179 results about "Simulation error" patented technology

The invention relates to the technical field of hydrological process simulation, in particular to an ecological hydrological process simulation method and system based on dynamic vegetation and a medium. The method comprises the following steps: acquiring a vegetation physiological monitoring parameter set and a root system water absorption dynamic data set; performing time-space unified registration on the vegetation physiological monitoring parameter set and the root system water absorption dynamic data set to obtain a vegetation physiological dynamic parameter set; performing soil dry and wet process division on the target area based on the vegetation physiological dynamic parameter set to obtain a segmented soil moisture state sequence; dividing the vegetation physiological dynamic parameter set according to the segmented soil moisture state sequence to obtain a state classification vegetation physiological data set; and constructing a vegetation water stress adaptation strategy model based on the state classification vegetation physiological data set. According to the method, the simulation precision of vegetation coverage and soil moisture dynamic balance is improved, and the problem of simulation errors caused by vegetation parameter static processing in the prior art is solved.

The invention relates to the technical field of cables, and particularly discloses a cable terminal simulation and state evaluation method and system in a humid environment, multiple types of sensor arrays are deployed, distributed temperature and humidity sensors are installed at key parts of a cable terminal, and aiming at the technical problems of one-sided single-dimensional monitoring, simulation model solidification and insufficient static weight adaptation, the cable terminal simulation and state evaluation method and system are provided. Cooperative acquisition of multiple parameters such as temperature and humidity, partial discharge and corrosion current is achieved through deployment of multiple types of sensor arrays, the nonlinear interaction relation between humidity permeation and electric field distortion is dynamically reflected in combination with a multi-physics field coupling simulation model, the one-sidedness of single parameter monitoring is solved, and the risk misjudgment rate is reduced by 40% or above; a dynamic water film thickness model is adopted to update interface parameters of the sealing ring in real time, a simulation result is updated per hour in combination with a finite element-boundary element hybrid algorithm, an electric field distortion simulation error is controlled within 12%, and the simulation precision is improved by more than one time compared with a fixed boundary condition.

The invention relates to the technical field of wind field simulation, and provides a wind field numerical simulation method based on multiple meteorological data sources. The objective of the invention is to solve the problems of large simulation error, rough terrain boundary processing and poor turbulence model parameter adaptability caused by non-uniform coverage of a single data source, insufficient precision and unscientific multi-source fusion. The method is characterized by comprising the following steps: step 1, constructing a CFD three-dimensional grid based on a geometric model of a target area; 2, collecting original data of a ground station, satellite remote sensing, numerical forecasting and the like; 3, performing standardized preprocessing (abnormal value elimination, missing interpolation, radiation / geometric correction and resampling), determining a multi-source fusion weight by combining historical data analysis, and generating comprehensive meteorological data by adopting a weighted average method; and 4, inputting the comprehensive data into a CFD-RANS model, dynamically adjusting turbulence parameters, accurately setting terrain boundary conditions, and obtaining a wind field space-time distribution result through numerical solution. According to the invention, through combination of multi-source data fusion and CFD simulation, the wind field simulation precision and stability are improved.

The invention discloses an electric power system simulation scene modeling method based on big data, which comprises the following steps: collecting preset scene data and monitoring data of an electric power system, and preprocessing the preset scene data and related data; performing power distribution network aggregation on the monitoring data to obtain equivalent load node data, and performing high-dimensional classification on the equivalent load node data according to the preset scene data to obtain node classification; the node classification comprises a normal scene and an extreme scene; performing time sequence causal feature extraction on the equivalent load node data according to the node classification to obtain time sequence causal features, and constructing a power system simulation scene model based on a Bayesian network according to the time sequence causal features; and performing uncertainty evaluation on the power system simulation scene model according to a simulation error to obtain uncertainty, performing parameter layered online correction on the power system simulation scene model based on a physical model according to the uncertainty, and outputting a target model.

The invention provides an RCS prediction method based on deep learning, computer equipment and a readable storage medium. The method comprises the following steps: firstly, constructing an electromagnetic simulation model of a target, and resolving to obtain noise-free RCS sample data corresponding to different incident wave angles; and then simulation errors are introduced to form noise sample data. Then, constructing a convolutional auto-encoder network as an RCS denoising model, and training the RCS denoising model by using noise-containing and noise-free sample data; and then a deep neural network model is built based on a grey wolf algorithm to serve as an RCS prediction model, the trained denoising model is used for preprocessing the training data set, then the prediction model is trained, and finally prediction of a target RCS value is achieved. According to the method, the GWO-DNN deep learning model is constructed for prediction, so that the dependence on a physical mechanism and the manual parameter adjustment cost are reduced. Meanwhile, noise reduction preprocessing is carried out on the actually measured RCS result through CDAE, the too high influence of measurement errors on the prediction result is overcome, and the RCS prediction precision is improved.

The invention discloses a complete machine variable-dimension simulation method based on a circumferential average flow model, belongs to the technical field of aero-engine numerical simulation and multi-scale modeling, and solves the problem that complete machine simulation efficiency and local flow detail precision are difficult to consider at the same time in the prior art. And traditional full-three-dimensional computing is too high in resource consumption and cannot quickly evaluate the performance of the whole machine. According to the method, a zero-dimensional complete machine and component two-dimensional model of the aero-engine is firstly established, dimensionality reduction is carried out by using a circumferential average N-S equation, the two-dimensional model is embedded into the zero-dimensional model, an equivalent replacement variable reconstruction equation set is solved, synchronous convergence of the complete machine and component models is realized, and simulation precision and efficiency are improved. According to the method, a full coupling mode is adopted, two-dimensional dimension reduction modeling is combined, minute-level convergence is achieved, the simulation error is not larger than 5%, the simulation precision is improved, the calculation efficiency is improved, rich S2 flow field information can be provided, and the research and development cost and period are reduced.

The invention relates to the technical field of flood forecasting, in particular to a flood forecasting method, device and equipment based on field monitoring and real-time modelling, and the method comprises the steps: collecting a topographic data file and an earth surface data text of a target area to construct a sub-meter DEM digital elevation model; radar real-time base data, radar and ground rainfall historical observation data, brightness temperature data and geographical coordinate information of a target area are obtained to solve actual radar real-time base data, rain short-term forecast data and soil moisture data, and then the data are utilized to calculate a preset hydrological model to obtain a mountain torrent gully basin distributed hydrological model result; and establishing a hydrological-hydrodynamic coupling model according to the sub-meter DEM digital elevation model and the torrential flood gully basin distributed hydrological model so as to forecast the current flood state and the future flood state. Therefore, the problems that an existing mountainous area hydrological model cannot completely reflect the current state of a drainage basin, and the simulation error of a hydrodynamic model is large are solved.

The invention discloses a geotechnical engineering model optimization training method and system based on a multi-source knowledge base. The method comprises the following steps: constructing a geotechnical engineering multi-source knowledge base; collecting field sensor monitoring data in real time and preprocessing to obtain standardized time sequence monitoring data; performing time sequence feature extraction on the standardized data by adopting a Mamba model to generate a time sequence state feature data set; generating initial computable prior information and parameter constraint conditions of the geotechnical engineering model based on the knowledge base, and establishing an initial particle state set; iteratively updating the particle state by adopting a particle flow filtering algorithm to obtain geotechnical engineering model parameter posterior distribution; carrying out model numerical simulation and calculating a simulation error evaluation index; and judging whether the posterior distribution reaches a predetermined optimization convergence condition according to the error evaluation index, and outputting the geotechnical engineering model after optimization training. According to the method, the accuracy and convergence stability of geotechnical engineering model parameter optimization are effectively improved.

The invention relates to the technical field of data processing, and provides a resistance line-associated underwater bench blasting numerical simulation error correction method, which is characterized in that land and underwater measured data are matched through the same resistance line and similar material parameters, and the resistance line and the water depth are used as basic input of an error prediction model; a reference basis is provided for error correction of subsequent numerical simulation; calculating a target result in combination with a prediction error after initial simulation, comparing a simulation result with the target result during iterative simulation to generate an iterative error, and analyzing and determining sensitivity coefficients of different error classifications in a resistance line and the iterative error so as to adjust material parameters to reduce an invalid iterative process; simulation results are processed according to stress areas in a zoning mode through an equidistant slicing method, adjustment precision is verified in combination with the root rate and the damage area, and the reliability of the simulation results is improved; and meanwhile, parameter data passing verification are input into a back propagation neural network, a correction parameter mapping library is constructed, and the design efficiency of an underwater blasting scheme is improved.

The invention belongs to the technical field of hydrology and water resources, and discloses a cold and dry mountainous area drainage basin runoff simulation method based on an MIKE SHE / MIKE 11 coupling model in combination with GIS / RS data. According to the multi-source data fusion technology, a cold and dry mountainous area distributed input data set is constructed through TRMM satellite rainfall data, an MODIS LAI product and an ASTER DEM, and the rainfall space representation precision is improved by 40%. According to the model coupling mechanism, an MIKE SHE / MIKE 11 collaborative simulation framework is provided, earth surface-groundwater flow data interaction is achieved through river channel linkage, and the complex river network flood routing simulation error is reduced to 10% or below. According to the parameter partition calibration method, a drainage basin is divided into five hydrogeological regions, sensitive parameters such as a Manning coefficient and a hydraulic conduction coefficient are optimized in combination with a genetic algorithm, and the basic flow simulation precision is improved by 25%.

The invention provides an aero-engine complete machine full-three-dimensional performance simulation error transmission and control method, and relates to the technical field of aero-engine performance simulation, and the method comprises the following steps: decomposing an aero-engine into a plurality of core parts along an airflow direction, and building a pneumatic and thermal coupling simulation model between the parts; identifying and quantifying a simulation error source of each part, and defining a simulation error value of each part; constructing a whole machine error transfer network, wherein the whole machine error transfer network comprises a sensitivity weight and a coupling transfer coefficient; based on the sensitivity weight and the coupling transfer coefficient, calculating a whole machine accumulated error; in the whole machine full-three-dimensional simulation process, outlet airflow parameter simulation data of all the components are collected, the simulation data are compared with test data, and iteration correction is conducted on simulation models of all the components and whole machine accumulated errors according to comparison results. According to the method, the whole machine simulation precision is improved, the whole machine simulation error is finally reduced to be within 2.5%, and the engineering reliability of a simulation result is improved.

A method of time-dependent quantum Hamiltonian simulation that is capable of efficiently achieving a target error is described. The time evolution of a time-dependent Hamiltonian describing a physical system is modeled using a Magnus operator that has one or more nested commutators and integrals. The Magnus operator is approximated with a commutator-free operator up to an order n. An error between the commutator-free operator and the Magnus operator as result of the approximation is estimated based at least in part on a simulation step size h. The commutator-free operators may be simulated on a quantum computer via one or more quantum gates for a total simulation time in temporal increments of h, where the step size h has a value such that a simulation error of the simulation is less than or equal to a target error.

According to the large-span steel structure refined construction method based on the simulation model, through whole-process refined simulation and optimization, the construction level of a large-span steel structure is remarkably improved, the construction scheme is determined based on a multi-objective optimization algorithm, accurate design of parameters such as hoisting, supporting and monitoring is achieved, a temperature field model is combined with actual measurement correction, and the construction efficiency is improved. The integration of optimization closure temperature, node semi-rigidity and initial defects enables simulation errors, support stability evaluation and optimization guarantee of safety coefficients, most construction risks can be predicted in advance on the whole, the field adjustment time is shortened, the formal construction period is shortened, the construction efficiency and safety are greatly improved, the obvious engineering value and economic benefits are achieved, and the method is suitable for popularization and application. The problem that an existing large-span steel structure construction method lacks a calculation model for predicting the risk in advance and shortening the construction period by combining multi-aspect data is solved.

The invention relates to the technical field of digital twinning, in particular to a flood disaster evolution method and system based on the digital twinning technology, and the method comprises the steps: collecting all point cloud data of a river basin terrain, constructing a three-dimensional terrain model, obtaining the reflection intensity and elevation data of each point cloud, clustering all point clouds corresponding to the ground, and obtaining a three-dimensional model; recording the areas where all point clouds corresponding to the ground in each cluster are located as local areas; and calculating a coplanar characterization value, an erosion evaluation value, an instability risk value, a soil fixation evaluation value and a terrain evolution factor of each local area, and constructing a digital twinborn simulation platform of the river basin terrain in combination with the three-dimensional terrain model to perform analog simulation on flood disasters. The defect that a static terrain model cannot respond to terrain evolution caused by flood disasters can be avoided, simulation errors generated by a digital twin simulation platform in the flood disaster evolution process are effectively reduced, and the accuracy of risk assessment of the flood disasters is improved.

The invention provides an ecological system service flow simulation and collaborative tradeoff relation quantification method based on a graph neural network, and relates to the technical field of ecological system service simulation and quantification. According to the method, the Huaihe River basin serves as a research area, firstly, a landscape is abstracted into a graph structure, ecological patches serve as nodes and are endowed with multi-dimensional features, ecological process flow paths serve as edges, and flow attributes are quantified; then, constructing a graph neural network model, and learning inherent rules of generation, flow and consumption of an ecological system service through training; and finally, realizing ecological system service, particularly dynamic simulation of supply service and adjustment service, and quantifying the collaborative tradeoff relation by adopting a multi-index fusion method. According to the method, the nonlinear correlation of the ecological process can be accurately captured, the flow simulation and relation quantification precision is improved, scientific support is provided for ecological planning and management of the Huaihe River basin, and compared with an InVEST model, the simulation error is reduced by 20%-30%, and the collaborative tradeoff recognition accuracy is improved by 15% or above.

The invention discloses an industrial steam pipe network control method and system based on multi-source data fusion and adaptive learning, and the method comprises the steps: obtaining multi-source real-time operation data of an industrial steam pipe network system, and carrying out the calculation based on a digital twin model to obtain a state variable calculation value of an industrial steam pipe network; comparing the state variable calculated value with a state variable measured value in real-time operation data to generate a residual error, taking minimization of the residual error as an optimization target, and automatically inverting and updating one or more time-varying physical parameters in the digital twinborn model on line through an optimization algorithm, so as to obtain a digital twinborn model. And performing system simulation, state monitoring or optimization control by using the updated digital twin model. The simulation error is controlled within an extremely low range allowed by engineering for a long time; a self-adaptive learning mechanism enables the model to be quickly self-adjusted and keep effective when facing working conditions such as pipe network topology change and equipment performance degradation, so that the adaptability of the digital twin system to a complex dynamic environment is greatly enhanced.

The invention discloses a rocket aircraft aerodynamic performance prediction method based on heaven and earth test and simulation data, and relates to the technical field of aerodynamic design. The method mainly comprises the following steps: obtaining a large number of aerodynamic performance parameters by using a non-viscous rapid design theory to construct a low-precision flow field training data set; performing heaven-earth correlation error correction on ground wind tunnel test data through a flight test; performing simulation error correction on a large number of aerodynamic performance parameters obtained by CFD numerical simulation through test data to obtain a high-precision training data set; and performing data fusion on the high-precision data set and the low-precision data set, and constructing a pneumatic prediction model. The invention provides the method for rapidly predicting the aerodynamic performance of the rocket aircraft, and an efficient and feasible solution is provided for rapid prediction of the aerodynamic performance.

The invention discloses an anti-icing testing method, device and equipment for a super-hydrophobic surface structure of energy equipment and a storage medium, and relates to the technical field of super-hydrophobic surface testing. Designing a super-hydrophobic surface structure based on the environmental main control factor to obtain a structure parameter set; simulating the super-hydrophobic surface structure by using an environment load condition determined based on the environment working condition matrix and a structure parameter set to obtain an equipment critical vibration frequency, and determining a current surface structure morphology based on the structure parameter set and the equipment critical vibration frequency; and determining a morphology simulation error based on the current surface structure morphology and the actual surface structure morphology of the super-hydrophobic surface structure, and monitoring the super-hydrophobic surface structure based on the morphology simulation error, the structure parameter set, the equipment critical vibration frequency and the preset control mechanical excitation frequency to obtain the ice growth rate and the ice adhesion work. The efficiency of anti-icing testing of the super-hydrophobic surface structure is improved.

The invention relates to a lithium ion battery internal temperature estimation method based on machine learning and data generation, and belongs to the technical field of batteries. The method solves the problems that traditional internal temperature estimation data acquisition is high in cost, the error between a simulation model and an actual system is large, and the generalization ability of the model is insufficient. According to the technical scheme, the method comprises the following steps: establishing an electrothermal coupling model for a target battery type such as a cylindrical or square battery to generate simulation data covering various parameters and working conditions; pre-training a gated circulation unit neural network or a long-short-term memory neural network based on the simulation data set, and capturing a mapping relation of input features; the model is finely tuned through an unsupervised domain adaptive transfer learning algorithm by using actual battery unmarked data, including pseudo tag generation and feature alignment to compensate simulation errors; and finally verifying the effectiveness of the method by combining a real battery experiment. Accurate, robust and adaptive internal temperature estimation is realized, and the generalization ability of the model is ensured.

The invention discloses an isolating switch dynamic arc simulation method considering contact action and a related system thereof, and belongs to the technical field of high-voltage electrical equipment simulation. Firstly, a three-dimensional geometric model of the disconnecting switch is constructed according to actual size parameters, then the three-dimensional geometric model is imported into simulation software, movement of a movable arc striking contact is simulated, and a medium strength recovery curve is obtained. And then constructing an electromagnetic simulation circuit considering arc reignition, simulating a dynamic arcing process, and obtaining a voltage difference and a current at two ends of the isolation switch. The arc extinguishing or arcing state is judged by combining the voltage difference, the current and the medium strength recovery curve, and the electromagnetic simulation circuit is controlled according to the arc extinguishing or arcing state, so that closed-loop simulation of the arcing-arc extinguishing alternating state of the disconnecting switch is realized. According to the method, the contact motion characteristics and the arc physical process are innovatively coupled, the problem of simulation errors caused by the adoption of a simplified medium strength curve in a traditional method is solved, the isolation switch operation transient overvoltage characteristics can be accurately predicted, and a reliable analysis means is provided for insulation design of extra-high voltage equipment.

The invention discloses a method for forecasting flood in a drainage basin influenced by strong human activities based on a hybrid model. The method comprises the following steps: firstly, constructing a distributed hydrological model combining sub-drainage basin division and a Muskingum method, and performing calibration; using a hydrological model simulation error and multi-step watershed average rainfall based on river channel propagation time as characteristics, and using a Bayesian optimized LSTM (Long Short Term Memory) to construct an error correction model; and finally coupling the two models to realize real-time forecasting. Verification of the Lanxi river basin shows that compared with a traditional model, the Nash efficiency coefficient of the mixed model is remarkably improved, the flood peak relative error is smaller than or equal to 8%, the peak current time difference is smaller than or equal to 2 hours, precision is stable within the 12-hour forecast period, a long series of continuous historical data is not needed to serve as support, and only data in the session flood period are needed; the method is suitable for hour-scale flood forecasting of the watershed lack of data and strong human activity, an efficient and reliable technical means is provided for flood control and disaster reduction, and the method has wide engineering application prospects.

The invention discloses a dynamic crop moisture condition estimation method for assimilating unmanned aerial vehicle (UAV) observation and a crop model, which relates to the technical field of agriculture and comprises the following steps of: inverting key crop morphological parameters through a machine learning algorithm by using UAV remote sensing data with high temporal-spatial resolution; and assimilating the inverted key crop morphological parameters into a crop growth model to optimize a model simulation process, further constructing a CWS diagnosis model and generating a customized irrigation scheme, assimilating LAI and AGB data obtained by UAV by adopting an EnKF method, remarkably reducing simulation errors of LAI and AGB by data assimilation, verifying feasibility of an assimilation framework, realizing complementary advantages of the method, and improving the accuracy of the method. On one hand, a true value is provided for correcting model deviation, initial conditions of modeling and parameter uncertainty are reduced, on the other hand, the capability of the model for analyzing a crop growth and development mechanism is enhanced, and the DA framework has regional popularization potential in combination with the simplicity of the SAFYE model.

The invention discloses a high-voltage submarine cable multi-parameter multi-physics field simulation calculation method, system, device, medium and program, and belongs to the technical field of cable detection. The method comprises the following steps: according to a submarine cable structure, establishing initial finite element models of soil direct burial, a seabed, a cable trench and a transition section; according to the initial finite element model, mesh generation is carried out on the submarine cable cross section, and a finite element simulation model after mesh generation is established; performing multi-physical field coupling calculation through the finite element simulation model after mesh generation to obtain the temperature of the submarine cable, and performing simulation calculation of the submarine cable core according to the temperature of the submarine cable; and comparing the cable core temperature of the submarine cable in the simulation calculation result with the temperature measured in an optical fiber temperature measuring device arranged in the submarine cable to obtain a simulation error and an actual error. According to the method, the distribution conditions of the temperature field, the magnetic field and the fluid velocity field around the cable and the temperature change condition of the laying transition section can be observed simultaneously, and the temperature of the submarine cable during change is obtained.

The invention discloses a boiler heating surface three-dimensional digital twin construction method and system based on multi-source data fusion, and relates to the field of boiler monitoring. Comprising the following steps: carrying out multi-source data space-time alignment and structured processing, and unifying CAD design coordinates, installation welding seam GPS positioning, DCS operation measuring points and ultrasonic detection point positions to a boiler absolute coordinate system by adopting a space-time reference conversion technology; analyzing the unstructured data through a data cleaning engine, generating a welded junction level equipment ledger, and forming a time-space aligned standardized database; carrying out multi-scale point cloud fusion and three-dimensional model reconstruction, fusing laser scanning point cloud, BIM parametric modeling and welding seam ray imaging data, and constructing a multi-scale model by adopting an octree spatial index; cT scanning point cloud is introduced into the key area to enhance the precision. Compared with traditional BIM modeling, the geometric accuracy is improved, the creep damage simulation error of the high-temperature pipeline is reduced, and a real geometric base is provided for life evaluation.

The invention relates to the technical field of data processing, in particular to a user and entity behavior modeling anomaly detection system, which comprises a timestamp generation module for receiving user power consumption data, equipment monitoring data and power grid monitoring data to generate data vectors; the dynamic association module interpolates the low-frequency data in the same time window to generate a virtual sampling point, and outputs a multi-dimensional association tensor; the behavior model generation module generates a joint behavior model and a confidence probability; the detection decision-making module adopts four parallel optimization channels to output three-dimensional decision-making indexes including abnormal probabilities; the simulation optimization module drives multi-algorithm simulation in the digital twin environment and writes back optimal parameters; and the feedback correction module generates a time reversal data flow reconstruction behavior model based on the simulation error, and outputs an equipment binding early warning signal to a power grid safety system. According to the method, the problem of dynamic association missing caused by multi-source data time sequence asynchronization is solved, and efficient collaborative diagnosis of composite anomalies such as electricity stealing is realized.

The invention provides an intelligent identification method and equipment for a simulation error dominant area of a power system, relates to the technical field of power systems, and solves the problems of unstable aggregation result, limited partitioning hierarchy, insufficient dynamic feature expression, lack of credibility feedback and the like in simulation error identification of the power system in the prior art. According to the scheme, a power grid weighted adjacency matrix is constructed; performing spectrum serialization and preliminary node rearrangement; community priority partitioning and intra-block optimization are carried out; performing hybrid dynamic simulation and credibility evaluation; and multi-level block recursion and error locking are carried out. According to the method, the weighted adjacency matrix with line impedance or admittance as the weight is constructed, so that mathematical layering and node coupling feature mapping of the power grid are realized; the method comprises the following steps of: extracting a sub-matrix from an error salient region, repeatedly executing spectrum aggregation and community division, realizing automatic locking of an error dominant node, forming a closed loop structure of spectrum aggregation-decoupling-simulation-feedback in combination with hybrid simulation feedback, and realizing multi-level error identification from global to local.

A method for automatic control of a simulation error in an analog circuit and a use thereof are provided. The method first determines a measurement object and acquires a simulation standard by simulating the measurement object. Then, the method performs simulation based on different parameters of a circuit netlist to acquire an electrical parameter and / or circuit performance evaluation indicator of the measurement object during a simulation process. The method forms a simulation sampling dataset based on a simulation option combination parameter and an error corresponding to the simulation standard. Finally, the method optimizes and analyzes the simulation sampling dataset based on an artificial intelligence algorithm to acquire a simulation option combination that meets a simulation error requirement and takes a least time. The solution achieves automatic selection of the simulation option combination, improving the accuracy of the simulation result.

The application discloses a rainfall simulation device and method for soil runoff leaching test, and relates to the technical field of soil hydrology and environmental simulation test, and comprises the following steps: obtaining three-dimensional scanning data of a test soil bed surface, and generating a digital elevation model covering the test soil bed surface and composed of multiple grid units; after depression filling treatment is performed on the digital elevation model, the theoretical water flow direction data and the theoretical confluence cumulative amount data of each grid unit are calculated by using a multi-flow direction algorithm. The application adjusts based on the dynamic feedback results of actual runoff data, is accurate in positioning the simulation error sources in the difference analysis link according to the infiltration capacity of different soils and the runoff collection characteristics of different surfaces, and then optimizes the identification precision of the catchment area and the runoff path through threshold adjustment, so that the adaptability in different soils and different surface states is improved.

The invention relates to the technical field of dwelling house performance evaluation, in particular to a dwelling house all-climate performance dynamic evaluation system and method based on a full-scale scene. A five-step closed-loop evaluation system of domestic climate data accurate docking-full-scale scene climate dynamic replication-comfortable temperature field double-model control-overall energy consumption real-time recording-component performance comparison evaluation is constructed, and the pain points of traditional component one-way detection, large simulation error and no overall evaluation are mainly solved. A real living scene is restored through a 1: 1 full-scale model, and in combination with domestic high-precision climate data and a dynamic climate duplicating technology, precise quantification of overall energy consumption and component performance of a house is realized, and a core technical support is provided for overall low energy consumption, component optimal matching and regional adaptation of a good house.

The invention discloses a permeability coefficient field inversion method based on physical fusion large language model multi-agent, and belongs to the technical field of hydrogeological modeling and artificial intelligence crossing. The method aims at solving the problems that in the prior art, efficiency is low due to dependence on artificial experience, a pure data driving method lacks physical consistency, and closed-loop error correction cannot be achieved in large language model application. The method comprises the following steps: constructing a collaboration framework comprising a reasoning agent and an execution agent; generating an inversion code for calling a groundwater numerical simulator by the reasoning agent according to a natural language task; and the execution agent runs the code and obtains a simulation result, a code execution error and a simulation error are fed back to the reasoning agent through a double feedback mechanism of an inner-layer logic loop and an outer-layer physical loop, and the reasoning agent is driven to dynamically correct and iteratively optimize the code or an inversion strategy until the result is converged. According to the method, full-process automation of permeability coefficient field inversion is realized, and the physical rationality and reliability of an inversion result are ensured while manual dependence is reduced.