82 results about "Probabilistic computing" patented technology

A large language model (LLM) may be used to classify an input into one of a plurality of categories. However, given the machine-learning operation of the LLM, the output of the LLM does not represent a definitive statement, but is based on probability computations of the machine learning model. Therefore, the classification performed by the LLM might not be correct. Classification into the wrong category by the LLM results in downstream technical problems. In some implementations, when an LLM generates a response that classifies an input, one or more probability values associated with a token that forms the basis of the response may be used to determine a confidence value. The confidence value is indicative of confidence in the classification performed by the LLM. An action may be taken based on the confidence value.

Computer-implemented methods and systems are disclosed for Information Technology Service Management (ITSM). The pioneering AI-driven system revolutionizes IT service management by uniquely validating, suggesting, and inferencing incident and problem data. At its core are cutting-edge generative AI techniques like GANs and LLMs, requiring intricate training and iterative refinement on varied data sets, showcasing a depth of expertise in database structures and AI concepts. It bridges critical gaps in incident resolution and classification through cognitive computing, AI, NLP, and deep learning, applied to both historical and current data. The system comprises modules for Incident Validation & Classification, Resolution Validation, Generative Intelligence, Problem Probability Calculation, and Prevention Recommendation, each employing AI to enhance standard compliance, predictive analysis, and proactive management, thereby setting new standards for IT service management efficiency and effectiveness.

The invention provides an urban park risk toughness assessment method and system based on probabilistic reasoning, and the method comprises the steps: carrying out the recognition of risk factors of a park target region, and building a risk toughness assessment index system suitable for a park; constructing a park risk toughness assessment Bayesian network model according to the risk toughness assessment index system and the dependency relationship of the influence factors in the system; determining prior information of the conditional probability of a root node in the Bayesian network by adopting a fuzzy Bayesian method, and initializing model parameters to obtain the prior probability of the root node of the Bayesian network model; the non-root node conditional probability of the Bayesian network model is calculated, and the risk transfer relation between the nodes is quantified; and based on the actual situation of the park, performing reasoning calculation on the target node and the related father node to obtain a park risk toughness evaluation result. The method can scientifically evaluate the toughness characteristics of different areas, especially parks, in a city, and provides a decision basis for improving the toughness of the city.

The invention provides an automatic data exception root cause positioning and playback repair method and system and related equipment. The method comprises the following steps: constructing a dynamic heterogeneous topological graph containing data logic nodes and physical resource nodes in real time, and establishing real-time directed dependency connection between the nodes; in response to a monitored abnormal signal, generating an anti-fact intervention set assuming that an upstream node is in a reference state by using a Do operator based on the dynamic heterogeneous topological graph; the anti-fact intervention set is substituted into a causal reasoning model for simulation, and the abnormal maintenance probability of the abnormal signal still existing under the condition that the anti-fact intervention set takes effect is obtained; and calculating a causal contribution degree according to the exception maintenance probability to lock a root cause node, and transmitting state data of the root cause node to a repair module to trigger playback repair. According to the invention, the accuracy and stability of abnormal root cause positioning are improved.

A large language model (LLM) may be used to classify an input into one of a plurality of categories. However, given the machine-learning operation of the LLM, the output of the LLM does not represent a definitive statement, but is based on probability computations of the machine learning model. Therefore, the classification performed by the LLM might not be correct. Classification into the wrong category by the LLM results in downstream technical problems. In some implementations, when an LLM generates a response that classifies an input, one or more probability values associated with a token that forms the basis of the response may be used to determine a confidence value. The confidence value is indicative of confidence in the classification performed by the LLM. An action may be taken based on the confidence value.

The invention discloses a fault tree Boolean function equivalent mapping method based on an untrained neural network, and relates to the field of fault tree analysis. In order to solve the problems that in the prior art, a Boolean function mapping structure is not beneficial to parallel expansion, the calculation efficiency is limited, and the Boolean function mapping structure is difficult to efficiently realize on high-parallel platforms such as a GPU, the invention provides a method for generating topological structure data by analyzing a fault tree model; the basic events, the intermediate events and the top events are mapped into neurons of an input layer, a hidden layer and an output layer respectively, a feedforward network with fixed weight and bias is constructed, and a logic activation function is defined in nodes to realize Boolean logic propagation. The input layer receives a basic event state vector, outputs a top event result through forward propagation, and can realize large-scale Boolean function mapping on a parallel platform through batch input matrixes. The method is suitable for reliability analysis, minimum cut set simplification, top event probability calculation, parallelization fault tree solving and the like of a large-scale complex system.

A pbit device, in one embodiment, includes a first field-effect transistor (FET) that includes a source region, a drain region, a source electrode on the source region, a drain electrode on the drain region, a channel region between the source and drain regions, a dielectric layer on a surface over the channel region, an electrode layer above the dielectric layer, and a ferroelectric (FE) material layer between the dielectric layer and the electrode layer. The pbit device also includes a second FET comprising a source electrode, a drain electrode, and a gate electrode. The drain electrode of the second FET is connected to the drain electrode of the first FET.

The automatic control method for oil and gas pipeline detection specifically comprises the following steps: step 1, providing basic data; step 2, predicting a future state based on the data; step 3, converting prediction into probability distribution; step 4, calculating a dynamic factor based on probability; by collecting physical attributes of oil and gas pipelines and detecting multi-dimensional state parameters of equipment, comprehensiveness and accuracy of scheduling decision making are achieved; the TCN network based on multi-head attention can effectively capture the time sequence dependency relationship of the pipeline and equipment states, so that the accuracy of state prediction is improved, and a reliable basis is provided for scheduling decision making; the factor graph model realizes probability quantitative analysis of the pipeline state and the equipment state, and can scientifically evaluate the execution risk of the detection task; a time decay factor and a residual electric quantity redundancy value factor are introduced, dynamic changes of task urgency and equipment electric quantity redundancy are considered, and task priority ranking is optimized; through a real-time feedback and dynamic adjustment mechanism, the scheduling scheme can adapt to the state change in the detection process, and the detection efficiency and the resource utilization rate are improved.

The invention relates to the technical field of building engineering construction, in particular to a building engineering construction collaborative management method and system based on the Internet of Things. The method comprises the following steps: acquiring multi-modal data of a construction site through Internet of Things equipment; constructing a construction knowledge graph; selecting a small amount of data marked with construction risks from multi-modal data as a sample data set to train an initial risk prediction model, generating a cross-modal consistency constraint extension training set through pseudo labels, and dynamically updating the risk prediction model based on an incremental learning method of a sliding window; inputting the construction knowledge graph into the heterogeneous graph neural network, and propagating and aggregating the characteristics of nodes and edges; identifying a risk causal chain in the construction knowledge graph, performing virtual intervention based on a causal inference method, calculating the intervention probability of subsequent node risks, and calculating the causal priority of each risk event according to the intervention probability; and retraining the risk prediction model according to feedback of the management platform.

The invention discloses a probability symbol generation method, a probability symbol operation method and equipment, relates to the field of digital signal processing, and solves the problems of high hardware complexity, low nonlinear operation efficiency, insufficient probability calculation precision and large conversion loss of traditional fixed-point calculation. According to the technical scheme, by fusing the advantages of fixed-point and probability calculation, a numerical value is divided into a high-bit fixed-point integer and a low-bit probabilistic part, a multi-bit probability symbol which meets the requirement for expectation unbiasedness and is controllable in variance is constructed, a class fixed-point, class probability and iterative calculation triple system is designed, and the probability of the multi-bit probability symbol is calculated. The multiplexing standard arithmetic unit processes the probability symbol stream to guarantee the linear calculation precision; single-stage multiply-accumulate operation and nonlinear interpolation are realized through a multiplexer, and discrete value selection and weighting are driven by probability; iterative resources are optimized in combination with dynamic filtering conversion. According to the method, a new normal form is provided for a high-energy-efficiency communication chip and a storage and calculation integrated framework, and digital signal processing is promoted to evolve towards the high-precision and low-power-consumption direction.

The invention discloses a maintenance strategy optimization and task assignment method for new energy equipment, and the method comprises the steps: collecting multi-source operation data of a new energy station, and carrying out the preprocessing of the multi-source operation data, so as to form an operation state data set; establishing an operation life and performance attenuation model to predict a fault probability, and calculating a power generation loss risk, a safety risk and a chain shutdown risk of each device of the station; identifying a maintenance opportunity window, and constructing a risk-opportunity joint index; aggregating the maintenance objects into a task package according to geographical proximity, an isolation relationship and an operation type, and determining a task step chain in the task package according to operation dependence and process logic; in a rolling time domain, an execution sequence and a time interval of a task package are optimized, a maintenance task plan and a resource scheduling scheme are generated, risk analysis, opportunity recognition, task aggregation, optimization scheduling and dynamic feedback are integrated, and intelligent decision making, real-time optimization and system collaboration of a new energy station maintenance process are realized.

The invention discloses an elevator prediction scheduling method and system based on user habit self-learning, and the method comprises the steps: carrying out the statistical analysis based on historical elevator taking event data, and judging whether a floor combination meeting a preset condition exists or not; combining all the independent floors with the floors meeting the preset conditions to serve as a self-learning unit; for each self-learning unit, updating the elevator calling probability of the respective learning unit by using an exponential weighted average algorithm; when the elevator does not have the real-time task, all elevator calling probabilities corresponding to the current time window are inquired, and the self-learning units meeting the triggering condition are extracted as a target candidate set; calculating an optimal pre-stop layer by utilizing a mathematical model based on the target candidate set, and calculating a comprehensive probability corresponding to the optimal pre-stop layer; and calculating an income evaluation function based on the comprehensive probability, and generating an instruction to drive the elevator to run to an optimal pre-stop layer to enter a prediction waiting state when an obtained value meets a condition.

The invention belongs to the technical field of data filling, and particularly relates to a data completion method based on inverse probability and computer equipment, and the method comprises the following steps: filling a missing data set by using a trained improved machine learning filling model, the improvement comprises that a loss function used for training the machine learning filling model is an improved loss function, a data set in the improved loss function is a data set obtained by weighting each piece of data, the weighted weight of the data is obtained according to a corresponding inverse probability, and the lower the inverse probability of the data is, the larger the corresponding weight is; the process of calculating the inverse probability of the data comprises the following steps: carrying out preliminary filling on a data set for training, calculating the missing probability of each piece of data in the data set subjected to preliminary filling, and calculating the inverse probability of each piece of data according to the missing probability of each piece of data in the data set. The filling capability of the machine learning filling model for non-random missing data is enhanced, and the filling precision of the machine learning filling model is improved.

This invention relates to the field of artificial intelligence technology, providing a text segmentation method, system, computer device, and storage medium, comprising: acquiring the text to be segmented; dividing each sentence of the text into multiple semantic blocks according to a left-to-right order, with the end point as the boundary; if two consecutive characters do not have a connected record in the vocabulary of the text to be segmented, then the preceding character of the two consecutive characters is recorded as the end point; performing full segmentation on each semantic block to obtain all possible segmentation methods for that semantic block; calculating the probability of traversing all segmentation methods for each semantic block in a left-to-right order, and selecting the segmentation method with the highest probability as the final segmentation result. The segmentation scheme of this invention is based on probability, traverses all solutions within a block, and comprehensively considers the context of the text, resulting in more accurate segmentation results, reducing labor costs, and improving segmentation accuracy.

The invention relates to the technical field of data processing, and provides an unsupervised power text grading rewriting method and system, and the method comprises the following steps: obtaining a to-be-rewritten original power knowledge point text and target user group identification information; selecting a continuous prompt vector group corresponding to the target group; inputting the original electric power knowledge point text and the continuous prompt vector group into a trained text rewriting model to obtain a rewritten text meeting the style requirement of the target population; in the text rewriting model training process, information reconstruction rewards and style conversion rewards are calculated through the estimated output probability of the large language model, the rewards and the comparison rewards form a total reward for unsupervised comparison learning, and a trained text rewriting model is obtained. An unsupervised comparative learning mechanism is introduced, information reconstruction and style conversion rewards are constructed based on the output probability of a large language model to train a text rewriting model, and personalized style rewriting of the electric power knowledge text is achieved.

The invention relates to the technical field of artificial intelligence, and discloses a data processing method and device, computer equipment and a storage medium, and the method comprises the steps: obtaining to-be-processed data, and carrying out the preprocessing of the to-be-processed data, so as to obtain prompt data corresponding to the to-be-processed data; the prompt data is input into a pre-trained complete credible classification model to obtain a classification result and a corresponding first classification probability and second classification probability, and the credible classification model comprises a classification sub-model and a confidence sub-model externally hung on the classification sub-model; and calculating the confidence coefficient of the classification result based on the first classification probability and the second classification probability. The method can be applied to business system platforms of financial science and technology, medical health and the like, and solves the technical problem that the accuracy of a large model prediction classification result cannot be evaluated in the prior art.

The invention belongs to the technical field of artificial intelligence, and provides a task planning method and device based on large model feedback optimization, and the method comprises the steps: converting the natural language description of a problem into a PDDL symbol sequence, and calculating an output probability corresponding to the PDDL symbol sequence according to an LoRA parameter; calculating a loss value between the output probability and the target PDDL symbol sequence so as to update LoRA parameters, and training to obtain a large language model with a planning generation capability; and performing executable verification on the candidate PDDL planning scheme output by the model, adjusting input context information input into the model by using an error log, and obtaining an executable planning scheme through an iterative verification process. According to the method and the device, new errors caused by full-amount rewriting are avoided by limiting the modification range, and solvability and verification pass of a planner are taken as a stop condition during iteration of the model, so that a result has evaluability and reproducibility.

The invention relates to a power network lightweight attack path generation method and system, and belongs to the technical field of attack path generation, and the method comprises the steps: obtaining the power network flow data of a target regional power grid in real time, carrying out the preprocessing, eliminating the sampling difference between heterogeneous devices, and obtaining the preprocessed time sequence aligned heterogeneous data; carrying out heterogeneous graph modeling based on the preprocessed time sequence aligned heterogeneous data to obtain a combined heterogeneous graph; wherein the heterogeneous graph modeling comprises the steps of extracting differentiated node features based on a source equipment type, and constructing edges with interaction features based on power business logic and a protocol type to form a protocol interaction element path; inputting the combined heterogeneous graph into a pre-trained potential attack path generation model to obtain a potential attack path probability; wherein the potential attack path generation model is obtained by performing joint adversarial training on the potential attack path generation model and the noise generation model; a suspicious path score is calculated based on the potential attack path probability, and an attack path is generated based on the suspicious path score.

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.

This invention provides a spatially parallel hybrid multiplier based on probabilistic computation and its operating method. The spatially parallel hybrid multiplier based on probabilistic computation includes a traditional multiplier and two high-precision probabilistic multipliers. Each high-precision probabilistic multiplier includes a random sequence generator, a random computation circuit, a probability estimator, and a two's complement conversion circuit connected in sequence. The spatially parallel hybrid multiplier based on probabilistic computation provided by this invention successfully utilizes the characteristics of probabilistic computation, implementing multiplication operations using only a few wires, logic gates, and single-bit addition, thus reducing computational complexity and resource overhead. Simultaneously, the spatially parallel hybrid multiplier based on probabilistic computation provided by this invention strikes a good balance between computational accuracy and hardware resources, ensuring computational accuracy while significantly reducing hardware resources. This saves area and reduces power consumption.

A large language model (LLM) may be used to classify an input into one of a plurality of categories. However, given the machine-learning operation of the LLM, the output of the LLM does not represent a definitive statement, but is based on probability computations of the machine learning model. Therefore, the classification performed by the LLM might not be correct. Classification into the wrong category by the LLM results in downstream technical problems. In some implementations, when an LLM generates a response that classifies an input, one or more probability values associated with a token that forms the basis of the response may be used to determine a confidence value. The confidence value is indicative of confidence in the classification performed by the LLM. An action may be taken based on the confidence value.

The invention provides a probabilistic nearest neighbor query method on high-dimensional continuous uncertain data, and relates to a method for quickly retrieving the nearest neighbor of the uncertain data by combining a locality sensitive hashing technology and probability calculation. The method comprises the following steps: firstly, constructing locality sensitive Hash for mapping, and mapping the uncertainty of a data object into a plurality of Hash tables through sampling; in a query stage, candidate adjacent objects are extracted from a given query point by using an index, and the probability that each candidate object becomes the nearest neighbor of the query point is calculated through Monte Carlo simulation. And finally outputting the neighbor object with the maximum probability and the probability value thereof. According to the method, the efficiency of high-dimensional uncertain data nearest neighbor query is greatly improved while the query accuracy is guaranteed, and the method can be widely applied to the fields of big data analysis, uncertain information retrieval and the like.

This application discloses an automatic control method for oil and gas pipeline inspection, specifically comprising the following steps: Step 1 provides basic data; Step 2 predicts future states based on the data; Step 3 transforms the predictions into probability distributions; Step 4 calculates dynamic factors based on probability; Step 5 achieves optimal scheduling using probability and factors; and Step 6 maintains scheduling timeliness through real-time feedback. By collecting the physical properties of oil and gas pipelines and multi-dimensional state parameters of inspection equipment, comprehensive and accurate scheduling decisions are achieved. A multi-head attention-based TCN network effectively captures the temporal dependencies between pipeline and equipment states, improving the accuracy of state prediction and providing a reliable basis for scheduling decisions. A factor graph model enables probabilistic quantitative analysis of pipeline and equipment states, scientifically assessing the execution risks of inspection tasks. The introduction of time decay factors and remaining power redundancy value factors considers the dynamic changes in task urgency and equipment power redundancy, optimizing task priority ranking. Through real-time feedback and dynamic adjustment mechanisms, the scheduling scheme adapts to state changes during the inspection process, improving inspection efficiency and resource utilization.

The application discloses a time sequence early classification method, a terminal device and a storage medium, constructs a training set by using time sequence data of human body actions; trains a neural network by using the training set; inputs the training set into the trained neural network to obtain classification probabilities of all moments of all data of the training set, calculates a probability exit threshold value by using the classification probabilities; inputs observable data of a moment t into the trained neural network to obtain a classification probability Pt of the moment t, takes a maximum value of the Pt, and if the maximum value is greater than the probability exit threshold value, stops inputting the observable data, and takes a classification result of the moment t as a final classification result of the observable data of the moment t. The application can adapt to continuously increasing new data, extract more distinguishing features, and improve the accuracy of early classification of time data; the application can adapt to sample content and difficulty, extract more class-specific features, and improve the accuracy of early classification.

The present invention discloses a method, system, device and storage medium for probabilistic analysis of available transfer capability, which includes obtaining probability calculation samples of random input variables; substituting the probability calculation samples of random input variables into an available transfer capability calculation model based on a radial basis neural network to solve the available transfer capability corresponding to each probability calculation sample of random input variables; and obtaining the probability distribution of the available transfer capability based on the available transfer capability and outputting it. The present invention can obtain an available transfer capability calculation model based on a radial basis neural network in an offline state. This model has the characteristics of accurate results and high calculation efficiency, and can be used for online probabilistic calculation and analysis of available transfer capability.

A large language model (LLM) may be used to classify an input into one of a plurality of categories. However, given the machine-learning operation of the LLM, the output of the LLM does not represent a definitive statement, but is based on probability computations of the machine learning model. Therefore, the classification performed by the LLM might not be correct. Classification into the wrong category by the LLM results in downstream technical problems. In some implementations, when an LLM generates a response that classifies an input, one or more probability values associated with a token that forms the basis of the response may be used to determine a confidence value. The confidence value is indicative of confidence in the classification performed by the LLM. An action may be taken based on the confidence value.

The invention discloses a probabilistic power flow algorithm based on a linear power flow model and a total probability formula, and aims at a scene that a conventional probabilistic power flow calculation method based on semi-invariant cannot be carried out due to the ill-conditioned Jacobian matrix in probabilistic power flow analysis of an active power distribution network. And an optimal state variable function is selected to improve the calculation precision. A total probability formula is introduced in probability calculation, N output interval combinations are obtained by performing segmentation processing on the new energy unit, and a joint conditional probability density function and each-order semi-invariant of the new energy unit under the N output interval combinations are calculated. The method comprises the following steps of: calculating a semi-invariant of a state variable based on a linear power flow model under each output interval combination, finally fitting a distribution function by adopting a Gram-Charlier (GC) series to obtain N conditional probability distribution functions of the state variable, and aggregating by adopting a total probability formula to obtain a complete probability density function. According to the method, the precision of probabilistic power flow analysis is improved, and the technical method has relatively high implementability.

The invention provides a model reasoning method and device and related equipment, and relates to the technical field of artificial intelligence, the model reasoning method comprises the steps that a target cue word is sent to second electronic equipment, the target cue word is used for enabling a reasoning model deployed on the second electronic equipment to conduct reasoning according to the target cue word, and the target cue word is sent to the second electronic equipment; generating a first reasoning result; the first reasoning result sent by the second electronic equipment is received, the first reasoning result is input into a verification model for probability calculation, a target probability is obtained, the target probability is used for representing the probability that the first reasoning result is the reasoning result corresponding to the target prompt word, and the target prompt word is sent to the second electronic equipment. The reasoning model is determined according to the verification model; and under the condition that the target probability is greater than an acceptable probability, determining the first reasoning result as a reasoning result corresponding to the target cue word. Therefore, the output efficiency of the reasoning result corresponding to the target prompt word can be improved.

A computer-implemented system and method for determining collapse states in quantum and probabilistic systems using wave-interference-based computation are disclosed. The system models collapse as a deterministic process arising from interaction between a system wavefunction and one or more observer or environmental wavefunctions. A modified Schrödinger-type formulation is used to compute a real-valued collapse intensity based on amplitude coupling and phase alignment between interacting wavefunctions. The system further aggregates the collapse intensity over a spatial domain to generate a scalar collapse measure, which is compared against a predefined threshold to determine collapse conditions. The invention enables simulation, prediction, and control of collapse behavior across quantum systems, probabilistic computation, and collapse-driven decision architectures.

The application discloses a risk assessment method and system based on fault semantic driving, a medium and a product, and relates to the field of power new energy equipment detection. According to the method, after a power new energy equipment detection system obtains a power equipment fault description text input by an operation and maintenance personnel, the power new energy equipment detection system matches the fault description text with fault semantic elements corresponding to nodes in a fault evolution track graph, and calculates the current risk intensity of the target node. At the same time, the power new energy equipment detection system calculates the current risk transmission value of the target node through a target directed edge to connect a downstream node according to an initiation probability corresponding to the target directed edge, and determines a current fault risk vector of the power equipment at a current time by combining an initial fault risk vector of the power equipment at an initial time, so as to determine a current risk assessment value of the power equipment. The fault diagnosis method based on risk propagation and accumulation can dynamically track and evaluate the fault diffusion process, and improves the accuracy and timeliness of fault detection.