214144 results about "Algorithm" patented technology

The invention provides a dynamic health adaptive monitoring method and system using artificial intelligence, and relates to the technical field of health monitoring, and the method comprises the steps: carrying out the noise reduction through a hierarchical diffusion probability model through multi-source heterogeneous health data, extracting preliminary features through combining with an improved attention auto-encoder, learning discriminative features through a contrast learning network, and carrying out the recognition of the discriminative features; inputting the discriminative features into an adaptive wavelet neural network for multi-scale feature decomposition, inputting the discriminative features into a dynamic graph neural network to obtain a health feature tensor, obtaining time sequence features by using a hierarchical memory enhancement network, and generating a health knowledge graph in combination with a dynamic routing capsule network and a causal reasoning network; and inputting the time sequence characteristics and the health knowledge graph into a deep Bayesian network for health risk assessment, and obtaining an adaptive monitoring scheme through a model prediction control network by using a hierarchical reinforcement learning network and combining a multi-agent collaborative optimization system and a deep deterministic strategy gradient network.

The present application relates to the technical field of load prediction, and provides an electrical load prediction method and device based on spatial-temporal correlation. The method comprises: performing spatial-temporal modeling on the basis of historical electrical load data and historical environment data to obtain a multi-layer spatial-temporal model, wherein each layer model is a directed graph and represents spatial causality in the fixed time, and a directed edge between the layer models represents temporal causality; on the basis of an interpretable spatial-temporal attention transformer, performing feature extraction and priori knowledge fusion on the multi-layer spatial-temporal model to obtain spatial-temporal characteristics of an electrical load, wherein the interpretable spatial-temporal attention transformer comprises a spatial causal attention network, a temporal attention network, and a spatial dependence comparison module; and on the basis of the spatial-temporal characteristics of the electrical load, and the real-time electrical load data and the real-time environment data of a target electrical node, predicting by using a trained STEF-DHNet model to obtain the predicted electrical load data of the target electrical node. The present application can improve the prediction accuracy and the model applicability.

The invention discloses a decision-making method and model for off-line reinforcement learning and continuous on-line fine tuning, and the method comprises the steps: obtaining original text-image pair data and historical track data, and obtaining an enhanced state sequence through feature matching verification and standardization processing; performing sequence prediction by using a multi-level attention mechanism, and obtaining pre-training model parameters through knowledge distillation and task adaptability enhancement; on the basis of stability evaluation and plasticity control, online knowledge migration is carried out on the model, and optimized model parameters are obtained by combining experience playback optimization; and finally, performing multi-dimensional performance evaluation and resource optimization. According to the method, through multi-modal feature fusion, dynamic knowledge migration and an adaptive optimization mechanism, the problems of insufficient feature representation, disastrous forgetting, low optimization efficiency and the like are effectively solved, and the learning efficiency and generalization ability of the model are improved.

Provided is a road crack detection method, a medium and a product. The lightweight YOLO-MCS road crack detection model is constructed based on an improved yolov8 network. A method of improving the yolov8 network includes: replacing a feature extraction network backbone with a lightweight convolutional neural network MobileNetV3, and embedding a Coordinate Attention Mechanism (CA) module in the lightweight convolutional neural network MobileNetV3; adding a small target detection layer and a Squeeze and Excitation (SE) module at a neck end; and introducing a loss function of power Intersection over Union (IoU) into a head prediction structure. The road crack detection method overcomes the problem that the existing detection algorithm has low detection precision for small cracks and is difficult to be applied to edge devices with limited computing resources.

A multi-layer artificial intelligence system includes a foundation layer comprising at least one general-purpose large language model (LLM); an expert array layer comprising a plurality of specialized reasoning models; and a meta-reasoning model configured to coordinate operations between the foundation layer and the expert array layer to generate a reasoned analysis.

A modeling method for three-dimensional real-time measurement reconstruction, comprising: on the basis of a three-dimensional camera, a laser radar, and an infrared sensor device, generating a multi-source original data set; performing data preprocessing; performing feature extraction and data fusion; performing three-dimensional space construction; by means of a deep machine learning algorithm, performing real-time dynamic adjustment and optimization on a model; and, by adopting an advanced image processing algorithm, generating a high-precision three-dimensional model.

A system and method for extending AI-enhanced decision platforms with deontic and normative reasoning capabilities that enhance adjustably autonomous decision-making through a novel integration of symbolic and neural approaches alongside quantum-inspired token management. The invention uses hierarchical and fuzzy deontic logic implementations and quantum-inspired state representations that combine complex amplitudes and phase information to manage obligations, permissions, and prohibitions while maintaining observer awareness to achieve complex goals while incorporating knowledge across multiple expert domains. The system employs dynamic event and spatio-temporal knowledge graphs along with debate mechanisms, enabling high-assurance automated reasoning while preserving explainability through neuro-symbolic integration and information-theoretic metrics. The platform is capable of operating through a federated distributed computational graph architecture that allows for arbitrary scaling while maintaining system coherence and logical consistency using quantum-inspired token operations and phase alignment transformations for optimizing information transfer between states.

The invention provides a method for participating in primary frequency modulation control of a power system based on a modular large-scale hydrogen production power supply, and the method comprises the steps: obtaining an original data flow from a high-precision synchronous phasor measurement unit, an electrolytic cell and a submodule, and carrying out the multi-scale decomposition and noise reduction processing after time synchronization and anomaly detection restoration; performing state estimation by using unscented Kalman filtering, extracting frequency disturbance characteristics in combination with empirical mode decomposition and Hilbert transform, and estimating the inertia of the power grid; an electrolytic cell dynamic model is established, and parameter identification and optimization are carried out through a recursive least square method; a control strategy is designed based on a multi-time scale control framework, virtual synchronous generator parameters are optimized through online Gaussian process regression, and frequency modulation control is realized in combination with scene tree random model predictive control and minimum-maximum regret learning; and finally, carrying out coordination control through a distributed consistency algorithm and carrying out multi-target performance evaluation optimization. And the frequency modulation performance and the operation efficiency of the system are improved.

The invention relates to the technical field of model analysis, in particular to a time-space fusion neural network line topology analysis method for a power distribution network. The method comprises the following steps: obtaining original line topology data corresponding to a power distribution network, and carrying out structured disassembly and preprocessing to construct a space-time double graph structure; constructing a bidirectional dynamic feature interaction mechanism based on the space-time double graph structure, performing multi-scale topological feature extraction, and generating a space-time separated feature vector set; performing deep coupling fusion on the feature vector set subjected to time-space separation to generate corresponding unified topological feature representation containing abnormal topology; and constructing a dynamic topology state prediction model based on the unified topology feature representation to optimize a space-time joint loss function and output a corresponding real-time topology connection relationship and an equipment state change trend, and meanwhile, performing dynamic topology reconstruction to generate a current-moment reliable topological graph corresponding to potential branch disconnection and temporary tripping. The topology analysis accuracy of the power distribution network can be improved.

The invention relates to an intelligent reflux valve adjusting method based on a self-adaptive control algorithm, and aims to improve the accuracy and response efficiency of fluid adjustment. The method comprises the steps that fluid flow, pressure, temperature and flow direction data collected by sensors installed at an inlet and an outlet of the reflux valve are obtained, and the initial target valve opening degree under the current fluid working condition is calculated through a working condition opening degree prediction model in combination with structural parameters of the reflux valve; based on the relation among the flow, the pressure and the valve opening, the initial target valve opening is adjusted through a fuzzy rule base, so that the final target valve opening is obtained, and therefore the dynamic change of the complex fluid working condition is adapted; according to the final target valve opening degree, an execution mechanism is controlled to adjust the operation state of the reflux valve, and precise adjustment is achieved; working condition prediction and fuzzy control are combined, the adjusting precision and stability of the reflux valve are improved, and the method is particularly suitable for fluid conveying systems with large flow and pressure fluctuation and has wide application prospects in the industries of energy, chemical engineering, environmental protection and the like.

Systems and methods of sensor data fusion including sensor data capture, curation, linking, fusion, inference, and validation. The systems and methods described herein reduce computational demand and processing time by curating data and calculating conditional entropy. The system is operable to fuse data from a plurality of sensor types. A computer processor optionally stores fused sensor that the system validates above a mathematical threshold.

The present invention belongs to the technical field of land change detection. Disclosed are a land change detection method combining matrix decomposition and adaptive propagation, and a system. A deep learning semantic segmentation model combining matrix decomposition and adaptive propagation is used for detecting a land change. The model is of a twin double-branch structure, which can receive both an input of a pre-temporal remote sensing image data set and an input of a post-temporal remote sensing image data set and can share network parameters, and which has the advantage of end-to-end feature learning. In the present invention, an attention method based on matrix decomposition is used to replace a conventional attention method based on manual design; moreover, in view of adaptive propagation technology for pixel offsets of neighboring points, a deep learning twin differential semantic segmentation network is established to realize automatic and precise land utilization / land coverage change detection on the basis of high-resolution optical data.

The invention discloses an early recognition method and system for a dam crest crack of a high-core-wall rockfill dam, and the method comprises the following steps: marking InSAR data, unmanned plane oblique photography data and three-dimensional laser scanning data through a spatial-temporal index structure, carrying out the abnormal value cleaning through a dynamic multi-dimensional threshold system, and carrying out the early recognition of the crack at the crest of the high-core-wall rockfill dam. A registration data set is obtained through processing of a segmented adaptive standardization processing unit and a space-time registration unit; extracting local features by utilizing probability tensor decomposition, constructing a global feature matrix by combining map entropy calculation, and obtaining a fusion feature set through multi-dimensional probability combination; constructing a multi-level early warning index based on hierarchical sparse representation, identifying a critical state by adopting a probability migration matrix, and determining an early warning level through an adaptive threshold network and probability combination discrimination; and carrying out multi-dimensional verification by adopting a combined verification matrix, and realizing system optimization and updating through a multi-objective optimization network and an incremental learning matrix. According to the method, the accuracy and reliability of early crack recognition are improved.

An example operation may include one or more of receiving a request to test a software program from a user device, the request comprising a description of requirements of the software program, generating a plurality of testing elements based on execution of a generative artificial intelligence (GenAI) model on the description of the requirements of the software program and a repository of test cases, generating a test case for testing the software program where the test case comprises the plurality of testing elements generated by the GenAI model, and storing the test case within a storage device.

The invention relates to the technical field of meteorological data analysis, and provides an aerosol prediction method and system based on artificial intelligence, which are used for accurately identifying an abnormal aerosol gathering mode in an extreme meteorological event. The method comprises the steps that an original multi-mode meteorological observation data set is acquired, and the original multi-mode meteorological observation data set comprises an earth surface aerosol concentration sequence, three-dimensional atmosphere state variables and boundary layer dynamic parameters; performing space-time heterogeneity correction on the original multi-modal meteorological observation data set to generate a space-time aligned target multi-modal meteorological observation data set; inputting the target multi-modal meteorological observation data set into a spatial-temporal feature fusion network to extract multi-scale spatial distribution features and cross-modal time sequence correlation features, and generating a spatial-temporal feature matrix based on the multi-scale spatial distribution features and the cross-modal time sequence correlation features; and performing multi-stage recursive optimization on the spatial-temporal characteristic matrix through a hierarchical time domain aggregation algorithm to generate an aerosol concentration prediction sequence of the target region.

The invention provides a numerical control machine tool wear automatic detection and compensation method based on artificial intelligence, and the method comprises the steps: collecting the cutting force data of a high-curvature region in real time through multi-sensor fusion, and obtaining the cutting force fluctuation characteristics; cutting temperature data of the high-curvature area are monitored and obtained, the cutting temperature change rate is extracted, whether the temperature exceeds a preset threshold value or not is judged, and if yes, an alarm mechanism is triggered, and cutting parameters are adjusted; predicting the tool wear rate in combination with the co-evolution relationship between wear and temperature, the online monitoring data and the processed time, and generating a wear prediction curve in a preset time period; and performing trend analysis and feature extraction on the wear prediction curve to obtain wear parameter changes of the cutter in a preset time, and if the prediction curve shows that the wear parameter changes at a certain time point in the future exceed a preset critical value, adjusting the cutting parameters and generating a target cutting parameter combination.

In some cases, flood risk quantification computing systems and methods are computationally intensive and in some cases are inaccurate. A flood risk computing system and method are provided that quantifies a flood vulnerability within a specified area of interest, irrespective of flood event characteristics; estimates and maps a flood hazard probability; integrates the flood vulnerability and the flood hazard probability to quantify a flood risk; and develops a rapid flood risk software tool, stored in the memory, to directly quantify flood risk characteristics using deep learning. The rapid flood risk took using a plurality of hierarchical deep neural networks.

The invention relates to a water quality change trend rapid prediction method based on multi-source data fusion and physical constraint, and the method specifically comprises the following steps: 1, synchronously collecting spectral information, DO, COD, temperature, pH and other data at a key monitoring station, constructing a hydrodynamic water quality coupling equation, simulating the spatial-temporal dynamic distribution of water quality parameters, and calculating the water quality change trend; 2, outputting a water quality sensitive area through a hydrodynamic force-water quality model, screening sensor layout point positions in combination with information entropy evaluation and spatial clustering, realizing low-cost water quality sensor network deployment through a multi-objective optimization algorithm, and calculating a water body global water quality distribution diagram by adopting a spatial interpolation method, 3, synchronously collecting spectral information according to key monitoring sites, analyzing main pollution sources, and adopting a principal component analysis and attention mechanism neural network; 4, based on real-time optical characteristic value-DO data, in combination with a spatial topology network, a water quality gradient and a cross-regional covariance, capturing water quality parameter spatial correlation among different sites, and determining the water quality parameter spatial correlation among different sites; an optical characteristic value-DO-COD dynamic prediction model is constructed; a COD predicted value is corrected by combining pollution traceability and spectral characteristics, and multi-source data is assimilated by adopting ensemble Kalman filtering, so that the model precision is improved.

Disclosed in the present invention is a dynamic human modeling method based on three-dimensional Gaussians. In the present method, a multi-view human video is given firstly, and in order to correct and transform the dynamic human appearance, a latent code and a set of blend weights are added to each Gaussian; and the latent code and a target pose are inputted to an MLP to correct the Gaussian in a canonical space, so as to capture an appearance change under the target pose. The corrected Gaussian is transformed into the target pose by means of the blend weights thereof in a linear blend skinning (LBS) manner. Finally, a realistic human image at a new viewing angle or a new pose can be rendered in real time by means of Gaussian splatting. Compared with an existing method based on an implicit neural radiance field (NeRF), the three-dimensional Gaussian representation of the present invention can better capture high-frequency details while achieving optimal rendering performance.

The systems and methods disclosed herein receive an output generation request from that includes input for generating an output using a language model. The input includes a set of alphanumeric characters associated with operative standards for a first set of actions. The system divides the set of alphanumeric characters into text subsets. For each text subset, a vector representation is determined. Prompts are created for each vector representation including the set of alphanumeric characters, query contexts, keywords, and / or the text subset. Each vector representation's prompt is input into the language model, which generates a second set of actions of related actions, where subsequently generated actions are based on prior generated actions. The system aggregates the second set of actions into a third set of actions and displays a graphical layout. The graphical layout displays a representation of the set of alphanumeric characters and the corresponding actions.

The invention relates to the technical field of marine environment monitoring, in particular to a time-space diagram neural network anomaly detection method and system for marine ecology. The method comprises the following steps: acquiring ocean monitoring data; performing data preprocessing on the acquired marine monitoring data; constructing a space-time diagram structure based on the preprocessed marine monitoring data; performing spatio-temporal feature extraction on the constructed spatio-temporal diagram structure by using an anomaly detection model; carrying out feature fusion on the extracted spatial-temporal features; and carrying out local anomaly and global anomaly scoring on the marine ecosystem based on a multi-scale anomaly scoring mechanism. According to the method, a unified data processing framework is designed, multi-source heterogeneous data such as satellite remote sensing data, water quality monitoring data and biological survey data can be processed at the same time, information contained in various kinds of data is fully utilized, and the comprehensiveness and accuracy of anomaly detection are improved. Through the construction of a space-time diagram structure, the incidence relation between different data sources is effectively captured, and more comprehensive feature expression is provided for anomaly detection.

The invention provides a robot alarm processing method and system based on a target detection algorithm and a cloud platform, and relates to the technical field of robot alarm processing, and the method comprises the steps: carrying out the detection of an abnormal target based on a convolutional neural network target detection model of the cloud platform in combination with a residual structure, a channel attention mechanism and an improved anchor-frame-free target detection algorithm, performing feature fusion through a feature fusion network guided by depth information to obtain accurate three-dimensional representation data, constructing a dynamic anomaly association graph by using an improved time sequence graph neural network, and generating a multi-level decision sequence in combination with an improved hierarchical reinforcement learning algorithm, and finally, collaborative task planning is carried out through a task decomposition network to obtain a task allocation scheme, and the robot carries out exception handling according to the optimal motion track, obstacle avoidance track data, real-time pose data, key operation sequence data and contact force feedback data.

Systems and methods for computing time series predictions are provided, including a two-stage classifier-and-regressor processor. The classifier is trained on the complete data set while the regressor is trained on a pruned dataset. The classifier includes an Extreme Gradient Boosting classifier. The regressor includes an attention mechanism and a Long Short-Term Memory (LSTM) neural network. For a series of successive time period computations, a current output of the LSTM neural network is recursively fed back as an input to the attention mechanism for a subsequent time period computation. The output of the regressor is scaled by the output of the classifier to adjust for overfitting caused by the pruned training dataset.

According to some embodiments, a method is performed by a wireless device for fallback operation of a machine learning (ML) model. The method comprises: transmitting a message indicating a capability of the wireless device for supporting a combination of at least one ML-based feature for a functionality and at least one fallback feature for the functionality to a network node; operating the at least one ML-based feature for the functionality; and operating the at least one fallback feature for the functionality.

Methods, apparatuses, and products for providing a code analysis feedback loop for code created using generative AI, including: performing a code analysis on code generated by a generative AI model; determining, based on the code analysis, whether the code satisfies one or more conditions; responsive to the code failing to satisfy the one or more conditions, until the code satisfies the one or more conditions, repeatedly updating the code by receiving, from the generative AI model, updated code based on a previously performed code analysis; and providing the code generated by the generative AI model.

The invention provides an intelligent inspection robot path optimization method and system based on an edge inference model, and the method comprises the steps: obtaining an environment feature topological graph of a target inspection region, and determining an initial edge inference model; incremental training is carried out on the initial edge reasoning model through the real-time environment perception data flow, and a dynamic reasoning model adaptive to the current environment characteristics is generated; performing priority scoring on each path node in the environment characteristic topological graph based on a dynamic reasoning model, and generating an initial optimization path sequence; triggering a feedback calibration mechanism of the dynamic reasoning model according to the environment perception data flow updated in real time, performing dynamic path node replacement on the initial optimization path sequence, and generating a final inspection path; and controlling the intelligent inspection robot to execute an inspection task according to the final inspection path, and continuously collecting new environment sensing data streams in the inspection task process to update a parameter set of the dynamic reasoning model. According to the invention, the adaptability and reliability of path planning to a complex dynamic environment can be improved.

The invention discloses a trajectory optimization method based on a multi-source heterogeneous positioning data fusion algorithm, and relates to the technical field of intelligent navigation and high-precision positioning, multi-modal data are acquired through a multi-modal sensor array, positioning redundancy of scenes such as tunnels and indoor scenes is enhanced, a weight distribution strategy is dynamically adjusted through an Actor-Critic network architecture, and the positioning accuracy is improved. The state space input comprises an environment semantic tag, a historical error sequence and a real-time noise variance, the output action space is continuous weight distribution of each data source, a multi-target reward function optimization strategy is combined, scene adaptability is realized, a local SLAM map, inertial navigation error parameters and a weight distribution strategy are shared in real time based on a V2X protocol, and the real-time performance of the system is improved. According to the method, a single device accumulative error is compensated by using adjacent vehicle data, a terminal locally trains an error compensation model, parameters are uploaded to a cloud end through differential privacy encryption, the cloud end adopts a FedAvg algorithm to aggregate a global model and issue the global model, the error difference between devices is inhibited, and dynamic road network updating and scene differentiation model distribution are supported at the same time.

A generative artificial intelligence (AI) driven code fixing pipeline has been created that uses a transformer-based large language model (LLM) to patch flawed program code. A pre-trained LLM is fine-tuned to generate a response that is a modified version of a code fragment in a prompt to the pre-trained model. After fine-tuning, the pre-trained LLM (hereinafter “code fix model”) is integrated into a pipeline that includes a program code cybersecurity scanner and a prompt generator. The scanner generates indications of flaws in program code and weakness types for those flaws. These indications flow into the prompt generator. The prompt generator retrieves reference code pairs based on weakness type and programming language to generate a batch of prompts to run inference on with the code fix model. The responses generated by the code fix model are presented as patching alternatives.

The invention provides an unmanned aerial vehicle multi-system navigation decoy system based on intelligent algorithm optimization, and relates to the technical field of unmanned aerial vehicle processing, a sensing module comprises a cognitive radar, a photoelectric / infrared sensor, a magnetic and acoustic sensor and an electromagnetic environment scanning device, and a digital twinning and countermeasure AI module comprises a digital twinning model module and a countermeasure AI engine; the cross-domain multi-source cheating module comprises a multi-system navigation signal synthesis module and a sensor-level cheating module; the reconfigurable intelligent antenna array comprises a metamaterial unit, a real-time beam controller and a biological heuristic intelligent algorithm module, and the system security hiding module comprises an encryption access control unit, a micro-power frequency expansion and hopping unit and a redundant backup and disaster recovery unit. The response time of the system from detection to interference can be remarkably shortened by performing high-fidelity simulation on the flight control logic of the unmanned aerial vehicle and the actual electromagnetic environment in the digital twin environment and combining the hybrid decoy strategy generated by the adversarial AI.

The invention discloses an underground construction decision-making method based on three-dimensional geological modeling and risk hot area identification, and relates to the field of fusion of artificial intelligence and geological engineering. The method comprises the following steps: firstly, acquiring drilling data, geological radar images and seismic reflecting layer information, constructing a three-dimensional geological voxel model with spatial topology constraints, and accurately describing a geological unit structure by adopting an irregular grid mode; and then, extracting a time sequence characteristic index under construction disturbance, forming a continuous time sequence characteristic vector, inputting the continuous time sequence characteristic vector into a convolutional recurrent neural network model with a space attention aggregation mechanism and a deep memory unit, and predicting a risk heat value of each space position. And on the basis, through heat gradient clustering and neighborhood consistency analysis, a dynamic high-risk hot area is identified, and a risk hot area map is constructed. And finally, in combination with the construction stage, the equipment plan and the sensor feedback information, constructing a multi-target auxiliary decision function, and generating a construction decision result including operation path reconstruction, rhythm adjustment and power limit and control suggestions.