90 results about "Intelligent design" patented technology

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 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 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 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 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 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.

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 relates to the field of intelligent design, provides a cultural and creative product design method and system based on perceptual engineering, provides effective basic information for personalized emotion demands and market demands by obtaining basic form information and comprehensive emotion information of a target cultural and creative product, and designs an emotion demand analysis method. According to the method, weight combination calculation is carried out in combination with a CRITIC analysis method and an entropy weight method, emotion demand weights are accurately obtained, the design pertinence is further improved, then a design sketch generation method is designed, the design sketch of a target cultural and creative product is rapidly generated through contour extraction processing and diffusion generation processing, the design process efficiency is greatly improved, and the design cost is reduced. An emotion mapping model is further designed, the design sketch is optimized through an empirical mode decomposition module, a kernel principal component analysis module and a self-attention enhancement module, the comprehensiveness and adaptability of the design are further improved, and the pertinence and efficiency of the creative product design are improved.

The invention provides a multi-mode refrigerant thermophysical parameter prediction method, and belongs to the technical field of intelligent thermophysical parameter prediction and chemical artificial intelligence. The technical problems of high experiment cost and low precision of a traditional method in refrigerant thermophysical parameter prediction are solved. According to the technical scheme, the method comprises the following steps that S1, an SMILES molecular structural formula containing refrigerant molecules and corresponding thermophysical parameters of the SMILES molecular structural formula are collected and sorted; s2, mining and automatically extracting deep semantic features of the molecules; s3, mining molecular topological structure information by adopting a graph convolutional neural network; s4, designing and training a multi-layer perceptron regression model; and S5, outputting a prediction result and an uncertainty result. The method has the advantages that the accuracy and generalization ability of refrigerant thermophysical property parameter prediction are improved, powerful technical support is provided for efficient screening of novel refrigerants and intelligent design of green substitute molecules, and the method has wide application prospects in the fields of chemical energy, material research and development and the like.

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 application relates to the technical field of architectural decoration design, in particular to an architectural decoration intelligent design system and method, which comprises the following modules: an intelligent perception analysis module, which is used for collecting space characteristic information and environmental parameters; generating a fusion feature matrix based on the space characteristic information and the environmental parameters; an AI design engine module, which is in communication connection with the intelligent perception analysis module and is used for receiving the fusion feature matrix; generating a preliminary design scheme based on the fusion feature matrix; a multi-dimensional optimization evaluation module, which is in communication connection with the AI design engine module and is used for evaluating the preliminary design scheme in terms of functionality, aesthetics and economy; generating an optimized design scheme based on the evaluation results; and an immersive visual interaction module, which is in communication connection with the multi-dimensional optimization evaluation module and is used for receiving the optimized design scheme; generating a three-dimensional visual model of the optimized design scheme; and providing an interactive design adjustment interface, so that the design cycle can be significantly shortened and the labor cost can be reduced.

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 belongs to the field of design of bed boards or decks of offshore platforms, and provides a parameterized intelligent design method and system for ribbed bed boards of offshore platforms, and adopts the technical scheme that a CAD drawing based on a platform steel beam arrangement drawing of a to-be-arranged bed board is obtained to identify steel beam grids of the to-be-arranged bed board, and the size of the bed board is determined; judging the arrangement direction and the length of the ribs according to the size of the bed board; calling rib specifications from the constructed rib section library according to the rib arrangement direction and the rib length; according to calculation parameters and result control parameters required by design, whether the called rib specifications meet calculation requirements or not is judged, and if not, the rib rules are screened again until the rib specifications meeting the calculation requirements are obtained; determining a rib arrangement scheme according to the rib arrangement direction, the rib length and the rib specification; based on the arrangement scheme of the ribs, automatic arrangement and drawing of the ribs are carried out on the CAD drawing, a new drawing file is obtained, and a design report is generated.

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 present invention discloses an intelligent design system for high-level gas extraction drilling, which relates to the technical field of high-level gas extraction. The key points of its technical solution are: combining the actual exploration and measurement conditions of the mine and setting up scenes in advance in the system based on OpenGL technology, preliminarily determining five drilling parameters that mainly affect high-level gas extraction through big data cloud analysis and calculation based on the measurement results and national coal mine standards, and accurately designing the high-level drilling parameter calculation system to obtain the three main determining parameters: the horizontal angle between the borehole and the return air channel, the borehole inclination angle, and the borehole length. Then, based on the calculation results, a simulation and visualization demonstration of the gas extraction drill bit operation trajectory is performed, so that the drilling parameters can be adjusted in real time and the drill bit movement trajectory can be grasped. It provides an automated means and scientific basis for high-level gas extraction work, effectively improves extraction efficiency, reduces the workload of technical personnel, and ensures the life safety of operators.