389 results about "Monte carlo em" patented technology

The application relates to the technical field of server operation and maintenance, and discloses a server abnormality automatic detection and recovery method and system, the method comprising collecting real-time logs and monitoring data, extracting abnormal load characteristics to construct a system state vector; using a deep reinforcement learning model to generate a candidate recovery action, and simulating and predicting stability benefits in a digital twin environment; combining high-load resource estimation to generate an action priority sequence through Monte Carlo tree search; optimizing a recovery path according to topological dependence encapsulation, verifying overload risk based on real-time resource snapshots, and automatically deploying and executing after confirming feasibility. The method can solve the problem of low server automatic recovery success rate in the prior art.

This invention discloses a method for assessing the quality of carbon emission data, relating to the field of carbon emission data quality assessment. The method employs sensitivity analysis to determine the main parameters affecting total carbon emissions; it scores the quality of these main parameters across four dimensions: numerical accuracy, source reliability, evidence standardization, and the degree of digitization of evidence, converting the scores into parameter uncertainty U-values; it performs two rounds of Monte Carlo simulations based on the parameter uncertainty U-values ​​to construct the likelihood function corresponding to the current observation; it calculates historical uncertainty U-values ​​based on multi-period historical carbon emission data and fits the prior probability distribution of the uncertainty; and it applies a Bayesian update formula to fuse the likelihood function corresponding to the current observation with the prior probability distribution, deriving the posterior distribution of the current carbon emission data uncertainty to determine whether the current carbon emission data meets quality requirements. This method improves the scientific rigor, transparency, and operability of carbon emission data quality management.

This invention relates to the field of aero-engine maintenance engineering and quality assessment technology, and discloses an aero-engine overhaul assessment method based on a physical information Bayesian neural network. The method includes collecting multi-source heterogeneous data from maintenance and testing sites and converting it into key process indicator scores; extracting rotor dynamics, imbalance transmission, and empirical formula features to construct physical constraint regularization terms; establishing an initial Bayesian neural network containing deterministic and variational Bayesian layers; inputting the key process indicator scores into the network, and optimizing parameters by combining the physical constraint regularization terms with adaptive annealing and early stop mechanisms; performing multiple Monte Carlo samplings on the trained network to calculate the mean distribution of the output results and obtain distribution statistical characteristics; calculating the quality score, confidence interval, and attribution warning information based on the distribution statistical characteristics, and outputting an overhaul assessment report. This invention solves the problem that existing pure data models violate physical laws and cannot quantify confidence levels.

The application discloses a method for designing and calibrating a detection site of an unmanned aerial vehicle based on high-precision positioning, comprising the following steps: S1, setting a main control point and a standard point; S2, performing static relative measurement and free station measurement, and outputting a reference coordinate data set; S3, introducing a Boolean sand model, calculating accurate three-dimensional coordinate values of the main control point, evaluating residual errors, and generating reference data of the main control point; S4, performing multi-station redundant observation on the standard point, and outputting accurate coordinate data of the standard point; S5, constructing an improved Hamilton Monte Carlo-based Bayesian inference model, introducing a Gaussian mixture prior guide factor, calculating a posterior probability distribution and an extended uncertainty, and generating a calibration data report; and S6, performing independent measurement and comparative analysis, and outputting a final calibration result. The application realizes accurate measurement of point coordinates of a detection site and scientific evaluation of uncertainty, and effectively improves the reliability and robustness of value traceability.

The application provides a kind of sports non-material cultural heritage protection policy intervention effect evaluation method, comprising: collecting multi-source heterogeneous data, extracting structured policy-behavior-response elements, constructing dynamic policy knowledge graph and realizing the time series modeling of causal network;Adopting time series contrast learning encoder and twin network optimization policy variable embedding, realizing accurate extraction of policy effect after mixed variable elimination;Integrating do-calculus and Monte Carlo tree search for counterfactual causal reasoning, forming multi-path effect contribution and stability evaluation;Through Shapley value decomposition and visual report, the net causal effect of each policy tool is quantified and the effectiveness is determined, the application can realize multi-dimensional causal effect accurate identification for sports non-heritage policy intervention process, improve the scientificity and timeliness of attribution analysis.

The present application provides a kind of marine aquaculture risk prediction method based on marine multi-source observation data, belong to marine aquaculture risk technical field, the present application is reconstructed three-dimensional marine environment field by sparse bayesian field reconstruction algorithm based on the constraint of ocean dynamics, then the risk benchmark distribution of long historical observation aquaculture area is migrated along the manifold coordinate to new aquaculture area by marine risk field manifold alignment migration algorithm, and the physical correction is carried out to bottom shape data and tidal harmonic constant, the risk index uncertainty interval is obtained by non-stationary generalized extreme value distribution combined with bayesian markov chain monte carlo sampling, drive physical constraint artificial intelligence to complete local high-resolution field reconstruction, according to the comparison result of risk index and early warning threshold, corresponding early warning level is triggered, finally form marine aquaculture insurance risk index product, solve the technical problem that new aquaculture area cannot establish reliable risk benchmark distribution due to lack of long-term historical observation data.

This invention discloses an improved Monte Carlo electromagnetic reverberation chamber electromagnetic environment modeling method and system, relating to the field of electromagnetic environment effects. The method introduces a distribution factor function to describe the distribution weight of plane electromagnetic waves on the surface of the incident sphere, combines this with Monte Carlo sampling to generate non-uniformly distributed plane electromagnetic wave parameters, calculates the electromagnetic field distribution in the simulated space based on the principle of electromagnetic superposition, and finally outputs the electromagnetic environment characteristics through post-processing. The corresponding system includes a parameter setting module, a sampling generation module, an electromagnetic calculation module, and a result processing and output module. This invention solves the problem of deviation between the simulation results and the actual reverberation chamber environment caused by the uniform distribution of electromagnetic waves in the traditional Monte Carlo method, achieving accurate simulation of electromagnetic environment non-uniformity and providing reliable support for electromagnetic compatibility testing, antenna performance evaluation, and other applications.

This invention relates to a single-event latch-up evaluation method for advanced process technologies, comprising: constructing a high-energy particle incident model using the Geant4 Monte Carlo simulation tool; constructing and calibrating a three-dimensional basic device model using the TCAD simulation tool; constructing a standard cell circuit containing parasitic effects, extracting the parasitic thyristor structure of the standard cell circuit, and identifying sensitive regions of the parasitic thyristor structure; performing transient effect simulation on the identified sensitive regions based on the high-energy particle incident model to confirm whether latch-up is triggered; introducing anti-latch-up hardening measures for the sensitive regions, and performing simulation verification on the hardened standard cell circuit under the high-energy particle incident model; if the hardened standard cell circuit does not trigger latch-up, the anti-latch-up hardening measures are deemed effective. This method provides a systematic and automated solution that can fundamentally characterize the complex parasitic effects of advanced devices.

The present application belongs to the field of geological exploration and artificial intelligence technology, and discloses a mineral resource prediction method and system based on a Bayesian physical information neural network. The method comprises the following steps: obtaining and preprocessing multi-source geological data of a study area; constructing a Bayesian neural network based on variational inference; constructing a physical constraint loss with a geological source term; performing three-stage progressive training based on a multi-objective loss function; and performing Monte Carlo sampling prediction and uncertainty quantification. The present application has strong physical interpretability: by embedding a steady-state diffusion equation with a geological source term, the model prediction result conforms to the geological law of ore-forming element migration and enrichment, and the source term clearly corresponds to two geological actions of fracture channel and ore-forming parent rock. The present application realizes the organic combination of data driving and physical driving by using multi-source information such as geochemical data, fracture structure, rock mass distribution and known mine point labels.

This application relates to the field of big data acquisition technology and provides a carbon-saving deodorization control method and system based on online gas monitoring. The method includes: deploying a corresponding sensor array according to a preset sensor deployment strategy to collect gaseous environmental data, obtaining a multi-dimensional spatiotemporal dataset; extracting features from the multi-dimensional spatiotemporal dataset using a multi-layer convolutional neural network to obtain a diffusion feature set of odor pollutants; constructing an odor pollutant diffusion model based on the diffusion feature set; simulating the probability distribution of the diffusion path of the odor pollutants using a Monte Carlo algorithm based on the odor pollutant diffusion model to generate a diffusion topology; determining the control priority of a preset emission valve based on the diffusion topology; and controlling the opening and closing state of the preset emission valve according to the control priority to ensure that the concentration of the odor pollutants meets a preset concentration condition. This method helps improve the prediction accuracy of the diffusion path of gaseous pollutants.

This invention discloses a method for assessing the load-bearing capacity of bridges under random traffic flow, belonging to the technical field of bridge load-bearing capacity assessment. This method improves the accuracy and efficiency of the assessment by extracting the bridge deflection influence line and calculating the compliance matrix through traffic flow statistics, Monte Carlo simulation, finite element analysis, and signal processing. Employing non-destructive testing technology, this method provides real-time health status of the bridge. By analyzing dynamic response data during heavy vehicle travel and combining it with the deflection influence line model, the method predicts the bridge's load-bearing limit and potential damage areas, providing a scientific basis for bridge safety assessment and maintenance. This invention is characterized by high efficiency, accuracy, and strong applicability, avoiding the problems of long testing times, traffic disruption, and large workload associated with traditional static load tests. It is suitable for the load-bearing capacity assessment and condition monitoring of small and medium-span bridges.

This invention provides a method and system for assessing the reliability of wind turbine generator sets based on a directed fault graph. Addressing the challenges of complex structures and multiple failure modes in wind turbine generator sets, it employs fault tree analysis to establish a directed fault graph model. To address the difficulty of polymorphism at the root nodes of the directed fault graph, it considers the uncertain logical relationships between components and the system within the wind turbine generator set and uses probability values ​​to measure these relationships in a conditional confidence quality table. Then, it takes all importance rankings into account and performs directed fault graph reasoning according to the same importance ranking, selecting the maximum value to complete the multi-fault state reliability assessment of the wind turbine generator set. This invention effectively assesses the reliability of wind turbine generator sets while avoiding the low efficiency and low accuracy problems of the Monte Carlo method and confidence interval method, respectively.

This invention belongs to the field of intelligent transportation, specifically relating to a MaaS-IoT integrated method and system for crowd risk perception and intervention. The method first collects environmental data and travel orders for the target area through IoT devices and an online platform; then, it constructs a path decision model based on the Bandit algorithm; combining shared and unique feature vectors to achieve path recommendation. Next, it constructs a crowd risk event dataset based on historical data and generates an easily searchable vector database, and fine-tunes the LLM to obtain a crowd risk inference model. Then, it tokenizes the time-series data, and uses the crowd risk inference model to perform chain reasoning and Monte Carlo-Shapley attribution to generate corresponding results; finally, it generates intervention instructions based on the outputs of the two models, and issues them for execution after verification. This invention solves the problem that existing methods struggle to achieve timely risk perception and efficient intervention in public places where crowds gather.

This invention discloses a career path planning method and system based on LLM (Multi-Agent Learning). The method first generates a set of target constraints through intent and constraint parsing and requirement clarification. Then, it constructs individual ability profiles and job-specific quantitative profiles. Next, based on ability gaps, it generates task packages level by level along stage boundaries and supplements prerequisite dependencies, forming a set of candidate task packages. Finally, addressing the problem that traditional sorting or traversal methods struggle to balance search efficiency and path quality due to the large candidate task package combination space, complex dependencies, and coexisting time and risk constraints, an improved Monte Carlo tree search algorithm is employed to filter invalid search branches in real time, efficiently searching and outputting career goal paths with better comprehensive scores. This invention improves the search efficiency, path quality, and interpretability of career goal path generation under complex constraints and can be widely applied to career development scenarios such as internships, campus recruitment, social recruitment, and entrepreneurship.

This invention discloses a Monte Carlo-based mineral assemblage analysis method, belonging to the field of mineral analysis. The invention includes the following steps: multi-source data fusion and preprocessing; physical model construction and initial solution generation; multi-scale feature fusion and extraction; Monte Carlo random sampling and solution space exploration; physical-data dual-driven evaluation and screening; iterative update and model optimization; convergence judgment and result output. This invention effectively solves the problem of multiple solutions in mineral assemblage identification by combining multi-source data fusion, a physical-data dual-driven model, and the Monte Carlo method with multi-scale feature fusion and iterative optimization. This method can integrate geophysical, geochemical, remote sensing, borehole core, and mineral spectral data, and through iterative model updates, gradually converges to the most probable mineral assemblage, providing uncertainty assessment, thereby improving the accuracy and reliability of mineral identification.

The application belongs to the technical field of vehicle reliability evaluation, and discloses a vehicle component reliability evaluation method suitable for strong random working conditions. In the method, multiple random working condition parameters such as road surface grade and driving speed are uniformly represented as continuous variables, a single high-precision proxy model covering all working conditions is constructed, and advanced fast sampling algorithms (Latin hypercube sampling, mixed importance sampling, adaptive Markov chain Monte Carlo) and a segmented random task profile are combined to realize accurate and efficient evaluation of cumulative damage.

The present application relates to the technical field of real-time monitoring, in particular to a real-time monitoring system for road and bridge construction progress, which comprises a dynamic relationship analysis module, an event response decision module, a structure health monitoring module, a risk management and prediction module, a progress prediction and adjustment module, a resource optimization configuration module, an environmental impact assessment module and an overall progress management module.In the present application, the dynamic relationship in construction is captured through the graph neural network, the graph attention network and the dynamic graph convolution network, the construction model is updated in real time, the accuracy of progress analysis is ensured, the event response decision module quickly optimizes the construction plan through the predictive control algorithm, the strategy flexibility is improved, the structure health monitoring and risk management module combines the Markov decision process and the Monte Carlo tree search, effectively prevents risks and ensures safety, the progress prediction, resource optimization and environmental impact assessment module improves resource efficiency and environmental adaptability, and the application of comprehensive evaluation method realizes efficient and high-quality construction management.

This invention discloses an intent-consistent program repair and trajectory-driven feedback generation system for programming education, including a data acquisition and preprocessing module, an adversarial test case generation module, a Monte Carlo tree search program repair module, and a hierarchical feedback generation module. This invention can reliably improve the credibility and consistency of repair results in a real teaching environment, reduce the interference of pseudo-correct submissions on teaching evaluation, and reduce the burden of repetitive Q&A for teachers through trajectory-driven process feedback, thereby improving the overall teaching quality and intelligence level of online programming teaching systems.

This application provides a two-layer prevention and control method and system for grid cascading faults considering the integration of new energy sources. Based on the probability density function and output power function of wind farms and photovoltaic farms, the Monte Carlo method is used to calculate stochastic power flow. Assuming an initial fault occurs on a line and it is disconnected by backup protection, for the remaining lines other than the line with the initial fault, an expression for whether a cascading fault occurs in the grid is established based on the calculated power flow value and its corresponding setting value. The node injection power combination is obtained based on the calculated power flow value, and a grid safety margin is established based on the node injection power combination. An inner-layer optimization model is constructed with minimizing the grid safety margin as the first objective function, and an outer-layer optimization model is constructed with maximizing the grid safety margin and minimizing grid loss cost as the second objective function. The two-layer prevention and control model for cascading faults is solved to obtain a cascading fault prevention and control strategy, thereby enabling early identification and precise and effective prevention and control of cascading faults.

The application belongs to the technical field of pollution source automatic monitoring and supervision, and specifically discloses an abnormality identification method and system based on fixed pollution source automatic monitoring data. The method comprises the following steps: extracting data at a preset frequency or period to form a time-stamped data sequence; performing differential statistical test in the data sequence to generate a suspected abnormality sub-sequence; taking a historical segment that continuously meets the standard and has stable working conditions as a positive sample to perform double Monte Carlo prediction to obtain a mode coincidence rate sequence; performing robust trend estimation to obtain a dynamic baseline that changes over time; calculating the residual of the credibility of the emission mode relative to the dynamic baseline, and outputting an abnormality alarm signal when the residual exceeds an adaptive discrete threshold. The application completes one-dimensional abnormal boundary definition and high-dimensional emission mode drift test in the same system framework, forms a complementary supervision framework combining "short-time-long-time" and "point-surface", and improves the abnormality identification precision and the intelligent level of supervision.

The application provides a warning method and system for the tipping moment of a thermoacoustic system under Lévy noise, and relates to the technical field of thermoacoustic system stability analysis and early warning control, which comprises the following steps: based on the physical model of the thermoacoustic system, a non-uniform wave equation describing the pressure and velocity fluctuation of the system is constructed; the Galerkin modal expansion and Taylor expansion truncation are performed on the equation, and a random differential equation model is established by combining the time-varying control parameters and Lévy noise; the random differential equation model is solved by the Monte Carlo simulation and the random fourth-order Runge-Kutta numerical method, and the amplitude sequence of the thermoacoustic system changing with time is obtained; based on the probability distribution of the amplitude sequence, the change of the system dynamics characteristics is quantified by calculating the Shannon entropy, and the sequence of the Shannon entropy changing with time is obtained; the warning moment when the Shannon entropy sequence first passes through the preset threshold is determined, and the warning domain is generated according to the time difference between the warning moment and the actual tipping moment of the thermoacoustic system, so that the warning for the tipping moment of the thermoacoustic system is realized.

A novel data generation system is provided, which includes a first data processing device having a function of generating a netlist by converting a hardware description language into graph-structured data and performing arithmetic processing based on Monte Carlo tree search, a second data processing device having a function of converting the graph-structured data into matrix data using a graph neural network, and a third data processing device including a transformer model and generating a first command in accordance with an input token sequence. The first data processing device has a function of generating a random number, and as a command executed in the Monte Carlo tree search, the first command or a second command selected using a calculation formula of action selection is selected in accordance with a magnitude relationship between the random number and a specified value.

This invention discloses an automated tuning method and system for large language model service configurations, belonging to the fields of artificial intelligence and cloud computing technology. The invention includes an offline analysis module for performance profiling of the large language model (LLM) service system under a finite configuration set and for collecting training data; an online search module for exploring and evaluating the performance of different configurations in the configuration space based on a Monte Carlo tree search algorithm; and a simulator module for receiving the configurations generated by the online search module and predicting the performance metrics of the configurations under a target workload based on a logical step simulation model. This invention effectively solves the problems of low search efficiency and poor performance of existing automatic tuning methods in existing large language model service frameworks (such as vLLM, SGLang, etc.) due to the huge number of configuration parameters (reaching a petabyte-scale search space) and the complex dependencies and mutual exclusion relationships between parameters.

The application discloses a Monte Carlo metric SC-Flip decoding method based on LLR absolute value interval, and the method comprises the following steps: according to a GA construction algorithm, a bit channel is divided into information bits and frozen bits; LLR absolute value intervals are divided into subintervals, and error probabilities of the subintervals are obtained through Monte Carlo simulation; Monte Carlo metrics are simplified, and the simplified Monte Carlo metric values are calculated according to the error probabilities of the subintervals; SC decoding is performed according to root node LLR values received from a receiver, if CRC check is successful, decoding is ended, otherwise leaf node LLR values are arranged in descending order of the Monte Carlo metric values, flip indexes of the first T Monte Carlo metric values are obtained; unflipped index positions are found from the T flip index positions in sequence to redecode, if CRC check is successful or T times of decoding are completed, decoding is ended. The simplified Monte Carlo metric only needs to perform addition operation, and is convenient for hardware implementation; and error correction performance is not lost; and at low flip times, error correction performance is obviously improved.

This invention relates to the field of power system fault quantification technology, and discloses a method, system, equipment, and medium for quantifying distribution network line faults under typhoon weather. It calculates the wind-induced fault rate of distribution network lines by constructing a wind field correction model that incorporates micro-topography correction factors and wind direction-line angle correction factors. With the goal of minimizing power outage losses in the distribution network, a coupled influence model of transmission network faults on the distribution network line fault rate is constructed and solved to obtain the optimal load shedding amount for each load node in the distribution network and its corresponding operational constraint boundary. Under this boundary, the random process of load shedding allocation and load transfer of faulty lines within the distribution network is simulated using a double Monte Carlo sampling method, and the probability of line cascading faults caused by transmission-distribution coupling is calculated based on the simulation results. The combined time-varying fault rate and type of distribution network lines are determined by integrating the two fault rates, achieving accurate quantification of distribution network line faults under the influence of transmission-distribution coupling.

The application belongs to the technical field of signal processing, and particularly relates to a non-coherent scattering ionospheric parameter non-ambiguous estimation method based on Monte Carlo, aiming to solve the problems of inaccurate parameter estimation and poor robustness in ionospheric parameter inversion in the prior art. The method comprises the following steps: receiving measured echo data, estimating the signal noise level and determining the prior information combination mode; obtaining an expected index based on a signal noise threshold mapping library, and judging whether a preset decision threshold is met; if the preset decision threshold is met, starting an optimization selection strategy based on Monte Carlo, and obtaining a candidate solution set; performing cluster analysis on the candidate solution set, selecting a solution with the optimal fitting degree from the solution cluster containing the largest number of solutions, and taking the solution as the final non-ambiguous estimation value. Through systematic Monte Carlo simulation, the application can quantitatively evaluate the inversion reliability under the current data condition, effectively avoid invalid calculation under adverse conditions such as excessive noise or insufficient prior information, and improve the robustness and success rate of estimation.

This invention discloses a method, apparatus, equipment, and medium for detecting over- and under-excavation in tunnels, relating to the field of intelligent detection technology in tunnel engineering. The method includes: acquiring raw point cloud data; reconstructing a watertight actual triangular mesh model based on Poisson surface reconstruction; calculating coordinate transformation parameters based on common control points to globally align the actual model with the design model; traversing the vertices of the actual model and calculating the three-dimensional deviation value by determining effective projection points; setting a virtual sliding section along the tunnel axis, analyzing the intersecting contour lines, and integrating to obtain the over- and under-excavation volume; establishing an error propagation model, using Monte Carlo simulation for uncertainty analysis, and generating confidence intervals and confidence levels for the deviation values; finally, generating an over- and under-excavation analysis cloud map and a quantitative analysis report with confidence interval identifiers. This scheme achieves robust deviation calculation, volume integration, and uncertainty quantitative analysis between the design model and the measured model.