16 results about "A priori probability" patented technology

The invention discloses an airport operation robustness decision-making method and device based on stochastic optimization. The method comprises the following steps: constructing a low-altitude intrusion situation feature space through multi-source heterogeneous sensing data; on the basis of the feature space, constructing a runway recovery time Wasserstein uncertainty set capable of dynamically adjusting the boundary so as to describe uncertainty; establishing a distribution robust optimization model for coupling aircraft position distribution and ground support facility scheduling; a column constraint generation algorithm is used for solving, and decision making and closed-loop feedback are executed in a rolling time domain mode. The method does not need to depend on prior probability distribution of interference events, adaptive balance of decision robustness and economical efficiency can be achieved, the problem of space-time mismatching is effectively avoided through resource coupling scheduling, and improvement of the operation recovery efficiency and safety of an airport under high uncertainty is facilitated.

The invention provides a communication method and device, and the method comprises the steps: determining rewards corresponding to K actions based on a reward model, and K is a positive integer; and sending first information to a first network device, the first information being used for indicating rewards corresponding to the K actions, and the first information being used for updating prior probabilities corresponding to the K actions. The first network device obtains which of the actions (or actions) can obtain a higher reward, so that the first network device can make a better decision. Moreover, the award is higher in accuracy compared with the prior probability, so that the decision making based on the updated probability is higher in effectiveness and reliability compared with the decision making based on the prior probability corresponding to each action.

The application relates to the field of natural language processing, and discloses a risk text accurate semantic recognition method based on an improved deep learning model, which comprises the following steps: calculating the displayable width of a target segment by a character width counting rule and setting an upper limit, and establishing a trigger variable that automatically changes with a delivery condition; defining a display function under the width constraint to generate visible texts in different trigger states. Further, a trigger variable group is determined for each visible text, and a collision rate index is calculated based on the prior probability of the trigger variable, which is used to quantify the overlap degree of the multi-trigger mapping. Then, according to the index, masking sampling is performed after the trigger position to generate an input sequence, and segment representation is obtained through multiple sampling of the deep learning model; then, an adjustment parameter is calculated according to the collision rate index to adaptively adjust the temperature and noise of the model, and finally, a risk score is obtained through input of a classification layer.

The application relates to a causal DAG discovery method for an online service system based on fusion of soft priori. The method comprises the following steps: obtaining observation data and text meta-knowledge of the service system, preprocessing to form a standardized sample set, identifying variable types and semantics and outputting; generating a natural language description according to the variable semantics, querying a large language model for an ordered variable pair, analyzing to obtain three types of causal probability vectors, and calibrating to obtain an edge-level priori probability. An appropriate conditional independence test method is selected, high-confidence independent / dependent sentences are divided, and weights are assigned. A candidate directed acyclic graph is selected as an initial structure, parameters are estimated by linear regression, and data fitting scores are calculated, language priori scores, conditional independence penalty terms and counterfactual self-consistency penalty terms are calculated. Fusion is carried out into a hybrid score function, discrete optimization is carried out under the constraint of a directed acyclic graph, and a causal graph structure with the optimal score is output. The method can improve the efficiency and accuracy of a smart operation and maintenance system.

The present application relates to the technical field of data processing, and is a content data active exploration method and system based on cognitive graph and trace inference. The method comprises: encoding a multi-modal input signal of target content data into a trigger vector, and matching the trigger vector with a content cognitive ontology graph to anchor a target attribution node; automatically generating competitive hypotheses of each target attribution node in the content cognitive ontology graph, and constructing an exploration path; calculating the utility value according to the prior probability and the execution cost of each exploration path, and iteratively executing the exploration path in the order of utility value from large to small, updating the posterior probability of each competitive hypothesis in real time, and outputting an attribution diagnosis conclusion according to the posterior probability of each competitive hypothesis in response to the satisfaction of a preset termination condition. The present application can actively understand and verify the causality of content data, and improve the attribution accuracy of content data.

The invention provides a blasting vibration peak velocity prediction method fusing parameter uncertainty and data-driven optimization, which comprises the following steps of: firstly, acquiring data such as blasting parameters, lithologic indexes, geological conditions and actually measured vibration peak velocity (PPV), and establishing a basic database; a prior probability model is constructed, a joint uncertainty model is established in combination with probability disturbance and a fuzzy triangular number, a multi-dimensional disturbance sample is generated through a joint central value and a joint standard deviation and is fused with original data, and an extended database is formed. Feature analysis is carried out on the fused data, and input variables which have obvious influence on the vibration peak velocity (PPV) are screened; and constructing a BP neural network model based on the screened features, carrying out global optimization on a network weight and a threshold by adopting a PSO algorithm, and then carrying out local fine tuning by utilizing Adam. Finally, through training and verification, prediction of the vibration peak velocity (PPV) is realized, and model precision is evaluated through RMSE, MAE, MAPE, Rand other indexes. The influence of rock and soil parameter uncertainty on prediction precision can be effectively processed, and the reliability and applicability of blasting vibration prediction are improved.

The application discloses a device fault detection method and device and a storage medium. In one aspect, the prior probability of occurrence of each fault cause is determined based on the operation data of a screw compressor, and the likelihood probability of occurrence of a target fault under the condition of occurrence of each fault cause is determined, the posterior probability of occurrence of each fault cause corresponding to the occurrence of the target fault is calculated according to the prior probability and the likelihood probability, and the detection order of all fault causes when the target fault occurs is determined according to the size of the posterior probability of occurrence of each fault cause. In another aspect, the time sequence data of a characteristic parameter is monitored to determine whether the preset range under the current working condition is met, so that the reliability of subsequent fault causes is improved.

The application discloses a method and system for representing sigmoid probability distribution based on a memristor, and relates to the technical field of the memristor, and comprises the following steps: receiving a sigmoid curve extracted from a memristor unit, and marking the sigmoid curve as a probability storage sigmoid curve; receiving input data, quantifying the input data into a probability value, and marking the input data as a prior probability; mapping the prior probability to a corresponding gate-end voltage value through the probability storage sigmoid curve, storing the gate-end voltage value to a node memristor array, and marking the gate-end voltage value as a storage probability value; quantifying the storage probability value to obtain a voltage signal, inputting the voltage signal to a weight memristor array, obtaining an output current, inputting the output current to a next node, and thus realizing a sigmoid belief network; and the application can directly represent the sigmoid probability distribution by using the current limiting effect of a 1T1R unit transistor, and simultaneously stores the probability value at the gate end.

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

This invention provides an intelligent security risk prediction method and system based on a multimodal large model. The method includes the following steps: collecting video, audio, and sensor data, and outputting a unified event token sequence; generating natural language causal descriptions for the event token sequence, constructing an event causal graph with event tokens as nodes, causal descriptions as edges, and outputting logits as edge weights, and dynamically updating the graph structure; obtaining the prior probability of node risk using a Bayesian neural network, fusing the correlation information between nodes through a fusion graph neural network message passing mechanism, and using Do-Calculus intervention loss calibration to obtain the node risk probability; using a Continuous-Time Markov Network to infer future risk trends; outputting a decision report and executing a tiered response; and updating the end-to-end model under privacy protection through federated learning. The beneficial effects of this invention are: achieving cognitive-level risk identification and proactive intervention in complex scenarios; significantly reducing false alarm and false negative rates, and enabling early warning.

This invention discloses an artificial intelligence-based method and system for scheduling maintenance resources in hydropower stations, belonging to the field of power system maintenance and intelligent scheduling technology. By applying the Explosive Search algorithm and integrating it with a three-layer optimization decision model, it is applied to the scheduling of maintenance resources in a target hydropower station, forming a nested optimization architecture. The Explosive Search algorithm performs a global search for maintenance resource scheduling schemes, while the three-layer optimization decision model evaluates the minimum comprehensive maintenance loss of each scheme under different fault scenarios. This collaborative optimization effectively avoids the subjectivity of prior probability distributions and significantly improves the ability of scheduling schemes to resist the randomness of faults. Furthermore, by constructing a digital model linking maintenance tasks and resources, global observation and overall coordination are achieved for various potential maintenance equipment and resources in the target hydropower station, improving the comprehensiveness of fault scenario generation and the accuracy of maintenance resource scheduling scheme confirmation.

This invention discloses an image stitching and matching method based on neighborhood topological similarity and prior probability sampling. The method includes obtaining initial feature points in two images to be stitched; selecting calibration points from the initial feature points through brute-force matching to obtain calibration point pairs; performing a triangular network topology on the calibration points to obtain a triangular topological network of the image; obtaining neighborhood topological vectors of the calibration points through the triangular topological network; combining the neighborhood topological vectors of all calibration points in the same image to be stitched into a neighborhood topological matrix; calculating the prior probability and sampling probability of each calibration point pair using the neighborhood topological matrix; determining the optimal transformation matrix based on the calibration point pair with the higher sampling probability; and stitching the two images to be stitched together using the optimal transformation matrix. This invention introduces the concepts of prior probability and sampling probability, making calibration points with a high probability of mismatch less likely to be selected, effectively improving the efficiency and accuracy of mismatch removal of calibration points.

This invention discloses a method for modeling the average failure rate of aircraft equipment for maintainability design, belonging to the field of aircraft maintainability design and reliability engineering technology. The method includes: S1, object definition and data standardization; S2, similarity analysis and equivalent sample generation; S3, construction of the average failure rate a priori probability model; and S4, Bayesian posterior update and dynamic output. This method for modeling the average failure rate of aircraft equipment for maintainability design constructs a set of equipment difference features, quantifies the comprehensive difference between the target equipment and reference equipment of mature aircraft models, and combines a credibility discount factor to form a scientific information transfer weight. It transforms the historical failure information of the reference equipment into the equivalent failure sample size and equivalent sample exposure of the target equipment. This overcomes the limitation of insufficient measured data for new aircraft models and avoids systematic bias caused by simply applying data from similar aircraft models, allowing the massive historical data of mature aircraft models to be reliably utilized by new aircraft models.

The application provides a reservoir physical property parameter prediction uncertainty evaluation method, comprising the following steps: step 1, establishing a statistical rock physics model of a target area, thereby deriving a conditional probability of seismic attributes; step 2, performing statistical analysis on existing physical property parameter data, and establishing a corresponding physical property parameter prior distribution; step 3, based on the statistical rock physics model and the prior probability distribution obtained in steps 1 and 2, using a physical property parameter simulation sampling algorithm of facies variation, performing facies prediction and generating a physical property parameter sample set of the target area; and step 4, according to the physical property parameter sample set obtained in step 3, estimating the expectation, variance and confidence interval of the physical property parameter. The reservoir physical property parameter prediction uncertainty evaluation method realizes the statistical expectation, variance and confidence interval estimation of the shale content, porosity and saturation in the facies variation reservoir, further evaluates the potential risk of the target reservoir development, and provides technical support for the risk analysis of oil and gas exploration and development.

This application provides a method, system, equipment, and storage medium for analyzing the jamming risk of tunnel boring machines (TBMs), relating to the field of tunnel boring technology. The method includes: obtaining the causal hierarchy of construction risk factors for TBMs; converting the causal hierarchy into Bayesian network topological constraints; constructing a three-layer temporal risk Bayesian network based on the Bayesian network topological constraints, comprising a bottom-level causal layer, an intermediate symptom layer, and a top-level event layer; performing hybrid parameter learning on the three-layer temporal risk Bayesian network based on historical observation data to obtain the prior probability of the bottom-level causal layer and the conditional probability of the entire network; and performing dynamic risk deduction based on the prior probability of the bottom-level causal layer and the conditional probability of the entire network to identify the evolution path of the target risk. This application effectively improves the accuracy of TBM jamming risk assessment by explicitly and accurately quantifying the coupling effect of multiple factors through conditional probability.

The application provides a particle filtering method based on state trajectory clustering similarity and application, and comprises the following steps: S1, initialization; S2, assuming a prior probability density distribution as an importance probability density function, that is, a proposal distribution guides particles to perform sequential importance sampling, and then the importance weight of the sampling particles is recursively calculated; S3, combining the consistency principle of the system true state and the sampling particle state trajectory, similarity measurement is performed on the current filtering and the future multi-stage Gaussian filtering predicted measurement information by using a data mining thought, that is, a clustering analysis method; S4, the proposal distribution in S2 is modified by using the similarity measurement in S3; S5, the importance weight calculation in the sequential importance sampling process in S2 is updated by using the proposal distribution modified in S4, the particle weight is normalized, and then the estimation value of the system state at the current moment is obtained; S6, steps S2-S5 are repeated, and the estimation values of the system state at different moments are sequentially output. The application can improve the particle degradation phenomenon.