59706results about "Instruments" patented technology

The embodiment of the invention relates to the technical field of ship scheduling, in particular to a multi-source data-based intelligent auxiliary decision-making method for ship entering and leaving a port and berthing and leaving the port, and the real-time performance and accuracy of port scheduling decision-making can be improved by fusing multi-source heterogeneous data to construct a ship navigation situation awareness system. The space-time alignment technology effectively eliminates space-time dislocation of environment interference factors and enhances the characterization capability of the ship motion state and environment coupling relation. Quantitative evaluation of multi-dimensional navigation risks is realized based on a ship behavior prediction model, and potential risks such as channel deviation and berthing conflicts can be identified. A collaborative decision-making scheme considering safety and efficiency is generated through a collaborative optimization mechanism of the dynamic scheduling rule base and the risk indexes, and ship trajectory control precision is optimized through dynamic planning of navigational speed adjustment and steering time sequence. Closed-loop linkage of port resource allocation and ship navigation control is achieved through automatic instruction generation and execution, and the reliability and the response speed of the port operation process can be ensured.

The present invention relates to the technical field of heart rate monitoring, discloses a heart rate monitoring method, wherein the heart rate monitoring method comprises: obtaining initial electrocardio signals of a user by a preset heart rate sensor, and acquiring initial acceleration energies of the user by a preset acceleration sensor; pre-treating the initial electrocardio signals, obtaining pretreated target electrocardio signals, and pre-treating the initial acceleration energies to obtain pretreated target acceleration energies, the present invention can be used to help doctors and users to know current heart rate conditions, make personalized and well-targeted health management measures and improve living quality.

The invention provides a buried water delivery pressure steel pipe disease inspection method and system, and the method comprises the following steps: carrying out the time sequence alignment and quality evaluation of multi-source sensor data, such as ultrasonic waves, videos and positions, carrying out the feature extraction and optimization through adaptive filtering and frequency smoothing technologies, and carrying out the partitioning compression storage based on entropy and variability; carrying out decompression and feature reconstruction on the compressed data, carrying out state evolution prediction based on multi-scale state trajectory analysis, and carrying out anomaly detection and classification in combination with a dynamic reference model; health feature vectors are constructed, health state distribution is obtained through nonlinear feature fusion and probability density analysis, deterioration trend prediction is carried out based on environment coupling analysis, risk rating and early warning are carried out by adopting a multi-level dynamic threshold value, and finally a maintenance priority sequence and a maintenance scheme are generated. According to the invention, pipeline diseases can be accurately identified, and detection efficiency and reliability are improved.

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 artificial intelligence, in particular to an artificial intelligence risk level supervision system which comprises a link transaction recognition module, a behavior pattern mapping module, a path clustering evaluation module, a level label correction module and a segmentation level tracking module. According to the method, through linkage analysis of time records and positioning coordinates, rapid identification and abnormal track screening of transition nodes are realized, the spatial perception capability of dynamic abnormal nodes is enhanced, a cross behavior structure is constructed in combination with overlapping number collection, the consistency identification capability of multi-source behavior data is improved, illegal paths are subjected to frequency clustering to form atlas expression, and the accuracy and the reliability of the system are improved. According to the method, quantitative description and node positioning of risk paths are enhanced, correction feedback is generated through grade comparison, the risk assessment sensitivity is improved, progressive increase links are screened based on the grade variation trend, serialized extraction of high-risk paths is achieved, and a hierarchical controllable risk link identification and assessment closed-loop system is constructed.

The invention discloses a construction progress control method in project supervision, and the method comprises the steps: calculating the current punctual completion rate and process handover delay time of each subpackage provider according to a real-time progress data set and a pre-established project network diagram, recognizing the progress fluctuation modes of different project stages through a time sequence analysis method, and carrying out the construction progress control according to the progress fluctuation modes. Establishing a correlation model of the sub-packaging quotient and the engineering stage; according to the priority scores, taking minimization of overall construction period delay and resource idleness as objective functions, applying an optimization algorithm, dynamically adjusting a construction plan and a resource allocation scheme of each subpackage provider, and generating an optimized project progress plan which comprises specific work arrangement and resource allocation suggestions of each subpackage provider; a successful coordination strategy in a historical project is analyzed, coordination measures are recommended for project management personnel and a set of communication matrix is generated according to characteristics and progress conditions of a current project, and the coordination measures comprise resource allocation suggestions, process optimization schemes and communication strategy templates.

An example assistant system can use a multimodal multitask medical machine-learned model to perform image processing to answer natural language queries. A device can process speech data or other natural language inputs to obtain a query. The query can be processed alongside image data that provides context for the query. The example system can receive a query associated with a particular task domain; generate, based on the query, a query input that comprises query instruction data from a first modality and query context data from a second modality; generate a combined input comprising the query input and an exemplar input, wherein the exemplar input comprises exemplar instruction data from the first modality and an exemplar context placeholder in lieu of exemplar context data from the second modality; process the combined input with a multimodal machine-learned model to generate output data; and output a query response based on the output data.

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.

Systems and methods for providing a space launch service platform (SLSP) that integrates artificial intelligence and data analytics to support launch operations. The SLSP may connect to multiple data sources, collect data, and standardize the collected data according to regulatory and operational standards. The SLSP may evaluate launch safety and risks using standardized data and generate a situational analysis for decision-making. The SLSP may provide graphical overlays and decision-support tools to highlight optimal launch windows and potential risks.

The invention provides a prediction method and system for a rock stratum fracture disaster in tunnel engineering, and belongs to the field of tunnel engineering. Comprising the steps of multi-modal monitoring data acquisition and synchronization, data preprocessing and feature calculation, multi-modal information entropy evolution analysis, catastrophe point detection and precursor identification, risk assessment and early warning output and the like. Multiple types of sensors are arranged at key positions of a tunnel, an edge computing gateway is accessed through an industrial-grade field bus, and space-time reference unification of multi-source heterogeneous data is achieved; carrying out preprocessing and feature calculation on the collected original data; performing sliding window probability distribution modeling on each modal feature, calculating an information theory index, and analyzing non-linear relevance among the multi-modal features; judging whether a catastrophe precursor event exists or not by adopting multiple methods; and finally, based on a catastrophe point detection result, performing risk assessment on the tunnel region by using a multi-dimensional risk quantification model, and performing early warning output on high-risk and emergency states.

The AI-enhanced cellular modeling and simulation platform is a computational system designed to enhance biomedical research and development and personalized medicine and wellness. This platform integrates simulation modeling, machine learning and artificial intelligence, multi-omics data, and sophisticated data fusion and decision-support techniques to create comprehensive models of cellular systems and processes across multiple scales. It enables researchers and clinicians to simulate complex biological interactions, predict disease progression, and design or optimize treatment strategies or medical devices with improved accuracy and efficacy. The system's architecture allows for integration of various components, including real-time data processing, federated learning, and quantum computing enhancements. From personalized drug discovery and cancer therapies to synthetic biology and epidemiological analysis, this platform offers powerful tools for understanding and manipulating cellular systems and bioengineered systems. By bridging the gap between molecular-level interactions between cells and materials and organism-wide effects, it enables significant advancements in healthcare and biological sciences.

The invention provides a health management data mining method and system based on deep learning, and the method comprises the steps: firstly obtaining a target health data set, including physiological monitoring, behavior activity records and medical history texts, of a target user, and then carrying out the cross-modal noise filtering and feature alignment processing of the target health data set; the method comprises the following steps: generating a standardized health feature sequence containing a synchronous timestamp and the like, performing hierarchical feature abstraction by using parallel time sequence convolution branches and space attention branches through a cascaded spatial-temporal feature extraction network to obtain a multi-granularity health state characterization vector, inputting the multi-granularity health state characterization vector into a pre-training model, and training the multi-granularity health state characterization vector to obtain a multi-granularity health state characterization model. According to the method, a personalized health management scheme containing disease risk levels and the like is generated, finally, according to dynamic monitoring period parameters in the personalized health management scheme, the data acquisition frequency and the feature alignment strategy are adjusted in real time, a closed-loop health management data flow is formed, and precise and personalized health management is achieved.

This disclosure relates to methods for spatial profiling of analytes present in a biological sample. Also provided are methods for using spatially barcoded substrates to detect a biological analyte in a cell culture, an organism, and organoid. Also provided are methods for using spatially barcoded substrates to detect the temporal profile of a biological analyte.

The invention discloses an unmanned aerial vehicle scheduling method and system based on multi-level collaborative decision, and belongs to the technical field of unmanned aerial vehicle intelligent scheduling. Comprising the steps of obtaining a flight task of an unmanned aerial vehicle cluster, decomposing the flight task into a plurality of sub-tasks, and determining a sub-task allocation strategy in combination with single unmanned aerial vehicle performance and sub-task requirements; according to the sub-task allocation strategy, in combination with the feature importance of the flight influence factors, an objective function is constructed, iterative optimization is performed through a particle swarm optimization algorithm, and an optimal flight task execution scheme of the single unmanned aerial vehicle is determined; and according to the optimal flight task execution scheme, multi-unmanned aerial vehicle cooperative path planning is carried out through a path planning algorithm, and an optimal task execution path is generated. The task execution efficiency of the whole unmanned aerial vehicle system can be improved, the coping capacity of the unmanned aerial vehicle system in a dynamic complex environment is enhanced, and the problems that an existing unmanned aerial vehicle is low in response speed, low in accuracy, poor in environmental adaptability and the like in task allocation and resource scheduling are solved.

Provided is a process, including: executing, with the computer system, tasks in the workflow with AI agents; detecting, with the computer system, that a human is needed to assist with a given task among the tasks in the workflow after one of the AI agents attempts the given task; selecting, with the computer system, the human from among a set of humans based on a profile of the human and the given task and sending a message that causes the selected human to perform the given task; and receiving, with the computer system, the selected human's completion of the given task and inputting the human completion of the given task, or input, to one of the AI agents performing a downstream task in the workflow; and storing, with the computer system, a result of completing the workflow in memory.

The invention relates to the technical field of disease follow-up visit, in particular to a health state assessment system applied to disease follow-up visit, which comprises a data acquisition module, a data preprocessing module, a health state assessment module, a personalized health management suggestion module, a dynamic health risk prediction module and a visualization module, the data acquisition module acquires multi-dimensional data of a patient and performs data fusion and integration, the health state evaluation module evaluates the health state of the patient based on multi-level feature extraction and a deep learning model, and the personalized health management suggestion module combines a health state evaluation result and historical data to obtain a personalized health management suggestion result. Customized health management suggestions are generated through a rule engine, and the dynamic health risk prediction module predicts future health risks of patients and provides real-time early warning by using a time sequence analysis model. The visualization module displays health assessment and management suggestion results through a chart and an interactive interface, and the accuracy and individuation degree of health management are improved.

A federated distributed computational system enables secure collaboration across multiple institutions for multi-species biological data analysis. The system consists of interconnected computational nodes managed by a central federation manager. Each node contains specialized components that work together to process multi-species biological data while preserving privacy. These components include a local computational engine that handles data processing, a physics-information integration subsystem that combines physical state calculations with information-theoretic optimization, a privacy preservation module that protects sensitive information, a knowledge integration component that manages biological data relationships, and a communication interface that enables secure information exchange between nodes. The federation manager coordinates all computational activities and manages resource allocations across the network while ensuring data privacy is maintained throughout the process. This architecture allows research institutions to collaboratively analyze complex, multi-species biological systems through integrated physics-based modeling and information-theoretic approaches while maintaining security and confidentiality.

Provided are an anomaly detection method and system for multi-source heterogeneous data, an electronic device, a vehicle, a computer-readable storage medium, a computer program product, and a computer program. The method comprises: performing statistical modeling on multi-source heterogeneous data, and obtaining background distribution features of multi-source heterogeneous data distribution through a data splitting method; preprocessing the background distribution features by using a preset sliding window to obtain a background image sequence on a specific time series; on the basis of the size of a target in the background distribution features, performing adaptive partitioning on the background image sequence to obtain an adaptive partitioning result, and identifying single source points of each partition in the adaptive partitioning result through a decoupling representation method; and on the basis of the identified single source points, determining the location of anomalous data in the background image sequence, and on the basis of a comparison calculation result of the multi-source heterogeneous data and the background image sequence from which the anomalous data is removed, obtaining an anomalous data location of the multi-source heterogeneous data.

The invention discloses a neurology nursing potential safety hazard monitoring system and method, and relates to the technical field of medical information safety monitoring, and the system comprises a first collection processing unit which is used for collecting monitoring data of a target patient, inputting the monitoring data into a time sequence deep network for processing, and obtaining risk feature data; the second collecting and processing unit is used for collecting physiological state data of the target patient, constructing a risk assessment rule base based on a medical knowledge graph to analyze the physiological state data, and generating risk probability data; and the risk early warning unit is used for inputting the risk feature data and the risk probability data into a Bayesian fusion network, generating risk early warning information and sending the risk early warning information to the medical care terminal. According to the invention, collaborative analysis of multi-source information is realized through the Bayesian fusion network, the problem of low early warning accuracy of a traditional single monitoring method is solved, and the reliability of potential safety hazard monitoring is improved.

Surgical systems and related computer-implemented methods are provided, including during performance of a surgical procedure on a patient, receiving, at a first surgical system, a first dataflow from a second surgical system, the first dataflow including first data regarding a measured patient parameter that the first surgical system is configured to use in performing a function during the performance of the surgical procedure. The computer-implemented methods including determining that a trigger event occurred during the performance of the surgical procedure such that a sum of a first bandwidth of the first dataflow and a second bandwidth of a second dataflow exceeds an available bandwidth, and in response to determining that the trigger event occurred, and during the performance of the surgical procedure, adjusting at least one of the first and second dataflows such that the sum of the first and second bandwidths does not exceed the available bandwidth.

The invention relates to the technical field of artificial intelligence and Internet of Things, in particular to an inspection service management system based on artificial intelligence and Internet of Things, which comprises a data acquisition module, an edge calculation module, a task allocation module, a path planning module, an early warning module and a user interaction module. Wherein the data acquisition module is used for acquiring multi-dimensional data of environment and equipment in real time; the edge calculation module is used for carrying out preliminary analysis and processing on the multi-dimensional data; the task distribution module is used for generating inspection tasks according to inspection rules; the path planning module is used for generating an optimal path of the inspection equipment; the early warning module is used for generating an early warning signal when the potential risk is detected; and the user interaction module is used for displaying the state of the system through a visual interface. According to the invention, by realizing real-time data processing, dynamic task scheduling and path optimization, the response speed and efficiency of the system are improved, and meanwhile, the equipment fault prevention and decision-making capabilities of operators are enhanced.

The invention relates to the technical field of hydropower station operation and maintenance management, in particular to an operation and maintenance management method and system for a hydropower station, and aims to improve operation and maintenance efficiency and equipment reliability. Then dynamic feature extraction operation is executed, a state feature set containing equipment operation stability, environment coupling and abnormal fluctuation correlation features is generated, then abnormal state matching processing is conducted on the state feature set through a preset abnormal detection strategy network, the abnormal level and influence range parameters are determined, and the abnormal state is obtained. And finally, generating an equipment operation and maintenance optimization strategy set according to the parameters, including an equipment control instruction sequence and a maintenance resource scheduling scheme, and feeding back to a hydropower station control center to trigger operation and maintenance operation, thereby realizing intelligentization and precision of hydropower station operation and maintenance management, and ensuring safe and stable operation of the hydropower station.

The present disclosure relates to the field of biological information, and in particular to a gene regulatory network inference method based on spatiotemporal transcriptomic data, and the use thereof. In the present disclosure, on the basis of a graph neural network, a graph variational auto-encoder is used, an adjacency matrix A is used as node connection information, cells and genes are used as node feature information, and position information of the cells is fully used, so as to reconstruct a feature matrix of nodes by means of three hidden layers, thereby performing more accurate cell clustering and dimensionality reduction analysis while inferring a gene regulatory network.

A federated distributed computational system enables secure, multi-institutional biological data analysis and genomic medicine through interconnected, decentralized nodes in a federated distributed graph architecture. A federation manager coordinates computational resource allocation, control and data flows, establishes privacy and security boundaries, implements multi-scale spatiotemporal analysis and simulation modeling, models cross-species or intrapopulation elements, and maintains cross-institutional knowledge relationships. Each node includes a local processing unit for biological data analysis, including multiomics and gene editing, privacy-preserving protocols for secure multi-party computation, a hierarchical knowledge graph for managing multi-domain biological relationships across spatial and temporal scales, and encrypted network connections. The system implements cross-species genetic analysis via phylogenetic integration, environmental response modeling through spatiotemporal tracking, and multi-scale tensor-based data integration with adaptive dimensionality control. This architecture enables research institutions to collaborate on complex biological analyses and genomic medicine applications while maintaining strict data privacy and security controls.

The invention relates to the field of medical informatization. The invention discloses a thyroid cancer electronic medical record system based on multi-modal data fusion. The thyroid cancer electronic medical record system comprises a multi-modal data acquisition module which acquires patient texts, ultrasonic images, genes, biochemical indexes and clinical data and performs standardized calibration to generate standard data; the multi-modal feature extraction module extracts semantic, structure, mutation, change and fluctuation features of each standard data through multiple technologies; the single-mode prediction model construction module constructs single-mode prediction models of texts, images and the like based on the features and outputs results; and the multi-modal fusion prediction module fuses the single-modal model based on the deep learning framework to output a multi-modal fusion prediction result. According to the invention, multi-modal data are integrated, and the accuracy and comprehensiveness of thyroid cancer diagnosis are improved. The system ensures consistency through standardized data processing, and assists doctors to accurately judge pathological types, recommend therapeutic schedules and evaluate prognosis by means of a multi-modal feature extraction and fusion mechanism.

The invention provides a green agricultural product distribution path optimization and inventory control method, and the method comprises the steps: selecting a distribution path with the minimum agricultural product quality influence degree as an optimal path according to a prediction result of the agricultural product quality influence degree, and transmitting the optimized distribution path to a distribution vehicle terminal in real time; after the distribution vehicle terminal receives the optimal distribution route issued by the cloud, the distribution route is navigated in real time through a vehicle-mounted navigation system, a driver is guided to drive along the optimal route, and the distribution route is dynamically adjusted according to the real-time road condition information; meanwhile, according to the real-time position and the driving speed of the distribution vehicle, the arrival time of the agricultural products is dynamically estimated, and the estimated arrival time information is pushed to a destination customer to remind the customer to make preparation for receiving goods in advance.

The embodiment of the invention relates to the technical field of data processing, in particular to an intelligent monitor temperature drift correction method and system based on a temperature compensation algorithm, and the method comprises the steps: collecting the environment temperature data of an environment where an intelligent monitor is located in real time, and obtaining a preset sensor aging factor in the intelligent monitor; based on the environment temperature data and a preset temperature-drift characteristic model, the temperature drift characteristic of the intelligent monitor is extracted, and a nonlinear compensation curve matched with the temperature drift characteristic is dynamically generated; performing iterative optimization processing on compensation parameters of the nonlinear compensation curve by adopting a self-adaptive compensation parameter optimization algorithm, and performing dynamic correction on the compensation parameters by fusing sensor aging factors in the iterative optimization processing process to obtain an optimized compensation parameter set; and performing real-time correction processing on original measurement data of the intelligent monitor according to the optimized compensation parameter set, and outputting a target measurement result after temperature drift noise suppression.

The present invention provides systems, methods, and devices for electroporation-based therapies (EBTs). Embodiments provide patient-specific treatment protocols derived by the numerical modeling of 3D reconstructions of target tissue from images taken of the tissue, and optionally accounting for one or more of physical constraints or dynamic tissue properties. The present invention further relates to systems, methods, and devices for delivering bipolar electric pulses for irreversible electroporation exhibiting reduced or no damage to tissue typically associated with an EBT-induced excessive charge delivered to the tissue.

The invention discloses a critical patient nursing intelligent monitoring system and method, and relates to the technical field of intelligent monitoring. Collecting physiological data and activity data of a patient in real time by using a multi-sensor system, and establishing an initial health file of the patient; according to the collected physiological data and activity data, daily activities and behavior modes of the patient are analyzed, and an individualized health baseline is established; monitoring physiological data and activity data of the patient in real time, identifying an abnormal trend, and generating an early warning signal; preferentially distributing nursing measures according to the state of the patient; the nursing measure effect is evaluated, and if the expected nursing effect is not achieved, the monitoring strategy is readjusted; according to the invention, the health state of the patient is comprehensively monitored and accurately analyzed in real time, so that abnormity can be found and intervened in time when the abnormity occurs, the patient can be nursed and treated in time, waste of nursing resources and nursing errors are reduced, and the treatment effect and the nursing quality of the critical patient are improved.

A source-grid-load-hydrogen storage multi-stage planning method and system considering flexible resources, relating to the technical field of source-grid-load-hydrogen storage system planning. The method comprises: collecting source-grid-load-hydrogen storage data for data preprocessing; constructing a flexibility supply and demand characteristic model and a source-grid-load-hydrogen storage multi-stage dynamic planning model; calling a solver to solve the source-grid-load-hydrogen storage multi-stage dynamic planning model to obtain an optimal solution; and outputting a multi-stage source-grid-load-hydrogen storage investment result, a multi-stage source-grid-load-hydrogen storage operation policy, and a multi-stage flexibility supply evaluation result within a planning period. Using the minimization of investment costs, operation costs, and insufficient flexibility penalty costs within the whole planning period as target functions, various constraints such as a new energy permeability constraint and a load loss rate constraint are introduced, a dynamic planning method is proposed, and a source-grid-load-hydrogen storage multi-stage planning solution that has sufficiently economical planning operation and is sufficiently flexible is obtained. According to a processing method based on piecewise linearization, the model is simplified, and the computation speed is increased.