31 results about "Spatial Autocorrelations" patented technology

This application relates to a method, apparatus, and equipment for soil erosion analysis. A pre-set model is trained by comprehensively considering soil erosion driving factor data, spatial structure characteristics, and soil erosion modulus data of the sample area. During training, the spatial autocorrelation of the prediction residuals of the soil erosion prediction model is ensured to meet a pre-set non-significant condition, thereby overcoming the problem of existing machine learning models neglecting spatial characteristics. In the prediction stage, the trained model is used in conjunction with the soil erosion driving factor data and spatial structure characteristics of the target prediction area to obtain accurate soil erosion modulus data, thus obtaining reliable soil erosion analysis information.

This invention provides a method and system for analyzing anomalies in toxic and harmful gas concentrations based on spatial autocorrelation, relating to the field of mine ventilation safety technology. The method includes: tracking and sensing the concentration of toxic and harmful gases in a mine sensor network to obtain time-series slices of concentration; analyzing these slices to obtain a global Moran's index and multiple local spatial autocorrelation indices; when the global Moran's index deviates from the dynamic baseline, clustering of significantly anomalous sensor nodes based on geographical coordinate distances according to the Z-test results of multiple local spatial autocorrelation indices to locate areas of ventilation structural imbalance; performing spatiotemporal collaborative anomaly root cause analysis; and outputting a structured anomaly tracing report. This addresses the problem in existing technologies that use single-point sensors to independently monitor mine toxic gas concentrations and issue threshold alarms, which have blind spots in monitoring the spatial correlation characteristics of gases and ventilation structural anomalies. This leads to frequent false alarms and missed alarms in mine gas concentration anomaly identification and airflow control decisions, and also fails to dynamically adapt to changes in roadway engineering.

The application relates to the technical field of soil analysis, in particular to a cane field soil organic matter influencing factor analysis method and system based on multi-source data, which comprises the following steps: acquiring the spatial distribution of all sampling points, using different colors to distinguish different years and different cane fields; calculating a spatial autocorrelation index to determine whether the sampling point distribution is aggregated, discrete or random; dividing the cane fields and analyzing the soil nutrients of each cane field for comparative analysis; constructing a random forest regression model; extracting feature data of each cane field and using SHAP values for interpretation; drawing a single feature data of each cane field and a corresponding SHAP value relationship scatter diagram to obtain a SHAP dependence diagram; using a PLS-SEM algorithm to estimate the random forest model to obtain direct effects or indirect effects; and verifying the importance ranking of SHAP by comparing the total effects of different driving factors. The application realizes closed-loop analysis from spatial pattern identification to machine interpretation.

The application discloses an event camera-based event-by-event space-time representation method. The method comprises the following steps: acquiring original event sequence data collected by an event camera; performing feature embedding on the original event sequence data in the space and time dimensions, and converting the obtained feature embedding into a fixed-dimension embedding tensor containing N tensor elements; inputting the fixed-dimension embedding tensor into a triple attention network, and calculating the feature correlation between the embedding tensor elements; constructing a multi-layer perception machine, taking the local space-time autocorrelation weight, the local space autocorrelation weight and the global space-time autocorrelation weight of each event as inputs, and calculating a complete space-time representation tensor of the event sequence. Compared with the time granularity loss caused by the common event image stacking or voxel aggregation, the application can retain all the space-time information and time granularity of the original event sequence to the greatest extent, which is of great significance for the practical application of the event camera in a high-speed scene.

This invention belongs to the field of remote sensing technology and discloses a method for optimizing habitat quality parameters in the InVEST model based on Remote Sensing Ecological Indices (RSEI). This invention overcomes the technical bottleneck of traditional InVEST models, which rely on manual experience to set parameters and are difficult to update dynamically over time. By constructing a multi-source remote sensing data-driven RSEI, and utilizing its statistical characteristics and spatial distribution patterns across different land use types, it achieves objective quantification and dynamic updating of four key parameters: habitat suitability, threat weight, threat attenuation distance, and habitat sensitivity. Without requiring data from ecological monitoring stations, it directly incorporates continuous changes in ecological environment quality into the InVEST parameter system, resulting in higher objectivity, temporal consistency, and spatial autocorrelation in habitat quality calculations. This provides a novel technical approach for the automated and time-series assessment of regional-scale habitat quality.

This invention relates to the field of building material testing technology, specifically a method for testing the mechanical properties of corn stalk laminated timber. The method includes: planning a grid of testing points on the laminated timber; measuring the elastic modulus and local compressive strength of each testing point in both the longitudinal and transverse directions to form a measurement distribution matrix; based on the differences in values ​​of the measurement distribution matrix in each direction, performing multi-directional feature calibration on the laminated timber to delineate preliminarily identified performance defect areas; based on the performance defect areas under multi-cycle fatigue loading, using the residual elastic modulus under multi-directional cyclic testing, performing residual performance calibration to establish performance distribution weights for each testing point; based on the performance distribution weights configured for the testing points, performing spatial autocorrelation analysis to determine the spatial correlation data of performance defects; and interpreting the spatial correlation data in relation to the distribution of testing points to generate mechanical testing results. This method improves the accuracy and efficiency of laminated timber testing.

The application discloses a kind of face, block and point scale linkage flood warning method based on river forecast water level in plain area, according to the flood face scale early warning of plain representative site, for having face scale early warning area, using GIS technology and the spatial autocorrelation of plain area water level in flood forecast process, calculate submerged grid and depth, carry out block, point scale early warning.The method is a kind of simple, fast method.It includes the following steps: collecting plain area basic data;A kind of face, block and point multiscale linkage early warning mechanism;Compare regional representative site forecast water level and characteristic water level, dike elevation, and carry out face scale flood risk early warning;With face scale early warning area as unit, obtain regional forecast water level face data, generate the submerged grid and water depth of regional unit, and carry out block scale early warning;With face scale early warning area as unit, in combination with submerged face, submerged water depth and flood risk point data, utilize spatial analysis technique to carry out point scale early warning.

The present application belongs to the technical field of space planning, and discloses a land space planning suitability evaluation system based on space-time big data; by constructing a projection zone distribution topology graph and an anisotropic distortion tensor field, coordinate distortion at the junction of projection zones is accurately identified and corrected. By using a spatial autocorrelation analysis method, systematic deviation areas in the residual system in the process of digitizing historical maps are identified, and a residual propagation influence domain model is established, so that accurate coordinate correction of each position point is realized. Through an adaptive fusion mechanism of compensation components, the fusion accuracy of multi-source space-time data is improved. A coordinate correction confidence evaluation mechanism is introduced, data uncertainty information is included in the suitability evaluation process, and more scientific and reliable evaluation results and confidence intervals are provided for decision makers.

The application discloses a tree individual growth stress diagnosis and habitat optimization method, system and equipment, which firstly collects individual growth indexes and spatial positions of representative sample trees, and fuses multi-source remote sensing and geographic data and the like; dynamic quality control is used to ensure sample representativeness and model stability; innovatively, human influence is decomposed into urban influence indexes and agricultural influence indexes; after key stress factors are screened by using a random forest, a regional structural causal model is constructed under a linear mixed effect model framework, and spatial autocorrelation interference is eliminated; the effects of each factor on tree growth are quantified through path analysis; finally, stress diagnosis is carried out on any individual tree based on the solidified model, dominant stress factors, relieving factors and aggravating factors are identified, and scientific and operable one-tree-one-strategy optimization suggestions are generated. The application realizes a paradigm transition from correlation judgment to causal mechanism revelation, and has high interpretability, individualized precision and strong decision guidance ability.

The application relates to the technical field of carbon emission analysis, and discloses a contaminated soil carbon emission comprehensive analysis system and method, which comprises the following steps: obtaining initial process data of a whole process of contaminated soil treatment of each space unit and performing pretreatment, determining target process data, screening the target process data based on an association rule, determining analysis indexes of three dimensions of soil characteristics, transportation parameters and cement kiln operation, performing dimension correlation analysis on the analysis indexes, determining in-site carbon emission accounting and out-site carbon emission accounting by adopting a difference method and a proportional distribution method based on an influence relationship and index combination, generating model carbon emission scores based on carbon emission spatial clustering rules and all carbon emission accounting based on a spatial autocorrelation analysis algorithm and in combination with carbon emission accounting, and determining a carbon emission risk grade of a target region based on the model carbon emission scores. The application ensures the reliability of comprehensive analysis of carbon emission.

The application relates to a structure discretization damage morphology prediction method, which comprises the following steps: constructing a finite element grid model of a structure, and extracting spatial position information of random units; determining distribution characteristics and spatial distribution characteristics of each random material parameter, and correlation between the parameters; according to the obtained spatial position information of the random units and the distribution characteristics of the random material parameters, a double correlation conversion method is used to convert a completely randomly generated performance data set, so that a performance data set capable of simultaneously considering material parameter mutual correlation and spatial self-correlation is obtained; the performance data set is assigned to unit material properties of the structure finite element model, and combined with a damage constitutive relation of the material, simulation is performed to obtain distribution characteristics of a damage morphology of the structure. The application also relates to a structure discretization damage morphology prediction system. The application can improve the prediction accuracy of the discretization damage morphology.

This invention relates to the field of laser measurement instrument technology and discloses an automatic loss detection method for fiber optic connectors based on optical signal attenuation analysis. The method includes: acquiring the backscattered light power sequence of the fiber optic link under test; calculating the logarithmic rate of change of power behind the connector feature point to generate a logarithmic power attenuation rate sequence; processing the sequence through a sliding window to calculate the spatial autocorrelation coefficient and determine a stationarity discrimination index characterizing the evolution trend of mode distribution; identifying the spatial location where the index enters the noise baseline range and establishing the mode equilibrium inflection point; and based on the power evolution relationship between the inflection point and the feature point, stripping the power fluctuation component caused by mode field diameter mismatch and calculating the intrinsic insertion loss. This invention achieves deep decoupling of energy transient fluctuations caused by mode reconstruction and interface intrinsic loss in the spatial dimension, eliminating pseudo-loss interference generated by heterogeneous fiber interconnects.

The application discloses a kind of for non-destructive fast evaluation of bamboo physical mechanics property prediction model construction method, mainly including collecting the growth information of target bamboo and the positioning information of the sample site where it is located;The bamboo culm of target bamboo is determined for physical mechanics property, and the measured physical mechanics property data is obtained;Correlation analysis is carried out on growth information, positioning information and measured physical mechanics property data, and the index significantly related to physical mechanics property is selected as model input variable;Based on model input variable and measured physical mechanics property data, the prediction model for predicting the physical mechanics property of bamboo is constructed.The application introduces regional grouping effect and spatial autocorrelation structure when establishing the correlation model of bamboo culm growth character and bamboo physical mechanics property, so that the final prediction model can non-destructively determine the growth character of bamboo, quickly evaluate the physical performance of bamboo, and then greatly improve the grading utilization and high-value processing efficiency of different quality bamboo resources.

This invention provides a method for assessing the risk of urban groundwater inrush based on a 3D mesh model and the XGBoost algorithm, belonging to the field of geological disaster early warning technology. The method includes: acquiring geological, environmental, and historical disaster data in real time through a multi-source heterogeneous data interface; constructing heterogeneous mesh units based on 3D geological modeling, dynamically binding parameters such as groundwater level, fault activity, and lithological permeability coefficient; using the XGBoost algorithm combined with second-order Taylor expansion to optimize gradient calculation, and constructing a nonlinear risk assessment model through Bayesian hyperparameter tuning and SHAP value feature interpretation analysis; identifying contiguous hazardous areas based on spatial autocorrelation analysis and an adaptive threshold segmentation algorithm; and realizing risk heat maps, geological profile overlays, and real-time early warning through a 3D GIS platform. This invention solves the problems of traditional methods relying on linear models, limited data, and insufficient local risk characterization, significantly improving the accuracy, efficiency, and engineering applicability of risk assessment.

The application discloses a kind of rock mass mechanical parameter non-stationary random field construction methods based on seismic frequency domain amplitude, belong to engineering geology detection technical field.This method takes the seismic response signal of target engineering area as input, forms three-dimensional frequency domain amplitude data body by time-frequency transform;Then extract the deterministic trend item varying with depth from amplitude data, and estimate spatial correlation structure with detrended residual;Finally, using fast Fourier transform, realize power spectral density filtering in frequency domain to generate three-dimensional random disturbance with target spatial correlation, and superimpose it with trend item and depth layering variance, obtain three-dimensional rock mass mechanical parameter random field that meet the depth direction non-stationary, spatial anisotropic correlation.This application generated random field contains macroscopic trend item and microscopic random disturbance simultaneously, not only can accurately reflect the depth trend of rock mass mechanical parameter, but also can maintain spatial autocorrelation and randomness, more in line with the actual heterogeneous and layered structure characteristics of geological body.

This invention discloses a method for analyzing and optimizing the impact of landscape patterns on county-level carbon storage, relating to the field of energy conservation and carbon reduction technology. It addresses the problems of weak quantitative foundations, inaccurate analysis of impact mechanisms, and a lack of differentiated optimization strategies in existing technologies for analyzing the correlation between carbon storage and landscape patterns. This solution includes constructing standardized land use data and a regionally adapted carbon density parameter library to quantify the landscape pattern index and county-level carbon storage; determining the spatial clustering characteristics of both through spatial autocorrelation analysis and hotspot identification; analyzing nonlinear interaction effects and identifying optimal thresholds for key factors using the XGBoost-SHAP model; analyzing spatial heterogeneity using the GWR model; and revealing the impact mechanisms based on the analysis results to formulate differentiated landscape pattern and land space optimization strategies. This invention also proposes a method and analysis system for enhancing carbon sequestration capacity, achieving accurate analysis of the correlation between carbon storage and landscape patterns. The optimization strategies are highly targeted and practical, providing scientific guidance for enhancing county-level carbon sequestration.

This invention belongs to the field of arable land quality evaluation technology and provides an adaptive evaluation method for arable land quality based on soil physical indicators and yield limiting factors. The method includes: screening core soil physical indicators, constructing a multi-dimensional indicator system, constructing a weight training set, training a weight adaptive adjustment model, obtaining weight correction results, calculating initial evaluation grid data, and generating a localized arable land quality grade map. This invention quantifies key physical degradation processes that restrict arable land yield, such as soil compaction and compaction, by supplementing the traditional indicator system with soil physical structure indicators. Through the weight adaptive adjustment model and weight correction calculation, it achieves regional adaptive dynamic adjustment of indicator weights. By embedding a collaborative interpolation algorithm with environmental driving factors and a mechanism for determining the degree of influence of environmental indicators, it achieves the synergistic utilization of the spatial autocorrelation of the comprehensive score of sampling points and the global driving effect of environmental factors, improving interpolation accuracy at low sampling densities.

The application relates to the technical field of bioinformatics and system biology, and particularly relates to a path enrichment analysis method and device based on Moran's I. The method comprises the following steps: acquiring biological sample data, extracting molecular identifiers and corresponding expression values; mapping the molecular identifiers to nodes of a path network in a preset path database, and constructing a spatial weight matrix based on a topological structure of the path network; performing mask processing on nodes that are not mapped to expression data, and marking observation nodes through a mask vector; extracting a subgraph based on the mask vector, calculating global spatial autocorrelation of the path by using a global Moran's I statistic, evaluating significance by using a Monte Carlo permutation test, calculating a p value corresponding to the global Moran's I statistic, and determining an enrichment path list. By introducing the Moran's I index in spatial statistics, the application can comprehensively consider expression changes of molecules in a path and topological adjacent relationships, and can make up for defects of traditional enrichment analysis methods that cannot reflect upstream and downstream regulation relationships.

This invention provides a DPSIR-based method for assessing water ecological resilience, belonging to the field of water ecological assessment technology. The method includes: constructing a five-dimensional DPSIR index system; collecting historical data for each index and performing dimensionless processing to obtain standardized values; determining the weights of each index using the entropy weight method; calculating the evaluation indices for the five dimensions based on the weights and standardized values, and then summing the weighted evaluation indices; performing spatial autocorrelation analysis to reveal the spatial distribution characteristics of water ecological resilience; identifying key factors affecting water ecological resilience by progressively introducing and screening variables and using the least squares method to fit the optimal regression equation; and formulating strategies and recommendations to improve water ecological resilience based on the assessment results. This invention, employing the aforementioned DPSIR-based water ecological resilience assessment method, solves the limitations of traditional assessment methods and the insufficient targeting of water ecological governance.

The application provides a soil pollution cross-border risk assessment method based on spatial autocorrelation and differentiation, relates to the technical field of soil environment monitoring and pollution prevention, and comprises the following steps: S1, multi-source spatial data acquisition and preprocessing; S2, construction of a spatial weight matrix and parameter initialization; S3, global joint decomposition of spatial autocorrelation and hierarchical heterogeneity; S4, local spatial correlation index decomposition; S5, statistical inference based on conditional limited spatial replacement; and S6, target area risk assessment. The method can decouple the natural background structure and local pollution continuous diffusion, realize accurate detection of micro-real pollution hotspots, and quantitatively evaluate the effectiveness of natural / administrative boundary pollution interception.

This application provides a method and product for constructing a multi-scale soil parameter space, belonging to the field of data processing technology. The method includes: acquiring multiple soil samples from a target area; extracting soil particle size parameters from the soil samples to obtain soil particle size parameters, including: fine particle characteristic parameters, used to characterize the relative content of fine particle components in the soil; and coarse particle characteristic parameters, used to characterize the particle size characteristics of coarse particle components in the soil; modeling parameter distribution based on the soil particle size parameters to obtain a marginal distribution function and a joint distribution function; generating an initial parameter field based on the marginal distribution function and the joint distribution function through Monte Carlo sampling, and introducing a spatial autocorrelation function to correct the spatial correlation of the initial parameter field to obtain the constructed soil parameter space.

The application relates to the technical field of power system multi-source scheduling, and discloses an optimization scheduling method and system considering traction network resources, and an electronic device, which comprises the following steps: clustering analysis is performed on the data of each node of a power system to obtain extended power system data; a particle swarm algorithm is used to optimize a prediction model based on power market operation data to obtain a wind power prediction value; a Transformer model is used to extract features of photovoltaic power data to calculate a photovoltaic power prediction value; a spatial autocorrelation method is used to screen key station data to calculate a load power prediction value; a regulatable resource planning model is constructed based on the wind power prediction value, the photovoltaic power prediction value and the load power prediction value; and the regulatable resource planning model is iteratively optimized by taking multi-energy power generation and consumption time sequence information as a training set to obtain a final scheduling model. The application provides reliable technical support for the safe, stable and economic operation of a power system.

A functional near-infrared spectral signal enhancement and classification method based on spatiotemporal autocorrelation and attention encoding is proposed to address the limitations of limited sample size and insufficient generalization ability of classification models in functional near-infrared spectral signal data. This method is applicable to the auxiliary diagnosis of neurodevelopmental disorders such as attention deficit hyperactivity disorder (ADHD). The method first preprocesses the raw near-infrared spectral data, extracting oxyhemoglobin signals and calculating spatial autocorrelation parameters, channel-level temporal autocorrelation parameters, and the eigenvalue distribution of the functional connectivity matrix. Then, enhanced data is generated based on the spatiotemporal autocorrelation model. Finally, the raw and enhanced data are input into the STEAFNet deep learning model for accurate classification. This invention generates high-quality enhanced data, expands the sample size, and maintains high fidelity. Combined with deep learning, it improves classification accuracy and generalization ability, providing technical support for clinical applications.

The application relates to the technical field of machine learning, and particularly discloses a spatial local XGBoost machine learning model based on multi-scale geographical weighting, which comprises the following modules: a feature decoupling module for constructing a double-channel input structure of geographical features and non-geographical features, realizing feature decoupling and fusion through a multi-scale spatial weight matrix; a bandwidth allocation module for dynamically determining a bandwidth scale in an XGBoost tree splitting process and establishing dynamic weights of tree levels; a constraint gain module for generating a splitting point marked with a scale; a contribution decoupling module for extracting splitting features and correlating geographical features and non-geographical features, realizing salted prediction and contribution index extraction; and a verification optimization module for optimizing model parameters through multi-scale heat maps and spatial autocorrelation analysis. The model can capture geographical spatial effects of different scales, improve spatial local prediction accuracy, and be applied to a scene with spatial heterogeneity in soil salinization analysis.

This invention relates to the field of optical equipment technology and discloses an infrared superlens image quality enhancement method based on foreground-background joint modeling, comprising the following steps: Step S1, based on regional feature analysis of degraded infrared superlens images, obtaining the morphological features of the foreground target and the texture complexity information of the background environment. This infrared superlens image quality enhancement method based on foreground-background joint modeling introduces a joint foreground and background modeling mechanism, combining spatial autocorrelation analysis, channel-aware lookup tables, and a dynamic fusion network of spiking neural networks to construct an infrared image quality enhancement framework with high expressive power and low energy consumption. This method can effectively achieve region-aware reconstruction of degraded infrared images, maintain the clarity of target details and global contrast in complex backgrounds, significantly improve the visual quality and signal recovery accuracy of images, and provide a new technical path for tasks such as infrared superlens imaging and weak signal target detection.

The application discloses a kind of multi-parameter lognormal random field automation generation method based on finite element grid, the method first reads finite element grid file, parses the unit topological relation of target analysis area and automatically extracts the geometric center point coordinate of entity unit;Obtain multidimensional uniform distribution sample, and convert into independent initial standard normal random matrix;Respectively construct the cross correlation matrix of each parameter space autocorrelation matrix and parameter;Multi-parameter lognormal random field is generated using nonlinear mapping equation;Finally, the random field data is mapped according to unit number and verified.Outputs.Through the combination of original finite element grid analysis and multiple correlation decoupling calculation, the method can efficiently and accurately generate the random field subject to complex correlation structure, effectively eliminate the error caused by traditional independent grid interpolation mapping, provide a fully automated pre-processing tool for large-scale random finite element simulation and engineering reliability assessment.

The application provides a method and system for multi-layer fusion processing of urban and rural construction land resources, and relates to the technical field of data processing.The method comprises the following steps: calculating an adjustment coefficient according to the morphological characteristics of a local block, correcting the evaluation results of each evaluation unit by using the adjustment coefficient, and generating a modified two-class intensive evaluation result layer; calculating global spatial autocorrelation statistics and local spatial autocorrelation statistics based on the modified two-class intensive evaluation result layer, and generating a spatial clustering feature layer; generating a comprehensive identification result layer based on the spatial clustering feature layer and in combination with the modified two-class intensive evaluation result layer; and generating a management measure layer based on the comprehensive identification result layer and in combination with spatial planning management requirements.The application improves the accuracy of intensive evaluation of construction land in urban and rural transition areas.

The application discloses a multi-temporal super-resolution reconstruction method based on change prior guidance, and belongs to the technical field of remote sensing image processing. The method first pre-processes multi-temporal remote sensing images to construct a standardized time series dataset; then extracts multi-temporal change information to generate a pixel-level change prior map; further, low-resolution image features and the change prior map are jointly modeled, a change perception attention mechanism is used to adaptively assign temporal weights, and a change region enhancement branch and a non-change region fusion branch are used for differential reconstruction; meanwhile, physical constraints such as spectral consistency and spatial autocorrelation are introduced to establish a joint optimization framework for reconstruction and change detection; and finally, high-resolution reconstruction results and a change detection map are output. The application effectively solves the change region reconstruction artifact problem and realizes high-precision and high-consistency multi-temporal remote sensing image super-resolution reconstruction.