17 results about "Temporal models" patented technology

The application discloses a gate opening control system and method based on a force-displacement time error algorithm, and relates to the technical field of gate control, comprising the following modules: an actual comparison module is used for collecting the structure, transmission mechanism and motor performance parameters of a gate, and obtaining the actual measured running time; a core algorithm construction module is used for establishing a function of driving force changing with opening degree based on the relationship between mechanics and kinematics, and constructing a theoretical time-opening degree digital model; a theoretical calculation module is used for forming a fixed parameter combination to be calibrated, and outputting the theoretical running time under different working conditions; a model correction module is used for optimizing the fixed parameters in the theoretical model, so as to minimize the error between the theory and the measurement, and lock the optimal parameter combination; and an algorithm running module is used for inputting the target opening degree into the final control model, calculating the gate running time, and realizing the gate positioning control, so that the system can improve the accuracy and reliability of the gate control.

This invention relates to the fields of intelligent transportation and road construction management technology, and particularly to a multi-scale traffic operation state prediction method and system. The invention achieves high-precision traffic state prediction by constructing a three-layer spatial topology architecture, designing and building a dual-flow independent LSTM architecture, and establishing a recursive rolling and interactive input mechanism. Under complex conditions such as lane closures, temporary traffic diversions, and perception blind spots, this invention, based on a hierarchical spatial perception system and a dual-flow deep temporal model, can perform high-precision short-term predictions of micro, meso, and macro traffic states, providing decision support for lane-level refined management and control in construction areas.

This invention belongs to the interdisciplinary fields of marine engineering, marine geotechnical engineering, and artificial intelligence. Specifically, it relates to a method for predicting the seabed response around pile foundations based on a deep learning time-series model. The method includes generating multi-point time-series physical field data of multi-directional wave and seabed dynamic response through CFD-FEM coupled simulation, followed by random spatial sampling processing; enhancing wave nonlinear characteristics using multi-order difference; extracting spatiotemporal dynamic features using TimeDistributed-LSTM; constructing a four-dimensional spatiotemporal tensor based on relative coordinates and Euclidean distance; aggregating local and global features using a local multilayer perceptron and max pooling; and finally, using a multi-output decoding network to predict the seabed pore water pressure response in parallel. This invention eliminates dependence on fixed grids and measurement points, exhibits strong spatial adaptability and generalization capabilities, and can achieve real-time, high-precision prediction of the dynamic response of the seabed around pile foundations.

This invention relates to a method, device, and medium for multi-agent coverage search and task allocation path optimization. The specific method includes: scenario construction and problem description, which describes the characteristics of the task area, the attributes of agents and targets, and clarifies the area coverage search problem and the target task allocation problem; area coverage model design, which establishes a minimum task time model objective function and constraints, and then solves the area coverage model; and task allocation strategy and path planning algorithm, which, considering agent speed and target attributes, combines a greedy strategy and a genetic algorithm to allocate target exploration tasks to agents, optimize target exploration paths, and reduce total task time. Compared with existing technologies, this invention improves the efficiency of marine area search and target exploration through multi-agent cooperation, achieving full area coverage search and accurate target exploration.

This invention discloses a method and system for evaluating the quality of immersive videos based on multimodal perception, relating to the field of video evaluation. The method includes: acquiring a multi-viewpoint texture and depth format video and extracting keyframes; extracting texture and depth features using ConvNeXt, processing texture features through a frequency-space texture enhancement module, and combining depth features with a cross-modal collaborative representation module to obtain structural texture coupling features; extracting semantic distortion features using a semantic distortion perception module; concatenating the coupling features and semantic distortion features and inputting them into a temporal modeling module to capture temporal features, then generating global perception features through a viewpoint fusion module; and finally outputting the final video quality score through a quality regression module. This invention achieves a more accurate and robust evaluation of immersive video quality by fusing multi-viewpoint texture and depth information and sequentially performing frequency-space texture enhancement, cross-modal collaborative representation, semantic distortion perception, temporal modeling, and viewpoint fusion.

This invention relates to a satellite-ground collaborative edge computing method for 6G space-ground integrated networks, comprising: constructing a system model for a mobile edge computing scenario; based on the system model, constructing a computation offloading model, a low Earth orbit satellite communication time model, and a channel model; based on the computation offloading model, the low Earth orbit satellite communication time model, and the channel model, constructing an offloading optimization problem with the goal of minimizing the weighted sum of total system latency and energy consumption; and iteratively solving the offloading optimization problem using a block coordinate descent algorithm, wherein the offloading optimization problem is decomposed into two sub-problems: task offloading optimization and resource allocation, which are solved separately. This invention solves the task allocation problem between satellites and base stations for multiple users by establishing a refined satellite-ground geometric model and resource scheduling model, minimizing the total system overhead while satisfying task deadlines and satellite service window constraints.

A system generates a temporal model for a phishing resource from the database. The system evaluates the phishing resource based on the temporal model. The system determines that the phishing resource is non-phishing based on evaluation results generated during the evaluating. The system updates information about the phishing resource in the database.

This invention discloses an intelligent detection system for residual coal unloading in skips based on image recognition, comprising the following modules: a data acquisition module, using an intrinsically safe mining camera positioned at the skip unloading location to acquire unloading video sequences; a calibration and segmentation module, used to perform region calibration and frame-by-frame image segmentation to form a residual feature sequence; a temporal modeling module, including a neural oscillator network, used for rhythm modeling and phase discrimination, outputting temporal discrimination results; a fusion and judgment module, used to combine spatial parameters and temporal results to generate a fused residual score; an alarm recording module, used to trigger an alarm and generate snapshot and video recordings when residual anomalies occur; and a linkage control module, used to transmit the judgment status and output linkage signals to the hoisting system safety loop. This invention achieves accurate detection and safe control of residual coal unloading in skips through camera recognition and neural oscillator network modeling.

The application relates to an artificial intelligence-based intestinal tract cleanliness auxiliary evaluation method, system, device and medium. The method comprises the following steps: inputting an updated fusion feature vector sequence into a pre-trained time sequence model based on a multilayer gate recurrent unit to obtain a cleanliness state classification result of a current defecation reporting event and a next defecation state prediction result; according to the cleanliness state classification result and the next defecation state prediction result, an intestinal tract preparation progress quantitative index and an evolution trend index up to the current defecation reporting event are calculated through a state residence time model based on a state transition probability matrix; and based on the intestinal tract preparation progress quantitative index and the evolution trend index, a dynamic threshold judgment mechanism is triggered for personalized early warning of the intestinal tract preparation process in combination with a target examination time. The method can dynamically monitor, evaluate the progress and predict the trend of the intestinal tract preparation process.

The application provides a combined active learning modeling method and device for a time synchronization system, and the method comprises the following steps: step S1, configuring a corresponding component learner for each component of a target time synchronization system; step S2, setting a query adapter to convert a component-level query into a system-level query; step S3, maintaining a time observation table containing unknown items by using a weak equivalence relation; step S4, assigning values to the unknown items by using a solver to construct a component hypothesis model; step S5, combining the component hypothesis models in parallel to perform system-level equivalence determination; and step S6, classifying and backing up false counterexamples by projecting the counterexamples. The application breaks through the limitation that a single time learning method can only obtain an overall model, and can directly obtain a component-level time model, and improves the stability and implementability of the combined active learning process.

This invention provides a temporal model representation method based on task arithmetic and weight alignment, comprising: acquiring meteorological monitoring time-series data; preprocessing the meteorological monitoring time-series data to obtain processed meteorological time-series data; inputting the processed meteorological time-series data into a target host model for parameter fusion for prediction processing to obtain meteorological prediction results; the target host model for parameter fusion is a network model after covering the embedding layer of the target host model based on an enhanced embedding module; the enhanced embedding module is a feature processor based on a weighted linear interpolation method, which weights and fuses the parameters of the source auxiliary model and the target host model within the embedding layer; both the target host model and the source auxiliary model are pre-trained temporal models. This significantly reduces the deployment and operational overhead of meteorological forecasting systems, achieving high efficiency, high accuracy, and strong generalization ability in accurate prediction of meteorological elements.

This invention discloses a method for estimating the remaining service life (RSW) interval of aero-engines based on bidirectional Mamba evidence regression. The method includes: collecting aero-engine operating status data and performing feature selection, normalization, and sliding window sample construction; inputting the RSW prediction samples into a lightweight multi-scale convolution module and a bidirectional Mamba temporal modeling module to extract local degradation features and long-range temporal dependency features; performing adaptive fusion through a residual-gated fusion module to obtain fused degradation features; inputting the fused degradation features into an evidence regression module to output the mean of the aero-engine RSW prediction and uncertainty parameters, calculating the uncertainty to construct the RSW prediction interval, and finally outputting the predicted RSW point value of the aero-engine, the lower boundary of the prediction interval, and the upper boundary of the prediction interval. This invention can improve the accuracy and reliability of aero-engine RSW prediction, providing a basis for health management and maintenance decisions.

The invention discloses a psychological risk assessment method and system based on artificial intelligence. The psychological risk assessment method comprises the steps of data acquisition, representation learning, risk assessment and assessment result output. The invention relates to the technical field of psychological risk intelligent assessment, in particular to a psychological risk assessment method and system based on artificial intelligence. Dynamic modal gating is introduced to weight modals, unreliable signals are eliminated, and prototype memory is matched to quickly adapt to typical high and low risk modes; a parallel self-attention and microstable state space mixed time sequence model is adopted, local and global time sequence dependence is captured, and the robustness, the few-sample adaptability and the interpretability of feature representation are improved; meanwhile, classification scores and instantaneous danger rates are output, confidence intervals are constructed through uncertainty decomposition, criteria are fused, parameters are fitted, posterior risk probabilities are provided in psychological risk scenes, early warning accuracy is improved, false and missing reports are reduced, and clinical practicability is enhanced.

Disclosed are systems and methods for allocating resources for executing a simulation. These include receiving a simulation for execution, calculating an initial runtime of an initial time step of the simulation, determining a total runtime of the simulation based on the initial runtime, selecting a runtime model based on the initial time step, total runtime, or a parameter of the simulation, identifying, based on the selected runtime model, an allocation of a resource providing an increase in runtime speed, allocating the identified resource, and executing the simulation using the allocated resource.

This application relates to the field of image recognition technology, and in particular to a method and system for detecting heat-sealing anomalies in industrial heat-sealing equipment. The method includes: acquiring the temperature matrix of the heat-sealing area of ​​the industrial heat-sealing equipment; extracting spatiotemporal feature information from the temperature matrix; training a dynamic temporal model based on the spatiotemporal feature information; using the dynamic temporal model to detect the real-time temperature matrix to determine whether abnormal pixels exist; if so, clustering and morphological processing are performed to determine whether heat-sealing anomalies exist. This application effectively overcomes the temperature baseline drift problem caused by factors such as equipment preheating, workshop airflow, and changes in ambient temperature, avoiding the high false alarm rate of traditional methods; and solves the problem of difficulty in collecting negative samples covering all defect types in industrial settings. While ensuring low resource consumption and high real-time performance, it significantly improves the accuracy, robustness, and generalization ability of detecting heat-sealing anomalies in industrial heat-sealing equipment.

This invention discloses a generative statistical semantic guidance processing method for time series classification. Addressing the issues of attention diffusion in long sequences and overfitting in small samples, this method projects multivariate time series to generate morphological token sequences. It extracts high-order statistical features such as skewness and kurtosis from the tokens to construct statistical semantic cue vectors, calculates importance scores based on these vectors, and performs Top-K soft sparse reweighting. Simultaneously, redundant tokens are adaptively compressed into global context features. Finally, the fused features are input into a multi-scale conditional expert routing module for dynamic calculation, and a large temporal model is introduced during training for manifold distillation. This invention effectively eliminates computational redundancy while preserving global coherence without loss, significantly improving classification accuracy and robustness in small sample and noisy environments.