ARHCS (Automatic Rainfall Half-Life Cluster System): A Landslides Early Warning System (LEWS) Using Cluster Analysis and Automatic Threshold Definition

A significant portion of Landslide Early Warning Systems (LEWS) relies on the definition of operational thresholds and the monitoring of cumulative rainfall for alert issuance. These thresholds can be obtained in various ways, but most often they are based on previous landslide data. This approach introduces several limitations. For instance, there is a requirement for the location to have been previously monitored in some way to have this type of information recorded. Another significant limitation is the need for information regarding the location and timing of incidents. Despite the current ease of obtaining location information (GPS, drone images, etc.), the timing of the event remains challenging to ascertain for a considerable portion of landslide data. Concerning rainfall monitoring, there are multiple ways to consider it, for instance, examining accumulations over various intervals (1 h, 6 h, 24 h, 72 h), as well as in the calculation of effective rainfall, which represents the precipitation that actually infiltrates the soil. However, in the vast majority of cases, both the thresholds and the rain monitoring approach are defined manually and subjectively, relying on the operators’ experience. This makes the process labor-intensive and time-consuming, hindering the establishment of a truly standardized and rapidly scalable methodology on a large scale. In this work, we propose a Landslides Early Warning System (LEWS) based on the concept of rainfall half-life and the determination of thresholds using Cluster Analysis and data inversion. The system is designed to be applied in extensive monitoring networks, such as the one utilized by Cemaden, Brazil’s National Center for Monitoring and Early Warning of Natural Disasters.

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.

Analysis of rockburst mechanism and warning based on microseismic moment tensors and dynamic Bayesian networks

One of the major factors inhibiting the construction of deep underground projects is the risk posed by rockbursts.A study was conducted on the access tunnel of the Shuangjiangkou hydropower station to determine the evolutionary mechanism of microfractures within the surrounding rock mass during rockburst development and develop a rockburst warning model.The study area was chosen through the combination of field studies with an analysis of the spatial and temporal distribution of microseismic(MS)events.The moment tensor inversion method was adopted to study rockburst mechanism,and a dynamic Bayesian network(DBN)was applied to investigating the sensitivity of MS source parameters for rockburst warnings.A MS multivariable rockburst warning model was proposed and validated using two case studies.The results indicate that fractures in the surrounding rock mass during the development of strain-structure rockbursts initially show shear failure and are then followed by tensile failure.The effectiveness of the DBN-based rockburst warning model was demonstrated using self-validation and K-fold cross-validation.Moment magnitude and source radius are the most sensitive factors based on an investigation of the influence on the parent and child nodes in the model,which can serve as important standards for rockburst warnings.The proposed rockburst warning model was found to be effective when applied to two actual projects.

Temperature‑Arousing Self‑Powered Fire Warning E‑Textile Based on p-n Segment Coaxial Aerogel Fibers for Active Fire Protection in Firefighting Clothing

Firefighting protective clothing is a crucial protective equipment for firefighters to minimize skin burn and ensure safety firefighting operation and rescue mission.A recent increasing concern is to develop self-powered fire warning materials that can be incorporated into the firefighting clothing to achieve active fire protection for firefighters before the protective clothing catches fire on fireground.However,it is still a challenge to facilely design and manufacture thermoelectric(TE)textile(TET)-based fire warning electronics with dynamic surface conformability and breathability.Here,we develop an alternate coaxial wet-spinning strategy to continuously produce alternating p/n-type TE aerogel fibers involving n-type Ti_(3)C_(2)T_(x)MXene and p-type MXene/SWCNT-COOH as core materials,and tough aramid nanofiber as protective shell,which simultaneously ensure the flexibility and high-efficiency TE power generation.With such alternating p/n-type TE fibers,TET-based self-powered fire warning sensors with high mechanical stability and wearability are successfully fabricated through stitching the alternating p-n segment TE fibers into aramid fabric.The results indicate that TET-based fire warning electronics containing 50 p-n pairs produce the open-circuit voltage of 7.5 mV with a power density of 119.79 nW cm-2 at a temperature difference of 300℃.The output voltage signal is then calculated as corresponding surface temperature of firefighting clothing based on a linear relationship between TE voltage and temperature.The fire alarm response time and flame-retardant properties are further displayed.Such self-powered fire warning electronics are true textiles that offer breathability and compatibility with body movement,demonstrating their potential application in firefighting clothing.

Visualization and early warning analysis of damage degree of surrounding rock mass in underground powerhouse

Based on the underground powerhouse of Shuangjiangkou hydropower station,Octree theory is adopted to define the indices of the microseismic(MS)spatial aggregation degree and the deviation values of MS count and energy.The relationship between the MS multiple parameters and surrounding rock mass instability is established from three aspects:time,space,and strength.Supplemented by the center frequency of the signal evolution characteristics,A fuzzy comprehensive evaluation model and the evolution trend of the MS event center frequency are constructed to quantitatively describe the early warning state of the surrounding rock mass instability.The results show that the multilevel tree structure and voxels generated based on the Octree theory fit relatively well with the set of MS points in threedimensional space.The fuzzy comprehensive evaluation model based on MS spatial aggregation and MS count and energy deviation values enables three-dimensional visualization of the potential damage area and damage extent of the surrounding rock mass.The warning time and potential damage zone quantified are highly consistent with the characteristics of MS precursors,with wide recognition and field investigation results,which fully validate the rationality and applicability of the proposed method.These findings can provide references for the early warning of surrounding rock mass instability in similar underground engineering.

Identification of Monitoring Organ in Bivalves for Early Warning of Paralytic Shellfish Toxins Accumulation

Bivalve farming plays a dominant role in mariculture in China.Paralytic shellfish toxins(PSTs)can be accumulated in bivalves and cause poisoning the consumers.A sensitive detection of PSTs can provide early warning to decrease poisoning events in bivalve consuming.PSTs are traditionally examined using the whole soft-tissues.However,PSTs accumulation varies dramatically in different tissues of bivalves.Some tough tissues/organs(such as mantle),which account for a large proportion of the total soft body,exhibit a lower accumulation of PSTs and make the toxin extraction time-and reagent-consuming,potentially decreasing the accuracy and sensitivity of PSTs monitoring in bivalves.To develop a sensitive and cost-effective approach for PSTs examination in massively farmed bivalves,we fed three commercially important bivalves,Yesso scallop Patinopecten yessoensis,Pacific oyster Crassostrea gigas,and blue mussel Mytilus edulis with PSTs-producing dinoflagellate Alexandrium catenella,and detected PSTs concentration in different tissues.For all three bivalve species,the digestive gland accumulated much more PSTs than other tissues,and the digestive gland’s toxicity was significantly correlated with the PSTs toxicity of the whole soft-tissues,with r^(2)=0.94,0.92,and 0.94 for Yesso scallop,Pacific oyster,and blue mussel,respectively.When the toxicity of the whole soft-tissues reached 80μgSTXeq(100g)^(−1),the regulatory limit for commercial shellfish,the digestive gland’s toxicity reached 571.48,498.90,and 859.20μgSTXeq(100g)^(−1) in Yesso scallop,Pacific oyster,and blue mussel,respectively.Our results indicate that digestive gland can be used for the sensitive and cost-effective monitoring of PSTs in bivalves.

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.

Dis-NDVW: Distributed Network Asset Detection and Vulnerability Warning Platform

With the rapid development of Internet technology,the issues of network asset detection and vulnerability warning have become hot topics of concern in the industry.However,most existing detection tools operate in a single-node mode and cannot parallelly process large-scale tasks,which cannot meet the current needs of the industry.To address the above issues,this paper proposes a distributed network asset detection and vulnerability warning platform(Dis-NDVW)based on distributed systems and multiple detection tools.Specifically,this paper proposes a distributed message sub-scription and publication system based on Zookeeper and Kafka,which endows Dis-NDVW with the ability to parallelly process large-scale tasks.Meanwhile,Dis-NDVW combines the RangeAssignor,RoundRobinAssignor,and StickyAssignor algorithms to achieve load balancing of task nodes in a distributed detection cluster.In terms of a large-scale task processing strategy,this paper proposes a task partitioning method based on First-In-First-Out(FIFO)queue.This method realizes the parallel operation of task producers and task consumers by dividing pending tasks into different queues according to task types.To ensure the data reliability of the task cluster,Dis-NDVW provides a redundant storage strategy for master-slave partition replicas.In terms of distributed storage,Dis-NDVW utilizes a distributed elastic storage service based on ElasticSearch to achieve distributed storage and efficient retrieval of big data.Experimental verification shows that Dis-NDVW can better meet the basic requirements of ultra-large-scale detection tasks.

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.

Wind Turbine Spindle Operating State Recognition and Early Warning Driven by SCADA Data

An operating condition recognition approach of wind turbine spindle is proposed based on supervisory control and data acquisition(SCADA)normal data drive.Firstly,the SCADA raw data of wind turbine under full working conditions are cleaned and feature extracted.Then the spindle speed is employed as the output parameter,and the single and combined normal behavior model of the wind turbine spindle is constructed sequentially with the preprocessed data,with the evaluation indexes selected as the optimal model.Finally,calculating the spindle operation status index according to the slidingwindowprinciple,ascertaining the threshold value for identifying the abnormal spindle operation status by the hypothesis of small probability event,analyzing the 2.5 MW wind turbine SCADA data froma domestic wind field as a sample,The results show that the fault warning time of the early warningmodel is 5.7 h ahead of the actual fault occurrence time,as well as the identification and early warning of abnormal wind turbine spindle operationwithout abnormal data or a priori knowledge of related faults.

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.

Deformation warning index for reinforced concrete dam based on structural health monitoring data and numerical simulation

The material mechanical parameters of the dam body and foundation will change when a dam is reinforced during the aging process.This causes significant changes in the structural state of the project and makes it difficult to ensure its structural safety.In this study,a new deformation warning index for reinforced concrete dams was developed according to the prototype monitoring data,statistical models,three-dimensional finite element model(FEM)numerical simulation,and the critical conditions of the dam structure.A statistical model was established to separate the water pressure component.Then,a three-dimensional FEM of the reinforced concrete dam was constructed to simulate the water pressure component.Furthermore,the deformation components that affected the mechanical parameters of the dam under the same amount of reservoir water level change were separated and quantified accurately.In addition,the method for inversion of comprehensive mechanical parameters after dam reinforcement was used.The influence mechanisms of the deformation behavior of concrete dams under the reservoir water level and temperature changes were investigated.A new deformation warning index was developed by combining the forward-simulated critical water pressure component and temperature component in the period of extreme temperature decrease with the aging component separated by the statistical model.The new deformation warning index considers the structural state of the dam before and after reinforcement and links the structural strength criterion and the deformation evolution mechanisms.It provides a theoretical foundation and decision support for long-term service and operation management of reinforced dams.

Recognizing Early Warning Signs (EWS) in Patients Is Critically Important

Introduction: Monitoring vital signs is a basic indicator of a patient’s health status and allows prompt detection of delayed recovery or adverse effects and early intervention. Patients with adverse events during hospitalization often display clinical decline for several hours before the event is observed. Non-critical care Nurses’ inconsistent recognition and response to patient deterioration lead to an increase in the length of hospital stay, unexpected admissions to the ICU, and increased morbidity and mortality. Aim: The study aimed to assess the factors that facilitate or impede the detection of early warning signs among adult patients hospitalized in tertiary care settings. Training should be provided to improve nurses’ knowledge, practice and attitude toward early warning signs of deteriorating patients leading to enhanced clinical judgment, skills and decision-making in addressing alerts. Methodology: A literature search was carried out in various databases;these were Cumulative Index to Nursing and Allied Health Literature (CINHAL), Google Scholar, PubMed, Science Direct, and Sage. The search area was narrowed from 2017 to 2022. The keywords used were “prevalence” AND “unplanned ICU admission”, “the importance of early warning signs” “outcome failure in rescue” “patient deterioration, communication” “improvement in early detection” AND “patient outcome admission” AND “early warning signs” AND “Pakistan”. After the analysis process, around 33 articles that met the inclusion criteria and were most relevant to the scope and context of the current study were considered. Conclusion: Most of the studies had reviewed literature in a qualitative retrospective observational study, content analysis, mixed method, and quasi-experimental study. The literature review identified that long hours of shift, nurse staffing levels, missed vital signs, lack of nursing training and education, and communication impact nurses’ ability to recognize and respond to early warning signs.

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.

Application of the monitoring and early warning system for internal solitary waves:Take the second natural gas hydrates production test in the South China Sea as an example

Internal solitary waves(ISWs) contain great energy and have the characteristics of emergency and concealment. To avoid their damage to offshore engineering, a new generation of monitoring and early warning system for ISWs was developed using technologies of double buoys monitoring, intelligent realtime data transmission, and automatic software identification. The system was applied to the second natural gas hydrates(NGHs) production test in the Shenhu Area, South China Sea(SCS) and successfully provided the early warning of ISWs for 173 days(from October 2019 to April 2020). The abrupt changes in the thrust force of the drilling platform under the attack of ISWs were consistent with the early warning information, proving the reliability of this system. A total of 93 ISWs were detected around the drilling platform. Most of them occurred during the spring tides in October–December 2019 and April 2020, while few of them occurred in winter. As suggested by the theoretical model, the full-depth structure of ISWs was a typical current profile of mode-1, and the velocities of wave-induced currents can reach 80 cm/s and30 cm/s, respectively, in the upper ocean and near the seabed. The ISWs may be primarily generated from the interactions between the topography and semidiurnal tides in the Luzon Strait, and then propagate westward to the drilling platform. This study could serve as an important reference for the early warning of ISWs for offshore engineering construction in the future.

Prediction and Early Warning Indicators of Short-term Severe Convection Weather in Ulanqab City

Based on the disaster reports,NCEP2.5X2.5 reanalysis data and radiosonde data of 11 national stations in Ulanqab region from June to August during 2012-2017,the weather situation classification and warning indicators of thunderstorm and gale,hail and short-term heavy rainfall were studied.The results show that the cold vortex weather situation was easy to produce hail,and the falling area of severe convection could be found in the downstream of the cold vortex,the intersection area of jet stream at 200 and 500 hPa,and the wet area side of the 700 hPa main line.The cold trough type weather situation was easy to produce thunderstorm and gale,and the falling area of severe convection appeared on the right side of the upper jet stream axis,the left side of the lower jet stream axis,the wet side of the 700 hPa main line,and the east of the shear line at 700 hPa.The weather situation of the low trough and subtropical high type was dominated by short-term rainstorm,and the falling area of severe convection was on the right side of upper jet stream at 200 hPa,the left side of the low southeast jet stream,and the wet side of the 700 hPa main line.The warning index thresholds of the total index,the temperature change at 850-500 hPa with height,the height of 0 and-20℃layer,lifting condensation height,temperature dew point difference and mixing ratio were highly reliable.

Early warning systems for enteral feeding intolerance in patients with stroke

Objective:The objective of this study was to construct an early warning system(EWS)to facilitate risk assessment,early identification,and appropriate treatment of enteral nutrition feeding intolerance(FI)in patients with stroke,so as to provide a reference for risk classification standards and interventions toward a complete EWSs for nursing care of stroke.Materials and Methods:Based on evidence and clinical nursing practice,a structured expert consultation method was adopted on nine experts over two rounds of consultation.Statistical analysis was used to determine the early warning index for FI in patients with stroke.Results:The expert authority coefficient was 0.89;the coefficients of variation for the two rounds of consultation were 0.088-0.312 and 0.096-0.214,respectively.There were significant differences in the Kendall’s concordance coefficient(P<0.05).Finally,22 items in five dimensions of patient age,disease,treatment,biochemical,and enteral nutrition-related factors were identified.Conclusion:The early warning index for FI in patients with a history of stroke is valid and practical.It provides a reference for the early clinical identification of FI risk.

The SNAKE System: CEMADEN’s Landslide Early Warning System (LEWS) Mechanism

In Brazil, the prominent climate-induced disasters are floods and mass movements, with the latter being the most lethal. The spate of major landslide events, especially those in 2011, catalyzed the creation of CEMADEN (National Center for Monitoring and Early Warning of Natural Disasters). This article introduces one of CEMADEN’s pivotal systems for early landslide warnings and traces its developmental timeline. The highlighted SNAKE System epitomizes advancements in digital monitoring, forecasting, and alert mechanisms. By leveraging precipitation data from pluviometers in observed municipalities, the system bolsters early warnings related to potential mass movements, like planar slides and debris flows. Its deployment in CEMADEN’s Situation Room attests to its suitability for overseeing high-risk municipalities, attributed primarily to its robustness and precision.

A Personalized Adverse Drug Reaction Early Warning Method Based on Contextual Ontology and Rules Learning

Background: The fatality of adverse drug reactions (ADR) has become one of the major causes of the non-natural disease deaths globally, with the issue of drug safety emerging as a common topic of concern. Objective: The personalized ADR early warning method, based on contextual ontology and rule learning, proposed in this study aims to provide a reference method for personalized health and medical information services. Methods: First, the patient data is formalized, and the user contextual ontology is constructed, reflecting the characteristics of the patient population. The concept of ontology rule learning is then proposed, which is to mine the rules contained in the data set through machine learning to improve the efficiency and scientificity of ontology rule generation. Based on the contextual ontology of ADR, the high-level context information is identified and predicted by means of reasoning, so the occurrence of the specific adverse reaction in patients from different populations is extracted. Results: Finally, using diabetes drugs as an example, contextual information is identified and predicted through reasoning, to mine the occurrence of specific adverse reactions in different patient populations, and realize personalized medication decision-making and early warning of ADR.

Analysis of Multi-Drug Resistant Organism Surveillance and Antimicrobial Resistance Early Warning in a Hospital in 2022

Objective:To determine the clinical distribution of multi-drug resistant organism(MDRO)in Jiangyan Hospital and the monitoring and warning of drug-resistance bacteria to provide an important basis for guiding the application of broad-spectrum antibiotics in clinical treatment and reducing the occurrence of nosocomial infection.Methods:Retrospective screening and analysis were conducted on the pathogenic strains of hospitalized patients in our hospital in 2022.Results:A total of 2,769 strains of pathogenic bacteria and 390 strains of MDRO were detected and isolated in our hospital in 2022;the detection rate of MDRO was 14.08%.A total of 516 strains(18.64%)Klebsiella pneumoniae(KP)and 62 strains(12.02%)of carbapenem-resistant Klebsiella pneumoniae(CR-KP)were detected;436 strains(15.75%)of Escherichia coli(ECO)were detected,including 8 strains(1.83%)of CR-ECO;342 strains(12.35%)of Pseudomonas aeruginosa(PA)and 116 strains(33.92%)of CR-PA were detected;there were 194 strains(7.01%)of Acinetobacter baumannii(AB),among which 125 strains(64.43%)were CR-AB;there were 291 strains(10.51%)of Staphylococcus aureus,among which 79 strains(27.15%)of methicillin-resistant Staphylococcus aureus(MRSA)were detected;78 strains(2.82%)of Enterococcus faecalis were detected,and vancomycin-resistant enterococcus(VRE)was not detected.The first five MDROs were CR-AB,CR-PA,MRSA,CR-KP,and CR-ECO.The top five departments with the highest MDRO detection rate in 2022 were the ICU(37.44%),the Pulmonology Department(ward 13;31.03%),the Department of Rehabilitation(ward 5;6.67%),the Department of Neurosurgery(ward 11;4.62%),and the Department of General Surgery(ward 10;3.59 The resistance rate of antibacterial drugs is divided into four levels for early warning:30%to 40%,41%to 50%,51%to 75%,and 75%or more.Conclusion:Our hospital should strengthen the monitoring of antimicrobial resistance warning related to MDRO and the abuse of antimicrobial drugs.Based on the results of drug sensitivity and antimicrobial resistance warning,the use of antibiotics should be standardized in clinical practice to reduce nosocomial infection。