57 results about "Intelligent design" patented technology

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 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 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 present application relates to the technical field of shaft blasting, and particularly relates to a kind of intelligent design methods of shaft blasting excavation, comprising: constructing back-end database, the back-end database includes typical shaft blasting case knowledge base and shaft blasting engineering theoretical rule knowledge base;Shaft blasting design parameters are input into blasthole design model, to generate blasting design data, the blasthole design model is established based on the back-end database;Equal proportion layering method is used to calculate blasthole distribution radius, and blasthole plane coordinates are calculated based on polar coordinate positioning method;Based on the blasthole plane coordinates, different types of blastholes are visualized and rendered according to the preset graphic semantic system, and section boundary auxiliary line, coordinate axis, legend and title bar are automatically generated on the rendered graphics, to obtain the final blasthole layout diagram.The present application realizes the change of blasting design data from artificial experience decision to data and algorithm driven decision.

A strip mine blasting digital comprehensive processing system comprises a mine geological information management system, a blasting database management system and a blasting intelligent design system. The mine geological information management system is used for analyzing mine landform features and rock mass structure geological features; the blasting database management system is used for storing theoretical calculation formulas related to blasting intelligent design, laboratory test data and field blasting test parameters; the blasting intelligent design system is used for loose blasting design, is developed by utilizing a three-dimensional graphic display platform, and comprises visual operation of drawing, editing, three-dimensional view display, blasting design and simulation; intelligent combination of blasting design and geological information is achieved, and the blasting design is more reasonable, scientific and accurate; an intelligent blasting optimization design system is established, blasting design template management is achieved, a design parameter database is established, and basic data is provided for blasting effect prediction.

The application discloses a kind of based on digital twinning foundation pit blasting intelligent damping method, system and medium, the method is by constructing an integrated " perception-prediction-generation-regulation " intelligent closed loop, built-in online correction neural network intelligent fluctuation theory model, realizes the dynamic high-precision deduction of wave propagation;Proposed fusion strain energy and kinetic energy dissipation topology optimization objective function, realizes the automatic generation of damping structure from macro layout to micro composite material distribution;Adopt the edge reinforcement learning based on attention mechanism, realize millisecond level self-adaptive precision control.System, through the cooperation of digital twinning engine, intelligent design brain, edge control node and block chain storage platform, realize the digitization, intelligentization and credibility of whole process.The application significantly improves the scientificity, precision and adaptive ability of blasting vibration control.

The invention provides a concrete faced rockfill dam objectification and assembly type intelligent design method which comprises the following steps: S1) according to design logic, disassembling the whole section of a concrete faced rockfill dam into a plurality of detail structures; s2) manufacturing the detail structure into parameterized parts, and assembling a plurality of parameterized parts into a typical cross section; s3) in the three-dimensional terrain, designating a dam axis, designing a longitudinal section and a typical cross section, and creating a BIM model; s4) customizing various styles in advance, reading point and line codes of the BIM model, and assigning styles for the codes; s5) extracting a calculation section from the BIM model, starting seepage stability calculation by one key, and synchronously generating a calculation report text; and S6) setting a section line and a chart style, and automatically extracting engineering quantity and drawings from the BIM model.The method has the advantages that the intelligent degree is high, the quality is high, the progress is fast, repeated work is avoided, parameters can be updated through one key, the time for design, check and review is saved, and quality improvement and efficiency improvement are achieved.

The invention discloses a building house decoration intelligent design analysis system based on artificial intelligence, which belongs to the technical field of decoration design and comprises a platform end and a user end. The user side comprises an information module and an evaluation display module; the information module is used for performing information management on the user house, acquiring house information, a decoration design drawing and additional design data of the user house, and performing size supplementation on the decoration design drawing according to the house information and the additional design data; granting a sharing permission to the design evaluation module of the platform end; the evaluation display module is used for visually displaying the design evaluation data; the platform end comprises a design evaluation module; the design evaluation module is used for carrying out design evaluation, identifying user sides with sharing authority in real time, identifying corresponding decoration design drawings, additional design data and house information from the user sides, and carrying out application evaluation on the decoration design drawings to obtain design evaluation data of the decoration design drawings; and the evaluation display module is used for sending the design evaluation data to the user side.

The invention relates to the technical field of intelligent design of geotechnical engineering, and discloses a BIM prestressed pipe pile assembly optimization system based on multi-source geological data. The system comprises a data preprocessing module, a feature extraction module, a feature sensing module, a feature fusion module and a BIM pile matching generation module. The system performs standardization processing on the original geological data; clustering analysis is carried out from a historical engineering case library, and geological mode features with a space influence radius are extracted; current site data is coded through a multi-level feature perception architecture, and features of different levels are fused by using a bidirectional attention mechanism; and mapping the fused comprehensive geological features to a BIM environment, and automatically driving to generate an optimized pile matching scheme. According to the method, intelligent reuse of engineering geological knowledge and deep fusion of multi-scale geological features are realized, a prestressed pipe pile design scheme highly adaptive to complex site conditions can be automatically and accurately generated, and the design efficiency and the scientificity of the scheme are improved.

The invention discloses a turbine blade intelligent design and analysis method based on large model self-programming. The method comprises the steps that turbine blade design data are received and preprocessed; the intelligent agent analyzes the natural language task of the user, and identifies the required functional component after disassembling; if the component is missing, a compliance code is automatically generated, verification is executed through a sandbox, a design report is generated according to the result and fed back to a user, and a new component is registered to a component library for storage. According to the method, the constraint of a traditional predefined functional component is broken through, codes are dynamically generated, debugged and executed according to semantic requirements of a user, and a whole-process task including data preprocessing and optimization result analysis is autonomously completed; the newly generated component supports persistent reuse, all operations are realized in a readable code form, the design logic is transparent, traceable and intervened, and the inherent defects of deep learning black box decision are fundamentally overcome; technical bottlenecks that functions of an existing intelligent design system are solidified, expansion is difficult, and novel and complex tasks are difficult to deal with are solved.

The application relates to the technical field of artificial intelligence, and discloses a deep-buried multi-structural-face underground powerhouse side wall pre-supporting intelligent design method and system, which aims to solve the problems of supporting lag and poor precision of an existing method, and mainly comprises the following steps: setting the value range of structural face parameters and pre-supporting direction parameters, adopting discrete element numerical simulation to construct a data set under different working conditions; constructing a multi-structural-face surrounding rock deformation prediction model for predicting the maximum deformation of the side wall; taking the prediction model as a fitness function of a particle swarm optimization algorithm to build an optimization framework; obtaining actual structural face parameters of a region to be supported, and iteratively searching the particle swarm optimization algorithm to make the fitness function output the optimal pre-supporting direction parameter with the minimum value; and before the underground powerhouse side wall is excavated, the next layer of rock mass which has not been excavated is obliquely pre-supported according to the optimal parameter. The application realizes the advance of the pre-supporting time and improves the supporting precision, and is suitable for underground powerhouses in complex geological regions.

The invention relates to the technical field of computers, and discloses an intelligent design and propagation adaptation method and system for non-perpetual element digital cultural and creative products. The method comprises the following steps: digitally extracting non-abandoned patterns, processes and stories through image recognition and natural language processing, and constructing a structured database; based on user preferences and market trends, utilizing a generative adversarial network to generate a multivariate cultural and creative design scheme; according to platform characteristics of short videos, e-commerce, social contact and the like, a multi-modal content package is automatically adapted through a scenarized propagation model; and user feedback data is combined to reinforce a learning mechanism to optimize a design and propagation strategy. The system comprises a non-residual element database, a generative design module, a propagation adaptation model and a feedback optimization unit. By adopting the technical scheme, the design threshold can be reduced, the non-immature transformation efficiency and propagation precision can be improved, and digital inheritance and innovative propagation of non-immature culture can be assisted.

The invention provides a backfill process intelligent design method based on multi-dimensional data analysis and optimization control, which comprises the following steps: judging whether a material difference exceeds a preset threshold according to a classified material group, and if so, predicting non-uniform distribution of a soil body through a support vector machine model, and determining coordinates of a potential non-uniform region; side slope instability risk points are extracted from the coordinates of the potential non-uniform area, stress distribution under the backfilling technology is analyzed and simulated through finite elements, and a stress distribution map is obtained; for the stress distribution map, obtaining a material selection scheme, adjusting a material combination proportion through an optimization algorithm, and determining an optimized material matching sequence; generating an inner waste dump three-dimensional model through the backfill process parameters, and evaluating a long-term stability index by adopting a numerical simulation method to obtain a stability evaluation score; and optimized feedback data are extracted from the stability evaluation score, whether the long-term stability requirement is met or not is judged, and if yes, a final backfill process scheme is output to control settlement and slope instability.

The application belongs to the technical field of intelligent design and computing optimization, and provides a multi-agent-based cross-physical-field microstructure reverse design method and system, obtains a design intention, analyzes the design intention into a quantitative cross-physical-field attribute target vector; utilizes a pre-trained neural network model to map the design intention into an adjusting parameter in a latent representation space, decodes and generates a geometric configuration of an initial microstructure, and constructs an initial evolution population; performs cross-physical-field numerical simulation on the microstructure in the population; identifies an evolution direction of a parameter space through local gradient approximation, performs elite screening by using a non-dominated sorting driven by a Pareto, dynamically adjusts the adjusting parameter of the latent space, guides the evolution process to converge to a physically effective Pareto frontier, and obtains a design scheme according to an optimization result. The application realizes efficient and accurate design of a microstructure under complex constraints.

The invention discloses a blasting parameter intelligent design method, medium and equipment for a rock mass containing a karst cavity, and the method comprises the steps: constructing a three-dimensional geological model according to a geological exploration result, marking the position, size and contour of the karst cavity, carrying out the self-adaptive blast hole arrangement design according to a delimited blasting region, carrying out the automatic recognition of each blast hole, and carrying out the automatic design of the blasting parameters of the rock mass containing the karst cavity. Blast holes intersecting with the karst cave are extracted, the intersecting length L1 of the blast holes and the karst cave and the length L2 of the blast holes located above the karst cave are automatically calculated, finally, blasting intelligent design optimization is conducted according to the recognition result, and when L1 < gt >, L1 < gt >; when L2 is larger than 40 times of the diameter and 0.5-1 m and L2 is larger than 40 times of the diameter, the blast holes above the karst cave are reserved, the whole row of blast holes with the overlarge chassis resistance line in the rear row are deleted, otherwise, the original blast holes are reserved, and a cloth bag is adopted for charging and blocking. And finally setting a detonating network to ensure that the direction of the detonating network is consistent with that of the resistance line. According to the method, intelligent design of the blasting parameters of the rock mass containing the dissolving cavity is achieved, the blasting design efficiency can be effectively improved, and the intelligent blasting requirement is met.

This invention relates to the field of parametric modeling technology, specifically to a parametric-based intelligent design optimization method and system for construction drawings. The method includes the following steps: extracting boundary and annotation information to determine component orientation and attribution; analyzing endpoint coordinates to adjust edge line order and calculate occupancy offset; comparing intersection positions to identify and sort preceding nodes; extracting intersection point distribution to determine contact range and connection relationship; and determining drawing page numbers based on component start point and path direction. In this invention, spatial attribution relationships of components are established through geometric boundaries and annotation information; the orientation is adjusted based on the difference in edge line angles; connection strength is identified based on intersection angles and contact ranges; the drawing order is derived according to numbering order and intersection sequence; component combinations are divided using the continuity of fitting segments; and drawing page number distribution is located based on component start point and path direction. This achieves dynamic coordination of component arrangement, sequential organization, and drawing splicing, improving the accuracy of component structural expression and the completeness of construction drawing generation.

The application discloses an airplane overall concept scheme intelligent design method based on a large language model, and belongs to the field of airplane overall design. The method fully gives play to the capacity of the large language model in knowledge integration, multidisciplinary collaboration and optimization design. By constructing a knowledge base of overall parameters, engine parameters and aerodynamic layout parameters, and in combination with the powerful text understanding and reasoning capacity of the large language model, the method realizes the rapid generation and optimization of design. Compared with traditional methods and existing tools, the large language model can significantly improve design efficiency, optimize the design process and output high-quality parameters, thereby meeting the needs of modern airplane design for rapid response and innovative design. Advantages of the method are as follows. First, the large language model can efficiently process multidisciplinary knowledge and realize cross-field collaborative design. Second, the parameter estimation and design optimization capacity of the large language model is significantly improved, thereby providing more accurate support for design schemes. The method provides strong technical support for modern airplane design.

The invention relates to the technical field of artificial intelligence, discloses an intelligent design method and system for pre-support of a side wall of a deeply-buried multi-structural-plane underground powerhouse, and aims to solve the problems of support lag and poor precision of an existing method. The scheme mainly comprises the steps that the value ranges of structural plane parameters and pre-support direction parameters are set; adopting discrete element numerical simulation to construct data sets under different working conditions; constructing a multi-structural-surface surrounding rock deformation prediction model for predicting the maximum deformation of the side wall; taking the prediction model as a fitness function of a particle swarm optimization algorithm, and building an optimization framework; acquiring actual structural plane parameters of a to-be-supported area, and iteratively searching through a particle swarm optimization algorithm to enable the fitness function to output a minimum optimal pre-supporting direction parameter; and before the side wall of the underground powerhouse is excavated, the next layer of rock mass which is not excavated is subjected to oblique pre-supporting according to the optimal parameters. According to the method, the pre-supporting opportunity is advanced, the supporting precision is improved, and the method is suitable for underground workshops in geological complex areas.

The invention discloses an intelligent design method, device and equipment for a high-pressure water mist fire extinguishing system and a medium, which are applied to the field of fire-fighting engineering, and comprise the following steps: acquiring and extracting modeling basic data, identifying protection areas of the high-pressure water mist fire extinguishing system according to the modeling basic data, generating water mist fire extinguishing system inner components in each protection area, the method comprises the following steps: performing open and closed system distribution to obtain an open protection area and a closed protection area, replacing components in a water mist fire extinguishing system of each protection area according to a distribution result, generating a pipeline network for connecting each protection area and a water mist pump room, performing hydraulic calculation on system pipe networks corresponding to the open protection area and the closed protection area, and generating a water mist fire extinguishing system. And fire-fighting equipment is arranged in the water mist pump room, so that the design of the fire extinguishing system is completed. The design work of the high-pressure water mist fire extinguishing system is more efficient and quicker, the performance and reliability of the fire extinguishing system in practical application are ensured, and a more powerful guarantee is provided for fire safety.

This invention provides an intelligent design method for building walls based on a semantic-physical dual closed loop, comprising the following steps: acquiring project data and environmental element data of the target project, generating semantic tags and project context, retrieving standard provisions based on semantic tags, and compiling the standard provisions into constraint expressions; constructing an executable design grammar for wall design, establishing a bidirectional mapping relationship between the executable design grammar and the building information model, and generating multiple candidate wall schemes conforming to the executable design grammar under the constraints of the constraint expressions; using a surrogate model to perform multiphysics performance evaluation on the candidate wall schemes, triggering a high-precision solver to verify based on the uncertainty evaluation results, and selecting a set of compliant schemes; iteratively optimizing the set of compliant schemes under the penalty mechanisms of hard and soft constraints, outputting a Pareto optimal solution set, parsing the Pareto optimal solution set based on the executable design grammar, and generating design documents and a compliance audit report.

The application relates to the technical field of intelligent design of building materials, and discloses a reverse design method for mix proportion of ultra-high performance concrete based on probability constraint, which comprises the following steps: constructing a comprehensive data set of the ultra-high performance concrete; processing the data set to obtain a high-value feature subset; taking the high-value feature subset as input, taking toughness, compressive strength and fluidity as output, and constructing a multi-target performance prediction proxy model; performing explainability analysis on the proxy model by adopting a SHAP method to obtain key features influencing reverse optimization of the mix proportion; constructing a reverse design optimization model of the mix proportion under probability constraint; solving the reverse design optimization model of the mix proportion, selecting a candidate mix proportion by a limited expected improvement function, obtaining a candidate mix proportion solution set satisfying the probability constraint condition, and determining a recommended mix proportion scheme from the candidate mix proportion solution set. In this way, the reverse precise design of the ultra-high performance concrete with toughness improvement as the core and multi-dimensional engineering indexes controlled can be realized.

The invention is applicable to the technical field of computer-aided design, and provides a cabin door tool intelligent design method based on parameterization and rule driving, which comprises the following steps: S1, frame intelligent generation: 1, 1, input preprocessing: performing attitude calibration on a cabin door three-dimensional digital model as design input, automatically calculating a minimum axial bounding box, and calculating a cabin door model; determining a space size reference of the cabin door; the method comprises the following steps: 1, generating a basic framework scheme: generating a plurality of basic framework structures as initial values of mechanical optimization based on the size of a bounding box and the length, width and height of an intelligent recommendation framework; according to the cabin door tool intelligent design method based on parameterization and rule driving, through full-process automation, full-process automatic design from an overall frame to small connecting pieces is achieved, designers are liberated from repeated work, schemes are diversified and intelligent, and through parameterization variable combination and a random selection algorithm, the design efficiency is improved. And various design schemes can be quickly generated and managed.

This invention discloses an intelligent design method for the proportioning and anti-seepage performance prediction of loess water-blocking pile filler, belonging to the field of data-driven modeling technology. The method includes the following steps: Step 1, collecting multiple sets of proportioning test records for loess water-blocking pile filler, and generating a perturbation sample template based on equidistant logarithmic ratio coordinate components; Step 2, using the input feature vector as input, constructing a dual-response analytical model for filler performance; Step 3, jointly generating a set of candidate proportioning points within the normalized solid composition space and the engineering allowable range of the water-solid ratio, and outputting the optimal proportioning scheme set for loess water-blocking pile filler under a preset total solid mass benchmark after inverse equidistant logarithmic ratio transformation. This invention achieves the establishment of an interpretable explicit mapping relationship between proportioning parameters and performance indicators, ensures predictive stability under raw material fluctuation conditions, and achieves multi-objective collaborative optimization of fluidity and anti-seepage performance.

The invention provides a non-parametric MEMS pressure sensor structure intelligent design method based on deep learning. The method comprises the following steps: obtaining MEMS sensor experiment data based on finite element simulation; a verification network is constructed and trained, and forward design from a non-parameterized structure of the cross-resolution sensor to performance parameters is realized; and constructing and training a generative network CVAE-GAN to realize a reverse design from a performance parameter to a non-parameterized structure. Compared with a traditional MEMS sensor structure design method based on finite element simulation, time-consuming multi-physical field simulation and complex manual parameter adjustment do not need to be carried out, the design efficiency is remarkably improved, and the method has the advantages of being high in speed, high in precision and intelligent. Compared with other MEMS sensor intelligent design methods, the method has the advantages that a fixed boundary condition is optimized into a variable boundary condition, and the method is suitable for more application scenes; discrete images are optimized into continuous vectors which can be directly operated with gradients, so that high-difference multi-solution generation is facilitated, and structural expressions with different precisions are compatible; the generated multiple solutions can be used for comparing and screening tolerance of machining errors.

The application provides a building wall intelligent design method based on a semantic-physical double closed loop, comprising the following steps: obtaining project data and environmental element data of a target project, generating semantic labels and project context, retrieving specification articles based on the semantic labels, and compiling the specification articles into constraint expressions; constructing an executable design grammar for wall design, establishing a bidirectional mapping relationship between the executable design grammar and a building information model, generating multiple wall candidate schemes conforming to the executable design grammar under the limitation of the constraint expressions; performing multi-physical field performance evaluation on the wall candidate schemes by using a proxy model, triggering a high-precision solver review based on the uncertainty evaluation results, and screening out a compliance scheme set; iteratively optimizing the compliance scheme set under a penalty mechanism of hard constraints and soft constraints, outputting a Pareto optimal solution set, analyzing the Pareto optimal solution set based on the executable design grammar, and generating a design document and a compliance audit report.

The invention relates to the field of urban landscape perception evaluation and intelligent design, in particular to a green space perception evaluation system based on visual depth and landscape features. The system mainly comprises a data acquisition module, a specific element semantic segmentation module, a monocular vision depth estimation module, a data fusion and perception calculation module and an analysis evaluation and visualization output module, and has the core thought that the visual depth and green visibility index and the perception attenuation of the visual depth and vision in a three-dimensional space are taken as core indexes; a system process integrating five steps of data acquisition, semantic segmentation, depth estimation, fusion calculation and perception evaluation is constructed, and the whole process automation from green seeing to green quantification is realized.