Design Of Detection And Evaluation System For O2O Housing Air Quality Sharing

With the all-round development of the national economy,people’s accommodation methods are increasingly diversified,hotels,homestays,and apartments by O2 O sales method are more and more popular.O2 O allows people to view and select house online through mobile APP,then quickly check in with baggage.However,the air quality problems associated with it cannot be underestimated.In recent years,the health accidents caused by the air quality problems in O2 O housing have emerged one after another.The key is that users cannot acquire the accurate information of all aspects about the air quality.In view of this,this thesis designs a portable O2 O housing air quality detection and evaluation system.After check-in,guests can check the air quality of the house with the testing equipment and upload the results to the data sharing platform for other guests to view.One can also view the air quality information uploaded by others through the platform to realize the sharing of test data.The air quality detection terminal of the system is used to collect the common air parameters such as temperature,humidity,formaldehyde,PM2.5,PM10 and so on.The real-time data is transmitted by the GPRS module of the detection terminal to the Redis database of the server(The underlying relies on Apache Tomcat and is designed with Spring MVC+Spring+MyBatis software architecture.),then processed by the evaluation algorithm.The server sends the real-time detection data and the corresponding evaluation result to the client APP that establishes the corresponding connection through the WebSocket long connection channel.The APP displays the test data and evaluation results in real time,and can upload the test results to data sharing platform.The platform has the functions of multiple data display,multi-condition filtering and sorting.Through the platform,the guests can intuitively know the air quality of the O2 O housing.The back-end management and monitoring system is used to manage system users and the dynamic monitoring of real-time data and location of online devices.The system uses the K-nearest neighbor-based fuzzy analytic hierarchy process to evaluate the air quality,and the analytic hierarchy process determines the influence weights of formaldehyde,PM2.5 and PM10 in the indoor environment.In order to ensure the timeliness and accuracy of data evaluation,the experiment found that area method(high information utilization ratio)and maximum membership principle(simple calculation,strong real-time)commonly used in defuzzification are inconsistent in the evaluation results when the maximum and the next largest value of the membership set is too small.This thesis uses K-nearest neighbors to construct a classification model,taking the actual data and membership degree vectors of each index as input,defuzzifying the result as output,training the model,and finally determining the system defuzzification strategy.Different simulation environment are evaluated by a variety of commonly used algorithm to compare with system’s evaluation algorithm in this thesis,and actual data is considered to verify the objectivity.The functions of each module of the system are tested and verified,and the results show that the system achieve the goal of the design.