12 results about "Bayesian statistics" patented technology

This application provides a business data prediction method based on multi-model fusion, applicable to the fields of artificial intelligence, big data, and fintech. The method includes: predicting incremental data for a target year using a Bayesian statistical model based on multi-source business data; obtaining incremental data for a target quarter using a seasonal time-series prediction model based on the multi-source business data; performing long-term decomposition prediction using the seasonal time-series prediction model and a multinomial model based on the incremental data of the target quarter to obtain initial prediction data for the target year and fluctuation data for the remaining quarters; wherein the target quarter and the remaining quarters constitute the target year; and using the incremental data of the target year as trend reference data, and based on the fluctuation data of the remaining quarters, correcting the initial prediction data for the target year to obtain the business data prediction result. This application also provides a business data prediction apparatus, device, storage medium, and program product based on multi-model fusion.

This invention relates to a lane navigation method and system, a computer device, and a storage medium, comprising: receiving a forward image captured by an onboard camera within the current period; inputting the forward image into a pre-trained deep learning model for image recognition to obtain the speed (Speed_A) and brake light activation count (Count_A) of vehicle A in the current lane, and the speed (Speed_B) and brake light activation count (Count_B) of vehicle B in the adjacent lane; performing Bayesian statistics on the brake light activation count (Count_A) and brake light activation count (Count_B) to obtain the braking deviation probability of vehicle A or vehicle B at the next moment; determining whether to change lanes to the adjacent lane based on the relationship between the speeds (Speed_A and Speed_B) and the braking deviation probability of vehicle A or vehicle B at the next moment; and outputting corresponding navigation instructions based on the results of the above determination, thereby improving the accuracy of lane navigation.

The application discloses a complex system coupling coordination degree optimization method and device, and the method comprises the following steps: acquiring a multilevel coupling parameter set in a complex system; screening a to-be-optimized parameter variable from the multilevel coupling parameter set to obtain a to-be-optimized parameter variable set; constructing a Bayesian statistical model; taking the Bayesian statistical model as a proxy model for describing a probability mapping relationship between a target function and a parameter variable; determining a sampling function; and iteratively optimizing the to-be-optimized parameter variable by using the Bayesian statistical model and the sampling function to obtain an optimized parameter. The application can improve the coupling coordination degree of the complex system and meet the requirements of stable operation and intelligent control of the complex system.

This invention discloses a method and system for predicting the remaining lifespan of individual sensors based on Bayesian statistics, relating to the field of equipment health status monitoring and lifespan prediction technology. The invention provides a method comprising: establishing a general degradation model and prior distribution using historical degradation data; collecting monitoring data of a specific target sensor under real or accelerated stress in the field; updating the posterior distribution of model parameters based on Bayesian statistical inference and Gibbs sampling to generate a specific degradation model for that individual sensor; and calculating the remaining lifespan based on this specific model and a failure threshold. This invention achieves a breakthrough from "group lifespan assessment" to "accurate prediction of individual lifespan" through data-driven adaptive correction, significantly reducing over-maintenance costs and enhancing equipment operational safety.

The invention discloses a residential district spring water direct drinking pipe network hydraulic model construction and optimization design method. According to the method, a pipeline roughness coefficient slow-varying trend component is predicted based on a Langelier saturation index and a scaling rate physical equation, and a fast-varying disturbance component is extracted through variational mode decomposition, so that parameter multi-scale decomposition is realized; a physical information neural network is used as a hydraulic positive problem agent solver, whole-network hydraulic state parallel reasoning is completed within 50 milliseconds, and meanwhile an accurate analysis Jacobian matrix is obtained at zero extra cost through automatic differential; bayesian statistical fusion of multi-sensor observation and parameter estimation is realized through sparse ensemble Kalman filtering based on a pipe network observability coefficient, and pipeline roughness coefficient posterior probability distribution is output. According to the invention, parameter real-time calibration completely synchronous with the acquisition period of the sensor is realized, and the consumption of computing resources is reduced by more than 80%; water quality-hydraulic power double safety constraints are embedded; and whole-network high-precision state estimation under 20% of sensor coverage rate is supported.

The invention discloses a ground temperature field construction method based on Bayesian framework multi-source data fusion and integrated Kalman inversion, and relates to the technical field of ground temperature field calculation and geothermal parameter inversion, and the technical scheme is characterized in that the method comprises the following steps: obtaining preprocessed gravity and magnetic data; carrying out the inversion of the preprocessed gravity and magnetic data through the multi-source inversion of a Parker-Oldenburg algorithm and a LithoRef18 model, and carrying out the inversion of the preprocessed gravity and magnetic data; an additive Gaussian error model is established in combination with a heat conduction equation, posteriori distribution is taken as a core target under Bayesian statistics, an EKI algorithm is utilized to iteratively optimize parameters, and inversion parameters are output; and inputting the inversion parameters into a nonlinear heat conduction equation to obtain three-dimensional ground temperature fields with different depths, and reckoning surface heat flow distribution. According to the method, high-precision construction of the regional ground temperature field is achieved, compared with traditional interpolation and formula estimation, system errors are reduced, and the reliability of deep temperature and ground surface heat flow prediction is improved to a certain degree.

The application relates to the technical field of environmental management, and discloses a carbon footprint evaluation method and device, which comprises the following steps: modeling the probability distribution corresponding to the preprocessed activity data and background data based on a Bayesian statistical model; sampling from the posterior distribution of the Bayesian statistical model by using a Markov chain Monte Carlo method to generate the probability distribution corresponding to the activity data; generating an input variable combination by randomly sampling in the probability distribution through target Monte Carlo simulation to perform carbon footprint calculation; obtaining an uncertainty analysis result by analyzing the distribution of the carbon footprint calculation result; analyzing key variables in the carbon footprint evaluation result by calculating the first-order sensitivity index and the total effect sensitivity index corresponding to the input variables to generate a global sensitivity analysis result; and constructing a carbon footprint evaluation model based on the uncertainty analysis result and the global sensitivity analysis result, and generating a carbon footprint evaluation result based on the carbon footprint evaluation model.

This invention belongs to the field of computational materials and proposes a melting point calculation method based on first-principles molecular dynamics and Bayesian statistics. It employs a small-scale system for calculation, significantly reducing computational complexity compared to large-scale systems. Furthermore, it utilizes an on-the-fly machine learning force field to further accelerate melting point calculation. This method requires no manual intervention, automatically updating the force field during AIMD simulations to maintain the accuracy of first-principles calculations and significantly improve computational efficiency. This method calculated 16 solid-liquid configurations within the 800–1100 K range, consuming approximately 600 cores, which significantly shortens computation time compared to other methods, thus demonstrating great application potential.

The invention provides a rainfall-type landslide susceptibility evaluation method, system and device and a medium, and belongs to the technical field of geological disaster risk evaluation and spatial information modeling. The method comprises the following steps: collecting and preprocessing multi-source environment and rainfall data of a target area; constructing a multi-time scale rainfall factor system, and reducing variable colinearity through intra-group screening and a multi-stage feature engineering method; modeling the static environment factor by using a random forest model, obtaining a landslide background susceptibility probability, and converting the landslide background susceptibility probability into a logarithmic probability offset item; under the constraint of an offset term, constructing a Bayesian spatial statistical model, introducing a nonlinear effect term and a spatial random effect term of a rainfall factor, and carrying out joint modeling and Bayesian inference on the occurrence probability of the landslide; and landslide susceptibility dynamic prediction and spatial mapping are realized. According to the method, the nonlinear prediction capability of machine learning and the physical constraint and spatial modeling capability of Bayesian statistics are fused, so that the stability, prediction precision and mechanism interpretation of an evaluation result are improved.

The application discloses a kind of power transmission line defect monitoring method and system based on multi-source data fusion, it is related to electric power equipment state monitoring field, solved the technical problem that defect detection method reliability is insufficient, false alarm rate is high, lack of accurate positioning to defect reason.The application collects the multi-source sensor data of power transmission line, based on the measured physical characteristic value of measured component of multi-source sensor data extraction, and based on environmental parameter and electrical parameter calculation theoretical physical characteristic value, form feature pair set;Each feature pair is judged based on bayesian statistical test method to abnormal state, and the abnormal type identification is output;Abnormal type identification and multi-source sensor data are used as observation data, input into the defect causal diagram model of preposition and carry out probability reasoning, and defect type is determined according to maximum posterior probability principle;Based on defect type, call physical evolution model to predict the future evolution trend of defect state, and assess risk level and the remaining useful life of measured component.

The invention discloses a flow monitoring method and device fusing video spatio-temporal characteristics and physical parameters, and aims to break through the bottlenecks that a traditional contact type flow measurement method interferes with water flow, equipment is easy to damage, and the prediction reliability is insufficient due to dependence on a single data source in a complex environment. According to the method, video spatio-temporal dynamic features and hydrological physical parameters are deeply fused, spatio-temporal features are extracted from a video through an encoder based on a three-dimensional spatio-temporal convolutional neural network, and meanwhile two key hydrological physical parameters including the water level and the sectional area are collected. Denoising, normalization and dimension reduction processing are performed on multi-source data, a fusion feature set is constructed, then a combined kernel function Gaussian process regression model is adopted for flow prediction, a complex nonlinear relation is accurately captured, and an uncertainty quantization interval of a prediction result is output. The uncertainty quantification result is derived based on a Bayesian statistical framework, the prediction credibility degree is visually presented in a standard deviation form, and risk grading and early warning of hydrological decision can be directly supported.

This invention relates to the fields of stochastic processes and Monte Carlo computation, and particularly to a method for constructing a Markov transition operator. The aim is to improve the spectral properties of traditional Markov operators by introducing a higher-order transition structure, thereby enhancing sampling efficiency. First, starting with a basic Markov transition operator that satisfies the invariance of the target distribution, this method, based on the Metropolis–Hastings (MH) framework, introduces a two-step hybrid update mechanism to construct a polynomial Markov operator. Through spectral structure analysis of this operator, its eigenvalue transformation relationship is established, and it is proven that it has a larger spectral gap and better convergence performance compared to the original operator, while also reducing the asymptotic variance of the corresponding statistics. Second, further analysis of the autocorrelation function and integration time shows that the Ishikawa-MCMC algorithm can effectively suppress linear dependencies between samples, resulting in a faster decay rate of the autocovariance, thereby reducing the Monte Carlo covariance. The operator construction method proposed in this invention overcomes the problem of slow convergence of traditional single transition operators under high-dimensional or complex distributions by integrating multi-order transition information. It provides a new operator design framework for Markov chain Monte Carlo algorithms and can be widely applied in fields such as Bayesian statistical inference, complex probability distribution sampling, and stochastic simulation.