1098 results about "Synthetic data" patented technology

The invention belongs to the technical field of earthquake disaster scene recognition, and discloses an earthquake disaster scene recognition method based on deep learning. The method comprises the following specific steps: S1, data acquisition and preprocessing; S1.1, multi-source heterogeneous data acquisition and establishment of a comprehensive database containing seismic waveform data, surface deformation data, building structure data, geographic information data and historical disaster record data; through fusion of a 3D convolutional network, a graph attention mechanism, a space-time LSTM and an adaptive cross-modal attention fusion technology, combined modeling of a seismic waveform space-time evolution law, an earth surface deformation space distribution characteristic, a building group topology vulnerability and disaster chain time sequence association is realized, the characterization capability of a complex nonlinear disaster mode is effectively improved, and the method has the advantages of high adaptability and high reliability. And disaster assessment response time is shortened to a sub-second level through mixed precision quantification and edge computing deployment, and high recognition accuracy is still kept in a scene with strong noise and data missing in combination with a multi-task classifier and a physical constraint verification mechanism.

The invention discloses a semi-supervised identification method and system for a small target of a container, and belongs to the technical field of artificial intelligence and computer vision, and the method comprises the steps: carrying out the target detection of a container image through a pre-trained target detection model, intercepting a sub-image, and inputting the sub-image into an initial classification model, and obtaining a classification confidence coefficient; the uncertainty of the model on a sample classification result is quantified through a Monte Carlo Dropout method; a feature space distance filtering and dynamic threshold adjusting mechanism is combined, and samples with high confidence, low uncertainty and consistent feature space are screened out to serve as pseudo label data; pseudo label data and initial synthesis data are mixed, and the generalization ability of the model is gradually improved through semi-supervised iterative training. According to the method, the dependence on manual annotation can be remarkably reduced, meanwhile, the distribution difference between synthetic data and real scene data is gradually reduced, and finally, high-precision recognition and strong generalization ability of a classification model in a real scene are achieved.

The invention relates to the technical field of power equipment operation and maintenance management, in particular to a power equipment asset health management and predictive maintenance service system which comprises a data acquisition and integration module, a feature engineering module, a health assessment and prediction engine maintenance decision and early warning module and a service interface module. The data acquisition and integration module acquires equipment operation parameters through multiple types of sensors, and associates pre-stored equipment asset information to generate an equipment comprehensive data stream; the feature engineering module cleans and standardizes the equipment comprehensive data stream, and constructs a space-time correlation feature matrix; the health assessment and prediction engine comprises a health state assessment unit and a fault prediction unit, the health state assessment unit outputs a health index HI by using a gradient boosting decision tree, and the fault prediction unit outputs a fault probability and a remaining service life RUL in a future preset time period; and the maintenance decision and early warning module generates a grading early warning signal and a maintenance strategy scheme. The intelligent level of operation and maintenance of power equipment is improved, reliable operation of the equipment is guaranteed, and the operation and maintenance cost is reduced.

The invention discloses a lithium ion battery health state estimation method and system, a medium and equipment, and the method comprises the steps: enabling each client of a battery mechanism participating in collaborative modeling to extract a health factor based on the original data of local battery monitoring, constructing and training a deep convolution generative adversarial network, and obtaining local synthetic data; each client uploads the local synthetic data to a central server, and the central server aggregates the local synthetic data of each client to form global synthetic data and distributes the global synthetic data to each client; each client carries out federated transfer learning training based on local data and the global synthesis data, uploads a local model to a central server after local training is finished, carries out dynamic weighted aggregation to form a global model and issues the global model, and the local training and federated aggregation are continuously and iteratively executed until the whole training process is finished; and each client carries out personalized fine tuning on the global model based on local data to realize estimation of the health state of the battery.

The invention discloses a computer memory bank fault prediction method and system based on deep learning, and relates to the technical field of computer hardware fault diagnosis, and the system comprises a multi-source time sequence data collection module which is used for obtaining memory bank operation state data in real time; the dynamic feature enhancement module is based on a composite architecture of a generative adversarial network and transfer learning, comprises a fault mode generator, and generates synthetic data consistent with real fault distribution by using an LSTM network; aligning feature spaces of different hardware platforms through a maximum mean difference loss function; the multi-modal fusion deep learning model comprises a space-time convolutional network, a graph attention network and an adaptive weight adjustment mechanism; and the fault early warning analysis module is used for analyzing a fault probability predicted value, an interpretable thermodynamic diagram and a maintenance suggestion. According to the invention, passive maintenance is changed into active prevention and control, and preposition and precision of fault management are realized through dual mechanisms of long-term trend prediction and short-term risk early warning.

Techniques for determining a presence of an object, especially an object such as animal or debris, in a path of a vehicle, are discussed herein. For example, sensors of various modalities, which may include multispectral sensors, may capture data representing an environment the vehicle is traversing. In examples, one or more trained machine learned (ML) models, operating on a vehicle computing system, may detect and / or classify objects in the environment, based on input data of one or more modalities or spectral bands. The ML models may be pre-trained using training data including real sensor data, synthetic data, and / or augmented data, along with auto-generated annotations. In some examples, hyperspectral data may be used to identify materials associated with detected objects. A confidence score associated with the detection of the object may also be computed. The vehicle may be controlled based on detection of the object and its classification.

The invention relates to the technical field of wind field simulation, and provides a wind field numerical simulation method based on multiple meteorological data sources. The objective of the invention is to solve the problems of large simulation error, rough terrain boundary processing and poor turbulence model parameter adaptability caused by non-uniform coverage of a single data source, insufficient precision and unscientific multi-source fusion. The method is characterized by comprising the following steps: step 1, constructing a CFD three-dimensional grid based on a geometric model of a target area; 2, collecting original data of a ground station, satellite remote sensing, numerical forecasting and the like; 3, performing standardized preprocessing (abnormal value elimination, missing interpolation, radiation / geometric correction and resampling), determining a multi-source fusion weight by combining historical data analysis, and generating comprehensive meteorological data by adopting a weighted average method; and 4, inputting the comprehensive data into a CFD-RANS model, dynamically adjusting turbulence parameters, accurately setting terrain boundary conditions, and obtaining a wind field space-time distribution result through numerical solution. According to the invention, through combination of multi-source data fusion and CFD simulation, the wind field simulation precision and stability are improved.

The invention relates to a power system scheduling method and device based on multi-modal data fusion. The method comprises the following steps: acquiring multi-modal data of a power system, wherein the multi-modal data comprises power grid topology data, meter information, wind and light energy output prediction data, maintenance plan text data and energy comprehensive data; performing feature extraction, space-time alignment processing and hierarchical attention learning on each modal data to obtain a fusion feature vector; training the scheduling model by using the fusion feature vector until a total loss function meets a preset condition, the total loss function comprising a power flow equation constraint term; the method comprises the following steps: acquiring real-time multi-modal data of a power system, and obtaining a real-time fusion feature vector through feature extraction, space-time alignment processing and hierarchical attention learning; and inputting the real-time fusion feature vector into the trained scheduling model to obtain a real-time scheduling decision, and scheduling the power system according to the real-time scheduling decision. The method can improve the dispatching precision of the power system.

The invention provides a method for predicting the quality of a sparse small sample industrial process based on a stacked network model. The method comprises the following steps: collecting end point quality report data of an industrial production process; performing hierarchical processing on the acquired data according to the missing rate, and removing abnormal data in combination with a quartile method and production experience; generating a synthetic data expansion small sample data set by adopting a conditional generative adversarial network; obtaining a first-layer basic model based on an accumulated contribution rate screening method of an SHAP value; constructing a first layer of a stacked integrated learning model and adjusting hyper-parameters by using Bayesian optimization; constructing a Ridge meta learning device to integrate the output of the basic model and constructing a second-layer network; a six-fold cross validation training model is adopted; predicting performance through a multi-index quantitative model based on the test set; and verifying the prediction precision of the end-point phosphorus content and the temperature by using real converter production data. The method can realize high-precision prediction of the end point quality index of the complex industrial generation process, and is beneficial to ensuring the product quality and improving the production efficiency.

A bridge component identification method based on unmanned aerial vehicle point cloud reconstruction and three-dimensional synthetic data is characterized in that a point cloud is generated through video acquisition of an unmanned aerial vehicle, accurate identification of bridge components is realized by combining with an improved PointNet + + model, and a technical process of data acquisition-point cloud generation-model training-component identification is constructed. The method comprises the following specific implementation steps: (1) acquiring a bridge member field video by using an unmanned aerial vehicle; (2) building a three-dimensional reconstruction framework to generate dense point cloud of bridge components; and (3) generating diversified bridge point cloud synthetic data in batches. (4) constructing a point cloud semantic segmentation data set with labels, and preprocessing and enhancing the point cloud semantic segmentation data set for model training; (5) realizing point cloud identification based on an improved PointNet + + model; and (6) post-processing an identification result, and extracting geometric parameters of the component for bridge structure state evaluation. According to the invention, efficient generation and high-precision identification of the point cloud of the bridge member can be realized, and reliable data support is provided for quality evaluation of the bridge structure.

The invention discloses a robot operation track generation method based on structure perception and knowledge enhancement reasoning, and belongs to the field of artificial intelligence and machine learning, and the method comprises the steps: building a large-scale multi-modal database through obtaining sampling data of a real robot operation scene and synthetic data generated by a simulation environment; multi-source three-dimensional perception data is obtained, a large language model with a structure perception capability is input, and a machine execution instruction system with relatively strong three-dimensional space interaction understanding and dynamic modeling capability is obtained; multi-task progressive training is carried out based on spatial information understanding and interactive operation, capacity progressive unlocking is achieved, and robot tasks are strongly oriented; a large model knowledge graph enhancement method based on a three-dimensional scene object is introduced to assist in large model reasoning, and a large model which has reasoning ability and structure sensing ability and is higher in robustness to complex scenes is constructed and used for generating robot operation tracks.

The invention discloses a wind power prediction system for optimizing a neural network based on a genetic algorithm, relates to the technical field of new energy power system prediction, and improves the precision and adaptability of wind power prediction by fusing the genetic algorithm and a deep neural network. The system adopts multi-objective genetic optimization, randomly initializes a neural network parameter combination, evaluates the fitness by taking a prediction error and model complexity as double objectives, and screens out an optimal network architecture through evolution operation; in the aspect of neural network training, the system adopts an LSTM and TCN hybrid network as a basic model, dynamic weighting input features of a meteorological attention mechanism are combined, a learning rate and regularization parameters are optimized by using a genetic algorithm, model convergence is accelerated, and overfitting is prevented; in addition, for the space-time imbalance of the wind power data, a generative adversarial network is introduced to generate synthetic data in an extreme weather scene, and the generalization ability of the model is enhanced.

The invention discloses a personalized federated learning method based on knowledge fusion distillation and a storage medium, and belongs to the technical field of personalized federated learning, and the method comprises the steps: a server carries out the pre-training and distributes a diffusion model to each client, and generates a local synthesis data set, initializing a global model as a student model of a client to perform knowledge fusion distillation training, and guiding the student model training by the teacher model optimized in the last round; and after training is completed, the client uploads student model parameters to the server for federal aggregation to update the global model, and meanwhile, the personalized feature extraction capability and reliability of the next round of teacher model are enhanced by using synthetic data, so that a closed-loop learning framework of global cooperation and local personalized collaborative optimization is formed. According to the method, the problems of client model drifting, performance attenuation and convergence rate slowing caused by data heterogeneity can be solved.

A system and method for scalable generations of synthetic <logical form, utterance> pairs for training a semantic parser is disclosed. A semantic parser is trained on pairs of <logical form, utterance>. An ontology graph is constructed and derived from a plurality of enterprise documents and provides relationships among the concepts or classes of an organization. One or more paths are traversed among a plurality of source and destination node pairs, facilitating comprehensive semantic representation. Attributed query subgraphs are generated of source nodes, destination nodes, and hidden nodes. Each path is recognized among a variety of possible paths between source and destination nodes. Each path in the ontology query subgraph is validated by considering a plurality of predicates and a knowledge graph generates a natural language utterance. The utterances are refined and rephrased using a large language model, enhancing their coherence and linguistic quality.

Implementations relate to generating a plurality of training instances to train a generative model in performing tasks using external tool(s) / service(s) accessible via corresponding API(s). The plurality of training instances each includes a synthetic natural language user instruction and execution step(s) to perform a task (e.g., a single-API task or a multi-API task) specified in the synthetic natural language user instruction. The synthetic natural language user instructions selected for the synthetic training data can be generated based on processing textual prompt(s) using a first LLM, where the textual prompt(s) can each include a list of APIs and associated API documents, one or more seed examples, and a request to synthesize a natural language user instruction. The one or more execution steps for a corresponding synthetic natural language user instruction can be generated based on parsing the corresponding synthetic natural language user instruction using the first LLM or a second LLM.

The invention discloses a temperature dynamic control system for optical fiber optical wand processing. The temperature dynamic control system comprises an acquisition unit, a control unit, a learning unit and an integration unit. The acquisition unit captures production line material imaging data through a multi-macro camera array, acquires temperature, tension, humidity and speed data in real time by using a production line sensor network, and forms production state comprehensive data after edge computing node fusion preprocessing. The control unit establishes a dynamic mathematical prediction model based on the comprehensive data, predicts future temperature states of a plurality of temperature zones and generates an optimization control input instruction; the learning unit receives an optimization control input instruction and historical temperature data, and generates a self-adaptive control strategy by adopting a reinforcement learning algorithm; the integration unit integrates the control input instruction and the self-adaptive control strategy to form a final control instruction, the temperature of each temperature zone is adjusted, and the temperature control precision and the system stability are ensured.

The invention discloses a spacecraft space environment safety guarantee system and method based on three-step calculation, and the system comprises two subsystems: a space environment data service subsystem and a three-step calculation and analysis subsystem, and the three-step calculation and analysis subsystem calls data and an algorithm model from the space environment data service subsystem. The space environment safety guarantee analysis based on the three-step calculation is executed; the space environment data service subsystem comprises a metadatabase and a comprehensive database; the metadatabase comprises space environment space-based monitoring data, space environment foundation monitoring data and data from a specified space environment mechanism; the comprehensive database comprises a space environment numerical calculation model library and a spacecraft business application rule algorithm model library; the three-step calculation and analysis subsystem comprises a sun-earth space environment analysis and calculation module, a spacecraft orbit ring layer environment analysis and calculation module and a spacecraft task influence risk assessment and calculation module, and three-step calculation is executed through the three modules.

The invention discloses a drainage pipeline multi-parameter sensing trenchless repair dynamic regulation and control method and system. Through a fusion technology path of multi-source comprehensive data acquisition, edge calculation real-time cross validation of abnormity, pipeline state simulation analysis of defect levels, intelligent algorithm dynamic adjustment of repair parameters and geographic information visualization platform full-process monitoring, closed-loop management from abnormity identification to repair regulation and control is realized. A comprehensive data set is formed through multi-source data acquisition, an edge computing technology is utilized to quickly identify abnormities, a simulation technology is combined to evaluate the severity of defects, then process parameters are optimized and repaired based on defect levels, and a regulation and control instruction is generated through linkage of a visual platform and a pump station dispatching system. And finally, a pipeline repair and operation regulation and control scheme is formed through integration, and the repair effect and the system stability are ensured. According to the method, the pipeline abnormity processing accuracy and the repairing efficiency are remarkably improved, and a technical guarantee is provided for safe and stable operation of an urban drainage system.

Techniques for determining a presence of an object, especially an object such as animal or debris, in a path of a vehicle, are discussed herein. For example, sensors of various modalities, which may include multispectral sensors, may capture data representing an environment the vehicle is traversing. In examples, one or more trained machine learned (ML) models, operating on a vehicle computing system, may detect and / or classify objects in the environment, based on input data of one or more modalities or spectral bands. The ML models may be pre-trained using training data including real sensor data, synthetic data, and / or augmented data, along with auto-generated annotations. In some examples, hyperspectral data may be used to identify materials associated with detected objects. A confidence score associated with the detection of the object may also be computed. The vehicle may be controlled based on detection of the object and its classification.

The invention discloses a multi-mode rainy day area road flatness detection method by means of Carla training, and relates to the technical field of road flatness detection, and the method comprises the steps: collecting real rainy day road surface images and vehicle parameters, and constructing a real data set; a rainy day scene is simulated in Carla, and terrain, wet and slippery materials and weather are configured; the method comprises the following steps: acquiring multi-modal data such as simulation point cloud and acceleration through a laser radar and a vibration sensor, and constructing a simulation data set; fusing real and simulation data to train a multi-modal neural network; extracting and fusing point cloud and acceleration features; road height variances and ranges are predicted to assess flatness. According to the method, the problem of scarcity of real data in a rainy day environment is solved by using Carla to generate synthetic data, the real acquisition cost is reduced, the detection precision is remarkably improved, various rainfall intensities and terrains can be covered, data complementation of the laser radar and the vibration sensor is realized, and the robustness in a rainy day is improved through dynamic weighting of an attention mechanism.

The invention relates to the technical field of quality detection, in particular to an AI-based liquid crystal glass plate defect detection method and system, comprising multi-modal data acquisition, adaptive preprocessing, dynamic intelligent detection, defect cognition decision, closed-loop feedback execution, a safety guardrail mechanism and digital twin support. Compared with the prior art which adopts a single imaging mode for surface detection and has the defects that the omission ratio of tiny flaws is high and metal and PR defects are difficult to distinguish, the scheme innovatively introduces a multi-mode collaborative acquisition mechanism, and the detection accuracy is improved through millisecond-level synchronization and space registration of four sets of bright field, dark field, ultraviolet and structured light imaging systems. A comprehensive data cube containing textures, metal features, fluorescence response and three-dimensional morphology is constructed, full-dimensional capture and accurate characterization of micron-sized flaws are achieved, and the detection rate and classification accuracy of complex flaws are remarkably improved.

The invention discloses a differential privacy data set distillation method and system based on image generation data, and belongs to the technical field of data privacy protection and machine learning. The method comprises the following steps: firstly, synthesizing a synthetic data set meeting Gaussian differential privacy, training a feature extractor on the synthetic data set, and finely adjusting an expert model by using original data differential privacy; multiple rounds of iterative optimization are carried out on the distillation data set initialized according to the classes, wherein a feature extractor is randomly selected according to the classes in each round to align the features of the original data added with the noise and the distillation data, and an expert model is utilized to align the semantics of the distillation data hard labels and the semantics of the synthetic data soft labels; and finally outputting a distillation data set meeting the total privacy budget. According to the method, effective priori is provided by utilizing generated data, extra noise injection in the alignment process is reduced, convergence is accelerated, and better privacy protection and data availability balance compared with a previous method is realized under the same differential privacy budget.

The invention relates to a pathological section intelligent auxiliary differential diagnosis system based on multi-modal fusion, in particular to the field of clinical pathology, semantic unification of multi-modal data is achieved through meta-task construction and a cross-modal alignment technology, and transferable diagnostic knowledge is extracted by utilizing a meta-learning framework; the method combines a generative model and knowledge constraints to generate high-quality synthetic data, and finally fuses real and synthetic samples through a self-adaptive diagnosis mechanism, thereby remarkably improving the differential diagnosis capability of rare lesions, effectively solving the problem of model generalization in a training data scarcity scene, and improving the accuracy of model identification. And efficient and reliable intelligent auxiliary decision support is provided for clinical pathological diagnosis.

A computer-implemented method for developing a differential privacy model is provided. The method includes collecting a private and personal dataset comprising private and / or personal data and training the differential privacy model via backpropagation to optimize an expected accuracy of an adversarial loss and a privacy loss. The differential privacy model is associated with a continuous normalizing flow. The method further includes outputting the trained differential privacy model. The trained differential privacy model is configured to generate new synthetic datasets that are used to train one or more downstream tasks. The method has applications including, but not limited to, use cases in medicine / healthcare such as Electronic Health Record (EHR) generation, single and bulk cell sequencing data generation, and pre-training large multimodal language models (LLMs) associated with clinical data, and can further for example, be used to optimize machine learning tasks or to support decision making.

Systems, methods, devices, and computer readable storage media described herein provide techniques for generating synthetic data for use in query generation. In an aspect, a pair comprising a natural language (NL) query and a query language (QL) query and predicted catalog information are used to prompt a large language model (LLM) to generate an augmented pair that is a variation of the pair. Synthetic data is generated comprising the augmented pair. In another aspect, an indication of feedback for a QL query generated by a LLM is received and a corrected pair is generated based on the indication and a corresponding NL query, the corrected pair comprises a corrected QL query and the NL query. The corrected QL query is a syntactically valid conversion of the NL query. The corrected pair is determined to satisfy criteria of a data store and is stored as synthetic data of the data store.

The invention relates to the technical field of municipal comprehensive GIS data management, in particular to a municipal comprehensive GIS data integration and management system for hub airport construction. The system comprises a data acquisition unit which is used for acquiring spatial geographic parameters, construction parameters and traffic operation parameters to construct an original multi-source data set, preprocessing the original multi-source data set and outputting a standardized data set; the equipment scheduling and resource management unit generates a total conflict set based on the spatial fusion data set and the dynamic progress data set, and obtains a Pareto optimal solution set by constructing a multi-objective optimization function; and the multi-dimensional conflict early warning unit obtains a security conflict set based on the spatial fusion data set and the dynamic progress data set in combination with the total conflict set, and finally generates dynamic early warning information through risk grade division. The coordinated scheduling scheme can be automatically generated under the scene of progress conflict of multiple contractors, complete dependence on manual decision making is avoided, and the construction efficiency and collaboration are improved.

A system and method for comprehensive data loss prevention and compliance management designed to identify and prevent cybersecurity attacks on modern, highly-interconnected networks, to identify attacks before data loss occurs, using a combination of human level, device level, system level, and organizational level monitoring and protection.

The invention discloses a synthetic data set construction method and electronic equipment, and relates to the technical field of artificial intelligence, and the synthetic data set construction method comprises the following steps: dividing an original multi-source document of a target field into a plurality of word segmentation units by using a word segmentation device; obtaining representative scores of the plurality of word segmentation units on the original multi-source document; based on the representative scores, determining the word segmentation units with the representative scores higher than a first score threshold as candidate keywords; determining importance degree scores of the candidate keywords based on the representative scores of the candidate keywords; based on the importance score, determining the candidate keyword of which the importance score is higher than a second score threshold as a target keyword; and calling a pre-training language model, and based on the target keyword, generating a question and answer pair corresponding to the target keyword to obtain a synthetic data set of the target field. The technical problem that the data coverage rate and the field correlation of the generated synthetic data set are low in the prior art is solved, and the technical effect of improving the data coverage rate and the field correlation of the generated synthetic data set is achieved.