391 results about "Data human" patented technology

The invention relates to the field of medical robots, and discloses a meridian point foundation construction method for precise positioning of an acupuncture robot, which comprises the following steps: scanning skin texture, muscle contour and skeleton mark features of a human body surface, and establishing a human body surface feature digital model; the method comprises the following steps: acquiring temperature distribution data of a human body surface, human body surface feature digital model data and elastic modulus data, performing weight mapping feature level fusion to generate an enhanced acupuncture point feature map, and establishing an acupuncture point-meridian association relationship matrix based on the enhanced acupuncture point feature map; compared with the prior art, the method has the advantages that three-dimensional body surface scanning, infrared thermal imaging and elastic modulus data are integrated through the multi-modal data fusion technology to generate the enhanced acupoint feature map, the dynamic meridian-acupoint twinborn model is constructed in combination with double-ellipse section fitting and implicit curved surface reconstruction, and tissue deformation is simulated in real time.

The invention provides an infectious disease early warning and monitoring method and system based on big data and deep learning, and relates to the technical field of infectious disease early warning, and the method comprises the steps: obtaining infectious disease historical data, crowd movement track data, medical treatment data and environment monitoring data as a training data set; establishing a space-time propagation link diagram and carrying out dynamic segmentation coding to generate a propagation characteristic sequence; training a deep learning model based on the propagation feature sequence to predict an infectious disease propagation inflection point and a high-risk area propagation probability; and when the propagation probability exceeds a preset threshold value, generating early warning information and pushing the early warning information to a monitoring terminal. According to the invention, accurate prediction and timely early warning of the transmission trend of infectious diseases can be realized, and the epidemic prevention and control efficiency is improved.

The invention provides a rescue robot autonomous identification and positioning system and method based on industrial vision, and relates to the technical field of robots, and the system comprises a cooperative scanning module which is used for constructing a robot coordinate map; the data fusion module is used for establishing a data fusion frame with a unified time sequence to obtain multi-modal image data; the human body recognition module is used for mapping the thermal imaging image to a visible light image and executing human body local recognition to obtain human body structure features; the target marking module is used for determining a rescue target and marking the rescue target in the robot coordinate map; and the path planning module is used for performing path planning, constructing an updated path and performing rescue through the rescue robot. According to the method and the device, the technical problem of relatively low rescue efficiency caused by difficulty in accurately positioning the rescue target due to shielding and interference in a complex environment in the prior art can be solved, and the precision of positioning the rescue target is improved by combining industrial vision, so that the rescue efficiency is improved.

The invention discloses a water and soil loss dynamic risk assessment system based on a remote sensing image, and relates to the field of water and soil loss risk assessment. The invention discloses a water and soil loss dynamic risk assessment system based on remote sensing images. The system comprises a data acquisition and preprocessing module, a dynamic feature extraction and analysis module, a hierarchical risk assessment model and a risk grading and modeling module, the method further comprises the following steps: acquiring a remote sensing image and historical soil erosion map data of a specific administrative region in a specific period through a data acquisition and preprocessing module; the dynamic feature extraction and analysis module is used for extracting land utilization types, vegetation coverage and quarterly change features of topographic factors through the acquired data; according to the method, a three-layer model architecture is arranged, static historical data, human activity disturbance and social exposure are decoupled and quantified, and the evaluation dimension is improved, so that the accuracy of an evaluation conclusion is improved.

The invention provides an underground water pollutant concentration prediction method and system based on machine learning, and relates to the technical field of underground water pollutant concentration prediction.The method comprises the steps that historical data, hydrogeological parameters, meteorological data, human activity data and geochemical parameters of underground water pollutant concentration of a target area are preprocessed; dividing a training set, a verification set and a test set; constructing a preset resolution feature set based on a geochemical mechanism; selecting an adaptive machine learning model according to data characteristics and coupling a physical mechanism; performing hyper-parameter tuning by adopting Bayesian optimization, and supplementing small sample data in combination with transfer learning to complete model training; predicting the underground water pollutant concentration of the target area by using the trained model, and outputting a pollutant concentration prediction result with an uncertainty interval; the invention provides a technical scheme for predicting the concentration of underground water pollutants, which is efficient, accurate and high in adaptability.

The invention relates to the technical field of safety production management, and discloses a digital twinborn visual monitoring and early warning system based on production management, which comprises a data acquisition module, a data processing module, a digital twinborn visual module, a safety monitoring and early warning module, an emergency management module and an application module, the data acquisition module acquires total factor data in the enterprise production process, and the digital twinborn visual monitoring and early warning system based on production management constructs an intelligent monitoring and early warning system covering production total factors through deep fusion of digital twinborn, big data, artificial intelligence and three-dimensional GIS technologies. According to the method, efficient integration and intelligent analysis of production data, full-process three-dimensional visual management and control, dynamic risk grading early warning and emergency disposal cooperative linkage are achieved, the intelligence and precision level of enterprise safety production is remarkably improved, the method can be widely applied to high-risk industries, the accident rate is effectively reduced, and remarkable economic benefits and social benefits are achieved.

One or more systems, devices, computer program products and / or computer-implemented methods of use provided herein relate to assessing certainty of artificial intelligence models used for detection or segmentation of pathologies. Accordingly, a system can comprise a memory that can store computer executable components. The system can further comprise a processor that can execute at least one of the computer executable components. The computer executable components can comprise a synthetic data generation component that generates biologically-inspired synthetic data that approximates a task-specific data manifold of a medical image from a radiomic features perspective; an artificial intelligence component that uses an artificial intelligence model to learn relevant representations of the synthetic data for an at least one image task; and a training component that utilizes the relevant representations and the artificial intelligence model to generate a task-specific model for the at least one image analysis task.

The invention relates to the technical field of rural space governance and intelligent decision making, in particular to a rural space planning method and related equipment, and the method comprises the steps: obtaining multi-source data of a target rural, the multi-source data comprising land data, population data and rural economic data of the target rural; performing fusion processing of space-time alignment on the multi-source data to obtain fusion data; constructing a spatio-temporal knowledge graph based on the fused data, and extracting entity attributes and spatial relationships; and inputting the entity attributes and the spatial relationship into a pre-trained planning optimization model, iteratively optimizing a rural space matching strategy according to rigid constraints, and outputting an optimal planning result, the technical problems that in the prior art, rural planning faces data splitting, element analysis isolation, planning scheme staticization and actual development requirements are disjointed, the decision making process depends on subjective experience, and economic-ecological benefit tradeoff is difficult to quantify are effectively solved.

The invention relates to the technical field of hospital risk assessment, and discloses an index analysis system based on hospital risk assessment, comprising: a dynamic data acquisition module configured with a hospital multi-source heterogeneous data interface, the data acquisition module is used for acquiring medical quality monitoring data, patient safety event data, financial operation data, personnel behavior data and medical equipment operation data in real time. According to the system, the dynamic data acquisition module is arranged, hospital multi-dimensional data is acquired in real time through a multi-source heterogeneous data interface, a comprehensive real-time data basis is provided for risk assessment, the standardization processing module performs data cleaning, format standardization and dimension alignment, a cross-department associated data set is constructed, the data quality and availability are improved, and the risk assessment efficiency is improved. A dynamic analysis unit of the intelligent risk assessment engine calculates indexes in real time based on a sliding time window mechanism, generates a multi-dimensional risk evolution trend map, and can monitor risk changes of different time scales.

The invention belongs to the technical field of urban climate adaptability planning, and provides an urban toughness evaluation method, device and equipment based on heat island data and a medium, and the method comprises the following steps: collecting dynamic heat island data, humanity perception data and toughness potential data, and preprocessing multi-source data; constructing a three-dimensional index system of the pressure degree-coping capacity-toughness potential of the heat island; s3, determining an index weight by adopting a reinforcement learning adaptive weight model, and calculating to obtain a final toughness value; s4, predicting short-term and long-term toughness value changes based on an LSTM-attention mechanism model, forming a toughness level-potential level linkage matrix in combination with the final toughness value and the potential level, and determining an intervention priority; according to the method, short-term thermal risk early warning and long-term toughness trend prediction are realized through the LSTM-attention mechanism model, and a high-risk area is identified in advance; through the constructed toughness-potential linkage matrix, the intervention priority is defined, and the resource utilization efficiency is effectively improved.

Systems, devices, and methods related to a Deep Learning Accelerator and memory are described. For example, an integrated circuit device may be configured to execute instructions with matrix operands and configured with random access memory. The random access memory is configured to store input data from a sensor, parameters of a first portion of an Artificial Neural Network (ANN), instructions executable by the Deep Learning Accelerator to perform matrix computation of the first portion of the ANN, and data generated outside of the device according to a second portion of the ANN. The Deep Learning Accelerator may execute the instructions to generate, independent of the data from the second portion of the ANN, a first output based on the input data from the sensor and generate a second output based on a combination of the data from the sensor and the data from the second portion of the ANN.

The invention belongs to the field of intelligent robots, particularly relates to an intelligent wheelchair following system based on single target tracking, and aims to solve the problem of poor effect of effectively avoiding obstacles in a complex scene and continuously following a single target under the condition of multi-target interference. The system comprises a sensor module used for restoring scene image data; the man-machine interaction module is used for acquiring a to-be-tracked target; the single-target tracking module is used for generating a moving strategy of the intelligent wheelchair; the intelligent decision-making module is used for generating an optimized path and dynamically adjusting the moving direction and speed of the wheelchair; the driving control module is used for realizing smooth steering and emergency braking of the intelligent wheelchair; and the communication module is used for realizing communication among the modules. According to the invention, three-dimensional positioning precision, target identification accuracy and tracking stability are improved, safety control and man-machine interaction are optimized, the method is especially suitable for people having difficulty in moving, and life self-care and social participation are improved.

The invention relates to the technical field of infectious disease monitoring and early warning, and discloses an intelligent influenza early warning system based on community multi-modal data fusion. The community-level multi-modal data fusion architecture is constructed, medical health data, environmental data, crowd activity data and network behavior data are integrated, spatial-temporal features are dynamically extracted and fused in combination with a deep learning model, and the problem of community monitoring blind areas caused by a single data source of an existing early warning system is solved; a long short-term memory network and convolutional neural network cascade architecture is utilized to capture a localized propagation rule, and the defect that a region-level prediction model cannot adapt to community heterogeneity is overcome; the risk score is generated in real time, the grading response instruction is triggered, a'monitoring-early warning-intervention 'closed loop is established, the early warning timeliness is remarkably improved, a basic-level response chain scission gap is filled, early prevention and control of flu outbreak are finally achieved, and public health resource consumption is reduced.

The invention discloses a hydropower station data operation and maintenance method, which comprises the following steps of: arranging a sensor network on hydraulic facilities and electromechanical gold junction equipment, acquiring data, preprocessing the data, registering in space and time scales, converting data signals into acoustic, optical and electric signals, and transmitting the acoustic, optical and electric signals to a server; the method comprises the following steps: performing signal fusion of a three-dimensional sonar signal and an RGB image based on an attention concentration mechanism, performing deep analysis on data, extracting a sensor data mode of hydraulic facilities and electromechanical junction equipment under normal working condition operation, establishing a fault diagnosis model, performing real-time diagnosis on the hydraulic facilities and the electromechanical junction equipment, and constructing a response decision knowledge base. Establishing a hydropower optimization scheduling model, and displaying the information of the hydropower station; the system integrates the Internet of Things, big data, artificial intelligence, cloud computing, BIM three-dimensional modeling and digital twinning technologies, realizes real-time monitoring, intelligent diagnosis and optimal scheduling of equipment and facilities of the hydropower station, and improves the overall digital intelligent operation management level of the hydropower station.

This disclosure relates to an AI-driven, non-invasive system configured to analyze whiskey color and maturation outcomes based on sensor-tracked environmental and chemical variables through integrated machine-learning, high-accuracy volume sensing, dielectric-based proof monitoring, and environmental data analysis to forecast how whiskey will age inside a barrel over time, and by leveraging real-time barrel data such as time, proof, volume, temperature, humidity, and evaporation rates, the AI model can accurately analyze the final color, proof, and optimal aging duration for whiskey and other barrel-aged spirits, which can allow distilleries to reduce inconsistencies, improve yield management, and enhance quality control without relying on manual sampling or invasive testing.

The invention provides a self-adaptive rhythm electronic piano accompaniment system based on artificial intelligence, and the system comprises a single-key triggering module which is used for indicating an accompaniment process; the speed-control signal conversion module is used for converting the speed signal acquired by the inductive sensor into a music control signal; the storage module is used for storing accompaniment note sequence data of the song; and the artificial intelligence accompaniment generation module adjusts the accompaniment rhythm according to the trigger signal and the playing speed, and generates accompaniment sound or harmony in real time. The system further comprises a pitch calculation unit, a timbre parameter adjustment module, a timbre generation module, a dynamic range adjustment module, a timbre smooth transition module, an automatic harmony generation module, a volume dynamic adjustment module and an audio output module. The control unit receives a user instruction and adjusts system setting parameters. Intelligentization and self-adaption of the accompaniment can be achieved, self-adjustment of the playing speed of the accompaniment is achieved, the playing speed of the accompaniment is kept consistent with the singing speed of the user, the playing flexibility and expressive force are improved, and the playing experience is improved.

The invention relates to the technical field of epidemic disease prediction, and discloses an intelligent epidemic disease prediction system based on deep learning. A multivariate data acquisition module of the system acquires environmental meteorological data, population flow data, medical resource distribution data and historical epidemic situation propagation data related to epidemic diseases in real time; a deep learning prediction module analyzes regional propagation dynamic characteristics through a space-time diagram neural network according to the multi-source data; a risk area identification module constructs an area risk level thermodynamic diagram according to the area propagation dynamic characteristics, and identifies a high-risk area of which the risk level exceeds a preset threshold value; and the resource matching analysis module calculates the resource coverage range of each medical institution in combination with the medical resource distribution data and the spatial position of the high-risk region. The system can integrate multi-source data, accurately analyze the epidemic propagation trend, intelligently identify high-risk areas, realize reasonable matching of medical resources, and provide effective technical support for epidemic prevention and control.

The invention discloses a mechanical arm path planning system, and relates to the technical field of man-machine interaction and safety control, the mechanical arm path planning system comprises a sensing module, a man-machine cooperation module, an optimization module and a regulation and control module, the man-machine cooperation module identifies the behavior intention and the working state of an operator to generate second data, and the optimization module fuses the first data, the second data and the state data of the mechanical arm body, selects a path planning strategy in a grading manner according to the environmental complexity, generates and optimizes a target path, and outputs an optimization result comprising a standard path and an alternative path. And the regulation and control module dynamically regulates and controls the optimization result, generates a regulation and control result through instruction conversion, deviation compensation and multi-stage response and feeds back the regulation and control result, the dynamic environment can be accurately dealt with, man-machine safety cooperation is achieved, the efficiency, safety and adaptability of mechanical arm path planning are improved, and the method is suitable for complex man-machine cooperation scenes.

The invention discloses a robot dog target following method based on computer vision. The robot dog target following method comprises the following steps: S10, preparing human body data, human body weight identification data and a single target tracking data set; s20, designing a lightweight human body detection network architecture and a detection human body frame, and performing model training based on the human body data; s30, constructing a lightweight single-target tracking model, selecting a detection frame of a to-be-tracked target as initialization input, receiving an initial frame transmitted by a robot dog camera as a template image, and performing specified template tracking and following; s40, constructing a lightweight full-scale model, starting a relocation mechanism after a lost target is tracked by a single target, and controlling the robot dog to turn around in situ, globally search a specified target and relocate a disappearing target; and S50, based on the tracking result of the single target, converting the target position on the image to an actual three-dimensional coordinate, and controlling the motion displacement of the robot dog to complete the following operation.

The invention relates to the technical field of city updating simulation, and discloses a city updating multi-scene simulation evaluation system based on digital twinning. The system comprises a multi-source data acquisition module, a space grid division module, a scene simulation engine and a dynamic evaluation module. The multi-source data acquisition module acquires real-time monitoring data and historical city update project data of a city physical space, and the real-time monitoring data covers environmental parameters, infrastructure operation data and people flow dynamic data, so that a digital twinborn body is constructed. The space grid division module divides the digital twin into space grid units according to the urban functional area type and the updating target and gives attribute tags to the space grid units. And the scene simulation engine generates a simulation parameter combination based on the attribute tag, and drives the digital twin to carry out multi-scene dynamic simulation. And the dynamic evaluation module outputs a state evaluation value of each space grid unit according to a simulation result, the state evaluation value comprises an infrastructure load state and a people flow distribution state, and orderly promotion of city updating work is assisted.

Systems and methods presented herein include guest activity detection devices configured to detect activity of guests of an amusement park, and to send data indicative of the detected activity; an artificial intelligence entity management system configured to analyze the data indicative of the activity of the guests, and to modify properties of guest-specific artificial intelligence entities based at least in part on the analyzed data indicative of the activity of the guests; and park features disposed within a physical environment of the amusement park and configured to be instructed by the guest-specific artificial intelligence entities to modify a guest experience for the guests of the amusement park in accordance with the properties of the guest-specific artificial intelligence entities.

The invention discloses an intelligent operation decision-making method, system and device for a flash furnace and a medium, and the method comprises the steps: obtaining the smelting process data of the flash furnace, the manual observation information of the smelting process and the inspection and test data of a smelted product, and building a global database to carry out the unified management of the data; a fire observation opening area of the flash furnace, the interior of a flue gas pipeline and the furnace wall of a slag tap are monitored, and furnace condition sensing information is obtained; regulating and controlling loop setting of a reaction zone of the flash furnace according to the smelting process data, the manual observation information, the inspection and test data, the furnace condition sensing information and the first target index; and according to the smelting process data, the manual observation information and the second target index, electrode rising and falling of an electrode area of the flash furnace and the gear of the transformer are regulated and controlled. According to the embodiment of the invention, the working condition of the smelting process of the flash furnace can be monitored and analyzed, and the operation decision is intelligently executed according to the monitoring result. The method can be widely applied to the field of flash furnace smelting.

The invention discloses a low-efficiency land use identification method and device based on a pressure-state-response framework, and the method comprises the steps: obtaining the geographic position data, land use type data, social and economic data, population data, ecological environment data, infrastructure data and regulation and control response data of a plurality of target regions; calculating a spatial correlation coefficient between the first target area and the second target area based on the geographic position data, the land use type data, the social economic data and the ecological environment data to obtain a spatial correlation matrix; based on the social economic data, the population data, the ecological environment data, the infrastructure data, the regulation and control response data and the spatial incidence matrix, constructing a low-efficiency land use evaluation index system; determining a comprehensive evaluation index set of each target area through the low-efficiency land use evaluation index system and the land use type data; and determining the low-efficiency land use level of each target area based on the comprehensive evaluation index set, and obtaining a low-efficiency land use identification result. According to the invention, the accuracy of low-efficiency land use identification can be improved.

An improved parcel growth prediction system that uses parcel data, population data, and artificial intelligence to predict the growth of a geographic area at a micro level (e.g., a real estate parcel level) is described herein. For example, the improved parcel growth prediction system may generate a graph model and apply the graph model as an input to an artificial intelligence model to predict the likelihood that a particular parcel may be developed some time in the future. Ultimately, implementing the improved parcel growth prediction system described herein may lead to more precise placements of infrastructure projects and / or to infrastructure projects that more precisely support the needs of the population of a geographic area as time passes.

Health information is integrated (106) into a framework system (400). PGHD and clinical data are integrated (106) into a patient data model (240). The PGHD data as integrated may be compressed or otherwise processed (104) to reduce the volume and / or frequency of the data for greater ease in understanding the PGHD data. A user interface (220) for this data model (240) allows for access to both types of data (PGHD and clinical data) by a patient or a physician. Artificial intelligence may be used to further consolidate the data by providing one or more biomarkers (120) estimated from both types of data, allowing for patient and / or physician goal, treatment success, and / or adverse event monitoring.

The invention discloses a packaging unit front aluminum coil intelligent plane warehouse and an operation method thereof.The intelligent plane warehouse comprises a cache warehouse area composed of a plurality of plane saddles, the plane saddles are provided with sensor arrays for monitoring the storage position and number of coiled materials and the bearing condition of the plane saddles, and the warehouse inlet end and the warehouse outlet end of the cache warehouse area are provided with a designated position TOP1 and a designated position TOP2 correspondingly; an AGV ground automatic coiled material conveying trolley and a packaging unit feeding trolley are correspondingly arranged at the designated position TOP1 and the designated position TOP2, and an intelligent crown block is arranged above the buffer storage area; scratches caused by transferring the aluminum coil by a manual crown block are avoided; full automation of edge cutting discharging and packaging feeding of aluminum coils can be achieved, the labor intensity and the safety risk are reduced, and the production efficiency is improved. Production continuity between the edge cutting unit and the packaging unit is achieved, butt joint of production data is automatically completed, manual data input and proofreading time is saved, transportation time is shortened, and the automation level is improved.

The invention discloses an intelligent operation and maintenance management method and system based on multi-modal AI, and belongs to the technical field of data artificial intelligence and industrial intelligent operation and maintenance crossing. The method comprises the steps that multi-modal data is collected and preprocessed, an improved VMD is used for conducting modal component decomposition on vibration signals, a first-order energy moment and a second-order energy moment are calculated to generate an initial vibration feature vector, and the initial vibration feature vector is calculated; a de-noising auto-encoder SDAE is used to obtain a final vibration feature vector, and the multi-modal data comprises vibration signals, energy consumption, images and text data; an improved VMD method is introduced to be combined with a Lagrange multiplier and a self-adaptive step length mechanism, a frequency domain iteration optimization solving process is constructed, meanwhile, nonlinear and deep feature enhancement is achieved through a denoising self-encoder, and therefore the extraction capacity and stability of a system for key modal components in complex vibration signals are effectively improved.

The invention belongs to the technical field of flood emergency risk avoidance, and particularly relates to a flood avoidance transfer path planning and intelligent matching method based on multi-algorithm cooperation, and the method comprises the following steps: S1, multi-source data integration: integrating flood inundation data, population distribution data, administrative division data and dangerous area data; s2, dynamic avoidance area generation: respectively processing a dangerous avoidance area and a flood submerging avoidance area to finally obtain two accurate avoidance areas; s3, constructing a multi-algorithm collaborative flood avoidance transfer path planning model; s4, constructing an intelligent layered placement matching model: designing a layered matching algorithm model, and realizing intelligent matching of the transfer unit and the placement area; and S5, generating and optimizing a flood avoidance transfer scheme. According to the method, accurate planning of a flood avoidance transfer path is realized through multi-algorithm collaborative fusion, and meanwhile, a hierarchical matching strategy and a dynamic adjustment mechanism are constructed, so that the problems of scientificity and high efficiency of placement matching can be solved, and the quality and efficiency of flood avoidance transfer work are comprehensively improved.

The invention relates to the technical field of ability evaluation, and discloses a personnel ability evaluation method based on human intelligence and virtual reality, an edge computing device and a medium. Firstly, a target virtual scene is constructed based on post requirements and task parameters set by a target task; human body multi-modal data of the target object and simulator data of the simulator are collected when the target object executes the target task in the target virtual scene based on the simulator; then, feature extraction and weighted calculation are carried out on the human body multi-modal data and the simulator data, and objective adaptation degree scoring data are determined; and determining personnel state data and workload of the target object based on the human body multi-modal data. And matching the simulator data with preset operation data, and determining abnormal data in the simulator data. And finally, based on the objective fitness score data, the personnel state data, the workload and the abnormal data, evaluating the task execution capability of the target object to obtain a task execution capability evaluation result of the target object.

The invention relates to an Internet of Things data man-machine interaction visualization system based on artificial intelligence, which belongs to the field of man-machine interaction technology, industrial Internet of Things and artificial intelligence technology and comprises a multi-modal data acquisition unit, a cognitive load state real-time inference module, a man-machine interaction interface self-adaptive generation module and a closed-loop regulation control module. The multi-modal data acquisition unit is used for acquiring multi-modal data related to the state of an operator in real time; the cognitive load state real-time inference module is used for inferring and generating a probability vector representing the current cognitive load state of the operator based on the acquired multi-modal data; according to the scheme, a complete feedback control loop is constructed by arranging the multi-modal data acquisition unit, the cognitive load state real-time inference module, the human-computer interaction interface self-adaptive generation module and the closed-loop regulation control module.