124 results about "Graphical model" patented technology

The present invention provides a privacy-enhanced structured data simulation and generation method and system. The method comprises: step 1, a data conversion phase: performing normalization preprocessing on data; step 2, a probabilistic graphical model construction phase: on the basis of a Bayesian form, constructing the posterior distribution for variational inference from the data having undergone normalization preprocessing in step 1, obtaining an association relationship between the features of structured data by using a Stein variational gradient descent method, and when introducing differential privacy noise, using a Monte Carlo estimation algorithm to automatically obtain the amount of noise that needs to be added for each update step; and step 3: a data generation phase: using the association relationship obtained in step 2 as a metric set to generate more accurate simulation data than real data. The beneficial effect of the present invention is that: the method of the present invention avoids gradient clipping when applying DP-SGD, thereby avoiding the selection of clipping parameters and reducing the adverse effects of gradient clipping on an inference process.

The invention relates to the technical field of mode recognition, in particular to a transformer substation equipment temperature measurement method and system based on visible light guidance. Firstly, a visible light image, an infrared thermogram and vibration spectrum data are synchronously collected, a dynamic compensation coefficient matrix is constructed through illumination intensity analysis, the dynamic compensation coefficient matrix is applied to the infrared thermogram, and illumination interference compensation is achieved. And then, inputting the corrected infrared thermogram and visible light image into a pre-trained heterogeneous atlas matching model, and identifying the category and three-dimensional coordinates of the target equipment. And based on the equipment category information, calling a standard temperature field template in the digital twin model library, establishing a heat conduction path model in combination with the vibration spectrum data, and generating a temperature field prediction reference map of the equipment. And finally, through pixel-by-pixel deviation analysis, inputting the temperature deviation value, the image features and the vibration data into the probability graphic model, and outputting equipment fault information. The method can realize high-precision temperature measurement and fault diagnosis.

Systems and methods are for mitigating hindsight bias related to designing and using artificial intelligence models for outlier events. More specifically, systems and methods for the use of synthetic data in the training and / or validation of model predictions in order to prevent overfitting and generate predictions that attempt to predict the unpredictable.

The invention discloses a product collaborative design management method and system based on data driving, and relates to the technical field of product design management, and the method comprises the steps: constructing a double-flow heterogeneous quantitative collection network; establishing an incidence matrix and calculating dynamic coupling strength among the parameters; designing a recursive deep belief network based on the incidence matrix and the dynamic coupling strength, encoding product structure parameters and design process data into a probability graph model, and constructing a design knowledge base; dynamically distributing the design rules in the design knowledge base by adopting a swarm intelligent optimization algorithm, generating a collaborative decision-making unit, and establishing a constraint propagation link; and generating a multi-target collaborative optimization scheme group, and screening an optimal scheme group based on Pareto frontier. According to the invention, omnibearing acquisition of product structure parameters and design flow data is realized through the double-flow heterogeneous quantitative acquisition network, and the data integrity is improved.

The present disclosure relates to methods and systems that perform probabilistic reasoning on knowledge graphs. The systems and methods use path-based simulations over a knowledge graph to convert the knowledge graph into a probabilistic graphical model that supports probabilistic reasoning on the knowledge graph. The systems and methods use the probabilistic graphical model to discover paths of the knowledge graph in response to a query.

The invention relates to the technical field of software and hardware compatibility testing, in particular to a domestic software and hardware compatibility intelligent evaluation system and method, and the system comprises a compatibility analysis engine, a compatibility detection task execution engine, a software behavior analysis module, a fault positioning module, a knowledge graph module, a performance optimization suggestion module and a visual interface. The software behavior analysis module constructs a high-dimensional feature space of a software running state through deep learning, and extracts abnormal features based on a topological space mapping technology; the fault positioning module constructs a dynamic causal network based on a probability graph model, and identifies a root node through a chaos sensitivity analysis algorithm; the knowledge graph module constructs a software and hardware dependency relationship model and provides priori knowledge for fault location; and the performance optimization suggestion module generates an optimization scheme based on the root positioning result, so that the functions of automatically executing a compatibility test, accurately positioning the root of the compatibility problem and providing targeted optimization suggestions are realized, and the adaptation efficiency of domestic software and hardware is greatly improved.

The invention relates to the technical field of filter film testing, and discloses filter film thermal deformation testing equipment and a testing method. The method comprises the following steps: based on a test environment, carrying out a temperature cycle test on the filter film, and collecting a triple data set at the same time; performing image preprocessing and displacement field construction on the triple data set, and decomposing a displacement field into a linear combination of a standard deformation mode to obtain a thermal deformation response function; constructing a hierarchical probability graph model based on a thermal deformation response function, and extracting thermal deformation feature structural representation through variational inference; and performing deviation compensation on the structural representation of the thermal deformation characteristics to obtain environment self-adaptive thermal deformation characteristics, and calculating a thermal deformation reliability evaluation result comprising a thermal expansion coefficient matrix, a thermal deformation anisotropy index and thermal ripple strength. And comprehensive evaluation and reliability analysis of the thermal deformation characteristic of the filter film are realized.

The invention provides a psychological semantic scene image generation method, which comprises the following steps of: 1, processing data according to a psychological scale filled by a user to obtain a three-level level score of a psychological factor; 2, constructing a basic semantic concept group through hierarchical semantic mapping; 3, constructing structured cue words, wherein the structured cue words comprise a text cue word project and an image cue word project; 4, generating an image note through a large language model according to the text cue word, and generating a background image and page elements through a text-to-graph model according to the image cue word; 5, generating a semantic scene graph from the background picture and the page elements generated in the step 4 through a differentiable graph synthesizer; and step 6, generating a final psychological report in combination with the generated graph annotation and the semantic scene graph.

The invention belongs to the technical field of automatic testing, and particularly relates to a test task scheduling method and system of a control system based on a graph model and deep reinforcement learning. The method comprises the following steps: analyzing a test task demand, and then constructing a Single-rate DAG task scheduling model; according to a Single-rate DAG task scheduling model, generating a directed graph as the input of a GNN; establishing a heat map about the edge based on the directed graph and the GNN; generating a task scheduling sequence according to the heat map, and planning a scheduling scheme according to the task scheduling sequence; calculating a loss function, and updating the graph neural network according to the loss function; repeatedly establishing the heat map to calculate the loss function until a high-quality solution conforming to an expected target can be planned or a difference value between a mean value and a minimum value of the planned test scheme makespan is smaller than a certain threshold value, and ending iteration; and step 7, outputting an optimized solution. The method is used for solving the problem that a planning result of a high-correlation test task scheduling process is often high in randomness and is difficult to meet actual requirements.

Aspects of the disclosure relate to geometry model generation. A computing platform may receive a plurality of drawing models corresponding to different space designs. The computing platform may identify a plurality of design parameters associated with each drawing model of the plurality of drawing models corresponding to the different space designs. The computing platform may train a machine learning engine based on the plurality of drawing models corresponding to the different space designs and the plurality of design parameters associated with each drawing model of the plurality of drawing models corresponding to the different space designs, which may produce at least one geometry model corresponding to the plurality of drawing models. The computing platform may store, in a database storing one or more additional geometry models, the at least one geometry model corresponding to the plurality of drawing models.

Various embodiments include systems and methods to implement a security platform providing cyberattack detection. The security platform may, with respect to a cloud compute environment, use audit log data that is associated with a particular domain of operational activity within the cloud compute environment. Based on multiple baseline profiles associated with the operational activity, the security platform may use a probabilistic graph to determine a behavioral anomaly. The security platform may, based on the behavioral anomaly, identify a cyberattack.

A system, method, and computer-readable medium for generating factual and / or counterfactual data are described. This may have the effect of improving the complexity of data available for training machine learning models. The models may include, but not limited to, a probabilistic graphical model (PGM) and / or an agent-based model (ABM). Further aspects may provide for scrubbing actual data to create a data model that does not reveal the content of the underlying source data. Yet further aspects may provide for validating a data model.

Embodiments of the following disclosure provide a feature importance system and method to identify features relevant to determining whether a target variable will achieve a particular value. One example method includes generating a probabilistic graphical model to represent the performance of one or more entities in a supply chain and selecting or more target variables. The method further includes collating a list of features pertaining to the one or more selected target variables, pruning at least one of the one or more features from the list and generating one or more bins in which to distribute the one or more features in the list. The method further includes modeling a network graph incorporating the one or more features in the list and bins and determining one or more inferences pertaining to the one or more supply chain entity target variables.

A system and method are disclosed for applying machine learning to identify anomalous supply chain data that generates a probabilistic graphical model based on training data from historical attributes of a supply chain comprising supply chain entities to represent the performance of the supply chain entities in the supply chain, standardizes input features data related to the probabilistic graphical model, performs data anomaly detection within the probabilistic graphical model using one or more frequentist data anomaly detection algorithms, performs data anomaly detection within the probabilistic graphical model using one or more Bayesian data anomaly detection algorithms, combines according to one or more weighting methods, the data anomaly detection performed using one or more frequentist data anomaly detection algorithms with the data anomaly detection performed using one or more Bayesian data anomaly detection algorithms, and detects in response to the combining, an anomaly within the standardized input features data.

The invention discloses an asset data fusion and mapping analysis method and system in an industrial control network environment, which ensure that asset nodes are unique and the relationships are diversified through asset data fusion, improve the data quality and consistency, greatly improve the data storage and query efficiency through a graphical model, adapt to large data volume and high concurrency scenes, and improve the data storage and query efficiency. According to the method, the limitation of a traditional storage technology in processing industrial control network environment asset dynamic behavior data construction and display is solved, the expandability and the intelligent level of the whole system are improved, a powerful tool is provided for industrial control network management and safety monitoring, and efficient operation and maintenance decision is promoted.

A machine learning system is provided to enhance various aspects of machine learning models. In some aspects, a substantially photorealistic three-dimensional (3D) graphical model of an object is accessed and a set of training images of the 3D graphical mode are generated, the set of training images generated to add imperfections and degrade photorealistic quality of the training images. The set of training images are provided as training data to train an artificial neural network.

The present invention provides a sponge city design method and system, relating to the field of sponge city data processing technology. The method comprises obtaining information; simulating the waterlogging situation in the urban area using an agent-based model based on the information to obtain a waterlogging distribution map; constructing a graphical model based on the drainage system data; and constructing a drainage pipeline layout optimization model based on the graphical model and the waterlogging distribution map. The drainage pipeline layout optimization model includes an objective function and constraints, the objective function including minimizing the sum of the construction cost of the drainage pipeline and a penalty for insufficient drainage capacity. The constraints include flow balance constraints at the drainage points, capacity constraints of the drainage pipelines, drainage capacity constraints in the flood-prone areas, and drainage capacity constraints in the drainage bottleneck areas. The method then solves the drainage pipeline layout optimization model based on hypergeometric distribution sampling. The present invention takes into account the differences between different urban areas and significantly improves the design results of sponge cities.

Methods, systems and apparatus for simulating quantum circuits including multiple quantum logic gates. In one aspect, a method includes the actions of representing the multiple quantum logic gates as functions of one or more classical Boolean variables that define a undirected graphical model with each classical Boolean variable representing a vertex in the model and each function of respective classical Boolean variables representing a clique between vertices corresponding to the respective classical Boolean variables; representing the probability of obtaining a particular output bit string from the quantum circuit as a first sum of products of the functions; and calculating the probability of obtaining the particular output bit string from the quantum circuit by directly evaluating the sum of products of the functions. The calculated partition function is used to (i) calibrate, (ii) validate, or (iii) benchmark quantum computing hardware implementing a quantum circuit.

A building management system including one or more storage devices storing instructions thereon that, when executed by one or more processors, cause the one or more processors to ingest asset information; cause a graphical model of the building to include a fault indicator based on the asset information, the fault indicator corresponding to a fault occurring on a first physical asset corresponding to a first virtual asset; cause a display device of a user device to display the graphical model within a user interface; receive a selection of the fault indicator from a user via the user interface; and in response to receiving the selection, cause the user interface to navigate to a fault-driven view of the graphical model depicting the first virtual asset and one or more second virtual assets corresponding to one or more second physical assets affected by the fault occurring on the first physical asset.

The invention relates to the technical field of building energy-saving monitoring, and discloses a building energy-saving roof heat insulation monitoring system and a heat insulation monitoring method. The method comprises the following steps: acquiring real-time heat insulation monitoring data of a building roof of a plurality of sensors, comparing and analyzing the real-time heat insulation monitoring data with a pre-stored roof heat insulation historical database, and extracting a system change component and a random interference component; detecting an unexpected temperature fluctuation phenomenon in the real-time data by adopting a mode recognition technology; mining a causal relationship by using a graph model analysis method, performing mode co-occurrence association processing, and identifying a core influence variable; taking the core influence variable as input, combining reference performance information of the same heat insulation structure to establish a standard heat insulation performance reference line, comparing the offset of the standard heat insulation performance reference line with the offset of an actually measured heat insulation performance curve, and fusing a statistical inference model to evaluate the heat insulation performance and judge the risk level of a heat insulation weak point; according to the related information, a sequence prediction technology is used for estimating an area where heat insulation failure possibly occurs in a specified time interval, and a heat insulation optimization maintenance scheme document is generated.

The invention discloses a method and a device for determining airtight detection process duration, and relates to the technical field of airtight detection. According to the scheme, the method comprises the following steps: constructing an equivalent cavity graphic model of a to-be-tested electric control cavity; constructing a leak detection graphic model; the leakage detection graphic model comprises a standard cavity graphic model, an airtight leakage detection equipment graphic model and an electric control leakage graphic model; the airtight leakage detection equipment graphic model is respectively connected with the standard cavity graphic model and the electric control leakage graphic model; the electric control leakage graphic model is a graphic model for adding a leakage hole on the equivalent cavity graphic model; in response to a preset simulation parameter, performing airtight detection process simulation through the leakage detection graphic model, so that the airtight leakage detection equipment graphic model performs inflation simulation on the standard cavity graphic model and the electric control leakage graphic model respectively, and outputs a simulation result; and determining target process duration information based on the simulation result. The method can save the cost of experimental samples and improve the production efficiency.

This invention discloses a link encapsulation method and apparatus based on unified device monitoring modeling encapsulation. The method includes: acquiring link information between various types of devices to be monitored; determining a graphical monitoring interface, preset link algorithms, and communication interfaces based on the link information; constructing a device graphical model, a backup switching model, a combined usage model, a batch control model, a southbound communication interface model, a northbound communication interface model, and an external call interface model based on the graphical monitoring interface, preset link algorithms, and communication interfaces; and encapsulating the device graphical model, backup switching model, combined usage model, batch control model, southbound communication interface model, northbound communication interface model, and external call interface model into a link driver file based on object-oriented programming encapsulation and polymorphism techniques. This invention can greatly simplify the complexity of device monitoring construction, shorten the construction cycle, reduce investment costs, and provide customized device monitoring functions.

This invention discloses a low-light image quality enhancement system and method based on enhanced night scene modeling. The system includes: a sample data construction module, which generates enhanced low-light scene images based on an initial image dataset using a dual-channel hybrid generative network architecture and constructs an optimized training sample library; and a hybrid enhancement model, constructed based on an attention module and a probabilistic graphical model module. During the training phase, the hybrid enhancement model uses the optimized training sample library as input and employs a region-adaptive weighted loss function for parameter optimization. During the application phase, the optimized model receives the low-light image to be processed, estimates the illumination and noise distribution of the low-light image, and generates the enhanced image. This invention significantly improves the coverage of night scene training data and achieves adaptive enhancement of image features under low-light conditions, significantly improving the noise suppression effect in dark areas and the ability to restore image details.

The application provides a simulation method and device based on a graphical model, a terminal and a medium. The method comprises the following steps: obtaining a runnable simulation model in response to a configuration operation on a basic graphical primitive; analyzing the simulation model to generate a target logical framework and a complex behavior set corresponding to the simulation model; verifying the target logical framework and the complex behavior set, and performing model simulation based on the target logical framework and the complex behavior set when the target logical framework and the complex behavior set both pass the verification, to obtain a running simulation result corresponding to the simulation model. The application proposes a simulation running mechanism that is naturally runnable without code conversion based on simple primitives for quickly constructing a model, so as to improve the limitations or problems that cannot be realized in the existing modeling system.

A method for storing and processing product carbon footprint data includes: identifying entities, relationships and attributes involved in the entire life cycle of a product, establishing a directed graph model of product carbon footprint data; encoding the product carbon footprint data of the entire life cycle in time and space, and structurally organizing the product carbon footprint data; constructing a storage record of product carbon footprint data based on a graph database, and making version marks for the generation, modification and update process of emission accounting data in the entire life cycle; establishing index keys based on entity tags according to dimensions such as emission process and space, and establishing an efficient retrieval method for the entire life cycle of product carbon footprint data; defining the scope of product carbon footprint disclosure, identifying important carbon footprint data nodes and relationships, and implementing integrated processing and transformation of product carbon footprint data to achieve mapping and visual expression from product carbon footprint data to disclosure reports. The present invention proposes a product carbon footprint data storage and processing solution based on a graph model, which realizes unified digital expression and full-process data management in product carbon footprint records, and realizes the disclosure and visualization of product carbon footprint data based on spatial information, thereby supporting application needs in fields such as carbon tax mechanisms, sustainable supply chains and sustainable finance.

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

The invention discloses a highway bridge engineering construction safety monitoring method. The highway bridge engineering construction safety monitoring method comprises the steps of obtaining highway bridge construction historical accident data; extracting structured information from the accident report text to determine an accident label and risk factors associated with the accident label; processing co-occurrence data of the risk factors in the accident case by using a preset association rule mining algorithm, and generating a risk factor association rule set representing a potential causal relationship among the risk factors according to the co-occurrence data; constructing a probability graph model of the risk factors based on causal semantics of the risk factor association rule set, wherein the probability graph model represents a dependency relationship among the risk factors; based on the probability graph model, determining sensitivity values of at least part of risk factors by using a preset importance degree measurement algorithm; determining a key risk factor according to the sensitivity value of the risk factor and a preset sensitivity threshold; and determining safety monitoring indexes of highway bridge engineering construction according to the key risk factors.

A computing system generates transformation error probabilities by analyzing a training data set containing training strings, each transformation error probability indicating a probability that a per-character transformation applied to a character of a training string results in a typographical error in a resulting transformation string, wherein the training data set includes strings from a historical dataset of strings including typographical errors. The computing system populates a probabilistic graphical model with the transformation error probabilities corresponding to each resulting transformation string and predicts a likelihood that an input string contains a typographical error based on the probabilistic graphical model.

The invention discloses a multi-source data fusion method and system for mobile Beidou high-precision positioning, and relates to the technical field of high-precision navigation positioning, the problem of satellite observation value quality degradation in a complex environment is inhibited through tight coupling layer explicit modeling and online estimation of multipath errors, and in the tight coupling depth fusion and calculation process, the real-time performance of the satellite observation value is improved. The constructed first factor graph model takes the multi-path error as a state parameter to be estimated for explicit modeling, the multi-path error is incorporated into a Beidou original observation factor, and in the joint optimization solution process, the first factor graph model takes the multi-path error as an independent degree of freedom for online real-time estimation and updating, so that the multi-path error is estimated in real time; according to the method, the part, attributable to multi-path interference, in the observation residual error is actively distributed and absorbed into the state parameters, so that a tight coupling resolving module can still output a more reliable tight coupling optimal pose sequence with higher anti-interference capability and provide a stable absolute position even if the satellite observation condition is seriously polluted by multiple paths.

The present invention discloses a method for sorting multifunctional radar pulse sequences based on a probabilistic graphical model, belonging to the field of radar electronic reconnaissance technology. The method mainly comprises two steps: constructing a radar pulse sequence interleaving model and sorting the radar pulse sequence. First, a hidden Markov model is used to model each radiation source to adapt to the type of interpulse modulation of the multifunctional radar. Second, a Markov chain is used to control the interleaving process. The present invention simultaneously infers the component Markov chain and the interleaving Markov chain, and has the characteristics of fast convergence speed and the ability to adapt to pulse missing. The present invention can sort radars with agile parameters.