PLC數(shù)據(jù)庫(kù)系統(tǒng)畢業(yè)設(shè)計(jì)英文論文參考4.2.3DatabaseConfigurationInfluxDBSetup:1.Createabucket(`industrial_data`)witharetentionpolicy(RP)of30days(auto-deleteolddata).2.CreateanAPItokenwithwritepermissionsforthebucket.MySQLSetup:1.Createadatabase(`industrial_db`).2.Createtables(`user`,`device`,`alarm_rule`)usingSQLscripts.4.2.4VisualizationSetupGrafanaisusedtocreateareal-timemonitoringdashboard.Keysteps:2.Createadashboardwithpanels:TimeSeriesPanel:Displaysreal-timetemperaturetrends.GaugePanel:Showscurrentpressurevaluewithathreshold(e.g.,10bar).TablePanel:Listsrecentalarmevents.4.3TestingandResults4.3.1FunctionalTestingDataAcquisition:Connected5PLCs,eachwith3sensors.ThemiddlewaresuccessfullyacquireddatafromallPLCswithapollingcycleof100ms.DataStorage:VerifiedthatInfluxDBstored18,000datapointsperhour(5PLCs×3sensors×600polls/hour)withoutloss.DataQuery:Aqueryfor"temperaturedataofPLC_001from____10:00to____11:00"returned3,600pointsin800ms.AlarmManagement:Setanalarmrule(temperature>80°C).Whenthetemperaturereached82°C,thesystemtriggeredanemailalarmanddisplayedaredalertontheGrafanadashboard.4.3.2PerformanceTestingReal-TimeDelay:MeasuredusingWireshark.Theaveragedelaybetweenthemiddlewaresendingarequestandreceivingaresponsewas450ms,meetingtherequirement(<1s).WriteThroughput:InfluxDBachievedawritethroughputof12,000points/second,whichissufficientfor100PLCs(100PLCs×3sensors×10polls/second=3,000points/second).ConcurrentQueries:Simulated100concurrentusersqueryinghistoricaldata.Theaverageresponsetimewas900ms,whichisacceptableforindustrialapplications.4.3.3ReliabilityTestingFaultTolerance:DisconnectedonePLCduringoperation.Themiddlewaredetectedthefault,loggedanerrormessage,andcontinuedacquiringdatafromotherPLCs.Scalability:Added10newPLCstothesystem.Themiddlewareautomaticallyrecognizedthenewdevices(viaMySQL`device`table)andstartedpollingthemwithoutrestarting.4.4DiscussionThetestresultsshowthatthesystemmeetsallfunctionalandnon-functionalrequirements.Keysuccessesinclude:StorageEfficiency:Thehybriddatabasesolution(InfluxDB+MySQL)balancedreal-timedatastorageandrelationaldatamanagement.Scalability:ThemodulardesignallowedforeasyexpansionofPLCsandsensors.Potentialimprovements:PredictiveMaintenance:Integratemachinelearningmodels(e.g.,LSTM)topredictequipmentfailuresusinghistoricaldata.5.ConclusionReferences[1]ModbusOrganization.(2023).*ModbusTCP/IPSpecificationV1.1b*.[2]OPCFoundation.(2022).*OPCUASpecificationPart1:OverviewandConcepts*.[3]InfluxData.(2023).*InfluxDB2.0Documentation*.[4]Oracle.(2023).*MySQL8.0ReferenceManual*.[5]Li,X.,etal.(2021)."OptimizationofModbusTCPPollingStrategyforReal-TimeDataAcquisition."*JournalofIndustrialInformatics*.[6]Wang,Y.,etal.(2022)."DesignofaHybridDatabaseSystemforIndustrialIoTDataStorage."*IEEETransactionsonIndustrialInformatics*.[7]GrafanaLabs.(2023).*GrafanaDocumentation*.[8]Siemens.(2023).*TIAPortalV17Documentation*.AcknowledgmentsThisworkwassupportedbythe[NameofFundingAgency]underGrant[GrantNumber].Theauthorswouldliketothank[NameofInstitution]forprovidingtheexperimentalenvironment.AppendixAp