33 results about "A priori probability" patented technology

The purpose is to appropriately manage abnormal communications with a simpler configuration. [Solution] The abnormality management device 1 includes a learning unit 12 that uses the characteristic directions of normal data as training data to learn, by maximum likelihood estimation, parameters of a probability model that outputs the posterior probability that the number of packets received in each time period corresponding to each of the characteristic directions of the normal data is normal; a derivation unit 13 that derives a probability distribution for the characteristic directions of abnormal data that indicates the number of abnormal packets received in each time period that deviates from the range of normal packet numbers, based on the posterior probability estimated by the probability model learned by the learning unit 12, the probability distribution for the characteristic directions of the normal data, and the prior probability of normality; and a calculation unit 14 that calculates a first index value that indicates the degree of spatial agreement formed by the probability distribution for the characteristic directions of the abnormal data derived by the derivation unit 13 and the probability distribution for the characteristic directions of the normal data.

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.

A method for generating a target function is provided. The method includes: performing normalization processing on a vector corresponding to each pixel in a target feature map set to generate a target vector, so as to obtain a target vector set; generating hash coding corresponding to each vector in the target vector set, to obtain a hash coding set; determining a prior probability of each hash coding in the hash coding set; and generating a target function based on an entropy of the prior probability.

The invention relates to a method and apparatus for single epoch position bounding. A method for determining a protection level for a position estimate using a single epoch of GNSS measurements, the method comprising: specifying a prior probability density of states x P(x); specifying a system model h(x) relating states x to measured observations z; quantifying a quality metric q associated with the measurements; specifying a non-Gaussian residual error probability density model f(r|θ,q) and fitting the model parameters θ using a set of experimental data; defining a posterior probability density P(x|z,q,θ); estimating states x; and calculating the protection level by integrating the posterior probability density P(x|z,q,θ) over states x.

The invention discloses a binary directional grey box fuzzy test method and system based on regional probability accessibility. The method comprises the following steps: S1, recovering a potentially missing indirect edge according to memory layout characteristics of a binary file; s2, obtaining a matching similarity score for each indirect edge, taking the matching similarity score as a corresponding prior probability, and calculating an accuracy probability of each indirect edge; s3, clustering into different regions according to the degree that the accessibility is influenced by the indirect edge, and constructing a region graph; s4, calculating the depth in the region and the connectivity between the regions according to the probability that each indirect edge is correctly recovered, and calculating the probabilistic reachability score of each path; and S5, performing optimal configuration on the fuzzy test according to the probabilistic reachability score of each path. According to the method, the directional fuzzy test based on reachability analysis under the binary condition can be effectively realized, the test strategy is adaptively adjusted when the control flow diagram dynamically changes, and the test efficiency and precision are balanced.

The invention provides a fault identification method and device and a program product, and relates to the technical field of data processing, the method comprises the following steps: obtaining at least one first node state parameter of a network node, the network node being a network node having a fault; calculating posterior probabilities of a plurality of fault types based on a conditional probability table and the at least one first node state parameter through a preset network model, the conditional probability table comprising prior probabilities and conditional probabilities between each fault type in the plurality of fault types and different node state parameters, the preset network model is used for calculating a posterior probability of a fault type according to a prior probability, and the plurality of fault types are a plurality of fault types when the network node fails; and determining a first fault type based on the posterior probability, wherein the first fault type is the fault type with the maximum posterior probability in the plurality of fault types. According to the invention, the fault identification accuracy can be improved.

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 invention relates to the technical field of financial science and technology, and provides a claim assessment method, device, equipment and medium, and the method comprises the steps: obtaining multi-dimensional historical observation data of an insured object of a to-be-assessed insurance policy, and constructing historical observation features matched with the insurance policy type of the to-be-assessed insurance policy according to the multi-dimensional historical observation data; according to the historical observation characteristics, determining a prior probability that the to-be-evaluated insurance policy has a specified claim settlement event; obtaining multi-dimensional current observation data of the insured object, and constructing a current observation feature matched with the insurance policy type according to the multi-dimensional current observation data; and determining a target likelihood value of the current observation feature relative to the specified claim event, and correcting the prior probability according to the target likelihood value to obtain a posterior probability of the specified claim event of the to-be-evaluated insurance policy. The method can be applied to an insurance claim settlement evaluation task involved in the financial science and technology field, and the evaluation precision can be improved.

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.

This invention belongs to the field of wireless communication technology, specifically relating to a multi-user signal processing method for low-Earth orbit satellite communication. The method includes: acquiring a pre-compensated digital domain hybrid signal y; arranging the standard symbol sets of N users corresponding to y into a combined symbol set of N user combined signals s according to a preset user order; iteratively calculating the likelihood probability of all possible combinations of symbols corresponding to each element in y; and calculating, in the current iteration, the transmitted signal x of user n in the hybrid signal y based on all likelihood probabilities and the prior probability of each user updated through iterative feedback. n The posterior probability of all transmitted bits being 0 is calculated; for each user's posterior probability vector obtained in the current iteration, deinterleaving and channel decoding are performed; if the iteration terminates, a decision is made and the channel-decoded probability is output; otherwise, the channel-decoded probability is used as the new prior probability for that user, and the iteration is repeated. This invention ensures demodulation bit error rate performance.

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 invention discloses a search direction autonomous decision-making method fusing prior probability and a large language model, which belongs to the field of autonomous decision-making, and comprises the following steps of: firstly, aiming at a scene traversal problem caused by an unknown target position, performing scene search sequence decision-making through logical reasoning based on environment prior knowledge; generating an optimal search sequence as global guidance; and then, scene matching is realized through prior probability analysis and big language model reasoning based on visual observation, a region of interest is obtained based on semantic correlation evaluation of a big language model, and autonomous decision-making in the search direction is realized.

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 invention relates to a segmented polynomial LDPC (Low Density Parity Check) decoding method, which comprises the following steps of: calculating a prior probability of channel output data, and generating a variable node matrix according to the prior probability of the channel output data in combination with a random construction method; calculating a check node expression through a complex function according to the variable node matrix, and performing approximation on the complex function by using a piecewise polynomial to obtain a check node matrix; and calculating the posterior probability of the channel output data according to the check node matrix, judging the posterior probability of the channel output data through soft judgment, if a check rule is met, completing decoding, otherwise, updating the variable node matrix again according to the check node matrix and the prior probability of the channel output data until decoding is completed. According to the method, the decoding performance and the implementation complexity are effectively balanced, and the problems that hardware implementation of a traditional BP algorithm is difficult, the complexity of a Log-BP algorithm is high and the performance loss of a Min-Sum algorithm is large are solved.

The invention relates to the technical field of power transformation equipment, in particular to a power transformation equipment state evaluation probability graph network method and system considering operation conditions. The invention provides a power transformation equipment state evaluation probability graph network method and system considering operation conditions. The method comprises the following steps: acquiring original state parameters and operating environment data of the power transformation equipment; thirdly, establishing a state transition network, and training and solving an initial state and a prior probability of a model parameter through a deep learning algorithm; meanwhile, a working condition self-adaptive feature coding module is constructed to code the online state parameters to obtain a working condition prior probability. And finally, performing statistical evaluation by using a graph network reasoning algorithm, and outputting a result. According to the method, key problems in the prior art are effectively solved, and remarkable progress is made in the aspects of accuracy, adaptability, efficiency and interpretability. The method provides powerful technical support for improving the safety and reliability of the power grid, and has important theoretical value and wide application prospect.

The present application belongs to the technical field of tunnel and underground engineering geological detection, and discloses a method and device for sensing the lithology of a shield face, comprising the following steps: (1) selecting samples within a predetermined distance from a borehole in a sample point set to form a training sample set, and the remaining samples constituting a test sample set; (2) mapping the fluctuation scale of the vertical thickness of the lithology to an anisotropy scale factor in spatial interpolation, and then calculating the weight of each sample in the training sample set; (3) constructing an input feature vector; training a machine learning nonlinear model based on the input feature vector and a prior probability field to obtain a lithology real-time sensing model; (4) fusing the prior probability field and the sensing probability of the samples in the test sample set sensed by the lithology real-time sensing model to obtain a final lithology probability, and then reconstructing the shield face according to the final lithology probability. The present application improves the sensing accuracy.

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.

The invention provides an accident cause traceability early warning method and system based on an accident tree and a Bayesian network, and the method comprises the steps: constructing an accident tree model; converting the accident tree model into a Bayesian network model by using a predefined mapping relation; node prior probability: obtaining the prior probability of each leaf node, and obtaining the prior probability of each intermediate node based on a conditional probability table and the prior probability of each leaf node corresponding to each intermediate node, based on a conditional probability table and the occurrence probability of each root node corresponding to each intermediate node, obtaining the prior probability of the root node; priori early warning: performing early warning on an accident by utilizing the occurrence probability of a root node in the Bayesian network model; the method integrates the logic clearness of the accident tree and the bidirectional reasoning advantage of the Bayesian network, accurately locates the disaster source, quantifies the influence weight of each cause factor, and provides a scientific and effective disaster prevention, control and emergency management decision basis for a coal mine.

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.

Certain aspects of the disclosure provide for dynamically routing traffic in A / B testing environments. In examples, a method may include receiving reward data associated with each respective variant of a set of variants corresponding to continuous values representing an outcome measure for the respective variant; generating a posterior probability density function for each respective variant in the set of variants based on the reward data for the respective variant and a prior probability density function for the respective variant; performing a sampling operation to generate an expected value of reward associated with each variant in the set of variants; assigning new traffic proportionally across each respective variant in the set of variants based on the expected value of reward associated with the respective variant; and based on the assigned new traffic, causing content associated with a variant of the set of variants to be provided to a user interface.

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 user activity detection and user channel estimation method, an electronic device and a storage medium, and belongs to the technical field of communication. The method comprises the following steps: establishing a transmission system model of mMTC uplink; constructing a prior probability model according to the transmission system model and user activity; establishing a joint variable according to the user activity and the angle domain of the system channel; inputting the joint variable into the prior probability model to obtain the logarithmic probability density of the joint variable; performing likelihood probability calculation according to the pilot signal and the joint variable to obtain the logarithmic likelihood probability of the pilot signal with respect to the joint variable; constructing a maximum a posteriori probability problem according to the logarithmic probability density and the logarithmic likelihood probability; and performing maximum minimum solving on the maximum a posteriori probability problem to obtain the user activity detection result and the user channel estimation result. The scheme of the application can improve the accuracy of user activity detection and user channel estimation, and has good universality.

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.