Security Early Warning System Research Articles

Construction of a Climate Early Warning System: Predicting Future Temperatures and Climate Security Using BiLSTM

In light of the worsening global climate, providing predictive models for surface temperature and energy consumption is crucial for formulating effective climate action strategies. Initially, a Bi-directional Long Short-Term Memory (BiLSTM) network model is established to predict the maximum surface temperatures over the next century, with the Seasonal AutoRegressive Integrated Moving Average (SARIMA) model serving as a benchmark. To assess the risk levels of climate security, the k-means clustering algorithm is utilized to classify the growth rates of carbon dioxide emissions, enabling the construction of a three-tier climate security early warning index. Subsequently, a hybrid classification model based on Support Vector Machine (SVM) and Random Forest (RF) takes the energy consumption growth rates as inputs and the warning indices as outputs to construct a climate security early warning system. The BiLSTM model is employed to predict the energy consumption growth rates for the upcoming decade, and these rates are input into the SVM-RF model to forecast future warning levels. The study demonstrates that the model can effectively predict the maximum surface temperatures and provide a three-tier safety warning system for future climate risk management. The intent of this research is to offer a novel tool for global climate prevention and to deliver practical application value to policymakers in finance, energy, and environmental sectors.

Retracted: Construction of Economic Security Early Warning System Based on Cloud Computing and Data Mining.

[This retracts the article DOI: 10.1155/2022/2080840.].

An integrated ecological security early-warning framework in the national nature reserve based on the gray model

Nature reserves (NRs) play a pivotal role in minimizing habitat loss and protecting wild animals and plants, which are critical for human ecological security. However, focusing only on the construction of ecological security patterns of NRs without understanding their ecological security early-warning situations and their driving factors may fail to achieve protection goals. This study constructed an ecological security early-warning framework and index system based on the Driving force-Pressure-State-Impact-Response (DPSIR) framework model. The gray model (GM) was used to predict the ecological security early-warning situation, and the Geo-detector model was applied to explore the driving factors of the ecological security early-warning system in the Baishuijiang National Nature Reserve (BNNR). The results showed that the average ecological security index (ESI) value increased from 0.2796 in 2005 to 0.3171 in 2017, with an average increase of 11.82%. The ecological security early-warning index (ESEWI) value increased from 0.3171 in 2018 to 0.3622 in 2030, which was an average increase of 12.46%. These results indicated that the ecological security situation continually improved from 2005 to 2030. By 2030, the number of towns with a “no warning” grade increased to four, the number of towns with an “extreme warning” grade was zero, and the proportion of areas with early-warnings decreased from 100% to 33%. The q values of per capita forest land areas and per capita grassland areas were both 0.9334, which indicated that environmental characteristic factors were the primary driving factors in ecological security early-warning. Our results demonstrated that the ecological security early-warning index system based on the DPSIR model and grey model can well prediction ecological security situation and provide scientific support for the ecological protection and management of NRs.

Early warning and scenario simulation of ecological security based on DPSIRM model and Bayesian network: A case study of east Liaohe river in Jilin Province, China

Ecological security early warning research is an effective tool for statistical unsustainable bottom line and watershed management. Recent research has shown that climatic elements influence the characteristics of ecological security and its variability and that landscape patterns constrain the basis of regional ecological security. Thus, there is a need to identify trends in ecological safety under the driving forces of climate and landscape pattern changes to enable early warning measures. In this study, a new ecological security early warning system (ESEW) was established, and a comprehensive index was constructed to assess the ESEW in the East Liaohe River Basin (ELRB), China during 2000–2020. The system used a geographic detector to reveal the internal influences, external drivers, and synergistic effects of ecological security warnings in the basin in different periods. A Bayesian network (BN) model was used to simulate the risk of different ecological security warnings occurring under different scenarios. The results showed that the ESEW level is higher in the northwestern part of the ELRB, and the area occupied by extreme warning decreased simultaneously as the area occupied by no warning decreased. The changes of ecological security warning situation in the ELRB are not only influenced by internal factors but also driven by external factors, and the synergy between the core factors and external factors can better explain the driving mechanism of ecological security alarm than a single factor. The BN model was more sensitive to the simulation of no warning and extreme warning. This study provided insights into the maintenance of the security and sustainable development of watershed ecosystems and new perspectives for watershed ESEW research.

Food Security: 3D Dynamic Display and Early Warning Platform Construction and Security Strategy.

Since it affects a nation’s economy and people’s wellbeing, food security is a crucial national security requirement. In order to realize multi-angle grain data presentation and analysis and achieve the goal of deep mining, we propose a 3D dynamic visualization analysis method of multidimensional agricultural spatial–temporal data based on the self-organizing map. This method realizes the multi-angle display and analysis of grain data and achieves the purpose of deep mining. With the outbreak of COVID-19, the global food security situation is not optimistic, so it is necessary to use the food security early warning system to solve the food security issue. Machine learning has emerged widely in recent years and has been applied in various fields. Therefore, it is an excellent way to solve food security to apply the model in machine learning to construct a food security early warning system. Afterward, a food security early warning platform is developed with a support vector regression (SVR) model to ensure food security. Finally, we analyze China’s medium and long-term food security policy in line with modernization objectives. The experimental results show that the food security early warning platform based on the SVR model from 2007 to 2016 is effective compared with the actual situation every year. Through analyses, we should improve the stability, reliability, and sustainability of food supply, firmly hold the food security initiative, and construct a national food security guarantee system matching the goal of modernization.

Construction of Economic Security Early Warning System Based on Cloud Computing and Data Mining.

Economic security is a core theoretical issue in economics. In modern economic conditions, the ups and downs caused by economic instability in any economic system will affect the stability of the financial market, bring huge losses to the economy, and affect the development of the whole national economy. Therefore, research on the regularity of economic security and economic fluctuations is one of the important contents to ensure economic stability and scientific development. Accurate monitoring and forecasting of economic security are an indispensable link in economic system regulation, and it is also an important reference factor for any economic organization to make decisions. This article focuses on the construction of an economic security early warning system as the main research content. It integrates cloud computing and data mining technologies and is supported by CNN-SVM algorithm and designs an early warning model that can adaptively evaluate and warn the economic security state. Experiments show that when the CNN network in the model uses ReLU activation function and SVM uses RBF function, the prediction accuracy can reach 0.98, and the prediction effect is the best. The data set is verified, and the output Q province's 2018 economic security early warning comprehensive index is 0.893. The 2019 economic security early warning index is 0.829, which is consistent with the actual situation.

Research on Food Security Risk Assessment and Early Warning in China Based on BP Neural Network Model

China has always attached great importance to food security issues; especially in today’s changeable world, it is particularly important to build a feasible and accurate food security early warning system. According to the influencing factors in food security, this paper uses the PCA method and the AHP method to construct a food security early warning index system that includes 4 secondary indicators and 13 tertiary indicators of total security, trade security, ecological security, and food security. There are four security levels of no warning, light warning, moderate warning, and heavy warning, and finally the comprehensive evaluation of food security from 2000 to 2019 and the specific early warning levels of various indicators are obtained. This paper constructs a food security evaluation system from the perspective of data, breaks through the limitations of existing research, and improves the completeness of food security early warning indicators. Because the BP neural network is a multilayer feedforward neural network with strong adaptability, it is one of the most widely used and successful neural network models at present. Finally, BP neural network is used to simulate China’s food security early warning system and design standardized risk prevention and control processes and classified response strategies—routine monitoring, risk control, and emergency response—to provide signal guidance and reference for China’s food security to respond to risks early.

Auxiliary cognition system‐based management strategy optimization of supply chain of new energy in oil–gas enterprises

AbstractThe work focuses on the optimization of management strategies of energy supply chain in oil–gas enterprises to find out the optimal management strategy and overcome the fragility of the traditional energy supply system. Back propagation neural network (BPNN) is applied to energy supply predicting and energy security early warning in oil–gas enterprises, and the security early warning system is designed based on the auxiliary cognitive system. The security early warning system is used to predict the peak value of oil and gas supply, and then the detection performances and the data security transmission performances of different models are compared and analysed. Finally, a comprehensive evaluation model for security management of oil–gas supply chain based on the neural network is established. It is found that compared with convolutional neural network (CNNs), the improved BPNN model algorithm proposed shows increased accuracy by 5.4%, reduced model training time and test time, and improved accuracy and recall rate. The accuracy, recall rate, and F1 value of the proposed model are 88.48%, 75.88%, and 96.21%, respectively, which were obviously higher than those of the CNN. It suggests that the improved security management model of oil–gas supply chain shows better recognition and prediction accuracy. Based on the quantitative and qualitative analysis results, the optimization suggestions for management strategies in oil–gas enterprises are put forward, which is of strategic significance for improving the security of oil–gas supply.

Design of Intelligent Security Early warning system for unguarded railway crossing in mining area

There are many railway crossings in the mine, but there are not enough people to keep every crossing manned. For the traditional unguarded railway crossing, we set up a warning sign at a certain distance from the crossing to remind pedestrians and drivers. For some crossings with poor vision, both pedestrians and locomotive drivers at the crossings can’t make timely and accurate judgment on the surrounding real-time road conditions, which is easy to cause safety accidents. In order to solve this technical problem and demand, an intelligent security and early warning robot with image recognition module and intelligent control board was developed by combining advanced theories and technologies such as Internet +, image recognition, beidou+ GPS dual-mode positioning, wind and solar complementary power generation and multi-sensor fusion. The system can improve the intelligent level of the unguarded crossing in the mining area, realize the unmanned or less unmanned crossing work, and ensure the safety of railway transportation in the coal mine.

Data Transmission Security in Computer Network Communication

In recent years, computer network communication technology has made considerable progress and development, and its scale has now covered all aspects of society and people’s lives. However, while computer network technology promotes social development and improves the convenience of people’s lives, there are still many security problems, especially the difficulty of ensuring the security of user data, which has become a prominent problem in the further development and improvement of communication technology. This article takes the network communication security technology as an entry point, and analyzes its technical types with the aid of data encryption technology, and then analyzes and studies its security in computer network communication data transmission according to the specific points proposed. Through the substantial technical analysis of the computer network security system, the possibility of the system being attacked can be reduced to a certain extent, and the security of network data can be improved. Through the analysis of the integrity of the network system, the overall framework of a distributed network security early warning system based on the network is established, and various structures of the system are analyzed. The network load analysis module is responsible for using key performance index data, such as user traffic, number of users and wireless utilization, and analyzes and classifies the network cell load by combining the partial least square method and BP neural network, so that the key cell performance analysis module is mainly Analyze key performance index data of key communities, obtain early warning thresholds through clustering algorithms, and implement a dynamic early warning system to provide guidance for network planning and mining in key small areas. The database module is responsible for storing all performance data and performance analysis results; the performance query module is responsible for querying and analyzing performance data and static warnings, and presents them to users in the form of text and graphics. Experimental research results show that in this information age, the development of computer networks plays a very important role in the development of our country. Computer network operators make full use of cluster analysis, data mining, encryption algorithms, and early warning systems to conduct research to fully improve the security performance of data in network communications.

Building a better global health security early-warning system post-COVID: The view from Canada

In the years following the Severe Acute Respiratory Syndrome (SARS) outbreak in 2002–2003, the World Health Organization (WHO) created a new system for global disease outbreak surveillance. The system relied on timely reporting by nation-states and gave the WHO a leading role in the global response. It also recognized the value of a multiplicity of sources of information, including from open-source media scanning. The post-SARS system faced its most significant task with the outbreak of the COVID-19 pandemic in the People’s Republic of China and its rapid spread in 2020. The WHO architecture for early warning of disease outbreaks arguably failed and gives rise to questions about how the international community can better respond to pandemic threats in future. This article explores the inter-connectedness of Canada’s system for global health surveillance, featuring the work of the Global Public Health Intelligence Network and that of the WHO, and argues that, while Canada has positioned itself as a global leader, much work needs to be done in Canada, and globally, if the concept of collective health security and shared early warning is to be maintained in the future.

Study on early warning of E-commerce enterprise financial risk based on deep learning algorithm

With the development trend of economic progress, the capital business of e-commerce enterprises has become complicated. The financial risk of listed companies is a problem that needs to be paid attention to. The financial risk of e-commerce companies is a complex and gradual process, and its unique reasons may be many. E-commerce companies are facing financial risks or difficulties, and bankruptcy and liquidation are also increasing. Financial risk has seriously affected e-commerce companies and society. As a result, the early warning methods of financial risks have been constantly improved. With the arrival of the new economic era in the era of knowledge economy, the early warning of financial risks in e-commerce companies has become a hot issue in the financial management of e-commerce companies. Based on the deep learning algorithm, this paper studies from the perspective of establishing the financial early warning model based on deep learning and constructing the financial risk early warning mechanism of e-commerce companies, and analyzes and forecasts the financial risks of listed companies. Through the construction of financial security early warning system, crisis signals can be diagnosed as soon as possible, and crisis signals can be prevented and solved timely and effectively.

Design and Implementation of Marine Information Security Early-Warning System Oriented to Security Elements Association Analysis

Zhao, J.; Xiang, Y., and Liu, F., 2020. Design and implementation of marine information security early-warning system oriented to security elements association analysis. In: Yang, D.F. and Wang, H. (eds.), Recent Advances in Marine Geology and Environmental Oceanography. Journal of Coastal Research, Special Issue No. 108, pp. 266–269. Coconut Creek (Florida), ISSN 0749-0208.Marine security is particularly important in today's world economic and political structure. Especially as China is a big country in the world, marine rights and ocean security are becoming increasingly important, and they are gradually being valued by people, scholars, and even governments. Many factors, such as marine fishery resources, mineral resources, energy resources, and even geography and trade, determine the diversity of marine security. Therefore, this paper designs a marine information security early-warning system on the basis of the correlation analysis of security elements. Effective early-warning test results show that the actual application works.

SISTEM PERINGATAN DINI KETAHANAN PANGAN DAERAH: PENATALAKSANAAN DAN PELIBATAN SELURUH PEMANGKU KEPENTINGAN

Early Warning System (EWS) is a solution of food security of the most important in the management of the policy (Governance) food security. This study departs from best practices (best practices) in the regional autonomy that has been applied in the area surrounding the Surakarta and management of food security that is initiated by Bank Indonesia (BI) and the Central Statistics Agency (BPS) through Regional Inflation Control Team (TPID). This study uses FGD and AHP method are used to build the index of food security EWS. The results of this study indicate that areas that do not have high production in food commodities indeed have a higher susceptibility than having high production.

Design and Implementation of Marine Information Security Early-Warning System Based on Attack Behavior Pattern Analysis

Zhao, J.; Zhang. K., and Duan, T., 2019. Design and implementation of marine information security early-warning system based on attack behavior pattern analysis. In: Li, L.; Wan, X.; and Huang, X. (eds.), Recent Developments in Practices and Research in Coastal Regions: Transportation, Environment, and Economy, Journal of Coastal Research, Special Issue No. 98, pp. 175–178. Coconut Creek (Florida), ISSN 0749-0208.The offshore platform structure is major infrastructure for the development of offshore petroleum resources and a base for offshore production operations and life. The environment of the offshore platform structure is very complicated and bad, and under environmental influence, the resistance of the offshore platform structure may below. The failure of the offshore platform structure not only causes direct major economic losses and casualties, but also may cause serious environmental pollution and social and political impacts. This paper describes the establishment and modification of a real-time calculation model of the offshore platform structure, determines the real-time environmental load of the system based on marine environment monitoring, proposes a real-time safety assessment method of the offshore platform structure, and develops a Windows application using the Delphi development tool. The system was commissioned twice in the Bohai JZ20-2MUO platform during the winter of 1998–99 and 1999–2000 and the security status of the JZ20-2MUO platform in the winter was monitored in real time.

Study on Construction of Tourism Security Early Warning System in Tourist Destinations - Take Yangshuo, China as an Example

Tourism security is the necessary foundation for sustainable development of tourism destinations. The application of next-generation information technology for early warning of tourism safety can achieve the management goals of tourism destinations such as accuracy, economy, and rapid response. The tourist destination safety early warning system mainly consists of video surveillance-based passenger flow assessment, traffic safety early warning system, emergency intelligent guidance system and intelligent rescue simulation system.

The evolution of landscape ecological security in Beijing under the influence of different policies in recent decades

Urbanization is an important force driving the development of the social, economic, and ecological environments in urban China. As the capital of China, Beijing has experienced a shift in the development process from emphasizing economic development to emphasizing ecological livability in recent decades. During this period, the Olympic Games, real estate development, and environmentally friendly construction policies were major events that affected Beijing's urban ecosystem and its safety. Based on the Pressure–State–Response (P-S-R) framework model, this paper establishes an indicator system for assessing the ecological security of Beijing from 1995 to 2015. The indicators were generated through coupling of an ecological model with time-series multi-source remote sensing data such as night light images and Landsat ETM images. We assessed ecological security during different policy periods and developed an ecological security early warning system for Beijing. After the effects of the economic development policy and the bid for the Olympic Games from 1995 to 2005, the urban area of Beijing with falling ecological security continues to expand. From 2005 to 2010, due to the joint effect of 2008 Olympic venue construction, urban environmental remediation policies, and real estate policies, the overall safety level in the central city was better, but the suburbs showed the opposite trend. In 2010–2015, real estate developed explosively in Beijing, while environmentally friendly development became strongly emphasized and the economic status of the capital weakened. The ecological security of the main urban area began to improve significantly, but the outer urban area and suburban areas were greatly affected by real estate development and exhibited a clear decline in ecological security.

Design and Development of Gas Sensor Based On Acoustic Resonance

Determination of the specific toxic, harmful, or flammable gases concentration i.e. butane, cannot be done directly. It requires devices that can do this measurement without any direct contact between the gas and human (observer) i.e. gas sensors. These sensors are typically used in security systems or early warning system. This research is about design and development of a gas sensor based on acoustic resonance. The sensor that has been developed is acoustic resonator based sensor, with two speakers as the sources of acoustic vibrations. This sensor is made to work at its resonance frequency. Since the resonance frequency of acoustic resonator is influenced by the speed of sound in the acoustic resonator, and the speed of sound is influenced by the density and concentration of the gas in the acoustic resonator, the changing of gas concentrations will cause resonance frequency shifting of the acoustic resonator. So, by taking measurement of resonance frequency shifting of resonator, gas concentration can be determined. This research was conducted in four stages, the first stage is designing of acoustic resonator, the second stage is manufacturing and initial testing of the acoustic resonator, the third stage is conditioning stage to make acoustic resonator works at its resonance frequency automatically, and the final stage is the testing stage of acoustic resonator using butane. Based on the research conducted, it can be concluded that the acoustic resonator system can work accurately and precision to detect the changing of butane gas concentration. Absolute error and relative error are relatively small, the largest of absolute error is 7.69% and the largest relative error is 0.47%.

A framework for a regional integrated food security early warning system: a case study of the Dongting Lake area in China

Understanding the regional food security situation is of great importance to maintaining China’s food security. To provide targeted information to help regional policymakers monitor food security status, based on the differentiated foci during the phased development of food security, this paper was conceived from the perspective of the need for early warnings and proposes a framework for regional integrated food security that incorporates food quantity security, food quality security, and sustainable food security. In this framework, an indicator system is proposed, and the calculation of these indicators, as well as their warning thresholds and warning ranges, is discussed. To test this approach, a case study was conducted in one of China’s major grain-producing areas, the Dongting Lake area. The results showed that the overall integrated food security situation in this area was generally in the low-alarm range between 1986 and 2011; the primary causes of this status were food quality security, generally in the low- or medium-alarm range, and sustainable food security, which in 14 of the 26 years was in the low-alarm range. The government should establish a more robust system for monitoring the quality of agricultural products, controlling waste discharge, and guaranteeing poor individuals access to sufficient, safe, and nutritious food to meet their dietary needs. Policies on pesticide and fertilizer application should shift from actively encouraging more use to controlling excess application.

Research and Application Based on Combination of online-function and offline-function of Power System Online Dynamic Early Warning System

AbstractThe Online Dynamic Security Assessment& Early Warning System of Power System (PDSA) integrate grid data and model, adopt pre-allocation mechanism, make static stability analysis, transient stability analysis, dynamic stability analysis and advanced assessment of total transfer capacity(TTC). Real-time alarm information of power grid is shown which reflect weak points of the grid. However, with the development of grid structure and data model, only displaying current alarm results that is unable to meet dispatchers' demands through pre-allocation fixed parameters mechanism adopted. This paper introduced combination of dynamic warning online systems and offline systems, which achieve online calculation parameters dynamically adjustment, transient stability curve viewing, analysis and comparison of historical alarm results, report generation. The solution has been successfully applied to state grid, regional grids and provincial grids of china, such as state grid, central china power grid, Hubei grid...