338 results about "Kriging" patented technology

The invention discloses a distributed real-time monitoring and early warning system for a temperature field of a smelting furnace, and relates to the technical field of industrial process intelligent monitoring. The problems of accumulated measurement errors and non-stationary hotspot escape reconstruction hysteresis caused by static emissivity setting in an existing system are solved. Collecting multiband radiation intensity and voltage signals through time domain alignment of the multispectral sensor array and the thermocouple array; iterating emissivity parameters in real time by adopting a dynamic ash body spectrum ratio algorithm in combination with flue gas absorption characteristics; fusing non-contact and contact temperature measurement data based on weighted Kalman filtering and complementary filtering; constructing a space-time variable covariance function to carry out non-stationary Kriging interpolation; dynamically optimizing the local grid resolution by combining an adaptive grid module; the processing flow is accelerated through the parallel computing module; early warning is triggered based on abnormal probability judgment and is fed back to emissivity correction and grid optimization; according to the invention, the monitoring precision and real-time performance of the temperature field are obviously improved, and the risks of false alarm, missing alarm and equipment melting loss are effectively inhibited.

The invention provides a soft rock tunnel surrounding rock parameter dynamic identification method and system based on data driving, and relates to the technical field of underground tunnel mechanical parameter dynamic identification, and the method comprises the steps: obtaining multi-element tunnel surrounding rock parameters, and building a joint probability distribution model of the multi-element surrounding rock parameters based on a Copula theory; performing Monte Carlo simulation, and generating a high-dimensional parameter sample library meeting physical constraints in the parameter constraint space based on the joint probability distribution model; establishing a tunnel three-dimensional numerical model and performing automatic numerical simulation to generate multivariate response data; constructing a Kriging agent model of a Gaussian kernel function based on multivariate response data training, establishing a nonlinear mapping relation between parameter input and deformation output, constructing an inversion objective function by taking the minimum root-mean-square error of multi-measurement-point displacement as an objective, and performing inversion solution by using an adaptive particle swarm optimization algorithm to obtain inversion identification parameters, and a dynamic feedback mechanism is constructed to realize adaptive tracking of the time-varying characteristics of the surrounding rock parameters.

The invention provides a hydrological trend prediction method based on big data analysis, and the method comprises the steps: building an initial graph structure which takes a monitoring station as a node and geographic distance and water system connectivity as an edge weight, carrying out the iterative aggregation of node features through a message passing mechanism, and capturing the space interaction of hydrological variables between stations; dynamically adjusting a water system connectivity parameter and an edge weight based on rainfall change, and generating an adaptive graph structure; in combination with the graph neural network, the dynamic Bayesian network and the space-time collaborative Kriging interpolation algorithm, hour-level hydrological dynamic transmission features are extracted, and a space-time coupling prediction model is formed; model parameter optimization and message passing mechanism adjustment are triggered through a prediction deviation threshold value, and a self-adaptive prediction process of'dynamic modeling-feature fusion-closed loop optimization 'is realized. According to the method, hydrological element space-time correlation can be accurately described, the analysis precision of a complex hydrological process is improved, and the adaptability to sudden hydrological events and basin environment changes is enhanced.

The invention discloses an intelligent temperature and humidity regulation and control fungus cultivation method for edible fungus sticks, and relates to the technical field of agricultural Internet of Things. The problems that in the prior art, multi-variety / multi-stage adaptability is poor, sudden environment response lags behind, and equipment cooperation efficiency is low are solved. Cross-variety control strategy migration is realized through a multi-strain dynamic migration matrix and a migration learning algorithm, and a three-dimensional microenvironment map is generated in combination with a multi-source sensor network and Kriging interpolation; identifying a strain growth stage by using a time sequence convolutional network, loading a corresponding parameter set, and dynamically generating an anti-interference instruction and a power distribution sequence based on a double-delay depth deterministic strategy gradient algorithm and model predictive control; scheduling a conflict-free instruction queue by adopting a heterogeneous equipment collaborative framework, and updating model parameters through federated learning and a Shapley value evaluation closed loop; according to the invention, the intelligence, self-adaptability and precision of edible mushroom growth environment regulation and control are greatly improved, and the robustness of the system to environment mutation is enhanced.

The invention discloses an urban water supply management data trend analysis method based on space-time analysis, and relates to the field of data processing, and the method comprises the steps: collecting data in real time through an urban water supply pipe network sensor network, building a space-time unified coordinate system, building a space-time Kriging interpolation model based on pipe network topology, and achieving the space-time alignment of multi-source data; dividing an adaptive space-time grid by using a Voronoi diagram and a sliding window mechanism, and calculating multi-dimensional features; constructing a dynamic space-time diagram by taking a grid as a node, performing multi-step prediction in combination with a space-time diagram convolution circulation network, fusing a Kriging interpolation result, and evaluating an abnormal probability and a confidence interval through a Bayesian neural network; a monitoring layer, a prediction layer and a risk layer are overlaid in a three-dimensional GIS, a dynamic thermodynamic diagram is generated, an early warning path is optimized based on a Dijkstra algorithm, and a minimum risk topology path is output. The method has the advantages that through space-time analysis and accurate prediction, the intelligence, stability and emergency response efficiency of urban water supply management are remarkably improved, and powerful support is provided for smart city construction.

The invention discloses a virtual power plant planning-operation two-stage distribution robust optimization method based on scene driving, and the method comprises the steps: S1, building a capacity configuration model and a master-slave game model based on a master-slave game framework of a power supply, a load and energy storage; s2, considering the correlation and uncertainty of fan and photovoltaic output to construct an uncertainty set of wind and light output probability distribution, and generating a plurality of typical scenes through a sampling and clustering algorithm; s3, performing comprehensive norm constraint on the confidence interval of the uncertain set under the driving of a typical scene, and establishing a distribution robust optimization model of the virtual power plant in two stages of planning and operation, wherein the distribution robust optimization model considers uncertainty; wherein the distributed robust optimization model comprises a capacity configuration model and a master-slave game model; and S4, solving the distribution robust optimization model containing the master-slave game model based on the modified Kriging model and a column and constraint generation algorithm. By utilizing the method, the influence of uncertainty on the planning rationality and the operation economy of the virtual power plant can be reduced.

The invention provides a mining area deformation monitoring and risk assessment method, which comprises the following steps of: carrying out registration, de-leveling, filtering and phase unwrapping on acquired image data through InSAR (Interferometric Synthetic Aperture Radar), generating a coherence graph, a filtering interference graph and an unwrapping phase graph, carrying out phase decomposition by using a singular value algorithm, separating a deformation phase, an atmospheric delay phase and noise, and carrying out risk assessment. The method comprises the steps of expanding discrete deformation points into a continuous deformation field through Kriging interpolation, carrying out deformation grading by taking a deformation rate, an accumulated settlement amount and a deformation gradient as core indexes, identifying a deformation high-risk area, arranging measurement robot monitoring points in the high-risk area, and constructing a risk assessment model in combination with real-time deformation data of a measurement robot and geological parameters. And evaluating the risk grade of each part of the mining area through the dynamic risk index model, and triggering graded early warning. According to the method, the InSAR and measurement robot technology is combined to solve the data fusion problem of large-range deformation monitoring and local high-precision monitoring, and the dynamic risk assessment precision under the complex terrain of the mining area is improved.

The invention discloses a shield construction earthwork volume prediction method based on Kriging interpolation and a classification regression tree, and relates to the field of shield construction earthwork volume prediction. According to the method, construction parameters, geological parameters and environmental parameters are collected in real time, and a variation function and a multi-scale interpolation fusion strategy are dynamically adjusted, so that the prediction precision problem of traditional Kriging interpolation in a local geological change region is solved; meanwhile, the stratigraphic interpolation result is dynamically corrected through a Bayesian updating method, and the accuracy of space prediction is improved; besides, a dynamic weight adjustment strategy is adopted, a Bayesian fusion model is combined, prediction model weights in different stages can be adjusted according to error feedback in the construction process, and therefore more accurate earthwork volume prediction is achieved.

The invention provides a rapid random model correction method for an active learning type ocean platform structure, and the method comprises the steps: firstly carrying out the selection of correction parameters through considering the sensitivity, then carrying out the design of a test scheme, and building an initial Kriging model; a new correction parameter training sample is found, the frequency corresponding to the new sample is calculated, a Kriging model is reconstructed by adopting a new training set, random model correction is performed based on the reconstructed Kriging model, an objective function is constructed by adopting actually measured frequency data and initial finite element model frequency data, and a correction parameter mean value and a standard deviation are taken as solving objectives. And solving by utilizing an optimization algorithm to obtain a mean value and a standard deviation of correction parameters of the finite element model to be corrected, and repeatedly iterating to realize random correction of the ocean platform structure. According to the method, rapid, efficient and accurate model correction under small samples and noise errors is realized, and higher correction accuracy can be obtained by adopting fewer training samples.

The invention discloses an optimization design method for a control surface of a wide-speed-range large-attack-angle reusable carrier, and relates to optimization design of aerodynamic configuration of an aircraft. The method comprises the following steps: S1, setting a control surface aerodynamic configuration design variable range, and generating an initial sample library by adopting Latin hypercube sampling; s2, aerodynamic parameters are obtained through CFD simulation, and a Kriging proxy model is trained; s3, evaluating the precision of the proxy model by taking a U learning function as a criterion, and stopping adding points when the minimum U function value is smaller than a threshold value; s4, constructing an optimization model which takes maximization of the lift-drag ratio and the static stability margin under the hypersonic speed as a target and takes the condition that the aerodynamic parameters of the supersonic speed / subsonic speed are not lower than a reference value and the hinge moment as constraints; and S5, performing iterative optimization by adopting an improved multi-target particle swarm algorithm based on genetic algorithm crossover mutation operation, updating the proxy model after the optimal solution of each generation is subjected to CFD verification, and outputting an optimal solution set. The problem of wide-speed-range aerodynamic configuration contradictions is solved, and the comprehensive performance of the carrier is remarkably improved.

The invention provides an ionosphere inversion method and system based on space-ground multi-source data fusion, and belongs to the technical field of ionosphere inversion. The problems that an existing ionosphere inversion method is poor in precision, low in timeliness and poor in adaptability to ionosphere change characteristics in different regions and under different conditions are solved. According to the method, after a proper ionized layer model is selected, sky-ground data are acquired and processed, and the critical frequency of an ionized layer, the vertical height at which the electron density reaches the maximum value and the total electron content of the ionized layer are reconstructed by using an improved Kriging interpolation method; and based on the reconstruction result, analyzing the change rule of the ionized layer characteristics along with time and space distribution and under different disturbance conditions. The method can improve the precision and reliability of ionosphere inversion, so that key parameters such as electron density distribution, critical frequency, total electron content and the like of the ionosphere are described more accurately, and more accurate data support and theoretical guidance are provided for the fields of navigation positioning, communication system optimization and space weather monitoring.

The invention relates to the technical field of electric power topology identification, and discloses an electric power topology rapid identification method based on depth map matching, and the method comprises the following steps: S1, carrying out the multi-source heterogeneous data fusion and adaptive noise reduction, and employing an adaptive threshold noise reduction algorithm; s2, modeling a dynamic space-time diagram; s3, attention-driven graph matching is carried out, and a two-channel graph neural network is used; and S4, updating the incremental topology library. For the condition that the real-time data of the low-voltage power distribution network generates significant noise due to equipment aging and acquisition errors, a self-adaptive threshold noise reduction algorithm is adopted, high-frequency noise components are accurately filtered out according to a threshold value, missing data are intelligently repaired in combination with a space-time Kriging interpolation method, the purity and integrity of the data are ensured, and a data basis is provided for subsequent topology recognition.

The invention discloses an offshore wind turbine foundation scouring full-period intelligent monitoring method and system based on multi-source data fusion and a dynamic coupling model, and relates to the technical field of intelligent monitoring, and the method comprises the steps: deploying a multi-source monitoring module, and constructing a finite element model; carrying out load calculation and parameter inversion; and training full-cycle dynamic updating of the washout failure function model. According to the method, a self-adaptive Kriging-Bayesian method is adopted, a Bayesian inversion framework and a self-adaptive agent model are fused to solve optimal soil body parameters, full-period model dynamic updating based on dynamic monitoring data is achieved, a multi-fidelity deep kernel learning model is adopted, three types of data are fused into a training set, full-period intelligent monitoring of offshore wind turbine foundation scouring is achieved, and the method has the advantages of being simple in structure, convenient to operate and high in practicability. The dynamic identification of soil parameters is realized by combining a self-adaptive inversion framework with a displacement error closed-loop optimization mechanism, and the technical problem that a traditional static model cannot adapt to the spatial-temporal variability of seabed geology is solved.

The invention provides a rock-fill dam concrete panel cracking reliability calculation method and system, and the method comprises the steps: determining test parameters based on obtained physical parameters of a rock-fill material used by a rock-fill dam; duncan E-B model parameters are determined based on the test parameters, factor sensitivity analysis is conducted on the Duncan E-B model parameters based on an orthogonal test method, and sensitivity parameters are determined; carrying out random field characterization on the sensitivity parameters by using a normal random field, and discretizing random field data by using a Cholesky decomposition method to obtain a sample set and a test set of a training agent model; based on a Kriging model constructed by the sample set, performing optimization iteration through MPSO and a learning function to obtain an optimal agent model, and evaluating the optimal agent model; inputting the test set into the optimal agent model to calculate to obtain a predicted value and an evaluation index, establishing a structural performance function, and calculating to obtain the panel cracking reliability by adopting an important sampling subset simulation method; according to the method, the accuracy of the cracking reliability is improved.

The invention relates to the technical field of remote volume adjustment, in particular to a control system and method for stage intelligent sound control sound equipment, and aims to accurately capture time distribution and amplitude characteristics of volume fluctuation by collecting environmental noise data in real time and combining a dynamic data analysis method, predict a future volume trend based on historical data, and improve the sound control efficiency. According to the logic based on dynamic prediction, the response capacity to sound changes is enhanced in volume control, the Kriging interpolation method is adopted to accurately analyze sound field requirements, the requirements of different areas are matched by adjusting sound field parameters, the sound field uniformity and the coverage accuracy are improved, and the volume control accuracy is improved. Meanwhile, the intelligence and automation level of the sound system is improved, the output power and frequency distribution of the sound equipment are dynamically adjusted through the particle swarm optimization algorithm, the output volume can be continuously optimized according to the real-time environment and sound field requirements, and the individual requirements of different stage performances are met.

The invention discloses a well submersible pump hydraulic design data modeling method, which comprises five steps of sensing acquisition, hydraulic data enhancement, hydraulic performance prediction, intelligent material matching and modeling output, relates to the technical field of pump hydraulic modeling analysis, and particularly relates to a well submersible pump hydraulic design data modeling method and system. A physical information confrontation generative network is combined with a space-time Kriging interpolation algorithm to enhance three-dimensional flow field data; a multi-modal graph neural network fusing physical control loss is constructed, and dynamic prediction of performance indexes such as pump body efficiency, lift and net positive suction head is achieved; based on a corrosion-wear coupling model and a reinforcement learning algorithm, the service life, the cost and the machinability of candidate materials are evaluated, an optimal combination is intelligently recommended, and systematic support is provided for design of an efficient, low-cost and long-service-life submersible pump for a well.

The invention discloses an adaptive gradient enhanced Kriging agent modeling method suitable for spacecraft heat transfer model correction, and relates to the field of spacecraft heat transfer model correction. The method specifically comprises the following steps: S1, carrying out theoretical derivation construction on a related core function in a self-adaptive gradient enhancement modeling process of a gradient weighted Gaussian process prediction function and a target sample self-adaptive learning function Kriging; and S2, establishing a novel adaptive gradient enhanced Kriging proxy model of the spacecraft heat transfer model through a numerical method based on a proxy model related core function constructed by theoretical derivation in the step S1. The operation complexity that an adjoint function and a Taylor equation need to be solved in traditional gradient enhancement agent modeling is avoided, the gradient enhancement method is embedded into the iteration process of adaptive learning, and efficiency and accuracy are improved.

The invention discloses a rotor reliability constrained rolling bearing assembly parameter robust design method. The method comprises the following steps: constructing a dynamic model for an actual rotor-bearing system; constructing an uncertainty parameter vector and a design variable vector; a target function based on robustness and a constraint function based on reliability are constructed, so that an uncertainty optimization model is obtained; constructing an augmented input variable, and establishing a candidate orthogonal polynomial basis function set; on the basis, constructing and evaluating polynomial chaos-Kriging models for the target function and the constraint function respectively, and screening out an optimal polynomial chaos-Kriging model; calculating the expectation and the standard deviation of the target function and the failure probability of the constraint function under each design variable vector; and converting the uncertainty optimization model into an unconstrained single-target optimization model, randomly generating population individuals of a heuristic optimization algorithm in a feasible region of design variables, and iteratively searching an optimal solution of the unconstrained single-target optimization model as a rolling bearing assembly scheme.

The invention discloses a water quality safety monitoring and early warning method based on big data, and relates to the technical field of monitoring and early warning. Setting sampling points according to areas, pasting scene labels, and arranging sensors to obtain water quality information; establishing a water quality evaluation model based on a support vector machine improved model, inputting the water quality information into the water quality evaluation model, outputting to obtain a water quality category, and dividing the sampling points into normal sampling points and key sampling points according to the water quality category; based on the normal point data, using LSTM to predict water quality safety and performing graded early warning; based on the key point data, positioning a pollution source by using space-time Kriging interpolation, and simulating a pollution diffusion path by using multivariable collaborative interpolation; and combining normal point early warning and key point diffusion simulation to obtain a water quality safety monitoring early warning result. According to the method, a global risk grading report is generated by fusing a normal sampling point early warning result and a key sampling point diffusion path, and emergency response and long-term treatment strategies are matched.

The invention provides a Yangtze river export deep and far sea environment data association analysis method based on multi-modal fusion, belongs to the technical field of far sea environment analysis, and achieves accurate data registration by establishing a multi-source data space-time standardization model and adopting an adaptive space-time Kriging interpolation algorithm. A sparse coding ocean signal separation algorithm is utilized to construct an over-complete dictionary separation mixed signal to extract pure features, a feature extraction network is constructed based on an attention mechanism to automatically learn deep feature representation of physicochemical biological parameters, and a matrix rank loss detection algorithm is adopted to automatically supplement feature compensation vectors to ensure feature integrity. A cross-scale attention mechanism is constructed based on wavelet transform to fuse multi-scale features, a Shapley value interpretability evaluation system is established to quantify correlation feature importance, and the technical problem that correlation feature extraction is inaccurate in the multi-source heterogeneous marine environment data fusion process is solved.

The invention discloses an indoor temperature and humidity spatial data prediction method fusing spatial interpolation and a graph convolutional network, and relates to the field of smart city and environment monitoring, and the method comprises the steps: obtaining indoor multi-point temperature and humidity data actually measured by a sensor, and the spatial position information of a monitoring mark point corresponding to the indoor multi-point temperature and humidity data; the temperature and humidity data, the spatial position information and the time dimension features of the marked points are used as input of Kriging interpolation, and temperature and humidity linear predicted values of part of unmarked points are generated; constructing a multiple linear regression model taking the Kriging prediction result, the measured data of the mark points, the time dimension characteristics, the spatial characteristics and the external meteorological data as independent variables and the target temperature and humidity values as dependent variables, and performing multi-dimensional fitting on the Kriging interpolation result; and constructing a feature matrix by using a linear predicted value output by the regression model and other features, and taking the feature matrix and an adjacent matrix as input of a graph convolutional network to generate indoor temperature and humidity three-dimensional space distribution. According to the invention, high-precision indoor space data prediction can be realized.

The invention relates to the technical field of geological engineering safety risk evaluation, in particular to a regional tectonic stress risk quantitative analysis method for tunnel engineering. The method comprises the following steps: collecting multi-source geomechanical parameters, simulating and inverting a tectonic stress field, and verifying the tectonic stress field; constructing a multi-scale geomechanical model, coupling simulation tectonic stress and gravity stress, and generating a stress space basic layer; utilizing Kriging interpolation and inversion decomposition to obtain a stress value and a directional diagram layer; calculating an included angle between the maximum principal stress direction and the tunnel axis, and performing vector decomposition to obtain a vertical stress component; and carrying out standardized normalization on the factors, determining a weight calculation risk value, and finally generating a tectonic stress risk quantitative grading graph and carrying out GIS visual rendering. The tunnel engineering-oriented risk quantitative analysis mechanism is constructed by retaining the directivity and spatial heterogeneity characteristics of the tectonic stress tensor, and the authenticity, resolution and engineering applicability of tectonic stress identification are remarkably improved.

The invention discloses a trend Kriging-based ASF estimation method for improving the positioning and navigation precision of a Rowland system, and belongs to the field of positioning and navigation.The method is based on a Kriging model, an effective primary function is selected based on limited real ASF data to construct an optimal trend function, and the method comprises two nesting tasks; the optimal basis function is selected, and the optimal relevant parameters are searched; in order to overcome the challenge that the nesting property of the two tasks is easy to cause local optimum, the two tasks are integrated into an integral optimization framework; under the framework, the optimal solutions of the primary function and the residual related parameters are searched at the same time by adopting an NDE algorithm with high global search capability. In addition, residual covariance minimization is used as an optimization target of the two tasks, and geospatial correlation of sampling data is guaranteed.

The invention discloses a thin-wall surface deformation measurement method and device based on a Kriging model, and a medium. The method comprises the following steps: S1, generating an initial measurement point; s2, a Kriging model is constructed; s3, dynamically selecting a sampling mode; s4, acquiring a sampling point to be detected; s5, the deformation degree of the thin-wall surface is obtained through actual measurement; and S6, evaluating and iterating. Through prediction and selection of iterative sampling points and in combination with a dynamic adjustment sampling strategy, the number of measurement points is effectively reduced, and the method can overcome the problem of measurement errors caused by coordinate deviation in AACMM handheld measurement. Even if deviation exists between the position of an actual measurement point and the position of a theoretical sampling point, the method still can accurately capture the deformation degree of the thin-wall surface through prediction and iterative optimization of the Kriging model, the reliability of a measurement result is ensured, and the measurement precision, the measurement efficiency, the stability of the method implementation and the applicability and robustness of different workpiece applications are improved.

The invention relates to the technical field of remote sensing land prediction, in particular to a farmland non-grain spatial distribution prediction method and system, and the method comprises the steps: abstracting a farmland plot into a graph node, defining an edge weight as a weighted combination of an inter-plot Euclidean distance, crop type similarity and irrigation system connectivity, constructing a three-layer dynamic graph structure comprising land parcels, neighborhoods and administrative units; taking the three-layer dynamic graph structure as input, and performing node feature extraction by adopting a graph attention long-short-term memory network to obtain a non-grain ratio preliminary predicted value; non-stationary state space-variational Kalman filtering is adopted to correct the preliminary prediction value of the non-grain ratio, and a plot-level prediction value is obtained; and performing spatial reconstruction on the plot-level predicted value by using policy-sensitive Kriging interpolation. According to the invention, spatial continuity and time serialization cultivated land non-grain trend prediction is realized.

The invention provides a structure reliability analysis method based on an adaptive variable fidelity model, and the method comprises the steps: generating an initial sample point set which is uniformly distributed and has representativeness through an improved random sampling method KMODMC, enabling the initial sample point set to comprise a low-fidelity sample set and a high-fidelity sample set, training a BP neural network through employing the low-fidelity sample set, and carrying out the training of the BP neural network through employing the high-fidelity sample set; a low-fidelity BP neural network model is obtained; meanwhile, based on an error training Kriging model of a high-fidelity sample set and a low-fidelity model predicted value, an error correction Kriging model is constructed, the low-fidelity BP neural network model and the error correction Kriging model are combined to form a multi-fidelity mixed agent model, and adaptive iterative optimization is performed through a double-model alternate point adding sampling strategy. And finally obtaining a high-precision multi-fidelity hybrid agent model for structural reliability evaluation. According to the method, the problems of high cost of high-fidelity simulation calculation and insufficient precision of a low-fidelity model are solved, and the adaptive capacity and prediction precision of the model in a complex nonlinear problem are effectively improved.

The invention discloses a turbine disc baffle structure optimization method and device, equipment, a medium and a product and relates to the field of turbine rotors, and the method comprises the steps that actual structure information of a turbine disc-baffle rotor system is obtained; performing finite element modeling on the actual structure information to obtain a finite element model; transient thermal analysis and transient stress analysis are sequentially carried out according to the finite element model, and the most dangerous time step where the maximum equivalent stress and the maximum contact stress are located is obtained; establishing a response surface model based on a Kriging model according to the most dangerous time step where the maximum equivalent stress and the maximum contact stress are located and actual structure information; according to the response surface model, a multi-target genetic algorithm is used for optimization, and optimized turbine disc baffle structure information is obtained. Stress concentration is effectively reduced, and the fatigue life is prolonged.

The invention belongs to the technical field related to structure optimization, and discloses a cross-scale topological optimization method of an auxetic metamaterial with a specified deformation mode, which comprises the following steps: (1) according to a given metamaterial function target, introducing an elastic tensor component constraint condition to establish a negative Poisson's ratio microstructure topological optimization model; (2) gradually changing the volume rate constraint of the initial microstructure to obtain a series of negative Poisson's ratio gradient microstructures with different equivalent densities; (3) constructing a multi-phase material interpolation model based on a Kriging model, constructing an auxetic metamaterial cross-scale topological optimization model with a specified deformation mode, and deducing the sensitivity of a target function to design variables; and (4) calculating and updating the design variable until the output meets the convergence condition, and outputting the auxetic metamaterial with the target deformation mode. According to the method, the limitation that the deformation mode of the traditional auxetic metamaterial cannot be locally regulated and controlled is overcome, and the design efficiency of the cross-scale design problem of the two-dimensional auxetic metamaterial under the complex condition is improved.

The invention relates to the technical field of ventilation control, in particular to a multi-parameter real-time monitoring system of an indoor large ventilation system. The system uses a monitoring data acquisition module to acquire basic monitoring data. Kriging interpolation is carried out by using a spherical semi-variation function according to the collected temperature data to obtain an optimal unbiased estimated value under each coordinate, and an upflow tendency diagram is formed. And segmenting the real-time upflow tendency diagram to determine a plurality of connected domains, evaluating a pollution plume mode index of the current indoor environment by comparing particle concentration data and heat energy data, and further determining a control strategy mode. In each control strategy mode, regulation and control parameters can be determined according to the area of a connected domain or a pixel value in a real-time upflow tendency map. According to the embodiment of the invention, the real-time upflow tendency diagram is constructed through data monitoring and feature analysis, and indoor dynamic, low-disturbance and low-delay ventilation control of a large commercial room is realized.

The invention discloses a vibroflotation pile composite foundation quality evaluation method based on an AHP-entropy weight method. The method comprises the following steps: constructing a multi-dimensional analysis model; four master control influence factors of quality evaluation are obtained based on the multi-dimensional analysis model; weighting the influence factors based on an AHP subjective weighting method and an entropy weight objective weighting method, and obtaining a combined weight; obtaining a normalized thematic map of each influence factor evaluation index based on a Kriging interpolation method; superposing the normalized thematic maps based on a GIS superposition coupling function to obtain a quality quantification index type partition evaluation map of the whole area of the composite foundation; and carrying out partition evaluation on the quality of the vibro-replacement stone column composite foundation in the whole region. According to the method, the multi-dimensional analysis model is constructed to obtain the combination weight of each index, so that the evaluation is comprehensive, and the error of the evaluation result is greatly reduced; the influence factors are coupled in the same graph and are subjected to partition evaluation, so that the evaluation precision of the processed foundation is improved.