10 results about "Kriging algorithm" patented technology

The invention belongs to the technical field of engine air pipe network valve adjustment, and provides a combustion chamber air pipe network valve opening prediction model construction method and system, and the method comprises the steps: generating a test working condition set comprising a plurality of valve opening combinations for an established air pipe network system model; the test working condition set serves as input, and a mapping data set of gas parameters and valve opening degrees of an inlet and an outlet of each network management node is obtained through simulation of the air pipe network system model; and training the mapping data set by adopting a Kriging algorithm, and establishing a prediction model taking gas parameters of an inlet and an outlet of a network management node as input and valve opening as output under a test working condition. According to the prediction model constructed through the method, all valve opening strategies can be rapidly predicted for inputting the target flow and pressure, and the target state can be rapidly achieved through a small number of personnel control actions in the experiment process.

The invention discloses a differential active temperature control matching method and system based on fluid thermosetting coupling analysis. The method comprises the following steps: determining a multi-physical field action form of a machine tool structure; a fluid-heat-structure multi-physics field coupling simulation model is obtained, specifically, coupling simulation of a cooling fluid field, an endogenous heat field and a machine tool structure field is conducted in sequence, and an effective multi-physics field coupling simulation model is obtained; adopting a response surface design method to generate multiple groups of DOE analysis working conditions; a Kriging algorithm is adopted to construct an agent model and establish a mapping relation: based on the DOE sample points and simulation results thereof, the Kriging agent model is trained, and a mapping relation between machine tool detection parameters and active temperature control parameters is established through an automatic refining process; based on the mapping relation, performing multi-objective optimization on temperature control parameters by taking thermal deformation of the key position as a constraint, and realizing differential control strategy matching of each temperature control loop; the problem of thermal deformation caused by thermal characteristic change of a machine tool structure is solved.

The invention provides a local artificial intelligence earthquake sand liquefaction deformation prediction method and system, and relates to the technical field of geological disaster prediction, and the method comprises the steps: obtaining multi-source geological data; 3-5Hz soil mass frequency domain features are extracted through wavelet packet decomposition, a DEM and a historical liquefaction thermodynamic diagram are fused to divide a sedimentary facies control unit, a three-dimensional voxel model is generated by adopting a Bayesian constrained improved Kriging algorithm, and a four-dimensional space-time tensor is constructed in combination with real-time seismic oscillation dominant frequency offset; inputting the tensor into a local adaptive graph convolutional network, dynamically adjusting the weight of a convolution kernel through a space-time attention mechanism, and synchronously outputting a prediction result; triggering incremental learning based on MEMS acceleration and FBG strain data, correcting a predicted value of an unliquefied region, and feeding back the predicted value to the voxel model; and finally, generating a probabilistic disaster chain scene tree through spatial pattern matching. The technical bottleneck that a traditional model is poor in adaptability in a complex sedimentary environment and cannot be updated in real time is solved, and intelligent early warning support is provided for engineering safety of an earthquake active area.

The application belongs to the technical field of hydrology, and provides a watershed vegetation and precipitation characteristic model determination method based on evaporation force, an electronic device and a storage medium, which comprise data acquisition, evaporation force calculation model construction, evaporation force calculation model optimization, Gaussian interpolation processing, and evaporation force attenuation area and attenuation zone identification; the application can accurately invert the spatial distribution of evaporation force from reanalysis data such as temperature, humidity and air pressure by introducing the static force balance formula and the Clausius-Clapeyron equation, has good physical consistency and applicability; by fusing real-time leaf area index, the evaporation force model can respond to different underlying surface ecological characteristics, and the explanatory power of the model on the vertical water vapor transport mechanism is improved; by constructing the evaporation force field through Gaussian interpolation and Kriging algorithm, the evaporation force attenuation area is identified along the gradient direction, and then the precipitation spatial attenuation model is established, so that the response mechanism of precipitation on the watershed scale is effectively described.

The invention discloses an air quality monitoring and purification regulation and control system and method based on a sensor network, and relates to the technical field of environmental data monitoring processing and intelligent control. According to the invention, environmental data are collected through a heterogeneous sensor network, and abnormal values are cleaned; constructing a dynamic air flow field directed weighted graph based on time sequence correlation among sensor nodes, and reconstructing a global air quality thermodynamic diagram and positioning a pollution source by using a flow field corrected Kriging algorithm; introducing a graph neural network to construct a prediction model, and combining historical and real-time data to predict a future air quality trend; based on a prediction result, solving a dynamic cost function containing multiple targets of quality, energy consumption and noise by using a quadratic programming algorithm to obtain an optimal control sequence; fresh air, a purifier and doors and windows are cooperatively dispatched to execute strategies such as positive pressure blocking or negative pressure pollution discharge; according to the method, the problems that blind areas exist in traditional monitoring, control response lags behind and equipment lacks collaboration are solved, and active and comfortable air quality management based on prediction is achieved.

This invention discloses a smart city data visualization method and system based on multi-source data fusion, belonging to the field of smart city technology. It preprocesses data using weighted isolated forest and generalized kriging algorithms; resolves cross-source conflicts using improved D-S evidence theory; dynamically adjusts the fusion strategy under the allocation of viewpoint and computing power using DQN reinforcement learning; achieves precise desensitization by combining viewpoint depth-driven differential privacy and attribute-based encryption; completes multi-dimensional interactive display based on view frustum culling, dynamic LOD, and cloud-to-terminal off-screen rendering technologies; and achieves closed-loop iterative optimization by collecting user feedback through the PPO algorithm. This invention solves the problems of heterogeneous data conflicts and high hardware dependence in 3D rendering, significantly improving the rendering smoothness and system self-evolution capability of massive city-level data while ensuring the fidelity of situational analysis and individual privacy.

The invention discloses a dynamic evaluation method for regional soil environment quality. According to the method, multi-temporal soil sample actual measurement data, remote sensing image data, land utilization data and environmental assistance data of a target area are acquired, the multi-source data are preprocessed and fused, and a time sequence Kriging algorithm interpolation is combined with a machine learning regression model; and generating a soil environment index raster data set with high temporal-spatial resolution. And a mixed deep learning model combining a long-short-term memory network and a convolutional neural network is constructed to learn a space-time evolution rule of the soil environment index. And calculating a soil environment quality comprehensive index by using the model, generating a quality grade spatial distribution diagram, and analyzing a change trend. Furthermore, the prediction of the regional soil environment quality can be realized by inputting future scene parameters. According to the method, dynamic evaluation and prediction of the regional soil environment quality are realized, the time-space continuity and precision of evaluation are improved, and the method has early warning capability.

The invention is applicable to the field of energy-saving dust removal, and provides an environment-friendly energy-saving dust removal system for a building, which comprises a dust removal device and a data sensing module, the dust concentration prediction module is used for generating the dust concentration of the detection point in the collected block in the future time period on the basis of a preset dust concentration prediction model and in combination with the environment data and the man-machine data of the collected block; the dust distribution prediction module is used for generating a dust concentration distribution thermodynamic diagram of the construction site in the future time period based on a preset Kriging algorithm and according to the dust concentration of the detection points in the collected blocks in the future time period; and the dust removal control module is used for adjusting the power of the dust fall device in the corresponding block according to the dust concentration distribution thermodynamic diagram of the construction site in the future time period, so that the dust fall device can operate in the optimal state in advance, the dust fall energy consumption is greatly reduced on the basis of ensuring the dust fall effect, and energy conservation and environmental protection are achieved.

The invention relates to the technical field of grain storage monitoring, and particularly discloses a processing characteristic monitoring and analyzing system for early indica rice for rice noodles in a storage process, which comprises the following steps of: generating a three-dimensional quality deterioration risk map according to storage environment data, and performing layered space sampling according to the three-dimensional quality deterioration risk map to obtain a sample set with coordinate identification; performing differential scanning calorimetry analysis on the sample to obtain gelatinization enthalpy value data, and integrating the gelatinization enthalpy value data with space coordinates and risk levels of the sample into a structured database; by taking the space coordinate as a position variable, the gelatinization enthalpy value as a target variable and the risk value as an auxiliary variable, processing by adopting a covariant Kriging algorithm, and generating a continuous aging degree prediction curved surface covering the whole storage space and corresponding uncertainty evaluation data; and finally, constructing a three-dimensional visual distribution map based on a prediction result, calculating a whole warehouse statistical index, identifying a high-confidence local deterioration region, and forming a quality decision report. The problem of monitoring distortion of a traditional method is effectively solved.

This invention discloses an electromagnetic situational awareness and visualization system based on geospatial positioning, comprising: a geospatial grid construction module, which acquires GIS engine data and divides the target area into a multi-scale non-uniform three-dimensional computational grid according to monitoring requirements; a geographic constraint inference and modeling module, which executes an adaptive electromagnetic propagation model and calculates physical trend values; a multi-source residual compensation module, which acquires the measured signal strength values ​​of monitoring nodes, calculates the residuals between the measured signal strength values ​​and the physical trend values, performs Kriging interpolation on the residuals using the residual Kriging algorithm, and synthesizes a global reconstructed situational map; and an interactive multi-dimensional display module, which renders the global reconstructed situational map as a dynamic heatmap superimposed on the geographic model, and synchronously displays the instantaneous spectrum map and waterfall map of the associated frequency points, outputting the electromagnetic situational field. This system can achieve high-precision situational awareness in complex geographic environments, providing a reliable basis for the analysis and management of the electromagnetic environment.