94 results about "Temporal models" patented technology

The invention relates to the technical field of natural resource monitoring and spatial information processing, in particular to a method and a system for feeding back land utilization change based on a land spatio-temporal model. The method comprises the following steps: deploying multi-source land monitoring equipment, carrying out collaborative data acquisition and standardization processing, and constructing a land space-time reference data set; performing triple mapping on the land space-time reference data set to obtain a land semantic association graph; constructing a land utilization knowledge graph based on the land semantic association graph; constructing a land change detection initial model by using the land utilization knowledge graph; meanwhile, in a high-frequency change scene, such as an urban and rural ecologic zone or an ecological sensitive area, a traditional model is slow in response to short-term land utilization disturbance, automatic adjustment cannot be carried out through deviation feedback between historical errors and model output, and the reliability of the model in actual application scenes such as resource regulation and control is limited.

The invention relates to the technical field of Internet of Things anomaly identification, in particular to an Internet of Things anomaly traffic identification method and system based on a deep neural network. The method comprises the following steps: collecting communication data of each piece of IoT equipment in real time from an edge gateway of the Internet of Things; preprocessing the collected communication data, and constructing a multi-dimensional feature vector; based on a convolutional neural network and a bidirectional long-short-term memory network, performing time sequence feature extraction and anomaly discrimination on the multi-dimensional feature vector to output a traffic anomaly probability; and comparing the abnormal probability output by the depth time sequence modeling neural network with a dynamic threshold value, and if the abnormal probability exceeds a preset threshold value, determining that the traffic is abnormal. A gating mechanism is introduced into a bidirectional long-short-term memory layer, a gating coefficient is calculated at a time step level, the influence weight of time step information on final output is dynamically adjusted, feature expression of key time steps is strengthened, noise or irrelevant information is suppressed, and the sensitivity of a model to time sequence data is improved.

The invention discloses a light energy power station fault prediction system based on deep learning. The system comprises a data acquisition module used for reading equipment operation data from a sensor; the data preprocessing module is used for denoising, interpolating and standardizing the equipment data; the graph convolutional network construction module is used for constructing an equipment data graph structure and extracting features; the Lemap dimension reduction module is used for mapping the high-dimensional equipment features to a low-dimensional space; the time sequence modeling module is used for constructing a time sequence prediction model based on the low-dimensional features; the hyper-parameter optimization module is used for optimizing hyper-parameters of the time sequence model; the model verification module is used for evaluating the precision and response time of the fault prediction model; the model deployment module is used for deploying the prediction model to a monitoring system; the fault prediction and early warning module is used for monitoring in real time and generating fault early warning; and the continuous optimization module is used for regularly optimizing and retraining the fault prediction model. The method achieves the high efficiency of fault prediction of the light energy power station, remarkably improves the prediction precision and the reliability of equipment operation, and is widely suitable for equipment monitoring and early warning.

The invention provides a children practice situation evaluation method, medium and system, and belongs to the technical field of deep learning models.The children practice situation evaluation method includes the steps that children and standard playing audio signals are collected and preprocessed into time-frequency representation, a time window sequence is constructed to calculate window similarity, beat mark points are extracted to form a rhythm sequence, and the rhythm sequence is obtained; a rhythm perception analysis model is input to generate a rhythm feature representation vector, a multi-dimensional deviation vector is formed by applying multi-level time model analysis, a rhythm deviation tolerance range matrix is constructed, deviation similarity is calculated, and finally a rhythm performance score is output through a rhythm evaluation neural network model. According to the method, a convolutional neural network, a long-short term memory network and an attention mechanism are fused, and comprehensive quantitative evaluation of the playing rhythm performance of the children is realized through large-scale data set training and expert scoring verification.

The invention provides a weak supervision video anomaly detection method and system based on prompt learning, and belongs to the technical field of abnormal event detection based on computer vision, and the method comprises the steps: obtaining to-be-processed video data; and processing the acquired to-be-processed video data by using a pre-trained anomaly detection model to obtain a specific classification result of the abnormal events in the video. According to the invention, a video local and global adaptive time modeling module is introduced to capture local and global dependency relationships at the same time, and the relationship between the demand of detailed time modeling and the calculation efficiency is balanced; by utilizing an external knowledge base, the distinguishing capability of the model on different categories is improved; according to the method, a text-video comparison loss function is designed, the similarity of a correctly matched text-video pair is enhanced, the similarity of wrong matching is reduced, and too high similarity of a negative sample is effectively inhibited, so that the distinguishing capability of the model is improved, the matching of the text and the video is more accurate, and the video and text alignment capability of the model is enhanced.

The invention relates to the technical field of large model reasoning, in particular to a reasoning performance optimization method and system based on a large model, and the method comprises the following steps: initializing a reasoning performance optimization agent; collecting hardware environment indexes in real time, wherein the hardware environment indexes comprise a video memory utilization rate, a CPU (Central Processing Unit) exchange number, residual video card resources, storage IOPS (Input / Output Per Second) and network throughput; the method has the beneficial effects that related statistical indexes, including model types, model weight file total volume, model average sequence length, the number of tokens per second output by the model, first token time of the model, a display card list occupied by the model, the size of a KVcache block, the size of a KVcache sliding window and the like, of each model in a service system are comprehensively collected and analyzed; the system performance is comprehensively evaluated, and the defects that in the prior art, performance evaluation is not comprehensive, and a real-time monitoring mechanism for key indexes such as memory occupation and network bandwidth is lacked are overcome.

The invention discloses a soil texture remote sensing mapping method based on time window screening. The method comprises the following steps: step 1, collecting and testing a soil sample; 2, screening a remote sensing image time window; step 3, acquiring and preprocessing an environment covariable; 4, constructing a multi-source data set; 5, constructing a soil texture prediction model; 6, multi-temporal remote sensing covariable combination optimization modeling is carried out; and step 7, soil texture mapping based on the optimal multi-temporal remote sensing combination. And fusing the multi-source environment covariable and the multi-temporal remote sensing data, and constructing a soil texture prediction model based on the optimal multi-temporal model combination by adopting an extreme gradient lifting algorithm. The problems of insufficient representativeness and weak generalization ability of single-time-phase remote sensing data are solved. On one hand, the generalization ability of the model is improved, and on the other hand, quantitative analysis of the key environmental factors is realized in combination with the SHAP technology, the interpretability of the model is enhanced, and meanwhile, the soil texture prediction precision is effectively improved.

The invention provides an aerospace craft discrete trajectory precision reconstruction method based on a variable order, and the method comprises the following steps: calculating the order of a reconstruction model based on a given highest-order dynamic parameter of an aerospace craft discrete trajectory; calculating a coefficient of the reconstruction model; based on a given aerospace craft discrete trajectory, constructing a continuous time model of a flight trajectory; and substituting any moment into the continuous time model of the flight trajectory to obtain trajectory data of the aerospace craft at any moment. According to the method, reconstruction can be completed with higher precision based on given discrete trajectory data, and trajectory continuity is considered; order configuration can be flexibly carried out, and the adaptability to discrete trajectories with different dynamic change rules is improved.

In various examples, machine learning models that benefit from temporal context while being computationally efficient to train and use are described herein. For instance, the disclosed systems and methods may apply a temporal series of images to a model and use intermediate features output from one or more backbone layers of the model as training data. In some examples, one or more recursive layers and / or one or more head layers of the model—or another model—may be trained using the training data by applying the intermediate features to the recursive layer(s). The recursive layer(s) may output a state representative of a temporal combination of the intermediate features, and the state may be applied to the head layer(s) to make one or more predictions. During inference, the recursive layer(s) may, in some examples, continuously update the state based on previous states of the recursive layer(s).

In various examples, to improve path perception in machine learning implementations, a temporal model includes a backbone model trained to predict one or more path perception outputs, such as, path geometry, path class, path uncertainty and / or other path attributes, for a current input frame. To create temporal context, the temporal model enables the backbone model to separately operate (in parallel or otherwise) on a set of frames that are temporally related to the current input frame. The outputs of the separate executions of the backbone model are then concatenated and processed via one or more convolution operations to generate a set of features that will be fed to the final output layer of the pipeline that encapsulates one or more path perception outputs that are generated based on temporal context.

The invention discloses an airport operation situation dynamic prediction method and device, equipment, a storage medium and a program product, and relates to the technical field of airport management.The airport operation situation dynamic prediction method comprises the steps that multi-source heterogeneous data of airport operation is acquired; performing multi-modal fusion based on the multi-source heterogeneous data to obtain multi-modal spatio-temporal data; and inputting the multi-modal spatio-temporal data into a pre-trained airport operation situation spatio-temporal model for situation dynamic prediction to obtain a situation prediction result. Due to the fact that multi-modal fusion is carried out based on the multi-source heterogeneous data, complementarity and collaboration among the data can be mined, and therefore all-dimensional dynamic prediction of the airport operation situation is achieved. The situation dynamic prediction is carried out through the pre-trained airport operation situation spatial-temporal model, so that the instantaneity of processing the abnormal time is improved, and the application scene is enlarged.

The invention belongs to the technical field of control theories, and provides a ZNN model design method for solving a time-varying Sylvester equation based on a filter, and the method comprises the steps: building an error function based on the basic knowledge of the time-varying Sylvester equation; designing a predefined time function based on a high-gain method, introducing the predefined time function into a ZNN model, and considering unknown noise interference in a ZNN model solving process; for unknown noise interference, a filter containing a predefined time function is introduced, and a subsystem of a neural network model is constructed; a Lyapunov function is constructed, a ZNN is obtained through stability analysis, noise can be effectively suppressed, and a time-varying Sylvester equation is solved at predefined time; and solving a time-varying Sylvester equation by using the predefined time ZNN model based on the filter, and verifying the performance of the predefined time ZNN model based on the filter. The method is used for solving a time-varying Sylvester equation, and the influence of noise can be effectively suppressed.

A computer-implemented system for predicting financial behavior and adaptive budget optimization based on machine learning, consisting of: a multitude of distributed processing nodes to enable low-latency communication between the nodes; a transaction data ingestion processor configured to establish authenticated connections with a plurality of financial data sources, wherein the ingestion module is further configured to normalize received transaction records into a standardized schema comprising at least a merchant identifier, a transaction category, a timestamp, a transaction amount, and optional geolocation metadata; a preprocessing engine comprising a classification sub-module trained through supervised learning to assign transaction categories based on merchant identifiers and context attributes, and a feature extraction sub-module configured to compute temporal, statistical, and behavioral feature vectors from the normalized transaction data; a prediction control unit comprising a plurality of lightweight neural network architectures, including at least one recurrent neural network (RNN) and at least one attention-based temporal model, the prediction control unit configured to predict short-term and medium-term output trends by sequentially processing the feature vectors; a budget optimization computation unit configured to solve multi-constraint budget allocation problems using a hybrid approach comprising a primary linear programming solver and an additional heuristic optimization technique, wherein the budget optimization computation unit is further configured to dynamically adjust budget allocations based on updated forecasts and user-defined constraints; a security subsystem configured for encryption at rest and in transit, as well as secure key storage in a hardware-based Trusted Platform Module (TPM); and a user interaction interface configured to display budget recommendations and forecasted spending trends through at least one web application, mobile application, or hardware device interface.

The invention discloses a man-machine cooperation disassembly task dynamic planning method under uncertain operation time. The method comprises the following steps: 1) constructing a man-machine cooperation disassembly information model under the uncertain operation time; 1.1) constructing an uncertain operation time description model of the product parts; 1.2) constructing a disassembling tool, direction and station position switching time model by considering the operation characteristics of the robot and the disassembling personnel; 1.3) constructing a product part disassembly constraint relation model; 2) taking the man-machine cooperation disassembly information model under the uncertain operation time constructed in the step 1) as an environment of a reinforcement learning algorithm, and establishing a man-machine cooperation disassembly task dynamic planning model; and 3) using the trained man-machine cooperation disassembly task dynamic planning model to generate a man-machine cooperation disassembly task planning scheme under the uncertain operation time. The uncertainty of the structural adhesive softening time is quantified by constructing the uncertain operation time man-machine cooperation disassembly information model, and the characterization problem of the disassembly operation time is solved.

The invention relates to a two-dimensional multi-view brain tumor medical image segmentation method and a two-dimensional multi-view brain tumor medical image segmentation system based on a Vision Mama time sequence model, which utilize a novel visual representation model to complete focus segmentation of a brain tumor two-dimensional medical image. The method is used for solving the problems that when an existing two-dimensional segmentation method is used for processing two-dimensional brain medical images, space depth information cannot be fully utilized, and three-dimensional structure features are difficult to accurately capture. The method comprises the following steps: 1, acquiring and preprocessing data; 2, constructing a multi-view brain tumor segmentation network based on edge feature fusion and a spatial state model; 3, constructing a combined loss function of weighted cross entropy and weighted Dess loss, and meanwhile, storing an optimal model weight in training for prediction; and 4, predicting a brain tumor medical image by using the trained optimal model, calculating evaluation indexes and performing result comparison. Through the combination of the methods, the boundary feature extraction quality of the fuzzy edge of the complex focus and the two-dimensional segmentation precision of the model on the brain tumor are effectively improved.

The present application relates to the technical field of wisdom agriculture, and particularly relates to a cloud-edge collaborative computing offloading method for wisdom agriculture. The present application constructs an adaptive task quantity prediction function, a response time model, a power consumption model and a load model, converts actual problems in the system into basic mathematical models, and specifically abstracts problems into optimal solution problems of mixed integer nonlinear optimization problems. Finally, the optimal computing offloading result is found by using the defined adaptive function and the particle swarm algorithm. Compared with other cloud-edge collaborative computing offloading methods applied in wisdom agriculture, the present application overcomes the error conflicts caused by traditional static modeling, improves the accuracy of the computing offloading result, and greatly improves the real-time performance and accuracy of terminal task computing.

According to the multi-sensor fusion evaluation algorithm and device based on the continuous time sequence, modeling is carried out based on continuous time, physical reality is better met, motion modeling with better precision is achieved, intra-frame motion can be accurately described, smoother and more accurate track estimation can be provided, and the method and the device are suitable for being applied to multi-sensor fusion evaluation. Especially, the advantages are obvious in high-speed and high-frequency vibration scenes; meanwhile, according to the technical scheme disclosed by the invention, when LiDAR / Camera matching fails, the constraints of the IMU and the GNSS are seamlessly and continuously applied to the whole track through a continuous time model instead of only acting on a discrete frame, so that error accumulation is greatly inhibited, system collapse is avoided, the robustness of a degraded scene is enhanced, application in specific fields such as surveying and mapping is greatly facilitated, and the method is suitable for popularization and application. And the method has the capability of flexibly adding constraints.

A multi-objective optimization method and system for a comprehensive transportation hub conversion component design scheme, comprising the following specific steps: based on the building information model, generating the detailed structure of the comprehensive transportation hub conversion component under multiple design schemes, extracting the construction materials, machinery, types of work and processes under different design schemes, and constructing a comprehensive cost library and a process total time model; through a structure analysis software, establishing a generalized steel-concrete composite beam bending / shear bearing capacity calculation formula, combining the cross-section properties and design parameters to quantify the structure performance; taking the maximum bending bearing capacity, the maximum shear bearing capacity, the minimum construction cost and the shortest construction period as four-dimensional objective functions, using the grey wolf algorithm (GWO) for multi-objective optimization solution, and outputting the optimal design scheme. The present application realizes the cost-construction period-performance collaborative optimization by fusing BIM modeling, structure analysis and intelligent algorithm, improves the structure safety and economy, and is suitable for the design and construction decision of comprehensive transportation hub engineering.

Systems and methods for evaluating risks for physical locations are disclosed. A request for risk evaluation of transactions associated with a physical location located within a predetermined region including a plurality of neighboring locations is received. A spatial model is implemented to generate a first risk score based on chargeback data associated with the physical location and chargeback data of the plurality of neighboring locations. A temporal model is implemented to generate a second risk score for the physical location. A final risk score for the physical location is generated based on the first risk score and the second risk score. The final risk score is transmitted to a computing device for detecting fraudulent transactions associated with the physical location in response to the request.

Systems and methods are disclosed. The method includes obtaining seismic data for a geological region of interest, a velocity model, and an attenuation model. The seismic data includes an amplitude and a phase of seismic waves traveling from a seismic source location. The method further includes determining a traveltime model using the seismic source location, the velocity model, and a traveltime Eikonal function and determining an attenuated traveltime model using a parallel fast sweeping method. The parallel fast sweeping method includes Cuthill-McKee ordering a plurality of grid nodes. The plurality of grid nodes represents the geological region of interest. The parallel fast sweeping method further includes using the seismic source location, the velocity model, the attenuation model, the traveltime model, and an attenuated traveltime Eikonal function. The method still further includes generating an updated attenuation model using the attenuated traveltime model, the amplitude, and the phase.

The invention discloses a measurement task scheduling method for a quality robot system, and the method comprises the steps: carrying out the modeling of the measurement time of a detection delegation order of a to-be-planned batch, and obtaining a time model consumed by a measurement task; constructing an objective function based on the expected complete inspection time, the measurement time model and the certificate time model, and constructing a 0-1 integer programming model of the measurement task by taking the tray number of the weight tray rack and the maximum measurement time as constraint conditions and the complete inspection remaining time as weak constraints; and inputting a batch detection delegation list to be planned into the integer programming model to obtain a scheduling result. Measurement tasks are optimized under the constraint conditions of the number of weight trays, the maximum total measurement time and the like, the measurement capacity of a mass robot system is fully utilized, cooperation of automatic measurement time and on-off time of detection personnel is achieved, the automatic measurement system continues to carry out continuous measurement work at night, and the work efficiency is improved. And the utilization rate of the quality robot system can be effectively improved.

The invention discloses a simulation task load dynamic change-oriented end-cloud integrated cluster dynamic container capacity planning method, and relates to the technical field of computer services. Modeling a micro-service, a container, a user and a server; load sensing and triggering judgment, including calculation of micro-service total load and load variation, calculation of load intensity and triggering condition judgment; mDP differential capacity adjustment: constructing an MDP state space, formulating a container capacity expansion and shrinkage strategy, and calculating an adjustment correction coefficient; key index calculation including a response time model, a system total cost model and a fairness index model; and carrying out multi-objective solution and verification, namely constructing an objective function, setting constraint conditions and solving by adopting fusion MDP. Hierarchical modeling is carried out by adopting micro-services, containers, users and servers, MDP decision differentiation adjustment is triggered through load sensing, multi-target solving is carried out, a four-layer technical architecture of modeling-sensing-decision-solving is formed, and dynamic optimization of cluster capacity can be realized.

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.

A predefined time ZNN model design method for solving pseudo-inverse of a time-varying matrix comprises the steps that a pseudo-inverse matrix is solved based on the time-varying matrix, an error function is established, the time-varying matrix is known, and the pseudo-inverse matrix to be solved is unknown; designing a predefined time function based on a high-gain method, and introducing the predefined time function into the ZNN model; aiming at bounded noise interference in a ZNN model solving process, introducing a disturbance estimator comprising a predefined time function, and constructing a subsystem of a neural network model; a Lyapunov function is constructed for stability analysis, and a ZNN model obtained through analysis can effectively suppress noise and solve a pseudo-inverse matrix of a time-varying matrix at predefined time. And applying the predefined time ZNN model to control of the permanent magnet synchronous motor chaotic system. According to the designed predefined time ZNN model, a complex parameter adjustment process is avoided in parameter adjustment. The method has the advantages of high robustness, high calculation precision and high solving speed.

The application provides a monocular human motion trajectory estimation method and system based on a graph neural network, and relates to the technical field of human motion trajectory estimation. The application designs a human motion trajectory estimation network based on a graph neural network, uses multiple stacked space-time modules to mine time and space features in human motion, wherein the space model uses a graph neural network to explore joint features under human topological structure priori and uses limb grouping to explore spatial correlation of joints in the limb group, the time model uses a Transformer structure to model each joint motion trajectory and uses limb grouping to explore time trajectory features of the whole limb group, so that more accurate trajectory estimation is realized.

The application discloses a terrain self-adaptive rainstorm waterlogging spatio-temporal joint prediction method and system, and belongs to the field of alarm responding to disaster events. In order to overcome the defects of the existing method, such as insufficient terrain adaptability of fixed feature weight, and prediction fragmentation caused by independent operation of the spatio-temporal model, the technical scheme of the application is as follows: on the one hand, a terrain self-adaptive dynamic feature weight mechanism is constructed, an adjustment coefficient is generated by quantifying the terrain undulation, and the feature factor weight of the geographical spatio-temporal weighted regression model is dynamically adjusted to adapt to the complex underlying surface; on the other hand, a spatio-temporal joint weighted fusion framework is proposed, a random forest spatial model and a PredFormer time series model are combined, the spatial prediction result is taken as the initial condition of the time series, and the time series collaborative prediction of the flooded range and the water depth is realized. The application is suitable for rainstorm waterlogging prediction of different terrains in cities, and has the advantages of strong terrain adaptability, high prediction accuracy and stability, and unified spatio-temporal rapid response.

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.

The invention discloses a user potential intention modeling method based on continuous time dynamics, which comprises the following steps of: firstly, performing time sequence perception embedding on an original behavior sequence of a user; then, the embedded sequence is processed through a sequence encoder, and a state vector representing the initial potential intention of the user is obtained through variational inference; thirdly, performing dynamic evolution on the initial potential intention state in continuous time by using a neural network parameterized Sheng differential equation so as to solve the state at any time point in the future; and finally, mapping the evolved future potential intention state to a project space through a decoder module, and generating a recommendation result. According to the method, the discrete behavior sequence is mapped to the continuous potential intention evolution trajectory, and the problem that a traditional discrete time model is difficult to process irregular interaction intervals and simulate smooth evolution of user interests is solved.