140 results about "Building simulation" patented technology

The invention relates to the technical field of building simulation, and discloses a BIM-based special-shaped hyperboloid building site model generation method and system, and the method comprises the steps: collecting the multi-source heterogeneous point cloud data of a special-shaped hyperboloid building site to construct a three-dimensional feature tensor model, and recognizing the key curvature mutation point of the tensor model to construct a space reference topology network; based on the digital skeleton model, geometric contour features are analyzed, hyperboloid fitting is carried out, and an initial BIM model is obtained; performing multi-physics field coupling simulation on the initial model to obtain a target BIM model; extracting curvature distribution and structural stress parameters of the target BIM model to obtain basic model parameters, and constructing a building performance prediction model in combination with historical performance reference data; and inquiring a bearing capacity standard and a deformation threshold value, and analyzing the optimal curved surface parameter combination and a prestress applying strategy to obtain optimized simulation parameters, so as to simulate the special-shaped hyperboloid building by utilizing target model simulation. According to the method, the simulation precision of the special-shaped hyperboloid building can be improved.

The invention provides an intelligent building simulation method and system based on digital twinning, and the method comprises the steps: firstly collecting building structure information in a building, and constructing a twinning mapping model in the building; determining the association constraint of each sensing node in the building simulation process according to the topological structure characteristics among the sensing nodes and the association elements of each sensing node, and further determining the simulation granularity of each sensing node; then determining a dependency relationship of environment change in building simulation; the simulation control quantity of each virtual building scene in the twinning mapping model is determined through the dependency relationship of environment change and the simulation granularity of each sensing node in the building simulation process, and all the simulation control quantities are loaded into the digital twinning platform so as to update the twinning mapping model of the target building. By adopting the scheme of the invention, the collaborative response speed of multiple virtual building scenes in the digital twin system can be improved, so that the data consistency of virtual and real scenes is kept.

The invention discloses a drainage basin intelligent flood control scheduling method and system based on digital twinborn, and relates to the technical field of flood control and disaster mitigation, and the method comprises the steps: collecting static data and dynamic data of a drainage basin, building a hydrological and hydrodynamic coupling model based on the static data and the dynamic data, and forming a drainage basin digital twinborn body; inputting the received numerical weather forecast into the digital twin of the watershed for simulation, generating a plurality of flood routing scenes in a future time period, and calculating a dynamic flood risk probability graph; the method comprises the following steps: constructing a simulation training environment by using historical flood data and a high-precision drainage basin digital twinborn body, carrying out offline training on a scheduling strategy network in the simulation training environment based on a reinforcement learning algorithm, and outputting a scheduling instruction according to a real-time drainage basin state to complete training of the scheduling strategy network. According to the method, the core problem that the traditional method is insufficient in decision timeliness and weak in adaptive capacity in an uncertain environment is effectively solved.

The invention discloses a green low-carbon building simulation analysis method based on a three-dimensional model, and the method comprises the steps: 1, carrying out the pre-analysis, constructing a BIM model of an initial building design, obtaining the surrounding environment information of a building, and inputting the information into building simulation software; 2, environment simulation and design analysis are carried out, building design is optimized, and a first design stage is formed; forming a second construction stage; monitoring the energy consumption of the building in real time to form a third operation stage; step 3, simulation optimization; 4, simulating and analyzing data mining; and a fifth step of information sharing and assistance. According to the method, design decision and optimization are assisted through simulated beforehand analysis and simulation, so that energy consumption and carbon emission of the building in each life cycle are reduced, the green performance of the building is improved, efficient and accurate energy consumption evaluation and optimization of a building design scheme are achieved, and generation of a low-carbon and energy-saving building scheme is effectively promoted.

The invention discloses an analog IC netlist layout collaborative optimization method and system based on a reinforcement learning algorithm, and the method comprises the steps: generating an initial netlist of an analog IC, and inputting the initial netlist into a pre-built simulation platform; generating an analog IC circuit layout according to the initial netlist of the analog IC, and performing simulation through a simulation platform; the reinforcement learning agent builds a graph neural network to execute an optimization task based on a pre-built analog IC netlist multilateral heterogeneous graph, generates an optimized analog IC netlist and sends the optimized analog IC netlist to the simulation platform; a new analog IC circuit layout is generated according to the optimized netlist of the analog IC, the simulation platform simulates the optimized netlist of the analog IC and the new analog IC circuit layout, and the analog IC circuit layout is further optimized through a reinforcement learning agent; and continuously interactively optimizing the reinforcement learning agent and the simulation platform until a final parameter combination meeting the performance index is obtained. According to the invention, the efficiency and performance of circuit optimization are greatly improved.

The invention discloses an automatic building parameter calibration method and system based on multi-objective optimization. The method comprises the following steps of determining uncertain building parameters needing to be calibrated and a value range of the uncertain building parameters; building a building simulation model, and embedding the building uncertain parameters into the building simulation model; collecting indoor temperature data and humidity data, and synchronously collecting meteorological data; constructing a multi-objective optimization model; building temperature and humidity under different uncertain parameter values are simulated based on a non-dominated sorting genetic algorithm, the multi-objective optimization model is solved, an optimized non-dominated solution set is output, and a Pareto leading edge is formed; and screening an optimal solution set through Pareto frontier analysis, establishing a calibrated building simulation model, simulating building energy consumption under future weather conditions, and outputting an optimal transformation scheme. According to the method, the calibration requirements of a large-scale building group can be efficiently processed, and the precision and efficiency of building uncertain parameter calibration are remarkably improved.

The invention discloses a test device for simulating deformation of a foundation pit and an adjacent building under combined reinforcement. The test device comprises a box body, a foundation pit simulation structure, a building simulation structure, a foundation pit reinforcement simulation system, a constant head simulation system, a rainfall recharge simulation system and a monitoring system. The box body is divided into a soil box and water tanks on the two sides, and the water tanks communicate with the soil box. The foundation pit simulation structure comprises an underground diaphragm wall simulation structure which is relatively buried in the soil box and a plurality of inner supporting pieces; the building simulation structure is shallowly buried on the outer side of the foundation pit simulation structure; the reinforcing simulation system comprises a base reinforcing part, a retaining wall reinforcing part and an isolation pile structure; the constant head simulation system conveys water into a water tank through a first water pump, and a switch valve is arranged on the wall of the water tank to control the water level in the water tank; the precipitation recharge simulation system injects or pumps water into or out of a precipitation pipe inserted in the foundation pit simulation structure through a second water pump; the monitoring system comprises a soil pressure box, a water pressure box, a strain gauge, a displacement meter, an observation pipe and a liquid level meter.

The invention provides an unmanned aerial vehicle autonomous obstacle avoidance method and system based on an A star algorithm, belongs to the technical field of unmanned aerial vehicle flight control, and solves the problems of path planning and autonomous obstacle avoidance of an unmanned aerial vehicle in a complex environment. 2, performing unmanned aerial vehicle path planning in the constructed environment map based on an A star algorithm; 3, constructing a simulation platform, and verifying the A star algorithm planning path by using the constructed simulation platform; and step 4, evaluating a verification result, and outputting an unmanned aerial vehicle planning path conforming to evaluation parameters.

The invention discloses a TLS observation network planning method for a large-scale building, and relates to the technical field of building simulation. Comprising the following steps: acquiring grid three-dimensional information of an observation area; taking all grid points in the screened grid as observation candidate sites, performing observation simulation on each candidate site in a final observation target point union set, and taking a building feature line length value obtained by simulation scanning as a weight value of the candidate site; scanning contents of the selected candidate sites are deleted from the screened simulation observation contents; and performing iterative selection in the remaining candidate sites until the maximum weight value of the current candidate site is lower than a set weight threshold value, obtaining a simulated overlapping degree among the remaining candidate sites, and performing station supplementation operation on the remaining candidate sites with the simulated overlapping degree lower than the set threshold value to obtain the TLS observation network. The method restrains the number of observation objects, solves the problem of low efficiency of three-dimensional simulation planning of a TLS observation network, and has the capability of processing large-scale building groups.

The invention discloses a non-standard goods shelf intelligent design method and system based on a parameterized model, and relates to the technical field of computer aided design, and the method comprises the following steps: collecting parameters, and carrying out abnormal value elimination and dimension unified processing to generate a standardized demand parameter set; defining core structure parameters and establishing a component association rule, and constructing a parameterized basic model through three-dimensional modeling; the storage rack structure is optimized by combining storage mechanics and material mechanics characteristics, stress concentration is reduced, and deformation under rated load is controlled; a simulation environment is built for statics and dynamics simulation, and a simulation test report is output; screening an optimal parameter combination to generate a detailed design scheme; and receiving user feedback, adjusting parameters and material selection in combination with production feasibility and cost budget, and repeating optimization simulation steps to generate a final scheme. Efficient design of the non-standard goods shelf is achieved through parametric modeling and intelligent optimization, the structure is safe and reliable, the material utilization rate is high, and the design quality and efficiency are remarkably improved.

The invention discloses a reinforcement learning-driven public building user energy consumption behavior simulation method, which comprises the following steps of: 1, acquiring basic data, and building a simulation environment model for simulating a mapping relationship between a user energy consumption behavior and building energy consumption as well as an environment comfort degree; 2, defining a state space and an action space of the intelligent agent, selecting a depth deterministic strategy gradient algorithm as a core learning algorithm of the intelligent agent, designing a multi-target comprehensive reward function of the core learning algorithm, and embedding a public building energy consumption control target; step 3, using an intelligent agent to interact with a simulation environment, simulating an energy consumption process of a user, and identifying different scenes according to a preset rule; 4, the intelligent agent iteratively updates network parameters in the core learning algorithm based on the number of samples collected in the interaction process, and after training convergence, a user energy consumption behavior optimization scheme is output.

The invention relates to an electrothermal coupling simulation method for a charging connector, and belongs to the technical field of electrothermal coupling simulation, and the method comprises the steps: obtaining a simulation instruction, and constructing a geometric model of the charging connector; obtaining constraint parameters, and constraining the geometric model; wherein the constraint parameters at least comprise material parameters, power-on parameters and thermal simulation parameters; and importing the constrained geometric model into a pre-constructed simulation model, performing electrothermal coupling simulation on the constrained geometric model through the simulation model, and outputting a simulation result which at least comprises the temperature rise of each preset temperature measurement point in the geometric model. The electric heating coupling temperature rise simulation is carried out on the charging connector based on the finite element method, the temperature rise analysis efficiency can be improved, and manpower and material resources consumed for analyzing the temperature rise condition in a traditional test are reduced.

The invention discloses a stability evaluation method for water photovoltaic installation equipment, which belongs to the field of photovoltaic installation, and comprises the following steps: collecting regional hydrological information and environmental climate information, and obtaining regional extremum features; constructing a simulation space, wherein the simulation space comprises a front simulation scene module and a rear stability evaluation module; inputting the regional hydrological information, the environmental climate information and the regional extremum features into a parameter search configuration module to obtain configuration parameters; using the parameter search configuration module as a feedforward module of the front simulation scene module, and fitting configuration parameters to a simulation space; and performing stability evaluation by using a rear stability evaluation module to obtain a stability evaluation result. According to the method, the simulation space is constructed, and the parameter optimization is carried out by taking the maximum volatility as the guide, so that the comprehensive and accurate evaluation of the stability of the overwater photovoltaic installation equipment in the extreme environment is realized, and the long-term reliable operation of the equipment is effectively guaranteed.

The invention relates to a quadruped robot motion control method based on visual fault tolerance, and the method comprises the steps: building a simulation environment according to the motion control demands of a robot; defining a blind estimator and a visual estimator, organizing a training environment according to scene requirements, and training a switching strategy between the blind estimator and the visual estimator; determining the motion constraint of the robot based on a cMDP constraint Markov decision process, and solving a robot motion control strategy in combination with the trained blind estimator and visual estimator; and correspondingly controlling the motion state of the robot according to the robot motion control strategy. Compared with the prior art, the advantages of visual estimation and ontology perception are combined, the robot can move more flexibly and stably in the complex terrain, a switching strategy between estimators is designed, the visual fault-tolerant capability can be enhanced, the motion stability of the robot is guaranteed, in addition, an end-to-end one-stage training framework is adopted, and the training efficiency is improved. The training and deployment efficiency can be improved.

The invention belongs to the technical field of intelligent evacuation, and discloses an intelligent evacuation path automatic generation system and method based on artificial intelligence. The method comprises the following steps: collecting building modeling data, wherein the building modeling data comprises building key parameters and equipment resource parameters; modeling according to the building modeling data to obtain a building simulation model; defining personnel characteristic parameters and disaster characteristic parameters on the building simulation model to obtain an intelligent evacuation simulation model; self-adaptively generating an evacuation path according to the intelligent evacuation simulation model; according to the scheme, the congestion state can be quickly responded in the dynamic environment, the high efficiency and safety of evacuation are guaranteed to the maximum extent through the mode of combining shunting optimization and priority setting, and the method is suitable for various complex buildings and disaster scenes and has remarkable practical value.

The invention relates to the technical field of building simulation analysis, in particular to an integrated city building comprehensive evaluation method and device, and the method comprises the steps: obtaining the building operation state information of each building region in a to-be-evaluated integrated city building; for each building area, carrying out dimension division on the corresponding building operation state information to obtain a plurality of building area feature sets; for each building area feature set, performing situation analysis to obtain a basic operation feature subset and a dynamic operation feature subset corresponding to the building area feature set; inputting the basic operation feature subset and the dynamic operation feature subset into a building area feature evaluation model to obtain a building area feature evaluation index corresponding to the building area feature set; according to the method, comprehensive evaluation of the multi-dimensional characteristics of the building can be provided, the operation state and performance change of the building can be reflected in time, and a more accurate evaluation result is provided.

The invention relates to the technical field of building simulation, in particular to a virtual building simulation service platform and method based on digital twinning, and the platform comprises an infrastructure and database, a data management system, a simulation support system, a simulation evaluation management system and a public service system. The infrastructure and the database provide hardware and data support for the simulation service platform, data analysis and conversion of a simulation model are achieved through the data management system, IFC sub-model data conforming to the simulation type standard are formed, the simulation supporting system achieves virtual construction simulation based on digital twinning, and the simulation service platform is provided with the simulation support system. And the simulation evaluation management system generates evaluation of the target simulation algorithm to realize virtual construction simulation requirements of the user. Therefore, the problems that in the field of building simulation, multi-source heterogeneous data is difficult to integrate effectively, the collaboration efficiency is low due to scattered fragmentation of simulation tools, cross-simulation scene collaboration and real-time interaction are missing, and the application effect and efficiency are affected are solved.

The invention provides a building air conditioner load prediction method based on LTSM and ARIMA. The building air conditioner load prediction method comprises the following steps of S1, performing data acquisition; s2, a step of carrying out data preprocessing; s3, a step of carrying out modal decomposition; s4, a step of carrying out ARIMA model result analysis; and S5, carrying out result verification. According to the method, the problem of effectiveness of public building air conditioner load prediction is solved, that is, load prediction of a current public building air conditioner system is mainly based on traditional building simulation software or a single-module artificial intelligence algorithm, and the load prediction has certain limitations more or less. And a composite model in which a primary prediction model is established by adopting a long-short-term memory network and then a primary prediction result is corrected through a differential autoregression moving flat residual prediction model can effectively solve related problems in air conditioner load prediction.

The invention discloses a passive maintenance-free anti-floating experiment simulation device and an experiment method. The device comprises a foundation pit simulation box; the water injection simulation box is arranged on the outer side of the foundation pit simulation box, and a water injection cavity is formed between the water injection simulation box and the foundation pit simulation box; the bottom of the building simulation box is located on a soil filling layer at the bottom of the foundation pit simulation box, and the foundation pit simulation box and the building simulation box are connected through a pressure sensor; the passive drainage system is arranged in the fill stratum between the foundation pit simulation box and the building simulation box, a closed loop is formed in the foundation pit simulation box, small holes for water permeation are formed in the passive drainage system, the passive drainage system has a drainage gradient, an outlet pipeline is connected to the lowest point of the drainage gradient, and the outlet pipeline is communicated with the building simulation box. The outlet pipeline is connected with an external metering barrel; the water injection system is used for injecting water into the water injection simulation box. According to the method, the passive anti-floating design structure can be verified, the design parameters can be designed, and the anti-floating limit water level can be calculated.

The application discloses a kind of single vehicle control methods based on scene style migration and SAC network cooperation, including steps: S1, using SUMO tool to build simulation platform;S2, construct single vehicle longitudinal control model, SAC network is used to obtain traffic state from simulation platform, then take action control vehicle behavior, after action execution, agent receives the reward value updated agent parameter that simulation platform feedback;S3, define policy network, output the numerical value of acceleration;Define value function network, output layer represents the estimation of state value;S4, through the vehicle control experimental platform built in call SUMO to simulate different weather conditions, and build measurement model;S5, on the basis of single vehicle longitudinal control model build migration learning framework;S6, using migration learning will train convergent single vehicle longitudinal control model to be transplanted to different weather conditions and train.The application can effectively inhibit traffic stop and go wave, ensure traffic safety, reduce fuel consumption.

The invention discloses an old building simulation black brick wall surface construction method, and belongs to the technical field of wall surface structure.The old building simulation black brick wall surface construction method comprises the following steps that S1, a wall body is cleaned; s2, laying a steel wire mesh; s3, the exterior of the steel wire mesh is coated with a bottoming mortar layer, and leveling is conducted; s4, a gridding cloth layer is laid on the outer portion of the bottoming mortar layer, and a cover face layer is scraped on the outer portion of the bottoming mortar layer; s5, jointing is conducted on the cover face layer, and the outline of the black brick is drawn out; s6, the outer portion of the finishing coat is coated with a movable surface layer in a brushing mode through a brushing tool; and S7, the movable surface layer at the pointing joint is protected, the pointing joint is subjected to white dripping, and the simulated black brick wall surface is obtained. Therefore, by the adoption of the construction method for the simulated black brick wall face of the old building, the problems that an existing simulated black brick wall face is long in construction period and high in cost can be solved.

The invention relates to the technical field of data processing, in particular to a prefabricated building simulation construction method and system, and the method comprises the steps: constructing a building structure model in a prefabricated building construction state according to the point cloud data of all components and the installation sequence of each component in a prefabricated building construction process, obtaining the inclination angle of the component between each moment and the vertical direction and the position coordinates of all constructors in the prefabricated building construction process; determining a dangerous area in the fabricated building; determining a risk severity index of the component corresponding to the dangerous area at the current moment; and determining the safety early warning degree of the movement behavior of the constructor relative to the dangerous area, and when the safety early warning degree is greater than a first threshold value, performing safety early warning on the constructor and outputting the corresponding dangerous area. Therefore, the timeliness, the accuracy and the reliability of safety early warning are improved.

The invention relates to the technical field of virtual reality, in particular to a three-dimensional modeling space simulation system and method of a smart building, dynamic BIM data of a to-be-interacted building is collected according to a UE engine, a remote control instruction and an energy consumption evaluation instruction are completed for the dynamic BIM data of the to-be-interacted building, illumination simulation environment analysis is completed according to simulation environment elements, and a simulation result is obtained. The simulation environment elements are subjected to personalized correction; establishing a mapping key value pair for the correction parameter and the simulation environment item ID, and sending the mapping key value pair to a plurality of simulation sensor sets; and when the BIM data of the to-be-interacted building is randomly acquired, acquiring related reference simulation environment elements from the time sequence environment model according to the BIM data, and returning the reference simulation environment elements to the to-be-interacted building. According to the method, the fitness of the virtual reality technology in the engineering design building simulation environment training is evaluated, so that the energy consumption generated by building operation is reduced, the design cost and the construction cost are reduced, and meanwhile, subsequent design errors and construction risks are avoided.

The application relates to an optimization design method of a polygon-based multi-plane device, comprising the following steps: S1. determining parameters of a polygon-based MPLC device; S2. constructing a topological-mode coupling model to determine a phase compensation amount; S3. generating an initial phase mask according to the phase compensation amount calculated in step S2 and in combination with phase distribution characteristics of a target mode; S4. outputting optimized phase mask parameters based on a genetic optimization algorithm; and S5. simulation verification: building simulation models of different topological structures, inputting the optimized phase mask parameters, simulating a mode evolution process, and analyzing whether a performance threshold value meets a demand. The application solves the problems that a traditional algorithm ignores the influence of a topological structure and the generality of a phase mask is poor, realizes accurate design of different polygon devices, and thus effectively reduces loss and improves shaping effect.

The invention relates to the technical field of electric power systems and energy, and discloses an economic evaluation method and system for improving the consumption capability of a distributed power supply by an energy storage system. The method comprises the following steps: determining key economic indexes, collecting related data, constructing a simulation model and a scoring standard, then determining index weights by using an analytic hierarchy process and a CRITIC weight method, and combining the weights to calculate a comprehensive weight. And finally, constructing an economic evaluation model to evaluate a scheme for improving the distributed energy consumption capability of the energy storage system. According to the technical scheme, the environmental influence of the energy storage system is considered, the evaluation standard of the environmental carbon value is added into the economical efficiency evaluation model, the energy storage system can be promoted to achieve the dual-carbon target, and by evaluating the influence of the energy storage system on environmental carbon emission, the energy storage system can be promoted to develop in the more environment-friendly and more sustainable direction.

The present invention discloses a method for constructing a ferroelectric transistor simulation model, including: calculating a first nonlinear function based on the voltage across a ferroelectric layer in a ferroelectric transistor at a certain moment, the gate voltage of the ferroelectric transistor, the body voltage of the ferroelectric transistor, the drain voltage of the ferroelectric transistor, and the source voltage of the ferroelectric transistor; obtaining each parameter value in a simulation model formula through a logistic function and the first nonlinear function; obtaining the upper limit and lower limit of the polarization of the ferroelectric transistor, and fitting a second nonlinear function based on the voltage of the ferroelectric polarization layer, the upper limit and lower limit of the polarization of the ferroelectric transistor, and deriving a ferroelectric transistor simulation model based on the first nonlinear function, the second nonlinear function, the truncated Gaussian distribution function, and each parameter value in the simulation model formula. The ferroelectric transistor simulation model provided by the present application can meet the simulation requirements of ferroelectric transistors in storage-computation integrated circuit design.

The embodiment of the invention discloses a building multi-working-condition batch simulation data generation method and device and a medium. The method comprises the following steps: determining a target configuration file corresponding to a target batch simulation task; wherein the target configuration file is used for describing at least two target parameter adjustment modes adopted for a reference source file; performing parameter adjustment on the reference source file according to the at least two target parameter adjustment modes to obtain at least two target source files; and running the at least two target source files based on target building simulation software to generate target batch simulation data. According to the technical scheme, the source file can be quickly adjusted based on the configuration file, so that the building multi-working-condition batch simulation data can be quickly and conveniently generated, effective coverage of the simulation data on various scenes can be ensured, and the applicability, flexibility and generation efficiency of the simulation data are improved.

The application relates to the technical field of circuit design, and discloses a signal simulation analysis method and system. The method comprises the following steps: building a simulation structure; acquiring scattering parameters of a main control chip substrate, a PCB board and a storage chip particle substrate respectively according to the built simulation structure; optimizing a second wiring design according to the scattering parameters of the PCB board, optimizing a third wiring design according to the scattering parameters of the storage chip particle substrate, and obtaining an optimized simulation structure; inputting the optimized simulation structure into pre-configured simulation software for signal transmission simulation test, and obtaining eye diagram data; analyzing the signal quality index of corresponding pins based on the eye diagram data, and determining whether the optimized simulation structure meets the preset transmission rate requirement according to the signal quality index; and through the above method, the interconnection link outside the main control chip and the internal wiring structure of the storage chip can be optimized simultaneously.

The invention relates to an interpretable building reconstruction rapid optimization method considering parameter uncertainty. The method comprises the following steps: S1, collecting information and carrying out data processing on original data; s2, building a building simulation model and calibrating weather data; s3, uncertain parameter sensitivity is analyzed, and a simulation model is calibrated; s4, building reconstruction measures are set, and reconstruction multi-objective optimization is carried out; s5, training a rapid prediction model and explaining a black box model decision mechanism; the method is based on an optimization algorithm, a prediction algorithm and a simulation kernel, and aims to solve the technical problems of uncertainty, simulation speed and interpretability in building reconstruction multi-objective optimization.

The application discloses an extreme weather prediction and identification method based on multi-model integration and machine learning coupling, and belongs to the field of climate-adaptive building design and building environment engineering. The technical scheme comprises the following steps: taking the GCM data set of CMIP6 and the RCM data set of CORDEX as original data, combining ERA5 reanalysis data to perform deviation correction and time downscaling processing; constructing a prediction model integrating random forest algorithm and SSP-RCP multi-scenario framework, dividing training set and test set according to time sequence, and realizing multi-scale prediction after standardization and super parameter optimization; constructing an extreme weather identification system based on quantile threshold method, dividing heat wave and cold wave threshold, and quantifying the duration, intensity and severity thereof; and finally generating a high-granularity regional future extreme weather data set for 2024-2034. The application can provide high-precision and high-temporal and spatial resolution meteorological data, provide support for building simulation and climate-adaptive building design, and has good regional adaptability and promotion potential.