Occurrence Probability Of Events Research Articles

Spatial Development of the Dipolarization Region in the Inner Magnetosphere

AbstractThe present study examines dipolarization events observed by the Van Allen Probes within 5.8 RE from Earth. It is found that the probability of occurrence is significantly higher in the dusk‐to‐midnight sector than in the midnight‐to‐dawn sector, and it deceases sharply earthward. A comparison with observations made at nearby satellites shows that dipolarization signatures are often highly correlated (c.c. >0.8) within 1 hr in magnetic local time (MLT) and 1 RE in RXY, and the dipolarization region expands earthward and westward in the dusk‐to‐midnight sector. The westward expansion velocity is estimated at 0.4 hr (in MLT) per minute, or 60 km/s, which is consistent with the previously reported result for geosynchronous dipolarization. The earthward expansion is apparently less systematic than the westward expansion. Its velocity is estimated at 50 km/s (0.5 RE/min), comparable to the westward expansion velocity, but it is suggested that the earthward expansion slows down as the dipolarization region approaches Earth, and it eventually stops. This idea is consistent with the earthward reduction of the occurrence probability of dipolarization events. Whereas this earthward expansion is difficult to explain with the conventional wedge current system, it may be understood in terms of a current system with two wedges, one with the R1 polarity outside and the other with the R2 polarity closer to Earth. For such a current system the region of dipolarization is confined in radial distance between the two wedge currents, and it is considered to expand earthward as the R2‐sense wedge moves earthward along with injected plasma.

A study on the use of discrete event data for prognostics and health management: discovery of association rules

This study addresses prognostics and health management (PHM) for manufacturing machines. Different from previous researches where continuous monitoring is assumed for PHM, we investigate the issue with discrete event data. Various event data are recorded during system operation, which can provide useful information for fault diagnosis and failure prediction. We focus on discovery of association rules based on the industrial discrete data. Apriori algorithm is employed to discover the frequent event groups and identify strong association rules. To accommodate the algorithm, the initial event data is transformed into the form of transactional data as a first step. The obtained association rule estimates the occurrence probability of certain significant events within specified time interval. It is concluded through a case study that the number of frequent event groups and strong association rules increases with the time interval that the events are grouped as one transaction.

Fault trees analysis using expert opinion based on fuzzy‐bathtub failure rates

AbstractThe main objective of fault tree analysis method is to estimate the “Top Event occurrence probability”. This requires determination of failure time distribution functions also known as “Bathtub Curves” for each of the system elements/events. This paper introduces a novel method to determine the failure time distribution functions using possibility theory. For this purpose, fuzzy‐bathtub distributions using expert opinions are generated for basic events and fuzzy formulas are derived for static and dynamic gates fault tree constructions. This process completed by proposed fuzzy Monte Carlo simulation throughout the preferred operational time and uses the actual time‐to‐failure data. Accordingly, the Top Event failure curve and the reliability profile of the system are depicted based on the defuzzificated basic‐events' bathtub‐failure‐rates. The results show that the proposed method not only is feasible and powerful but can also be accurate more than the other probabilistic and possibilistic techniques because of the component failure rates follow the real failure distributions.

2444 - Analysis of Bicycle Environment Using Instrumented Probe Bicycle

Background Cycling has been an important mode of transportation that enhances transportation sustainability by reducing air and noise pollution as well as traffic congestion. This study seeks to improve the methodology for determining the relationship between cycling dynamic performance and roadway environment characteristics across different bicyclists’ skill levels. We examined two facets of performances: mobility and comfortability. Methods To achieve the goal of this study, an Instrumented Probe Bicycle (IPB) equipped with various sensors was built. Using sensors allowed us to monitor bicyclist interactions to roadway environment and dynamic movements. A naturalistic field experiment, including intersections, roundabout, alignment changes, and different road surface conditions, was conducted. The Pavement Surface and Evaluation Rating System (PASER) was used to determine the quality of road surfaces. In addition, two self-reported questionnaires were used in order to obtain each participant's skill level as well as their perception on the level of cycling comfortability. Results Fault Tree Analysis was employed to develop the performance measures for recognizing the probability of fault event occurrence. Bicycle speed is being used to measure the mobility performance of the bicycle environment system. The probability that a particular segment fails to support a rider is defined as mobility performance failure. Moreover, the bicycle dynamic movements as well as the PASER rating estimate the comfortability level of the bicyclists. The probabilistic outcome of the proposed Ordered Probit Model was considered to develop the Cycling Comfortability Index (CCI). CCI is a continuous value between 0 and 1, which is adopted to find the probability of comfort level for each observation. At the end, by having two failure probabilities (mobility and comfort), the probability of a fault event occurrence is calculated. Conclusions We found that the probability of a fault event occurrence, was strongly related to the bicyclist's level of experience. The fact that inexperienced bicyclists had higher failure ratio implies that their levels of mobility and comfortability are lower. In addition, the quality of road surface had a significant impact on the speed as well as the comfortability index. The Ordered Probit Model showed that cycling comfortability was significantly affected by the average Y-axis acceleration and the mean absolute deviation of Z-axis velocity. Steering movements and Z-axis angular movements (yawing) also affected cycling comfortability. The IPB developed in this study turned out to be very useful in collecting cycling maneuver data and in analyzing bicycle safety associated with bicycle infrastructure.

Regional rainfall thresholds for landslide occurrence using a centenary database

Abstract. This work proposes a comprehensive method to assess rainfall thresholds for landslide initiation using a centenary landslide database associated with a single centenary daily rainfall data set. The method is applied to the Lisbon region and includes the rainfall return period analysis that was used to identify the critical rainfall combination (cumulated rainfall duration) related to each landslide event. The spatial representativeness of the reference rain gauge is evaluated and the rainfall thresholds are assessed and calibrated using the receiver operating characteristic (ROC) metrics. Results show that landslide events located up to 10 km from the rain gauge can be used to calculate the rainfall thresholds in the study area; however, these thresholds may be used with acceptable confidence up to 50 km from the rain gauge. The rainfall thresholds obtained using linear and potential regression perform well in ROC metrics. However, the intermediate thresholds based on the probability of landslide events established in the zone between the lower-limit threshold and the upper-limit threshold are much more informative as they indicate the probability of landslide event occurrence given rainfall exceeding the threshold. This information can be easily included in landslide early warning systems, especially when combined with the probability of rainfall above each threshold.

Statistical analysis of annual maximum daily rainfall for Nelspruit and its environs.

Nelspruit and its environs frequently experience extreme high annual maximum daily rainfall (AMDR) events resulting in flood hazards. These flood hazards have caused flood disasters that have resulted in loss of property and lives. The main objective of this study was to carry out statistical analysis of extreme high AMDR events that have caused flood hazards, which in turn have caused flood disasters in Nelspruit and its environs. Empirical continuous probability distribution functions (ECPDF) and theoretical continuous probability distribution functions (TCPDF) were applied to carry out the statistical analysis of the extreme high AMDR events. Annual maximum daily rainfall event of magnitude 100 mm was identified as a threshold. Events > 100 mm were considered as extreme high events resulting in flood disasters. The results of empirical frequency analysis showed that the return period of flood disasters was 10 years. The occurrence probability of flood disaster event at least once in 1, 2, 3, 4 and 5 years was 0.10, 0.19, 0.27, 0.34 and 0.41, respectively. Generalised logistic PDF was identified as the best-fit theoretical PDF for statistical analysis of the extreme high AMDR events in Nelspruit and its environs. The results of this study contributed to the understanding of frequency and magnitude of extreme high AMDR events that could lead to flood disasters. The results could be applied in developing flood disaster management strategies in Nelspruit and its environs.

The impact of creating mathematical formula to predict cardiovascular events in patients with heart failure

Since our retrospective study has formed a mathematical formula, α = f(x1, …, x252), where α is the probability of cardiovascular events in patients with heart failure (HF) and x1 is each clinical parameter, we prospectively tested the predictive capability and feasibility of the mathematical formula of cardiovascular events in HF patients. First of all, to create such a mathematical formula using limited number of the parameters to predict the cardiovascular events in HF patients, we retrospectively determined f(x) that formulates the relationship between the most influential 50 clinical parameters (x) among 252 parameters using 167 patients hospitalized due to acute HF; the nonlinear optimization could provide the formula of α = f(x1, …, x50) which fitted the probability of the actual cardiovascular events per day. Secondly, we prospectively examined the predictability of f(x) in other 213 patients using 50 clinical parameters in 3 hospitals, and we found that the Kaplan–Meier curves using actual and estimated occurrence probabilities of cardiovascular events were closely correlated. We conclude that we created a mathematical formula f(x) that precisely predicted the occurrence probability of future cardiovascular outcomes of HF patients per day. Mathematical modelling may predict the occurrence probability of cardiovascular events in HF patients.

Spatial distribution and occurrence probability of regional new particle formation events in eastern China

Abstract. In this work, the spatial extent of new particle formation (NPF) events and the relative probability of observing particles originating from different spatial origins around three rural sites in eastern China were investigated using the NanoMap method, using particle number size distribution (PNSD) data and air mass back trajectories. The length of the datasets used were 7, 1.5, and 3 years at rural sites Shangdianzi (SDZ) in the North China Plain (NCP), Mt. Tai (TS) in central eastern China, and Lin'an (LAN) in the Yangtze River Delta region in eastern China, respectively. Regional NPF events were observed to occur with the horizontal extent larger than 500 km at SDZ and TS, favoured by the fast transport of northwesterly air masses. At LAN, however, the spatial footprint of NPF events was mostly observed around the site within 100–200 km. Difference in the horizontal spatial distribution of new particle source areas at different sites was connected to typical meteorological conditions at the sites. Consecutive large-scale regional NPF events were observed at SDZ and TS simultaneously and were associated with a high surface pressure system dominating over this area. Simultaneous NPF events at SDZ and LAN were seldom observed. At SDZ the polluted air masses arriving over the NCP were associated with higher particle growth rate (GR) and new particle formation rate (J) than air masses from Inner Mongolia (IM). At TS the same phenomenon was observed for J, but GR was somewhat lower in air masses arriving over the NCP compared to those arriving from IM. The capability of NanoMap to capture the NPF occurrence probability depends on the length of the dataset of PNSD measurement but also on topography around the measurement site and typical air mass advection speed during NPF events. Thus the long-term measurements of PNSD in the planetary boundary layer are necessary in the further study of spatial extent and the probability of NPF events. The spatial extent, relative probability of occurrence, and typical evolution of PNSD during NPF events presented in this study provide valuable information to further understand the climate and air quality effects of new particle formation.

Spatial pattern and influencing factors of landslide casualty events

Analysis of casualties due to landslides from 2000 to 2012 revealed that their spatial pattern was affected by terrain and other natural environmental factors, which resulted in a higher distribution of landslide casualty events in southern China than in northern China. Hotspots of landslide-generated casualties were in the western Sichuan mountainous area and Yunnan-Guizhou Plateau region, southeast hilly area, northern part of the loess hilly area, and Tianshan and Qilian Mountains. However, local distribution patterns indicated that landslide casualty events were also influenced by economic activity factors. To quantitatively analyse the influence of natural environment and human-economic activity factors, the Probability Model for Landslide Casualty Events in China (LCEC) was built based on logistic regression analysis. The results showed that relative relief, GDP growth rate, mean annual precipitation, fault zones, and population density were positively correlated with casualties caused by landslides. Notably, GDP growth rate ranked only second to relative relief as the primary factors in the probability of casualties due to landslides. The occurrence probability of a landslide casualty event increased 2.706 times with a GDP growth rate increase of 2.72%. In contrast, vegetation coverage was negatively correlated with casualties caused by landslides. The LCEC model was then applied to calculate the occurrence probability of landslide casualty events for each county in China. The results showed that there are 27 counties with high occurrence probability but zero casualty events. The 27 counties were divided into three categories: poverty-stricken counties, mineral-rich counties, and real-estate overexploited counties; these are key areas that should be emphasized in reducing landslide risk.

Seismicity Study of Botswana from 1966 to 2012

The history of Botswana’s major seismic events has been initiated by two earthquakes that occurred on the 11th of September and 11th of October 1952 with ML Richter magnitudes of 6.1 and 6.7, respectively, in the Okavango Delta Region (ODR). Not much is known and well documented about the seismicity of a larger coverage of Botswana primarily because over the years, local seismic stations were biased to the north-western part of Botswana, to monitor the more seismogenic ODR. The objective of this study is to use data from the International Seismological Centre (ISC) bulletin for the period 1966-2012 to estimate the relative size distribution of seismic events (b-value), the rate of seismic activity (a-value), and associated stress condition prevailing in Botswana to assist in the quest of hazard mitigation. This study shows that micro-seismic activities of magnitude ranging from 1.3 to 5.7 are distributed not only on the northern part of Botswana, but also in other parts of the country such as the Southern (including Kweneng East and West), Central and Eastern Botswana. Based on 327 events extracted from data compiled by the ISC, the frequency-magnitude distribution (FMD) of earthquakes in Botswana from 1966 to 2012 was estimated with b-value and a-value for the entire catalogue found to be 1.2 and 6.3, respectively, implying a region of low stress dominated by small to moderate events. The minimum completeness magnitude (MC), a value that indicates the lowest magnitude above which all seismic events are reliably recorded was found to be 3.8. From this analysis, annual probabilities of occurrence for M4 and M5 events were found to be 67.2% and 4.3%, respectively, while M6 or larger event has an annual probability of 0.3%.

Protein-Ligand Dissociation Simulated by Parallel Cascade Selection Molecular Dynamics.

We investigated the dissociation process of tri-N-acetyl-d-glucosamine from hen egg white lysozyme using parallel cascade selection molecular dynamics (PaCS-MD), which comprises cycles of multiple unbiased MD simulations using a selection of MD snapshots as the initial structures for the next cycle. Dissociation was significantly accelerated by PaCS-MD, in which the probability of rare event occurrence toward dissociation was enhanced by the selection and rerandomization of the initial velocities. Although this complex was stable during 1 μs of conventional MD, PaCS-MD easily induced dissociation within 100-101 ns. We found that velocity rerandomization enhances the dissociation of triNAG from the bound state, whereas diffusion plays a more important role in the unbound state. We calculated the dissociation free energy by analyzing all PaCS-MD trajectories using the Markov state model (MSM), compared the results to those obtained by combinations of PaCS-MD and umbrella sampling (US), steered MD (SMD) and US, and SMD and the Jarzynski equality, and experimentally determined binding free energy. PaCS-MD/MSM yielded results most comparable to the experimentally determined binding free energy, independent of simulation parameter variations, and also gave the lowest standard errors.

Effective Risk Management in Innovative Projects: A Case Study of the Construction of Energy-efficient, Sustainable Building of the Laboratory of Intelligent Building in Cracow

Many construction projects fail to meet deadlines or they exceed the assumed budget. This scenario is particularly common in the case of innovative projects, in which too late identification of a high risk of delays and exceeding the assumed costs makes a potentially profitable project untenable. A high risk level, far exceeding the level of risk in standard non-innovative projects, is a characteristic feature of the realization phase of innovative projects. This is associated not only with greater complexity of the design and construction phases, but also with the problems with application of new technologies and prototype solutions, lack of qualified personnel with suitable expertise in specialized areas, and with the ability to properly identify the gaps between available and required knowledge and skills. This paper discusses the process of effective risk management in innovative projects on the example of the realization phase of an innovative, energy-efficient and sustainable building of the Laboratory of Intelligent Building in Cracow - DLJM Lab, from the point of view of DORBUD S.A., its general contractor. In this paper, a new approach to risk management process for innovative construction projects is proposed. Risk management process was divided into five stages: gathering information, identification of the important unwanted events, first risk assessment, development and choice of risk reaction strategies, assessment of the residual risk after introducing risk reactions. 18 unwanted events in an innovative construction project were identified. The first risk assessment was carried out using two-parametric risk matrix, in which the probability of unwanted event occurrence and its consequences were analysed. Three levels of risks were defined: tolerable, controlled and uncontrolled. Risk reactions to each defined unwanted event were developed. The following risk reaction types were considered: risk retention, risk reduction, risk transfer and risk elimination. Three-parametric risk matrix was developed to make it possible to assess risk after implementing risk reactions. The possibility of implementing risk management was inversely proportional to the probability of unwanted event occurrence and its contribution to the project outcome. Introducing this risk management strategy allowed to significantly reduce the risk of the innovative construction project. It proved to be an effective tool to reduce risk to an acceptable level. It had a significant contribution to carrying out the project within the assumed time, budget and quality standards.

Generating Probabilistic Forecasts from Convection-Allowing Ensembles Using Neighborhood Approaches: A Review and Recommendations

“Neighborhood approaches” have been used in two primary ways to postprocess and verify high-resolution ensemble output. While the two methods appear deceptively similar, they define events over different spatial scales and yield fields with different interpretations: the first produces probabilities interpreted as likelihood of event occurrence at the grid scale, while the second produces probabilities of event occurrence over spatial scales larger than the grid scale. Unfortunately, some studies have confused the two methods, while others did not acknowledge multiple possibilities of neighborhood approach application and simply stated, “a neighborhood approach was applied” without supporting details. Thus, this paper reviews applications of neighborhood approaches to convection-allowing ensembles in hopes of clarifying the two methods and their different event definitions. Then, using real data, it is demonstrated how the two approaches can yield statistically significantly different objective conclusions about model performance, underscoring the critical need for thorough descriptions of how neighborhood approaches are implemented and events are defined. The authors conclude by providing some recommendations for application of neighborhood approaches to convection-allowing ensembles.

Determining vehicles’ radio transmission range for increasing cognitive radio VANET (CR-VANET) reliability using a trust management system

As a part of an intelligent transportation system, a Vehicular Ad-Hoc Network (VANET) requires reliability by way of an essential need. Timely and accurately delivering safety messages in VANET is one of the reliability issues affected by different factors such as the lack of frequency spectrum as well as nodes’ inabilities or misbehaviors. To overcome spectrum shortage, Cognitive Radio (CR) has been proposed while employing a trust mechanism can lessen any damage caused to reliability by incapable or misbehaver nodes. An effective trust management system can analyze ‘agree’ and ‘disagree’ messages and decide about an event to increase reliability. However, message transmission based on trust assessment imposes an extra decision delay causing a reliability cutback, especially in delay-sensitive safety messages. In this research, an effective trust management system for CR-VANET is proposed and analyzed based on a Birth and Death Process (BDP) model to evaluate the average delay imposed by the decision-making process. The probability of event occurrence is also derived in terms of arrival message rate. Moreover, the minimum and maximum permissible range of radio transmission to reach an acceptable degree of reliability, based on the above analysis, is determined. Our statistical analysis in base of 2k factorial methodology shows that determining the radio transmission range has a major impact on decision-making delay. Using the proposed radio transmission range, simulation results also show that reliability can be improved as well as increasing the accuracy of event detection and reducing decision-making and safety message delivery delays.

Probabilistic Risk Analysis of Diversion Tunnel Construction Simulation

AbstractComprehensive and effective risk analysis is significant for studying construction simulation of diversion tunnel. Existing tunnel risk simulation approaches mainly analyze ordinary risk factors, and cannot quantitatively study risk events considering their causes. Additionally, in other tunnel probabilistic risk analysis methods, although some studies have made full probabilistic estimates of tunnel schedule, risk factors are unable to be studied considering cyclic construction characteristics and occurrence probability of risk events cannot be determined quantitatively. To address the issues, a probabilistic risk analysis approach of diversion tunnel construction simulation is proposed. Based on hierarchical simulation model, risk factors can be analyzed at operation level of tunnel construction. Moreover, Bayesian network is embedded into simulation program to quantitatively calculate probability of risk events in each simulation cycle, considering geology, design, construction, and management conditions and their mutual dependence.

Fuzzy Logic-Based Operation of Battery Energy Storage Systems (BESSs) for Enhancing the Resiliency of Hybrid Microgrids

The resiliency of power systems can be enhanced during emergency situations by using microgrids, due to their capability to supply local loads. However, precise prediction of disturbance events is very difficult rather the occurrence probability can be expressed as, high, medium, or low, etc. Therefore, a fuzzy logic-based battery energy storage system (BESS) operation controller is proposed in this study. In addition to BESS state-of-charge and market price signals, event occurrence probability is taken as crisp input for the BESS operation controller. After assessing the membership levels of all the three inputs, BESS operation controller decides the operation mode (subservient or resilient) of BESS units. In subservient mode, BESS is fully controlled by an energy management system (EMS) while in the case of resilient mode, the EMS follows the commands of the BESS operation controller for scheduling BESS units. Therefore, the proposed hybrid microgrid model can operate in normal, resilient, and emergency modes with the respective objective functions and scheduling horizons. Due to the consideration of resilient mode, load curtailment can be reduced during emergency operation periods. Numerical simulations have demonstrated the effectiveness of the proposed strategy for enhancing the resiliency of hybrid microgrids.

Методика внутреннего аудита казенного учреждения

In the process of public administration reform there has begun the implementation of modern international control and auditing standards in the public sector. The need for the improving of the efficiency of public administration requires the use of enhancement processes of public finance application and use, which in turn depends on the quality of feedback. The feedback was provided earlier by the control and auditing services, which were recording the cases of violation but at the same time, the evolution of the public internal financial control requires a shift in emphasis from the detection of violations to the analysis of the processes of the use of the resources. Not downplaying the importance of the elimination of these violations, the analysis of the main processes which involved the spending of budgetary funds, the identification of bottlenecks in such processes and introducing recommendations aimed at minimizing of the occurrence probability of adverse events is now the prevalent task of the modern state of the public internal auditing. Effective public internal auditing is designed to provide continuous assessment of the state internal control and it should help in the conditions of modern changing environment and regulatory shifts, by providing modern public administration. However, the liquidation or slow transformation in the process of reorganization of existing control structures, creates the risk of loss of the previously accumulated experience, as well as ineffective implementation of international standards of internal control and internal auditing, testifying to the fact of relevance growth of the research topic. This paper provides with the technique of internal auditing of state-owned institutions, which was developed on the basis of synergy works of domestic and foreign scholars and practitioners.

Anthropogenic warming has caused hot droughts more frequently in China

Historical records have indicated an increase in high-impact drought occurrences across China during recent decades, but whether this increase is due to natural variability or anthropogenic change remains unclear. Thus, the shift toward dry conditions and their associated attributions across China are discussed in this study, primarily regarding the standardized precipitation evapotranspiration index (SPEI). The results show that drought occurrences across China increased consistently during 1951–2014, especially during the recent twenty years. Most of the increased drought events happened under warm-dry conditions that coincided with relatively high temperature anomalies but without large anomalies in annual precipitation, implying an increase in hot drought events across China. Further analysis revealed that the change in drought occurrences were mainly due to the combined activity of external natural forcings and anthropogenic changes across China. However, external natural forcings were mainly responsible for the variability of droughts and anthropogenic influences for their increasing trends, suggesting that anthropogenic warming has increased hot drought occurrences, associated risks and impacts across China. With continued warming in the future, the impact of anthropogenic warming on the increased hot drought events will be further amplified. The probability of warm years is projected to significantly increase, and the occurrence probability of hot drought events (SPEI<−1.0) will increase to nearly 100% by the year 2050, even though the annual precipitation is projected to increase across China in the future.

A semiparametric mixture cure survival model for left-truncated and right-censored data.

In follow-up studies, the disease event time can be subject to left truncation and right censoring. Furthermore, medical advancements have made it possible for patients to be cured of certain types of diseases. In this article, we consider a semiparametric mixture cure model for the regression analysis of left-truncated and right-censored data. The model combines a logistic regression for the probability of event occurrence with the class of transformation models for the time of occurrence. We investigate two techniques for estimating model parameters. The first approach is based on martingale estimating equations (EEs). The second approach is based on the conditional likelihood function given truncation variables. The asymptotic properties of both proposed estimators are established. Simulation studies indicate that the conditional maximum-likelihood estimator (cMLE) performs well while the estimator based on EEs is very unstable even though it is shown to be consistent. This is a special and intriguing phenomenon for the EE approach under cure model. We provide insights into this issue and find that the EE approach can be improved significantly by assigning appropriate weights to the censored observations in the EEs. This finding is useful in overcoming the instability of the EE approach in some more complicated situations, where the likelihood approach is not feasible. We illustrate the proposed estimation procedures by analyzing the age at onset of the occiput-wall distance event for patients with ankylosing spondylitis.

A Network Information Security Risk Assessment Method Based on Cloud Model

As an information security technology that can make the system more secure and robust, network security assessment not only can integrate various security assessment elements such as information assets, vulnerability and threats, but also can help users actively identify potential security threats that the system is encountering. Current network security assessment theories and methods require large amount of historical data to conduct statistics analysis or machine learning. Besides, the means of network threats change with each passing day, which implies that new network security assessment theories and methods should be formed by considering from network information’s own characteristics and finding out its behavior patterns of vulnerability and threats. Cloud model is an uncertainty analysis method, which can be used for the grading of network security status. Based on access graph model, this paper proposed the assessment method based on threat event occurrence probability and the assessment method based on information assets, and calculate the integrated security value of network information system. Then, the paper combined the network security assessment value with cloud model, obtained the eigenvalue of the cloud model after sampling and calculation, and established a certain cloud model. At last, the paper took the random sampling value as the input of the cloud model, and confirm the network security grade after judging the cloud model.