27results about How to "Reduce modeling costs" patented technology

The invention discloses a transformer substation three-dimensional parametric modeling method and system based on engineering design drawings. The method comprises the following steps: standardizing and correcting a design completion drawing of the transformer substation, and outputting a standardized drawing; the method comprises the following steps of: identifying buildings and power grid equipment and facility components in a transformer substation based on a standardized drawing, extracting geometric information and converting spatial positions to obtain a topological relation, converting extracted geometric parameters into information with definite constraint semantics, and forming modeling parameters according to the obtained spatial positions, semantic attributes, model parameters and the topological relation; and selecting a proper parameterization modeling method to carry out automatic model construction according to the modeling parameters in combination with machine account data, equipment, implementation classification and a predefined model component library. According to the method, the automatic construction technology of the parameterized model is researched by focusing on pattern recognition and semantic and topology extraction, 'associated automatic generation 'of the three-dimensional model of the transformer substation from zero to integer is realized, and the method has low-cost whole-station modeling capability.

The invention discloses an underground water level prediction method and system based on a space-time attention gating neural network. The method comprises the following steps: constructing a historical underground water level space-time sequence data set; performing principal component analysis on the space-time sequence data set to obtain a dimension-reduced space-time sequence data set; for the spatio-temporal sequence data set after dimension reduction, constructing a plurality of spatio-temporal characteristic matrixes through a sliding window method; training an underground water level prediction model by using the plurality of spatial-temporal characteristic matrixes to obtain a trained underground water level prediction model; obtaining time-space sequence data of historical underground water level of a to-be-measured position, constructing a time-space characteristic matrix of the underground water level, and predicting the underground water level of the to-be-measured position by using the trained underground water level prediction model; according to the method, rapid and accurate prediction of the underground water level is realized, and the generalization ability, the prediction precision and the interpretability of an underground water level prediction model are greatly improved while the modeling cost is reduced.

The invention discloses a quick and automatic layout method for a warehouse logistics conveying system based on Unity 3D and CAD. The method comprises the following steps: extracting detail data of warehouse logistics conveying equipment; exporting and optimizing warehouse logistics conveying equipment detail data, and adjusting data in exported equipment detail table grids and a list arrangement sequence; the optimized details of the warehouse logistics conveying equipment are imported into Unity; and querying and analyzing data in the equipment details on the basis of a linear traversal algorithm in combination with matrix transformation, and automatically substituting corresponding equipment position information, rotation parameters and scaling into matrix transformation to complete spatial layout and attribute assignment of the warehouse logistics conveying system. Compared with an existing traditional conveying system layout technology, the data conversion step can be reduced, and data interaction of Unity3D and CAD is improved; the modeling cost is reduced, and the project economic benefits are improved; errors of spatial layout and attributes are reduced, and the model precision is improved; compared with the prior art, the layout efficiency of the conveying system can be improved by 70%-85%.

The invention discloses a transformer substation model construction method based on laser point cloud and visual data, and relates to the technical field of transformer substation model construction.The transformer substation model construction method based on the laser point cloud and the visual data comprises the following steps that an equipment model library is constructed based on equipment data in a transformer substation; obtaining a substation field depth prediction map by using monocular depth estimation; calibrating the substation field depth prediction map based on the point cloud data; obtaining three-dimensional model data of the substation equipment based on the calibrated substation field depth prediction map; according to the transformer substation model construction method based on the laser point cloud and the visual data, by means of monocular depth estimation and automatic processing of the point cloud data, dependence on manual modeling is reduced, and the modeling process is more efficient; the equipment model library is constructed based on the equipment data, so that high equipment precision in the model library is ensured; and the depth prediction map is calibrated by using the point cloud data, so that the accuracy of the three-dimensional model is improved.

The invention provides a model and scene fusion rendering method and device, equipment and a storage medium. The method comprises the following steps: displaying a three-dimensional scene; displaying a skeleton model representing a skeleton structure of the three-dimensional model in the three-dimensional scene; in response to an adjustment confirmation operation for the skeleton model, sending a fused image generation request to a server to obtain a target fused image, the target fused image being obtained by fusing the model effect picture and the rendering effect picture of the three-dimensional scene; the model effect picture is obtained by performing background fusion on a three-dimensional model corresponding to the skeleton model and a rendering effect picture of the three-dimensional scene by using a large language model.

A method, apparatus, device, and storage medium for determining the output force of a hydraulic muscle include: acquiring geometric characteristic parameters, material characteristic parameters, and friction characteristic parameters of a target hydraulic muscle; acquiring the current operating parameters of the target hydraulic muscle; determining the elastic equivalent force based on the geometric characteristic parameters, material characteristic parameters, and current operating parameters; determining the frictional force and basic output force based on the geometric characteristic parameters, material characteristic parameters, frictional characteristic parameters, and current operating parameters; and determining the final output force of the target hydraulic muscle based on the basic output force, the elastic equivalent force, and the frictional force. This application establishes a simulation model through physical mechanism derivation. The modeling process only requires conventionally obtainable core parameters, eliminating the need for repeated complex experiments and resulting in low modeling costs. Furthermore, the simulation model comprehensively simulates the rubber elasticity and frictional hysteresis effect of the hydraulic muscle, improving simulation accuracy and achieving low-cost and high-accuracy modeling of the hydraulic muscle's output force.

The application discloses a kind of remote sensing time series data analysis method and system considering the spatio-temporal correlation of geographical object, belong to data analysis technical field.The method includes: obtaining multi-source multi-temporal remote sensing image and auxiliary data and pre-processing, generate consistent time series data stack;In three-dimensional space-time domain, image is divided into voxel and is adjacent tracking, identify geographical process object with evolution behavior and record its attribute;Determine the spatio-temporal topological relationship between geographical process object, generate unified spatio-temporal relationship table by composite reasoning;With geographical process object as node, spatio-temporal relationship as edge constructs geographical process object spatio-temporal graph model, extracts and standardizes the attribute characteristics of node and edge;Importance index of each node is calculated by using graph convolution network and topological dynamic mechanism joint modeling.The application realizes closed-loop analysis from data processing, relationship modeling, importance evaluation to decision support, improves the interpretability and decision effectiveness of remote sensing time series change detection.

The invention discloses a three-dimensional precise modeling method for electric power communication equipment, which comprises the following steps of: constructing a basic primitive, converting the basic primitive into a gridding primitive based on basic primitive parameters, and obtaining a triangular patch of the gridding primitive and a corresponding triangular vertex index; the method comprises the following steps: acquiring a vertex mapping image of the electric power communication equipment, processing the vertex mapping image, constructing a color mapping set, acquiring an unshielded triangular patch, mapping the color mapping set with a triangular vertex corresponding to the unshielded triangular patch, and dyeing the triangular patch. Parametric modeling is realized according to the parameters of the equipment drawing of the electric power communication equipment, so that the modeling cost is reduced, and accurate modeling is realized; and vertex color assignment of the parameterized model is realized by adopting a photo mapping mode, so that the sense of reality of the equipment is improved.

The application provides a three-dimensional head model reconstruction method and device and a terminal, and belongs to the technical field of three-dimensional modeling. The method comprises the following steps: obtaining target video data, which is obtained by shooting around a human head; obtaining three-dimensional point cloud and camera pose information corresponding to a video frame image in the target video data; determining position information of a three-dimensional head model according to the three-dimensional point cloud and the camera pose information based on an initial three-dimensional head model, wherein the position information comprises position information of facial feature points; adjusting the three-dimensional head model according to the three-dimensional point cloud, the camera pose information and the position information of the three-dimensional head model to obtain a target three-dimensional head model; and performing texture rendering on the target three-dimensional head model according to the camera pose information and a target texture generation model. In the embodiment of the application, the modeling process is relatively simple without the need for complex hardware devices or special environment sites, and the cost of three-dimensional head model reconstruction can be reduced.

This invention discloses a method for predicting the dynamic evolution of pollution plumes based on a physical perception spatiotemporal large model, comprising the following steps: acquiring multi-source heterogeneous monitoring data and constructing a static spatial topology matrix; constructing a PF-STGNN model, which adopts an encoder-decoder architecture; the encoder captures the long-term dependencies of pollution plume evolution through a context-aware temporal self-attention mechanism, and couples dynamic graph convolution with the static spatial topology matrix through a dynamic flow field interactive spatial solution module, outputting a feature matrix containing historical diffusion memories; the decoder ensures temporal unidirectionality through a causal masking temporal self-attention module, injects historical evolution laws into future predictions through a cross-spatiotemporal evolutionary context cross-attention module, and performs spatial morphology verification again through a dynamic flow field interactive spatial solution module; finally, a concentration prediction matrix for future periods is output through a parallel generator. The advantages of this invention are: it can be applied to groundwater pollution monitoring and environmental risk early warning systems.

The embodiment of the invention provides a molecular-level oil refining whole-process model construction method and device and computer equipment. The method comprises the steps that an SOL molecular library is constructed according to input SOL molecular data; constructing a reaction rule base according to the reaction rules; constructing a single device model according to the SOL molecular library, the reaction rule library and the reaction parameter library, and storing the single device model in a whole-process model construction system; and according to the constructed single device model in the construction system or the single device model obtained by importing the drawing and the logistics connection line between the single device models, a whole-process model is constructed and obtained. According to the method, the single device model can be constructed and stored as a system resource, and in the construction process of the whole-process model, the existing single device model can be reused, so that the manual operation of a user is reduced, and the modeling cost of the whole-process model is effectively reduced. And a single device model can be obtained by supporting a drawing import mode, so that the requirements of various whole-process model construction scenes are met.

The invention discloses a cognitive state recognition system based on single-channel electroencephalogram signal task perception and multi-scale modeling. The cognitive state recognition system comprises a task perception time domain feature extraction module, a frequency domain independent feature extraction module, a task perception frequency domain feature fusion module, a self-adaptive feature fusion module and a result output module. The task sensing time domain feature extraction module captures multi-time scale features from local to global by adopting a multi-scale convolutional neural network in combination with a Transform architecture; the frequency domain independent feature extraction module generates time-frequency representation through continuous wavelet transform, and designs an independent encoder for different frequency bands to extract specific features of the frequency bands; the task perception frequency domain feature fusion module adaptively identifies a task related frequency band through a significant frequency band attention mechanism and guides cross-frequency band feature fusion; the adaptive feature fusion module integrates time domain and frequency domain features by using an attention mechanism; and the result output module is used for completing cognitive state classification and providing an interpretable analysis result. According to the method, task related features can be efficiently and accurately extracted from the single-channel EEG signals, high-precision cognitive state recognition is achieved, and the method is suitable for various application scenes such as emotion recognition and workload evaluation.

The invention discloses a modeling method and device for a double-cylinder shock absorber, and relates to the technical field of shock absorbers, and the method comprises the steps: building simplified models of a damping valve, a one-way valve, an oil compression process and an oil leakage process of the double-cylinder shock absorber; establishing pressure flow equations of a compression cavity, a stretching cavity and an oil storage cavity of the double-cylinder shock absorber; establishing a displacement equation of a piston rod of the double-cylinder shock absorber; and based on each simplified model, each pressure flow equation and the displacement equation of the piston rod, establishing a model of the double-cylinder shock absorber. According to the method, a damping valve, a one-way valve, an oil compression process and an oil leakage process which are complex in modeling and time-consuming in calculation are reasonably simplified, so that the calculation complexity and the modeling cost are reduced, and pressure flow equations of a compression cavity, a stretching cavity and an oil storage cavity and a displacement equation of a piston rod are constructed; the modeling precision lost when models of a damping valve, a one-way valve and the like are constructed is made up, and therefore overall modeling can be rapidly and accurately conducted on the double-cylinder shock absorber.

This application relates to the field of medical image processing technology, and discloses a method, system, device, and medium for predicting tumor status, including: acquiring a set of CT images of the lungs of a target object during the current respiratory cycle; constructing a three-dimensional tumor model of the lung tumor based on the CT image set; the lung tumor has a real volume and real centroid coordinates; performing finite element analysis to obtain a finite element tumor model, calculating the simulated volume and simulated centroid coordinates of the lung tumor; comparing and obtaining the volume retention ratio and centroid offset; predicting the motion state of the lung tumor in the next respiratory cycle to obtain the initial motion state parameters of the lung tumor in the next respiratory cycle; if the motion state prediction quality of the lung tumor meets the preset motion state prediction quality based on the volume retention ratio and centroid offset, then the initial motion state parameters are used as the target motion state parameters to predict the motion state of the lung tumor, thereby improving the accuracy and correctness of radiotherapy.

The invention discloses an energy-saving control system and method for heating and ventilation equipment, and belongs to the technical field of building energy conservation, and the system comprises a data obtaining module which is used for obtaining environment observation data of a target space; the flow field prediction module constructs a first fusion convolutional neural network and is used for predicting target space flow field information at a future moment and obtaining steady-state heat and humidity characteristics based on the target space flow field information; the operation backstepping module constructs a second fusion convolutional neural network and is used for predicting heating and ventilation equipment operation state data corresponding to the candidate environment data; and the operation optimization module constructs an optimization objective function, optimizes the environment data to be adjusted from the candidate environment data, and determines a heating and ventilation equipment control strategy. Through the method of fusing the convolutional neural network and data driving, the dependence on complex modeling and simulation tools is reduced, so that the implementation cost of the system is reduced, and the real-time responsiveness and adaptability of the model are improved.

The application discloses a physical information multi-fidelity neural network three-dimensional cascade flow field reconstruction method based on Fourier characteristics, and relates to the field of aero-engine turbine machinery aerodynamic design. The method comprises the following steps: generating high / low fidelity data sets with boundary consistent and gradient perception sampling; constructing a multi-fidelity neural network containing Fourier characteristic embedding, multi-fidelity collaborative network and RANS equation residual constraint; adopting a two-stage training strategy, first optimizing data fitting loss to learn the flow field mapping relationship, and then jointly improving the conservation with physical loss; finally, realizing fast, high-precision and physically consistent reconstruction of three-dimensional cascade space coordinates to key physical quantities such as velocity, static pressure and temperature. The method significantly reduces the dependence on high-fidelity data, improves the prediction accuracy in high gradient areas, and is suitable for rapid iterative design of aero-engine cascades.

The invention relates to the technical field of heat pump fault detection and diagnosis, and discloses a heat pump system fault prediction method based on a time sequence chain diagram, which comprises the following steps of: configuring and deploying a sensor group, acquiring monitoring variable data of a heat pump system from normal operation to fault occurrence time, and recording a fault type of the heat pump system at the same time; constructing a degeneration time sequence data set of the heat pump system; a time sequence chain graph degradation model of the heat pump system from normal operation to the fault occurrence moment is constructed; constructing a hierarchical graph model of the monitoring variables, the residual life and the fault type; constructing a competition risk time sequence chain graph degradation model, and calculating a competition risk cumulative generation function; obtaining time sequence data monitored in the operation process of the heat pump system, and predicting the residual life and the fault type of the heat pump system based on the competitive risk cumulative occurrence function; according to the method, the accuracy and efficiency of heat pump fault detection and diagnosis are improved.

The invention belongs to the field of discrete event system control, intelligent manufacturing and reinforcement learning, and discloses a state feedback control strategy generation method and system of a time delay Petri network system in a non-deterministic environment, and the method comprises the steps: constructing a time delay Petri network model containing controllable / uncontrollable transition and minimum transmission delay constraint, defining a time extension state formed by the identification and the enable transition residual delay; a two-stage random judgment mechanism is introduced, two types of non-deterministic factors of control execution errors and uncontrollable event preemption are modeled, and the state transition probability is deduced; a control problem is formalized into a Markov decision process, and a reward function with time consumption as a negative reward and deadlock and an ultra-long path as strong punishment is designed; and adopting a table type Q learning algorithm based on dynamic learning rate scheduling, iteratively updating an action value function in interaction with a simulation environment, and exporting a state feedback control strategy. The method can be widely deployed in discrete event system control scenes such as flexible manufacturing, logistics scheduling and multi-robot cooperation.

The present invention relates to the technical field of three-dimensional reconstruction, and discloses a method and system for three-dimensional reconstruction of urban space based on big data, comprising the following steps: step S101: collecting shooting information of a plurality of multi-angle spectral images of a target urban space and corresponding spectral images; step S102: preprocessing the photographed spectral images to obtain the preprocessed spectral images; step S103: collecting wide-area data to train an improved adversarial neural network; step S104: using a graph network of the target space to replace a preset random occlusion module, and reversely updating the graph network of the target space; step S105: modeling the target space based on point cloud data of the target space; the present invention realizes the generation of a high-quality and structurally reasonable three-dimensional model when the target space data is incomplete through the collaborative work of the improved adversarial neural network and the graph network.

The invention discloses a method and system for establishing a complementary metal oxide semiconductor tube model at extremely low temperature, and the method comprises the steps: obtaining an electrical characteristic curve of a CMOS tube at least at two extremely low temperature points, or carrying out the curve fitting of a known CMOS tube of a model; on the basis of the obtained CMOS tube electrical characteristic curves of the at least two low-temperature points, predicting the CMOS tube electrical characteristic curve under the target temperature Ttarget; modeling software is adopted to carry out curve fitting parameter extraction on the CMOS tube under the Ttarget, important parameters are corrected, and an electrical characteristic curve of the CMOS tube under the Ttarget is reconstructed based on the extracted parameters and the corrected parameters; complete modeling of low-temperature electrical behaviors of the CMOS tube is further completed after parameter extraction and modeling of the CMOS tube under the Ttarget are completed through modeling software, and accurate matching of a device characteristic curve and a real physical state under the extremely low temperature is achieved.

The embodiment of the invention provides a fissure connectivity modeling method and device based on surrounding rock fissure JRC, and the method comprises the steps: building a fissure quantitative analysis model based on a fissure image obtained through borehole imaging, and describing the geometric morphology of a fissure through a sine curve; fitting the fracture quantitative analysis model, and extracting characteristic parameters representing the geometrical morphology of the fracture; calculating a roughness coefficient JRC of the fracture surface based on the characteristic parameters, and representing the fracture as a fracture plane equation in a three-dimensional space through coordinate transformation; establishing an extension length probability model of the crack based on the JRC and the crack plane equation; and for any two fractures from different drill holes, calculating a spatial intersection line according to respective plane equations, and then calculating a connectivity probability along the intersection line based on an extension length probability model to complete quantitative modeling of fracture connectivity. According to the method, the fracture connectivity probability criterion is established by combining the fracture roughness coefficient JRC with the geometric parameters, and quantitative evaluation of the fracture connectivity is achieved.

The invention relates to the technical field of nuclear reactor core design and safety, in particular to a quasi-three-dimensional transportation method and system based on multi-layer coarse net improvement and a medium, a pressurized water reactor three-dimensional reactor core to be simulated is divided into a plurality of transportation layers, and each transportation layer is radially divided into a plurality of fine nets; dividing each transport layer of the pressurized water reactor into a plurality of differential layers; radially dividing a plurality of coarse nets in each difference layer, and establishing a coarse net finite difference model on each coarse net; adopting rhombus difference in each difference layer to assume linear change of neutron angular flux density; and establishing a new quasi-three-dimensional transport model for surface neutron standard flux density correction of the differential layer central surface and the interface surface on each transport layer. According to the method, the axial height of quasi-three-dimensional transport calculation can be expanded, the number of modeling layers of quasi-three-dimensional transport calculation is reduced, the memory is saved, the calculation efficiency is improved, and the method can be used for pressurized water reactor numerical reactor simulation calculation and engineering application.

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 provides a correction method and system of an OPC model and a computer readable storage medium, and is applied in the field of semiconductor technology. In the application, two fixed safety intervals currently set by the industry, i.e. a first minimum interval of side-to-side in a mask rule constraint condition and a second minimum interval of angle-to-angle, can be determined through an initial OPC model; then a new parameter, i.e. a direct length, constructed by the application is used to establish the correlation between the interval between adjacent test patterns and the change of the direct length of side-to-side between adjacent test patterns, so that the two independent safety intervals of side-to-side and angle-to-angle in the mask rule constraint condition in the prior art are integrated, so that the safety interval of the OPC model can dynamically change with the actual geometric shape (direct length) of the test pattern, i.e. a new mask rule constraint condition more in line with the constraint rule between adjacent patterns in the actual photolithography process is proposed, the photolithography process window is expanded, and the convergence is improved.

The invention discloses a power transmission and transformation project evaluation system and method based on three-dimensional design, comprising: an engineering information collection module, a database, an engineering data analysis module, an engineering evaluation module and an engineering application test module, wherein the engineering information collection module is used to collect the power transmission and transformation project data currently available and designed by users, and the database is used to store all the collected data, and the engineering data analysis module is used to analyze the compatibility of the power transmission and transformation project and generate the modification impact path of the power transmission and transformation project model, and predict the workload required for modifying the power transmission and transformation project, and the engineering evaluation module is used to establish an engineering evaluation function according to the analysis result, substitute the user demand data, and evaluate the applicability of the power transmission and transformation project as a basic model to the user, and the engineering application test module is used to update the user demand data, and the most suitable three-dimensional model of the power transmission and transformation project is selected as the basic model, thereby reducing the modeling times and costs of the power transmission and transformation project and improving the efficiency of the three-dimensional design of the power transmission and transformation project.

The invention discloses an active power distribution network dq decoupling model sample expansion information mapping construction method, and belongs to the technical field of power distribution networks. The invention aims to provide an active power distribution network dq decoupling model sample expansion information mapping construction method for simulating and expanding limited working condition test sample data of a power distribution area system by using an offline mechanism so as to construct an online physical-information mapping model of the power distribution area system. According to the method, firstly, a multi-element equipment mechanism mapping model is constructed, a dq decoupling mapping model is identified and corrected, and finally, a physical-information data mapping model is constructed based on expansion of a small sample space of the mapping model. The method is separated from a commercialized mechanism simulation system, the modeling cost is saved, the modeling efficiency is improved, and the method is used for online mapping model construction and intelligent analysis decision making of the smart power grid.

The present invention discloses a method, device and storage medium for predicting the loss of high-quality customers based on supervised learning, wherein the method includes: obtaining high-quality customers to be predicted to be lost, and extracting the loss feature data of the high-quality customers to be predicted to be lost within a preset time period; inputting the loss feature data of the high-quality customers to be predicted to be lost into a pre-trained high-quality customer loss prediction model, and outputting the high-quality customer loss prediction result; wherein the high-quality customer loss prediction model is obtained by training the prediction model constructed based on the XGBoost algorithm using the constructed high-quality customer loss prediction sample data set. Selecting high-quality customer groups as the research object reduces the category imbalance of training data, improves the training speed and accuracy of the model, and can also grasp the core customers of the bank; using the XGBoost algorithm, the algorithm is insensitive to missing values ​​and does not need to interpolate the missing values; and has strong learning ability, fast training speed, and fast convergence speed.