72 results about "Probabilistic modelling" patented technology

The invention relates to the technical field of industrial equipment intelligent operation and maintenance, and discloses a self-adaptive predictive maintenance system and method for industrial equipment. A physical mechanism and neural residual parallel mixed digital twinborn model is constructed, predictive deviation is explicitly modeled into a residual, and through a meta-learning technology, the prediction deviation and the residual are subjected to prediction prediction; a twin mother set with universal knowledge is trained offline from historical data of an equipment group, for new equipment, a small amount of initial data can be used for rapid fine adjustment based on the mother set, personalized twin is efficiently generated, a residual sequence is continuously monitored in an online diagnosis stage and a system, probability modeling is performed on health residual distribution, and the probability modeling result is obtained. Sensitive detection of early and unknown faults is achieved, and after individual twins obtain new knowledge through self-adaptive learning, the knowledge is shared through a federated network in a model updating quantity mode and used for iteratively optimizing a global twinborn mother set, and intelligent population evolution between devices is achieved.

The invention discloses a numerical value conversion method, system and device based on an LLM large model and a medium, and the method specifically comprises the steps: carrying out the multi-modal feature preprocessing of the inputted online game novel content, and generating structural data containing a semantic tag and an entity association relationship; extracting role attributes, equipment description and entity features in a plot event from the structured data through a field adaptive analysis module of the self-developed LLM large model, and generating an initial numerical value mapping set; on the basis of the initial numerical value mapping set and in combination with an occupational balance template, probability modeling is carried out on a role growth track, and a first numerical value mapping set is output; on the basis of the first numerical mapping set, candidate numerical combinations are generated through Monte Carlo tree search, the balance loss of each combination is calculated through a differentiable combat formula, and a numerical framework conforming to game type constraints is generated. According to the method, the game numerical value design efficiency is remarkably improved, the numerical value balance is effectively guaranteed, and the requirement for manual intervention is reduced.

The invention discloses an engine state estimation and system modeling correction method based on double-layer variational inference, which relates to the field of engine state estimation and comprises a variational inference stage aiming at component performance states and kinetic model parameters; a system output prediction stage based on an observation equation; and a solving stage of performing objective function optimization through an evidence lower bound. The structure clearly presents information flow and key calculation links of the proposed algorithm in state estimation and model learning. According to the method, combined reasoning of state variables and model parameters is achieved by building a probability modeling structure, the modeling problem when system dynamics is partially or completely unknown is solved by combining a modeling method of a stochastic differential equation, and while the state variables and the model parameters are optimized, the modeling efficiency is improved. Precise inference of component states and reliable identification of fault features are achieved, the fault detection accuracy of sudden gas circuit abnormity reaches the standard, and meanwhile the performance is better in the aspect of tracking long-term performance degradation.

The invention discloses a multi-rover coverage method in a priori unknown environment, and relates to the technical field of probability modeling and cooperative control. The method comprises the following steps: deploying and forming a multi-rover network in a target area, virtually distributing and uniformly driving rovers to respective sub-target areas according to Voronoi segmentation; based on a sampling method, generating uniform sampling points in a sub-target area in charge of each rover in a self-adaptive manner, and continuously iterating and updating a continuous round-trip sampling path; according to the sampling path, each rover iterates in an online and distributed manner and updates estimation on unknown priori, and respective estimation mapping on target distribution is formed; based on a multi-source fusion mechanism, through weighted fusion of estimation data of each rover, the overall estimation accuracy and consistency of the system are improved; according to the Zensen-Xiang divergence, calculating change differences between self iterations of the rovers and between the rovers, and taking a double-threshold mechanism as a basis for estimating iteration stop; and iteratively driving each rover to the optimal position based on the control rate of the rover.

The invention discloses an articulated coordinate measuring machine error prediction compensation method and system based on a digital twin model. The method comprises the following steps: constructing a real-time synchronous articulated arm digital twinborn model; probability modeling is carried out on a plurality of error sources of the articulated coordinate measuring machine through a Monte Carlo method, and then error statistical characteristics are obtained. And inputting the error statistical characteristics, the joint angle during measurement of the real joint type coordinate measuring machine and the environmental parameters into an error compensation network. And the error compensation network outputs the end position compensation amount of the current coordinate measurement result. According to the method, the digital twinborn model of the articulated coordinate measuring machine is constructed, measurement point distribution is simulated, high-frequency online real-time compensation is realized in cooperation with an error compensation network, the whole process of'measurement-simulation-prediction-compensation 'can be completed through a unified system, and the real-time performance of error compensation is remarkably improved.

The invention discloses a Bayesian optimization-based hydrate reaction condition calculation method. The method comprises the following steps of S1, obtaining an initial data set through experimental measurement and numerical simulation; s2, establishing mathematical modeling of a hydrate reaction process based on chemical reaction engineering knowledge, and generating a simulation model; s3, constructing a proxy model by using a Bayesian optimization algorithm, and performing probability modeling and prediction on the target performance index to obtain performance distribution under a given experimental condition; s4, calculating and recommending optimal experiment parameters according to an expected improvement criterion; s5, performing an experiment or simulation under a recommendation condition, and merging new data into an existing data set; s6, repeating the steps of Bayesian optimization modeling, experiment recommendation and data updating until a predetermined termination condition is reached; and S7, outputting the optimized optimal reaction condition parameters and performance indexes. According to the method, the optimization efficiency and the data utilization rate are improved, multiple targets and complex constraints can be considered, and the scientificity and practicability of industrial process parameter optimization are remarkably enhanced.

The invention provides a method, device and equipment for promoting retention through interaction scene prediction and a storage medium, and the method comprises the steps: obtaining a current multi-modal interaction data stream between users in an interaction scene, carrying out the preprocessing of noise filtering, framing processing, size normalization, time aggregation and the like, extracting emotion features, and generating a real-time multi-dimensional observation vector sequence. The sequence captures complementarity and time sequence dependence of multi-modal information, and overcomes limitation of single-modal static analysis. The observation vector sequence is input into a pre-trained hidden Markov model, and the model is trained based on historical sequences and defines a hidden state set, an initial probability, a transfer matrix and an emission probability; the optimal emotional state path at the current moment is obtained through Viterbi algorithm reasoning, and probability modeling and dynamic prediction of emotion transfer uncertainty are achieved. The current emotion state is extracted from the path, the mimicry representation of the virtual pet is driven to be displayed on the interactive interface, visual feedback is formed, emotion connection is enhanced, and the user retention rate is increased.

This invention provides a probabilistic modeling method and system for characterizing cell capture performance in single-cell immunoblotting (scWB). First, considering the random cell settling process in a polyacrylamide gel microwell array under gravity, a probabilistic distribution model of the number of non-empty microwells is constructed. The evolution of the number of non-empty microwells under different cell loading amounts is analyzed, and the model's calculation results are validated using single-cell capture experimental results. Considering the constraint that trace protein bands in adjacent microwells may overlap, a probabilistic distribution model of the effective number of microwells is established, and a method for calculating the average number of effective microwells is given, revealing the variation of the average number of effective microwells with the spacing between microwell columns under different cell loading amounts. This invention provides a theoretical basis for further optimization of microwell array structures and significantly improves the cell sample utilization rate of scWB technology.

The invention relates to a visible light and synthetic aperture radar (SAR) image registration method based on a Gaussian process and a Transform, which effectively solves the problem of accurate registration of a repetitive scene of a different-source image by combining the feature refinement capability of the Transform and the probability modeling capability of the Gaussian process. According to the specific scheme, a visible light image, an SAR image training data set and a to-be-registered image are manufactured, a Gaussian process is used for embedding higher dimensionality after a feature pyramid is processed, Fourier transform is carried out, repetitive features are mapped to a specific region of a frequency domain, a plurality of matching candidates are generated, and a matching result with the highest probability of global judgment fitting is obtained. Then refining features from a large amount of image data by using a Transform module, screening a corresponding relation with high confidence, and training the network by taking cross entropy as a loss function; then, image registration can be carried out on a visible light image and an SAR image by using the network, and a transformation matrix is obtained;

The invention relates to the technical field of machine vision and intelligent perception, in particular to a line laser center positioning method and system based on column-level probability modeling, and the method comprises the steps: obtaining an input image containing line laser, and carrying out the normalization processing of the input image; based on a preset line laser mask image, constructing a column-level supervision label used for representing laser existence and center position offset of each image column, wherein the column-level supervision label comprises a laser existence confidence label and a center offset label; a line laser prediction network is trained by using the column-level supervision label, and the line laser prediction network takes the normalized image as input and outputs the confidence coefficient and the central position offset prediction value of each column of line laser; and inputting an input image into the trained line laser prediction network, outputting the confidence coefficient and the center offset prediction value of each column of line laser, and reconstructing a line laser center track. According to the invention, efficient and controllable prediction of the center position of the line laser can be realized, and the technical requirements of real-time detection and continuous center extraction are met.

This invention discloses a video anomaly detection method based on scene-conditional normalized flow. The method includes the following steps: Step A, processing a continuous video frame sequence to obtain a foreground target sequence; Step B, dividing the foreground target sequence into blocks to obtain a block-level feature sequence; Step C, obtaining complete spatiotemporal structural information features; Step D, obtaining high-level semantic foreground features; Step E, extracting overall scene information corresponding to the foreground target from the original video frames; Step F, performing conditional probability mapping modeling on the scene features; Step G, mapping the foreground target sequence extracted from the video to be detected to the latent space and determining whether the corresponding foreground target or video is an anomalous event. This invention, through probabilistic modeling, effectively avoids the misjudgment problem caused by uniformly modeling the same behavior in different scenes in existing methods. The overall method is simple and can effectively reduce interference from background areas and irrelevant pixel information.

The invention relates to the field of medical data processing, and discloses an imaginary medical data generation method, which comprises the following steps: S1, constructing a dynamic medical knowledge graph containing rare disease knowledge; s2, constructing a virtual patient model, and carrying out probability modeling on a disease development process; s3, using a deep learning algorithm to generate virtual patient data under the guidance of the virtual patient model, and synchronously recording associated knowledge path metadata; s4, performing post-processing on the generated data; s5, providing the data to a medical diagnosis platform; s6, establishing a performance feedback module, and collecting and evaluating performance indexes of the downstream medical diagnosis model; and S7, executing attribution analysis and adaptive optimization. According to the method, the medical logicality of the data is ensured through the strong constraint of the knowledge graph, and the continuous iterative improvement of the data quality according to the application effect is realized through a closed-loop feedback mechanism.

The invention discloses a Bayesian learning-based tensor interpolation decomposition method and device, and the method comprises the following steps: S1, expanding a multi-order tensor into a matrix according to one dimension, carrying out the clustering of the matrix, extracting a certain number of column vectors from different clustering types, and organizing the column vectors into a skeleton matrix; s2, regarding the skeleton matrix as a factor matrix in CP decomposition, and regarding the remaining factor matrixes as weight matrixes; carrying out probability modeling on tensor interpolation decomposition by utilizing leaf bass learning, and adding prior to the tensor; s3, setting prior for the weight matrix, drawing a probability graph model according to the prior of the weight matrix, and exporting model posterior distribution; s4, using a Gibbs sampling algorithm to carry out posterior approximate calculation of the weight matrix; and S5, calculating a model error value based on the tensor approximate value, if the model error value is smaller than a set threshold value, outputting a weight matrix, and otherwise, carrying out iteration on the step S4. The method has the effect of high interpretability.

The invention belongs to the technical field of industrial Internet of Things, and discloses a multi-sensor-based intelligent inspection method and system for a yarn twisting machine bearing, and the method comprises the steps: collecting data according to a multi-source sensor installed on a yarn twisting machine inspection robot, and obtaining the state information of the robot; according to the state information of the robot, the robot is driven to move to reach the to-be-detected bearing set of the yarn twisting machine; acquiring temperature distribution and acoustic signals of a to-be-detected bearing pack of each yarn twisting machine through an infrared thermal imaging sensor and an acoustic sensor mounted on a yarn twisting machine inspection robot; and fault diagnosis is carried out on the bearing pack according to the temperature and the acoustic signal. According to the method, a positioning framework of deep fusion depth probability modeling and physical law constraint is adopted, and the problem of high-precision pose estimation in an industrial isomorphic scene is effectively solved through a data-driven feature learning and environment priori knowledge collaborative optimization mechanism; and the practical engineering problem of fault early warning of the yarn twisting machine bearing is solved.

The invention discloses a strategy generation method and system for participation of an iron and steel enterprise in an electric power-carbon coupling market, and belongs to the technical field of industrial energy system optimization, and the method comprises the steps: obtaining multi-source heterogeneous data of the iron and steel enterprise; dividing a plurality of energy consumption units in the iron and steel enterprise into at least one production agent according to the energy consumption and carbon emission characteristics; probabilistic modeling is carried out on the energy consumption and carbon emission of the intelligent agent based on a production plan, and the adjustable potential of the intelligent agent is determined; fusing the market price information and the intelligent agent adjustable potential, and constructing an optimization model with the goal of comprehensive cost minimization and carbon quota income maximization; and solving by adopting a multi-agent reinforcement learning algorithm, and generating an optimal transaction and production adjustment strategy of the iron and steel enterprise participating in the electricity-carbon coupling market. The method solves the problems that a traditional method is difficult to dynamically respond to market fluctuation and cannot collaboratively optimize electric power cost and carbon assets, and provides scientific and efficient collaborative decision support for iron and steel enterprises.

The invention provides a soft measurement modeling method based on a VMD-BiLSTMA-GPR model, and relates to the field of artificial intelligence, and the method comprises the steps: S1, obtaining the direct measurement data of a process variable and the offline detection data of a target variable; s2, performing variational mode decomposition on the data of the target variable to obtain a series of intrinsic mode function subsequences and a residual sequence; s3, adopting a bidirectional long short-term memory network and self-attention mechanism fusion model to train and predict each intrinsic mode function sub-sequence and the residual sequence obtained in the step S2; s4, superposing the prediction results of the sequences obtained in the step S3, and reconstructing to obtain a point prediction value of the target variable; and S5, based on the point prediction value obtained in the step S4, adopting a Gaussian process regression algorithm to carry out probability modeling on the prediction residual error, and outputting an interval prediction result with a specific confidence interval. According to the method, high-precision point prediction and reliable interval prediction of variables difficult to measure in a complex industrial process are realized.

The invention provides a power system low-carbon economic dispatching method based on probabilistic carbon emission flow. The method comprises the following steps: determining input data of a probabilistic carbon emission flow model; constructing a carbon flow database according to the input data; performing random injection and linear regression modeling on the carbon flow database to obtain an updated database; probability carbon emission flow calculation is carried out on the updated database to obtain a probability density function; calculating a dynamic node electricity price based on a probability density function; performing demand response adjustment on the dynamic node electricity price to obtain a response node load; and performing low-carbon economic dispatching optimization on the power system according to the probability density function and the response node load. According to the method, iterative feedback optimization scheduling is carried out on the basis of combination of probability modeling and a price mechanism, and low-carbon economic operation of a full-power system is realized.

The invention discloses a micro-grid operation random optimization method based on source load probability prediction. The method comprises the following steps: firstly, performing energy-sensitive self-organizing segmentation on source-load historical time sequence data, and extracting morphological fingerprint features; and adopting an improved affinity propagation clustering algorithm fused with a power system operation constraint penalty mechanism to identify a typical operation mode. Secondly, a quantile regression model based on a gated pulse neural P system is established in each mode for probability prediction, and a probability scene library with weights is generated; and finally, constructing a two-stage stochastic optimization model with the goal of minimizing the expected operation cost, and solving by adopting a Benders decomposition algorithm to obtain a fixed equipment plan and a flexible operation strategy. According to the method, through refined mode recognition and probability modeling, on the basis of fully considering the uncertainty of the source load, robust optimization of economic operation of the micro-grid is realized, and the expected operation cost of the system is effectively reduced.

A processor-implemented method for soft example-by-example invariance includes receiving, by an artificial neural network (ANN), an input. The ANN selectively performs transforms on the input to generate reconstructed inputs that are invariant to a subset of the set of transforms. The ANN generates an inference based on the reconstruction input.

This application relates to a method and device for drone target selection based on Markov games and Bayesian optimization. The method includes extracting drone target feature data, sparsifying the target feature data based on the distance between the drone and the target drone, generating a global representation, constructing a Markov game model, defining a reward function as the objective function for Bayesian optimization, probabilistically modeling the objective function using a Gaussian process as a proxy model, and iteratively solving the optimal reward function output value. Based on the optimal reward function output value, the Markov game model outputs the drone target selection result. This method can optimize drone selection in complex situations.

The present invention discloses a method and device for dividing a practical stability domain of a wind power grid-connected system considering probability characteristics, which relates to the field of data processing technology. By performing probability modeling analysis on the uncertainties of the active power and load power of a wind farm, a first probability parameter of the active power change and a second probability parameter of the load power change are obtained. The probability parameter item is updated based on the first probability parameter and the second probability parameter. When it is determined that the value of the probability parameter item reaches a specific critical value, it is judged whether the Hessian matrix of a differential equation group at an equilibrium point in an equilibrium point set is a singular matrix. If it is a singular matrix, a bifurcation analysis is performed on the wind power active power and the load reactive power of a target node to obtain a bifurcation curve and each bifurcation point. The practical stability domain is divided based on each bifurcation point and the bifurcation curve, so that the influence of the uncertainty of the wind power injected active power and the load reactive power on the practical stability domain is taken into account when dividing the practical stability domain.

The invention provides a civil aviation operation-oriented airspace probability modeling and risk quantification method, which uses ADS-B data to construct layered space-time probability density, adopts an anisotropic kernel which is widened along the course and tightened in the transverse direction, and realizes short-time smoothing by using a time kernel. Inverse detection probability correction is introduced into the sample weight to reduce the coverage deviation. The reference density is subjected to multi-day exponential weighting generation according to hour data so as to describe day and night laws and workday characteristics. Under an information geometry framework, a relative entropy item, a Fisher information item, a capacity penalty item and a conflict kernel item are combined into a unified risk functional, and calculation of density evolution and a congestion dissipation direction is realized by a Wasserstein gradient flow. The system is composed of a data preprocessing module, a detection probability correction module, a layered anisotropic space-time kernel density estimation module, a reference density generation module, a risk functional and gradient flow module and an increment updating and index output module. The technology has unified modeling and clear interpretable and engineering landing characteristics, and can be used for situation analysis and collaborative decision support of air routes, sectors and terminal areas.

A method for probabilistic modeling for intention estimation in an operator-robot teleoperated assembly task is provided. The method may estimate the assembly task to be completed, wherein the assembly task comprises a sequence of actions. The method may predict a next action of the sequence of actions to be performed for the assembly task to be completed.

This invention discloses a stochastic optimization scheduling method for power systems based on LSTM-t distribution, belonging to the field of power system scheduling technology. The method includes: 1) constructing a two-layer LSTM wind power prediction model, obtaining predicted wind power values ​​and residual sequences after data preprocessing and model training; 2) fitting the residual sequences using a t-distribution, determining the distribution parameters through maximum likelihood estimation, and verifying the fitting effect using Q-Q plots and K-S tests; 3) generating initial wind power scenarios based on the t-distribution parameters through Monte Carlo simulation, reducing them to typical scenarios through K-means clustering, constructing a two-stage chance-constrained stochastic programming model with constraints, and solving the scheduling scheme using the Gurobi optimizer. This method accurately characterizes the heavy-tailed nature of wind power prediction errors, achieves deep integration of LSTM prediction and probabilistic modeling, and synergistic optimization of energy storage and thermal power, significantly improving the economy and robustness of power system scheduling.

The present invention discloses a coding and decoding method and system with controllable entropy decoding complexity. The method uses a highly scalable and complexity-controllable entropy decoding scheme, so that the decoding of semantically structured code streams can support the complexity requirements of any external setting, thereby adapting to intelligent analysis tasks in different application scenarios, and improving the versatility and flexibility of the semantically structured image coding and decoding scheme. At the same time, each target in the encoding process can adopt entropy coding reference dependency relationships of different complexities for probabilistic modeling, further enhancing the flexibility and scalability of the coding and decoding scheme, making it more suitable for real application scenarios.

The invention relates to the technical field of intelligent traffic, and discloses a multi-target path recommendation method considering congestion probability and application thereof, and the method comprises the steps: firstly calculating the congestion probability of a road section based on the probability distribution of a travel time index TT I, constructing a congestion amplification coefficient to correct a classical BPR function, and obtaining more accurate expected travel time; secondly, constructing a generalized road section impedance unified model fusing time value, energy cost, road toll and road type penalty; a toll station delay model and a bypass threshold mechanism are introduced in a path layer to screen effective candidate paths; and finally, taking minimization of total time cost and economic cost as double targets, obtaining a Pareto optimal solution set by using a multi-target genetic algorithm, and calculating comprehensive effectiveness in combination with user preference to perform final recommendation. According to the method, the congestion risk is quantified through probabilistic modeling, dynamic and personalized optimal path recommendation under the multi-dimensional cost constraint is realized, and the accuracy, practicability and user satisfaction of path planning are remarkably improved.

This invention discloses a DDoS attack detection method and system based on VAE-SAC collaboration, belonging to the field of network security detection technology. The invention includes probabilistic modeling of normal network traffic based on VAE, modeling DDoS attack detection as a Markov decision process, and performing feature extraction and VAE state representation calculation on real-time network traffic. This invention uses a variational autoencoder to probabilistically model the high-dimensional statistical features of normal network traffic, mapping the input features to a Gaussian distribution in the latent space, and calculating the normal deviation index based on the reconstruction error and the KL divergence of the latent space. The hybrid reward function, which integrates sparse label rewards and VAE deviation rewards, helps solve the policy optimization problem of reinforcement learning in sparse label environments, improves the stability and sample efficiency of model training, facilitates the synergy between feature extraction and decision optimization, and enhances the detection capability of DDoS attacks.

The invention discloses a volume rendering technology-based interactive three-dimensional visualization reconstruction system for a liver and gall system, which belongs to the technical field of medical image three-dimensional visualization and comprises an organ perception optimization transfer function adaptive optimization module, a multi-level adaptive ray casting rendering module, a virtual anatomy interaction control module and a quantitative measurement labeling module. An organ perception optimization transmission function adaptive optimization module performs probability modeling on CT value distribution of different tissues of the liver and gall system based on a Gaussian mixture model, and automatically generates an optimization transmission function; the multi-level self-adaptive ray casting rendering module adopts GPU acceleration and a self-adaptive sampling strategy to realize efficient volume rendering; the virtual anatomy interaction control module supports plane cutting and transparency adjusting operation; and the quantitative measurement labeling module provides distance, angle and volume measurement functions, and triggers adaptive adjustment of an optimized transmission function through a closed-loop feedback mechanism, so that the display quality and interaction performance of three-dimensional visualization of the liver and gall system are remarkably improved.

The invention discloses an infrared image compression method based on Mama linear recursive scanning, and belongs to the crossing field of computer vision image processing and infrared thermal imaging technologies. According to the method, an end-to-end deep learning framework is adopted, and the method comprises the steps that firstly, global physical parameter locking and normalization preprocessing are carried out; and 2, feature extraction and coding based on a thermodynamic state space module. And 3, carrying out super-prior feature analysis and probability modeling. And 4, entropy parameter prediction based on the bionic pulse decoder. And step 5, performing sub-pixel resolution reconstruction and physical field inversion. According to the method, through a full-process penetration and linear inversion mechanism of global gain and offset factors, based on the linear complexity advantage of the Mama architecture, and in combination with a four-way scanning causal deviation elimination and sub-pixel convolution artifact-free up-sampling strategy, reasoning delay and video memory occupation are remarkably reduced on the basis of ensuring global thermal field topological continuity, and the method has the advantages of high efficiency and high reliability. And a real-time and high-fidelity infrared image compression solution is provided for resource-limited edge end equipment.