138 results about "Intelligent design" patented technology

The invention discloses a multi-modal-based software architecture intelligent design and optimization system, which relates to the field of intelligent design and optimization, and comprises the steps of performing modal perception and preprocessing on architecture design demand information input by a user, converting the architecture design demand information into structured semantic data, and optimizing and enhancing semantic expression by introducing a reinforcement learning strategy, forming a structured user intention vector set; through a semantic mapping and reasoning processing unit, user intention vectors in the user intention vector set are constructed into a semantic-component alignment graph, and graph structure modeling and confrontation generation algorithm combination are adopted to generate a candidate structure graph set; a structure diagram generation step in the structure generation module effectively improves the rationality, diversity and adaptation capability of an automatically generated structure, and provides core support for realizing automatic construction of a target-demand-oriented architecture structure.

The invention provides a heat exchanger intelligent design method based on deep reinforcement learning, and relates to the technical field of data processing. Establishing a flow model for simulating the flow path, speed distribution and pressure change of the fluid in the heat exchanger; according to the geometrical shape of the heat exchanger, the physical property of a heat exchanger material and fluid flowing in the heat exchanger, a heat exchange model is established by combining the thermodynamic principle, and the heat exchange efficiency under different fluid flowing conditions is calculated; the flow model and the heat exchange model are coupled, so that a coupling performance evaluation model for evaluating the overall performance of the heat exchanger under the specific working condition is obtained; and determining a state space, an action space and a reward function according to the flow heat exchange intensity coupling performance evaluation model. The design efficiency is improved.

The invention discloses a building decoration intelligent design method and system based on a user portrait, and relates to the technical field of smart home and artificial intelligence, and the system comprises the following components: user data collection and portrait construction, a biological rhythm analysis model, an intelligent design algorithm, an interactive design platform, and implementation and feedback. Based on biology, psychology and environmental science theories, a biological rhythm analysis model is established, the potential influence of different building decoration elements on the biological rhythm of a user can be predicted and evaluated, through application of an intelligent design algorithm, the system can automatically match and optimize a building decoration design scheme, and the design efficiency is improved. The design scheme is ensured to meet the personalized requirements of the user, and meanwhile, the biological rhythm harmony of the user is promoted to the maximum extent, which is of great significance for improving the physical and psychological health level of the user and improving the quality of the living environment.

The invention belongs to the technical field of bioengineering and artificial intelligence crossing, and particularly discloses a microbial fermentation experiment intelligent design method and system based on a large language model (LLM). According to the invention, a historical database can be automatically retrieved and an experiment resource state can be inquired according to an experiment purpose input by a user intention, an experiment scheme is generated through large language model reasoning, and a task structure which can be identified by an experiment system is synchronized to a digital system so as to support confirmation, optimization and execution of tasks.

The invention relates to the technical field of design service, in particular to a roof waterproof structure intelligent design optimization system which comprises a node contour extraction module, a contact segment recognition module, a close boundary evaluation module, a disturbance area labeling module and a waterproof configuration reconstruction module. According to the invention, by extracting the direction vector change of the boundary point cloud sequence, the continuity and stability of the turning trend in the contour structure can be accurately obtained, the boundary of the roof structure can be identified, and the contact type of the boundary joint can be determined in combination with the direction change rate distribution of the contact surface, so that the boundary sealing state has quantitative index output; in addition, local positioning information of a fitting path and a structure defect can be provided, a node contour reconstruction path with an adjustment space is provided based on a pressing path continuity judgment and simulation edge shift strategy, and the adaptability of a node structure under the action of complex stress and the configuration optimization capability of the overall waterproof performance of a roof are improved.

The invention discloses a method for intelligently designing a tunnel grouting scheme and an expert system. The method comprises the following steps: step 1, establishing a three-dimensional geological tunnel model; 2, grouting design parameters and the value range of the grouting design parameters are determined, a grouting design parameter set is generated randomly, and numerical simulation is conducted; 3, generating a simulation result parameter set, and combining the simulation result parameter set with the grouting design parameter set to form a data sample; various simulation result parameters in the data sample are set as input layer data, and various grouting design parameters are set as output layer data; 4, repeating the step 2 and the step 3, generating a data sample set and training a neural network prediction model; 5, establishing a plurality of independent neural network models according to different geology and excavation face characteristics, and integrating the models to an expert system; and step 6, inputting field parameters through an expert system, and intelligently generating a grouting scheme. The problems that an existing traditional method is low in efficiency, depends on manpower and lacks accuracy are solved.

The invention relates to a large-scale language model-based solid waste cementing material intelligent design and performance prediction system, and relates to the technical field of material science and artificial intelligence, the system integrates multi-source heterogeneous data through a multi-task data set construction module, and establishes association mapping of a solid waste material formula and performance; the performance prediction module based on the large-scale language model realizes high-precision cross-modal prediction of key indexes such as compressive strength and permeability; the reverse design module generates a formula scheme conforming to chemical components and industrial constraints through dynamic embedding of material scientific rules and reinforcement learning multi-objective optimization, and forms a closed loop verification link based on physical rule verification and an experimental feedback mechanism. The research and development efficiency of the solid waste cementing material is remarkably improved, the trial and error cost and resource waste are effectively reduced, rapid conversion of laboratory achievements to industrial production is promoted, and intelligent technical support is provided for solid waste resource utilization and green building material development.

The invention relates to an intelligent design method of a radiation shielding material based on cross-scale error feedback lock step, which comprises the following steps: determining a material component set and acquiring an atomic-scale graph structure of the material component set, and extracting intrinsic performance parameters such as heat conductivity and attenuation coefficient through an atomic-scale prediction module; and then, constructing a design variable vector in combination with a multi-physical field condition, calculating equivalent performance parameters of the composite material, establishing a cross-scale lock step mechanism, and triggering a molecular dynamics and Monte Carlo recalculation module to carry out correction when the deviation between a predicted value and a mesoscopic recalculation result exceeds a limit. Based on the correction parameters, a candidate material scheme is generated by using a multi-objective optimization algorithm; and performing gamma-ray and neutron shielding performance simulation on the candidate schemes through a Monte Carlo radiation transfer model accelerated by a deep neural network, and comparing with an experimental result to realize self-adaptive material design driven by cross-scale errors. According to the method, the design precision and efficiency of the radiation shielding material are remarkably improved.

The invention provides a terahertz fingerprint sensing-oriented multiplexing meta-structure device intelligent design method, which comprises the steps of obtaining expected resonant frequencies, and covering a target fingerprint frequency range through equal-interval sampling to obtain a series of expected resonant frequencies; setting expected structure parameters, and setting the expected structure parameters of the target super-structure device according to actual physical limits and processing conditions; combining the obtained expected resonant frequency with the set expected structure parameters to form an input vector of a reverse design network; and predicting target structure parameters, constructing and training a reverse design network based on deep learning, and predicting other structure parameters of a series of meta-structure surface modules capable of realizing a target fingerprint frequency range. The reverse design of the multiplexing super-structure device is realized through the deep learning neural network, and the device structure meeting the target fingerprint requirement can be quickly and accurately deduced. The design precision and efficiency are remarkably improved, and the capability of responding to sensing demand changes in real time is achieved.

The invention discloses an intelligent design and precise preparation method of structure-controllable non-spherical particles based on a microfluidic technology, and relates to the field of microfluidic technology and artificial intelligence cross application, and the method comprises the following steps: S1, realizing continuous preparation of various non-spherical particles based on a microchannel confinement effect, and obtaining a data set by combining high-throughput numerical simulation; s2, constructing a controllable prediction model matched with various non-spherical liquid drops, and training and verifying the controllable prediction model based on the data set; s3, selecting a corresponding controllable prediction model based on the particle shape, inputting device parameters, physical property parameters and target deformation by a user, and performing reverse search and iterative optimization to obtain operation working conditions; and S4, completing preparation of non-spherical particles based on the predicted spherical droplet template size and working conditions. According to the reverse intelligent design strategy based on artificial intelligence, target-oriented non-spherical particle micro-fluidic precise preparation is achieved, and the micro-fluidic preparation efficiency of the non-spherical particles is greatly improved.

The invention relates to the technical field of sliding bearing design in mechanical engineering, in particular to a sliding bearing multi-mode end-to-end intelligent design system based on a large model. Comprising a multi-modal input and feature recognition module, a high-fidelity performance prediction module, a structural parameter intelligent optimization decision module and a parametric modeling and multi-modal output module which are connected in sequence and form an intelligent design closed loop, and all the modules are seamlessly connected through data interfaces. According to the method, multi-modal input and a high-fidelity simulation closed loop are driven through a large language model, the sliding bearing design efficiency is remarkably improved, the technical threshold is greatly reduced, interaction between a natural language and a drawing is supported, and non-experts can complete high-performance design; full-process automation from requirements to drawings is achieved, and manual intervention errors are avoided; optimizing in a wide-area parameter space by using the large model reasoning capability to realize multi-target global optimization; and multi-modal input and output of texts, drawings and models are supported, and the engineering applicability is enhanced.

The invention discloses an intelligent design method and system for a well track of a high-density cluster well. The method comprises the steps that a three-dimensional collision detection route map is constructed; performing path search and intelligent obstacle avoidance on the three-dimensional collision detection route map by using a path search algorithm, judging whether the search paths meet the dog-leg degree constraint in the search process, performing path cost calculation on the paths meeting the dog-leg degree constraint in sequence, and sorting and storing the search paths meeting the dog-leg degree constraint according to the path cost; and according to a sorting result, selecting an optimal path, and smoothing the segment of which the dog-leg degree exceeds a preset threshold value. According to the method, a route map from a wellhead position to a target spot for three-dimensional collision detection is automatically constructed through a probability route map algorithm, then an automatic path search algorithm is utilized to perform path search under the condition that the set maximum dog-leg degree is met until an optimal feasible path is found, and local smoothing processing is performed on the found path, so that the optimal path is obtained. The design time can be effectively saved, and the collision risk of the borehole track is reduced.

The invention relates to the technical field of artificial intelligence, in particular to a concrete faced rockfill dam profile intelligent design method based on data feature fusion, and the method comprises the steps: building a learning prediction model to predict the height and slope ratio of a dam body through collecting initial data and historical data such as feature water levels, anti-seismic attributes, stone parameters and the like; dam body contour parameters are calculated, and the area proportion of each partition is predicted; thirdly, the dam body contour parameters, the feature water level, the anti-seismic attribute and the partition area proportion are fused into a multi-dimensional feature vector, the multi-dimensional feature vector is input into the generative adversarial network model, and a partition design image of the concrete faced rockfill dam is generated. According to the method, intelligent design of the concrete faced rockfill dam profile scheme is achieved, the design efficiency is greatly improved, and the intelligent level of concrete faced rockfill dam design in China is further improved.

The invention discloses a water conservation digital intelligent design method for a power transmission line project. The method comprises the following steps: constructing a water conservation design principle structured table, a modular basic water conservation model warehouse and a water conservation plant structured table; establishing a three-dimensional scene digital elevation model; carrying out automatic plotting and precise modeling on each iron tower leg of a specific power transmission line project, and recommending a corresponding water conservation plant type and a growth scheme; determining whether the actual situation of a specific power transmission line project is matched with the recommended water conservation measures; comparing the preliminary design scheme with a water conservation reply file, and automatically generating a detailed feedback report; and verifying the feedback report. According to the method, automatic modeling and plotting can be completed, plant types are automatically recommended, water conservation measure design and engineering quantity statistics are intelligently completed, and the design efficiency is improved; meanwhile, for the water conservation measures which do not meet the requirements, the purpose that the deviation between the water conservation measures and the water conservation reply file is within a reasonable range is achieved by adjusting design parameters.

The invention relates to the technical field of parameterization modeling, in particular to a construction drawing intelligent design optimization method and system based on parameterization, and the method comprises the following steps: extracting boundary and annotation information, judging the orientation and affiliation relation of components, analyzing the coordinates of end points, adjusting the order of side lines, calculating the occupation offset, comparing the positions of intersection points, recognizing preorder nodes, and sorting. Extracting junction point distribution to judge a contact range and a connection relation, and determining a drawing page number by combining a component starting point and a path direction. According to the method, the spatial affiliation relation of the components is established through geometric boundaries and labeling information, the trend is adjusted according to the included angle difference value of side lines, the connection strength is recognized by combining the joint angle and the contact range, the drawing sequence is deduced according to the serial number sequence and the intersection point sequence, and the component combinations are continuously divided through attachment sections. The drawing page distribution is positioned based on the starting point of the component and the path direction, dynamic cooperation of component arrangement, sequence organization and drawing splicing is achieved, and the accuracy of component structure expression and the integrity of construction drawing generation are improved.

The invention relates to a Nusselt number prediction method, program and equipment based on a heat transfer associated hybrid model of a shell-and-tube heat exchanger and a storage medium, and belongs to the technical field of intelligent design and performance optimization of industrial heat exchange equipment. The objective of the invention is to solve the problems of low prediction precision, poor adaptability and lack of physical interpretability of a traditional empirical correlation under complex working conditions. Through an adaptive weight mechanism, the final hybrid model can automatically select an optimal prediction strategy according to different working conditions, and in four typical working condition cases, the average decision coefficient is up to 0.9930, and the average absolute percentage error (MAPE) is only 1.66%, which is significantly superior to that of a single model. According to the system, high-precision and high-robustness prediction of the heat transfer performance of the shell-and-tube heat exchanger is achieved, more importantly, the corrected correlation formula has clear physical significance and high interpretability, and a revolutionary tool is provided for intelligent design, real-time performance monitoring and energy efficiency optimization of the heat exchanger.

The invention discloses a fabricated building intelligent design system based on multi-source data analysis, and relates to the technical field of building engineering, and the method comprises the steps that an information collection module establishes equipment collection data to generate an initial data set; and the information labeling module performs position identification and position association on the initial data set. And the extraction processing module obtains a building demand, performs feature evaluation on the initial data set, and inputs a result to a decision result of the adaptive scale decision model. The fusion extraction module establishes and fuses features. The additional feature extraction module identifies an additional feature set. And the search module performs similar search to obtain building search results. And the evaluation selection module performs adaptive evaluation sorting on the search results based on the building requirements to complete the prefabricated building design. The technical problem that it is difficult to achieve efficient, high-quality and low-cost design meeting diversified requirements when the building scale and complexity are improved in existing building design is solved, and the technical effect of improving the quality, efficiency and comprehensive benefits of fabricated building design is achieved.

The invention discloses an intelligent design method for an ocean observation station based on deep reinforcement learning, which belongs to the technical field of intelligent design of observation stations, is used for intelligent design of observation stations, and comprises the following steps: acquiring multi-source ocean data, making a deep learning sample set, and constructing and training a deep neural network model in combination with deep reinforcement learning. And performing ocean observation station reasoning by using the trained deep neural network. According to the method, rapid, batch and optimal design of regional ocean observation stations is realized, and the accuracy of an ocean observation reconstruction field is improved, so that guarantee and service are provided for related application requirements depending on an ocean environment field.

The invention discloses an interactive catalysis method and platform based on molecular simulation and artificial intelligence. According to the method, intelligent design of a catalyst is achieved through data classification, condition optimization, structure modeling and mechanism analysis. Catalytic data uploaded by a user is subjected to standardization processing and then is divided into two types: a data set A is used for recommending an optimal reaction condition by a multi-objective optimization algorithm; a three-dimensional model is constructed after a catalyst structure of the data set B is optimized through quantum chemistry software, a plurality of intermediates and transition state structures are optimized, and geometric / electronic descriptors are calculated. And constructing a dual-model collaborative prediction system of the model E and the model F through a feature screening and dimension reduction technology, and finally screening the high-performance catalyst by utilizing a model F driven molecule generation algorithm. According to the method, flexible adaptation to specific application scenes can be achieved, the urgent requirements of the industry for efficient and accurate catalyst performance prediction and intelligent design are met, cost reduction and efficiency improvement of the industry are effectively assisted, and the research and development process of the catalyst is accelerated.

The invention relates to the technical field of intelligentization and digitization of straightening machines, and particularly discloses a knowledge engineering-based intelligent design method of an automatic shaft part straightening machine, which comprises the steps of constructing a rule library, an ontology library and a case library, reasoning of unknown parameters of the shaft part automatic straightening machine is achieved based on the knowledge base in a case reasoning and rule reasoning mode. And parametric modeling is carried out to obtain a three-dimensional model and an engineering drawing of the automatic straightening machine for the shaft parts, which meet the design requirements. And performing simulation verification on the established model, and performing parameter optimization according to a simulation result. And updating the knowledge base through the online learning module. According to the method, the design efficiency of the non-standard straightening machine can be improved, and the workload of engineers and the design cost are reduced. Meanwhile, an online learning module is added, dynamic updating of a knowledge base is achieved based on real-time data of the automatic straightening machine for the shaft parts and latest design cases, and the problem that non-standard working conditions are difficult to cover when the number of rules in an initial rule base is small is solved.

The invention relates to a data knowledge-driven intelligent design method for the mix proportion of foam light soil. The method comprises the steps of obtaining foamed light soil performance data, and performing preprocessing to obtain a complete data set; respectively establishing an LSTM-based learner model, an SVM-based learner model and an XGBoost-based learner model, and establishing a foam light soil performance prediction model; training the foam light soil performance prediction model by adopting a complete data set to obtain a performance prediction value, calculating a precision difference between the performance prediction value and a performance true value, and optimizing the foam light soil performance prediction model according to the precision difference; inputting a formula ratio to be predicted into the trained foam light soil performance prediction model to obtain a foam light soil performance prediction value; and constructing a multi-objective design optimization model of the foam light soil, and solving the multi-objective design optimization model of the foam light soil by adopting a multi-objective optimization algorithm to obtain an intelligent design scheme of the foam light soil.

The invention discloses a data-driven intelligent material design research and development platform system and method, and the system comprises a multi-scale simulation module which comprises nano-scale simulation software, micro-scale simulation software, mesoscale simulation software and macro-scale simulation software, matches the simulation software based on material data inputted by a user, configures simulation information, and carries out simulation operation through the simulation software obtained through matching; the high-throughput module is used for performing node operation based on all nodes in a preset process template corresponding to the task file to obtain a task operation result; the AI model module comprises an AI model deployed on nodes in a containerization manner, generates input parameters of a current task based on a parameter template of the AI model, performs model prediction, and is used for online deployment of the AI model of a user; the material database stores material data and is used for a user to inquire the material data online and display the material data in a templated mode. Aiming at special requirements in the field of material science, the limitation of an existing cloud computing platform is overcome, and the resource utilization rate is improved.

The invention discloses an intelligent design method for a dual-frequency wide-angle high-transmission metasurface, which comprises the following steps of: performing layered sampling on geometric structure parameters of a metasurface unit, performing equal-interval sampling on incident angles, performing full-wave electromagnetic simulation on a parameter sample group to obtain transmission electromagnetic response data, and constructing a database; a multi-layer perceptron neural network is used for learning a nonlinear mapping relation among geometric structure parameters, incident angles, frequencies and transmission coefficient amplitude values, and an independent test sample is used for evaluating the prediction precision and generalization ability of the trained multi-layer perceptron neural network under wide angles and dual frequency bands. A substitution model is constructed based on the trained multi-layer perceptron neural network, the substitution model is embedded into a global optimization process, structural parameters are iteratively optimized in combination with a heuristic search algorithm, an optimal design meeting a dual-band high-transmittance index is obtained, the precision and efficiency of metasurface design are remarkably improved, and the performance of the metasurface design is improved. While the wide-angle double-frequency high transmission performance is ensured, the design period is greatly shortened.

The application provides a laminated core structure design method, relates to the technical field of intelligent design, and comprises the following steps: constructing a design parameter library, inputting basic design parameters of the core structure, and keeping the center of the window unchanged in each level window; taking the center of the main level window as a fixed reference, inputting the horizontal and vertical numbers of the main level window, the width and height of the main level window, combining column section level definition data, and sequentially deriving the width, height and center position of other level windows; according to the size and position of each level window, combining column section level definition data and splicing modes, calculating the vertical column size and horizontal column size, and generating a sheet type shape and size range suitable for each window level; and according to local design requirements, the sheet type and splicing structure are specially adjusted. The application can solve the problems of high artificial dependence, low efficiency, poor adaptability and material waste in the traditional design method, and provides support for the automation, digitization and intelligentization of transformer production.

The invention relates to the technical field of supporting structures, and discloses a foundation pit supporting structure construction method which comprises the following steps: S1, supporting structure design and model selection; a three-dimensional model of a supporting assembly is constructed through the BIM technology, the spatial layout and size parameters of components such as a base plate and anchor rods are optimized, visual construction simulation and error control are achieved, and a supporting form is selected according to the grade of a foundation pit; and S2, a supporting structure is installed, specifically, base plates with preset drainage holes are installed on the two opposite sides of the side wall of the foundation pit, the length of a fixing rod is adjusted through a first rod body and a second rod body which are in sliding connection, reverse locking is achieved through a locking piece, and the two ends of the fixing rod are connected with the base plates. The slope stability and the surrounding environment deformation are effectively controlled by adopting the combination form of the row pile support, the soil nailing wall and the like, intelligent design, modular construction and precise monitoring are fused, the method is suitable for medium-deep foundation pit engineering, and efficiency and safety are both considered.

The invention relates to an intelligent design method for an equivalent single beam of a cable bearing bridge. The intelligent design method comprises the steps that basic design indexes of the cable bearing bridge are set; based on a BP neural network model, the basic design indexes are used for generating design parameters of an equivalent single beam of the cable bearing bridge, and the design parameters comprise section characteristics, dead load set degree and equivalent torsion mass; and establishing a finite element model of the equivalent single beam based on the design parameters of the equivalent single beam. The method comprises the following steps: firstly, setting a basic design index, and then quickly generating design parameters of an equivalent single beam based on a BP neural network model so as to establish a finite element model; dependence on experience of designers is reduced, determination difficulty of design parameters is reduced, particularly, when scheme comparison and selection are needed, design parameters corresponding to equivalent single beams are generated according to different basic design indexes, scheme comparison and selection efficiency is greatly improved, and reference is provided for main beam design decision making of cable bearing bridges.

The invention discloses an advertisement design assistance method and system based on artificial intelligence, and relates to the technical field of intelligent design assistance, and the method comprises the steps: carrying out the data collection of a target user for advertisement design, constructing a behavior matrix, determining an earliest time point as a reference point, carrying out the standard deviation analysis, and determining a diffusion parameter; time spread values of user interests are analyzed, and a single time spread vector is calculated. According to the method, a local activation factor based on advertisement composite embedding is introduced, so that the weight of each behavior event can be dynamically associated with the difference of advertisement characteristics, user behaviors are divided according to users to which the user behaviors belong, and interest distribution is counted in combination with a distribution matrix subjected to space-time weighting, so that the user behavior analysis accuracy is improved. And further reducing into a user specific interest vector, generating a category number-based distribution vector for the user interest graph by combining a width-depth model, and further defining an aggregation proportion of different interest categories to user behaviors and advertisement contents.

The embodiment of the invention discloses a physical embedded closed-loop high-entropy alloy laser additive manufacturing performance oriented intelligent design method. By constructing a high-entropy alloy mechanical property database, training a multi-algorithm collaborative integrated agent model and fusing thermodynamic and physical laws and a multi-target Bayesian optimization algorithm, combined reverse design of high-entropy alloy components and LDED process parameters is realized, the applicability, accuracy and robustness of performance prediction are improved, and the performance prediction efficiency is improved. The research and development period of new materials is shortened, and the test cost is reduced. By embedding physical constraints such as valence electron concentration, mixing enthalpy, mixing entropy, electronegativity difference and atomic size difference and comprehensively considering the influence of process factors such as laser power, scanning speed and powder feeding rate, an optimal Pareto component-process scheme is obtained under the support of finite element simulation closed-loop verification and an incremental learning mechanism; and the tensile strength and the yield strength of the high-entropy alloy under different conditions are accurately predicted and reflected.

The application discloses an intelligent design method for a tunnel open cut top drainage structure, relates to the technical field of tunnel design, and comprises the following steps: creating a natural terrain model according to a tunnel open cut local coordinate system; generating a peripheral three-dimensional terrain model after backfilling of the open cut based on the natural terrain model; determining an open cut top terrain line according to the peripheral three-dimensional terrain model, and determining a drainage structure type corresponding to the tunnel open cut top according to the open cut top terrain line; setting a limit boundary condition, and determining an optimal start and end point of a drainage structure center line according to the drainage structure type within the limit boundary condition; determining an optimal three-dimensional center line of the drainage structure under the constraint condition of the optimal start and end point; optimizing the cross-sectional size of the drainage structure according to different segment slopes in the drainage structure, obtaining an optimal cross-sectional size, and generating a drainage structure three-dimensional entity model based on the optimal three-dimensional center line and the optimal cross-sectional size. The application can improve the design efficiency of the drainage structure.