161 results about "Data human" patented technology

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.

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 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.

A system and method for closed-loop tracking of advertising events through offer redemption with artificial intelligence feedback. A session identifier links device, pass, content, and contextual data throughout a content playback session. Advertisements dynamically inserted into the content stream inherit the session identifier. An offer platform generates offers linked to the session identifier and pass identifier, enabling tracking through distinct lifecycle states from push to redemption. A database captures timestamped and geo-referenced data for each event. An artificial intelligence engine uses redemption and avail events as verified ground-truth outcomes for training machine learning models, enabling supervised learning that establishes causal relationships between ad exposure and purchasing activity. An inventory gateway receives merchant product inventory data. An optimization engine employs demand forecasting and reinforcement learning to generate inventory-driven triggers and offer recommendations. A merchant interface presents analytics for inventory optimization.

The invention discloses a warehousing network model and algorithm based on two-stage model collaborative optimization, and belongs to the technical field of warehousing network models. The invention discloses a warehousing network model and algorithm based on two-stage model collaborative optimization. The warehousing network model comprises an analysis and prediction module, an inventory collaborative management module and an optimization model. According to the invention, the problem of lack of overall planning of the full-link cost of the storage network in the prior art is solved. Through collaborative optimization of the first stage model and the second stage model, balance of long-term cost and short-term cost is realized, full-link cost optimization is achieved, the storage space utilization rate is improved, maximum utilization of manpower, equipment and space resources is realized, and through integration of regional economic data, population density, a traffic network, competitor layout and other multi-dimensional data, the real-time performance of the system is improved. Costs, efficiency and risks of different site selection schemes are calculated, a clear quantitative basis is provided for long-term decisions such as storage node layout and facility scale determination, and large deviation of demand probability fitting is avoided.

The invention discloses an artificial intelligence-driven urban potential updating area identification and division method, which comprises the following steps of: firstly, constructing an index system according to three dimensions of building space, industry form composition and crowd vitality by using the collected building number, land function data, POI (Point of Interest) industry form data, LBS (Location Based Service) crowd data, high-definition remote sensing image data and road network data; the method comprises the following steps: carrying out hierarchical classification identification on an update object through a natural discontinuity point method and a K-Means clustering algorithm, carrying out intelligent division and optimization on a city update unit through a DBSCAN clustering algorithm and a reinforcement learning model of a graph network structure database, and finally carrying out division and boundary optimization on a city update area. According to the invention, automatic identification and division of potential updating objects, updating units and updating areas of the city can be realized, and digital and intelligent application conversion of city planning work is promoted.

The invention relates to the technical field of wireless charging assembly line automatic control, in particular to an intelligent cooperative control system for a wireless charging assembly line, and adopts the technical scheme that a central scheduling module and a beat cooperative algorithm unit can construct a dynamic state model based on global real-time data; a digital twin sub-module is introduced to perform simulation deduction verification on an instruction in a virtual environment, a beat redistribution strategy can automatically adjust a material flow when a fault occurs, and a visual positioning and compensation module can identify a material position and attitude offset in real time and generate compensation data. The man-machine interaction interface can visualize the whole line state, prediction information and alarm information in real time, the historical database and optimization learning unit can store data and iteratively optimize algorithm parameters, uncertain interference is analyzed through Monte Carlo simulation, and a Pareto optimal solution set is solved through an NSGA-II algorithm. The production takt of the three stages of feeding, dispensing assembling and baking discharging is dynamically matched, and dynamic takt synchronization and productivity optimization of the whole production line are achieved.

The invention relates to the technical field of flood control and disaster reduction, and particularly discloses a flood storage and detention area dynamic personnel transfer early warning method and system, and the method comprises the steps: collecting and fusing hydrological monitoring data, personnel dynamic distribution information and geographic information in real time, and constructing a unified dynamic data base; based on the dynamic data base, generating a gridded real-time risk level by fusing a flood routing model and real-time personnel density data; according to the real-time risk grade, dynamically triggering and issuing a grading early warning instruction; based on the real-time submerging prediction result and the dynamic road condition information, generating and dynamically updating an optimal transfer path for the threatened individual; pushing customized early warning information corresponding to the early warning instruction and the optimal transfer path to an individual terminal; according to the invention, based on hectometer-level gridding real-time risk assessment and a three-level early warning mechanism, a submerging prediction model and a special group positioning technology are fused, and high-risk point location coverage is realized.

The application discloses a method for intelligent identification of changes in river and lake water areas by a UAV and early warning of abnormal problems in rivers and lakes, comprising the following steps: constructing a digital twin bottom plate data of rivers and lakes; manually editing a basic flight route of the rivers and lakes, intelligently generating a shooting flight route based on the basic flight route and the digital twin bottom plate data of the rivers and lakes, splicing the basic flight route and the shooting flight route, and obtaining a dynamic flight route of the UAV; performing dynamic flight route flight according to the dynamic flight route, collecting data information during flight, and returning the data information to a UAV base station through a network and transmitting the data information to an edge computing device; generating river and lake orthographic images and river and lake spatial terrain data by combining the data information collected by the UAV with water area management lines; and identifying and comparing the orthographic images before and after the two times by using a water area surface identification model and an image difference method, so as to obtain a water area surface change area and abnormal problems in the rivers and lakes. The method solves the problems of low identification accuracy, low image spatial resolution and unintelligent flight route scheduling in the existing UAV patrol of the rivers and lakes.

The invention provides a blue space degradation diagnosis method and system, computer equipment and a medium, and belongs to the field of ecological environment monitoring, and the method comprises the steps: obtaining a remote sensing image, climate data and human activity data of a to-be-diagnosed blue space, and extracting water body images of the to-be-diagnosed blue space at a plurality of time points through the remote sensing image; calculating a multi-class landscape form index of each grid in the water body image; clustering the water body image based on the multi-class landscape form index of each grid to obtain a blue space combination mode corresponding to each grid; inputting the climate data, the human activity data and the landscape form index into a random forest model to obtain a contribution degree; and based on the contribution degree, screening from the climate data, the human activity data and the landscape form index to obtain a water body change driving factor, and determining a degradation diagnosis result of the blue space to be diagnosed. The blue space combination mode constructed by the method can reflect the structural difference of the blue space, and the accuracy of degradation diagnosis of the blue space is guaranteed.

The present invention relates to a method and an apparatus for detecting anomalies in an operation behavior of a device under test (DUT), in particular during electromagnetic susceptibility (EMS) measurements. The apparatus comprises a monitoring unit adapted to generate a first set of observation data of the operation behavior of a DUT while the DUT is not subjected to disturbances and adapted to generate a second set of observation data of the operation behavior of a DUT while the DUT is subjected to disturbances and comprising an AI module trained with the first set of observation data generated by the monitoring unit and adapted to process the second set of observation data to detect anomalies in the operation behavior of the DUT while being subjected to the disturbances and comprising a reporting unit adapted to reporting anomalies in the operation behavior of the DUT detected by the trained AI module.

The present application relates to the field of electric digital data processing, and in particular to a method for sensing and decision-making of a door lock interaction system based on embodied intelligence, which comprises collecting sensing data, labeling an intention label and an environmental noise label corresponding to each sensing data to obtain labeled sensing data; constructing a sensing and decision-making model and training the sensing and decision-making model to obtain a trained sensing and decision-making model; collecting new sensing data, inputting the voiceprint data, the relative distance data between the person and the door lock, and the door lock state data in the new sensing data into the trained sensing and decision-making model after aligning them to a unified reference according to a time stamp to obtain an intention probability distribution, selecting the intention with the highest probability as a decision result and triggering a corresponding door lock control instruction. The existing sensing and decision-making method of the door lock interaction system based on embodied intelligence has the problem of poor user experience. The method provided by the present application can provide good user experience.

The embodiment of the invention provides an intelligent scheduling method and system for constructors. According to the method, firstly, original data including work order data, personnel state data and external environment data are collected in real time, and a data foundation is laid for subsequent decision making; then generating an order dispatching scheme based on a pre-trained scheduling strategy model, and preliminarily screening out candidate schemes conforming to a global optimization direction; the reward value of each scheme is calculated through a preset reward function, so that resource waste caused by single-dimensional optimization is avoided; and finally, executing the target scheduling scheme, and combining an execution result, historical work order data and a real-time network state optimization strategy model to realize accurate matching of skills of construction personnel and work order requirements and global balance of personnel load, thereby remarkably improving resource configuration efficiency.

The application discloses an osteoporosis risk grading prediction method based on clinical prior logic gating, acquires multi-modal clinical data of a patient to obtain a standardized feature vector group, constructs and trains an osteoporosis risk grading prediction neural network model, including a double-flow feature coding module, a logic gating fusion module and a cascaded classification prediction module, inputs the standardized feature vector group after splicing processing to the trained neural network model, and outputs an osteoporosis risk grading prediction result. The application converts the diagnosis logic in the clinical guideline into an attention gating mechanism in the neural network by designing a double-flow architecture, and combines a screening-grading cascaded strategy, so that the existing structured data (demography, test single, medical history) in the hospital information system can be directly used to realize low-cost, high-precision and clinically interpretable osteoporosis risk grading without additional image examination.

The disclosed principles provide a system and method for improving brain health in a virtual reality environment. The system includes a virtual reality appliance and an artificial intelligence enabled program for providing and customizing the virtual reality environment with a treatment plan. The artificial intelligence enabled program includes an initial treatment phase wherein a user's baseline metrics are measured. Using these metrics, a treatment plan is created during a treatment plan development phase. The treatment plan is implemented during a treatment phase through a series of repeated mindfulness, cognitive, and physical exercises presented in a virtual reality environment. Data corresponding to the user's inputs during treatment is collected and stored. An artificial intelligence program analyzes user input data and modifies the treatment plan accordingly to optimize treatment. Treatment and evaluation and modification are alternately repeated according to the personalized, artificial intelligence-modified treatment plan to strengthen neural pathways.

The invention discloses a smart city public resource dynamic distribution system based on AI, and the system comprises a data collection and fusion module which obtains and fuses resource state data and public resource demand data of a smart city, and generates multi-source heterogeneous real-time city data; the artificial intelligence demand prediction module is used for outputting a quantitative demand prediction result according to the multi-source heterogeneous real-time city data; the dynamic optimization decision module is used for generating an optimized dynamic resource allocation strategy based on a quantitative demand prediction result, an allocation optimization target and a current public resource constraint condition; and the resource scheduling execution and feedback module is used for converting the optimized dynamic resource allocation strategy into a target control instruction, issuing the target control instruction to a public resource execution terminal to complete resource scheduling and allocation, collecting actual state data after resource allocation and feeding back the actual state data. According to the invention, the dynamic prediction and optimization decision of the resource demand are realized, the emergency response speed and the global optimization capability are improved, and the efficient and intelligent resource allocation demand of the smart city is met.

The application provides an attendance management method and device, a terminal and a medium. The method comprises the following steps: acquiring multi-modal identity data of an attendance staff, environment light intensity data where the attendance staff is located, and two-dimensional code display data, and acquiring historical attendance data. The multi-modal identity data comprises face image data, infrared living body detection data, and electronic access code data. The two-dimensional code display data comprises two-dimensional code image definition and two-dimensional code scanning angle. A modal consistency evaluation value is obtained according to the multi-modal identity data, the face image data, the electronic access code data, the environment light intensity, the two-dimensional code image definition, and the two-dimensional code scanning angle. A face similarity judgment value is obtained according to the face image data, the infrared living body detection data, and the historical attendance data. An attendance result is determined according to the modal consistency evaluation value and the face similarity judgment value. The real attendance situation of the staff is comprehensively considered.

The application discloses a construction site personnel safety behavior analysis and management and control system and relates to the technical field of building safety management. Through multi-modal heterogeneous data fusion collection, multi-source information such as video data, personnel positioning data, environment data and equipment state data of a construction site is acquired. Data preprocessing technology is used to clean, space-time align, feature extract and standardize different types, formats and frequencies of data, and unified format space-time behavior data is generated. On this basis, a standardized shadow behavior classification system is established, a large-scale shadow behavior sample library is constructed, a space-time causal graph neural network algorithm is adopted, chain causal correlation rules between shadow behaviors and accidents are mined, and an accident evolution chain model is constructed, which can accurately predict the probability and time of accidents. Through real-time analysis of the current personnel behavior sequence, a hierarchical early warning can be given 10-30 minutes before the occurrence of a violation behavior.

The application provides an interaction method, device, electronic equipment and storage medium, candidate interaction content is selected from an interaction content set based on current time and triggerable time of each interaction content in the interaction content set; human-computer interaction is performed based on the candidate interaction content; the each interaction content is obtained by content classification on historical interaction data, the interaction content set is maintained based on a memory period of the each interaction content, and the memory period and the triggerable time of the each interaction content are determined based on an interaction category to which the each interaction content belongs. The method, device, electronic equipment and storage medium provided by the application reduce terminal data storage, reduce terminal calculation, and thus improve the response speed of the terminal during interaction. Meanwhile, the candidate interaction content is selected from the interaction content set, human-computer interaction is performed, appropriate and close-to-user historical interaction data human-computer interaction is provided, and the experience of the user is improved.

A method of training a submodule and preventing capture of an AI module is disclosed. Input data received from an input interface is transmitted through a blocker module to an AI module, which computes a first output data by executing an AI model. A submodule in the AI system trained using methods steps processes the input data to identify an attack vector from the input data. The submodule comprises an xai classification model and at least a preprocessing block. The xai classification model runs a pre-trained AI model on xai signatures. The submodule distinguishes between a genuine input and an attack vector by identifying one or more xai signature features in the input.

The disclosure is that of a new flutter application using artificial intelligence in order to create an application that makes it easier and more fun to apply for a US immigrant or non immigration visa. The application guides the applicant through the process, and also provides invaluable information which was obtained and developed by the inventor over 30 years of practicing immigration law in the United States. The additional information being provided in the application form includes the types of questions that the person may be asked at the interview, as well as tips for preparation for the interview.

The invention discloses an experimental animal intracerebral target accurate injection system and method based on artificial intelligence and ultrasonic guidance, and relates to the technical field of target injection, the system comprises an ultrasonic probe for emitting ultrasonic waves, receiving experimental animal craniocerebral echo signals and outputting radio frequency signal data; the image processing module is used for executing time gain compensation and beam forming on the radio frequency signal data and outputting gray scale image data and blood flow image data; the artificial intelligence reasoning module is used for inputting the gray-scale image data and the blood flow image data into a convolutional neural network model and outputting corresponding coordinates; the path planning unit is used for generating an entry point coordinate, an angle parameter and a depth parameter through an optimization algorithm; the puncture guide frame adjusts the spatial position of the puncture needle according to the angle parameter and the depth parameter; and the needle point tracking unit is used for calculating a deviation value between the needle point coordinate and a preset track and generating an early warning signal. The problem of inaccurate positioning caused by individual difference in a traditional method is fundamentally solved.

The invention provides a brain-computer interface-based cell culture optimization method and system, and belongs to the technical field of cell culture automation and intelligent control. The method comprises the following steps: inputting a cell type and a culture target, and calling database parameters and data; the artificial neural network learns and outputs a culture parameter range; a medicament proportioning scheme and a physical environment regulation and control strategy are generated through multi-algorithm optimization; the medicament precise proportioning module and the closed-loop optical-electric multi-mode regulation and control module are used for executing a scheme and a strategy respectively; and the result verification and feedback module is used for detecting the cell culture effect, feeding back data, optimizing model parameters and forming closed-loop iterative optimization. The system is composed of an input and data unit, an intelligent decision-making unit, an execution unit and a result verification and feedback unit. The device can accurately control a chemical microenvironment and a physical illumination environment, solves related problems in the prior art, stably produces high-quality cells, is especially suitable for brain-computer interface research, realizes culture process standardization and intelligence, and provides reliable cell materials for downstream applications.

The invention discloses an attendance abnormity real-time processing method and system, and relates to the technical field of attendance information processing, and the method comprises the steps: responding to a voice instruction input by a user, carrying out semantic analysis on the voice instruction, calling multi-source data associated with a to-be-queried target employee in real time based on the analysis content, and sending the multi-source data to the to-be-queried target employee; the multi-source data at least comprises employee identity data, time sequence attendance data, commuting feature data, scheduling data and business scene data; and extracting specified features of the multi-source data, and inputting the specified features into a pre-trained anomaly detection model to identify an attendance abnormal event of the target employee. The full-dimensional data complementation information gap is combined with the model accurate mining rule, the pain points of high misjudgment rate, more missed judgment, low efficiency and poor scene adaptation in the traditional single data and manual judgment mode are solved, and the labor intensive industry brings the values of recognition precision improvement, management efficiency optimization, business adaptation enhancement and employee experience improvement.

The invention relates to the field of manufacturing execution systems and intelligent manufacturing, in particular to a manufacturing execution system management method oriented to production data, which comprises the following steps: a multi-source data acquisition step: acquiring physical sensing state data, manual interaction time data and system scheduling data of a manufacturing execution system, and integrating to generate multi-source heterogeneous production data; a trust quantitative evaluation step of quantifying the distortion degree of the execution state based on the multi-source heterogeneous production data to obtain an execution trust entropy; a cross validation prediction step: comparing the physical sensing state data with the man-machine interaction time data to calculate execution trust entropy, and predicting system failure risk; a dynamic scheduling decision step: setting a scheduling degradation adjustment mechanism based on execution trust entropy, and outputting a target scheduling instruction to adjust the running state of the system; according to the method, the dynamic balance between pursuit of efficiency and maintenance of authenticity of underlying data is realized, and the vulnerability resistance of the complex manufacturing network is effectively improved.

The invention discloses a closed-loop mine atmosphere intelligent control system and method based on a space-time prediction model. The closed-loop mine atmosphere intelligent control system comprises a sensing layer which is used for collecting multi-dimensional data including atmospheric environment parameters and mine operation data in real time; the artificial intelligence processing core is in data communication with the sensing layer, and the artificial intelligence processing core is configured to receive multi-dimensional data collected by the sensing layer; utilizing a space-time prediction model to predict the three-dimensional space distribution and migration track of the to-be-measured object in the atmosphere in a preset time period in the future according to the current and historical data; based on the prediction result, generating a preemptive control instruction; the execution layer is in instruction communication with the artificial intelligence processing core, the execution layer is configured to receive and execute the preemptive control instruction, and before the prediction risk of the to-be-tested object reaches a critical threshold value, physical intervention is carried out on a prediction migration path of the to-be-tested object; the problems that an existing mine atmosphere monitoring system is lagged in response, sensing and action are disjointed, and the control strategy is extensive are solved.

The present application relates to the technical field of machine learning, and specifically provides a power plant violation behavior real-time identification method, system, device and storage medium, comprising: collecting multi-source monitoring data of a power plant operation area in real time, wherein the multi-source monitoring data comprises video data, personnel position data and environment state data; extracting features from the multi-source monitoring data, and fusing the extracted features into multi-dimensional feature information for representing personnel behavior state; inputting the multi-dimensional feature information into a pre-trained risk level evaluation model to obtain a multi-level risk evaluation result corresponding to current personnel behavior; the multi-level risk evaluation result at least comprises a high risk level and a low risk level; according to the multi-level risk evaluation result, starting a graded early warning response matched with the risk level. The present application can identify different levels of risk before an accident occurs and trigger real-time early warning, changing passive response to active prevention.

An application programming interface, API, server comprising an API gateway and at least one API comprising a plurality of callable API endpoints, and an associated method, the server further comprising: an API monitor configured to retrieve API call data related to API calls made by a client to one or more API endpoints of one or more APIs in order to time-stamp each API call in the retrieved API call data, and to send the time-stamped API call data to a data storage system; the data storage system that is configured to store the received time-stamped API call data; an artificial intelligence, AI, copilot configured to be trained using the data stored in the data store and usable in a development environment in order to provide the user with suggestions for generating code for a new application.

1. The name of the design product: limited space operation terminal. 2. The use of the design product: for lighting, environmental monitoring, collecting environmental data, and receiving audio and video data, human body sign data, etc. collected by other intelligent terminals located in the limited space, and then transmitting the above data to the ground terminal equipment. 3. The design points of the design product: in shape. 4. The picture or photo that best indicates the design points: perspective view.

The utility model relates to electrical measurement and automation test technical field, concretely relates to a kind of AC-DC withstand voltage insulation tester, it includes: main control unit, for executing test program and controlling each module coordinated work;High voltage output module, for generating the AC / DC high voltage required by test;Precise current detection module, for detecting the leakage current of measured piece;Multi-channel test interface, for connecting multiple measured equipment;Automatic switching unit, for automatically switching test channel;Data storage module, for automatically recording test data;Man-machine interface, for displaying test information and receiving operation instruction.The utility model is jointly constituted by innovative design an advanced test system with automation, high precision, high efficiency, intelligent management in one, realizes qualitative leap in test quality, operation convenience, production efficiency and safety etc., fully meets the rigorous requirement of modern industry to electrical safety test equipment.