29 results about "Probability assessment" patented technology

The invention relates to the field of low-altitude navigation, and particularly provides a low-altitude route conflict and dynamic early warning data processing method and system, a terminal and a medium, and the method comprises the steps: constructing a comprehensive state vector fusing task roles and formation constraints; spatial domain situation structured perception is realized through dynamic spatial domain gridding modeling and spatial domain state attribute updating; performing grid-level macroscopic conflict identification based on airspace state attributes, and screening potential conflict hotspot regions; for aircrafts in the hot spot area, trajectory prediction of intention and dynamics fusion is carried out based on the comprehensive state vector, and conflict risk probability assessment is carried out by adopting space-time probability envelope; and when the conflicting parties belong to the same group, a collaborative collision avoidance strategy taking formation maintenance as a primary target is generated, the strategy preferentially tries to integrally and uniformly avoid, and if the strategy fails, differential instruction distribution based on task role priority is started. According to the method, the high efficiency and the real-time performance of conflict early warning and releasing are improved.

The invention discloses a network packet capture and analysis method and device based on artificial intelligence. And constructing a dynamically updated normal traffic baseline by using a lightweight AI model, and extracting real-time traffic features to perform abnormal probability evaluation. And when the abnormal probability exceeds a risk threshold, triggering a precise packet capture strategy. Extracting multi-modal features from the captured abnormal traffic, and fusing the multi-modal features into a high-dimensional feature vector which retains behavior semantics through a deep neural network; and carrying out anomaly classification by using a special AI large model, and carrying out causal reasoning in combination with a knowledge graph to locate a root cause. And generating a disposal strategy based on the root cause, and performing risk grading according to the influence range and the service interruption probability to realize grading closed-loop disposal. According to the method, full-process automation from anomaly perception, intelligent analysis to adaptive disposal is realized, the traditional technical bottlenecks of encrypted traffic detection, unknown threat identification, root cause positioning and the like are effectively solved, and the efficiency and response speed of network security operation and maintenance are remarkably improved.

The invention discloses a distributed photovoltaic aggregation frequency modulation capability probability evaluation method considering distribution network operation safety constraints, and the method comprises the steps: firstly carrying out the modeling of the probability distribution of distributed photovoltaic power generation power through employing a multivariate Gaussian mixture model, so as to describe the non-Gaussian characteristics and spatial correlation of a plurality of distributed photovoltaic outputs; secondly, taking maximization of distributed photovoltaic aggregation upward and downward power regulation ranges as a target, considering power distribution network line power flow constraints, node voltage safety constraints and inverter capacity constraints, and constructing a second-order cone programming model to realize distributed photovoltaic aggregation frequency modulation capability deterministic evaluation; and finally, proposing a multivariate mixture Gaussian point estimation algorithm, constructing a special sampling set, executing a small amount of deterministic calculation, and combining statistical analysis and weighted mixing to obtain the probability distribution of the distributed photovoltaic aggregation frequency modulation reserve capacity. According to the method, the large-scale distributed photovoltaic polymerization frequency modulation capability is effectively analyzed, the uncertainty is quantified, and the calculation precision and efficiency are both considered.

SYSTEM AND METHOD FOR EVALUATING UNIFORM RESOURCE LOCATOR 5 Disclosed is a system (100) and a method (500) for evaluating a Uniform Resource Locator (URL). The method (500) comprises extracting the URL from a message and assigning an initial risk score. The method (500) further comprises performing a protocol verification to verify a protocol of the URL and a domain assessment operation to determine whether the URL is an IP-address based URL, or FQDN 10 based URL. Furthermore, the method (500) comprises calculating a risk score for the URL based on protocol verification, domain assessment, evaluation of length of the URL, association with reputed domains, analyzing mean value of the URL, and identifying whether an entry corresponding to the URL is stored in a distributed ledger (108). Thereafter, the method (500) comprises determining a probability of 15 the URL being secure based on the calculated risk score and evaluating the credibility of the URL based on the probability.

The invention belongs to the technical field of oil and gas drilling, and particularly relates to a casing tripping-in risk quantitative evaluation method considering well bore quality uncertainty, which realizes probability evaluation of casing tripping-in friction resistance by utilizing Monte Carlo simulation and multi-distribution random sampling. The core thought of the method is as follows: the uncertainty of well diameter and dog-leg-degree well bore quality parameters is taken as an entry point, and the accuracy and decision scientificity of casing tripping-in performance evaluation are improved through the process of'probability modeling-related sampling-smooth constraint-friction resistance calculation-risk evaluation '. According to the method, uncertainty and spatial distribution characteristics of key well bore quality parameters such as the well diameter and the dogleg degree can be systematically represented; a large number of random borehole quality parameter sequences conforming to actual geology and engineering rules are generated; on the basis, Monte Carlo simulation is carried out, and probability distribution of casing running friction resistance is calculated; and finally, the risk that the casing can be put down is quantified in a probability form, and a visual probability basis is provided for engineering decision making.

The invention discloses an active power distribution network bearing capacity probability modeling method related to virtual power plant demand response, and aims to solve the problems that in the prior art, evaluation of photovoltaic bearing capacity depends on certainty, and an uncertainty processing method and an efficient probability evaluation framework are lacked. According to the method, uncertainty is quantified through a probability density function, a complex random problem is converted into a multi-scene deterministic optimization problem containing key uncertainty information by utilizing Monte Carlo simulation and scene reduction technologies, and demand response of a virtual power plant is integrated as an active control means. And finally, accurately calculating the photovoltaic bearing capacity and the system performance index of the active power distribution network under the condition of meeting multiple security constraints.

The present application relates to railway route selection technical field, and relates to a kind of railway route selection scheme across seismic fault's total probability risk assessment method.The assessment method uses Monte Carlo simulation, establishes the total probability risk analysis model suitable for railway route selection design across fault zone, is based on the theory of performance-based earthquake engineering, integrates fault zone displacement probability risk analysis, railway structure object probability vulnerability analysis and railway fault rear probability economic loss analysis, can be combined with fault zone characteristics (position, geometric shape, seismic intensity) The annual exceedance probability of specific displacement value is calculated, the damage state of different railway structure (usually avoid using bridge mode to cross fault zone, therefore, the present application mainly considers the intersection of tunnel, embankment section and fault zone) under the influence of fault displacement is evaluated, simultaneously, total probability risk analysis model across fault zone and cost minimization model form cost-risk net present value model, and construction cost and cross-fault zone risk are simultaneously evaluated.

This invention discloses a method and system for optimizing medical consignment inventory based on kit integrity constraints. The method includes: constructing a material dependency graph for surgical kits, representing each surgical kit as a graph structure containing nodes and edges; constructing a shortage risk propagation model based on historical borrowing and returning data, calculating the risk transmission coefficient of material shortages on kit availability, and forming a kit integrity probability assessment system; establishing an optimization model aimed at minimizing the probability of kit shortages under total safety stock budget constraints, dynamically allocating the safety stock level of each material within the kit; integrating surgical scheduling data with the inventory calculation model, and implementing differentiated inventory reservation and sharing strategies based on surgical certainty levels to achieve synergistic optimization of kit integrity and inventory costs. This effectively solves the problem that traditional single-material inventory calculation methods cannot guarantee kit integrity, significantly improving material availability and reducing redundant inventory costs.

The invention provides a power grid forest fire potential intensity probability assessment method based on comparative learning, and belongs to the technical field of power grid safety assessment. Comprising the steps of 1, defining parameters; 2, collecting input data and processing the input data; 3, classifying the processed data into dynamic and static variables; 4, building a model; 5, initial data are sent to the step 2, results are sequentially output to the next step until the step 4, and network training is carried out; 6, after training is completed, learning network training results are compared; and step 7, obtaining a power grid mountain fire potential probability evaluation result after training is completed. According to the model, input and target features are aligned to a unified high-dimensional hidden space by using comparative learning in a pre-training stage, uncertainty of potential fire intensity is described by using a multi-quantile head in a regression stage, and a constructed two-stage learning framework is also suitable for other power grid forest fire behavior feature probability modeling and is high in universality and expandability.

This invention discloses a multispectral remote sensing disaster monitoring and early warning system utilizing deep learning. The system includes: a spectral data acquisition and preprocessing module, used to continuously record image data from different time periods based on surface reflectance information to obtain a raw dataset, and preprocess the raw dataset to obtain a clear image dataset; a pattern recognition module, used to classify patterns in the clear image dataset to obtain potential disaster-related feature combinations; a disaster risk assessment module, used to perform time-series analysis on potential disaster-related feature combinations to obtain probability assessment values ​​associated with disaster risk; a disaster risk early warning module, which constructs a quantitative index system for disaster risk association based on the probability assessment values, and determines early warning reference standards based on the constructed quantitative index system for disaster risk association; and a closed-loop monitoring module, which continuously monitors the real-time acquired multispectral data based on the early warning reference standards to generate early warning information output including risk levels.

The invention relates to the technical field of geological disaster monitoring and early warning, and discloses a geological disaster probability assessment method based on multi-source big data fusion analysis. The method comprises the steps of three-dimensional coordinate system layout and multi-point sensor acquisition, standardization, memory kernel construction, time sequence convolution fusion, anomaly recognition, probability mapping and spatial interpolation, and high-risk area output. By unifying a spatial reference system, the spatial consistency of data of each monitoring point is ensured; the whole-process data quality inspection and standardization are realized, and the dimension unification and comparability of the multi-source heterogeneous data are realized. A finite memory weight model and time sequence weighted convolution are innovatively adopted, the historical cumulative effect is comprehensively reflected, the anomaly discrimination robustness is improved, and the defect that only short-term data is used is overcome. Probabilistic mapping and feature distance-based spatial smoothing endow the risk expression with continuity and adaptivity, and realize scientific determination of regional high-risk partitions.

The application discloses a global sensitivity calculation method of a power system risk event based on subset simulation, establishes a representation method of a risk event in power system uncertainty quantification considering uncertainty of a random source model, applies subset simulation to decompose a rare event to reduce sampling times, and efficiently calculates global sensitivity of uncertainty of probability distribution parameters of a random source in a power system to a probability of a rare event. The scheme greatly reduces a sample size while guaranteeing accuracy of rare event probability evaluation, effectively solves the problem of low efficiency of a traditional Monte Carlo sampling algorithm in simulating a rare event, and can quickly evaluate the influence degree of uncertainty of probability distribution parameters of a random source in a power system to a probability of a rare event of power grid operation.

This invention relates to the field of PCB board technology, and particularly to a method and system for assessing the defect probability of PCB boards. By acquiring multiple feature data and constructing a three-dimensional defect feature map, this invention effectively assesses not only surface defects but also internal structural defects and latent defects. Real-time acquired environmental interference parameters are used to correct the basic assessment factors, ensuring that the defect probability assessment considers not only the material's inherent characteristics but also the impact of environmental factors on the test results. By individually assessing each preset detection area, the defect situation and its impact in different areas can be clearly identified, providing a precise basis for subsequent production adjustments and defect rectification. The calculation method based on single-area defect probability and area ratio ensures the authority and accuracy of the final overall defect probability, providing solid data support for PCB quality inspection.

This invention discloses a method for assessing the overall performance degradation probability of box girder bridges based on DBN and Gamma distributions, belonging to the technical field of box girder bridge performance degradation probability assessment. The method includes: determining the composition of the box girder bridge; determining the dwell time of each component; calculating relevant parameters of the Gamma distribution probability density function; calculating the cumulative probability density function and cumulative survival probability function of each component; modeling the performance degradation of the box girder bridge to obtain the transition probability; calculating the state probability distribution of each component; forming the probability degradation curves of each component and the overall structure through a dynamic Bayesian network; and assessing the impact of each component on the probability degradation curves of its component and the overall structure. This invention, employing the aforementioned method for assessing the overall performance degradation probability of box girder bridges based on DBN and Gamma distributions, can scientifically predict the performance degradation of box girder bridges from component to component and from component to the whole, thereby predicting the service life of box girder bridges from a probabilistic perspective.

This invention relates to the field of precipitation forecasting technology, and discloses a precipitation forecasting and probabilistic prediction method, system, equipment, and medium based on physical consistency constraints. The method includes: in the training phase, inputting preprocessed first meteorological data into the model to obtain preliminary prediction results, and constructing physical consistency constraints by combining the physical fields in the results; constructing classification prediction losses for precipitation magnitude intervals; performing joint optimization by combining various losses to obtain a trained precipitation forecasting model; inputting preprocessed second meteorological data into the trained model, and outputting continuous prediction results of future precipitation intensity through a physical enhancement feature construction module, a multi-scale circulation feature fusion module, a spatiotemporal feature fusion module, and a feature reconstruction and prediction module. This invention can improve the stability and generalization ability of the model, and provide direct and usable magnitude event probability information for precipitation forecast results, thereby meeting the needs for precipitation risk classification and early warning and extreme event probability assessment.

This invention relates to the field of transaction data management technology, and in particular to a method and system for transaction data anomaly analysis and processing based on feature correlation dimensionality reduction. This method determines the multi-dimensional original features of transaction data, performs dimensionality reduction based on feature correlation to obtain target features, and calculates the initial anomaly probability by combining historical normal feature deviations to screen out suspected abnormal transactions. It then uses the dimensionality-reduced target features to extract behavioral data from suspected transactions, determines the behavioral anomaly probability and specific abnormal transactions by the deviation from historical behavioral data, and comprehensively assesses the overall transaction risk of the platform based on the initial and behavioral anomaly probabilities. If the risk does not meet the requirements, the entire dataset undergoes anomaly review, and the transaction risk label status is updated accordingly. This method removes redundant noise through feature dimensionality reduction and employs a two-stage probability assessment to achieve precise identification from coarse to fine, effectively reducing the false alarm rate and improving the efficiency and accuracy of anomaly detection.

This application discloses an integrated assessment method and device for multivariate seismic vulnerability of tunnels that incorporates physical priors, relating to the technical field of seismic analysis and seismic risk probability assessment of underground engineering structures. The method includes: generating predicted samples based on a logarithmic domain joint distribution description; inputting each predicted sample into a heteroscedastic moment estimation substitution model to obtain predicted values ​​of the conditional mean and conditional variance of the logarithmic domain demand response; reconstructing and dynamically anchoring the conditional marginal distributions of each response based on the predicted values ​​and residual information, and coupling each anchored conditional marginal distribution to obtain a multi-response joint probability model; calculating the point-state failure probability of each projected sample under a preset damage state based on the multi-response joint probability model, constructing discrete sample pairs; and fitting each discrete sample pair to obtain seismic vulnerability curves under each preset damage state. This application improves the accuracy, robustness, and engineering usability of tunnel seismic vulnerability assessment.

This invention relates to a method, system, and equipment for establishing a pipeline risk analysis model based on fuzzy Bayesian networks, belonging to the field of pipeline risk analysis technology. The method for establishing the pipeline risk analysis model includes: constructing a pipeline failure analysis Bayesian network based on historical pipeline accident data and the Apriori algorithm; calculating the prior probabilities of basic risk factors based on expert opinions on basic pipeline risk factors and fuzzy comprehensive evaluation; obtaining the conditional probabilities of sub-nodes in the pipeline failure analysis Bayesian model based on expert pairwise evaluation of the relative importance of basic pipeline risk factors, as well as the analytic hierarchy process (AHP) and the ranking node method; and establishing the pipeline risk analysis model. This invention can calculate the failure probability of pipelines with complex multi-risk factors and identify key pipeline failure events. Based on the probabilities, it can assess the safety and reliability of pipeline systems, prevent accidents, reduce costs, and optimize resource allocation.

This invention relates to a multi-dimensional evaluation method and system for the carrying capacity of distribution networks based on probabilistic assessment. The method includes: collecting distribution network operation data and constructing a distributed generation processing probability model and a load demand uncertainty model; performing sampling and power flow calculations based on the distributed generation processing probability model and the load demand uncertainty model; calculating and normalizing multi-dimensional indicators based on the calculation results at each sampling time; the multi-dimensional indicators include resilience indicators, including transient voltage stability index, islanding reconstruction power, and energy storage throughput margin; calculating the subjective and objective weights of the multi-dimensional indicators and fusing them to obtain a comprehensive weight; calculating the score for each sampling and the expected score based on the comprehensive weight and the normalization result; calculating the risk probability based on the expected scores of all samplings; and classifying the carrying capacity level based on the expected score and the risk probability. Compared with the prior art, this invention improves the reliability of carrying capacity evaluation for distribution networks with uncertainties.

The invention belongs to the technical field of health management, and particularly relates to a risk assessment and personalized scheme generation method based on multi-source health data. The method comprises the following steps: collecting health data of a user; cleaning, converting and standardizing the collected health data, and constructing a user health portrait and derivative features; inputting the processed health data into a pre-trained artificial intelligence model cluster which comprises a stacked integrated learning model and an interpretable random forest model which are respectively used for critical disease risk probability assessment and specific health risk classification assessment; dividing risk grade categories according to the risk assessment result; based on the health data and the risk level category, generating a personalized health intervention scheme by retrieving the enhancement generation architecture; and dynamically updating a risk assessment and intervention scheme according to follow-up visit data to form a self-adaptive health management closed loop.

The invention discloses a receiving-end power grid-oriented new energy power supply off-network risk calculation method, relates to the technical field of power system automation, and aims to solve the problems that an existing risk assessment method cannot distinguish the probability and consequence difference of different scenes and is difficult to adapt to the real-time changing operation state of a power system. The method comprises the steps that a multi-time-scale operation scene set is constructed, scene reduction is carried out, and a typical operation scene set is generated; calculating a new energy power supply off-network probability caused by a fault in the typical operation scene; evaluating a system consequence caused by the new energy power supply off-network event; and on the basis of the new energy power supply off-network probability and the system consequence, calculating a new energy power supply off-network risk, and evaluating the overall new energy power supply off-network risk of the DC receiving end power grid. According to the method, a probability-consequence-control three-in-one risk comprehensive assessment framework is established, and decision support of which control measures are taken in which scenes to reduce the risk to an acceptable level at the minimum cost is provided for scheduling personnel.

This invention provides a method and apparatus for in-depth comprehensive typhoon disaster risk assessment in inland areas. The method includes: constructing a typhoon path probability assessment model based on the subtropical high; calculating the spatial distribution simulation results of the typhoon path probability based on the subtropical high based on the typhoon path probability assessment model; simulating the spatial distribution probability of typhoons of different intensity levels based on historical typhoon wind field data to obtain the typhoon intensity probability; and calculating the spatial distribution of comprehensive typhoon disaster risk based on the spatial distribution simulation results and intensity probability results of the typhoon path probability based on the subtropical high based typhoon high, combined with regional disaster system theory. This invention can simulate and assess the typhoon catastrophic risk in typhoon-prone and typhoon-scarce areas, greatly improving the scientific rationality of typhoon disaster risk assessment results in inland and mid-to-high latitude regions lacking data.

This application proposes a multi-sensor data fusion dust concentration monitoring system and method, relating to the field of dust concentration monitoring technology. The system includes: a laser emitting component, multiple laser receiving components, and a processing component. The laser emitting component comprises a rotation drive mechanism, a pitch drive mechanism, and a laser emitter. The laser emitting component sequentially emits lasers to each laser receiving component. Multiple laser receiving components are distributed at different locations in the space to be measured, with each location having the same number of laser receiving components. The laser receiving components generate measurement data based on the received lasers. The processing component determines a first probability assessment distribution of the safety level for each location based on the measurement data of the current measurement cycle, and performs fusion processing on the first probability assessment distribution using a preset first data fusion algorithm to obtain a fused evaluation of the target dust concentration. This monitoring system can achieve dust concentration monitoring and provide a safety risk assessment result.

This invention discloses a method and apparatus for assessing the probability of vegetation ecosystem loss under combined meteorological-agricultural-hydrological drought stress. The method includes: collecting standardized precipitation evapotranspiration index (SPEI), standardized runoff index (SRI), and standardized soil moisture index (SSMI) on a grid-by-grid, monthly timescale in the study area, as well as total primary productivity (GPP) representing the health status of the vegetation ecosystem in each season; calculating the lag response time of GPP to different drought indices in different seasons, and determining the season in which GPP is most sensitive to drought stress; for the most sensitive season, selecting SPEI, SSMI, and SRI sequences at the scale corresponding to the lag response time, and establishing a four-dimensional Vine Copula model grid-by-grid; setting different drought and GPP loss level classification criteria, and establishing a vegetation productivity loss probability assessment model based on Bayesian theory. This method can more comprehensively and accurately assess the probability of vegetation ecosystem loss under drought stress.

This invention discloses a method and system for assessing the probability of vessel operability, belonging to the field of marine engineering technology. It includes: constructing and calibrating a wave numerical model of the target construction area and a motion model of the target vessel to determine the operational wave threshold; generating wave field data based on the numerical model, fixed-point wave forecast data based on machine learning, and real-time wave data based on vessel motion inversion in parallel; spatially adapting the three types of forecast data according to the vessel's real-time position, and adaptively weighting and fusing them based on the dynamic error indices of each forecast source to obtain a fused wave forecast value; finally, comparing the fused forecast value with the wave threshold, and calculating the vessel's operability probability at a future time using a probability function. This invention achieves complementary advantages of multi-source data and on-board dynamic forecasting, and elevates operational assessment to a probabilistic risk indicator, significantly improving the utilization efficiency of construction windows and the scientific nature of decision-making.

The present application belongs to the technical field of oil and gas drilling, and particularly relates to a casing lowerability risk quantitative evaluation method considering wellbore quality uncertainty. The present application uses Monte Carlo simulation and multi-distribution random sampling to realize the probability evaluation of casing lowerability friction. The core idea is: taking the uncertainty of wellbore quality parameters such as hole diameter and dogleg severity as the starting point, through the process of "probability modeling -> related sampling -> smoothing constraint -> friction calculation -> risk evaluation", the accuracy and decision-making scientificity of casing lowerability evaluation are improved. The method can systematically represent the uncertainty and spatial distribution characteristics of key wellbore quality parameters such as hole diameter and dogleg severity; generate a large number of random wellbore quality parameter sequences conforming to actual geological and engineering laws; on this basis, Monte Carlo simulation is carried out to calculate the probability distribution of casing lowerability friction; finally, the risk of casing lowerability is quantified in the form of probability, providing intuitive probability basis for engineering decision-making.

This invention discloses a method for detecting consistency alarms in regulatory instruction paraphrasing based on intelligent completion. The method includes: acquiring the current dialogue; classifying and filtering the regulatory text in the current dialogue using an SVM model to obtain regulatory-paraphrased text; identifying regulatory text elements in the regulatory-paraphrased text to obtain the identified regulatory elements; completing the simplified and omitted information in the regulatory-paraphrased text; and using a matching-comparison mechanism to check the consistency of each element in the completed regulatory-paraphrased text. If the paraphrased text is inconsistent with the regulatory text, the probability of the inconsistent text information is calculated to facilitate confirmation of paraphrasing inconsistency alarms. This invention reduces misjudgments of paraphrasing consistency caused by simplification and omission, improving the effectiveness of the technology. While providing the paraphrasing consistency judgment result, it also provides the judgment criteria and probability assessment, adapting to the paraphrasing consistency detection needs of corresponding scenarios and improving reliability.

This invention relates to the field of vulnerability analysis technology, and particularly to a method for calculating the damage probability of target components based on automatic damage feature identification. The method includes: batch simulating space debris impacting an equivalent target at different angles and velocities to obtain simulation result files for each working condition; batch reading the simulation result files, and extracting the damage type and damage feature size of each component based on its component type, pre-impact particle set characteristic parameters, and post-impact particle set characteristic parameters; using damage criteria, determining the damage degree of each component based on its damage type and damage feature size; and for each working condition, statistically determining the damage probability of each component based on the damage degree of each component obtained from multiple simulations under that condition. By directly extracting the damage feature size of each component from the simulation result files obtained from numerical simulation, bypassing the ballistic limit equation and impact debris cloud modeling process, the accuracy and efficiency of component damage probability assessment can be improved.