78 results about "Probability assessment" patented technology

The invention relates to the technical field of fire early-stage recognition, in particular to a multi-source information-based conveying belt fire early-stage recognition and early-warning system, which comprises a sudden change detection module, a synchronous analysis module, a spatial trend recognition module, a coupling fluctuation screening module and a probability evaluation module. According to the invention, through multi-source information linkage acquisition, collaborative monitoring of parameters such as along-line temperature, smoke, gas, images, load and heat source temperature difference, combined characteristic analysis among parameters and synchronous response trend determination are driven, and active revelation of early risk hidden dangers and dynamic discrimination of spatial distribution consistency and fluctuation continuity are realized. Potential abnormal focusing locking under a high-interference complex working condition is promoted, collaborative fluctuation between a load and a heat source temperature difference further eliminates environmental noise influence, risk weight dynamic adjustment strengthens classification sensitivity of probability identification, fire risk clustering division promotes accurate mastering of distribution of tiny initial hidden dangers, and the probability identification accuracy is improved. And the reliability of fire early warning in a coal mine area conveying belt scene is obviously improved.

The invention discloses an underground-ground-sky combined rock burst and earth surface deformation early warning method, which comprises the following steps of: S1, arranging an underground monitoring system, and acquiring underground monitoring data; s2, setting a ground monitoring system, and collecting ground monitoring data; s3, setting a sky monitoring system, and collecting sky monitoring data; s4, calibrating the time scale difference of the underground monitoring data, the ground monitoring data and the sky monitoring data through a multi-scale time alignment algorithm; and S5, constructing a multi-stage joint probability assessment framework to carry out mining area rock burst and earth surface deformation risk assessment. According to the method, multi-source monitoring data are integrated through a multi-scale space-time fusion framework, and a full-chain association model of'microcosmic fracture-mesoscopic deformation-macroscopic accumulation 'is constructed. And on the basis of cross-scale time sequence alignment, spatial heterogeneity is optimized and eliminated, a multi-stage joint probability assessment framework quantifies a cross-scale collaborative risk, full-period intelligent management and control of'monitoring-early warning-feedback-optimization 'is realized, and early warning accuracy and timeliness are improved.

The invention relates to the technical field of corrosion detection, in particular to a grounding grid corrosion intelligent monitoring system which comprises an electrochemical data capture module, a corrosion behavior analysis module, a corrosion probability evaluation module and a monitoring strategy optimization module. According to the method, data errors and abnormal values are filtered and corrected, the overall quality of data is improved, the accuracy of analysis results is ensured, and in corrosion behavior analysis, the future trend of corrosion is effectively predicted through dynamic change detection of electrochemical properties and calculation of the corrosion rate; the time sequence analysis of the electrochemical impedance data not only enhances the prediction accuracy, but also improves the understanding depth of corrosion behaviors, the corrosion probability evaluation module adds consideration of random changes to the whole prediction model through the application of the Markov chain, so that the model is closer to the uncertainty of the real world, and through the optimization of the processing flow, the prediction accuracy is improved. The real-time performance and the early warning capability of the monitoring system are obviously improved, and the reliability and the operation accuracy of the system are enhanced.

The invention discloses a warehouse safety management platform and method based on the wireless radio frequency technology, and relates to the technical field of monitoring analysis, and the method comprises the steps: collecting environment sensing data and radio frequency signal data, carrying out the three-dimensional space topology construction of a warehouse, generating a warehouse three-dimensional dynamic environment model, and carrying out the monitoring of the environment sensing data and the radio frequency signal data. Performing correlation analysis on temperature and humidity data in the environment sensing data and warehouse material attributes in the warehouse, generating material abnormal vibration trajectory data, and determining an environment parameter correlation matrix; inputting the environmental parameter incidence matrix and the material abnormal vibration trajectory data into a preset dynamic risk model for security threat probability assessment, generating an equipment abnormal operation characteristic spectrum, and outputting a warehouse regional risk grade distribution diagram; and fusing the warehouse regional risk level distribution diagram and the equipment abnormal operation characteristic spectrum, constructing a potential safety hazard identification optimization model, and pushing the model to a terminal to execute real-time safety early warning and equipment linkage management. The method has the effect of improving the safety management efficiency of the storeroom.

The invention relates to the technical field of drainage basin water environment pollution traceability, and discloses a drainage basin non-point source pollution accurate traceability method based on deep learning, which comprises the following steps: constructing a generation and verification dual-network collaborative framework comprising a pollution source hypothesis generator and a hypothesis verifier, and establishing a collaborative optimization mechanism of the pollution source hypothesis generator and the hypothesis verifier; the generation and probability evaluation of the pollution source hypothesis are realized, and an evidence link tracker is constructed to analyze the relationship between the pollution source candidate hypothesis and the support evidence; further analyzing the relationship between the pollution source candidate hypothesis and the support evidence; the analysis result of the relation between the pollution source candidate hypothesis and the support evidence is integrated, and the calculation efficiency and the system performance are optimized; according to the method, probability evaluation is provided for each pollution source hypothesis through a Bayesian posterior inference framework, the uncertainty of a traceability result is quantified, and a reliable reliability index is provided for decision making.

The invention discloses a weather big data-based rainstorm and flood composite disaster early warning method, which comprises the following steps of: extracting periodic characteristics and abnormal fluctuation information of rainstorm frequency by acquiring long-term weather data and performing hierarchical processing, and analyzing and determining the change trend of the rainstorm frequency in combination with a time sequence. And constructing an incidence matrix based on the rainstorm frequency change trend and the topographic hydrological characteristic data, determining an initial parameter range of flood probability assessment, introducing dynamic correlation analysis, fusing meteorological data and environmental conditions, and obtaining a dynamic adjustment coefficient of probability assessment. Through mapping the adjustment coefficient and the rainstorm frequency change trend, a quantitative expression of a rainstorm and flood coupling structure is constructed, and an early warning model is obtained based on the quantitative expression. And finally generating multi-scene early warning signal distribution by performing adaptive adjustment on the model response and verifying the accuracy of the model response. According to the invention, accurate early warning of flood disasters in a complex environment is realized, and the timeliness and reliability of early warning are significantly improved.

The invention discloses a design and manufacturing method of a polyamideimide composite coating gear, which comprises the following steps: S1, collecting customer demands, sequencing the priorities of the customer demands, and establishing a quality house based on a quality function expansion method; s2, formulating a design project according to the priority of the customer demand; s3, preliminarily evaluating the feasibility of the design project through probability evaluation and risk analysis tools; s4, key quality characteristics are obtained, and a tolerance limit range is determined; s5, on the basis of a multi-objective optimization machining parameter reverse adjustment method, a parameter-driven input and output model is established; s6, inputting the key quality characteristics into the input and output model to obtain a design scheme; s7, inputting the design scheme into the multi-objective optimization model to obtain optimized key quality characteristics; s8, planning a manufacturing process based on customer requirements and optimized key quality characteristics; and S9, manufacturing a composite coating gear sample, testing the composite coating gear sample, and verifying the service performance of the composite coating gear sample.

The invention discloses a landslide risk assessment method and system, and relates to the field of geological disaster assessment, and the method comprises the steps: training a landslide risk probability model through a positive sample and a uniform sample, predicting the landslide disaster occurrence probability of a sample point, and carrying out the optimization through an artificial bee colony algorithm; in the training process, the prediction density for measuring the size of the landslide disaster occurring range in the research area is compared with a set threshold value, the corresponding fitness is obtained according to the comparison result, the fitness serves as the negative constraint of the model, an artificial bee colony algorithm is used for iteratively searching a new constant term and a disaster-pregnancy factor weight term, and the disaster-pregnancy factor weight term is obtained. Obtaining a trained landslide risk probability model; and obtaining a geological disaster occurrence probability according to the trained landslide risk probability model, and evaluating the landslide risk through the geological disaster occurrence probability. According to the method, the spatial distribution of the landslide hazard risk probability can be considered while the landslide hazard risk probability model is trained by only using the positive sample.

The invention discloses an electric power system safety assessment method and system considering multiple meteorological sensitive factors, and relates to the technical field of electric power system safety assessment, and the method comprises the steps: building a fault model under the influence of multiple meteorological factors based on the coupling effect of different meteorological factors on electric power system elements; and calculating the fault probability of each element of the power system under the extreme weather condition by using a multi-modal data fusion and neural network technology. And evaluating the safety of the power system through a Monte Carlo method in combination with the fault probability of the system element, and outputting a reliability index. According to the method, the dependence of traditional safety assessment on single-factor hypothesis, a static model and low-efficiency calculation is broken through, and a full-link innovation path of multi-factor dynamic perception-deep learning fusion modeling-global state probability assessment is realized; finally, the technical effects of improving the evaluation accuracy, adapting to extreme weather evolution and enhancing the emergency response capability of the power system are achieved, and the method has remarkable social value and engineering application prospects.

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 provides a causal relationship and time sequence characteristic-considered cascading failure rapid screening method and system, and relates to the technical field of power grid cascading failures, and the method comprises the steps of obtaining cascading failure scene data; based on cascading failure scene data, extracting a causal relationship between an initial short-circuit fault and a subsequent fault by using logistic regression and a neural network model; different states of the fault event are converted into graph structures, time delays between the different states of the fault event are obtained based on cascading fault scene data statistics, and time sequence features in the transient evolution process of the cascading fault event are extracted; the probability that the initial fault causes a subsequent fault event is obtained based on a causal relationship, the duration of different states of the fault event is generated by adopting a Monte Carlo sampling method based on time sequence characteristics, and a cascading fault scene is generated; and carrying out risk assessment and probability assessment on the cascading failure scene generated based on the causal relationship and the time sequence characteristics, and finally screening to obtain a high-risk cascading failure scene.

The present disclosure provides a method and system for quickly screening cascading failures that consider causal relationships and timing characteristics, and relates to the technical field of cascading failures in power grids. The method comprises the following steps: obtaining cascading failure scenario data; extracting the causal relationship between an initial short-circuit fault and subsequent faults using logistic regression and a neural network model based on the cascading failure scenario data; converting different states of a fault event into a graph structure, and obtaining time delays between different states of the fault event based on statistics of the cascading failure scenario data, and extracting timing characteristics during the transient evolution of the cascading failure event; obtaining the probability of an initial fault triggering a subsequent fault event based on the causal relationship, and generating a cascading failure scenario using a Monte Carlo sampling method based on the timing characteristics; and performing risk assessment and probability assessment on the cascading failure scenarios generated based on the causal relationship and timing characteristics, and ultimately screening out high-risk cascading failure scenarios.

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 relates to a slope instability probability evaluation method based on burial depth constraint space random field simulation, and belongs to the technical field of slope engineering. The method comprises the following steps: acquiring shear strength parameters c and phi at different buried depths of a slope soil body in a research area; the obtained shear strength parameters are expanded according to Gaussian process regression in combination with a radial basis function, the reliability of the expanded parameters is determined through K-S inspection, and cross correlation between c and phi is obtained; determining statistical characteristics of the c and phi expansion data; under the burial depth constraint condition, slope model determinacy research is carried out, a safety coefficient is calculated, the model is compared with a traditional model, and the difference between the potential slip plane of the slope under the burial depth constraint and the potential slip plane of the traditional slope is obtained; the traditional Monte Carlo simulation MCS is optimized; according to deterministic analysis and a random field theory, carrying out uncertainty research on the side slope under the burial depth constraint, and calculating the instability probability of the depth evolution random field side slope model.

The invention discloses a multispectral remote sensing disaster monitoring and early warning system using deep learning, and the system comprises a spectral data collection and preprocessing module which is used for continuously recording image data in different time periods based on surface reflection information to obtain an original data set, preprocessing the original data set to obtain a clear image data set; the mode recognition module is used for carrying out mode classification on the clear image data set to obtain a potential disaster related feature combination; the disaster risk assessment module is used for performing time sequence analysis on the potential disaster related feature combination to obtain a probability assessment value associated with the disaster risk; the disaster risk early warning module is used for constructing a quantitative index system associated with the disaster risk based on the probability evaluation value and determining an early warning reference standard based on the constructed quantitative index system associated with the disaster risk; and the closed-loop monitoring module is used for continuously monitoring the multispectral data acquired in real time based on an early warning reference standard to generate early warning information containing risk levels and outputting the early warning information.

The invention discloses a personnel vulnerability probability assessment method considering landslide migration and personnel escape. The method comprises the following steps: investigating a living environment of residents around a landslide, and compiling land coverage types in the living environment into a geographic vector file; dividing a land coverage area for personnel escape in the geographic vector file into a plurality of sub-areas, and determining a personnel escape network; estimating available time for residents in each sub-region to escape; estimating the escape demand time of residents in each sub-region; and establishing a performance function representing successful or failed escape of the personnel, and respectively establishing personnel vulnerability probability evaluation formulas of an individual level and a group level according to the performance function representing successful or failed escape of the personnel. The method can be reproduced in other areas with landslide risks, so that an optimal escape route before a disaster is effectively planned, and important scientific basis and practical reference are provided for landslide disaster prevention and reduction work.

The embodiment of the invention discloses a regional water shortage risk and space-time imbalance evaluation method, system and device and a storage medium, and the method and system construct a multi-dimensional water shortage risk evaluation system through the combination of kernel density estimation, a Copula function and a centralized index, and remarkably improve the evaluation precision and practicability. Compared with a traditional parametric distribution method, kernel density estimation does not need to preset a data distribution form, the method can flexibly adapt to complex and diversified supply and demand sequences, high fitting precision is still kept under the condition of insufficient data or extreme distribution, experiments show that the root-mean-square error is reduced by more than 30% compared with the traditional method, and the adaptability to unknown distribution is remarkably enhanced. The joint probability distribution of the water demand and the water supply is quantified through the Copula function, and the severity level is dynamically mapped in combination with the water shortage rate and the duration, so that the limitation of single probability evaluation is solved, the time persistence is brought into risk calculation for the first time, and the evaluation result is closer to the actual water use scene.

The invention relates to the technical field of power systems, and particularly discloses a data-driven power transmission safety grading risk assessment method, which comprises the following steps: constructing an intelligent real-time assessment model of power transmission section limit transmission capability based on a KAN framework; historical operation data and simulation supplementary data are collected to construct a training sample set; training an intelligent real-time evaluation model of the transmission section limit transmission capability through a loss function comprising a TTC evaluation precision loss item and a physical consistency regularization item, and ensuring that the evaluated TTC meets section power flow safety constraints; performing multi-confidence scene sampling on wind power and photovoltaic output to generate a new energy uncertain output scene set under different confidence levels; and inputting the new energy output scene set into a trained intelligent real-time evaluation model of the transmission section limit transmission capability, obtaining TTC probability evaluation sets corresponding to different confidence coefficients, and realizing transmission safety grading risk evaluation. The method has the advantage that intelligent and real-time risk confidence assessment of the operation mode can be realized.

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.

The present invention discloses a method for assessing the vulnerability of a commutating branch tower to wind disasters that takes into account the variation of material parameters. When calculating the wind-induced response of a commutating branch tower, a sampling operation is used to consider the variation patterns of various structural material parameters, and an intelligent prediction model is used to learn the relationship between wind speed and the mean and standard deviation of structural demand indicators. Finally, the exceedance probability of the structure under a series of wind speed values ​​is predicted to form a comprehensive wind disaster vulnerability result that takes into account the variation of multiple structural material parameters. The method of the present invention provides a more accurate exceedance probability assessment based on the existing wind disaster vulnerability assessment of transmission towers, taking into account more uncertain risks. The present invention considers the uncertainty of multiple types of variation parameters through random sampling, reducing the amount of calculation. By using an intelligent prediction model, the quantitative relationship between wind speed and exceedance probability, as well as the relationship between wind speed and multiple types of material parameters and exceedance probability are obtained. Under a specified wind speed, the exceedance probability can be calculated more quickly through the intelligent prediction model, and vulnerability curves or surfaces under multiple wind speeds can be obtained, greatly improving the analysis efficiency.

The present invention discloses a method and application for determining the hazard of a moraine lake outburst. The method calculates a moraine lake outburst susceptibility factor and its conversion value, a moraine lake outburst debris flow probability assessment factor and its conversion value, a moraine lake outburst probability factor for debris flow blocking the main river and its conversion value, and a moraine lake outburst probability factor for the main river's peak flow. Based on the conversion values, a comprehensive moraine lake outburst hazard assessment value is calculated and the moraine lake outburst hazard level is determined. This method addresses the characteristics of moraine lake outburst hazard and comprehensively considers multiple factors that influence the risk of a moraine lake outburst, including outburst floods, debris flows, blocking the main river, and floods caused by re-outburst. This improves the accuracy of the moraine lake outburst hazard assessment results and prioritizes the peak flow factor, which has the greatest impact on the hazard, making the hazard assessment results more reasonable and reliable.

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 present invention relates to a stress intensity limit S t A failure probability assessment method is provided, comprising: obtaining creep rupture test data of specimens under different working conditions, including creep rupture life and creep strain-time curves; determining the standard error of the logarithmic creep rupture life; determining the average stress required for the total strain to reach 1%, the minimum stress at the onset of the third stage of creep, and the minimum stress causing creep rupture under each working condition, as the first criterion stress, the second criterion stress, and the third criterion stress, respectively; determining creep life models corresponding to the first, second, and third criterion stresses; determining creep life models considering safety factors corresponding to the first, second, and third criterion stresses; determining probability parameters corresponding to the first, second, and third criterion stresses based on the creep life model after introducing probability parameters and the creep life model considering safety factors for each criterion; and calculating the failure probabilities of the first, second, and third criterion stresses under different working conditions.

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 present invention discloses a method for probabilistic assessment of human vulnerability considering landslide movement and human evacuation. It includes the following steps: investigating the living environment of residents around the landslide and compiling the land cover types in the living environment into a geographic vector file; dividing the land cover areas available for human evacuation in the geographic vector file into multiple sub-areas to determine the human evacuation network; estimating the available time for residents in each sub-area to escape; estimating the required time for residents in each sub-area to escape; establishing a function to represent the success or failure of human evacuation, and based on the function representing the success or failure of human evacuation, establishing individual-level and group-level probabilistic assessment formulas for human vulnerability. The present invention can be replicated in other areas with landslide risks, so as to effectively plan the optimal pre-disaster evacuation route and provide important scientific basis and practical reference for landslide disaster prevention and mitigation work.

The invention relates to the field of power grid data scheduling, and discloses a power grid business data intelligent scheduling method based on multi-source data fusion, which comprises the following steps: fusing different business data of a power grid for risk assessment and fault probability assessment of the power grid and user loads, and combining a fault probability assessment result to obtain a power grid business data intelligent scheduling result; and carrying out load intelligent scheduling strategy construction and fault correction strategy construction on the user load and the power grid in the high-risk state. The system can accurately and actively identify the operation risk of the power grid, achieves the intelligent load transfer of main and distribution cooperation, can prevent faults in advance, achieves the quick response when the faults occur, effectively reduces the power-off time and range, guarantees the normal power demands of all walks of life and residents, and improves the power utilization efficiency. And meanwhile, unnecessary equipment overload and redundant operation can be avoided, the operation cost of the power grid is reduced, the service life of equipment is prolonged, and the operation economic benefit of the power grid is improved on the whole.

The present invention generally provides systems and methods for autonomous space characterization, data analysis, anomaly detection, hazard probability assessment, and profile deviance assessment and incident response action, and firefighting employing robotically controlled firefighting equipment, autonomous selection and release of firefighting agent, and the autonomous prediction, detection, classification, and location of existing and impending threats and fire events.

The present invention provides a method for evaluating the seismic loss probability of a building complex based on the simulation of ground motion intensity random field, belonging to the technical field of seismic loss assessment. The method provided by the present invention first uses the ground motion intensity random field simulation technology to simulate the ground motion of the region, obtains the spatial distribution of ground motion intensity at different sites, and then estimates the loss probability distribution of each building by using the building seismic performance model and the building vulnerability database. This method can more quickly and accurately evaluate the occurrence probability of extreme seismic losses of large-scale building complexes, provide important support for seismic risk assessment and post-disaster emergency decision-making, thereby enhancing the disaster resistance and recovery ability of cities and improving the overall resilience of cities.

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.