Research on a Comprehensive Monitoring System for Tunnel Operation based on the Internet of Things and Artificial Intelligence Identification Technology

This article proposes a comprehensive monitoring system for tunnel operation to address the risks associated with tunnel operations.These risks include safety control risks,increased traffic flow,extreme weather events,and movement of tectonic plates.The proposed system is based on the Internet of Things and artificial intelligence identification technology.The monitoring system will cover various aspects of tunnel operations,such as the slope of the entrance,the structural safety of the cave body,toxic and harmful gases that may appear during operation,excessively high and low-temperature humidity,poor illumination,water leakage or road water accumulation caused by extreme weather,combustion and smoke caused by fires,and more.The system will enable comprehensive monitoring and early warning of fire protection systems,accident vehicles,and overheating vehicles.This will effectively improve safety during tunnel operation.

Intelligent Protection and Monitoring System of Lightning Disaster

Based on Internet technology,modern wireless communication technology and new sensor technology,an intelligent protection and monitoring system of lightning disaster was established to collect lightning current(lightning intensity,frequency and time),induced lightning current and leakage current of surge protector(SPD),earthing resistance,working voltage,temperature and humidity in the lightning environment and other data in real time.Through the online analysis of visual data and automatic alarm beyond the preset value of cloud platform,it can realize the intelligent online monitoring and management of lightning protection devices,providing scientific and reliable lightning protection technical support for lightning protection and disaster reduction,and ensuring the smooth development and efficient management of lightning protection safety work.

Failure process and monitoring data of an extra-large landslide at the Nanfen Open-pit Iron Mine

An extra-large landslide occurred on June 19,2021,on the footwall slope of the Nanfen Open-pit Iron Mine in Liaoning Province,China,with a volume of approximately 1.2×107 m3.To elucidate the causative factors,development process,and destructive mechanisms of this catastrophic landslide,comprehensive field tests,investigations,and laboratory experiments were conducted.Initially,the heavily weathered rock mass of the slope was intersected by faults and joint fissures,facilitating rainwater infiltration.Moreover,the landslide contained a substantial clay mineral with highly developed micro-cracks and micro-pores,exhibiting strong water-absorption properties.As moisture content increased,the rock mass underwent softening,resulting in reduced strength.Ultimately,continuous heavy rainfall infiltration amplified the slope's weight,diminishing the weak structural plane's strength,leading to fracture propagation,slip plane penetration,and extensive tensile-shear and uplift failure of the slope.The study highlights poor geological conditions as the decisive factor for this landslide,with continuous heavy rainfall as the triggering factor.Presently,adverse environmental factors persistently affect the landslide,and deformation and failure continue to escalate.Hence,it is imperative to urgently implement integrated measures encompassing slope reinforcement,monitoring,and early-warning to real-time monitor the landslide's deformation and deep mechanical evolution trends.

Application and Development of Protected Horticulture Intelligent Monitoring System

Protected horticulture makes use of related facilities, engineering technolo- gy and management technologies to create or improve local environment in order to provide optimal environment concerning controllable temperature, humidity, and light for farming and breeding industry, as well as product storage. Protected horticulture is independent to some extent, instead of relying greatly on nature, targeting full use of soil, climate and biological potential. The research concluded production characteristics of protected horticulture and analyzed the application of protected hor- ticulture intelligent monitoring system in protected greenhouse cultivation. In addition, the future development was proposed on protected horticulture intelligent monitoring system.

Smart Healthcare Activity Recognition Using Statistical Regression and Intelligent Learning

In this present time,Human Activity Recognition(HAR)has been of considerable aid in the case of health monitoring and recovery.The exploitation of machine learning with an intelligent agent in the area of health informatics gathered using HAR augments the decision-making quality and significance.Although many research works conducted on Smart Healthcare Monitoring,there remain a certain number of pitfalls such as time,overhead,and falsification involved during analysis.Therefore,this paper proposes a Statistical Partial Regression and Support Vector Intelligent Agent Learning(SPR-SVIAL)for Smart Healthcare Monitoring.At first,the Statistical Partial Regression Feature Extraction model is used for data preprocessing along with the dimensionality-reduced features extraction process.Here,the input dataset the continuous beat-to-beat heart data,triaxial accelerometer data,and psychological characteristics were acquired from IoT wearable devices.To attain highly accurate Smart Healthcare Monitoring with less time,Partial Least Square helps extract the dimensionality-reduced features.After that,with these resulting features,SVIAL is proposed for Smart Healthcare Monitoring with the help of Machine Learning and Intelligent Agents to minimize both analysis falsification and overhead.Experimental evaluation is carried out for factors such as time,overhead,and false positive rate accuracy concerning several instances.The quantitatively analyzed results indicate the better performance of our proposed SPR-SVIAL method when compared with two state-of-the-art methods.

Real-Time Monitoring Method for Cow Rumination Behavior Based on Edge Computing and Improved MobileNet v3

[Objective]Real-time monitoring of cow ruminant behavior is of paramount importance for promptly obtaining relevant information about cow health and predicting cow diseases.Currently,various strategies have been proposed for monitoring cow ruminant behavior,including video surveillance,sound recognition,and sensor monitoring methods.How‐ever,the application of edge device gives rise to the issue of inadequate real-time performance.To reduce the volume of data transmission and cloud computing workload while achieving real-time monitoring of dairy cow rumination behavior,a real-time monitoring method was proposed for cow ruminant behavior based on edge computing.[Methods]Autono‐mously designed edge devices were utilized to collect and process six-axis acceleration signals from cows in real-time.Based on these six-axis data,two distinct strategies,federated edge intelligence and split edge intelligence,were investigat‐ed for the real-time recognition of cow ruminant behavior.Focused on the real-time recognition method for cow ruminant behavior leveraging federated edge intelligence,the CA-MobileNet v3 network was proposed by enhancing the MobileNet v3 network with a collaborative attention mechanism.Additionally,a federated edge intelligence model was designed uti‐lizing the CA-MobileNet v3 network and the FedAvg federated aggregation algorithm.In the study on split edge intelli‐gence,a split edge intelligence model named MobileNet-LSTM was designed by integrating the MobileNet v3 network with a fusion collaborative attention mechanism and the Bi-LSTM network.[Results and Discussions]Through compara‐tive experiments with MobileNet v3 and MobileNet-LSTM,the federated edge intelligence model based on CA-Mo‐bileNet v3 achieved an average Precision rate,Recall rate,F1-Score,Specificity,and Accuracy of 97.1%,97.9%,97.5%,98.3%,and 98.2%,respectively,yielding the best recognition performance.[Conclusions]It is provided a real-time and effective method for monitoring cow ruminant behavior,and the proposed federated edge intelligence model can be ap‐plied in practical settings.

Hyperspectral Intelligent Monitoring System of Major Soil Nutrients Based on ArcGIS Engine

Based on the object-oriented concept,the hyperspectral intelligent monitoring system of major soil nutrients was designed and developed by using C# and ArcGIS Engine in combination with Microsoft SQL Server.The system mainly includes the following functions：file operation,basic map operation,spectral preprocessing,model management,nutrient content quick calculation,spatial distribution analysis,user management and so on.This system can accomplish the input and preprocessing of soil hyperspectra,and calculate the content of major nutrients,such as soil organic matter,nitrogen,phosphorus as well as potassium quickly and intelligently based on hyperspectral data.Thereby,the soil nutrients regional distribution in the research area can be analyzed by using the principle of geostatistics.This study can not only promote the practicability of soil quantitative remote sensing,but also provide references for the decision-making of agricultural fertilizing.

An intelligent singular value diagnostic method for concrete dam deformation monitoring

Extracting implicit anomaly information through deformation monitoring data mining is highly significant to determining dam safety status.As an intelligent singular value diagnostic method for concrete dam deformation monitoring, shallow neural network models result in local optima and overfitting, and require manual feature extraction.To obtain an intelligent singular value diagnosis model that can be used for dam safety monitoring, a convolutional neural network (CNN) model that has advantages of deep learning (DL), such as automatic feature extraction, good model fitting, and strong generalizability, was trained in this study.An engineering example shows that the predicted result of the intelligent singular value diagnostic method based on CNN is highly compatible with the confusion matrix, with a precision of 92.41%, receiver operating characteristic (ROC) coordinates of (0.03, 0.97), an area-under-curve (AUC) value of 0.99, and an F1-score of 0.91.Moreover, the performance of the CNN model is better than those of models based on decision tree (DT) and k-nearest neighbor (KNN) methods.Therefore, the intelligent singular value diagnostic method based on CNN is simple to operate, highly intelligent, and highly reliable, and it has a high potential for application in engineering.

China agricultural outlook for 2015–2024 based on China Agricultural Monitoring and Early-warning System(CAMES)

The primary goal of Chinese agricultural development is to guarantee national food security and supply of major agricultural products. Hence, the scientiifc work on agricultural monitoring and early warning as wel as agricultural outlook must be strengthened. In this study, we develop the China Agricultural Monitoring and Early-warning System (CAMES) on the basis of a comparative study of domestic and international agricultural outlook models. The system is a dynamic and multi-market partial equilibrium model that integrates biological mechanisms with economic mechanisms. This system, which includes 11 categories of 953 kinds of agricultural products, could dynamical y project agricultural market supply and demand, assess food security, and conduct scenario analysis at different spatial levels, time scale levels, and macro-micro levels. Based on the CAMES, the production, consumption, and trade of the major agricultural products in China over the next decade are projected. The fol owing conclusions are drawn:i) The production of major agricultural products wil continue to grow steadily, mainly because of the increase in yield. i ) The growth of agricultural consumption wil be slightly higher than that of agricultural production. Meanwhile, a high self-sufifciency rate is expected for cereals such as rice, wheat, and maize, with the rate being stable at around 97%. i i) Agricultural trade wil continue to thrive. The growth of soybean and milk im-ports wil slow down, but the growth of traditional agricultural exports such as vegetables and fruits is expected to continue.

Data-driven intelligent monitoring system for key variables in wastewater treatment process

In wastewater treatment process(WWTP), the accurate and real-time monitoring values of key variables are crucial for the operational strategies. However, most of the existing methods have difficulty in obtaining the real-time values of some key variables in the process. In order to handle this issue, a data-driven intelligent monitoring system, using the soft sensor technique and data distribution service, is developed to monitor the concentrations of effluent total phosphorous(TP) and ammonia nitrogen(NH_4-N). In this intelligent monitoring system, a fuzzy neural network(FNN) is applied for designing the soft sensor model, and a principal component analysis(PCA) method is used to select the input variables of the soft sensor model. Moreover, data transfer software is exploited to insert the soft sensor technique to the supervisory control and data acquisition(SCADA) system. Finally, this proposed intelligent monitoring system is tested in several real plants to demonstrate the reliability and effectiveness of the monitoring performance.

A new early-warning prediction system for monitoring shear force of fault plane in the active fault

The most common method used to describe earthquake activity is based on the changes in physical parameters of the earth＇s surface such as displacement of active fault and seismic wave.However,such approach is not successful in forecasting the movement behaviors of faults.In the present study,a new mechanical model of fault activity,considering the shear strength on the fault plane and the influence of the resistance force,is established based on the occurrence condition of earthquake.A remote real-time monitoring system is correspondingly developed to obtain the changes in mechanical components within fault.Taking into consideration the local geological conditions and the history of fault activity in Zhangjiakou of China,an active fault exposed in the region of Zhangjiakou is selected to be directly monitored by the real-time monitoring technique.A thorough investigation on local fault structures results in the selection of two suitable sites for monitoring potential active tectonic movements of Zhangjiakou fault.Two monitoring curves of shear strength,recorded during a monitoring period of 6 months,turn out to be steady,which indicates that the potential seismic activities hardly occur in the adjacent region in the near future.This monitoring technique can be used for early-warning prediction of the movement of active fault,and can help to further gain an insight into the interaction between fault activity and relevant mechanisms.

Sparsity-Assisted Intelligent Condition Monitoring Method for Aero-engine Main Shaft Bearing

Weak feature extraction is of great importance for condition monitoring and intelligent diagnosis of aeroengine.Aimed at achieving intelligent diagnosis of aero-engine main shaft bearing,an enhanced sparsity-assisted intelligent condition monitoring method is proposed in this paper.Through analyzing the weakness of convex sparse model,i.e.the tradeoff between noise reduction and feature reconstruction,this paper proposes an enhanced-sparsity nonconvex regularized convex model based on Moreau envelope to achieve weak feature extraction.Accordingly,a sparsity-assisted deep convolutional variational autoencoders network is proposed,which achieves the intelligent identification of fault state through training denoised normal data.Finally,the effectiveness of the proposed method is verified through aero-engine bearing run-to-failure experiment.The comparison results show that the proposed method is good at abnormal pattern recognition,showing a good potential for weak fault intelligent diagnosis of aero-engine main shaft bearings.

The safety parameters monitoring system for the coal mine based on CAN bus communication and intelligent data acquisition

In this paper,a monitoring and controlling system for the safety in production and environmental parameters of a small and medium-sized coal mine has been developed after analyzing the current domestic coal production and security conditions. The client computer can convert the analog signal about the safety in production and environmental parameters detected from the monitoring terminal into digital signal,and then,send the signal to the coal mine safety monitoring centre. This information can be analyzed,judged,and diagnosed by the monitoring-management-controlling software for helping the manager and technical workers to control the actual underground production and security situations. The system has many advantages including high reliability,better performance of real-time monitoring,faster data communicating and good practicability,and it can effectively prevent the occurrence of safety incidents in coal mines.

Research and Application of 3G Electrical Safety Job Site Intelligent Monitoring Device

This paper describes the shortcomings and difficulties of power company security construction, such as site management for construction site security monitoring personnel is limited, in recent years , rural power grids and Urban Network alteration Faced with new situation. The use of advanced science and technology and communication terminal in order to better strengthen the means of power construction site safety supervision, improve the level of safety production supervision, design and development of a new electrical safety job site intelligent monitoring devices. The device consists of three parts of the remote wide angle 360 degrees of portable video surveillance equipment and 3G smart terminal equipment and portable battery. Through the application of such a device, professionals can remotely monitor the construction job site safety, diagnose, and effectively improve the security of the electricity sector management and reduce security risks and personnel on-site monitoring costs for improving the security of the entire power industry field operations with significance.

Research on Intelligent Monitoring System Based on Raspberry Pi 3

In order to monitor environmental parameters for food storage more effectively in real time,an intelligent monitoring system was designed and implemented based on Raspberry Pi.Based on the Raspberry Pi 3 Model B,the system connects environmental sensors such as temperature,humidity,light intensity and CO2 concentration.It can be used to monitor environmental parameters such as air temperature and humidity,light intensity and CO2 concentration during food storage.Based on Raspberry Pi 3,this study successfully built an intelligent monitoring system,and developed web version for computer terminal and app(iOS version and Android version)for mobile terminal.The data about the environmental factors during food storage were obtained.The system is reliable,simple and practical in operation,and highly expandable,laying a foundation for the next step of research on food safety storage environmental parameters and intelligent control.

Research on Technology Early-Warning System Based on Dynamic Information Monitoring

Relying on the advanced information technologies, such as information monitoring, data mining, natural language processing etc., the dynamic technology early-warning system is constructed. The system consists of technology information automatic retrieval, technology information monitoring, technology threat evaluation, and crisis response and management subsystem, which implements uninterrupted dynamic monitoring, trace and crisis early-warning to the specific technology. Empirical study testifies that the system improves the accuracy, timeliness and reliability of technology early-warning.

Self-Powered,Long-Durable,and Highly Selective Oil-Solid Triboelectric Nanogenerator for Energy Harvesting and Intelligent Monitoring

Triboelectric nanogenerators(TENGs)have potential to achieve energy harvesting and condition monitoring of oils,the“lifeblood”of industry.However,oil absorption on the solid surfaces is a great challenge for oil-solid TENG(O-TENG).Here,oleophobic/superamphiphobic O-TENGs are achieved via engineering of solid surface wetting properties.The designed O-TENG can generate an excellent electricity(with a charge density of 9.1μC m^(−2) and a power density of 1.23 mW m^(−2)),which is an order of magnitude higher than other O-TENGs made from polytetrafluoroethylene and polyimide.It also has a significant durability(30,000 cycles)and can power a digital thermometer for self-powered sensor applications.Further,a superhigh-sensitivity O-TENG monitoring system is successfully developed for real-time detecting particle/water contaminants in oils.The O-TENG can detect particle contaminants at least down to 0.01 wt%and water contaminants down to 100 ppm,which are much better than previous online monitoring methods(particle>0.1 wt%;water>1000 ppm).More interesting,the developed O-TENG can also distinguish water from other contaminants,which means the developed O-TENG has a highly water-selective performance.This work provides an ideal strategy for enhancing the output and durability of TENGs for oil-solid contact and opens new intelligent pathways for oil-solid energy harvesting and oil condition monitoring.

Progress of Intelligent Monitoring Technology for Wheat Fusarium Head Blight

Fusarium head blight is one of the most important diseases affecting wheat yield and quality.It is of great significance to carry out intelligent monitoring of wheat Fusarium head blight for high yield,high quality and sustainable development of wheat.On the basis of identifying the harms of wheat Fusarium head blight,this paper analyzed the monitoring technology of wheat Fusarium head blight based on satellite remote sensing,hyperspectral,near-infrared,Internet of things and photoelectric system,to provide a reference for the intelligent monitoring of wheat Fusarium head blight.

A Preliminary Study on Application Effect of Intelligent Insect Sexual Attraction Monitoring System

[Objectives]The paper was to verify and explore the application effect of intelligent insect sexual attraction monitoring system.[Methods]The data of Helicoverpa armigera and Spodoptera frugiperda monitored by intelligent insect sexual attraction monitoring system,manual survey and traditional pest monitoring tool were compared and analyzed,and the application effect of guiding field pest control was investigated.[Results]The statistical data of intelligent insect sexual attraction monitoring system were highly consistent with that of manual survey,and were consistent with that of traditional pest monitoring tool,which had good effect in guiding field control.[Conclusions]The monitoring data of intelligent insect sexual attraction monitoring system are accurate,efficient,real-time and practical.It can solve the problem of high monitoring intensity for the monitoring personnel and conform to the development direction of modern agriculture.

Integrated Monitor System of Water and Fertilizer of Greenhouse Intelligent Irrigatio

The integration of water and fertilizer is a comprehensive technology combined irrigation and fertilizer, which has outstanding advantages of saving fertilizer, saving water, saving labor, protecting environment, high yield and high efficiency. Currently, most of the water and fertilizer integrated irrigation and fertilization and irrigation operation in the production-based greenhouse is achieved relying on artificial experience, which is hard to achieve timely, scientific and intelligent irrigation. In this study, the application of STM32 embedded system realized the real-time collection of the data from the humidity sensors buried in top, middle and low depth of soil, and water and fertilizer integrated irrigation work was completed in the greenhouse through automatic control according to the predetermined fertilization and irrigation strategies for different crops. Moreover, the system had remote monitoring function, which used the global system for mobile （GSM） module to provide users with remote short message services, and therefore, the users could not only achieve the remote intelligent monitoring on the irrigation, light, ventilation of the greenhouse through short messages, but also could start and stop the remote control system operation, so as to realize the automatic management of the greenhouse environment, achieving the purpose of remote fertilization and water-saving irrigation.