Research Article Design and Implementation of IoT Data ...

Research ArticleDesign and Implementation of IoT Data-Driven Intelligent LawClassroom Teaching System

Gaoyan Shi

Shijiazhuang Vocational and Technical College Shijiazhuang 050081 China

Correspondence should be addressed to Gaoyan Shi shigysjzpteducn

Received 20 October 2021 Revised 6 December 2021 Accepted 9 December 2021 Published 25 March 2022

Academic Editor Akshi Kumar

Copyright copy 2022 Gaoyan Shiis is an open access article distributed under the Creative Commons Attribution License whichpermits unrestricted use distribution and reproduction in any medium provided the original work is properly cited

In this paper we conduct in-depth research and analysis by building an IoTdata-driven intelligent law classroom teaching systemand implementing it in the actual teaching process Firstly the application requirements and main functions of the classroominteractive system are analysed and studied in depth the overall design of the classroom interactive system based on wirelesscommunication is carried out and the classroom interactive system consisting of teacherrsquos receiver wireless receiver studentrsquoshandheld terminal teacherrsquos receiver upper computer software studentrsquos handheld terminal upper computer software and datamanagement website is developed Platform and communication part middleware comprises multiple modules such as real-timememory event database task management system and event management system e system uses USB communication serialtime-sharing communication Zigbee wireless communicationWebSocket and other technologies to realize data communicationbetween several modules and some key technologies are studied and improved in detail Finally the system is deployed to theactual experimental application scenario and the software and hardware construction of the IoT innovation lab is completed elab equipment is officially put into use the information platform is also in the trial run stage and the system is tested in timeduring the operation stage to provide the basis for the improvement of the system functions Use data to conduct an in-depthanalysis of learning situations and learning process analysis optimize the reasonable construction of a personalized learningenvironment serve personalized learning and achieve the most optimal learning effect e secondary development of the Turingrobot provides a personalized auxiliary QampA system for teaching allowing artificial intelligence-assisted teaching to be integratedinto the curriculum With the help of teaching law courses the empirical study demonstrates that the personalized learningenvironment has significant advantages in improving teaching effectiveness

1 Introduction

e rapid development of information technology hasaccelerated the deep integration of information technologyand education teaching information technology platformsand artificial intelligence-assisted teaching other informa-tion technology resources have emerged and a variety ofnew learning methods have emerged to show that educationis making headway towards the information technologyprocess However due to the complexity of informationtools teaching reform often tends to go astray and veryoften information technology tools become just a landmarkapplication to highlight new learning styles staying only forinformation technology competitions or open or observedclasses and not falling into the classroom reality for students

[1] e main research purpose of this paper is to selectintegrate and develop informatization resources under adata-driven perspective and build a personalized informa-tization learning environment that meets the schoolteaching and learning conditions Network nodes are re-quired to have the self-adjustment ability self-adaptationability and robustness and complete tasks such as networkinitialization and fault self-repair through topology controlmechanisms network protocols and cooperation tomaintain the normal operation of the network Traditionalschool education cannot teach each student according to hisor her ability but the information age has brought neweducation opportunities [2] With artificial intelligence andbig data the whole process of student learning can bemonitored and tracked in real time and the problems and

HindawiComputational Intelligence and NeuroscienceVolume 2022 Article ID 8003909 11 pageshttpsdoiorg10115520228003909

deficiencies of students can be analysed so that teachers cantailor their learning plans to the specific situation of stu-dents e emphasis on process-oriented data on studentlearning guides students to adjust their learning stylespromptly making large-scale personalized learning possibleWhile offline traditional education allows teachers to impartknowledge and solidify the foundation online educationbreaks the time and space constraints from which studentscan access personalized resources and learning styles

Changing the traditional education model through neweducational technology equipment has received increasedattention from schools [3] At present the informationmanagement system for teachers and students has beenwidely used in colleges and universities realizing thefunctions of integrated management of teacher and studentinformation online course selection online grade entry andquery and integrated query of school information Inparticular the online grade entry and query function ofstudents is especially widely used among teachers andstudents However the current teaching situation in do-mestic universities is still the traditional irrigation methodinterspersed with multimedia teaching methods such asPPT Teachers still count studentsrsquo knowledge proficiencythrough traditional after-class assignments and randomlychecking studentsrsquo answers to questions in class [4] At thesame time according to the studentrsquos learning data feedbackthe learning environment resources are adjusted in real timeand finally a dynamic and personalized learning environ-ment that conforms to the academic conditions is con-structed Once students do not keep up with the teacherrsquosteaching ideas it is difficult to listen to the later knowledgeAs for the studentrsquos attendance teachers usually still confirmit through the roll call

About the methodological aspects of rule of law edu-cation rule of law education is most often based onproblem-based and case-based approaches [5] e prob-lem-based approach requires students to identify constructand analyse problems and students therefore spend moretime on learning activities and perform better than those inthe case-based approach Based on the needs of laboratorymanagement and practical training teaching it is necessaryto research and explore laboratory intelligence and infor-matization for the management of university laboratories Ifthe requirements are met open the serial port communi-cation switch for communication and close the serial portswitch after the communication is completed If it does notmeet the requirements wait for a period continue to queryuntil there is no other coordinator communication and thenturn on the serial port switch In this paper based on theexisting perfect facilities the structure and characteristics ofIoT technology in smart home and smart building are ap-plied to the laboratory management system exploring themodel of intelligent laboratory constructing IoT innovationtraining room and developing smart campus class terminalmanagement system which integrates the use of IoT mobileInternet big data intelligent perception and other emerginginformation technologies on the basis of traditional campusto provide timely feedback to parents and teachers onstudentsrsquo safe attendance and abnormal attendance to

organically combine all aspects of home-school interactioncampus information announcement release class cultureteacher-student interaction class walking teaching andteaching management to build an information managementplatform for campus and class management and studentgrowth enrich the overall school information technologyenvironment and information dissemination channels ef-ficiently and conveniently deliver various knowledge in-formation and notice information provide timely feedbackon classroom information realize transparent managementof classes and provide strong technical support for thedigitization and information of education and teaching inour school

2 Status of Research

e spreading factor and the communication rate are foundto have a significant impact on the coverage capability of theLoRa network through field tests e limitations of the LoraWAN protocol are proposed through the analysis of LoraWAN protocol the size of the Lora WAN network is muchsmaller than the theoretical value under the limitation ofduty cycle transmission and the adoption of transmissionacknowledgment answering mechanism for reliable trans-mission also makes the network capacity smaller in the sameway [6] rough different IoT application cases the ad-vantages and disadvantages of Lora WAN in differentscenarios are analysed and its applicability scope is illus-trated e authors argue that the random-access method ofAloha for nodes in Lora WAN networks is not the optimalsolution and a more complex TDMA access method can beconsidered By modelling and simulating a large Lora WANnetwork using the NS-3 network simulator the simulationresults show that the limited downlink communication inthe Lora WAN network reduces the packet delivery rate ofthe uplink communication that needs to be acknowledgedand adding gateways can improve but not eliminate thiseffect [7] A comprehensive analysis of the features andlimitations of Lora WAN gives the current problems of theLora WAN protocol that is high data loss rate at high dataconcurrency and long retransmission time for the timelyarrival of data [8] e physical layer transmission charac-teristics of the Lora WAN network are analysed the packetloss rate and conflict probability of different data frames atthe gateway are derived by building a signal receptionmodeland the variation of packet loss rate and conflict probabilitywith different spreading factors and sending cycles are il-lustrated by simulation results [9]

With the development of communication technologyclassroom interactive systems have gradually entered uni-versity campuses and are playing an increasingly importantrole in student learning Multimedia teaching has also be-come a standard part of daily university teaching activities[10] Many university research project groups and somesocial research institutions at home and abroad have alsoconducted a series of research on education informatization[11] ere are currently two ideas for developing classroominteractive systems One idea is to upgrade the hardwarefacilities completely and transform them into intelligent

2 Computational Intelligence and Neuroscience

classrooms [12] However these devices require a large-scaleand comprehensive upgrade of the classroom hardware theamount of engineering time costs and transformation costsare relatively large Moreover if a certain aspect needs to beupgraded at a later stage it will be more difficult [13] edata management web terminal is mainly used to bind theuserrsquos school number and the MAC address of the studentrsquoshandheld terminal e teacher downloads the student listand the corresponding MAC address list of the class to betaught stores and analyses the studentsrsquo classroom answerand question data collected by the teacher and visualizesthese data For the student user it can display the studentsrsquousual classroom performance in real time and show it in achart for the teacher user it can display his classroomperformance in real time [14] For teachers they can displaythe class performance of students in their classes in real timeand display it in charts [15]

With the continuous development and updating ofcomputer technology big data cloud computing and ar-tificial intelligence will be widely used in all areas of sociallife For example big data can be used to predict the oc-currence of crimes companies can analyse the sales ofproducts based on customer reviews on shopping websitesthe National Centre for Disease Control and Prevention cananalyse the spread of diseases based on Internet usersrsquo searchrecords and so on [16] For the large amount of datagenerated the integration and analysis of the data allowpeople to discover new knowledge and create new value Asthe volume of IoT business increases the amount of datastorage and computation will bring the need for ldquocomput-ingrdquo power to the cloud Cloud computing will provide theappropriate services in infrastructure platforms and soft-ware Artificial intelligence is a branch of computer scienceand new technical science that combines the application ofcomputers and other disciplines which focuses on the studyof theories methods and technologies to simulate extendand expand human intelligence [9]

3 IoT Data-Driven Intelligent Law ClassroomTeaching System Design

31 IoTSmartClassroomTeachingSystemDesign ere is nodoubt that the communication among many nodes in IoTinvolves addressing the problem and IPv4 addresses aregetting scarce Its address space can hardly meet the demandfor address space in todayrsquos IoT Moreover IPv4 has ashortcoming for the mobility of the Internet and the MIPv4mechanism proposed in the IETF to support the mobility ofnodes will lead to rapid consumption of network resources formany nodes resulting in network paralysis [17] IPv6 tech-nology not only has a huge address space but also adopts astateless address allocation scheme to solve the problem ofallocation efficiency of massive addresses For node mobilityIPv6 proposes the concept of IP address binding buffer andintroduces a method to detect node movement At the sametime IPv6 design considers the quality of service such asdefining the traffic class field and flow label field in the packetstructure and using the flow label carried by the packet to do adetailed division of services It is a protocol designed for

communication between remote sensors and controlequipment that have limited computing and storage resourcesand work on low-bandwidth unreliable networks It has thecharacteristics of simplicity strong scalability and low traffic

e IoT application layer is the ultimate purpose layerwhich is the combination of IoT and industry needs bydocking IoT technology with industry technology and usingvarious solutions to provide a good IoT business experiencefor a wide range of users to achieve a wide range of intelligentapplications so that people can feel the great impact that IoTbrings to real life e main function of the IoT applicationlayer is to process the massive information coming from thenetwork layer and use this information to provide relevantservices to userse key issue of the application layer is howto achieve information sharing and security Among themthe rational use as well as efficient processing of relevantinformation is an urgent IoT problem and to solve thistechnical challenge the IoTapplication layer needs to utilizetechnologies such as middleware Machine to Machine(M2M) and edge cloud computing In building the infor-mation network of IoT middleware is mainly applied todistributed systems to connect various technologies andshare resources Middleware can be divided into two partsthe platform and the communication part e middlewareconsists of various modules such as a real-time memoryevent database task management system and event man-agement system which can be implemented to connect twoindependent applications as shown in Figure 1

It is a protocol designed for communication betweenremote sensors and control equipment that have limitedcomputing and storage resources and work on low-band-width unreliable networks It has the characteristics ofsimplicity strong scalability and low traffic A wirelesssensor network consists of tiny nodes deployed in themonitoring area with data processing units miniaturesensors and communication modules through a self-orga-nizing wireless self-organizing network system With thehelp of the nodesrsquo built-in sensors measuring the signals ofthe objects around their location the observer can obtaininformation about the sensed objects in the network cov-erage area e three elements of wireless sensor networksare sensor sensing object and observer [18] e charac-teristics of wireless sensor networks are mainly reflected inthe size of scale and the purpose of application In theapplication of a wireless sensor network the location ofsensor nodes cannot be set precisely in advance ereforethe network nodes are required to have self-regulationability self-adaptive ability and robustness to maintain thenormal operation of the network through topology controlmechanism and network protocols andmutual collaborationto complete such work as network initialization and faultself-healing e basic structure of wireless sensor networkscan be divided into node types node structures andfunction-based models of wireless sensor network struc-tures Its technical research is to be mainly focused onmedium access control protocols network topology controlrouting protocols node positioning clock synchronizationdata management data fusion embedded operating sys-tems and network security

Computational Intelligence and Neuroscience 3

Based on the in-depth analysis of constructivist theorywe propose the theory of realm pulse learning and build apersonalized learning environment with an informationplatform and AI teaching assistance It is a new paradigm toguide the transformation of learning which allows the in-teraction between the many learning elements of the learnerand the external elements of the teacherrsquos guidance to form aself-renewal force In many cases informatization methodshave only become an iconic application for demonstratingnew ways of learning ey only serve for informatizationcompetitions or open classes and observation classes andhave not been implemented in the classroom to serve stu-dents e AI-assisted teaching of the information platformcan undoubtedly provide a better learning environment forthe student-cantered teaching model while adjusting thelearning environment resources in real time based on thefeedback of studentsrsquo learning data finally building a dy-namic and personalized learning environment in line withthe learning situation

en to make data-driven decision-making applied toteaching in addition to the basic elements some conditionsare needed One is based on the concept of big data teachingeducation data as a practical tool and means to promote theimprovement and perfection of teaching continuouslySecond for the correct understanding of the application ofdata in teaching the big data should have a cautious attitudethere may be some personal privacy data security and otherissues and we should adhere to the principle of science andobjectivity combined with the actual situation and flexible

choice e third is the construction of an effective platformfor data collection and application to be able to use theplatform to carry out teaching activities inside and outsidethe classroom collect learning data carry out learningtracking and make data decisions truly applied to teachingas shown in Figure 2

To ensure that more student handhelds can be connectedin a wireless-based classroom interactive system we need ateacher receiver and 4 Zigbee coordinators to transmit in-formation to each other Reasonably use educational data tocustomize a personalized learning environment to meet thelearning needs of students at different levels so that artificialintelligence and big data can truly promote more person-alization thereby increasing the attractiveness of theclassroom e teacher receiver uses one serial port toconnect to 4 wireless receivers respectively At the sametime there are 4 LCD lights connected to each hardwarechannel to show which coordinator of the wireless receiverthe teacher receiver is communicating with ere is also aserial communication switch connected to each serial pathwhich can only be communicated when the switch is closedWhen the teacher needs to send a message to the 4 serialports with the receiver the 4 serial communication switchescan be turned on and off in sequence to ensure that only oneserial communication switch is on for each communicationWhen a coordinator needs to send a message to the teacherreceiver it also needs to make sure no other coordinator iscommunicating first If the requirements are met then turnon the serial communication switch to communicate and

IoT application data

Coming from the network layer

Information to provide relevant services to

users

Great impact that IoT brings to real life

Process the massive information

Intelligent applications wide range of users to achieve

IoT technology

Various solutions

Ultimate purpose layer

Industry needs

middleware

Key issue of the application layer

Achieve information sharing and security

The middleware consists of various modules such as a real-time memory

event database

task management system and event management system

IoT business experience

Senser Senser

Senser Senser

Senser

Senser

Senser

Senser Senser Senser

Senser

Senser

Information network of IoT Connect two independent

Information network of IoT


Figure 1 IoT framework design


turn off that serial switch when the communication iscomplete If the requirement is not met then wait for sometime and continue to query until no other coordinator iscommunicating before turning on the serial switch

Reasonably use educational data to customize a per-sonalized learning environment to meet the learning needs ofstudents at different levels so that artificial intelligence and bigdata can truly promote more personalization thereby in-creasing the attractiveness of the classroom A typical LoraWAN network architecture consists of four parts nodesgateways web servers and application servers e nodes aremainly responsible for the collection and reporting of sensingdata and the reception and execution of command data on theserver side e nodes adopt the ALOHA access method torandomly access the network and send data on demand edata reported by a node can be received by multiple gatewaysthus forming a star network topology with the gatewayswhich can ensure the reliability of the connection and realizethe access of a larger number of devices in the network egateway is the relay device between the node and the networkserver in the Lora WAN network as the central node of thestar network forwarding the LoRa RF data fromto the nodeand TCPP network data for processinge network server isan important part of the Lora WAN network for data pro-cessing and device access responsible for LoRa node dataparsing and encapsulation encryption decryption LoRanode entry authentication Adaptive Date Rate (ADR) reg-ulation and other functions e application server is builtand developed by the user side and has different applicationfunctions for different IoTapplications Building a database toprocess and store the application data providing an appli-cation logic interface and using a visual interface such asWEB or APP to connect the user side with the applicationserver mean building a LoRa IoT system from the sensinglayer to the application layer

32 Data-Driven Intelligent Law Classroom AnalysisDepending on the starting point for considering theproblem research scholars have proposed model-drivengoal-driven product-driven business-driven technology-driven decision-driven task-driven and other forms ofdrivers Data analysis emanating from the model side iscalled model-driven and the mode of operation with thetechnology side as the trigger is called technology-drivenWith the advent of the intelligence and data era knowledgeacquisition is no longer entirely dependent on expert ex-perience e use of some intelligent methods can startfrom the data mining to get the valuable informationbehind the data which is data-driven and one of the mainreasons why data-driven methods have developed morerapidly than other methods and attracted widespread at-tention from scholars [19] From the perspective of thegeneration environment the concept of data-driven is aproduct of the era of big data inspired by data-intensivescientific discoveries Data has become the starting pointand perspective for people to understand analyse and solveproblems resulting in the idea of using a data mindset tosolve problems Furthermore the scientific preprocessingof data the constant statistics of data the real calculation ofdata data modelling machine learning data visualizationdata management and other technologies provide thetechnical support for data-driven in addition to these datascience methods data mining neural networks artificialintelligence metadata and all other technologies andmethods of data-based design are the technical guarantee ofdata-driven Reasonably use educational data to customizea personalized learning environment to meet the learningneeds of students at different levels so that artificial in-telligence and big data can truly promote more personal-ization thereby increasing the attractiveness of theclassroom

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Reset

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Load

Carry outEN

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2

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8

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GND

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a2

a3

a4

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0

b2

b3

b4

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4

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Figure 2 Time-sharing communication serial port principle


Tp

x2

+ y2

+ z2

1113969

C

Ts L

Vminus

x2

minus y2

minus z2

1113969

C

(1)

e essence and value of big data are to eliminateuncertainty and the value of data drive lies in the kineticoutput of the drive and the means to realize the value of bigdata data analytic thinking data-driven applications indifferent scenarios the formation of data-driven decision-making data-driven design data-driven procedures data-driven products data-driven innovation and other resultsoutput Among them the so-called data-driven decision-making is to help patronize through data includingproduct improvement operation optimization marketinganalysis and business decision-making With data supportwe can better determine which channels convert better andwhich feature styles are more popular with users andsupport the decision through data Data-driven productintelligence is the ability to empower products to learnthrough data models e so-called intelligence can beunderstood as a model with a certain database setting analgorithmmodel on it then the results of the data obtainedback to the product are intelligent In this way the producthas a certain learning ability and can constantly update anditerate itself for example the literature recommendationsystem by analysing the userrsquos historical behaviour recordsdiscovering the userrsquos interest points building a targeteduser model and finally realizing personalized recom-mendations as shown in Figure 3

Scientific discovery has shifted from the traditionalhypothesis-driven to a scientific approach based on sci-entific data for exploration For some scientific problemsthat are difficult for humans to understand researchersare gradually unravelling the mystery of these scientificpuzzles through data monitoring and analysis using dataas a tool and object of research and designing and con-ducting research from data Data are no longer just theresult of scientific research but have become the livingfoundation of scientific research People are concernedwith not only modelling describing organizing pre-serving accessing analysing reusing and building sci-entific data infrastructure but also how to use theubiquitous network and its inherent interactivity andopenness to construct an open and collaborative discoveryand creation model with the help of massive data thusgiving birth to data-intensive scientific discovery ere isa lack of effective and detailed information interactionbetween students and teachers Teachers cannot specifi-cally and comprehensively understand studentsrsquo knowl-edge proficiency ey can only arrange teaching contentand teaching progress based on their own teaching ex-perience and random checks in the classroom e de-velopment of a data-intensive scientific research paradigmoffers new opportunities for collaborative efforts betweenacademia industry and government is research par-adigm relies on sensing tools to acquire data or computer

simulations to generate it process it through software forcleaning store it in computers and analyse it using dataanalysis software Data from scientific research can begrouped into four categories data generated by experi-mental instruments data generated by computer simu-lations data collected by observation and data generatedon the Web Data generated on the Web include historicalbehavioural data of Internet users and data on Webtransactions

a hm( 1113857 (125gf + 07)hm minus (125gf minus 07)

Pcollsf 1 + e2Gsf

(2)

Data visualization is an interactive visual representationof data using the intelligence of the human brain and theperceptive ability of the human eye to convert data that isdifficult to display directly into graphics symbols and so onto convey useful information efficiently rough this visualrepresentation of data the information hidden in the dataincluding various attributes is presented in some summaryform From the point of view of aesthetic requirements andfunctional requirements data visualization requires thesame for both and guarantees a balance between design andfunctionality by directly expressing special aspects andspecial features and thus gaining insight into very sparse butparticularly complex data collections

e conflict model of the LoraWAN protocol shows thatthe probability of conflict during transmission of datapackets sent by nodes using ALOHA access is closely relatedto the combination of bandwidth coding rate and theproportion of nodes using the same spreading factor Whenthe Lora WAN protocol is simulated in the LoRa networkcreated in this paper the corresponding proportion of nodesusing the same spreading factor bandwidth coding rate andother parameters are substituted into the formula and theprobability of conflicts occurring in the data transmissionprocess of nodes using different spreading factors is close to1 At this time the conflicts in the network are more fre-quent the data loss caused by communication conflicts ismore serious and the reliability of network communicationis poor at this time

Gsf λ2zsfTsf (3)

As the information transfer channel in the whole IoTarchitecture the network layer is responsible for trans-ferring the information obtained from the sensing layer andsubmitting it to the application layer for processing eapplication layer is the interface between the IoT systemand the user which mainly solves the problems related toinformation interaction human-machine processing andso on It combines the needs of various IoT industries tocomplete specific business functions and realize the in-telligent application of the whole IoT industry e maintasks of the application layer include data analysis andprocessing data storage and mining and communicationhandover between people and things and things and things[20] To adapt to the ubiquitous situation of devices and atthe same time play the powerful computing ability storageability and network communication ability of cloud


computing platform the cloud platform is used as theapplication layer in the IoTarchitecture to build the IoTcloudplatform architecture establish the unified management ofpeople and things in the IoT application establish andmaintain the communication channel between people andthings and realize the functions of remote control datamanagement business logic processing of industry applica-tions and so forth e IoT system architecture based on thecloud platform is shown in Figure 4 which mainly consists ofterminal layer network access layer and cloud platform

For aesthetic effect adjust the label size to be lt10 so thatthe information about the research user can be betterpresented e base feature is more important in under-standing the researcherrsquos information and is also the leastvariable Set the base feature label size to 10 It is necessary toresearch and explore the intelligence and informatization oflaboratories is article takes the Computer EngineeringDepartment of Fujian Institute of Information Technologyas an example Based on the existing facilities the structureand characteristics of the Internet of ings technology insmart homes and smart buildings are applied to the labo-ratory management system

it σ Wi htx2t

11138681113868111386811138681113868111386811138681113868 + bi1113872 1113873 (4)

Research preference label and research relationship la-bel the user portrait of the digital library knowledge dis-covery platform designed in this paper is essentially a labeledformal description of the user model of the platform usersand the user portrait is constructed using the userrsquos basicattributes research results and search target informationand displayed through the word cloud visualization Finallythe real users of the knowledge discovery platform are se-lected for the method validatione user portrait of the digital

library knowledge discovery platform constructed in this papercan depict user information in all aspects and intuitively displayuser information to accurately provide personalized servicesand accurate recommendations and provide a real and effectivereference basis for the decision-making layer

4 Results and Analysis

41 IoT Smart Classroom Teaching System Performancee key to achieving remote management from the cloud tothe device side is to establish and maintain instantaneousand reliable communication between the cloud and thedevice side Instant communication requires that the device

097

5

042

2

072

1

042

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045

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1

2

3

Val

ues

GC D HEBA F JILabel

Figure 4 Label weights

Data

Stop word filtering

Basic dictionary

Negative Words

Adverbs of Degree

Python programming

Multiplication factor

Text preprocessing

Word segmentation

Adverbs of Degree

Negative Words

OtherPositive detection of

before and after words

Online dictionary professional

dictionary

Feature vector processing

Negative Words

Words before and after detection

Multiplication factor Negative

Judging the part-of-speech category

Figure 3 Data-driven intelligent law classroom architecture


side can report data to the cloud receive messages from thecloud in real time and ensure that the communication haslow latency Since in the actual application environment thenetwork environment at the device side cannot ensurestability and reliability so based on establishing the con-nection between the device side and the cloud side aheartbeat mechanism is required to maintain the longconnection from the device side to the cloud side and at thesame time TCP-based protocols are preferred to select thecommunication protocols to ensure the reliability of theconnection Based on the above analysis this paper selectsthe MQTT protocol to solve the problem of end-to-endinstant and reliable communication during remote com-munication MQTT protocol is a lightweight agent-basedpublishsubscribe model messaging protocol designed forcommunication of remote sensors and control devices withlimited computing and storage resources and working in lowbandwidth and unreliable networks with features of sim-plicity scalability and traffic saving e MQTT protocolenables network communication betweenmultiple clients byspecifying topic subscriptions and message distributionwith the server acting as a proxy to forward messages basedon the topic number By decoupling the senders and re-ceivers of messages in space and time the system has goodscalability and scaling capability

e rapid development of IoTapplications has led to theemergence of various types of IoT products In various IoTproducts the nodes can be either sensing devicesactuationdevices with sensing data collection modules and controlmodules or subsystem devices connected through fieldbuses By accessing different functional module devicesdifferent IoTproducts can be quickly constructed to achievefunctional expansion of IoT applications For the charac-teristics of multiple and complex product functions in IoTapplications this paper completes the description of devicefunctions by defining attributes services and events fordevices referred to as the object model to construct datamodels of device entities under different products digitizethe entity objects in physical space and digitally representthem in the cloud as shown in Table 1 for the definition ofattributes and services events By linking the Internet ofings technology with industry technologies various so-lutions are used to provide users with a good Internet ofings business experience to achieve a wide range of in-telligent applications so that people can truly feel the hugeimpact of the Internet of ings on real life

Different IoT products are used in different applicationscenarios and the hardware architecture and software en-vironment of the devices in the products as well as theirfunctions show great differences which determine differentforms and functions of the device side leading to the gradualldquofragmentationrdquo of IoT applications Any user-oriented IoTproduct needs to break the full-stack communication pro-tocol of ldquodevicendashnetwork platform applicationrdquo but theldquofragmentedrdquo IoT feature leads to great differences in theformat length and precision of data from different devicese communication protocols of different products are alsovery different which will lead to the problem of intercon-nection between IoT products and become a bottleneck for

further development of IoT In the traditional IoT appli-cation development mode first the device developer needsto define the communication protocol from the device sideto the application side according to the system function eapplication side has a strong dependency on the device sidedevelopment so the device side must be done first and thenthe application side which cannot be developed synchro-nously e application developer must wait for the devicedeveloper to complete the development of the device sideand then carry out the development and testing of theapplication side which undoubtedly increases the devel-opment time and cost and consumes huge human andmaterial resources as shown in Figure 5 Data-driven de-cision-making data-driven design data-driven programsdata-driven products and data-driven innovations areformed

In order to avoid the communication conflict caused bymultiple nodes communicating with the gateway at the sametime on the same channel CSMA presentation monitoringand random delay backoff strategy are used to avoid theconflict Presend listening is to detect the presence of packetleading code on the channel through CAD If the CADdetects the presence of leading code transmission in thecurrent channel then the current channel is occupiedOtherwise the current channel is in an idle state If thechannel is idle then the service transmission can be carriedout Otherwise random back-off is performed When thechannel is occupied the timer is set to wait for a randomdelay after the delay the current channel state is detecteduntil the channel is idle and the data is sent successfully orthe number of random evasions reaches the upper limit Ifthe number of random evasions reaches the upper limit thecompeting channel is randomly switched and the data issent again

Since the SNR values of the three groups of nodes to thegateway are similar in this test the gateway assigns the threenodes to the same TDMA channel after admission ey areassigned communication time slots consecutively in theorder of admission e three nodes continuously occupythe time slots for periodic service reporting Use data asresearch tools and objects design and conduct researchfrom data Data is no longer just the result of scientificresearch but becomes the living foundation of scientificresearch eoretically there is a difference of 3 secondsbetween the time slots of each node for periodic reportingHowever the test data shows that there is a delay of severaltens of milliseconds due to the software and hardwarefactors e corresponding communication cycle has acertain delay but in general it is verified that the LoRanetwork communication with TDMA access is feasible anddifferent nodes will actively report at different times withoutconflict with each other to ensure the reliability of com-munication is ensured

42 Results of the Data-Driven Smart Law ClassroomExperiment Finally classroom observations revealed astrong interest in the course most notably in the reluctanceof students to leave the classroom after class and continue to


modify the code to create their work Several problems werealso revealed firstly the lack of basic computer skills due tothe low exposure to computers which was a major obstacleto the completion of the first three weeks of the assignmentand secondly the repeated shouting at the teacher during theclass In the teacher workshop after the class we analysedtwo reasons behind this students were afraid of using thesoftware badly because they were not familiar with the use ofScratch programming so they were afraid to try boldly whenthere were problems with the code students needed theteacher to affirm the quality of their work and encourage andevaluate it before they would proceed to the next stage of thetask after completing their work Show it in some form ofsummary From the perspective of aesthetic requirementsand functional requirements the requirements of data vi-sualization are the same for both and by directly expressingspecial aspects and special characteristics we can gain in-sight into very sparse but particularly complex data sets toensure balance between design and function e datacollected was divided into three parts the first part was aboutmathematical achievement evaluated with a pre- andposttest of mathematical academic achievement related toproject progress the second part was computationalthinking divided into three areas (computational conceptsand computational practices were evaluated by Bibrascompetition questions computational perspectives weretested by scales and collaborative perceptions in compu-tational perspectives were analysed separately) and the third

area was a problem-driven learning environment perceptualanalysis as shown in Figure 6

Use the template to design a display page for the newseasonal products of Sun King Sportswear According to theweb design imitation +microinnovation concept studentscan imitate the teacherrsquos work or modify their preclass workMoreover according to their actual learning situation theycan choose a learning method that suits them for examplestudents with weaker abilities can log on to the Super Starplatform to watch tutorials and microlessons on demandand dialogue with robot assistants to help them learn keepup with the pace of the class and exercise their independentinquiry skills Students upload the designed layout in realtime to an online discussion group to exchange ideas withgroup members is session uses online resources and arobot assistant to assist students in their task explorationexercising their independent inquiry skills Students discussthe layout online to improve their collaborative learningskills e ldquoshake it uprdquo spot check of task completion addsinterest to the classroom and instantly determines the levelof proficiency in key content recording student errors formore intuitive analysis and summary e information re-sources include Super Star Online Learning Space TuringRobot Learning Pass ldquoShake Onerdquo and Super Star Inter-active Group After the teacherrsquos lecture students can accessthe online learning space to learn more or ask questions tothe robot assistant according to their proficiency In thissession students can access the Superstar platform video towatch the learning data and discussion and the studentsrsquopersonalized learning mode is greatly reflected Onlinegroup interaction makes group learning more conveniente use of ldquoshakerdquo instead of roll call to check the com-pletion of tasks increases the interest of the classroom andimproves studentsrsquo participation in the classroom as shownin Figure 7

As can be seen from Figure 7 the accuracy of the re-search object is 090 the recall is 084 and the F1 value is087 e research object category is higher than the othercategories in all indicators of discrimination because theresearch object generally has an established authorizedname For example in the case of research objects in specificfeature domains the corresponding conventions can usuallybe found and the model can better identify and determinethem Research questions and research methods on thecontrary are more variable and complex in their formalcomposition so the discriminative F1 values are relativelylow e change of knowledge discovery services in data-driven digital libraries has put forward higher requirementson the semantic understanding of data resource contentsespecially the full text of scientific research papers Completespecific business functions and realize intelligent applica-tions in the entire Internet of ings industry e main

Table 1 Properties events and service definitions

Project Describe ValueAttributes Parameters describing the operating state of the equipment 13Service e current temperature and humidity values collected by the temperature and humidity sensing device 14Event e attributes of the device support read-only write-only read and write and users can read and set them 54

Working frequencyTransmit powerReceiving sensitivity

bandwidthData rateSpreading factor

SX12

85

SX12

78

SX12

79

SX12

84

SX12

81

SX12

82

SX12

86

SX12

76

SX12

80

SX12

77

SX12

83

Module

0

2

4

6

8

10

Val

ues

Figure 5 Comparison of the communication bloc


tasks of the application layer include data analysis andprocessing data storage and mining communication be-tween people and things and things and things To assist inachieving semantic identification annotation and under-standing of data resource content this section designs akeyword-based approach to identify the problems methodsand research objects of data resource content using key-words of full-text scientific research papers and implementsthe approach in the field of agriculture Experiments showthat the method achieves satisfactory identification results

is section focuses only on identifying the research objectproblem and method vocabulary without exploring thefunctions assumed by the vocabulary in the text and whetherthe problem corresponds to the method erefore futureresearch will further explore which specific function thevocabulary assumes in the text and what the correspondingsolution to the problem is rough the experiments theeffectiveness of the deep learning model is verified whichprovides an effective general idea for the research designfingerprint extraction of the full text of scientific papers inthe knowledge discovery service platform of the digital li-brary and methodological references for other types of datacontent extraction and identification studies

5 Conclusion

e LoRa IoT system is designed with the help of IoTplatform the access process from the device side to the cloudside is developed and the authentication mechanism is usedto ensure the legal access of the device side e front-endweb page of the data management website design is notbeautiful enough and the functions are not perfect ejQuery and Bootstrap frameworks used in developing thefront-end of the web page are not beautiful enough and thewebsite currently only does some simple data processing andvisualization Now it does not support the function of bigdata analysis e MQTTprotocol is used to achieve reliableremote long connection communication between the cloudand the device the device function is abstracted through theobject model and the object model is described using theJSON data interaction format so that the relevant com-munication protocol can be developed to realize the datatransmission between the end and the end e wireless

-196 SD-3370932

+196 SD3757599

Mean1933330

66 68 70 72 74 76 78 80 82 84 86 88 90 9264Mean

-60

-40

-20

0

20

40

60

Diff

eren

ce

Figure 6 Test performance statistics

Recall rateAccuracyF1

method problem otherObjectType

3

4

5

6

7

8

9

Val

ues

Figure 7 Comparison of inspection results


communication-based classroom interaction systemdesigned in this paper can help teachers realize the collectionof feedback data data visualization display and storage ofstudentsrsquo roll call multiple-choice questions of classroomquizzes and YN judgment questions in classrooms edata management website also allows students and teachersto view the actual classroom performance of students in theirclass or the teacherrsquos class Although the system can achievethe basic functions of the classroom interactive system dueto the limited personal ability there are still numerousshortcomings and there are some gaps between the researchwork of the thesis and the actual use of the needs which needto be continuously improved at a later stage

Data Availability

e data used to support the findings of this study areavailable from the corresponding author upon request

Conflicts of Interest

ere are no conflicts of interest regarding this article

Acknowledgments

is article was supported by the Shijiazhuang Vocationaland Technical College

References

[1] M Kwet and P Prinsloo ldquoe ldquosmartrdquo classroom a newfrontier in the age of the smart universityrdquo Teaching in HigherEducation vol 25 no 4 pp 510ndash526 2020

[2] Z Engin J van Dijk T Lan et al ldquoData-driven urbanmanagement mapping the landscaperdquo Journal of UrbanManagement vol 9 no 2 pp 140ndash150 2020

[3] V Hillman and V Ganesh ldquoKratos a solution for dataprivacy literacy and student agency in a data driven schoolecosystemrdquo International Journal of Innovation in Educationvol 6 no 3-4 pp 184ndash201 2020

[4] A Bennett ldquoAutonomous vehicle driving algorithms andsmart mobility technologies in big data-driven transportationplanning and engineeringrdquo Contemporary Readings in Lawand Social Justice vol 13 no 1 pp 20ndash29 2021

[5] Z Y Dong Y Zhang C Yip S Swift and K Beswick ldquoSmartcampus definition framework technologies and servicesrdquoIET Smart Cities vol 2 no 1 pp 43ndash54 2020

[6] A M Shahat Osman and A Elragal ldquoSmart cities and big dataanalytics a data-driven decision-making use caserdquo SmartCities vol 4 no 1 pp 286ndash313 2021

[7] S Y Hong and Y H Hwang ldquoDesign and implementation foriort based remote control robot using block-based pro-grammingrdquo Issues in Information Systems vol 21 no 4pp 317ndash330 2020

[8] K Coatney and M Poliak ldquoCognitive decision-making al-gorithms internet of things smart devices and sustainableorganizational performance in Industry 40-basedmanufacturing systemsrdquo Journal of Self-Governance andManagement Economics vol 8 no 4 pp 9ndash18 2020

[9] N Petrovic and D Kocic ldquoData-driven framework for en-ergy-efficient smart citiesrdquo Serbian Journal of Electrical En-gineering vol 17 no 1 pp 41ndash63 2020

[10] C Edwards ldquoReal-time advanced analytics automatedproduction systems and smart industrial value creation insustainable manufacturing internet of thingsrdquo Journal ofSelf-Governance and Management Economics vol 9 no 2pp 32ndash41 2021

[11] T Cerquitelli D J Pagliari A Calimera et alldquoManufacturing as a data-driven practice methodologiestechnologies and toolsrdquo Proceedings of the IEEE vol 109no 4 pp 399ndash422 2021

[12] D C Nguyen P Cheng M Ding and D Lopez-PerezldquoEnabling AI in future wireless networks a data life cycleperspectiverdquo IEEE Communications Surveys amp Tutorialsvol 23 no 1 pp 553ndash595 2020

[13] M C Lucic X Wan H Ghazzai and Y MassoudldquoLeveraging intelligent transportation systems and smartvehicles using crowdsourcing an overviewrdquo Smart Citiesvol 3 no 2 pp 341ndash361 2020

[14] Y Zhang Z Y Dong C Yip and S Swift ldquoSmart campus auser case study in Hong Kongrdquo IET Smart Cities vol 2 no 3pp 146ndash154 2020

[15] HMihaljevic C J Larsen S MeierW Nekoto and FMoronZirfas ldquoPrivacy-centred data-driven innovation in the smartcity Exemplary use case of traffic countingrdquo Urban Planningand Transport Research vol 9 no 1 pp 426ndash449 2021

[16] B Schneider J Reilly and I Radu ldquoLowering barriers foraccessing sensor data in education lessons learned fromteaching multimodal learning analytics to educatorsrdquo Journalfor STEM Education Research vol 3 no 1 pp 91ndash124 2020

[17] G Chen P Wang B Feng Y Li and D Liu ldquoe frameworkdesign of smart factory in discrete manufacturing industrybased on cyber-physical systemrdquo International Journal ofComputer IntegratedManufacturing vol 33 no 1 pp 79ndash1012020

[18] I H Sarker M M Hoque M K Uddin and T AlsanoosyldquoMobile data science and intelligent apps concepts AI-basedmodeling and research directionsrdquo Mobile Networks andApplications vol 26 no 1 pp 285ndash303 2021

[19] C She R Dong Z Gu et al ldquoDeep learning for ultra-reliableand low-latency communications in 6G networksrdquo IEEENetwork vol 34 no 5 pp 219ndash225 2020

[20] G B Rehm S HWoo X L Chen et al ldquoLeveraging IoTs andmachine learning for patient diagnosis and ventilationmanagement in the intensive care unitrdquo IEEE PervasiveComputing vol 19 no 3 pp 68ndash78 2020


deficiencies of students can be analysed so that teachers cantailor their learning plans to the specific situation of stu-dents e emphasis on process-oriented data on studentlearning guides students to adjust their learning stylespromptly making large-scale personalized learning possibleWhile offline traditional education allows teachers to impartknowledge and solidify the foundation online educationbreaks the time and space constraints from which studentscan access personalized resources and learning styles

Changing the traditional education model through neweducational technology equipment has received increasedattention from schools [3] At present the informationmanagement system for teachers and students has beenwidely used in colleges and universities realizing thefunctions of integrated management of teacher and studentinformation online course selection online grade entry andquery and integrated query of school information Inparticular the online grade entry and query function ofstudents is especially widely used among teachers andstudents However the current teaching situation in do-mestic universities is still the traditional irrigation methodinterspersed with multimedia teaching methods such asPPT Teachers still count studentsrsquo knowledge proficiencythrough traditional after-class assignments and randomlychecking studentsrsquo answers to questions in class [4] At thesame time according to the studentrsquos learning data feedbackthe learning environment resources are adjusted in real timeand finally a dynamic and personalized learning environ-ment that conforms to the academic conditions is con-structed Once students do not keep up with the teacherrsquosteaching ideas it is difficult to listen to the later knowledgeAs for the studentrsquos attendance teachers usually still confirmit through the roll call

About the methodological aspects of rule of law edu-cation rule of law education is most often based onproblem-based and case-based approaches [5] e prob-lem-based approach requires students to identify constructand analyse problems and students therefore spend moretime on learning activities and perform better than those inthe case-based approach Based on the needs of laboratorymanagement and practical training teaching it is necessaryto research and explore laboratory intelligence and infor-matization for the management of university laboratories Ifthe requirements are met open the serial port communi-cation switch for communication and close the serial portswitch after the communication is completed If it does notmeet the requirements wait for a period continue to queryuntil there is no other coordinator communication and thenturn on the serial port switch In this paper based on theexisting perfect facilities the structure and characteristics ofIoT technology in smart home and smart building are ap-plied to the laboratory management system exploring themodel of intelligent laboratory constructing IoT innovationtraining room and developing smart campus class terminalmanagement system which integrates the use of IoT mobileInternet big data intelligent perception and other emerginginformation technologies on the basis of traditional campusto provide timely feedback to parents and teachers onstudentsrsquo safe attendance and abnormal attendance to

organically combine all aspects of home-school interactioncampus information announcement release class cultureteacher-student interaction class walking teaching andteaching management to build an information managementplatform for campus and class management and studentgrowth enrich the overall school information technologyenvironment and information dissemination channels ef-ficiently and conveniently deliver various knowledge in-formation and notice information provide timely feedbackon classroom information realize transparent managementof classes and provide strong technical support for thedigitization and information of education and teaching inour school

2 Status of Research

e spreading factor and the communication rate are foundto have a significant impact on the coverage capability of theLoRa network through field tests e limitations of the LoraWAN protocol are proposed through the analysis of LoraWAN protocol the size of the Lora WAN network is muchsmaller than the theoretical value under the limitation ofduty cycle transmission and the adoption of transmissionacknowledgment answering mechanism for reliable trans-mission also makes the network capacity smaller in the sameway [6] rough different IoT application cases the ad-vantages and disadvantages of Lora WAN in differentscenarios are analysed and its applicability scope is illus-trated e authors argue that the random-access method ofAloha for nodes in Lora WAN networks is not the optimalsolution and a more complex TDMA access method can beconsidered By modelling and simulating a large Lora WANnetwork using the NS-3 network simulator the simulationresults show that the limited downlink communication inthe Lora WAN network reduces the packet delivery rate ofthe uplink communication that needs to be acknowledgedand adding gateways can improve but not eliminate thiseffect [7] A comprehensive analysis of the features andlimitations of Lora WAN gives the current problems of theLora WAN protocol that is high data loss rate at high dataconcurrency and long retransmission time for the timelyarrival of data [8] e physical layer transmission charac-teristics of the Lora WAN network are analysed the packetloss rate and conflict probability of different data frames atthe gateway are derived by building a signal receptionmodeland the variation of packet loss rate and conflict probabilitywith different spreading factors and sending cycles are il-lustrated by simulation results [9]

With the development of communication technologyclassroom interactive systems have gradually entered uni-versity campuses and are playing an increasingly importantrole in student learning Multimedia teaching has also be-come a standard part of daily university teaching activities[10] Many university research project groups and somesocial research institutions at home and abroad have alsoconducted a series of research on education informatization[11] ere are currently two ideas for developing classroominteractive systems One idea is to upgrade the hardwarefacilities completely and transform them into intelligent

















users




IoT technology

Various solutions


Industry needs

middleware




event database



Senser Senser

Senser Senser

Senser

Senser

Senser


Senser

Senser









Q

QSET

CLR

S

R

u1

x2

x1

f (x1xn)

A

H

UD

Reset

B1

B8

Load

Carry outEN

B

amp0

00

amp0

00

amp0

00

1a1

a2

a3

a4

b1

Vcc1

GND

0

b2

b3

b4

2

3

4

5

6

7

8

a1

a2

a3

a4

b1Vcc1

GND

0

0

b2

b3

b4

1

2

3

4

5

6

7

8

a1

a2

a3

a4

b1Vcc1

0

b2

b3

b4

1

2

3

4

5

6

7

8



Tp

x2

+ y2

+ z2

1113969

C

Ts L

Vminus

x2

minus y2

minus z2

1113969

C

(1)





Pcollsf 1 + e2Gsf

(2)



Gsf λ2zsfTsf (3)





it σ Wi htx2t

11138681113868111386811138681113868111386811138681113868 + bi1113872 1113873 (4)





097

5

042

2

072

1

042

5

045

2

078

8

088

6

066

7

084

4

043

064

3

091

1

085

3

050

7

075

1

069

9

090

2

072

8

057

5

071

3

095

7

077

8

091

1

085

1

087

070

9

056

8

067

3

051

079

7

1

2

3

Val

ues

GC D HEBA F JILabel


Data

Stop word filtering

Basic dictionary

Negative Words

Adverbs of Degree

Python programming


Text preprocessing

Word segmentation

Adverbs of Degree

Negative Words




dictionary


Negative Words






















SX12

85

SX12

78

SX12

79

SX12

84

SX12

81

SX12

82

SX12

86

SX12

76

SX12

80

SX12

77

SX12

83

Module

0

2

4

6

8

10

Val

ues





5 Conclusion


-196 SD-3370932

+196 SD3757599

Mean1933330

66 68 70 72 74 76 78 80 82 84 86 88 90 9264Mean

-60

-40

-20

0

20

40

60

Diff

eren

ce




3

4

5

6

7

8

9

Val

ues




Data Availability




Acknowledgments


References





































users




IoT technology

Various solutions


Industry needs

middleware




event database



Senser Senser

Senser Senser

Senser

Senser

Senser


Senser

Senser









Q

QSET

CLR

S

R

u1

x2

x1

f (x1xn)

A

H

UD

Reset

B1

B8

Load

Carry outEN

B

amp0

00

amp0

00

amp0

00

1a1

a2

a3

a4

b1

Vcc1

GND

0

b2

b3

b4

2

3

4

5

6

7

8

a1

a2

a3

a4

b1Vcc1

GND

0

0

b2

b3

b4

1

2

3

4

5

6

7

8

a1

a2

a3

a4

b1Vcc1

0

b2

b3

b4

1

2

3

4

5

6

7

8



Tp

x2

+ y2

+ z2

1113969

C

Ts L

Vminus

x2

minus y2

minus z2

1113969

C

(1)





Pcollsf 1 + e2Gsf

(2)



Gsf λ2zsfTsf (3)





it σ Wi htx2t

11138681113868111386811138681113868111386811138681113868 + bi1113872 1113873 (4)





097

5

042

2

072

1

042

5

045

2

078

8

088

6

066

7

084

4

043

064

3

091

1

085

3

050

7

075

1

069

9

090

2

072

8

057

5

071

3

095

7

077

8

091

1

085

1

087

070

9

056

8

067

3

051

079

7

1

2

3

Val

ues

GC D HEBA F JILabel


Data

Stop word filtering

Basic dictionary

Negative Words

Adverbs of Degree

Python programming


Text preprocessing

Word segmentation

Adverbs of Degree

Negative Words




dictionary


Negative Words






















SX12

85

SX12

78

SX12

79

SX12

84

SX12

81

SX12

82

SX12

86

SX12

76

SX12

80

SX12

77

SX12

83

Module

0

2

4

6

8

10

Val

ues





5 Conclusion


-196 SD-3370932

+196 SD3757599

Mean1933330

66 68 70 72 74 76 78 80 82 84 86 88 90 9264Mean

-60

-40

-20

0

20

40

60

Diff

eren

ce




3

4

5

6

7

8

9

Val

ues




Data Availability




Acknowledgments


References

























Q

QSET

CLR

S

R

u1

x2

x1

f (x1xn)

A

H

UD

Reset

B1

B8

Load

Carry outEN

B

amp0

00

amp0

00

amp0

00

1a1

a2

a3

a4

b1

Vcc1

GND

0

b2

b3

b4

2

3

4

5

6

7

8

a1

a2

a3

a4

b1Vcc1

GND

0

0

b2

b3

b4

1

2

3

4

5

6

7

8

a1

a2

a3

a4

b1Vcc1

0

b2

b3

b4

1

2

3

4

5

6

7

8



Tp

x2

+ y2

+ z2

1113969

C

Ts L

Vminus

x2

minus y2

minus z2

1113969

C

(1)





Pcollsf 1 + e2Gsf

(2)



Gsf λ2zsfTsf (3)





it σ Wi htx2t

11138681113868111386811138681113868111386811138681113868 + bi1113872 1113873 (4)





097

5

042

2

072

1

042

5

045

2

078

8

088

6

066

7

084

4

043

064

3

091

1

085

3

050

7

075

1

069

9

090

2

072

8

057

5

071

3

095

7

077

8

091

1

085

1

087

070

9

056

8

067

3

051

079

7

1

2

3

Val

ues

GC D HEBA F JILabel


Data

Stop word filtering

Basic dictionary

Negative Words

Adverbs of Degree

Python programming


Text preprocessing

Word segmentation

Adverbs of Degree

Negative Words




dictionary


Negative Words






















SX12

85

SX12

78

SX12

79

SX12

84

SX12

81

SX12

82

SX12

86

SX12

76

SX12

80

SX12

77

SX12

83

Module

0

2

4

6

8

10

Val

ues





5 Conclusion


-196 SD-3370932

+196 SD3757599

Mean1933330

66 68 70 72 74 76 78 80 82 84 86 88 90 9264Mean

-60

-40

-20

0

20

40

60

Diff

eren

ce




3

4

5

6

7

8

9

Val

ues




Data Availability




Acknowledgments


References






















Tp

x2

+ y2

+ z2

1113969

C

Ts L

Vminus

x2

minus y2

minus z2

1113969

C

(1)





Pcollsf 1 + e2Gsf

(2)



Gsf λ2zsfTsf (3)





it σ Wi htx2t

11138681113868111386811138681113868111386811138681113868 + bi1113872 1113873 (4)





097

5

042

2

072

1

042

5

045

2

078

8

088

6

066

7

084

4

043

064

3

091

1

085

3

050

7

075

1

069

9

090

2

072

8

057

5

071

3

095

7

077

8

091

1

085

1

087

070

9

056

8

067

3

051

079

7

1

2

3

Val

ues

GC D HEBA F JILabel


Data

Stop word filtering

Basic dictionary

Negative Words

Adverbs of Degree

Python programming


Text preprocessing

Word segmentation

Adverbs of Degree

Negative Words




dictionary


Negative Words






















SX12

85

SX12

78

SX12

79

SX12

84

SX12

81

SX12

82

SX12

86

SX12

76

SX12

80

SX12

77

SX12

83

Module

0

2

4

6

8

10

Val

ues





5 Conclusion


-196 SD-3370932

+196 SD3757599

Mean1933330

66 68 70 72 74 76 78 80 82 84 86 88 90 9264Mean

-60

-40

-20

0

20

40

60

Diff

eren

ce




3

4

5

6

7

8

9

Val

ues




Data Availability




Acknowledgments


References
























it σ Wi htx2t

11138681113868111386811138681113868111386811138681113868 + bi1113872 1113873 (4)





097

5

042

2

072

1

042

5

045

2

078

8

088

6

066

7

084

4

043

064

3

091

1

085

3

050

7

075

1

069

9

090

2

072

8

057

5

071

3

095

7

077

8

091

1

085

1

087

070

9

056

8

067

3

051

079

7

1

2

3

Val

ues

GC D HEBA F JILabel


Data

Stop word filtering

Basic dictionary

Negative Words

Adverbs of Degree

Python programming


Text preprocessing

Word segmentation

Adverbs of Degree

Negative Words




dictionary


Negative Words






















SX12

85

SX12

78

SX12

79

SX12

84

SX12

81

SX12

82

SX12

86

SX12

76

SX12

80

SX12

77

SX12

83

Module

0

2

4

6

8

10

Val

ues





5 Conclusion


-196 SD-3370932

+196 SD3757599

Mean1933330

66 68 70 72 74 76 78 80 82 84 86 88 90 9264Mean

-60

-40

-20

0

20

40

60

Diff

eren

ce




3

4

5

6

7

8

9

Val

ues




Data Availability




Acknowledgments


References






































SX12

85

SX12

78

SX12

79

SX12

84

SX12

81

SX12

82

SX12

86

SX12

76

SX12

80

SX12

77

SX12

83

Module

0

2

4

6

8

10

Val

ues





5 Conclusion


-196 SD-3370932

+196 SD3757599

Mean1933330

66 68 70 72 74 76 78 80 82 84 86 88 90 9264Mean

-60

-40

-20

0

20

40

60

Diff

eren

ce




3

4

5

6

7

8

9

Val

ues




Data Availability




Acknowledgments


References
























5 Conclusion


-196 SD-3370932

+196 SD3757599

Mean1933330

66 68 70 72 74 76 78 80 82 84 86 88 90 9264Mean

-60

-40

-20

0

20

40

60

Diff

eren

ce




3

4

5

6

7

8

9

Val

ues




Data Availability




Acknowledgments


References























Data Availability




Acknowledgments


References






















Research Article Design and Implementation of IoT Data ...

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