38179 results about "Robot" patented technology

A federated distributed computational system enables secure biological data analysis and genomic medicine with enhanced oncological therapy capabilities. The system implements patient-specific tumor-on-a-chip analysis through microfluidic control systems and cellular heterogeneity preservation, while integrating fluorescence-enhanced diagnostics using CRISPR-LNP targeting and robotic surgical navigation. The architecture coordinates spatiotemporal analysis of gene therapy delivery through molecular imaging and immune response tracking, and implements bridge RNA integration with multi-target synchronization. Treatment selection is optimized through multi-criteria scoring and patient-specific simulation modeling. Each federated node contains a local processing unit for biological data analysis, privacy preservation protocols, and a hierarchical knowledge graph structure. The system implements cross-species genetic analysis, environmental response modeling, and multi-scale tensor-based data integration, enabling research institutions to collaborate on complex, large-scale biological analyses while maintaining strict data privacy controls.

The invention relates to the technical field of multi-modal data processing, and discloses a multi-modal sensor fusion inspection method and system, and the method comprises the steps: collecting the multi-modal original data of power equipment through a multi-modal sensor in an inspection robot, and constructing a feature vector set; performing adaptive weight calculation on the multi-modal sensor according to the feature vector set to obtain a sensor weight set; carrying out conflict identification and resolution on the multi-modal original data to obtain a fusion data set; performing abnormal feature extraction on the power equipment based on the fused data set to obtain an abnormal feature set; and carrying out routing inspection trajectory optimization based on the abnormal feature set to obtain a target routing inspection path sequence, and carrying out equipment state joint prediction in combination with historical equipment routing inspection data to obtain an equipment fault prediction result. And thus, more accurate equipment state joint prediction is realized.

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.

A federated distributed computational system enables secure oncological therapy optimization through robotic integration. The system establishes a distributed graph architecture with secure communication channels connecting computational nodes, implementing encryption protocols for cross-institutional data exchange. Each node contains processing capabilities for fluorescence-guided imaging, uncertainty quantification, and expert knowledge integration while maintaining hierarchical knowledge graphs of oncological biomarkers, interventions, and outcomes. The system coordinates domain-specific knowledge through token-space communication and implements an advanced robotic integration system for surgical interventions using spatiotemporal tumor mapping, multi-modal fluorescence imaging, surgical robot coordination, and space-time stabilized mesh management. Key capabilities include wavelength-specific multi-modal fluorescence detection, combined epistemic and aleatoric uncertainty estimation, tensor-based data integration with adaptive dimensionality control, and light cone search for adaptive treatment optimization—all while maintaining strict privacy controls.

A robotic surgical system includes a surgeon consol coupled to a patient consol, and the patient consol coupled to surgical instruments. A surgeon computer is coupled to or at the surgeon consol that is coupled to to one or more surgical instruments. A robotic surgery control system includes an artificial intelligence (AI) system with one or more deep learning algorithms. A feedback loop monitors and collects data from the one or more sensors. One or more cameras provide feedback to the robotic surgical system, and are configured to provide images of an anatomical object in at least a two dimensional (2D) arrangements of pixels / Deep learning algorithms of the AI system distinguish different anatomical objects from the images.

An example operation may include one or more of receiving context of a user during an inquiry of a feature via a software application, executing a waiting period via the software application, during the waiting period, selecting an animation to display via the software application based on the context of the user and the feature inquiry wherein the animation provides contextual data associated with the feature, wherein the contextual data is based on a determined need of the user, displaying the animation via the software application during the waiting period, and determining if the user has accepted the feature via the software application. At least one portion of the example operation: integrates with an artificial intelligence (AI) chatbot, interacts with the AI chatbot, is performed by the AI chatbot, and / or is associated with an AI model.

An example operation may include one or more of executing a waiting period of time within a software application being accessed by a user, executing an animation via the software application during the waiting period, determining a result of the software application being accessed by the user, determining additional time to add to the waiting period of time based on the result, via the software application, executing the waiting period with the additional time, augmenting the animation based on the additional time to add to the waiting period, via the software application, and executing the augmented animation via the software application during the additional time. At least one portion of the example operation: integrates with an artificial intelligence (AI) chatbot, interacts with the AI chatbot, is performed by the AI chatbot, and / or is associated with an AI model.

An example operation may include one or more of registering a user for a first feature by a software application, wherein the registering of the user comprises receiving information about the user, determining that the received information does not meet a first condition related to a first feature, determining that the received information meets a second condition related to a second feature, generating an offer to the user the second feature based on the information, detecting acceptance of the second feature, by the software application, and enabling the second feature by the software application for the user in response to the acceptance. At least one portion of the example operation: integrates with an artificial intelligence (AI) chatbot, interacts with the AI chatbot, is performed by the AI chatbot, and / or is associated with an AI model.

The invention discloses a logistics robot path planning method based on multi-modal perception, and relates to the technical field of robot path planning. Laser radar, visual camera and IMU data are fused, and environment state feature vectors are generated through multi-modal data synchronization and space-time alignment; the method comprises the following steps: analyzing environmental semantics by using models such as PointPill and YOLOv8, extracting dynamic characteristics, and identifying obstacles; constructing a space-time risk field, searching a path by a space-time algorithm, converting path points into a continuous trajectory, and optimizing the continuous trajectory; deviation is evaluated in real time, dynamic re-planning is triggered, and multi-robot cooperation and environment semantic understanding are included. Through multi-mode perception fusion, hierarchical planning, multi-target optimization and a cooperation mechanism, the obstacle detection accuracy and the obstacle avoidance success rate are improved, the path planning time is shortened, the energy consumption is reduced, the multi-robot conflict is reduced, the task efficiency is improved, the environment semantic understanding and task adaptive ability is enhanced, and the method is suitable for scenes such as intelligent storage and the like and has wide application prospects. The automation level is improved.

The invention discloses a point cloud data rapid processing system based on 3D vision, and relates to the technical field of point cloud data processing. The binocular acquisition module generates an anti-interference high-precision point cloud; the preprocessing module is used for reducing noise and dimensionality and retaining key geometric features; the hierarchical feature extraction module fuses curvature weight and a non-maximum suppression strategy, and improves the robustness of an ORB algorithm in rotation, scale and noise scenes; the industrial scene segmentation module accurately separates stacked objects through normal vector clustering and a dynamic region growing algorithm, and secondarily verifies boundaries in combination with a lightweight semantic model; the two-stage registration module dynamically adjusts a threshold value based on error feedback to realize pose optimization from coarse registration to fine registration; the coordinate mapping module establishes a space mapping model through multi-attitude calibration and robot multi-axis linkage trajectory optimization. The problem that in the prior art, a 3D machine vision algorithm is high in time delay is solved, the real-time performance and rapidity of 3D model recognition and feature extraction are improved, and the efficiency requirement of industrial production is met.

The invention discloses a robot real-time potential safety hazard recognition system based on multi-modal sensor fusion, and particularly relates to the technical field of intelligent inspection and safety monitoring, the system comprises five parts of data acquisition, information fusion, behavior response, trajectory analysis and risk output, and the potential safety hazard recognition system is used for recognizing potential safety hazards through image acquisition, thermal imaging, gas concentration and temperature and humidity information. Carrying out numerical value normalization and feature extraction, identifying potential abnormity and generating early warning; triggering data enhanced acquisition and track recording in the target area, and constructing a space-time path model to analyze an abnormal evolution trend; and finally, outputting a potential safety hazard assessment result according to a risk level classification rule by combining the enhanced information and the trajectory features. According to the method, high-precision early warning is realized through multi-source data acquisition and normalization fusion, the local recognition capability is improved based on dynamic enhanced acquisition of a behavior response mechanism, an abnormal development trend is tracked by combining track evolution modeling, risk level assessment is output according to the abnormal development trend, and accurate recognition and dynamic management and control of hidden dangers are realized.

The invention discloses a power industry robot collaborative inspection and fault self-diagnosis system and method, and belongs to the technical field of power inspection, and the system comprises a management module which receives an inspection task instruction and obtains inspection task information according to the inspection task instruction; the environment identification module is used for acquiring inspection environment data and identifying obstacles; the path planning module is used for generating an inspection path set by adopting a multi-target particle swarm optimization algorithm according to the obstacle and inspection task information; the scheduling module is used for acquiring the state data of each robot and distributing the routing inspection paths in the routing inspection path set to each robot; the fault feature extraction module is used for acquiring multi-sensor data acquired by the robot and generating a fault feature vector; and the fault diagnosis module performs fault analysis on the fault feature vector to obtain a fault risk analysis report. The obstacle is recognized by acquiring the environment data, the inspection path set is generated by combining the inspection task information and adopting the multi-target particle swarm optimization algorithm, and the method can adapt to the complex inspection environment.

The invention belongs to the field of intelligent edge systems and privacy enhancement computing, and particularly relates to a privacy protection-oriented robot large model cloud edge end collaborative reasoning and federated learning system, which comprises a cloud server layer used for deploying a large-scale pre-training model and executing complex reasoning and global federated learning coordination; the edge calculation layer is used for deploying an intermediate layer model and executing local data aggregation, privacy protection processing and intermediate feature calculation; the terminal equipment layer is used for deploying a lightweight model and executing data acquisition, primary processing and lightweight reasoning; the federated learning framework is used for optimizing the model; the privacy protection module is used for integrating data localization, differential privacy, homomorphic encryption, secure multi-party computing and a block chain verification mechanism; the adaptive allocation module is used for dynamically adjusting computing resources. According to the method, the problems of privacy leakage risk, computing resource limitation, network delay, insufficient data isolation and the like of the traditional AI service in a robot scene are solved, and efficient privacy protection and data security isolation are realized.

The invention provides a teaching-free welding robot path planning system based on visual autonomous learning, and relates to the technical field of industrial welding, and the teaching-free welding robot path planning system comprises a pose compensation module which is used for arranging three infrared reflective mark points on the surface of a clamp base in an L shape, collecting original point clouds of the mark points and a workpiece by using a stereo camera, and preprocessing the point clouds; generating a pose compensation matrix based on the offset of the actual coordinate and the theoretical coordinate of the mark point, and outputting a de-noising point cloud data set and a compensation matrix; and the pose correction module is used for fitting a workpiece plane equation by adopting a random sampling consistency algorithm on the basis of the de-noised point cloud data set, acting a pose compensation matrix on the workpiece plane equation, correcting a pose error caused by clamp drifting, calculating a welding seam track end point coordinate and outputting a corrected welding seam pose parameter and a scanning pose instruction. According to the method, clamp drift can be compensated, weld joint poses can be corrected, three-dimensional features are extracted, a collision-free path is planned, and the welding precision, efficiency and automation degree are improved.

The invention discloses a robot grabbing posture generation method and a related device, and the method comprises the steps: obtaining an RGB image, a depth image and a position coordinate of a target object, and generating a spatial calibration matrix through data preprocessing; target object segmentation is carried out on the calibrated RGB matrix according to the target point coordinate sequence, a segmentation mask is generated, and object mass center coordinates are calculated; generating a three-dimensional point cloud by using the calibrated depth matrix, the segmentation mask and the camera parameters, extracting a plane point set and a non-plane point set through an RANSAC algorithm, and analyzing to obtain an axial feature vector and a plane normal vector; finally, grabbing parameters are calculated according to the feature vectors and the centroid coordinates, and a grabbing posture transformation matrix is generated through vector operation. According to the technical scheme, under the condition of not depending on a preset model library, the grabbing posture of an unknown object is generated by analyzing the geometrical characteristics of the object, the accuracy problem in the process of converting two-dimensional image information into three-dimensional grabbing parameters is solved, and the adaptability and reliability of a robot grabbing task are improved.

The invention relates to the technical field of target detection, in particular to a robot three-dimensional environment sensing method and device based on deep visual learning, and the method comprises the steps: collecting visual data based on a sensing system carried by a robot, carrying out the synchronous processing, extracting the spatial structure characteristics of a synchronous visual data stream and a preliminary semantic segmentation map, and carrying out the recognition of a target image; combining the spatial structure features with the preliminary semantic segmentation map to generate a geometric semantic feature map; extracting and optimizing local, regional and global features of the geometric semantic feature map, and performing three-dimensional modeling processing according to a multi-scale feature tensor to generate a three-dimensional geometric model; and carrying out fusion optimization on the structure information of the three-dimensional geometric model and the geometric semantic feature map, and carrying out verification processing based on a verification framework to obtain three-dimensional environment perception data. Through deep visual learning and visual data fusion, the defects of insufficient accuracy and limited deep semantic understanding ability in complex three-dimensional environment perception in the prior art are solved.

The disclosure provides an intelligent horizontal transportation system for automatic loading or unloading at a container terminal. The system includes a plurality of autonomous transport robots (ATRs) and an ATR control system. The plurality of ATRs are configured to perform horizontal transportation tasks. The ATR control system is in real-time communication with the plurality of ATRs to manage and control the plurality of ATRs; the ATR control system is in real-time communication with a terminal management system, an automated yard crane, and an automated quay crane, so as to coordinate task scheduling between the automated yard cranes, automated quay cranes, and the plurality of ATRs. The ATR control system includes a task scheduling module, a dynamic path planning module, a standardized control interface module, a traffic management module, a lock station management module, a vehicle sequencing module, a charging scheduling module, a parking management module, and a remote driving module.

The invention discloses a pig breeding intelligent management and control system based on multi-modal data monitoring, and belongs to the technical field of pig breeding management, and the system comprises a data collection and individual recognition module which comprises an RFID ear tag, an inspection robot, an environment sensor and an intelligent feeder, and is used for determining the identity information of a pig individual based on the data collected by the RFID ear tag and the inspection robot, associating the collected multi-modal data of the live pigs in the pig house, and uploading the multi-modal data to edge computing equipment through an MQTT protocol for preprocessing; the feature extraction module extracts physiological and behavior features of live pigs by using a multi-modal deep learning fusion model; the key breeding index analysis module constructs a live pig health index and an environment quality index; the abnormity early warning module adopts a CUSUM control chart to accumulate index deviation and respond to abnormity; the management regulation and control model constructs a Markov decision process by minimizing energy consumption and maximizing health indexes, intelligently adjusts the rotating speed of a fan and a feeding strategy, and realizes precise environmental control and feeding optimization.

A structural-health-monitoring system is disclosed for real-time detection and autonomous maintenance of physical structures. The system includes a sensor network comprising at least one strain gauge and one tri-axial accelerometer mounted on the structure to generate real-time sensor signals. A perception module filters and normalizes the signals and extracts numerical features such as peak amplitude and dominant frequency. A digital-twin module maintains a finite-element model updated in response to the extracted features. A data-driven surrogate model predicts sensor behavior and refines itself using machine-learning techniques. An anomaly-detection module computes an anomaly score from model residuals or classifier outputs. Upon exceeding a threshold, a maintenance module initiates a maintenance action, including generating an inspection schedule or issuing a control signal to an autonomous inspection or repair device. A learning module continuously improves system performance using reinforcement learning based on historical outcomes. The system supports predictive diagnostics, robotic repair, and automated optimization for long-term structural integrity.

The invention discloses a surgical robot resection navigation method based on multi-modal image registration. The method comprises the following steps: acquiring a preoperative multi-modal medical image of a patient and reconstructing a three-dimensional model containing a focus and a dangerous structure; acquiring an intraoperative real-time image; extracting cross-modal invariant features from the pre-operative and intra-operative images by using a first deep learning network, and establishing an initial corresponding relation; predicting an initial non-rigid deformation field by using a second deep learning network, based on the pre-operative / intra-operative image and the initial corresponding relation and in combination with physical model constraints; tracking a surgical tool in real time, and updating a non-rigid deformation field in real time by adopting an incremental calculation strategy; applying the updated deformation field to the preoperative three-dimensional model to generate a deformed model; and mapping the tool position to the deformed model, and generating a navigation instruction. According to the method, soft tissue deformation can be accurately compensated, multi-modal image information is effectively fused, navigation information is dynamically updated in real time, and the precision and safety of robot surgery are remarkably improved.

The invention discloses a monorail crane inspection robot intelligent test method based on data analysis, and relates to the technical field of intelligent detection, and the method comprises the following steps: synchronously collecting track images, point cloud and attitude data, carrying out time alignment and preprocessing, and outputting a standardized data packet; detecting an inspection target by using a YOLO detection network, and outputting a multi-scale feature vector in combination with a point cloud feature hierarchy extraction network and a time sequence convolutional network; predicting a fault development trend in combination with an improved A-star algorithm and a long short-term memory network, optimizing an inspection path through reinforcement learning, and outputting a maintenance decision scheme; the maintenance decision scheme is converted into a control instruction, the robot is driven to execute an inspection task and feed back the operation state in real time, incremental learning and point cloud reconstruction are combined, and a visual diagnosis report is output. According to the method, the dynamic threshold algorithm is adopted for self-adaptive analysis, and the key geometric indexes are calculated in combination with the cross-modal attention mechanism, so that the recognition capability of structural anomalies is improved.

In one implementation, a computer-implemented method involves receiving a user message corresponding to a query or a statement to AI chatbot, performing preprocessing operations resulting in generation of initial context of the user message by extracting text of the user message, metadata of the user message, and a conversation identifier, obtaining historical context pertaining to the user message from a plurality of storage mechanisms provided in differing formats including a knowledge graph, a vector database comprised of vector embeddings, and a database comprising text summaries of prior conversations between the user and the AI chatbot, generating a prompt for a LLM that instructs the LLM to generate a response to the user message that is based on and consistent with the user message, the initial content, and the historical context, and providing a final response to the user that is corresponds to an LLM-generated response.

The invention discloses a multi-source information collaborative power equipment three-dimensional temperature field construction method. Firstly, an infrared camera, an IMU and a laser radar are utilized to obtain an accurate external parameter relation through joint calibration, point cloud distortion is eliminated, angular points and plane points are extracted, a re-projection residual error, a distance sampling residual error and an IMU pre-integration residual error are constructed, an error state iteration Kalman filter is adopted to optimize a global pose, and positioning is achieved. Providing a self-supervised depth completion network, combining an infrared temperature image and a sparse depth map generated by a laser radar as input, adopting a depth completion strategy guided by an infrared image, estimating relative motion of adjacent frames by using pose information, introducing a feature alignment module to reduce alignment errors, and combining the depth map, the infrared image and IMU data to obtain a self-supervised depth completion algorithm; and efficient construction of the three-dimensional temperature field of the power equipment is realized. According to the invention, the three-dimensional temperature field of the power equipment is constructed more accurately, and the capability of the substation inspection robot for state monitoring and fault diagnosis of the power equipment is improved.

The invention discloses a defect repair method based on digital twinning and friction stir welding technologies, and relates to the technical field of intelligent manufacturing and digital twinning, and the defect repair method comprises the following steps: synchronously capturing full-dimensional data of a welding area through a multi-mode sensor array integrated by an actuator; secondly, segmenting defect boundaries by adopting a deep learning algorithm, constructing a dynamic twin model in combination with thermal-force field coupling simulation, and accurately mapping defect three-dimensional features; generating a repair track according to the twinborn model, converting the repair track into a robot joint instruction through a curved surface parameterization mapping algorithm, and implanting real-time anti-collision constraint; in the repairing process, based on reinforcement learning control of the material rheological resistance and the temperature gradient, the rotating speed, the advancing speed and the down force of the tool are dynamically adjusted; and after repairing, micro-focus CT scanning is started immediately, actually measured data is compared with twinborn prediction, and when the deviation exceeds a threshold value, a re-repairing process is triggered automatically. The method solves the problems that a traditional method depends on manual intervention and the precision of a sensor is easily interfered by the environment.

The invention provides a data center inspection robot monitoring analysis method and system based on machine vision, and relates to the technical field of inspection robots, and the method comprises the steps: obtaining a video stream and environment parameter data collected by an inspection robot, and carrying out the detection and recognition of an abnormal state through a deep learning target; and constructing a multi-modal data fusion analysis model to form a knowledge graph to generate a root cause analysis result, and planning an inspection path based on priority scores. According to the invention, intelligentization and precision of data center monitoring are realized, and inspection efficiency and fault diagnosis accuracy are improved.

The invention provides an intelligent material damage diagnosis method and system based on magnetic characteristic data fusion, and relates to the technical field of nondestructive testing, and the method comprises the following steps: collecting magnetic characteristic data by using a magnetic stress detection robot; performing multi-scale decomposition on the data to construct a magnetic domain evolution characteristic spectrum; inputting the characteristic spectrum into a composite neural network to extract a material damage characteristic matrix; constructing a fusion feature vector by using tensor dimensionality reduction and a multilayer belief transfer network; and realizing damage calculation through temperature field modulation and self-adaptive weight distribution to obtain the damage grade and the residual life. According to the invention, accurate diagnosis of the material damage in the welding seam area of the pressure vessel is realized.

The embodiment of the invention provides a swimming pool robot positioning and trajectory prediction method and system fusing sonar and vision. The method comprises the following steps: collecting terahertz sonar data for a target area; based on terahertz sonar data, constructing a motion track model of the swimming pool robot; acquiring multi-source image data by adopting a multi-spectrum and event camera collaborative acquisition strategy; a dynamic SLAM algorithm based on deep learning is adopted, dynamic objects in the swimming pool environment are recognized and processed in real time based on the multi-source image data, and the environment image data with the dynamic objects removed are adopted to obtain a swimming pool environment model; performing deep fusion on the motion track model and the swimming pool environment model through a knowledge graph network, and predicting the operation state of the swimming pool robot by adopting a quantum machine learning algorithm to obtain a panoramic model; and creating and updating a digital twinborn model corresponding to the panoramic model in real time. The real-time performance and accuracy of positioning and track prediction of the swimming pool robot are improved, and a user can conveniently maintain and manage the swimming pool robot.

The invention provides an intelligent storage robot group cooperative scheduling method based on deep reinforcement learning, and relates to the technical field of intelligent storage, and the method comprises the steps: constructing a group perception module through a hierarchical attention mechanism, and generating a dynamic cooperative perception matrix; establishing a deep reinforcement learning model for strategy learning; and designing a multi-level reward function and optimizing a training process through an adaptive weight adjustment mechanism. According to the invention, the cooperative efficiency of warehouse robot group scheduling is improved, the task conflict rate is reduced, and the adaptability of the system to a complex dynamic environment is enhanced.

The invention provides a robot cable manufacturing process optimization method and system based on deep reinforcement learning, and relates to the technical field of intelligent manufacturing, and the method comprises the steps: collecting cable manufacturing data, constructing a digital twin model, and achieving the process flow simulation through a graph network; building a deep reinforcement learning environment by taking the manufacturing data and the simulation data as state input; analyzing a causal dependency relationship of process parameter adjustment; constructing an industrial knowledge graph expert system to generate an optimization strategy; and a process optimization closed loop is formed. According to the invention, self-adaptive optimization of the cable manufacturing process is realized, and the manufacturing efficiency and quality are improved.

The invention relates to the technical field of artificial intelligence, can be applied to business scenes of robot strategy training, financial science and technology, medical treatment and health and the like, and discloses a strategy optimization method and device based on an interaction track, equipment and a medium. The reinforcement learning agent interacts with the environment to generate an action and state sequence and record an interaction track, an optimal action strategy is generated based on a track optimization strategy, an original empirical data set is further generated, and a pre-training model is optimized through supervised learning to obtain a target strategy model. According to the method, a task-related high-quality trajectory is generated through reinforcement learning agent and environment interaction, original empirical data is extracted on this basis, and a pre-training strategy model is optimized in combination with a supervised learning mechanism, so that the sample utilization efficiency is effectively improved, and the generalization ability and execution robustness of the model under a multi-task condition are enhanced.