68 results about "Time model" patented technology

The invention relates to the technical field of risk analysis, in particular to a supply chain risk quantitative evaluation method and system based on a dynamic affair atlas, and the method comprises the steps: collecting multi-source heterogeneous data, constructing a four-dimensional space-time model comprising a time dimension, a geographic space dimension, a supply chain network space dimension and a risk influence space dimension, representing the supply chain event as four-dimensional spatio-temporal data; a supply chain entity is identified from the four-dimensional spatio-temporal data, risk events are extracted, a affair graph is constructed, and the affair graph takes the risk events as nodes and the evolution relation between the events as edges to calculate the relation weight between the events; calculating a probability quantized value of the risk conduction path based on the affair map, and obtaining a comprehensive risk score of the target entity; generating a risk mitigation strategy based on the comprehensive risk score; and monitoring the deviation between the actual risk occurrence condition and the prediction result, and updating the affair map and the risk mitigation strategy through adaptive parameter optimization and an incremental learning mechanism to form a self-evolutionary risk assessment system.

The invention belongs to the technical field of air traffic management, and relates to an air flight flow prediction method based on a lightweight adaptive network, which comprises the following steps of: firstly, acquiring data and fusing the data to generate a flow time sequence matrix indexed by airport identification and timestamp; secondly, constructing a self-adaptive dynamic graph; performing mixed graph convolution on the output dynamic graph and node features to form a spatial feature tensor, and performing frequency domain enhancement on the spatial feature tensor to obtain an event enhancement sequence; then processing the event strengthening sequence through an hour-level large convolution kernel and a minute-level expansion small convolution kernel, amplifying a congestion peak based on trend-fluctuation gating after time alignment, and outputting a fusion feature sequence; and finally, carrying out recursive decoding and prediction to obtain the predicted air flight flow. According to the method provided by the invention, under the conditions of dynamically changing airspace topology and strong noise and multi-scale coupled time sequence data, a set of lightweight spatial-temporal model is constructed and updated online in a self-adaptive manner, so that high-precision multi-step prediction of the multi-element flight flow can be realized.

The invention discloses a heavy traffic asphalt pavement damage evolution prediction system, and relates to the technical field of road engineering, and the system comprises a data acquisition module which is used for obtaining continuous multi-frame time sequence images of pavement cracks, a vehicle-mounted axle load spectrum and environment temperature and humidity data; the image processing and space-time modeling module is used for performing multi-scale fractal processing and dynamic time warping on the time sequence image and calculating a cross-scale fractal dimension sequence; the material performance association module is used for establishing and updating a probability association model of the fractal dimension and the shear modulus in real time according to the test data of the dynamic shear rheometer; the multi-physics field data fusion module is used for performing multi-physics field feature fusion through the fractal attention network to generate a multi-physics field collaborative damage correction factor; and the damage prediction and early warning module is used for predicting a fractal dimension growth trend through a fractal domain adaptive transfer learning algorithm, and outputting damage evolution early warning based on a viscoelasticity phase consistency verification result.

The invention discloses a traffic image labeling method and device based on a D-S evidence theory and a medium, and relates to the technical field of image labeling. According to the method, two different types of real-time models are simultaneously utilized to process the same image in parallel, and two uncertainty detection results of the same image are obtained; a KM algorithm is utilized to match targets in two uncertainty detection results, then a prior credibility score is utilized to reduce a conflict coefficient, and two different types of small model prediction results are combined through an uncertainty theory. According to the traffic image labeling method and device based on the D-S evidence theory and the medium, the strong generalization ability of the open set model is utilized to make up for the defects of a closed set model, the use of a large model to improve the hardware dependence is avoided, the precision problem of a single small model is made up under the condition that the efficiency is ensured, and the iteration duration of the model is shortened.

The invention provides a traffic flow prediction method combining a dynamic graph neural network and a Mama mechanism. The future short-term traffic flow is predicted by using historical traffic data. According to the method, firstly, normalization preprocessing is carried out on traffic state data collected by multiple sensors, a training sample is generated by adopting a sliding window, and a traffic flow value in the next one hour is predicted according to data in the past 24 hours. On the basis of the model structure, a time modeling module composed of multiple layers of MambaBlocks is constructed and used for capturing historical time sequence dependence; constructing a spatial modeling module of dynamic graph convolution, and combining a static adjacency matrix of the road network with a learnable adaptive adjacency structure to extract spatial association; and finally, the outputs of the modules are fused, and a prediction result is obtained through a prediction output module. In the training process, a Huber loss function is used as an optimization target, and evaluation indexes such as a mean absolute error (MAE), a root mean square error (RMSE) and a mean absolute percentage error (MAPE) are used for evaluating the performance of the model. According to the method, the traffic space-time dynamic characteristics are effectively mined, and the long-range dependence modeling capability and the prediction precision are improved.

The invention discloses a vibration temperature control cooperative control method and system for concrete forming, and the method comprises the steps: building an initial prediction model of a vibration temperature control bidirectional coupling effect through concrete mix proportion parameters, historical vibration control parameters and initial temperature field data; the model is dynamically corrected through vibration time sequence data and temperature field space data which are collected in real time to obtain a real-time coupling effect prediction model, and then the real-time model is used for predicting the compactness deviation and the temperature gradient of the next control period; then vibration parameter adjustment amount and temperature control measure intervention intensity are calculated based on the predicted value through a multi-target collaborative optimization algorithm to generate a collaborative control instruction, and finally collaborative optimization of vibration and temperature control in the concrete pouring process is achieved according to the instruction. According to the invention, precise collaborative optimization of vibration and temperature control in the concrete pouring process can be realized, and the stability of concrete forming quality is effectively guaranteed.

The invention provides a typhoon path prediction method and system fusing physical constraint and path mutation recognition. The method comprises the steps that a multi-dimensional input feature tensor is obtained through a feature tensor generation module; performing time modeling through a multi-scale modeling module to obtain a deep feature tensor, and performing significance guidance through a significance guidance module to obtain a prediction path sequence; the bending event identification module identifies a bending angle to obtain a plurality of path abnormal scores, the adversarial disturbance analysis module introduces a plurality of preset disturbances to determine the path confidence, and the path prediction output module outputs a path prediction result. According to the technical scheme of the embodiment of the invention, refined modeling can be carried out on the typhoon path, the PINNs physical constraint module is utilized to ensure that the prediction result meets the physical conservation principle, the path mutation detection capability is enhanced through bending event recognition, uncertainty evaluation is realized through disturbance analysis, and the accuracy of the typhoon path detection is improved. And a typhoon path prediction result which is more accurate and credible and has physical consistency is output.

The invention relates to the technical field of water, wind and light storage combination, in particular to a wind, light and water storage combination robust tracking method and device and a medium, which can uniformly represent the output uncertainty of extreme renewable energy sources and quickly map a safe feasible region in a high-dimensional scheduling space. And safe and economic cooperative control of the hydroelectric generating set, the reservoir water level and the energy storage resources within the resolution ratio of 15 minutes is realized. Firstly, unified normalization and sliding window time embedding are performed on wind speed, irradiation and load sequences in a data layer, and a dynamic error envelope is constructed through exponential weighting of a high-tail sample, so that a measurable extreme interval is directly obtained in an original observation space. And then introducing a physical climbing limit and a unit inertial constraint, sequentially expanding a disturbance scene tree on nodes at two ends of the error envelope, carrying out error envelope-physical mapping coupling modeling, enabling the system to keep time-space correlation, and directly incorporating unit dynamic characteristics in a scene generation stage.

The invention relates to a decision-making method and a prediction model based on brain-like intelligent decision-making, and belongs to the technical field of decision-making generation, and the method comprises the steps that a multi-modal feature extraction and fusion module carries out the feature extraction after receiving the input information of a to-be-optimized decision-making, and generates a fusion feature vector; a Transform space-time modeling module performs space-time modeling on the fusion feature vector to obtain a space-time feature matrix; an EW-TOPSIS dynamic evaluation module processes the spatial-temporal characteristic matrix to generate a decision candidate sequence; the dynamic memory module screens the decision candidate sequence to obtain a decision candidate set; the reinforcement learning module is used for updating the decision gradient of the prediction model based on the brain-like intelligent decision; and the behavior collection module is used for constraining the decision candidate set to obtain an optimal decision. According to the invention, through close cooperation among a plurality of modules, full-link closed-loop optimization from multi-modal to-be-optimized decision input to high-robustness decision is realized.

This invention relates to the field of meteorological services and provides a method and system for correcting subseasonal model precipitation based on climate zoning and sea surface temperature (SST) background constraints. The method includes: climate zoning, adding SST background constraints, reanalysis data modeling, model historical return data modeling, real-time prediction model matching, and prediction result correction. The system includes: a data preprocessing unit, a climate zoning unit, an SST background constraint unit, a reanalysis data modeling unit, a model historical return data modeling unit, and a real-time model matching and prediction result correction unit. This invention considers the importance of regional differences in precipitation and SST background constraints, solving the problems of significant regional differences in daily summer precipitation in my country, the tendency for overfitting in single-point modeling, and the lack of physical constraints that make correction methods unsuitable. It effectively utilizes physically meaningful predictability sources to modify the cumulative probability density correction method based on percentile mapping, thereby improving my country's summer subseasonal precipitation prediction capabilities.

The invention discloses a multi-scale time sequence prediction method based on an adaptive sparse expert selection strategy and a closed continuous time neural network. The method comprises the following steps: carrying out normalization and low-dimensional feature mapping based on RevIN; performing trend-seasonal structure enhancement processing on the feature sequence after linear mapping; constructing a multi-scale expert model based on the feature sequence after trend-season enhancement; self-adaptive sparse expert selection and load balancing loss calculation are carried out; carrying out weighted aggregation and residual fusion on multi-scale expert output; global modeling of a closed continuous time neural network based on channel weighting is carried out; and finally performing prediction generation and reverse normalization. The multi-scale time series prediction method has the multi-time-scale adaptive modeling capability, the sparse expert efficient selection mechanism and the global continuous time modeling capability, and can be applied to various multivariable time series prediction scenes such as power load prediction, weather prediction, industrial production monitoring, traffic flow prediction and financial price prediction.

The invention discloses a video action recognition method based on an auxiliary time modeling module, and belongs to the field of mode recognition. According to the method, a series of time modeling modules (ECTLs) are designed, and the advantages of Transform and LSTM in the aspect of processing sequence information are efficiently combined, so that the potential of CLIP in the aspect of improving the efficiency and universality of an action recognition technology is explored. The ECTL module can extract local and global features from the sequential sequence, thereby improving the efficiency of sequential modeling. Experiments carried out on three data sets (HMDB-51, UCF-101 and Someting-Someting V2) show that the ECTL series time modeling module provided by the invention has effectiveness, and has universality in various video recognition environments. According to the method, the extremely competitive performance is kept under complete supervision, and the accuracy which is equivalent to or even higher than that of the most advanced method is realized on three public reference data sets.

The invention discloses a sequential optimization method and device for multi-team collaborative repair of a power distribution network under a multi-source factor. The method comprises the following steps: firstly, constructing a line fault probability model based on a first external source and internal source set factor, and constructing a line maintenance time model based on a second external source and internal source set factor; in the prevention stage, a reinforcement strategy is generated according to the fault probability model, and a preventive island is constructed; in the degradation stage, a fault line set is determined according to a fault probability model and a threshold; in the recovery stage, the two models are combined, and a multi-team composite VRP scheduling decision model is constructed by using a variable time scale strategy; and finally, integrating information of each stage, constructing a multi-stage toughness improvement model by taking minimization of active weighted loss as a target, and solving to obtain an optimal pre-disaster reinforcement and post-disaster recovery strategy. According to the invention, the adaptation and recovery capability of the power distribution network to extreme weather can be effectively improved.

The invention provides a multi-disaster-type linkage-oriented grain and cotton crop meteorological risk grading early warning method and system, and relates to the technical field of risk grading early warning. The first information comprises soil salinity change data, frost meteorological data, drought meteorological data, wind disaster meteorological data and crop monitoring data in a preset time period; performing disaster type space-time relation analysis on the first information to generate a multi-disaster type linkage space-time model; performing fusion processing on the multi-disaster linkage spatial-temporal model through spectral clustering and a preset gradient boosting tree to obtain a disaster influence prediction model; carrying out adaptive filtering and grading judgment processing on the disaster influence prediction model to obtain a disaster influence grade; and generating an adversarial network based on the disaster influence levels and historical early warning coping strategies, and determining coping strategies corresponding to all disaster influence levels of each crop. According to the invention, targeted disaster grading can be carried out according to soil types and crop planting characteristics in different areas.

This invention relates to the field of oil and gas field exploration technology, and discloses a method and equipment for predicting the time required for the first discovery of a low-exploration-level depression. The method includes: acquiring geological structural characteristic parameters of the target depression; determining the exploration efficiency of the target depression based on the structural characteristic parameters and pre-determined exploration effectiveness characterization parameters; and predicting the time required for the first discovery of the target depression based on the exploration efficiency of the target depression and a pre-generated first discovery time model. The first discovery time model is generated from the exploration efficiency and the time required for the first discovery of other depressions within the work area where the target depression is located. This invention provides a method for characterizing the time required for the first discovery of a depression based on exploration efficiency constructed from depression structural parameters, which can lay the foundation for medium- and long-term proven reserve prediction in low-exploration-level depressions.

The application discloses a new energy automobile energy supply and conversion facility site selection decision method and device, wherein the method comprises the following steps: acquiring traffic flow data and energy supply and conversion facility data in a planning area; dividing the planning area according to the traffic flow data and the energy supply and conversion facility data to obtain a plurality of traffic zones; establishing a nonlinear high-dimensional space-time model of the traffic flow data and the traffic zones, and obtaining a feasible decision set by using the nonlinear high-dimensional space-time model; performing dimension reduction processing on the nonlinear high-dimensional space-time model, and obtaining suitable strategy parameters in combination with a case data knowledge base; establishing a prediction model to evaluate the traffic flow state and the energy supply and conversion facility service level in the future planning area, and screening in the feasible decision set according to the evaluation result to obtain a plurality of optimal decisions; performing rank-sum ratio sorting on the plurality of optimal decisions to obtain a final decision result; and thus the operation efficiency of the regional traffic system and the energy supply and conversion infrastructure service level can be improved.

This invention relates to a lightweight adaptive method for testing non-stationary data streams. Its core lies in constructing a two-branch adaptive architecture that decouples observation and execution statistics. A shadow observation branch, independent of the main inference branch, maintains a statistical reference baseline in real time, unaffected by adaptive calibration actions, thus suppressing the closed-loop coupling problem between drift perception and statistical updates at the mechanism level. Based on this decoupled architecture, the shadow branch calculates a composite drift surrogate metric to map and generate dynamic momentum parameters, and introduces relaxed cumulative variables to perform three-level state determination and spatiotemporal misalignment mask generation. Finally, during inference forward propagation, dynamic online calibration of statistics is performed only for the key levels selected by the mask. This invention alleviates the adaptation lag and statistical oscillation risks caused by fixed momentum, suppresses the statistical degradation risk caused by single-sample noise, and is suitable for real-time model optimization in single-sample, non-stationary streaming scenarios.

The invention relates to the technical field of BIM-based construction management, in particular to a BIM-based construction progress control management method and system, and solves the technical problem of relatively large prediction deviation in BIM-based construction progress management establishment in the prior art. Factors occurring in the construction process are selected through field investigation, the influence degree is analyzed, the actual progress is extracted according to a 4D real-time model established through BIM, the influence coefficient is calculated through a regression model, and the predicted construction period and the actual construction period are compared to verify the effectiveness of the model; in addition, according to the technical scheme, the BIM technology is applied to the construction process, project actual conditions can be visually inquired through progress visual management based on a real-time 3D model, and progress deviation is analyzed; and meanwhile, the predicted construction period is used as a time parameter to be linked with the BIM three-dimensional model, so that the dynamic management of the subsequent project progress is realized.

The present application relates to a kind of typical mechanical and electrical system performance prediction method based on small sample data extension, applied in the field of intelligent mechanical and electrical manufacturing technology, comprising: S1, based on digital twinning mechanical and electrical system real-time model building;S2, based on time series data enhancement method TimeGAN mechanical and electrical system small sample data extension;S3, mechanical and electrical system performance prediction and real-time monitoring technology.The present application is aimed at the reduction of model generalization ability and prediction accuracy caused by small sample data in mechanical and electrical system, difficult to detect and diagnose and other problems, proposes a kind of small sample data extension method TimeGAN, and on this basis, mechanical and electrical system is expanded to performance prediction;While the real-time characteristics of digital twinning are ingeniously combined with mechanical and electrical system, further strengthen the centralized management and predictive maintenance of mechanical and electrical system, provide strong support for its life prediction, fault diagnosis, performance optimization and other fields of research.

The invention discloses a multi-source multi-scale intelligent fusion scenic spot passenger flow prediction method, device and equipment and a storage medium, and relates to the technical field of data processing. According to the scheme, the method comprises the steps that feature fusion and multi-scale time modeling module processing are conducted on multi-source data of stations in a scenic spot, prediction of passenger flow data of all the stations is achieved through a regression decoder, multi-source deep fusion is conducted on the multi-source heterogeneous data with the stations in the scenic spot as the minimum prediction unit, and a space fine-grained result is output. The problems of insufficient multi-source heterogeneous data fusion capability and rough spatial prediction granularity in the prior art are solved.

The application provides a typhoon path prediction method and system fusing physical constraints and path mutation identification. The method comprises the following steps: obtaining a multidimensional input feature tensor through a feature tensor generation module; performing time modeling through a multiscale modeling module to obtain a deep feature tensor, performing significance guidance through a significance guidance module to obtain a prediction path sequence; identifying a bending angle through a bending event identification module to obtain a plurality of path anomaly scores, introducing a plurality of preset disturbances through a counter disturbance analysis module to determine path confidence, and outputting a path prediction result through a path prediction output module. According to the technical scheme of the embodiment of the application, the typhoon path can be finely modeled, the PINNs physical constraint module is used to ensure that the prediction result meets the physical conservation principle, the bending event identification is used to enhance the path mutation detection capability, the disturbance analysis is used to realize the uncertainty evaluation, and the typhoon path prediction result is more accurate, reliable and physically consistent.

The invention discloses a low-cost AI auxiliary forklift operation system which comprises a data collecting and preprocessing module used for collecting and processing video data in a forklift operation environment; the target detection module is used for identifying and positioning goods, obstacles and personnel by using an RT-DETR target detection model, and extracting a target category, position information and a motion trend; the space-time modeling module is used for carrying out space-time information modeling on the target features by adopting an improved TimeSform network and extracting a motion trail and an environment relation of the target; the operation scene understanding module is used for analyzing the operation state of the forklift, calculating the relative speed and distance change of a target, detecting an abnormal operation mode and predicting the operation risk; and the path planning and decision-making module is used for adjusting the driving route and the operation strategy of the forklift and generating navigation and safety early warning information. According to the method, AI visual perception and space-time modeling are utilized, the understanding ability of the forklift operation environment is improved, and intelligent auxiliary operation is achieved.

This invention discloses an intelligent correction method for the physical consistency of sub-seasonal model forecast products, belonging to the interdisciplinary field of artificial intelligence and meteorological forecasting. Multiple meteorological elements and multi-level circulation elements from the model forecast are used as raw data. First, the deviation field between atmospheric reanalysis data and model forecast data is calculated. Then, a multi-scale feature extraction network is used to obtain deviation features at different spatial scales. These multi-scale deviation features are concatenated with the original input features and input into a U-Net. The U-Net encoder and decoder are used for deep feature fusion and mapping. Simultaneously, a composite loss function that integrates numerical accuracy and physical consistency is designed to guide the collaborative training of the multi-scale feature extraction network and the U-Net network, resulting in a trained intelligent correction model. In application, the intelligent correction model outputs a physically consistent deviation correction amount to intelligently correct real-time model forecast data, ultimately obtaining a sub-seasonal forecast field that combines prediction progress and physical consistency.

The application relates to an EMD-LSTM-based wind speed prediction method fusing spatial features. The method obtains time series and spatial data from a monitoring site, pre-processes and constructs a data set, establishes a time-space prediction model to predict wind speed, fuses two prediction results by adopting weight Bayesian optimization, and obtains a prediction result. In the time model, EMD decomposition is performed on the wind speed rate feature, multiple LSTMs are input, the prediction components of each LSTM are accumulated to obtain a wind speed prediction value considering only the time series feature. In the space model, the wind speed of the adjacent station is vector decomposed and interpolated, the wind speed vector is restored to obtain the interpolation of the wind direction and the wind speed rate, and the distance from the actual value is calculated to obtain the spatial independence weight. In the model fusion, the weight Bayesian optimization is performed according to the spatial independence weight to obtain the wind speed value prediction fusing the time-space features. The application simultaneously considers the influence of the related time features and the spatial features in the wind speed prediction task, and the model structure and the prediction method are more reasonable.

The application discloses a kind of capacity limited vehicle path problem solving method based on timing coding and attention mechanism, first construct the initial feasible solution satisfying constraint condition, and current solution is expressed as containing node level, journey level and solution level hierarchical feature structure;By timing modeling to node sequence in journey and aggregation, journey level feature is obtained, and then the feature set of multiple journeys is modeled by attention, to generate solution embedding vector representing the overall structure of solution;The solution embedding vector is fused with environmental state features and input into the policy decision network, and the selection result of local optimization operator is output, and the solution is iteratively updated according to the selection result until the termination condition is met.The application can enhance the modeling capability of node order change and inter-journey correlation, and improve the stability and generalization performance of the algorithm under different scales.

The invention discloses a 3D human body posture estimation method based on DBTSnet, and relates to the technical field of computer vision. According to the method, a TSM module is innovated, and different from a traditional time modeling method for uniformly processing the whole sequence, the TSM module introduces an innovative block attention mechanism, an input sequence is divided into time periods, each fragment is independently processed through time attention and then is recombined to keep a full-sequence context, and therefore the whole sequence can be processed more accurately. The module exclusively addresses the challenge to enable focused attention to model remote dependencies within a local time window while preserving global time consistency. The time period module operates as a plug-and-play component and can be integrated into an existing architecture-in the reasoning process, it effectively processes a variable-length sequence, and architecture modification is not needed.

The application discloses a high-water-pressure shield double-liquid slurry regulation method based on phase state transition, which comprises the following steps: S1, acquiring double-liquid slurry discrete proportioning measured data; S2, constructing a viscosity-intensity evolution prediction model based on the measured data, the model being used for predicting the evolution law of viscosity time variability and intensity of any proportioning combination within a limited interval; S3, acquiring a phase state curve and a defined physical interval based on the evolution law of viscosity time variability and intensity; S4, formulating a water pressure-space coupling phase state strategy based on the phase state curve and the defined physical interval, wherein the coupling phase state strategy is used for determining a target phase state; S5, constructing a comprehensive target function, and acquiring the optimal proportioning of the double-liquid slurry based on the comprehensive target function; and S6, constructing a two-stage time model based on slurry pipeline residence time and segment back filling diffusion time based on the target phase state and the optimal proportioning, wherein the two-stage time model is used for reverse regulation of the double-liquid slurry discrete proportioning.

The present application relates to the technical field of risk analysis, in particular to a supply chain risk quantitative evaluation method and system based on a dynamic matter graph, comprising collecting multi-source heterogeneous data, constructing a four-dimensional space-time model containing a time dimension, a geographical space dimension, a supply chain network space dimension and a risk influence space dimension, and representing a supply chain event as four-dimensional space-time data; identifying a supply chain entity from the four-dimensional space-time data, extracting a risk event and constructing a matter graph, the matter graph taking a risk event as a node and an evolution relationship between events as an edge, and calculating a relationship weight between events; calculating a probability quantitative value of a risk transmission path based on the matter graph, and obtaining a comprehensive risk score of a target entity; generating a risk mitigation strategy based on the comprehensive risk score; monitoring a deviation between an actual risk occurrence and a prediction result, and updating the matter graph and the risk mitigation strategy through an adaptive parameter optimization and an incremental learning mechanism, thereby forming a self-evolving risk evaluation system.