Intelligent Early Warning System Research Articles

Construction of an Adolescent Spinal Health Intelligent Early Warning and Intervention System under the Proactive Health Model

This study explores the construction of an intelligent early warning and intervention system for adolescent spinal health and its application under a proactive health model. The research demonstrates that through real-time monitoring and personalized interventions, the Spinal Health Index of adolescents in the intervention group significantly improved, increasing from 65 to 85 points, while the control group's index slightly decreased from 60 to 58 points. Personalized intervention strategies, such as the combination of exercise and nutritional interventions, were found to be the most effective, indicating that daily activity levels significantly impact spinal health. The system plays a crucial role in the management of adolescent spinal health by facilitating real-time monitoring, personalized interventions, and modifications in health behaviors. Despite limitations including constraints in sample size and geographical scope, a relatively short intervention period, and insufficient data diversity, future research can enhance universality and model generalizability by expanding sample sizes, prolonging the intervention period, and increasing data diversity. Looking forward, the integration of multimodal data, optimization of the user interface, and establishment of long-term tracking mechanisms will further enhance system performance and promote the improvement of spinal health management in adolescents.

Data Analysis and Algorithm Innovation in Power System IntelligentMonitoring and Early Warning Technology

Data analysis and algorithm innovation play a pivotal role in enhancing power system intelligent monitoring and early warning technology. With the increasing complexity of modern power grids, the integration of advanced data analytics enables real-time monitoring, fault detection, and predictive maintenance. By leveraging machine learning algorithms, anomaly detection techniques, and big data analytics, power systems can efficiently identify potential risks and failures before they escalate into serious issues. These innovations not only improve grid reliability and resilience but also optimize resource utilization. Early warning mechanisms based on intelligent algorithms provide timely alerts, allowing for preventive measures that ensure the stability and safety of the power network. This approach fosters a smarter, more adaptive power infrastructure capable of meeting growing energy demands while minimizing downtime and disruptions.This paper presents a comprehensive investigation into the development and efficacy of an intelligent early warning system for power systems. Leveraging machine learning algorithms, IoT sensors, and cloud computing frameworks, the system aims to enhance real-time monitoring capabilities and facilitate proactive intervention and maintenance. Through a series of simulations and iterations, the study demonstrates significant improvements in performance metrics such as accuracy, precision, recall, and F1 score. The integration of data analytics and classification techniques enables the system to accurately predict and classify anomalies, thereby minimizing risks and ensuring the reliability and efficiency of power systems. Through a series of simulations and iterations, the study demonstrates significant improvements in performance metrics such as accuracy, precision, recall, and F1 score. Specifically, the system achieves an average accuracy of 95%, precision of 92%, recall of 94%, and F1 score of 92% across multiple iterations. The integration of data analytics and classification techniques enables the system to accurately predict and classify anomalies, thereby minimizing risks and ensuring the reliability and efficiency of power systems.

Research on Intelligent Monitoring System of Intelligent Transmission Line Based on Computer Internet of Things Technology

The author proposes an intelligent early-warning system for preventing external damage of transmission lines. The system uses TI-TMS320DM6446 as the core, the peripheral circuit configuration and video monitoring; The moving objects captured by PTZ camera are detected and identified by using mixed Gaussian model, background difference and minimum tangent matrix, and alarm signals are provided. According to the particularity of the power supply of the equipment on the transmission line, the basic power management of the system is analyzed in detail. A reliable power management scheme is designed. The practical application proves that this method has good real-time performance and has obvious effect on preventing power cable damage by force.

Optimization of Network Security Intelligent Early Warning System Based on Image Matching Technology of Partial Differential Equation

In order to effectively avoid network information leakage and computer network paralysis, and improve the ability of computer network security early warning, it is necessary to design a computer network security intelligent early warning system based on network behavior. Security warning is considered as the second defense mechanism behind the firewall. It can monitor and warn the network without affecting the network performance, so as to provide real-time protection for external attacks, internal attacks and misoperations, and improve the network security. This paper designs an intelligent early warning system for network security based on partial differential equation image matching technology. The system adopts B/S development mode, and the server and browser are located in the campus network. Data fusion technology is used to assess network security, predict potential threats, and add new intrusion features to the feature database for subsequent use. In this paper, a target matching algorithm based on partial differential equation is proposed by using partial differential equation. The algorithm calculates the phase difference through the algebraic combination of orthogonal filter outputs. For scenes with continuous disparity changes, the error matching rate of this algorithm is lower than that of Michael Bleyer algorithm and vertical constraint algorithm. In general, the disparity map generated by this algorithm has high matching accuracy. The results show that the distribution of alarm information is reasonable and in line with the actual situation.

Detection of Safety Signs Using Computer Vision Based on Deep Learning

Safety signs serve as an important information carrier for safety standards and rule constraints. Detecting safety signs in mines is essential for automatically early warning of unsafe behaviors and the wearing of protective equipment while using computer vision techniques to realize advanced safety in the AI and IoT era. This work aims to propose an improved YOLOV4-tiny safety signs detection model applying deep learning to detect safety signs in mines. The dataset employed in this study was derived from coal mines and analogous environments, comprising a total of ten types of safety signs. It was partitioned into training, validation, and test sets following a distribution ratio of (training set + validation set) to test set = 9:1, with the training set to validation set ratio also set at 9:1. Then the attention mechanism ECANet was introduced into the model, which strengthened the network’s learning of places that need attention. Moreover, the Soft-NMS algorithm was used to retain more correct prediction frames and optimize the detection model to further improve the detection accuracy. The Focal Loss function was introduced to alleviate the problem of category imbalance in one-stage safety signs detection. Experimental results indicate that the proposed model achieved a detection precision of 97.76%, which is 7.55% and 9.23% higher than the YOLOV4-tiny and Faster RCNN algorithms, respectively. Besides, the model performed better in the generalization because it avoided the over-fitting phenomenon that occurred in the YOLOV4-tiny and the Faster RCNN. Moreover, the advantages of the improved model were more prominent when detecting small target areas and targets under dim conditions in coal mines. This work is beneficial for the intelligent early warning system with surveillance cameras in coal mines.

An Intelligent Early Warning System for Harmful Algal Blooms: Harnessing the Power of Big Data and Deep Learning.

Harmful algal blooms (HABs) pose a significant ecological threat and economic detriment to freshwater environments. In order to develop an intelligent early warning system for HABs, big data and deep learning models were harnessed in this study. Data collection was achieved utilizing the vertical aquatic monitoring system (VAMS). Subsequently, the analysis and stratification of the vertical aquatic layer were conducted employing the "DeepDPM-Spectral Clustering" method. This approach drastically reduced the number of predictive models and enhanced the adaptability of the system. The Bloomformer-2 model was developed to conduct both single-step and multistep predictions of Chl-a, integrating the " Alert Level Framework" issued by the World Health Organization to accomplish early warning for HABs. The case study conducted in Taihu Lake revealed that during the winter of 2018, the water column could be partitioned into four clusters (Groups W1-W4), while in the summer of 2019, the water column could be partitioned into five clusters (Groups S1-S5). Moreover, in a subsequent predictive task, Bloomformer-2 exhibited superiority in performance across all clusters for both the winter of 2018 and the summer of 2019 (MAE: 0.175-0.394, MSE: 0.042-0.305, and MAPE: 0.228-2.279 for single-step prediction; MAE: 0.184-0.505, MSE: 0.101-0.378, and MAPE: 0.243-4.011 for multistep prediction). The prediction for the 3 days indicated that Group W1 was in a Level I alert state at all times. Conversely, Group S1 was mainly under an Level I alert, with seven specific time points escalating to a Level II alert. Furthermore, the end-to-end architecture of this system, coupled with the automation of its various processes, minimized human intervention, endowing it with intelligent characteristics. This research highlights the transformative potential of integrating big data and artificial intelligence in environmental management and emphasizes the importance of model interpretability in machine learning applications.

Edge-Centric Intelligent Early Warning System for Residual Soil Stability Prediction in Slope

Edge-Centric Intelligent Early Warning System for Residual Soil Stability Prediction in Slope

Research on the design and precise monitoring technology of intelligent early warning system for power tower settlement based on multi-dimensional information fusion technology

Abstract Multi-dimensional information fusion technology helps to realize the synergy and complementarity between the information, which provides a guarantee for the settlement monitoring and early warning of electric power pole towers. The settlement monitoring algorithm for electric power pole towers is improved by this paper based on this technology and a settlement warning model is proposed. The traditional PDA algorithm can be enhanced by enhancing the pole tower image acquisition capacity with the wavelet transform algorithm and achieving the fusion and evaluation of multidimensional information through an isolated forest algorithm. The relationship functions of tilt angle, settlement wind speed size and other indicators are established, and the system of super-definite equations is used to solve the correlation coefficients, and the early warning system of pole tower settlement is constructed. The analysis results show that the monitoring results of the monitoring algorithm are fluctuating and stable; the absolute value of the error in the horizontal direction is not more than 9 mm, and the absolute value of the error in the vertical direction is not more than 14 mm. The predicted values of the maximum displacement and stress R 2 are close to 1, and the MAPE is 0.436% and 1.123%, respectively. It indicates that the improved power pole tower settlement monitoring algorithm and early warning system in this paper have satisfactory performance.

Research on intelligent identification algorithm for preventing external damage behavior of power cable based on machine learning

Abstract As the key carrier of power distribution line power transmission and the supporting artery of urban functioning, power cables are widely used in electric power engineering construction. The number of users is increasing, and the distribution of locations is becoming more complex. However, if a power cable failure occurs, if not eliminated in time, it will threaten the safety of the power grid, seriously affecting the production of enterprises and the lives of residents. The article first on the power cable vibration signal recognition algorithm design, extraction of power cable external damage fault signal characteristics, proposed based on PSO-SVM power cable external damage fault waveform recognition algorithm, and the design of power cable anti-external damage intelligent early warning system, to achieve a certain period according to the trend of signal changes in the vibration event to discriminate. Research results show that under the same computer operating conditions, the average running time of the algorithm proposed in this paper is only 3.43. Compared to the other two algorithms, the algorithm has the highest recognition accuracy, the fastest convergence speed, and the optimal comprehensive performance. In the external damage signal recognition and warning test, the recognition rate of the excavator passing through defense zone one and two at a distance of 3 meters is 100%, which is greater than 80%, in line with the expected effect.

Overflow Intelligent Early Warning System Based on BP Neural Networks

Overflow is one of the most serious drilling accidents that affect the safety of drilling construction. The analysis of integrated logging parameters and the judgement of overflow situation in China still remain in the stage of "manual judgement" and "threshold warning". Based on this, we propose the method of combining the real time measurement information of comprehensive logging instrument with artificial intelligence technology, and design a BP neural network based intelligent early warning software system using SQL Server 2008 database management platform and C# program development language. Through the experimental test, the system runs well, the timeliness of overflow early warning is good, the accuracy of early warning results is high, and it can meet the needs of field application, and it can provide effective technical support for the field overflow early warning, and it has a better prospect of field application.

Securing drinking water supply in smart cities: an early warning system based on online sensor network and machine learning

Abstract To enhance the quality of life and ensure sustainability in crowded cities, safe management of drinking water using cutting-edge technologies is a priority. This study developed an intelligent early warning system (EWS) for alarming and controlling risks from bacteria and disinfection byproducts in a drinking water distribution system (DWDS), named BARCS (Bacterial Risk Controlling System). BARCS adopts an artificial intelligence (AI) approach to data-driven prediction and considers total chlorine (TCl) concentration as the pivot indicator for risk identification and control. First, the machine learning-based AI model in BARCS can provide a reliable prediction of TCl concentration in a DWDS, with an average R2 of 0.64 for the validation set, while offering great flexibility for BARCS to adapt to various conditions. Second, TCl concentration was proven to be a good indicator of bacterial risk in a DWDS, as well as a cost-effective surrogate variable to assess disinfection byproduct risk. Third, the robustness analysis demonstrates that with state-of-the-art water quality monitoring technologies, online implementation of BARCS in real-world settings is feasible. Overall, BARCS represents a promising solution to the safe management of drinking water in future smart cities.

Financial Risk Intelligent Early Warning System of a Municipal Company Based on Genetic Tabu Algorithm and Big Data Analysis

This paper proposes an intelligent early warning system for financial risk of listed companies based on genetic tabu algorithm and big data analysis. Establish the role application, analyze and complete the financial risk intelligent early warning system of listed companies and query the analysis results. Through the early warning source data management module, financial early warning module and countermeasure management module, the hardware part of the system is designed, the functional modules are divided, the relationship between the internal functions of the system is clear, and the hierarchical structure and call relationship between the modules are established. The key technology of CBR retrieve the matching case. Genetic tabu algorithm establish fitness function, search fitness measure, optimize attribute weight, and design system software. The experimental results show that the financial risk intelligent early warning system has high early warning accuracy, can effectively shorten the early warning time, and meet the needs of financial risk management of listed companies.

Application of Intelligent Inspection Robot in Coal Mine Industrial Heritage Landscape: Taking Wangshiwa Coal Mine as an Example.

This study is developed to explore the role of intelligent inspection robot in the protection and utilization of coal mine industrial heritage. Based on the actual situation of the coal mine, the underground planning protection scope is analyzed. Aiming at the problems of imperfect fire early warning detection technology, management mechanism, high labor cost and low work efficiency in underground protection, the intelligent inspection robot technology is proposed to realize safety tour, underground intelligent management and early warning of underground security, fire protection facilities construction, and intelligent early warning system. This paper analyzes the key technology of intelligent inspection robot in coal mine industrial heritage protection, introduces the composition, structure and implementation method, and proposes its construction path and method. Besides, the path planning, motion obstacle avoidance and sensing detection of the robot are studied. The research shows that the intelligent inspection robot has comprehensive functions and stable performance, and can realize the scientific, intelligent and refined management of industrial heritage protection, which provides a guiding basis for the intelligent protection of coal mine industrial heritage.

An intelligent early warning system of analyzing Twitter data using machine learning on COVID-19 surveillance in the US

The World Health Organization (WHO) declared on 11th March 2020 the spread of the coronavirus disease 2019 (COVID-19) a pandemic. The traditional infectious disease surveillance had failed to alert public health authorities to intervene in time and mitigate and control the COVID-19 before it became a pandemic. Compared with traditional public health surveillance, harnessing the rich data from social media, including Twitter, has been considered a useful tool and can overcome the limitations of the traditional surveillance system. This paper proposes an intelligent COVID-19 early warning system using Twitter data with novel machine learning methods. We use the natural language processing (NLP) pre-training technique, i.e., fine-tuning BERT as a Twitter classification method. Moreover, we implement a COVID-19 forecasting model through a Twitter-based linear regression model to detect early signs of the COVID-19 outbreak. Furthermore, we develop an expert system, an early warning web application based on the proposed methods. The experimental results suggest that it is feasible to use Twitter data to provide COVID-19 surveillance and prediction in the US to support health departments’ decision-making.

Epidemiological characteristics of imported respiratory infectious diseases in China, 2014\u20122018

BackgroundWith the progress of globalization, international mobility increases, greatly facilitating cross-border transmission of respiratory infectious diseases (RIDs). This study aimed to analyze the epidemiological characteristics and factors influencing imported RIDs, with the goal of providing evidence to support adoption of high-tech, intelligent methods to early find imported RIDs and prevent their spread in China.MethodsWe obtained data of imported RIDs cases from 2014 to 2018 from the Inbound Sentinel Network of Customs and the National Notifiable Diseases Reporting System in China. We analyzed spatial, temporal, and population distribution characteristics of the imported RIDs. We developed an index to describe seasonality. Pearson correlation coefficients were used to examine associations between independent variables and imported cases. Data analyses and visualizations were conducted with R software.ResultsFrom a total of 1 409 265 253 inbound travelers, 31 732 (2.25/100 000) imported RIDs cases were reported. RIDs cases were imported from 142 countries and five continents. The incidence of imported RIDs was nearly 5 times higher in 2018 (2.81/100 000) than in 2014 (0.58/100 000). Among foreigners, incidence rates were higher among males (5.32/100 000), 0–14-year-olds (15.15/100 000), and cases originating in Oceania (11.10/100 000). The vast majority (90.3%) of imported RIDs were influenza, with seasonality consistent with annual seasonality of influenza. The spatial distribution of imported RIDs was different between Chinese citizens and foreigners. Increases in inbound travel volume and the number of influenza cases in source countries were associated with the number of imported RIDs.ConclusionsOur study documented importation of RIDs into China from 142 countries. Inbound travel poses a significant risks bringing important RIDs to China. It is urgent to strengthen surveillance at customs of inbound travelers and establish an intelligent surveillance and early warning system to prevent importation of RIDs to China for preventing further spread within China.Graphical

Forestry Fire Prevention Detection and Emergency Command System Based on Internet of Things and Infrared Light Sensing Technology

Forest fire is a sudden, destructive and difficult natural disaster, which not only burns a lot of forests, but also harms the animals in the forests, and also reduces the forest's reproductive capacity, causing the soil to be barren, destroying the forest's water conservation and even causing the ecological environment to lose balance. The forest is harmful and difficult to extinguish, so it is particularly important to extinguish the fire immediately when it is still in the bud. However, because the fire often originates in the deep mountains and forests, it is difficult to find it, so it is of great significance to find out the fire for early extinguishing. Compared with the construction of watchtower and video surveillance system, the establishment of new intelligent early warning system can realize the intellectualization and informatization of forest fire prevention, and can detect and extinguish the fire in the early stage of fire.

Design and implementation of intelligent monitoring system for public transport applications

With the rapid development of automobile technologies and great economic growth, cars have become more and more popular for their convenience. However, the increase in cars has caused serious air pollution and increased the probability of traffic accidents. This paper investigates the design and implementation of intelligent monitoring and early warning system, which can detect the traffic conditions and provide environmental monitoring data. To achieve a comprehensive monitor for both inside and outside the public transport, this system is based on the STC12C5A16S2 micro-controller and loaded with all kinds of sensors, such as alcohol sensors, temperature and humidity sensors, toxic gas sensors, photosensitive sensors and light sensors etc. If the system detects any abnormal situation, it will generate early warning signs in time to inform the driver and the traffic regulatory departments. Consequently, our intelligent monitoring system can protect the safety of passengers and reduce the incidence of traffic accidents.

Design on Early Warning System for Renal Cancer Recurrence Based on CNN-Based Internet of Things

Kidney cancer is a type of urinary system tumor. The incidence of kidney cancer, which is second only to bladder cancer, has shown an overall upward trend in recent years. However, the early judgment of kidney cancer is still in the imaging biomarker discovery stage. Early detection and treatment cannot be achieved. Based on the natural advantages of the Internet of Things in the medical field, we focused on an intelligent early warning model of renal cancer recurrence and built a renal cancer early warning system integrated with the Internet of Things. We integrated the influencing factors of renal cell carcinoma, constructed a sample set, conducted data analysis and optimized the dataset. Aiming at the instability of renal cancer recurrence, five supervised learning prediction algorithms, including multiple linear regression, Bayesian ridge regression, gradient boosting tree, support vector regression, and convolutional neural network were used to develop a renal cancer recurrence prediction model. The predictive performance of these five algorithms were compared and discussed. Finally, the best renal cancer recurrence prediction model was established by combining a convolutional neural network with an Internet of Things medical framework. This design provided an intelligent early warning system to predict the recurrence time of renal cancer patients. In addition, the warning prompts provided in accordance with the model results can assist doctors in making preliminary judgments of the patient’s condition and has a certain auxiliary effect on the clinical diagnosis and treatment of cancer and kidney cancers.

Electric Equipment Transportation Condition Monitoring and Intelligent Early Warning System Based on Big Data Technology

Electric Equipment Transportation Condition Monitoring and Intelligent Early Warning System Based on Big Data Technology

Research on intelligent early warning system of rail transit line trip based on pscada

In this paper, an intelligent early warning scheme for rail transit line trip based on PSCADA system is proposed. The scheme takes into account the defects of low prediction accuracy and real-time prediction caused by the lack of power data in the traditional line trip prediction method. At the same time, a large number of power data generated by PSCADA system in the long-term application process are ignored in the field of rail transit[1]. Based on this situation, the prediction data set is constructed by combining the historical power data collected by PSCADA system in rail transit and the lightning weather data in traditional prediction methods. On this basis, the lightgbm machine learning intelligent algorithm is used to compare the similar support vector machine (SVM) and logistic regression algorithm to obtain a model with good prediction effect. In practical application, the real-time data set is constructed by using the real-time power data and real-time weather data collected by PSCADA system to predict, and an intelligent early warning system with the dual advantages of real-time and high accuracy is obtained.