38 results about "Computational design" patented technology

The invention relates to a heat supply prediction method and system based on spatial-temporal feature fusion deep learning, and the method comprises the following steps: S1, carrying out the collection and fusion of multi-source heterogeneous data, and constructing an integrated data set; s2, preprocessing the data; s3, constructing a graph structure model of the heat supply system, and constructing a weighted undirected graph; s4, constructing and executing forward calculation of the space-time double-flow deep network; s5, designing a composite loss function including mean square error loss and physical constraint loss, and performing joint optimization training on the space-time double-flow deep network; and S6, performing multi-step heat supply load prediction by using the trained model, outputting a heat supply load curve of each heat exchange station in a specified time period in the future, and integrating a prediction result with a heat supply scheduling system. The method has the advantages that the prediction precision is improved compared with that of a traditional machine learning model by capturing the spatial-temporal characteristics at the same time, and the advantages are more remarkable in extreme weather.

The invention provides a theme skin changing method and system for automatically compiling an LESS design variable into a CSS design variable, and the method comprises the steps: 1, configuring a directory structure of an LESS file, and setting a variable recognition rule; 2, analyzing the content of the LESS file into an abstract syntax tree; 3, traversing the abstract syntax tree, and marking nodes meeting a variable recognition rule as design variable nodes; 4, calculating a final value of an LESS design variable represented by the design variable node, establishing a mapping relation table, and modifying the content of the design variable node to obtain a modified abstract syntax tree; 5, adding a global CSS variable statement to the modified abstract syntax tree to obtain the abstract syntax tree added with the global statement; and 6, converting the abstract syntax tree added with the global declaration into a CSS code, and outputting the CSS code. The method supports dynamic theme switching, can be seamlessly integrated into an existing Web development process, and can realize rapid and stable theme skin changing without modifying an existing LESS code.

The invention relates to the field of computer numerical calculation, in particular to an optimal design method of a special disc spring for an ultrasonic motor, which comprises the following steps of: firstly, in finite element software, performing parametric modeling on a disc spring stress model, setting according to related material attributes, and setting and constraining the characteristics of a contact surface; boundary conditions are set according to parameter characteristics of the disc spring, then grids are divided and submitted for finite element software calculation, and initial data are collected; and then modifying parameters, running a Python script to control finite element software to perform batch operation, and outputting a disc spring characteristic curve. And an optimization algorithm model combining a particle swarm optimization (PSO) and a convolutional neural network (CNN) is constructed, the optimization algorithm model realizes calculation design optimization through iterative interaction, and optimal structure parameters and characteristic curves are obtained. According to the method, the structure parameters of the disc spring are calculated, designed and optimized, a convenient and efficient calculation mode is provided for the ultrasonic motor disc spring, and the specific working condition requirements of the ultrasonic motor are met.

The invention discloses an energy collaborative optimization method under interaction of a multi-region micro-grid and an electric vehicle load. The method comprises the following steps: constructing a graph neural network architecture oriented to a multi-micro-grid system; local feature extraction is carried out on each microgrid node, and multi-head attention calculation is carried out on the extracted local features; designing a micro-grid optimization model based on the graph neural network, constructing a local optimization target, and carrying out local distributed training; and co-scheduling among micro-grids based on federated learning. According to the method, efficient configuration of internal resources of a single micro-grid is ensured, collaborative complementation between micro-grids is realized, and data privacy of each micro-grid is effectively protected through a federated learning mechanism. The finally output scheduling scheme can balance the overall benefit and individual benefit of the system, and provides reliable decision support for energy management of the multi-microgrid system.

This invention belongs to the field of enzyme engineering technology, specifically relating to an enzyme engineering method and its application in biocatalysis. Based on the excellent performance optimization capabilities of the deep learning-guided protein sequence design model ProteinMPNN, this invention developed a computational design framework called DeepPCD. This framework integrates deep learning-guided sequence design, physical energy calculation, and molecular dynamics simulation, constructing a three-level intelligent screening process from "massive generation" to "high-precision prediction." Using this strategy, nitrile hydrolase (EC 3.5.5.1) was mutated, resulting in a nitrile hydrolase mutant with improved stability, specificity, and enzyme activity, capable of efficiently and specifically catalyzing the hydrolysis of nitrile compounds to carboxylic acids.

The invention discloses a small-deformation contact nonlinear simulation method, which belongs to the technical field of contact nonlinear simulation, and comprises the following steps: S1, carrying out contact simulation at a contact interface by adopting a friction spring; s2, setting a nonlinear analysis working condition in finite element analysis software; and S3, performing small deformation contact nonlinear simulation and calculation on the structure. According to the small-deformation contact nonlinear simulation method, important references are provided for accurate calculation and design, material saving, energy consumption reduction, production efficiency improvement and the like of structures such as modular units and beams, laying of tunnel linings and the like, the application mode of the small-deformation contact nonlinear simulation method can be embedded into a program to form automatic options, and digital twinning is achieved.

The present disclosure provides a ketoreductase mutant having activity on both (R)-(-)-1,3-butanediol and (S)-(-)-1,3-butanediol, and a computational design method for obtaining the mutant. The ketoreductase mutant disclosed in the present application can catalyze the reaction of racemic 1,3-butanediol to synthesize 4-hydroxy-2-butanone, which has good industrial application value.

The invention relates to the field of computational chemistry, in particular to universality optimization in a transition metal chromophore computational design process. According to the technical scheme, the high-universality design method of the transition metal chromophore comprises the following steps: establishing a molecular database; a calculation model is adopted to scan and calculate molecules in the molecular database, molecules with calculation results meeting screening requirements are regarded as to-be-selected molecules, and DFA is adopted as the calculation model; and the calculation model adopts a plurality of DFAs for calculation. The invention aims to provide a high-universality design method and equipment for transition metal chromophores and a storage medium, and functional molecules of metal chromophores can be designed for different design systems. According to the method, the reliability of the output result of the VHTS in the design process of different metal chromophores is greatly improved, better training data is provided for subsequent AI model training, and therefore the accuracy of the design result output by the AI model is improved.

The application discloses a kind of novel photovoltaic device transient dynamics calculation design system and method, system includes: user parameter input module is used to receive and manage the input parameter required by simulation;Device structure construction module is used to construct the layered structure model of target device based on input parameter, and define the space interval, physical property and interface boundary condition of each layer;Physical model selection and coupling module is used to construct corresponding coupled partial differential equation set according to the device type of target device;Space grid division module is used to construct the space discrete grid of layered structure model, and the calculation precision of interface and thin layer area is enhanced using non-uniform grid division mode;Core numerical solution module is used to solve coupled partial differential equation set, and obtain simulation result;Data export module is used to output simulation result as general data format.

The application provides a kind of klystron high-dimensional parameter optimization method and system, comprising: using klystron one-dimensional large signal simulation software KlyH, the input design parameter is simulated and calculated, and initial data set containing design parameter and corresponding output parameter is generated;Regression prediction model of design parameter to output parameter is constructed, the normalized output change rate of design parameter is calculated based on regression prediction model, and the sensitivity of each design parameter to output parameter is determined according to the normalized output change rate;According to the sensitivity, the range of each design parameter is dynamically adjusted, the optimization search interval of each design parameter is obtained, and multi-objective optimization algorithm is used to optimize and solve in the optimization search interval, and the optimal solution set is obtained.The dimensionality reduction and search space compression of klystron design parameter are realized, the calculation efficiency and search quality are further improved, and reliable support can be provided for intelligent optimization design of high-performance klystron.

The invention discloses a high-rigidity graphene / magnesium-based composite material design method based on multi-modal feature fusion, and belongs to the field of calculation design of metal-based composite materials. The method comprises the following steps: collecting and preprocessing multi-source heterogeneous data; feature extraction and structure representation modeling; performing multi-modal feature fusion and performance prediction; and performing configuration parameter optimization design. According to the method, high-precision prediction of key performance indexes (elastic modulus, fracture strain, limit stress and the like) is realized by constructing a depth feature extraction network of an image mode and a text mode, unifying heterogeneous feature space representation and combining a sequence modeling and regression prediction algorithm. And finally, reversely deducing an optimal configuration parameter combination through a multi-objective optimization algorithm.

The invention relates to the technical field of intelligent manufacturing, and provides an intelligent lightweight method and device for an additive manufacturing support structure based on a virtual test. According to the method, a design area is formed according to the expansion of an original model structure, the material distribution of the design area is calculated according to the load, constraint and optimization target of a scaffold structure, and a scaffold structure topological optimization Pareto optimal solution set is formed based on interaction between a reinforcement learning agent and a virtual topological optimization environment by means of an artificial intelligence technology; according to stress simulation analysis of the optimal solution set of the support topology, the lattice gradient is calculated, and based on reinforcement learning and virtual lattice structure reinforcement environment interaction, a lightweight Pareto optimal solution set of the support structure is formed. According to the method, based on the mode that the topological optimization agent and the lattice strengthening agent carry out reinforcement learning in the virtual simulation environment, on the premise that frequent manual debugging and material object printing are not needed, the lightweight degree and structural strength of the support structure are improved, and efficient and intelligent lightweight design is achieved.

The invention discloses a glass composition optimization method and system based on a neural network and a goat optimization algorithm, and relates to the technical field of glass composition optimization. The method comprises the following steps: optimizing hyper-parameters of a neural network by using a goat optimization algorithm, and constructing a high-performance neural network prediction model; then, the optimized neural network prediction model is embedded into a fitness evaluation link of a goat optimization algorithm, in the glass component optimization process, performance indexes of different component combinations are rapidly predicted through a neural network, the performance indexes serve as fitness values of the goat optimization algorithm, and the algorithm is guided to efficiently search for the optimal component combination. According to the method, through the high-precision prediction capability of the neural network and the global search and adaptive capability of the goat optimization algorithm, the efficiency and accuracy of glass component design are remarkably improved, an innovative solution is provided for research and development of high-performance glass materials, the separation dilemma of'prediction-design 'of glass components is broken through, and the method is suitable for large-scale popularization and application. And the core challenge of high-dimensional constraint space optimization is solved. And glass research and development is pushed from trial and error experiments to calculation design.

Disclosed is a method of operating a computing system. The method performed in the computing system having one or more processors and a memory storing one or more programs executed by the one or more processors, includes receiving one of a plurality of artificial neural network architectures as a backbone architecture, determining a structure of a DCIM (Digital Computing-in-Memory) macro based on the backbone architecture, generating an approximate addition candidate group of the DCIM macro based on a first algorithm, generating a heterogeneous approximate DCIM based on the structure of the DCIM macro and the approximate addition candidate group, and mapping channel-specific weights with respect to the heterogeneous approximate DCIM based on a second algorithm.

The application relates to the technical field of the cross of protein engineering and computational biology, and discloses a uracil-DNA glycosylase de novo design method based on physical priori and deep learning. A fixed function motif containing a catalytic core layer, a DNA binding interface layer and a structural support layer is extracted, and the fixed function motif and a substrate analog are used as geometric constraints; a full-atom diffusion model is used to generate a new protein topological skeleton; iteration optimization is carried out through deep learning sequence generation and physical force field refinement; sub-Angstrom level precision control of an active pocket and sequence-structure self-consistency are realized; finally, multi-dimensional screening is carried out by using an orthogonal prediction model and a Pareto optimal strategy. The application can break through the natural evolution limit, obtain a de novo designed UNG which is unique in sequence, has a sub-Angstrom level precision catalytic pocket and is compact in structure, and provide a new computational design strategy for the development of programmable gene editing tool enzymes.

The invention relates to the field of computational chemistry, in particular to design accuracy and efficiency optimization in a transition metal chromophore computational design process. The invention aims to provide a complex design method based on a full historical data retention strategy, which not only retains failure data in an iteration process, but also classifies and differentiates the failure data. The situation of information overload and noise interference under a full historical data retention strategy is changed, and the learning efficiency and reasoning quality of LLM are improved.

Organic field-effect transistors (OFETs) have some advantages over inorganic transistors, for example, due to being physically flexible and lightweight. Additionally, OFETs can cover large-areas and can potentially be mass-produced at a low cost. It is recognized herein, however, that a technical challenge related to OFET design is evaluating effects at multiple length scales during the design process.

This invention relates to the field of computer-aided design and discloses a computational design method for negative thermal conductivity metamaterials based on topology optimization algorithms. The method includes the following steps: defining the material's relative density field and the active heat source's power density field as dual design variables; performing active-passive collaborative optimization iteration using the adjoint variable method and the moving asymptote method; introducing a hyperbolic tangent function to quantize the continuous power field, extracting the coordinates away from the heat source and applying repulsive constraints; performing cross-scale mechanical-thermal decoupling reconstruction, replacing stress concentration regions with microscopic metalattices; calculating the Jacobian matrix to generate a feedforward compensation network and performing engineering-level derivation. This invention breaks the theoretical limit of thermal conductivity in passive metamaterials, achieves interference-free physical mapping of standard hardware in topological space, eliminates destructive thermal stress through lattice reconstruction, generates a physical system with extremely low computational latency, and realizes a dynamic adaptive negative thermal conductivity representation under complex perturbations.

The invention belongs to the technical field of protein engineering and biological catalysis application, and discloses a high-expression efficient PET hydrolase mutant and application thereof. The PET hydrolase mutant with high activity, high thermal stability and high expression level obtained through calculation design and site-specific mutagenesis shows great potential to be applied to industrial application of PET recovery. Experiments prove that the fusion temperature of the modified mutant protein reaches 92.4 DEG C; under the industrial-grade PET substrate concentration, the maximum reaction speed of catalyzing PET hydrolysis is 1.76 times that of the template enzyme; and the expression level of the gene in a shake flask is 3.3 times that of a template enzyme, and the protein yield in a fermentation tank reaches 1.49 g / L.

This invention discloses a small-deformation contact nonlinear simulation method, belonging to the field of contact nonlinear simulation technology, including the following steps: S1, using friction springs at the contact interface for contact simulation; S2, setting nonlinear analysis conditions in finite element analysis software; S3, performing small-deformation contact nonlinear simulation and calculation on the structure. This invention, employing the above-mentioned small-deformation contact nonlinear simulation method, provides important reference for the accurate calculation and design of modular units, beam placement, tunnel lining, and other structures, saving materials, reducing energy consumption, and improving production efficiency. Its application can be embedded into programs to form automated options, realizing digital twins.

The invention is suitable for the technical field of mold development, and provides a mold development and design process-based supervision method and system, and the method comprises the following steps: receiving a mold system architecture which comprises a plurality of mold subsystems, and the mold subsystems are provided with an interaction relationship; a logic stress flow model is determined, wherein each mold subsystem is made to be a node in the model, and directed connecting edges are determined according to the interaction relation; receiving a design decision parameter set, inputting the design decision parameter set into a node corresponding to the logic stress flow model, and determining a cost change degree, a periodic change degree and a performance change degree; calculating an influence index of the design decision parameter set on the global situation based on the cost change degree, the periodic change degree and the performance change degree; and determining optimal design parameter information according to the influence index. According to the method, propagation and influence of the design decision in a mold system can be simulated, the influence of the design decision on the whole mold can be evaluated in advance, and the optimization and modification cost is reduced.

A CPU parallel parametric level set topology optimization method for arbitrarily shaped CAD models belongs to the field of high-performance design technology. It solves the problems of the split between CAD and CAE, poor and time-consuming mesh generation, and low efficiency of large-scale mesh calculations. The topology optimization parallel computing design framework of the present invention significantly improves the efficiency and accuracy of topology optimization of complex structures by introducing parametric level set methods, parallel finite element analysis technology, and seamless integration with CAD models, and has broad engineering application prospects. It not only solves many problems of traditional topology optimization methods, but also provides an efficient and practical optimization design tool for the engineering design field. It can significantly improve the efficiency and accuracy of topology optimization when processing complex CAD models, and solves the problems of insufficient communication between CAD and CAE, poor mesh generation quality, low calculation efficiency, etc. in the existing technology, and has broad engineering application prospects.

The present invention discloses a method for topological optimization of a thermally stable load-bearing support structure, which relates to the field of structural optimization and includes the following steps: defining a design domain and a non-design domain, and completing initialization settings; converting design variables into physical variables based on a density filter function and a projection function; establishing an interpolation model for elastic coefficients and thermal conductivity coefficients; constructing and assembling a heat transfer matrix and a stiffness matrix based on a finite element mesh model; performing a heat transfer simulation to obtain an equivalent thermal load of a unit; performing a statics simulation to calculate a design response; calculating the sensitivity of the design response to the design variables; establishing an optimization model, solving the optimization model based on a moving asymptote algorithm, and updating the design variables; post-processing the optimization results to generate a support structure with a smooth surface. The present invention addresses the problem of thermomechanical coupling optimization under environmental thermal fluctuations, and obtains a load-bearing structure with high specific stiffness and resistance to thermal deformation, solving the problem of low stiffness and easy failure of traditional methods under large environmental fluctuations.

The invention relates to the field of research and development assistance systems, in particular to a research and development system based on multi-objective optimization and knowledge mining, which is characterized in that a demand task generation module matches an objective function and a constraint condition according to a design demand to form an initial task book; the case retrieval module retrieves a plurality of historical cases with high similarity in the knowledge base to generate a historical case set; the sensitivity analysis module calculates the sensitivity of each adjustment value under the constraint of the constraint condition of the design demand, and generates a sensitivity report; the multi-algorithm fusion optimization module uses multiple algorithms to carry out parallel iterative analysis and fusion communication according to the initial task book, the historical case set and the sensitivity report to obtain an optimal scheme set, and then the digital twinborn verification module carries out multi-physics field simulation analysis to generate a simulation report; the final review module performs comprehensive review on the initial task book, the optimal scheme set and the simulation report, and outputs a review scheme and a modification suggestion; the problem of low research and development efficiency caused by multi-person cooperation in the research and development process is solved.

The application provides a control surface structure flutter margin optimization method based on a Kriging surrogate model, comprising the following steps: establishing a control surface structure reference model; performing quality discrete processing on the reference model, updating the quality of each point of the control surface structure reference model according to the perturbation method, performing control surface structure flutter analysis, performing sensitivity analysis on each quality point of the control surface, obtaining the positions of a plurality of quality points with the greatest influence on the quality point discrete flutter speed, and marking the plurality of quality points as flutter significant points; updating the quality data of the plurality of flutter significant points in batches, establishing a flutter speed surrogate model based on the Kriging method; testing the flutter speed surrogate model by using a test data set; and performing rudder surface flutter optimization analysis based on the control surface flutter speed surrogate model and a genetic algorithm. The technical scheme of the application is used to solve the technical problem that the traditional design method usually depends on experience, experiments or a large amount of calculation, and the design cycle is long and the cost is high.

This invention provides a method for automatic generation and energy-saving optimization of three-dimensional building functional layout, belonging to the field of automatic generation and optimization of building models in computational design. The method includes: acquiring basic modeling data of the target building; inputting the basic modeling data into model generation software; determining fixed parameters for the layout scheme and generating rule parameters; using a three-dimensional spatial layout algorithm to perform three-dimensional spatial layout on the mesh model; calculating the annual load per unit building area and the applicability score of the building functional layout of the basic building model; setting the running parameters of a multi-objective evolutionary algorithm; optimizing the basic building model using the multi-objective evolutionary algorithm; and performing cluster analysis and visualization processing. This invention provides an automatic three-dimensional modeling method for energy-saving buildings, simultaneously utilizing a three-dimensional spatial layout algorithm and a multi-objective evolutionary algorithm to increase the efficiency of modeling and optimization, and resulting in a model with better applicability.

The invention discloses an intelligent auxiliary review method and system for a power distribution network project. The method comprises the following steps: collecting multi-source heterogeneous data, preprocessing, constructing a space-time reference, and forming an equipment space topology database and a typical cost case feature library; thirdly, extracting engineering entity characteristics based on the preprocessed data by adopting a detection model, establishing a three-dimensional space mapping model, calculating design layout offset, and generating a resource scheduling signal when the design layout offset exceeds a threshold value; meanwhile, an abnormal diagnosis result and a high calculation demand signal are obtained through analysis of an event influence quantitative model by using historical cost data and an event graph. And formulating a hierarchical scheduling strategy according to the signals to optimize computing power resource allocation. And finally, under the resource constraint, integrating the three-dimensional evidence chain to output a review report, determining an actual deviation and a correction suggestion, and realizing intelligent review and dynamic resource management. By implementing the method provided by the invention, efficient integration of spatial topology verification, cost anomaly prediction and resource scheduling and closed-loop feedback optimization can be realized.

The application discloses a multi-region micro-grid and energy collaborative optimization method under the interaction of electric vehicle load, which comprises the following steps: constructing a graph neural network architecture for a multi-micro-grid system; performing local feature extraction on each micro-grid node and performing multi-head attention calculation on the extracted local features; designing a micro-grid optimization model based on the graph neural network, constructing a local optimization target, and performing local distributed training; and performing inter-micro-grid collaborative scheduling based on federated learning. The application not only ensures efficient configuration of internal resources of a single micro-grid, but also realizes collaborative complementation between micro-grids, and effectively protects the data privacy of each micro-grid through the federated learning mechanism. The finally output scheduling scheme can balance the overall benefit and individual benefit of the system, and provides reliable decision support for energy management of the multi-micro-grid system.