62 results about "Computational probability" patented technology

The invention discloses a hierarchical contrast anti-fact learning method and system for a visual question and answer model. The method comprises the steps of obtaining original image data and question text data, constructing multi-modal joint tensor data, calculating probability distribution based on a projection matrix, screening samples and dynamically adjusting weights. Constructing a multi-modal graph structure, performing modal alignment based on the feature enhancement matrix, and executing dynamic feature fusion; establishing a multi-level probability knowledge structure, generating an anti-fact sample, and executing knowledge fusion operation; constructing a multi-level distance measurement system, executing adaptive weight optimization, and performing spectral clustering optimization and boundary adjustment; and constructing a multi-dimensional evaluation index system, executing stability analysis and dynamic performance tracking, and obtaining final performance evaluation index data. According to the method, the performance of the visual question and answer model is improved through a hierarchical comparison anti-fact learning framework, and optimization of feature representation, dynamic adjustment of a knowledge structure and accurate evaluation of the model performance are realized.

The invention discloses a cable joint health state assessment method and system based on multi-dimensional data fusion, and relates to the technical field of cable state assessment, and the method comprises the steps: collecting cable joint operation parameters, unifying a time sequence, and forming a two-dimensional matrix; performing spectral analysis on the operation parameters, and calculating energy characteristics of each sub-band by applying multi-stage wavelet decomposition; and constructing a Bayesian network structure, and calculating probability distribution according to the energy characteristics of the sub-bands. According to the method, spectral analysis is carried out through Fourier transform, the effect of identifying main frequency components and noise characteristics in signals is achieved, the effect of extracting energy characteristics of the signals in different frequency ranges is achieved by decomposing a two-dimensional matrix into a plurality of sub-bands by applying multi-stage wavelet decomposition, and the method has the advantages of being simple in structure and convenient to operate. By constructing a Bayesian network structure and defining feature nodes and state nodes, the health state evaluation of the cable can be dynamically updated according to data collected in real time.

The invention discloses a co-evolution method for distributed heterogeneous flexible job shop scheduling, which designs a three-section type coding and full-active decoding method based on three coupling sub-problems, and constructs a search space for the distributed heterogeneous flexible job shop scheduling problem. On the basis of a key path and key block theory, various neighborhood structures are designed for three-section type coding and serve as individual search directions; a deep reinforcement learning technology is utilized to perform feature extraction on a key factory, and a training algorithm is designed to train an intelligent agent, so that customized neighborhood structures can be selected for different individuals, and the diversity of a population is increased; a probability matrix is calculated in the training process, a high-quality population is generated in the co-evolution process, and convergence of the algorithm is accelerated through crossover and mutation operators among sub-populations. The method can also adapt to different scales and complexity problems through learning and optimization of the intelligent agent, and the overall performance and the resource utilization rate of a production system are remarkably improved.

Systems and techniques for are described herein. A content passage is received from a corpus of training data comprising labeled training data and unlabeled training data. A query is received from a query hierarchy for a classification domain. The content passage and the query are embedded to form a passage-query pair. A predicted result for the passage-query pair is generated based on a calculated probability of the predicted result being within an answer threshold. A passage-query-result triplet is generated that comprises the passage-query pair and the predicted result according to the query hierarchy for the classification domain. Vectors of the content classification large language model are updated using the passage-query-result triplet.

The present disclosure details systems, computer program products, and methods for machine engine analysis of network interaction data to identify conflicts. This involves the activation of an intelligence engine that connects to a backend data engine to access a database of conflict of interest (COI) datasets. The intelligence engine analyzes incoming applicant data and the COI datasets to identify potential conflicts. The identified conflicts are validated using stochastic metrics via a rules engine. A quantifiable probability metric is calculated, representing the probability of conflict from both organizational and individual perspectives. Based on the potential conflict of interest, a recommendation is generated that aligns with the calculated probability. The system then provides recommendations via API or on a user interface that is communicatively coupled to the processor.

The invention discloses an electric power work order visual management early warning detection system and method, and the system comprises a data preprocessing module which is used for preprocessing historical work order data; the first model training module is used for training a first model based on the preprocessed historical work order data to extract features; the feature fusion module is used for fusing features extracted by the first model to construct a prediction model; the real-time data processing module is used for inputting the real-time data into the prediction model, extracting the most probable early warning label and calculating a probability difference; the result output and model optimization module is used for outputting a result and iteratively optimizing a prediction model according to real-time data; wherein the prediction model adopts a uniflow model to fuse text, audio and structured features, and optimization is carried out through a Transform layer and cross entropy loss. According to the system, the data quality is improved, text semantics are accurately captured, time sequence and local features are mined, real-time early warning is achieved, a model is dynamically optimized, and efficient operation and maintenance of power enterprises are assisted.

In one example, the component analyzer calculates an individual part score for each part of the platform based on the property table and an individual connection score for each connection of the platform based on the connection property table. The component analyzer provides the individual part scores and the individual connection scores as score data to an architecture modeling engine to calculate a probabilistic model based on the score data and the architecture model. The probabilistic model includes part probability values ​​and connection probability values, and the architecture model characterizes a target architecture of the platform. The fault tolerance analysis engine evaluates the probabilistic model and the architecture model to determine the likelihood that one or more potential cyber attacks against the platform based on the target architecture will be successful or unsuccessful in compromising at least a portion of the platform.

The invention relates to a missing mode robustness enhancement training method based on multi-mode probability shape-preserving distillation, and the method comprises the following steps: obtaining a first multi-mode training set with a complete mode, defining an auxiliary indication vector to represent whether the mode is missing or not, and constructing a second multi-mode training set with mode missing; respectively mapping each input mode in the second multi-mode training set into independent D-dimensional normal distribution, and fusing to obtain fused D-dimensional normal distribution; end-to-end distillation is performed based on a self-distillation framework, in a training stage, a teacher model processes a first multi-modal training set and provides complete modal representation, and a student model processes a second multi-modal training set and provides complete modal representation supervision. And on the basis of the independent D-dimensional normal distribution of each input mode, the D-dimensional normal distribution and the complete mode representation are fused to calculate probability extremum optimization loss and geometric consistency optimization loss, and a loss function is constructed to carry out model training. Compared with the prior art, the method has the advantages that the effectiveness of information migration can be improved, and the processing capacity of the model to the missing mode is improved.

The invention provides a game situation deduction and path reasoning method and device based on a dynamic knowledge graph, and a storage medium, and the method comprises the steps: collecting multi-source game data, and obtaining a multi-modal data set; establishing a dynamic knowledge graph according to the multi-modal data set; based on the dynamic knowledge graph, utilizing a Bayesian network to carry out probability dependency relationship modeling to obtain a probability graph model; calculating the confidence of each path in the probability graph model according to parameters corresponding to a set initial event, and generating a high-confidence reasoning chain set; performing situation evolution prediction on the key indexes in the high-confidence reasoning chain set by using a pre-trained time sequence model to obtain a plurality of situation prediction results; performing multi-scene simulation on each situation prediction result, and determining evaluation information of each situation prediction result; and generating a Pareto optimal strategy path set according to the evaluation information of each situation prediction result. By means of the scheme, the intelligent level of game situation prediction can be improved, and more reliable and efficient technical support is provided for various complex game decision-making scenes.

The invention belongs to the technical field of path planning, and particularly provides a multi-agent path planning method based on an attention mechanism, and the method comprises the following steps: S1, selecting path planning training data, and constructing a training set; s2, respectively designing an encoder and a decoder by using an attention mechanism, and constructing a path planning model based on an encoder-decoder structure; s3, designing a reinforcement learning framework based on a rollback reference, and constructing a multi-agent task-oriented reward function; and S4, inputting the data set obtained in the S1 into the path planning model constructed in the S2, training the path planning model by using the reinforcement learning framework designed in the S3, and applying the trained model to multi-agent autonomous path planning. According to the multi-agent path planning method, node high-dimensional feature representation is obtained through an encoder, probability distribution is calculated through a decoder, the model is trained through a reinforcement learning framework combined with a rollback reference, the multi-agent path planning cost is reduced, and the path planning quality is improved.

The invention provides an urban power distribution network probabilistic load flow calculation method for large-scale access of demand side resources, and belongs to the technical field of power system power distribution network analysis and optimization. The method comprises the following steps: constructing a dynamic Copula model in combination with space-time sequence analysis, introducing a Markov chain, and carrying out multi-dimensional uncertainty coupling modeling; a probabilistic power flow-multi-objective optimization model is constructed, and energy router cooperative control is carried out based on distributed model predictive control; sparse expansion is carried out by adopting error feedback adaptive sampling and sparse Bayesian learning in combination with polynomial chaos expansion, and probability power flow is calculated through OpenDSS; establishing a three-phase probabilistic power flow model and a DG-EV-DR collaborative probability model; and evaluating the extreme scene risk, and visualizing the result through a digital twin platform. According to the method, the accuracy and practicability of distribution network probabilistic load flow calculation in a demand side resource large-scale access scene are effectively improved, and a risk quantification basis is provided for power grid dispatching.

To provide a residual life prediction with high accuracy.SOLUTION: A residual life prediction device comprises: a simulation processing part 102 for simulating signal intensity with respect to an inspection condition being a factor of variation of a defect detection signal by a non-destructive test, and a defect condition being a condition of a defect which is an object of the non-destructive test; a probability distribution estimation part 104 for estimating a probability distribution related to the defect condition estimated from actual signal intensity, on the basis of an analysis result by the simulation processing part 102 and actual signal intensity being signal intensity of a response signal obtained by an actual inspection; and a residual life prediction part 105 for calculating a time change of the probability distribution related to the defect condition estimated by the probability distribution estimation part 104, estimating as a residual life, a time period where an overlapping part between the probability distribution related to the defect condition after lapse of a prescribed period, and a preset destruction probability distribution of an inspection object, satisfies a prescribed condition, then outputting the estimated residual life to an output part.SELECTED DRAWING: Figure 1

The invention provides a deep neural network model fingerprint generation and verification method based on decision consistency, and the method comprises the steps: inputting an original sample to a source model, generating a boundary sample through a gradient-based optimization method, carrying out the feature shielding of the boundary sample through a random binary mask matrix, so as to generate a fingerprint sample set, the size of the mask matrix is dynamically adjusted according to the feature complexity of the data set; respectively inputting the fingerprint sample set into a source model and a suspicious model, obtaining output probabilities of boundary samples and fingerprint samples of the two types of models, calculating a probability difference, and respectively calculating feature attribution weight matrixes of the two types of models through regularization linear regression; carrying out binarization processing on the weight matrix according to positive and negative of elements; obtaining binary weight matrixes corresponding to the source model and the suspicious model, and calculating element-by-element decision consistency similarity of the source model and the suspicious model; and when the decision consistency similarity does not exceed a preset threshold, determining the consistency of the two models.

Provided is a method for correcting bias introduced by weighted training in machine learning, comprising: labeling the number of examples of each class in weighted data used by a machine learning classifier; adding a weight to training data, and calculating a weight wij of each data example j in class i of the training data according to a user-given data weighting method; calculating a mean weight wi for examples of each class; conducting classification and logistic regression against features of the examples in the weighted data and labels corresponding to the features; after training, when calculating probabilities Pw(i) of class after machine training using the machine learning classifier, correcting the probabilities P(i) by applying a deweighting formula to obtain corrected probabilities P(i); making a classification decision based on the corrected probabilities P(i). The method improves the accuracy of classifiers in assigning probabilities to new data in machine learning applications.

The invention provides a method for automatically labeling work order types on the basis of agents, which comprises the following steps of: performing punctuation standardization processing on an original work order, and converting spoken and non-standardized work order texts into segmented word segments conforming to field specifications in combination with word segmentation in a power field dictionary; unifying and normalizing the segmented word segments through a preset synonym mapping table to obtain a standardized text sequence; the standardized text sequence is input into a bidirectional encoder expression model to output a semantic vector sequence, and deep semantic understanding of the work order text is achieved; related external information is called to be coded into a feature vector, and then the feature vector is fused with the semantic vector sequence through an attention mechanism to generate an enhanced semantic vector; multi-dimensional label prediction tasks are executed in parallel based on a multi-task learning architecture, and multi-class labels and probability distribution are output; the confidence coefficient is obtained by calculating the maximum value of the probability distribution, and the preset process is executed, so that automation and quality management and control of label generation are realized, and the problem of low efficiency of manual power work order processing in the prior art is solved.

The invention provides an available power transmission capacity evaluation method considering new energy uncertainty, which relates to the field of new energy, and comprises the following steps: firstly, based on output characteristics and historical data of new energy power generation, establishing a probability model of fan power generation and photovoltaic power generation; thirdly, establishing a probabilistic available power transmission capacity calculation model associated with the power transmission network by utilizing a continuous power flow model; and finally, a proxy model is established based on a data-driven polynomial chaos expansion method and is used for rapidly calculating a probability available power transmission capacity model, and the evaluation precision and the calculation efficiency of the power transmission network under the uncertainty of new energy are improved. By comprehensively considering uncertainty factors of new energy power generation, a scientific and reliable basis can be provided for scheduling and planning of a power transmission system.

Certain aspects involve building timing-prediction models for predicting timing of events that can impact one or more operations of machine-implemented environments. For instance, a computing system can generate program code executable by a host system for modifying host system operations based on the timing of a target event. The program code, when executed, can cause processing hardware to a compute set of probabilities for the target event by applying a set of trained timing-prediction models to predictor variable data. A time of the target event can be computed from the set of probabilities. To generate the program code, the computing system can build the set of timing-prediction models from training data. Building each timing-prediction model can include training the timing-prediction model to predict one or more target events for a different time bin within the training window. The computing system can generate and output program code implementing the models' functionality.

The invention discloses an end-to-end sparse trajectory recovery method and device based on road network constraint, electronic equipment and a storage medium, and solves the problem of error accumulation of an existing map matching and trajectory completion two-stage method. The method comprises the following steps: acquiring a sparse trajectory and road network data; and constructing a unified end-to-end deep learning model, and deeply coupling map matching and track completion tasks. The core is that after a candidate road segment set is generated for each GPS point and the probability is calculated, a differentiable virtual road segment embedding mechanism is introduced, and a gradient return channel from track completion to map matching is constructed through probability weighted candidate road segment embedding. The trajectory decoding module fuses the spatio-temporal features with virtual road segment embedding to generate a complete trajectory. The model performs end-to-end optimization through a joint loss function, so that a trajectory reconstruction supervision signal directly guides an upstream candidate selection process. According to the method, error accumulation caused by one-way information transmission in a traditional method is avoided, and the global precision, continuity and robustness of track recovery are remarkably improved.

The invention discloses a named entity recognition method and system based on multi-target learning, and relates to the technical field of natural language processing. The method comprises the steps of obtaining a source text sequence and a target text sequence; according to the source text sequence, the prediction language model and double affine transformation, constructing a span representation matrix to obtain a representation coding layer; according to the span representation matrix, the inter-span relative position information and the matrix region information, span feature representation is constructed by using cavity convolution and a nonlinear activation function, and a span dependency layer is obtained; according to the span feature representation and a multi-layer perception mechanism, calculating a span category probability to obtain a span classification task; and according to the span feature representation, the automatic regression bidirectional transducer and the multi-layer perception mechanism, calculating a probability maximum value, obtaining a boundary generation task, finally obtaining a multi-target learning named entity recognition model, and performing named entity recognition on a target text sequence. According to the method, the problem that span boundary feature representation is fuzzy due to context sharing is solved.

The application discloses a kind of electric information collection intelligent configuration method, it is related to power distribution automation technical field, including, convergence equipment account, topology, historical operation and configuration record are standardized, form data set and candidate template;Extract operation, topology, equipment and alarm features, build comprehensive feature vector;With comprehensive features and candidate template respectively by SVM, random forest and CNN calculate matching probability, form probability matrix;With odds ratio, consistency coefficient and historical success rate fusion calibration, get robust matching probability;Sensitivity detection is carried out to core operation index, together with matching probability determines optimal template, and according to three levels segmentation, in turn, under the parameter of roll-back is sent out.This application determines optimal template by the ingenious combination of the probability calculated by three models, ensures the reliability and accuracy of optimal template.

The application discloses a simulation job scheduling method and system based on a hybrid expert model and a medium, and relates to the field of data processing.In the method, the job parameters of a simulation job to be scheduled and host state information of computing nodes in a cluster are acquired; a feature vector representing job features and cluster states is generated based on the job parameters and the host state information; the feature vector is input into a routing module in the hybrid expert model to obtain the probability and probability distribution of the simulation job relative to a plurality of preset subject experts; the information entropy of the probability distribution is calculated, and the information entropy is compared with a preset information entropy threshold; if it is determined that the information entropy is less than the preset information entropy threshold, a target subject expert with the highest probability is determined from the plurality of preset subject experts to obtain a scheduling decision; and the scheduling decision is sent to a job executor on a target host, so that the job executor executes the simulation job according to the scheduling decision.Implementation of the technical solution provided by the application improves the accuracy of the scheduling decision.

The invention is suitable for the technical field of well drilling safety monitoring, and provides a well drilling overflow prediction method and system based on an expert network model, and the method comprises the following steps: carrying out the Fourier transform of preprocessed multi-dimensional time series data, dividing a frequency range into a plurality of non-overlapping frequency bands, carrying out the frequency domain mask processing, and obtaining a frequency domain mask; recovering the signals into time domain signals corresponding to the frequency bands; respectively inputting the time domain signal of each frequency band into a corresponding expert network model, and outputting a feature vector of the frequency band; global pooling is carried out on feature vectors output by the expert network models, and probability weights are calculated; according to the feature vectors output by the expert network models and the probability weights corresponding to the feature vectors, carrying out weighted fusion to obtain fused feature vectors; and inputting the fusion feature vector into a classification head, and outputting an overflow prediction probability. According to the method, through collaborative learning of frequency domain decomposition and the expert network, random noise and burr interference are effectively suppressed, and the reliability and interpretability of overflow prediction are remarkably improved.

A method includes obtaining a first input instance and a first class corresponding to the first input instance, from an input instance dataset. The method further includes calculating a probability distance score based on a set of probability values by a distance function generator to transform the first input instance. The method further includes processing the first input instance and the probability distance score by a counterfactuals generator to generate a counterfactual instance. The method further includes executing a machine learning model to process the counterfactual instance and assign a second class, different from the first class, to the counterfactual instance.

The invention discloses a long-tail target detection method based on expanded data feature distribution variance, which solves the problem that inter-class and intra-class feature distribution in a long-tail distribution data set is unbalanced, and comprises the following steps: acquiring the long-tail distribution data set; the method comprises the following steps: extracting original backbone features by adopting ResNet-50-FPN; through a feature cluster compression method, the tail class cluster is more compact by using an adaptive scaling factor, and the separation degree of the tail class cluster and the head class cluster is kept; calculating a probability weight based on the number of category samples, and introducing a dynamic bias term to expand a feature distribution variance; the offset is adjusted in combination with the Euclidean distance, and the dispersion and stability of the sample are balanced; the processed features are used for model training, and the original features are input during testing to obtain a detection result. According to the method, through a cooperative strategy of feature cluster compression and bias term calculation, inter-class discrimination is enhanced, intra-class feature diversity is improved, the long tail problem is effectively relieved, and the detection precision and generalization ability of the model to the tail class are improved.

Systems, methods, and computer-readable media for providing low-latency markets for wagering on sporting events are disclosed. In some embodiments, an outcome matrix may be generated by a plurality of contest simulations utilizing statistical data of a history of sporting events. Outcome data of the outcome matrix may be indicative of probabilities of events occurring during an upcoming contest. The probabilities may be calculated, and markets may be priced based on the calculated probabilities. The priced markets may be provided to users by a graphical user interface by user computing device. Furthermore, users may request user-requested markets by inputting different markets into the GUI. The user-requested markets may then be priced using the outcome data of the outcome matrix and stored calculations thus, providing new markets based on the user-requested markets from the outcome matrix without generating new simulations.

A method to provide a model-assisted inline wafer-level inspection during high volume manufacturing is disclosed. More particularly, a method for using a computational model to generate fingerprint wafer defect maps and then guide wafer selection for inline inspection is disclosed. A computational probability prediction model is disclosed to generate defective die probability estimates with improved accuracy and versatility to guide different wafers for inspection.

The invention discloses an Ethereum network account classification method and device based on a graph neural network, and the method and device can capture a low-order neighborhood mode and a high-order neighborhood mode at the same time, improve the richness and discrimination of node representation, achieve the local adaptive feature transformation, effectively cope with challenges caused by the diversity of feature distribution in a disparate graph, enhance the discrimination capability of account behavior modes, and improve the user experience. Key modes and features in account prediction are better captured, and the prediction accuracy is improved. The method comprises the following steps: (1) representing an account and a transaction behavior as a graph structure; (2) inputting the feature information into a KAN to extract Fourier features of a transaction, extracting a global feature mode of an account, and enhancing periodic information of transaction data; (3) after feature enhancement is obtained, performing neighbor aggregation by using GraphSAGE, and extracting an account low-order neighborhood mode; and (4) inputting the last layer of GraphSAGE for classified output, and calculating probability distribution through softmax.

The application provides a radar signal open set identification method based on SoftMax information entropy, research is carried out on the basis of SoftMax probability distribution, and the probability distribution information entropy is calculated for unknown discrimination; the information entropy is calculated based on the known type SoftMax probability distribution of output; the uncertainty degree of the identification result is reflected through the information entropy; whether the uncertainty degree of the identification result is higher than the preset is judged; if yes, the identification result of the radar signal to be identified is updated as an unknown radar signal category and is output; otherwise, the radar signal category with the highest probability value in the identification result is output. Compared with directly using the maximum probability, the threshold discrimination interval is improved, so that the unknown sample can be more easily identified; the application does not need additional network design and training, is simple to realize, has high real-time performance, has good open set performance, can be applied to any network based on the SoftMax layer classification, has wide application range and does not bring too much time consumption, can be used immediately, and has good practicability and real-time performance.

The invention provides a smart grid false data injection attack detection method, a terminal and a storage medium, and relates to the technical field of power system information security. The method comprises the steps that heterogeneous measurement data of a target smart grid are collected, a three-dimensional space-time tensor is constructed based on the heterogeneous measurement data, and the heterogeneous measurement data comprise node measurement data, branch measurement data and network topology data; inputting the three-dimensional space-time tensor into a constructed attack feature extraction model, and outputting a target feature corresponding to the three-dimensional space-time tensor; based on the target features, calculating an attack prediction probability, and based on the attack prediction probability, determining whether the target smart grid is subjected to a false data injection attack; and when the target smart power grid is subjected to the false data injection attack, calculating a probability distribution vector by using the target features, and determining an attack type of the false data injection attack on the target smart power grid according to the probability distribution vector. According to the invention, the accuracy and reliability of detection can be improved.

The invention belongs to the field of natural language processing, and particularly relates to the field of large language models, in particular to a subject education large model knowledge boundary exploration method. In order to solve the problems of capability decline and illusion of a large model in a special field, the method comprises the following steps: firstly, acquiring a token generation probability based on a softmax function by utilizing a large language model principle, then designing an algorithm to calculate probability volatility and measure reply confidence, and then setting a data set and carrying out manual annotation and statistics to obtain a volatility threshold in a knowledge boundary. And finally, performing specific instruction fine tuning, labeling data, replacing original replies of data outside the boundary to serve as a fine tuning data set, and correcting model knowledge boundary cognition. According to the method, through evaluation of model reply and knowledge boundary exploration, knowledge outside the boundary is effectively extracted, the model is finely adjusted, illusion caused by knowledge deficiency is reduced, and the practicability and accuracy of the model in the field of subject education are improved.