Introduction:This study presents empirical evidence from the implementation of an automated infectious disease warning system utilizing multi-source surveillance and multi-point triggers in Yuhang District,Hangzhou Ci...Introduction:This study presents empirical evidence from the implementation of an automated infectious disease warning system utilizing multi-source surveillance and multi-point triggers in Yuhang District,Hangzhou City,Zhejiang Province,so as to provide reference for more extensive practice of infectious disease surveillance and early warning in the future.Methods:The data were obtained from the Health Emergency Intelligent Control Platform of Yuhang District from January 1 to April 30,2024,encompassing warning signal issuance and response documentation.Descriptive epidemiological method was used to analyze the early warning signals.Results:From January 1 to April 30,2024,the Health Emergency Intelligent Control Platform in Yuhang District generated 4,598 valid warning signals,with a warning signal positive rate of 36.43%.The early warning system detected 71 infectious disease outbreaks reported through the Intelligent Control Platform,including 24 single-source early warning and 47 multi-source early warning.The sensitivity was 78.02%,demonstrating improved performance compared to existing infectious disease surveillance and warning systems.Conclusions:This represents the first domestic publication evaluating an automated multi-source surveillance and multi-point trigger warning system.By integrating and correlating multi-source data,the system can efficiently and accurately detect warning signals of infectious disease incidents,which has significant practical implications for early surveillance,warning,and management of infectious diseases.展开更多
Ultra-high-voltage direct current wall bushings are critical components in direct current transmission systems.Temperature variations and abnormal distributions can signal potential equipment failures that threaten sy...Ultra-high-voltage direct current wall bushings are critical components in direct current transmission systems.Temperature variations and abnormal distributions can signal potential equipment failures that threaten system stability.Therefore,monitoring these critical multi-point temperature variations is essential.However,the unique design of the bushings,featuring insulation sheds of periodic shape,distorts infrared temperature measurements by introducing interference points.These interference points,dependent on the measurement's angle and distance,appear irregularly in infrared images,severely impacting the accuracy of multi-point temperature distribution assessments.To address this challenge,an anomaly detection method is proposed that adaptively identifies interference points.The method identifies interference points by comparing pixels and uses a voting mechanism to improve identification accuracy.Compared with traditional methods,this approach presents two main advantages:adaptive identification capability,which enables it to recognise interference points and adapt to changing conditions,and unsupervised learning,which enables it to work effectively without requiring manually labelled data.Experimental tests on 161 bushing infrared images demonstrate the effectiveness of the method,achieving a 100%success rate in identifying localised overheating issues.The method has been integrated into high-voltage direct current transmission anomaly systems and can be used to monitor critical equipment,enhancing system reliability and safety.展开更多
基金Supported by the Major Science and Technology Project of the Science and Technology Department of Zhejiang Province(2021C03038,2022C03109)the Medical and Health Science and Technology Project of Zhejiang Province(2024KY895,WKJ-ZJ-2522,2025KY774).
文摘Introduction:This study presents empirical evidence from the implementation of an automated infectious disease warning system utilizing multi-source surveillance and multi-point triggers in Yuhang District,Hangzhou City,Zhejiang Province,so as to provide reference for more extensive practice of infectious disease surveillance and early warning in the future.Methods:The data were obtained from the Health Emergency Intelligent Control Platform of Yuhang District from January 1 to April 30,2024,encompassing warning signal issuance and response documentation.Descriptive epidemiological method was used to analyze the early warning signals.Results:From January 1 to April 30,2024,the Health Emergency Intelligent Control Platform in Yuhang District generated 4,598 valid warning signals,with a warning signal positive rate of 36.43%.The early warning system detected 71 infectious disease outbreaks reported through the Intelligent Control Platform,including 24 single-source early warning and 47 multi-source early warning.The sensitivity was 78.02%,demonstrating improved performance compared to existing infectious disease surveillance and warning systems.Conclusions:This represents the first domestic publication evaluating an automated multi-source surveillance and multi-point trigger warning system.By integrating and correlating multi-source data,the system can efficiently and accurately detect warning signals of infectious disease incidents,which has significant practical implications for early surveillance,warning,and management of infectious diseases.
基金National Natural Science Foundation of China,Grant/Award Numbers:62106033,42367066。
文摘Ultra-high-voltage direct current wall bushings are critical components in direct current transmission systems.Temperature variations and abnormal distributions can signal potential equipment failures that threaten system stability.Therefore,monitoring these critical multi-point temperature variations is essential.However,the unique design of the bushings,featuring insulation sheds of periodic shape,distorts infrared temperature measurements by introducing interference points.These interference points,dependent on the measurement's angle and distance,appear irregularly in infrared images,severely impacting the accuracy of multi-point temperature distribution assessments.To address this challenge,an anomaly detection method is proposed that adaptively identifies interference points.The method identifies interference points by comparing pixels and uses a voting mechanism to improve identification accuracy.Compared with traditional methods,this approach presents two main advantages:adaptive identification capability,which enables it to recognise interference points and adapt to changing conditions,and unsupervised learning,which enables it to work effectively without requiring manually labelled data.Experimental tests on 161 bushing infrared images demonstrate the effectiveness of the method,achieving a 100%success rate in identifying localised overheating issues.The method has been integrated into high-voltage direct current transmission anomaly systems and can be used to monitor critical equipment,enhancing system reliability and safety.
