506 results about "Data human" patented technology

A system and method for an AI-controlled sensor network for threat mapping and characterization. The system deploys a network of honeypots and sensors across various geographic locations and network segments, collecting and aggregating data on network traffic and potential threats. An AI orchestrator analyzes this data using advanced machine learning models, generating dynamic honeypot profiles and a comprehensive threat landscape. The system can adapt in real-time to emerging threats, optimize resource allocation, and provide actionable intelligence. By correlating data across multiple points, the system offers enhanced threat detection capabilities and proactive cybersecurity measures, surpassing traditional security information and event management (SIEM) tools.

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 relates to the technical field of natural resource investigation, in particular to a GIS-based natural resource investigation system and method, which comprises data acquisition, data preprocessing, spatial data integration, dynamic monitoring and analysis, resource utilization evaluation and decision support. Firstly, environment data, human activity data and resource distribution data are collected in real time by arranging multiple kinds of sensors, remote sensing satellites, unmanned aerial vehicles and other devices, and cleaning and denoising processing is carried out. And then, spatial integration is performed on data from different sources by utilizing a GIS platform, and a geographic information data set is formed by combining regional map information. Through a dynamic monitoring system, in combination with spatial-temporal change analysis, resource change and fluctuation of environmental factors are monitored in real time, and resource consumption and environmental influence trends are predicted. Based on the analysis results, the system generates a resource optimization utilization scheme and environmental protection measures, scientific decision support is provided for managers, and sustainable utilization of resources and protection of the ecological environment are achieved.

The invention is suitable for the field of logistics transportation, and provides a hazardous chemical substance transportation path planning method and system, and the method comprises the steps: collecting road network information, road condition real-time data, weather data, population density distribution data, environment sensitive point distribution data, and hazardous chemical substance characteristic data; based on the collected data, establishing a risk assessment model, and performing risk assessment on each preset transportation path; constructing a multi-objective function, and distributing a corresponding weight for each objective; and searching and generating an optimal or suboptimal transportation path in the road network based on the multi-objective function, and when the change of the road condition data or the weather data exceeds a preset threshold value, performing risk assessment and path planning again, and regenerating the optimal or suboptimal transportation path. And the transportation data security is ensured. A path can be adjusted in real time to cope with road conditions and weather changes, the transportation risk is reduced, and the transportation cost, time and risk are balanced.

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 an artificial intelligence perioperative period anesthesia depth detection system which comprises a data acquisition module which is provided with a multi-mode sensor and is used for acquiring physiological data of a patient in a perioperative period, and an artificial intelligence model module which is used for constructing an anesthesia depth prediction model based on a deep learning algorithm. The result output and display module is used for outputting the prediction result of the anesthesia depth in real time and visually displaying the prediction result on a display screen in the form of digits and charts, and the communication and interface module is provided with an interface for data communication with a hospital information system and an electronic medical record system. By means of multi-mode collection and innovative design, the anesthesia depth detection accuracy is improved, precise monitoring and intelligent auxiliary decision making are achieved, the method can be used for various scenes, the safety of patients in the perioperative period is effectively guaranteed, and the medical 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 relates to the technical field of building energy consumption analysis, and discloses a building energy consumption analysis method and system based on multi-modal perception. According to the method and the system, building environment data, energy consumption data, personnel flow data and equipment state data are collected from a plurality of data sources, the data are cleaned, abnormal values and noise information are eliminated, the data collected at different time points are integrated through a time alignment method, and the data are stored in a database. Data from different sources and dimensions are standardized, feature extraction and modeling are carried out on the preprocessed data, time features, environment features and personnel flow features are extracted, an energy consumption change mathematical model is established, energy consumption analysis is carried out on the extracted features, total energy consumption of a building is calculated for energy efficiency evaluation, and energy efficiency evaluation is carried out. The energy consumption intensity and the energy efficiency ratio are obtained, time sequence analysis is conducted through historical data, the future building energy consumption trend is predicted, the analysis result is visualized, and energy conservation and emission reduction suggestions and optimization decisions are provided based on the visualized analysis result.

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.

The invention discloses a clinical depression and melanoma patient nursing intervention system and method, which utilizes big data and artificial intelligence technology to establish a digital twinborn model according to the information, illness state, depression state and the like of a patient, collects existing cases for training to continuously optimize the model, and improves the precision of the digital twinborn model. And then similar cases are recommended by utilizing the database, the patient information and the digital twinborn model, a nursing scheme is generated, and then the nursing scheme is input into the digital twinborn model to be tested and predicted and is continuously improved. According to the mode, the efficiency of making the nursing scheme can be greatly improved, personalized customization can be carried out according to the actual condition of the patient, and the nursing effect is greatly improved. Meanwhile, the physiological data of the patient is detected by adopting the wearable equipment, whether the patient is in a depression state or in a depression mood is speculated by utilizing the physiological data, and psychological nursing is carried out through nursing intervention measures set in advance, so that the depression state of the patient is effectively improved.

The invention relates to the technical field of nuclear emergency decontamination management, and discloses a nuclear emergency decontamination station partition management method and device based on artificial intelligence, and the method comprises the steps: carrying out the three-dimensional laser scanning and data collection of a pollution region, a decontamination region and a clean region of a nuclear emergency decontamination station, and constructing a decontamination station digital twinborn model; according to the digital twin model of the decontamination station, performing multi-dimensional analysis and weight calculation on the polluted area, the decontamination area and the clean area by adopting an analytic hierarchy process to obtain a partition evaluation parameter matrix; inputting the partition evaluation parameter matrix into a deep reinforcement learning network for dynamic optimization calculation to obtain partition management control parameters; and performing decision analysis and threshold judgment on the radiation dose rate data, the pollution range data, the personnel quantity data and the equipment state data according to the partition management control parameters, and generating an emergency disposal control instruction, thereby realizing cooperative linkage of the pollution area, the decontamination area and the clean area, and improving the accuracy and timeliness of emergency disposal.

The embodiment of the invention provides a personnel behavior detection method based on multi-modal data and related equipment, and is applied to the technical field of image recognition processing. A to-be-processed video is acquired, and a to-be-processed image frame is extracted from the to-be-processed video. And extracting multi-modal data from the to-be-processed image frame by using a target detection algorithm, a target tracking algorithm and a human body posture estimation algorithm, wherein the multi-modal data at least comprises a personnel detection frame, a personnel image and a personnel posture feature map. And inputting the personnel detection frame, the personnel image and the personnel posture feature map into a pre-constructed abnormal behavior detection model to obtain a behavior detection result output by the abnormal behavior detection model. Image data is obtained from the video data, the personnel action has continuity, and the image data is close to an actual website scene; a multi-modal data fusion abnormal behavior detection model is used to make up for the data limitation and fusion suitability among the personnel image, the detection frame and the attitude feature, so as to improve the behavior abnormality recognition accuracy.

To provide a system.SOLUTION: A system includes: means for connecting corporate data including financial data, personnel data, and sales data to a database for storage; means for acquiring the corporate data from the database; means for preprocessing the acquired corporate data; means for calculating an achievement index on the basis of the preprocessed corporate data; means for outputting the calculated achievement index; means for totalizing a management index including an outputted achievement index; and means for using a generative AI model to generate a strategy proposal on the basis of the totalized management index.SELECTED DRAWING: Figure 1

The invention discloses an intelligent crowd monitoring system based on deep learning, and relates to the technical field of intelligent monitoring, and the system comprises a data collection module which collects monitoring video information in real time and generates a monitoring video stream; the video processing module is used for performing target detection on the acquired monitoring video stream and extracting crowd position and quantity information according to a target detection output result; the image recognition module is used for calculating the crowd density, tracking the movement track of the crowd to obtain movement track data, and calculating crowd distribution change data according to the crowd density; the crowd analysis module is used for predicting and analyzing crowd behaviors by using a deep learning model to obtain a predicted congestion path; and the decision adjustment module is used for judging whether the crowd advancing route needs to be dynamically adjusted based on fuzzy reasoning and warning the crowd to avoid the congestion area. The problem that multi-dimensional comprehensive analysis of crowd behaviors, flowing directions and the like is lacked in the prior art is solved, and the crowd monitoring accuracy and the congestion avoiding efficiency are improved.

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.

An intelligent, predictive customer loyalty system for social media marketing, consisting of: a data acquisition unit configured to collect user interaction data, demographic attributes, and sentiment analysis metrics from multiple social media platforms via platform-specific application programming interfaces (APIs), web scraping techniques, and direct data ingestion pipelines; a real-time engagement prediction engine comprising a machine learning model trained on historical engagement patterns, sentiment fluctuations, and content performance metrics, with the model dynamically predicting user responses, determining the optimal engagement time, and prioritizing interaction opportunities based on a probabilistic ranking technique; a real-time engagement prediction processing unit configured to generate and optimize social media posts, ads, and interactive media using a neural network-based natural language generation (NLG) model, where the model processes contextual user behavior and trending topics to dynamically adapt marketing content; an AI-powered automation unit for interactive engagement, comprising a deep learning-based chatbot engine integrated into a dialogue management system, with the chatbot engine analyzing the user's intentions, sentiment development, and engagement history to enable predictive, context-aware conversations with users; a cloud-connected processing unit that includes an AI inference accelerator configured to perform deep learning calculations for real-time engagement prediction units, personalized content recommendations, and chatbot response generation, with the processing unit integrated into a distributed computing framework to optimize inference latency.

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 discloses a full-induction type intelligent digital interaction system based on cloud edge computing driving. The full-induction type intelligent digital interaction system comprises the following steps: step S1, voice input and understanding; s2, environment perception and analysis; s3, generating response content; s4, matching the response voice with the action; s3, converting the response content in the step S3 into response voice, and synchronously generating corresponding action data; the data character image on the display screen is driven to perform corresponding action through the action data; and S5, continuously collecting user interaction data by the system, and continuously optimizing response content and an interaction mode by the system through big data analysis and a machine learning algorithm. According to the method, through the steps of voice input and understanding, environment perception and analysis, personalized learning optimization and the like, the system can understand user requirements, analyze the situation where the user is located and intelligently generate personalized response content, and therefore the interaction experience of the user and the system is 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.

A system for AI-driven predictive healthcare for pandemic prevention, which includes the following: a data collection module configured to capture health-related data from IoT-based wearable devices, electronic health records (EHRs), environmental sensors, and social media in real time; an AI-driven analysis module that is operationally linked to the data collection module and configured to use deep learning and natural language processing (NLP) techniques to process and analyze collected data in order to detect potential disease outbreaks; a predictive modeling module configured to use hybrid epidemiological and AI models to predict disease transmission patterns based on mobility data, population density, and historical infection trends; a warning and response module that automatically generates and disseminates notifications to health authorities, medical professionals, and vulnerable individuals based on predicted outbreak risks; a blockchain-based data security module configured to ensure the secure storage, access control, and verification of health-related data through decentralized ledger technology and smart contracts; and a decision support module that is operationally linked to the predictive modeling module and provides AI-driven recommendations for healthcare resource allocation, vaccination strategies, quarantine measures and public health measures.

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.