17results about How to "Quick forecast" patented technology

The present application relates to a kind of lake reservoir type chlorophyll concentration prediction method, device and medium, method includes the following steps: obtaining the water quality, water dynamics and weather of the online automatic monitoring data of lake reservoir including forecast point and upstream point;The online automatic monitoring data is preprocessed, and the data after processing is obtained;The data after processing is carried out feature extraction to obtain the water quality feature and water dynamics index feature of forecast point and upstream point, and construct cumulative illumination feature;Upstream water quality feature, water dynamics index feature and illumination feature are input into the fusion prediction model pre-trained, to obtain upstream transport chlorophyll prediction result and time series influence chlorophyll prediction result, using error reciprocal method upstream transport chlorophyll prediction result and time series influence chlorophyll prediction result are fused;The chlorophyll prediction result obtained by fusion is output.Compared with prior art, the present application has the advantages of high accuracy, strong stability and the like.

This invention relates to the field of navigation and timing technology, specifically to a GNSS timing data prediction method and related equipment. The method involves acquiring Roland timing data when GNSS timing data is invalid and preprocessing it. The preprocessed data is then input into a trained single-hidden-layer extreme learning machine model. This model calculates the hidden-layer output matrix by randomly initializing the input layer weight matrix and bias vector, and solves for the output weights using regularized least squares. The model outputs a normalized GNSS timing data prediction value, which is then denormalized to obtain the final GNSS timing prediction result. This method combines the high stability of the Roland system with the efficient computational power of the extreme learning machine to achieve accurate GNSS timing data prediction. It is suitable for enhancing the resilience of positioning, navigation, and timing systems in critical infrastructure scenarios, ensuring the continuous operation of the system even when GNSS signals are interfered with or fail.

The application discloses a method for optimizing process parameters of metal type nuclear fuel preparation process, collecting and arranging metal type nuclear fuel preparation process parameters and performance parameters, post-preparation performance parameters and microstructure metallographic pictures; establishing a metal type nuclear fuel solidification phase field model according to a solidification thermodynamic path under different process parameters; solving the metal type nuclear fuel solidification phase field model by using a Fourier spectrum method to obtain a phase field variable numerical solution; post-processing and visualizing the phase field variable numerical solution to generate a microstructure distribution map of the metal type nuclear fuel after solidification, and comparing and verifying the phase field model with the metal type nuclear fuel microstructure metallographic picture; calculating the post-solidification performance parameters of the metal fuel based on the microstructure distribution of the metal type nuclear fuel after solidification; taking the preparation process parameters as input and the performance parameters as output, constructing a nonlinear mapping proxy model between the metal type nuclear fuel preparation process parameters and the performance parameters; and solving the optimal preparation process parameters by using the nonlinear mapping proxy model given the target performance parameters of the metal type nuclear fuel.

The application discloses a kind of infinite long cylinder group target strength calculation method of arbitrary arrangement, comprising: establishing cylinder group, the radius and center coordinates of each cylinder are determined;Determine the first-order scattering expression of each cylinder, and carry out local coordinate system conversion, the first-order scattering expression of each cylinder after conversion is superimposed, and the sound field coupling relationship between each cylinder in cylinder group is obtained;Rigid boundary condition is applied to each cylinder surface in cylinder group, and the first-order scattering coefficient of single cylinder is solved;On the basis of considering multiple scattering effect between cylinders, the first-order scattering coefficient of single cylinder is used to recursively obtain the multi-order scattering coefficient of cylinder group;The scattering sound field of each order of cylinder group is calculated;Each order scattering sound field is superimposed, and the total scattering sound field of cylinder group is obtained;The target strength of cylinder group is calculated.The application can solve the problem that the efficiency of underwater cylinder group structure target strength prediction is lower and modeling is complex in the prior art.

The application discloses a high-rise building cluster typhoon resistance resilience prediction method based on a double-flow convolutional neural network, and aims to solve the problem that the existing high-rise building cluster typhoon resistance resilience prediction method based on physical simulation cannot meet the large-scale rapid prediction demand.The prediction method comprises the following steps: 1, a typhoon time series is combined with a virtual high-rise building cluster to obtain a virtual typhoon disaster scene, and the typhoon resistance resilience indexes of each building in the virtual high-rise building cluster are calculated; 2, the preprocessed typhoon time series and the building cluster space configuration are taken as input data, and a pixel-level label image of the typhoon resistance resilience indexes is taken as output data to construct a data set; 3, a double-flow convolutional neural network model is constructed; and 4, the double-flow convolutional neural network model is trained.Through the technical path of "simulated generation of a data set-double-flow network fusion of space-time features-end-to-end mapping of output resilience distribution", the high-rise building cluster typhoon resistance resilience under typhoon disasters is rapidly and accurately predicted.

The present application relates to a kind of power transmission network impedance envelope fast prediction method based on graph structure learning and its electronic equipment, for the fast performance evaluation under complex environment, method includes: first, according to standard unit cell theory, PDN is discretized into Unit cell, and training and test data set containing different environment and process parameters are constructed by EDA tool simulation.First, pre-processing is carried out to data, relevant parameters are extracted from PDN netlist file, and they are abstracted as graph structure: node represents Unit cell, includes L, C, position and five attributes of distance from port distance;Edge indicates connection relationship and contains R attribute.Subsequently, graph data is normalized and converted into node feature vector, and graph neural network model is constructed, with the second upper envelope line of impedance-frequency curve as label, graph level regression training is carried out using training set.Finally, the model is verified using test set, to realize the fast, high-precision prediction of PDN impedance under unknown parameters.The method has good generalization and universality.

The application discloses a coal seam lithium-rich layer detection method based on well logging curves and statistical analysis, and belongs to the technical field of mineral resource exploration. The method comprises the following steps: selecting a series of representative coal seam positions for systematic sampling, and preparing the samples into piston samples and powder samples; determining the kaolinite and lithium content of the powder samples, and distinguishing lithium-rich and ordinary coal samples; systematically determining various geophysical response parameters of the piston samples, such as density, resistivity and wave velocity, and screening out sensitive discrimination indexes for lithium enrichment; setting a window size, calculating the moving statistical value curves of the density, resistivity, wave velocity and other well logging curves sensitive to the lithium-rich layer, calculating principal components, and using the selected principal components and a support micro machine to train a prediction model, so as to predict the lithium-rich layer in the coal seam. The application combines geophysical logging, statistical analysis, principal component analysis and a support vector machine, and provides a fast, economical and non-destructive means for lithium resource exploration in coal.

The application relates to the field of structural engineering and artificial intelligence, and particularly relates to a long-span space steel structure temperature field prediction method based on a space-time graph neural network, which comprises the following steps: establishing a three-dimensional geometric model of a steel structure to be measured, performing transient thermal analysis on each node in the steel structure to be measured by using a finite element method; converting the three-dimensional geometric model into a graph model; constructing a temperature field prediction model according to the graph model; configuring the temperature field prediction model to take a node feature matrix and an edge feature matrix as input, extracting spatial dependence features of each node by using a graph neural network; extracting time sequence features representing temperature changes of the nodes in the spatial dependence features by using a recurrent neural network; determining periodic trend features of the time sequence features by using an attention mechanism, and mapping the fused features into node temperature prediction values; inputting limited monitoring point data on the steel structure to be measured into the trained temperature field prediction model, and determining a temperature field distribution of the steel structure to be measured.

This invention discloses a machine learning-based method and system for predicting the production capacity of gas-water two-phase gas in tight sandstone. The method includes: constructing production capacity prediction models for different well types using an artificial neural network; inputting data such as critical flow saturation, water saturation, water phase relative permeability curve index, starting pressure gradient, permeability, reservoir thickness, fracture conductivity, fracture length, cluster number, and stress sensitivity coefficient as input variables into the production capacity prediction model, and using cumulative gas and water production as output variables. The input variables are then imported into the corresponding production capacity prediction model for different well types to obtain the cumulative gas and water production for each well type. This method uses an artificial neural network to construct a production capacity prediction model to predict the cumulative gas and water production in tight sandstone, achieving rapid and accurate prediction of the production capacity of the gas-water two-phase gas in tight sandstone, avoiding complex calculation processes, and reducing computation time and production costs.

This invention discloses an intelligent operation and maintenance method and system for ship software platforms based on big data. By integrating multi-source heterogeneous data from the entire ship lifecycle, it can achieve comprehensive perception of the platform's operational status, predictive early warning of potential faults, and intelligent diagnosis of root causes of faults. The system utilizes historical data to offline train a health assessment model and a fault prediction model based on time-series networks. During online operation, the system analyzes real-time data streams, continuously quantifies the platform's health status, and issues early warnings of faults. When a warning or fault occurs, the system can automatically activate a knowledge graph-based inference engine to quickly locate the root cause and provide decision support for operation and maintenance personnel, realizing a shift from passive response to proactive intervention. This invention provides key technical support for realizing intelligent operation and maintenance and autonomous navigation of ships, and has been verified in a simulation environment, demonstrating its application potential in actual ship deployments.

This invention discloses a method for predicting the T2 spectrum of rock nuclear magnetic resonance (NMR) based on image feature transfer learning, relating to the field of oil and gas exploration and development technology. The method includes: S1. Standardization and enhancement preprocessing of rock casting thin section images; S2. Hybrid feature extraction fusing pre-trained deep features and handcrafted rock physical features; S3. Construction and training of a regression model from high-dimensional features to T2 spectrum under small sample conditions; S4. Direct prediction of rock NMR T2 spectrum. By employing image feature transfer and small sample learning strategies, this method overcomes the limitations of traditional NMR experiments, such as high requirements for core samples, long measurement cycles, and high costs. It enables rapid and efficient prediction of rock NMR T2 spectra using a small number of casting thin section images. Simultaneously, it significantly reduces the cost and time of rock property analysis, providing rapid support for rock property parameters in oil and gas reservoir evaluation, and has broad application prospects and economic benefits.

This invention discloses a blade structure stress prediction method driven by the fusion of multi-source data and a reduced-order model. Through a reduced-order surrogate model combining unidirectional fluid-structure interaction (FSI) simulation of the blade, it achieves rapid prediction of the blade structure stress field under different wind speeds, rotational speeds, and pitch angles. The method includes: constructing a unidirectional FSI simulation model of the wind turbine blade; extracting reduced-order modes and coefficients from high-fidelity blade stress field data to reduce the order of the full-order simulation results; constructing and training a surrogate model based on the reduced-order modes and coefficients, and obtaining the mapping relationship between different wind speeds and reduced-order mode coefficients by optimizing the model hyperparameters; the surrogate model rapidly predicts the modal coefficients of the blade stress field based on the real-time wind speed of the wind turbine, and reconstructs the blade stress field results with the reduced-order modes. This invention can rapidly predict the blade stress field under real-time wind speed conditions based on a small number of high-fidelity simulation samples, which is significant for the intelligent operation and maintenance of offshore wind turbines.

The application discloses the field of welding stress, and discloses a kind of welding stress strain fast prediction control method and system based on LSTM network, the method is first based on original welding simulation and actual welding process in high fidelity data, using laser power, welding speed, defocusing amount and spot diameter and other welding process parameters and welding workpiece and other parameters as input, corresponding welding stress and strain as the output of prediction to train LSTM agent model.The application can be implemented in the butt joint state of various welding, can adapt to a variety of states, the accuracy of cross-condition identification exceeds 80%, and can meet the requirements of normal work.The application realizes the rapid calculation of welding stress and strain based on LSTM network training model, the detection of welding process for convenient backtracking adjustment, and dynamic adjustment of welding process based on the advantages of model to realize closed-loop optimization of welding process.

The embodiment of the application provides a kind of fast estimation method, device, equipment and storage medium of wind resistance coefficient, comprising: the target vehicle windward area, the test mass of the target vehicle and the target vehicle sliding resistance coefficient are obtained;The target vehicle windward area, the test mass of the target vehicle and the target vehicle sliding resistance coefficient are input into wind resistance coefficient prediction model, and the wind resistance coefficient of the target vehicle is obtained.To realize fast and high-precision estimation wind resistance coefficient.

The application discloses a smart contract code completion method and device based on a graph neural network, wherein the method comprises the following steps: constructing a plurality of smart contract source code data sets according to version numbers of smart contract source codes; constructing corresponding code representation graphs according to semantic information of the smart contract source codes in each data set and industry security practices; constructing a code completion model based on a gated graph neural network, training the code completion model by using the code representation graphs to obtain a trained code completion model; embedding a code representation graph corresponding to a smart contract to be completed into the trained code completion model, and performing code completion prediction to obtain a word completion list. When the code representation graph is constructed, the semantic information of the smart contract source code is considered, the code word used for completing the smart contract can be quickly and accurately predicted, and the safety of the code word used for predicting the smart contract is improved in combination with the industry security practice mode.

The application discloses a kind of vehicle-mounted hydrogen storage bottle gas hydrogen filling limit condition prediction methods, it is related to the technical field of hydrogen energy and fuel cell, in view of the existing filling strategy safety boundary fuzzy, the problem of low prediction efficiency, the method first obtains the geometric and physical parameters of hydrogen storage bottle;Then establish fluid-structure coupling numerical model, the lumped parameter model of hydrogen zone is coupled with the one-dimensional unsteady heat conduction model of solid wall surface and is calculated in batches, to construct filling process data set;Then based on the data set, adopt and optimize XGBoost algorithm to construct prediction model;Finally, under the premise of setting filling temperature safety threshold, the limit value of filling parameter is predicted by model reverse, and the quantitative relationship between each limit parameter is polynomially fitted.The application considers the high fidelity and calculation efficiency of modeling, can give the quantitative safety filling boundary, effectively improve filling rate and reduce precooling energy consumption.