Intelligent identification and real-time warning method of diverse complex events in horizontal well fracturing

The existing approaches for identifying events in horizontal well fracturing are difficult, time-consuming, inaccurate, and incapable of real-time warning. Through improvement of data analysis and deep learning algorithm, together with the analysis on data and information of horizontal well fracturing in shale gas reservoirs, this paper presents a method for intelligent identification and real-time warning of diverse complex events in horizontal well fracturing. An identification model for "point" events in fracturing is established based on the Att-BiLSTM neural network, along with the broad learning system (BLS) and the BP neural network, and it realizes the intelligent identification of the start/end of fracturing, formation breakdown, instantaneous shut-in, and other events, with an accuracy of over 97%. An identification model for "phase" events in fracturing is established based on enhanced Unet++ network, and it realizes the intelligent identification of pump ball, pre-acid treatment, temporary plugging fracturing, sand plugging, and other events, with an error of less than 0.002. Moreover, a real-time prediction model for fracturing pressure is built based on the Att-BiLSTM neural network, and it realizes the real-time warning of diverse events in fracturing. The proposed method can provide an intelligent, efficient and accurate identification of events in fracturing to support the decision-making.

Real-time lane departure warning system based on principal component analysis of grayscale distribution and risk evaluation model

A technology for unintended lane departure warning was proposed. As crucial information, lane boundaries were detected based on principal component analysis of grayscale distribution in search bars of given number and then each search bar was tracked using Kalman filter between frames. The lane detection performance was evaluated and demonstrated in ways of receiver operating characteristic, dice similarity coefficient and real-time performance. For lane departure detection, a lane departure risk evaluation model based on lasting time and frequency was effectively executed on the ARM-based platform. Experimental results indicate that the algorithm generates satisfactory lane detection results under different traffic and lighting conditions, and the proposed warning mechanism sends effective warning signals, avoiding most false warning.

Ecological risk assessment and early warning of heavy metal cumulation in the soils near the Luanchuan molybdenum polymetallic mine concentration area,Henan Province,central China

The Luanchuan molybdenum polymetallic mine concentration area is rich in mineral resources and has a long history of mining.The environmental impact of long-term mining activities cannot be ignored.It is of great significance to study the ecological risk and the accumulation trends of heavy metals in the soil of mining areas for scientific prevention and control of heavy metal pollution.Taking the Taowanbeigou River Basin in the mine concentration area as the research object,the ecological pollution risk and cumulative effect of heavy metals in the soil of the basin were studied by using the comprehensive pollution index method,potential ecological risk assessment method and geoaccumulation index method.On this basis,the cumulative exceeding years of specific heavy metals were predicted by using the early warning model.The comprehensive potential ecological risk of heavy metals in the soil near the Luanchuan mine concentration area is moderate,and the single element Cd is the main ecological risk factor,with a contribution rate of 53.6%.The overall cumulative degrees of Cu and Pb in the soil are“none-moderate”,Zn and Cd are moderate,Mo has reached an extremely strong cumulative level,Hg,As and Cr risks are not obvious,and the overall cumulative risks order is Mo>Cd>Zn>Cu>Pb>Hg.According to the current accumulation rate and taking the risk screening values for soil contamination of agricultural land as the reference standard,the locations over standard rates of Cu,Zn and Cd will exceed 78%in 90years,and the over standard rate of Pb will reach approximately 57%in 200 years.The cumulative exceeding standard periods of As,Cr and Hg are generally long,which basically indicates that these elements do not pose a significant potential threat to the ecological environment.Mining activities will accelerate the accumulation of heavy metals in soil.With the continuous development of mining activities,the potential pollution risk of heavy metals in the soil of mining areas will also increase.

Pattern changes and early risk warning of Spartina alterniflora invasion:a study of mangrove-dominated wetlands in northeastern Fujian,China

The exotic saltmarsh cordgrass,Spartina alterniflora(Loisel)Peterson&Saarela,is one of the important causes for the extensive destruction of mangroves in China due to its invasive nature.The species has rapidly spread wildly across coastal wetlands,challenging resource managers for control of its further spread.An investigation of S.alterniflora invasion and associated ecological risk is urgent in China's coastal wetlands.In this study,an ecological risk invasive index system was developed based on the Driving Force-Pressure-State-Impact-Response framework.Predictions were made of'warning degrees':zero warning and light,moderate,strong,and extreme warning,by developing a back propagation(BP)artificial neural network model for coastal wetlands in eastern Fujian Province.Our results suggest that S.alterniflora mainly has invaded Kandelia candel beaches and farmlands with clustered distributions.An early warning indicator system assessed the ecological risk of the invasion and showed a ladder-like distribution from high to low extending from the urban area in the central inland region with changes spread to adjacent areas.Areas of light warning and extreme warning accounted for43%and 7%,respectively,suggesting the BP neural network model is reliable prediction of the ecological risk of S.alterniflora invasion.The model predicts that distribution pattern of this invasive species will change little in the next 10 years.However,the invaded patches will become relatively more concentrated without warning predicted.We suggest that human factors such as land use activities may partially determine changes in warning degree.Our results emphasize that an early warning system for S.alterniflora invasion in China's eastern coastal wetlands is significant,and comprehensive control measures are needed,particularly for K.candel beach.

Integrated water risk early warning framework of the semi-arid transitional zone based on the water environmental carrying capacity (WECC)

Water risk early warning systems based on the water environmental carrying capacity(WECC)are powerful and effective tools to guarantee the sustainability of rivers.Existing work on the early warning of WECC has mainly concerned the comprehensive evaluation of the status quo and lacked a quantitative prejudgement and warning of future overload.In addition,existing quantitative methods for short-term early warning have rarely focused on the integrated change trends of the early warning indicators.Given the periodicity of the socioeconomic system,however,the water environmental system also follows a trend of cyclical fluctuations.Thus,it is meaningful to monitor and use this periodicity for the early warning of the WECC.In this study,we first adopted and improved the prosperity index method to develop an integrated water risk early warning framework.We also constructed a forecast model to qualitatively and quantitatively prejudge and warn about the development trends of the water environmental system.We selected the North Canal Basin(an essential connection among the Beijing-Tianjin-Hebei region)in China as a case study and predicted the WECC in 25 water environmental management units of the basin in 2018–2023.We found that the analysis of the prosperity index was helpful in predicting the WECC,to some extent.The result demonstrated that the early warning system provided reliable prediction(root mean square error of 0.0651 and mean absolute error of 0.1418),and the calculation results of the comprehensive early warning index(CEWI)conformed to the actual situation and related research in the river basin.From 2008 to 2023,the WECC of most water environmental management units in the basin had improved but with some spatial differences:the CEWI was generally poor in areas with many human disturbances,while it was relatively good in the upstream regions with higher forest and grass covers as well as in the downstream areas with larger water volume.Finally,through a sensitivity analysis of the indicators,we proposed specific management measures for the sustainability of the water environmental system in the North Canal Basin.Overall,the integrated water risk early warning framework could provide an appropriate method for the water environmental administration department to predict the WECC of the basin in the future.This framework could also assist in implementing corresponding management measures in advance,especially for the performance evaluation and the arrangement of key short-term tasks in the River Chief System in China.

Construction of multi-factor identification model for real-time monitoring and early warning of mine water inrush

As a new technical means that can detect abnormal signs of water inrush in advance and give an early warning,the automatic monitoring and early warning of water inrush in mines has been widely valued in recent years.Due to the many factors affecting water inrush and the complicated water inrush mechanism,many factors close to water inrush may have precursory abnormal changes.At present,the existing monitoring and early warning system mainly uses a few monitoring indicators such as groundwater level,water influx,and temperature,and performs water inrush early warning through the abnormal change of a single factor.However,there are relatively few multi-factor comprehensive early warning identification models.Based on the analysis of the abnormal changes of precursor factors in multiple water inrush cases,11 measurable and effective indicators including groundwater flow field,hydrochemical field and temperature field are proposed.Finally,taking Hengyuan coal mine as an example,6 indicators with long-term monitoring data sequences were selected to establish a single-index hierarchical early-warning recognition model,a multi-factor linear recognition model,and a comprehensive intelligent early-warning recognition model.The results show that the correct rate of early warning can reach 95.2%.

Research and application of real-time monitoring and early warning thresholds for multi-temporal agricultural products information

Monitoring and early warning is an important means to effectively prevent risks in agricultural production,consumption and price.In particular,with the change of modes of national administration against the background of big data,improving the capacity to monitor agricultural products is of great significance for macroeconomic decision-making.Agricultural product information early warning thresholds are the core of agricultural product monitoring and early warning.How to appropriately determine the early warning thresholds of multi-temporal agricultural product information is a key question to realize real-time and dynamic monitoring and early warning.Based on the theory of abnormal fluctuation of agricultural product information and the research of substantive impact on the society,this paper comprehensively discussed the methods to determine the thresholds of agricultural product information fluctuation in different time dimensions.Based on the data of the National Bureau of Statistics of China(NBSC)and survey data,this paper used a variety of statistical methods to determine the early warning thresholds of the production,consumption and prices of agricultural products.Combined with Delphi expert judgment correction method,it finally determined the early warning thresholds of agricultural product information in multiple time,and carried out early warning analysis on the fluctuation of agricultural product monitoring information in 2018.The results show that:(1)the daily,weekly and monthly monitoring and early warning thresholds of agricultural products play an important early warning role in monitoring abnormal fluctuations with agricultural products;(2)the multitemporal monitoring and early warning thresholds of agricultural product information identified by the research institute can provide effective early warning on current abnormal fluctuation of agricultural product information,provide a benchmarking standard for China's agricultural production,consumption and price monitoring and early warning at the national macro level,and further improve the application of China's agricultural product monitoring and early warning.

Real-Time Warning System of Regional Landslides Supported by WEGISB and Its Application in Typhoon Rananim,Zhejiang Province,China

As one of the provinces of highest economic growth in coastal China,Zhejiang Province is experiencing serious geological disasters during the past development of economy.The main kinds of geo-hazards include landslides,rock falls and debris-flows in Zhejiang Province,which are mainly induced by intensive rainfall during typhoon season or by long-term rainfall from May to June every year.Thus,

Effect of an inpatient nursing risk early warning and control system in Shanghai:A retrospective study of adverse events

Purpose:To avoid the nursing risk of inpatients,reduce the occurrence of nursing errors and improve the safety of inpatients.Methods:We established a nursing risk early warning and control system,which includes a safety supervisory network,risk screening and early warning tools,and a risk control process.Results:The qualified rates of risk control measures to prevent pressure ulcers,unplanned extubation and fall/fall from bed all increased.The incidence of reported nursing errors decreased.The number of mistakes in medication-giving decreased.Conclusion:The establishment of an inpatient early warning and control system could effectively avoid nursing risk,improve risk prevention abilities,improve patient safety,and improve nursing quality.

The Application of Big data Mining in Risk Warning for Food Safety

Comprehensive evaluation and warning is very important and difficult in food safety. This paper mainly focuses on introducing the application of using big data mining in food safety warning field. At first,we introduce the concept of big data miming and three big data methods. At the same time,we discuss the application of the three big data miming methods in food safety areas. Then we compare these big data miming methods,and propose how to apply Back Propagation Neural Network in food safety risk warning.

Early Warning of Commercial Housing Market Based on Bagging-GWO-SVM

A number of risks exist in commercial housing,and it is critical for the government,the real estate industry,and consumers to establish an objective early warning indicator system for commercial housing risks and to conduct research regarding its measurement and early warning.In this paper,we examine the commodity housing market and construct a risk index for the commodity housing market at three levels:market level,the real estate industry and the national economy.Using the Bootstrap aggregating-grey wolf optimizer-support vector machine(Bagging-GWO-SVM)model after synthesizing the risk index by applying the CRITIC objective weighting method,the commercial housing market can be monitored for risks and early warnings.Based on the empirical study,the following conclusions have been drawn:(1)The commodity housing market risk index accurately reflect the actual risk situation in Tianjin;(2)Based on comparisons with other models,the Bagging-GWO-SVM model provides higher accuracy in early warning.A final set of suggestions is presented based on the empirical study.

An Artificial Intelligence Algorithm for the Real-Time Early Detection of Sticking Phenomena in Horizontal Shale Gas Wells

Sticking is the most serious cause of failure in complex drilling operations.In the present work a novel“early warning”method based on an artificial intelligence algorithm is proposed to overcome some of the known pro-blems associated with existing sticking-identification technologies.The method is tested against a practical case study(Southern Sichuan shale gas drilling operations).It is shown that the twelve sets of sticking fault diagnostic results obtained from a simulation are all consistent with the actual downhole state;furthermore,the results from four groups of verification samples are also consistent with the actual downhole state.This shows that the pro-posed training-based model can effectively be applied to practical situations.

Designing of Commercial Bank Loans Risk Early Warning System Based on BP Neural Networks

According to the index early warning method, a commercial bank loans risk early warning system based on BP neural networks is proposed. The warning signal is mainly involved with the financial situation signal of loaning corporation. Except the structure description of the system structure the demonstration of attemptive designing is also elaborated.

Risk Early-Warning Method for Natural Disasters Based on Integration of Entropy and DEA Model

Risk early-warning of natural disasters is a very intricate non-deterministic prediction, and it was difficult to resolve the conflicts and incompatibility of the risk structure. Risk early-warning factors of natural disasters were differentiated into essential attributes and external characters, and its workflow mode was established on risk early-warning structure with integrated Entropy and DEA model, whose steps were put forward. On the basis of standard risk early-warning DEA model of natural disasters, weight coefficient of risk early-warning factors was determined with Information Entropy method, which improved standard risk early-warning DEA model with non-Archimedean infinitesimal, and established risk early-warning preference DEA model based on integrated entropy weight and DEA Model. Finally, model was applied into landslide risk early-warning case in earthquake-damaged emergency process on slope engineering, which exemplified the outcome could reflect more risk information than the method of standard DEA model, and reflected the rationality, feasibility, and impersonality, revealing its better ability on comprehensive safety and structure risk.

Study of Enterprises Marketing Risk Early Warning System Based on BP Neural Network Model

For effectively early warning the marketing risk caused along with the varied environment, a BP neural network method was introduced on the basis of analyzing the shortcomings of the risk early warning method, and combined with the practical conditions of dairy enterprises, the index system caused by the marketing risk was also studied. The principal component method was used for screening the indexes, the grades and critical values of the marketing risk were determined. Through the configuration of BP network, node processing and error analysis, the early warning results of the marketing risk were obtained. The results indicate that BP neural network method can be effectively applied through the function approach in the marketing early warning with incomplete information and complex varied conditions.

Study on Positioning System of the Risk Early Warning for Enterprise Overseas Investment

From the perspective of internal and external environment analysis,we construct the risk identification index system for overseas investment enterprises.Combined with the theory of comprehensive evaluation and risk early warning,the risk location system of overseas investment is established.The risk intelligence decision model is constructed by rough set theory,and the risk identification,risk location and risk decision of overseas investment are studied,and are empirically analyzed with cases in overseas investment.

Flood Risk Forecasting and Early Warning Technology for Medium and Small Rivers in the Yellow River Basin Induced by Heavy Rain

The Yellow River Basin is one of the important sand-producing and sediment-transporting areas in China,and one of the three most important sand-producing areas in the world.The amount of sand and dust days in the“Three Norths”(Dongbei,Xibei,and Huabei)area has increased,and regional sand and dust storms have occurred frequently.There are generally more serious hidden danger points of debris flow geological disasters in small and medium-sized river basins.The technical achievements of flood risk forecasting and early warning for medium and small rivers in the Yellow River Basin based on rainstorm-induced floods are important technical supports for flood forecasting and early warning for medium and small rivers.Based on this,a case study was carried out on the problems such as the weak forecasting and early warning ability of flood disasters induced by heavy rain and the low accuracy of flood disaster loss assessment in the flood disasters of medium and small rivers,for the reference of relevant personnel.