collaborative office and better services. And managed the city, enterprises and citizens, and realized the role of a smart city public information platform.

Figure 6

System operation core

Conclusion

In the design of the system, the GSM mobile communication network is selected to realize the mining and analysis of the public information of the smart city. The system realizes the mining and analysis of the public information of the smart city with high accuracy, good reliability and low cost. Through the final test of the system, the system can be fully applied to the smart city public information platform.

In order to better meet the requirements of the times, the system adopts digital design ideas. The system associates the GSM network with the single-chip microcomputer, and makes full use of the advantages of GSM to provide multiple interfaces to send instructions and smart city data to TC35. At the same time, it uses the cellular design of GSM for remote transmission. Data, compared with traditional transceiver modules, has stronger reliability. When designing a smart city public information mining and analysis system, this paper chooses to use relatively simple TEXT coding to realize data transmission, which further streamlines the coding process of the program. This article uses the existing GSM mobile communication network to build a smart city public information mining and analysis system. This is a brand-new design for the network operation of the smart platform, and it also opens a new way of thinking for follow-up research. As a result, the potential of the GSM mobile communication network can be further explored. Of course, there are more and richer technologies to choose from in today's society. In future research, a variety of information collection techniques can also be used for effective supporting applications to realize the mining and analysis of public information in smart cities.

Figure 1

GSM network structure diagram
GSM network structure diagram

Figure 2

System overall architecture diagram
System overall architecture diagram

Figure 3

System operation process
System operation process

Figure 4

Server deployment structure
Server deployment structure

Figure 5

Smart City Information Platform
Smart City Information Platform

Figure 6

System operation core
System operation core

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