60 results about "Real time modeling" patented technology

The invention belongs to the technical field of computers, and particularly relates to an Internet of Things security data real-time monitoring and protection method and system, and the method comprises the steps: collecting equipment communication and state data through an edge agent, and analyzing and extracting standardized metadata; constructing an equipment behavior contour vector based on a sliding window, and dynamically maintaining a global equipment topological graph; triggering a primary alarm in combination with behavior deviation detection and topology abnormity; outputting a threat score and an attack intention through rule matching and Bayesian network double-engine collaborative reasoning; and executing automatic response according to grading, and feeding back and correcting a behavior baseline to realize closed-loop optimization. The system comprises a data acquisition module, a protocol analysis module, a behavior modeling module, a topology maintenance module, an anomaly detection module, a collaborative reasoning module, an automatic response module and a baseline correction module. Through full-link real-time modeling and cross-device collaborative analysis, the attack detection rate is significantly increased to 98% or above, the false alarm rate is lower than 2%, the response delay is controlled within 800 milliseconds, and the security and adaptive ability of the Internet of Things system are enhanced.

The invention relates to the technical field of laser deposition control, in particular to an unmanned aerial vehicle metal part ultrasonic field auxiliary laser deposition system which comprises the following modules: a data acquisition module for acquiring a molten pool thermal field signal, a sound field signal and a laser scattering signal, and performing sampling, filtering and synchronous processing to obtain molten pool original data; the micro-flow digital twinning module is used for establishing a molten pool micro-flow digital twinning model according to the molten pool original data and simulating molten pool micro-flow behavior to obtain micro-flow prediction data; and the microflow ultrasonic regulation and control module is used for generating an ultrasonic pulse sequence according to the microflow prediction data and applying the ultrasonic pulse sequence to the molten pool to regulate and control the flow field. According to the invention, through the molten pool microflow digital twinning module and the microflow ultrasonic regulation and control module, real-time modeling and regulation and control of the molten pool microflow structure in the complex geometric part deposition process are realized, the problem that local vortexes, shear layer differences and flow field anomalies cannot be identified in real time in the traditional technology is solved, and the molten pool microflow evolution can be actively reconstructed.

The invention discloses a sea-air target trajectory prediction method and device based on dynamic graph learning and a generative adversarial network, and a medium, and belongs to the technical field of sea-air target trajectory prediction, and the method comprises the steps: S1, data preprocessing; s2, dynamic graph learning and generative adversarial network (GAN) training are carried out, feature dimension relevance is modeled in real time through a dynamic graph structure, and a GAN module is utilized to generate adversarial trajectory features; s3, CNN-Transform hybrid model prediction is carried out, local feature extraction is carried out through a CNN module, global dependency modeling is carried out through a Transform module, and a decoder part is associated with a historical trajectory and future prediction through a cross attention mechanism; and S4, performing visualization and evaluation based on dimension reduction. According to the invention, the precision, robustness and interpretability of trajectory prediction in a complex scene are significantly improved.

The invention relates to the technical field of unmanned aerial vehicle surveying and mapping, and discloses a pressure pipeline axonometric mapping system based on an unmanned aerial vehicle, and the system uses a pipeline anchoring relative navigation module to guide the unmanned aerial vehicle to fly along a line with a target pipeline as a reference; the outer diameter and three-dimensional center point coordinates of the pipeline are obtained in real time through an orthogonal contour scanning and parameter resolving module; a self-adaptive flight and measurement control module is used for adjusting the flight distance according to the measured outer diameter feedback, and adjusting sensor parameters according to the imaging quality feedback; meanwhile, the coordinates of the center point are incrementally constructed into a topological graph through an airborne real-time topological graph construction and node processing module, pipe fitting nodes are autonomously detected and recognized, and then a preset strategy is called to conduct refined scanning on the nodes. According to the invention, autonomous navigation independent of global positioning, self-adaptive closed-loop control of measurement and flight, and airborne integrated real-time modeling of pipe network topology and geometric information are realized, and the surveying and mapping precision, the automation level and the operation efficiency are remarkably improved.

The invention discloses an interactive behavior feature extraction method and device, equipment, a medium and a product, and relates to the technical field of computers, the method comprises the following steps: obtaining original live broadcast data generated in a live broadcast room, the live broadcast room comprising at least one interactive object; determining multi-modal live broadcast behavior data of the original live broadcast data in the target time window based on the multi-modal behavior data of the interactive object; dividing the multi-modal live broadcast behavior data into a plurality of behavior subsequences; based on the behavior data corresponding to the behavior subsequences, determining behavior cooperation features between the behavior subsequences, behavior interaction features between the interaction objects and time evolution features; and determining a target feature of the live broadcast room based on a fusion result of the behavior cooperation feature, the behavior interaction feature and the time evolution feature. By implementing the technical scheme of the invention, real-time modeling for the multi-behavior sub-sequence of the live broadcast room is realized, and the global feature of the live broadcast room level can be accurately extracted.

The invention provides an industrial robot anti-collision early warning method and system. The method comprises the following steps: environment perception and multi-source data fusion; predicting a dynamic obstacle track; robot posture real-time modeling is carried out; carrying out collision risk joint assessment; dynamically dividing early warning levels; intelligent planning of an obstacle avoidance path; dynamically compensating joint torque; multi-stage early warning collaborative response is carried out; performing iterative evaluation on the early warning effect; and self-evolving the anti-collision knowledge base. The three key technical problems in the industrial robot anti-collision field are solved, dependence on high-cost physical sensors (such as a torque sensor and a laser radar) is eliminated, collision detection of any position of the whole robot is achieved, and the cost is reduced by 60% or above. The limitation of a static obstacle model is overcome, and real-time track prediction and risk grading of dynamic targets such as moving personnel and conveying belt materials are achieved. Through multi-source data fusion and a risk probability model, the false alarm rate is reduced to 5% or below, and meanwhile it is guaranteed that the missing report rate is 0.

The invention provides a multi-source fusion heterogeneous data extraction method and system and a storage medium, and the method comprises the steps: constructing a dynamic hypergraph neural network and a dynamic hypergraph structure, carrying out the feature aggregation, and obtaining a space-time enhancement feature representation; performing causal reasoning and meta-learning optimization to generate robust feature representation; and by executing data reconstruction and fusion, outputting a heterogeneous data extraction result after multi-source fusion. According to the method, a dynamic hypergraph neural network, a sliding window and an attention mechanism are combined, data entity spatio-temporal evolution is modeled in real time, and the problem of correlation lag of a traditional static hypergraph is solved; then, a causal element learning confrontation module integrates causal reasoning, element learning and confrontation training to improve model robustness; finally, data reconstruction, meta-learning and adversarial loss are subjected to weighted fusion through a joint optimization strategy, the feature expression ability is improved, and the generalization performance of the model in a small sample scene is ensured.

The invention relates to the technical field of computers, discloses a panoramic situation intelligent emergency decision management and control method and system based on cloud computing, and aims to solve the problems of serious information island, response lag, weak intelligent decision capability, disjunction of resource scheduling and emergency strategies, man-machine interaction separation and the like in an existing emergency management system. The method comprises the steps that multi-source heterogeneous data are collected through a global data access engine, and a unified data lake is formed through data fusion and cleaning; constructing a dynamic three-dimensional digital twinborn model to realize panoramic situation real-time modeling; and carrying out risk identification and graded early warning by using a deep neural network. According to the technical scheme, global situation second-level perception, risk active prediction, scientific auxiliary decision making and integrated collaborative management and control are realized, and the emergency response efficiency and the decision making intelligence level are remarkably improved.

The invention discloses an AI-driven vehicle-mounted sound field real-time modeling and voice separation method, and relates to the technical field of voice signal processing. A main control unit comprising a time sequence synchronizer, a resource scheduler and a health monitor is constructed. 3D sound field modeling is carried out by adopting a lightweight STCN + bidirectional LSTM network, adaptive updating of the model is realized through EWC incremental learning, and a CNN-LSTM noise classification network and targeted suppression algorithms such as ANF / spectral subtraction are developed. The voice separation module adopts an improved Conv-TasNet architecture, 3D spatial constraint and a multi-task loss function are fused, and low delay is realized under INT8 quantization and pipeline processing. The system dynamically optimizes parameters through a real-time regulation and control unit, supports scene self-adaption, finally achieves a separation effect in a mixed noise scene, reduces the delay of the whole system, and effectively improves the definition and stability of vehicle-mounted voice interaction.

This invention provides a method and system for intelligent mixing of metallurgical dust and sludge based on deep machine learning. By mining data from metallurgical dust and sludge strong mixers, preprocessing data acquisition, real-time modeling of strong mixers, multi-step predictive modeling of strong mixers, intelligent control of water addition, intelligent control of binder, and intelligent control of return material, this invention solves problems such as uneven mixing and unsatisfactory mixing effects in the metallurgical dust and sludge field. It improves the processing capacity of the mixing system, enhances production quality and capacity, and paves the way for the digital and intelligent transformation of metallurgical dust and sludge.

This invention discloses a parameter estimation method and system for the forward and inverse models of an automotive electromagnetic valve-controlled shock absorber. The method includes the following steps: driving the shock absorber to perform simple harmonic motion at a set stroke and frequency under different currents; constructing a parameterized model of the shock absorber based on the changes in output force during the compression and recovery strokes; constructing a parameter estimation objective function to estimate the parameters of the parameterized model; fitting the estimated parameters as a function of the current to construct the forward model of the shock absorber, which calculates the damping force based on the current state of the shock absorber; selecting key variable parameters in the forward model that change with the current to calculate the sensitivity coefficient; constructing the inverse model of the shock absorber, generating the desired damping force using the forward model, and solving for the current corresponding to the desired damping force based on the inverse model. This invention can adapt to the dynamic characteristics of CDC shock absorbers under different road conditions, accurately meet the real-time modeling needs of complex road conditions, and significantly improve the practicality and response efficiency of the model.

The invention discloses a rain clutter modeling method based on an electromagnetic scattering principle. Firstly, a radar emission signal model and a rain field model are constructed, and the rain field model comprises raindrop size, raindrop last speed and rainfall distribution; then, uniformly dividing the detection space into a plurality of scattering units; calculating the backscattering sectional areas of the raindrops, and further superposing the backscattering sectional areas of all the raindrops in the unit volume to obtain the scattering rate of the unit volume; according to the scattering rate per unit volume and the volume of the scattering unit, calculating the backscattering sectional area of the scattering unit; and finally, calculating the rain clutter echo signal of a single scattering unit, and superposing the rain clutter echo signals of all scattering units in the distance ring to obtain the rain clutter echo signal of the distance ring. And superposing the rain clutter echo signals of all distance rings to obtain a rain clutter echo signal in the detection space. According to the method, the modeling accuracy is improved, the calculation complexity is greatly reduced, and the method is suitable for large-scale real-time modeling.

The invention relates to the technical field of risk assessment, and provides a multi-dimensional risk real-time assessment method and system for dynamic confrontation. The method comprises the following steps: accessing a data source to execute data acquisition, and establishing a multi-source dynamic data set; carrying out attack intention and behavior attribution analysis on the multi-source dynamic data set, and outputting an attacker strategy intention graph; modeling an attacker strategy by using a random game based on the strategy intention graph, and configuring a dynamic attack tree; constructing a conditional dependency graph according to the dynamic attack tree path dependency relationship, and mapping the conditional dependency graph into a Bayesian network structure; according to a Bayesian network, a risk propagation model is constructed, a node state is updated, and risk probability mapping is established, so that the technical problem that a traditional static risk assessment method cannot reflect risk evolution in a dynamic confrontation process in real time is solved, and the aim of improving the risk evolution in a dynamic confrontation process is achieved through collection and analysis of a multi-dimensional dynamic data source. And real-time modeling and risk propagation evaluation of attacker behaviors are realized, so that the technical effects of improving the adaptive capability and the real-time response capability of network security protection are achieved.

This invention relates to the field of Building Information Modeling (BIM) technology, specifically to a collaborative control system for high-fill open-cut tunnel construction based on BIM collaboration. The system includes a multi-source sensing and acquisition module, a BIM digital twin coordination module, an intelligent decision-making module, an execution control and feedback module, and a signal execution module. This invention collects various data from the construction site in real time and performs dynamic real-time modeling using a BIM digital twin model. The system generates control signals based on real-time data analysis, promptly identifying and locating hazardous areas on the construction site, effectively reducing safety risks during construction and ensuring the stability and safety of the construction process. This invention performs in-depth analysis of construction site data, calculates key construction indicators in real time, generates machinery scheduling schemes, and generates specific control signals for filling machinery, trigger signals for the support system, and adjustment signals for drainage equipment, enabling precise scheduling of construction machinery according to actual needs.

Medical systems are described, including a medical system including a display having a user interface that includes graphical elements corresponding to a first set of medical parameters and a type of intraluminal state, and a processor configured to receive user input selecting a status or value for at least one medical parameter of the first set of medical parameters and the type of intraluminal state, determine the intraluminal state over time based on the user input, and display on the user interface a graphical representation of the intraluminal state over time.

The application provides a kind of self-adapting environmental temperature electric fan wind speed intelligent control method and system, it is related to intelligent household electrical appliances control technical field, the method includes: based on the first temperature value, second temperature value and third temperature value calculate environmental average temperature, while combining wind speed correction value, according to the preset temperature-wind speed mapping rule generates initial wind speed gear parameter;Wind speed correction value is superimposed to initial wind speed gear parameter, generates final wind speed gear control parameter;Final wind speed gear control parameter is converted into motor speed control instruction, adjusts electric fan motor speed in real time;Wherein, temperature-wind speed mapping rule satisfies temperature rises and promotes wind speed gear, temperature reduces and reduces wind speed gear.The application is converted into motor speed instruction by synchronous acquisition, real-time modeling and dynamic correction closed loop process, quickly temperature change is realized electric fan wind speed intelligent control.

The invention belongs to the field of 3D GS scene modeling, and particularly relates to a 3D GS-based cross-scene adaptive modeling method and system, and the method comprises the steps: obtaining a multi-source image sequence, camera pose parameters and a scale classification identifier of a target scene; calling a corresponding preset modeling strategy parameter set according to the scale classification identifier, and executing sparse reconstruction and Gaussian field initialization to generate an initial three-dimensional Gaussian splashing model; in the iterative optimization process, real-time modeling parameters including the distribution density are maintained for different space areas respectively; obtaining a current frame through each round of training rendering, and calculating a pixel-level reconstruction error to generate an error distribution diagram; performing multi-scale frequency characteristic decomposition on the error distribution diagram, and extracting a high-frequency error component and a low-frequency error component; and calculating an energy ratio of the two and judging whether the energy ratio falls into a preset balance threshold interval, if not, calculating a microscopic region density increment and a macroscopic region merging threshold according to the deviation value, updating real-time modeling parameters and continuing iteration, and if so, outputting a final model.

Systems, methods and media for adaptive real time modeling and scoring are provided. In one example, a system for automatically generating predictive scoring models comprises a trigger component to determine, based on a threshold or trigger, such as a detection of new significant relationships, whether a predictive scoring model is ready for a refresh or regeneration. An automated modeling sufficiency checker receives and transforms user-selectable system input data. The user-selectable system input data may comprise at least one of email, display or social media traffic. An adaptive modeling engine operably connected to the trigger component and modeling sufficiency checker is configured to monitor and identify a change in the input data and, based on an identified change in the input data, automatically refresh or regenerate the scoring model for calculating new lead scores. A refreshed or regenerated predictive scoring model is output.

Medical systems are described, including a medical system including a display having a user interface that includes graphical elements corresponding to a first set of medical parameters and a type of intraluminal state, and a processor configured to receive user input selecting a status or value for at least one medical parameter of the first set of medical parameters and the type of intraluminal state, determine the intraluminal state over time based on the user input, and display on the user interface a graphical representation of the intraluminal state over time.

The application provides a power supply and a power conversion control system and method thereof. It belongs to the technical field of power electronics and intelligent management. The method comprises the following steps: dividing the power conversion demand of the power supply into multiple topological paths to generate dynamic topological configuration data; constructing a composite power conversion architecture according to the dynamic topological configuration data, and deploying edge computing nodes to form an intelligent topological reconstruction network; modeling the load behavior in real time through the intelligent topological reconstruction network to generate a load characteristic parameter set; performing multi-target collaborative scheduling based on the load characteristic parameter set, dynamically adjusting the topological path combination and the switching frequency, collecting multi-source power signals, and generating original power conversion data; performing harmonic suppression and aging compensation processing on the original power conversion data to generate purified power signal data; and through the integration of multiple topological paths, edge AI load prediction and multi-target collaborative control, the power conversion control method of the power supply is remarkably effective.

The invention discloses a high-precision 3D imaging plug-in system for mobile phone vertical screen close-range live broadcast, and belongs to the technical field of 3D imaging and live broadcast equipment. The system realizes high-precision real-time modeling of which the depth error is less than or equal to 0.03 mm within the distance of 0.2 m-0. 5m by arranging a double 5 million pixel main shooting module with the baseline distance of 10 + / -0.5 mm and combining a special depth processing chip and an improved semi-global matching algorithm. The system optimizes a field angle and double-channel annular light supplement for vertical screen live broadcast, synchronously transmits a depth map and a double-channel 4K video through a USB-C3.2 Gen2 interface, supports 30fps non-delay live broadcast application, and effectively solves the problems of insufficient precision, poor real-time performance and close-range adaptability existing in an existing scheme.

The invention relates to the technical field related to oral treatment and diagnosis, and discloses a 3D modeling assisted oral disease diagnosis platform which comprises a three-dimensional scanning device, a multi-source data fusion module and a biomechanical simulation engine. According to the invention, an oral cavity digital model is constructed through three-dimensional scanning and real-time modeling, and multi-modal data analysis and a deep learning algorithm are integrated, so that automatic identification and quantitative evaluation of a focus are realized; through cooperation of a three-dimensional scanning device, a multi-source data fusion module, a biomechanical simulation engine, an AI focus diagnosis module and a visualization terminal, the problems of insufficient three-dimensional visualization, low early-stage lesion recognition rate, low diagnosis efficiency and poor result consistency of oral disease diagnosis can be effectively solved; therefore, efficient and accurate oral disease diagnosis and judgment and subsequent continuous treatment are achieved, real-time three-dimensional modeling and multi-scale lesion analysis are integrated on the same platform for the first time, and diagnosis accuracy is improved through geometric + gray bimodal data.

The application discloses a kind of remote intelligent door lock control method and system based on Internet of Things, it is related to intelligent door lock control system technical field, including, based on TensorFlow Lite model is combined with LBP and cosine similarity algorithm optimization fingerprint matching reliability, after verification, based on integrated multimodal sensor, environmental state detection is carried out, after environmental state detection passes, using GMM model calculates behavior mode score, and based on time series convolutional neural network, the optimization of GMM model calculation result is carried out.LBP and cosine similarity algorithm are fused, the identification ability of system to low-quality fingerprint image and similar feature fingerprint is enhanced, the user operation time distribution is learned by Gaussian mixture model, and the continuity and stability of behavior are analyzed by combining time series convolutional neural network, realize the real-time modeling and credibility evaluation of user behavior, improve the identification ability of system to illegal operation, make up the short board that existing door lock system lacks the perception to environment.

The invention discloses an energy management method and system based on big data, and belongs to the technical field of energy management, and the method comprises the steps: building a digital twin model of an enterprise energy system through a digital twin modeling module, and building a physical and virtual real-time mapping relation; the method comprises the following steps: integrating multi-dimensional data such as energy consumption, business operation, environmental parameters and real-time electricity price through a multi-source data fusion module, and generating a unified energy state feature vector based on an adaptive fusion weight; learning an optimal energy configuration strategy by adopting a deep Q network algorithm through a reinforcement learning optimization module; the energy configuration effect is evaluated in real time through the closed-loop feedback adjustment module, feedback adjustment parameters are generated and transmitted to the front module, the four modules form a deep coupling closed-loop cooperative system, and real-time modeling, self-adaptive optimization and closed-loop feedback of an energy system can be achieved.

This invention relates to the field of storage scheduling technology, specifically to an intelligent storage scheduling method for computing clusters, comprising the following steps: acquiring link topology and status data through an interface, generating a dynamic link status set, inputting OPTICS clustering to identify high-load nodes, calculating load correlation strength, outputting high-load node clusters, querying routing paths and generating adaptive paths through multi-objective optimization, calculating heat gradients to trigger migration or archiving instructions, generating a policy adjustment table, inputting it into a scheduling engine to judge the load mitigation effect, and outputting a dynamic scheduling policy. In this invention, real-time physical link topology modeling is used, load intensity is calculated through access frequency and I / O queue changes, high-load nodes are located using density clustering, balanced paths are generated by combining global routing and multi-objective optimization, and data is dynamically migrated based on heat gradients to achieve self-optimization of storage resources, improve load balancing and scheduling response capabilities, and reduce maintenance costs.

The invention belongs to the field of structural engineering, and relates to a holographic environment real-time modeling method and system using quantum entanglement. The system comprises an environment data acquisition module, a quantum data processing module, a holographic model generation module and a real-time updating interaction module. According to the system, high-precision acquisition of multi-dimensional environment information is realized through an environment data acquisition module based on a multi-sensor collaborative acquisition technology of quantum entanglement enhancement; performing preprocessing, optimization and feature extraction on the acquired multi-source environment data through a quantum data processing module; converting the processed environment data into a high-resolution three-dimensional holographic model through a holographic model generation module; and real-time synchronization and intelligent interaction of the environment model are realized through the real-time updating interaction module. Through the modules, the real-time performance, the data processing efficiency and the modeling precision of holographic environment modeling can be effectively improved, and comprehensive and visual visualization support is provided for planning, monitoring and decision making of a complex environment.

This invention discloses a method for reliable communication using pilot square waves, relating to the field of two-bus communication technology for electronic detonators. The method includes the following steps: S1, Line Status Awareness: Before entering uplink communication, a pilot square wave is sent. Bus current sampling data is acquired within the sampling window corresponding to the pilot wave, forming a background reference feature quantity. Based on the background reference feature quantity, the line status is classified to obtain the line status category for subsequent decision-making. The line status category and the background reference feature quantity are cached as real-time modeling results. S2, Command Sending and Sampling: A downlink command containing a carrier square wave is sent. This invention utilizes pilot square waves to perform real-time modeling of line distortion and background current, and extracts key current features based on a dynamic sampling window to construct an adaptive threshold decision, thereby achieving reliable identification of uplink feedback current in complex line environments.

The invention discloses a method for realizing reliable communication by using a pilot frequency square wave, which relates to the technical field of electronic detonator two-bus communication, and comprises the following steps: S1, line state sensing: sending the pilot frequency square wave before entering uplink communication, acquiring bus current sampling data in a sampling window corresponding to a pilot frequency and forming a background reference characteristic quantity, the method comprises the following steps: S1, receiving a background reference characteristic quantity, classifying a line state based on the background reference characteristic quantity to obtain a line state category for subsequent judgment, and caching the line state category and the background reference characteristic quantity as a real-time modeling result, S2, instruction sending and sampling: sending a downlink instruction containing a carrier square wave, according to the method, line distortion and background current are modeled in real time by using pilot frequency square waves, and key current characteristics are extracted based on a dynamic sampling window to construct adaptive threshold judgment, so that reliable identification of uplink feedback current in a complex line environment is realized.