142 results about "Spatial Autocorrelations" patented technology

The invention relates to the field of security surveying and mapping, in particular to a network security space surveying and mapping method, system and device based on multi-source data fusion, and the method comprises the steps: obtaining network security data in real time, and constructing a dynamic network topological graph; calculating a time-varying vulnerability score based on the topological graph and a historical attack log, and predicting an attack path and a propagation probability through a Bayesian network; performing cross-domain fusion on equipment, service and user behavior characteristics by adopting a federated learning framework to generate a dynamic asset portrait; generating a risk thermodynamic diagram in combination with spatial autocorrelation analysis and a multi-index fusion algorithm; a defense strategy effect is simulated based on an attack graph reconstruction engine, a Pareto optimal strategy combination is generated through an NSGA-II algorithm, and closed-loop verification and dynamic parameter correction are realized by utilizing honeypot deployment and flow traction. Therefore, the problems of topology update lag, single risk assessment dimension, cross-domain threat association fracture, defense strategy static stiffness, non-closed loop of a verification system and the like in the traditional technology are solved.

The invention discloses a three-dimensional point cloud data filtering method and system based on adaptive clustering segmentation and gradient compensation, and the method comprises the steps: carrying out the data preprocessing of three-dimensional point cloud data through employing a statistical filtering method, obtaining the preprocessed point cloud data, extracting the point cloud features in the preprocessed point cloud data, and obtaining the point cloud feature data; according to the invention, a function of establishing multi-dimensional features including point cloud density, spatial autocorrelation and local curvature features by using an adaptive clustering segmentation algorithm based on density spatial distribution and carrying out dynamic clustering segmentation on a ground feature-ground cluster is realized; and residual mixed point clouds can be further separated through a coarse-fine granularity grid grading processing strategy, so that ground point clouds are separated, and the technical limitations of high parameter dependence, insufficient terrain adaptability and poor real-time performance in the prior art are broken through; and the filtering precision and robustness in a complex scene are remarkably improved through multi-dimensional collaborative optimization, and the method is suitable for being widely popularized and used.

The invention provides a Beidou ionosphere random error autocorrelation model establishment and correction method and device, and the method comprises the steps: building a deep learning neural network model for predicting a Beidou ionosphere random error through combining a convolutional neural network, a long-short-term memory neural network and an attention mechanism, and carrying out the correction of the Beidou ionosphere random error based on the predicted Beidou ionosphere random error. And respectively fitting the time autocorrelation function of the Beidou ionosphere random error and the space autocorrelation function of the Beidou ionosphere random error, and establishing a Beidou ionosphere random error autocorrelation model. The Beidou ionosphere random error prediction method can predict the Beidou ionosphere random error under the condition of not knowing an accurate physical model, and provides technical support for high-precision prediction of the Beidou ionosphere model and analysis of the Beidou ionosphere random error.

The invention discloses a coal seam frequency resonance seismic exploration system and method based on deep learning. The method comprises the following steps: S1, arranging passive source seismic equipment to collect background waveform data; s2, performing spatial autocorrelation spectrum analysis, and extracting a frequency dispersion curve; s3, speed inversion is carried out, and a speed profile map is output; s4, constructing a DeepLabV < 3 + > model to identify geological anomalies; s5, performing image preprocessing to generate standardized input; s6, performing model reasoning to obtain a segmentation result graph; s7, generating a structure map and a risk map in combination with the coordinates; and S8, exporting an interpretation result in a structured format. According to the method, a seismic data processing flow combining a spatial autocorrelation method and a DeepLabV3 + model is adopted, and automatic extraction of coal seam frequency resonance information and intelligent recognition and visual interpretation of a geological structure are achieved.

The invention provides an airfield pavement detection method based on an earthquake background noise imaging technology, and relates to the technical field of airfield pavement detection. Comprising the following steps: acquiring seismic background noise data of an airport pavement to be measured; preprocessing the seismic background noise data to obtain preprocessed noise data; carrying out cross-correlation calculation on the preprocessed noise data and carrying out empirical Green function extraction; according to the empirical Green function, a surface wave group velocity frequency dispersion curve is extracted by adopting spatial autocorrelation and multiple filtering methods; inverting shear wave velocity distribution of each layer of the pavement based on the surface wave group velocity dispersion curve, and constructing a three-dimensional velocity model; determining a shear wave velocity distribution diagram according to the three-dimensional velocity model; and determining a detection result of the airfield pavement to be detected according to the transverse wave velocity distribution diagram. According to the invention, the problems of low detection precision and poor real-time performance of a common detection technology in the prior art are solved.

The invention discloses a deep geothermal target area intelligent positioning method based on a machine learning algorithm, and relates to the technical field of geothermal resource exploration. Comprising the following steps: S1, multi-source heterogeneous data fusion acquisition and preprocessing; s2, geological feature entropy quantification and spatial autocorrelation analysis are carried out; s3, deep feature extraction and multi-modal feature fusion are carried out; s4, performing multi-model adaptive integration and dynamic weight optimization; s5, reinforcement learning dynamic adjustment participation abnormal threshold determination; and S6, performing intelligent positioning and risk assessment on the three-dimensional geothermal target area. According to the positioning technology, geological structure, geophysical field, geochemistry and remote sensing data are integrated through a machine learning algorithm, high-dimensional feature vectors are constructed, the limitation that the prior art depends on physical detection singly is solved, reinforcement learning is introduced to dynamically optimize model parameters, real-time geothermal well data updating is combined, and the positioning accuracy is improved. The model can adapt to geological condition changes, and the prediction precision is greatly improved compared with a traditional method.

The invention relates to the technical field of medical elastography and material multi-scale mechanical measurement, in particular to an elastography method and system based on an instantaneous scattering displacement field, and the method comprises the steps: driving a passive / active method scattering displacement field excitation device through a frequency domain controllable sine wave signal, and generating a scattering displacement field on a target material, an instantaneous displacement field is obtained through high spatial resolution measurement, then sliding window local interception, two-dimensional autocorrelation calculation and nonlinear fitting based on the Rayleigh / shear wave spatial autocorrelation theory are performed on the single-frame instantaneous displacement field, shear wave velocity distribution is obtained, and then the viscoelasticity modulus of a target area is inverted to achieve comprehensive characterization of the viscoelasticity of the material. According to the scheme, the single-frame scattering displacement field is excited and collected, and the elasticity inversion is performed based on the instantaneous displacement field, so that the dependence of traditional elasticity imaging on high-frame-rate equipment is broken through, the material viscoelasticity evaluation can be efficiently completed at low cost, and the application prospect in the fields of clinical diagnosis and mechanical measurement is wide.

The invention discloses a fish potential spawning site identification and positioning method based on habitat similarity and spatial autocorrelation, and belongs to the technical field of river ecological protection and habitat evaluation. The method comprises the following steps: step 1, obtaining state values of hydrodynamic force, water quality and substrate indexes of a typical target fish spawning site and target fish distribution information; 2, determining egg laying habitat requirements of target fishes; 3, performing comprehensive habitat suitability evaluation on the typical target fish spawning site; 4, constructing a target fish potential spawning site identification model combining habitat similarity and spatial autocorrelation; and 5, evaluating and verifying the feasibility and accuracy of the method. According to the method, habitat similarity and spatial autocorrelation are combined, a potential spawning site of target fishes in a drainage basin is identified and positioned, and feasibility and accuracy of the method are verified by combining habitat suitability evaluation and spatial position coincidence.

The invention relates to the technical field of regional ecological risk assessment and space management and control, in particular to a regional ecological risk assessment and partitioning method based on a multi-system spatio-temporal dynamic relationship, which comprises the following steps: collecting and preprocessing multi-source spatio-temporal data; a natural ecological risk index and a human social risk index are fused, and a comprehensive ecological risk assessment model based on a social-ecological system is constructed; divariate spatial autocorrelation analysis and cold and hot spot analysis are adopted, and a region is divided into three-level subareas including a management and control area, a monitoring area and a prevention area and six types of subareas; and analyzing the partition dominant driving factor by using the optimal parameter geographic detector, and generating a differentiated control strategy. The method realizes quantitative coupling of natural ecology and human social system risk spatio-temporal dynamic relationships, solves the problems of strong subjectivity, insufficient system comprehensiveness and lack of dynamic association mechanisms of a traditional method through multi-dimensional index fusion and spatial analysis technologies, and provides systematic technical support for accurate management of regional ecological risks.

The invention specifically discloses a long-time-sequence high-frequency ecological environment quality space-time differentiation and driving analysis method, and relates to the technical field of remote sensing and ecological environment. The method comprises the following steps: determining an evaluation area, and constructing a macroscopic ecological safety risk evaluation framework; ecological indexes of greenness, humidity, temperature and dryness are calculated, and a time sequence data set is constructed; reconstructing a time sequence data set and constructing a remote sensing ecological index model based on the reconstructed time sequence data set; verifying the precision of the model, and evaluating the reconstruction precision by taking the screened high-quality pixels as true values; analyzing the spatial and temporal change trend and significance output by the model by using slope estimation and trend test, outputting future change continuity by using a Hurst index analysis model, and measuring spatial autocorrelation output by the model by using a Moran index; remote sensing ecological index evolution factors are researched by means of an optimal parameter geographic detector. According to the invention, the precision and timeliness of ecological assessment are improved, and decision support is provided for ecological management.

The invention relates to the technical field of machine learning, and particularly discloses a spatial local XGBoost machine learning model based on multi-scale geographic weighting, and the model comprises a feature decoupling module which is used for constructing a two-channel input structure of geographic features and non-geographic features, and achieves the feature decoupling and fusion through a multi-scale spatial weight matrix; the bandwidth allocation module dynamically determines the bandwidth scale in the XGBoost tree splitting process, and establishes a dynamic weight bound by tree levels; the constraint gain module generates split points with scale marks; a contribution decoupling module extracts split features and associates geographic and non-geographic features to realize salinization prediction and contribution index extraction; and the verification optimization module optimizes model parameters through a multi-scale thermodynamic diagram and spatial autocorrelation analysis. The model can capture geospatial effects of different scales, improves the spatial local prediction precision, and is applied to soil salinization analysis scenes with spatial heterogeneity.

The invention discloses an ecological vulnerability evaluation method based on road domain landscape pattern optimization, and the method comprises the steps: obtaining expressway road domain environment data as training data, and obtaining ecological environment data; calculating an ecological environment quality index of the ecological environment data, and obtaining landscape structure features in the target area; integrating the landscape pattern index into an ecological sensitive factor, and constructing an ecological vulnerability evaluation model by adopting an SRP structure; performing spatial autocorrelation analysis on space-time evolution of the ecological vulnerability of the road domain according to the ecological vulnerability evaluation index, establishing an ecological vulnerability multi-scene simulation model, performing overlay analysis on an ecological vulnerability evaluation result and a land utilization type, obtaining a specific restoration strategy, tracking space-time evolution characteristics of the ecological vulnerability, and performing road region ecological vulnerability evaluation. Ecological vulnerability influence factors are adjusted based on different scenes, future possible ecological vulnerability risks are simulated, specific restoration strategies are made according to different partition standards, and effective configuration and use efficiency of resources are ensured.

The invention discloses a high-standard farmland construction time sequence optimization method, and relates to the technical field of agricultural information, and the method comprises the steps: superposing a permanent basic farmland distribution diagram layer and a high-standard farmland distribution diagram layer, and recognizing a permanent basic farmland region which is not constructed into a high standard; obtaining multi-source basic data in the region, and representing the grain yield by adopting an LAI average value after S-G filtering; constructing a high-standard farmland construction ecological toughness evaluation index system; the combined weight of each evaluation index is calculated according to the combination of an entropy weight method and a CRITIC method, and a set pair analysis model is established to calculate the farmland ecological toughness; a spatial interaction relationship between grain yield and farmland ecological toughness is obtained based on a bivariate local spatial autocorrelation analysis method, a preferential plot of high-standard farmland construction is identified in combination with a self-organizing mapping neural network and a K-means method, and a construction time sequence arrangement and spatial layout optimization scheme is formed. According to the invention, the problem of high-standard farmland construction priority can be solved.

The invention discloses a communication and inductance integrated channel modeling method with space consistency, which comprises the following steps: 1) setting an initialization scene, and configuring related parameters of an antenna; 2) generating powers, azimuth angles and pitch angles of the proprietary clusters and the common clusters and distances between the first and the last passing clusters and a transmitting and receiving end, and then generating azimuth angles, pitch angles and propagation distances of sub-paths in the clusters; 3) calculating the probability of existence of the proprietary cluster and the common cluster on the antenna array; 4) calculating and generating channel impulse response (CIR) of the sensing channel and the communication channel; and 5) simulating and calculating the existence condition of the clusters on the array surface, a time autocorrelation function (TACF) and a space autocorrelation function (SCCF), and judging whether the channel modeling method has accuracy and space consistency or not. The channel modeling method disclosed by the invention is based on a common cluster and a proprietary cluster of a sensing channel and a communication channel, the relationship between the two channels is considered, the channel space consistency is comprehensively analyzed, and the channel modeling method has important significance for guiding the design of an ISAC system.

The invention relates to the technical field of land utilization change evaluation, in particular to a carbon emission spatial autocorrelation evaluation method, model and system based on a land gradient utilization model and a medium. Calculating the carbon absorption amount of the to-be-evaluated area from the natural vegetation dimension, the crop dimension and the water area dimension; calculating the carbon emission of the to-be-evaluated area from the energy consumption dimension, the industrial production process dimension, the waste treatment dimension, the agricultural dimension, the respiration dimension and the water area carbon volatilization dimension; determining a carbon emission net value of the to-be-evaluated region according to a difference value between the carbon absorption amount and the carbon emission amount; determining a global spatial autocorrelation index and a local spatial autocorrelation index of the to-be-evaluated region according to the carbon emission net value; and determining a carbon emission spatial autocorrelation evaluation result of the to-be-evaluated region according to the global spatial autocorrelation index and the local spatial autocorrelation index. The objective of the invention is to solve the problem of how to carry out spatial autocorrelation evaluation on areas with obvious vertical zone performance and altitude gradient performance.

The invention belongs to the field of tunnel engineering, and relates to a tunnel defect detection data spatial resolution enhancement method and device based on a Vine Copula dependent structure and a conditional random field, a computer readable medium and a computer program product. The method comprises the following steps: firstly, separating an overall trend and random fluctuation from original tunnel defect monitoring data; then establishing a Vine Copula multivariable dependence model to represent a statistical correlation relationship among the plurality of defect indexes; and then generating defect distribution data with high spatial resolution by using a conditional random field interpolation simulation technology in combination with a spatial autocorrelation analysis result. According to the invention, while the accuracy of the existing measuring point data is ensured, the spatial correlation structure and multivariable joint distribution characteristics of the defect data are maintained, and the precision of spatial interpolation prediction is improved.

The invention discloses an expressway bridge construction state real-time monitoring method and system, and relates to the technical field of bridge construction monitoring. An expressway bridge construction state real-time monitoring system comprises a data acquisition and processing module, a data expansion and analysis module, a multi-point data fusion module, a state comprehensive evaluation module and a construction scheme adjustment module. According to the method, data interpolation and expansion are carried out on the preprocessed vibration monitoring data through the Kriging interpolation method, the spatial autocorrelation between the vibration monitoring data can be fully utilized, and the monitoring data are expanded, so that the problem of insufficient coverage of monitoring points can be effectively solved, and the integrity and precision of the monitoring data are improved; the change trend and the distribution rule of vibration in space can be reflected more accurately, a more comprehensive and reliable basis can be provided for evaluation of the highway bridge construction state, and the state evaluation accuracy of the highway bridge construction state real-time monitoring method and system is improved.

The invention provides an integrated circuit hot spot detection method based on multi-modal feature fusion and dynamic optimization, which relates to the technical field of integrated circuits, and comprises the following steps: acquiring multi-modal feature data, executing progressive block clustering to extract multi-scale features, fusing the features by using a multi-head attention mechanism, and constructing a hot spot feature map. A hot spot area is determined by combining spatial autocorrelation analysis, and a detection result is optimized based on synchronization analysis of a morphological evolution and energy evolution sequence. The hot spot detection accuracy and robustness can be improved, the false detection rate can be reduced, and the potential hot spot area in the complex layout can be effectively identified.

The invention discloses a carbon emission monitoring method and device considering a spatial topological structure, and a carbon emission reduction evaluation method. The carbon emission monitoring method comprises the following steps: S1, constructing a multi-dimensional data set of a clean energy base; s2, detecting spatial autocorrelation in the data by using a Moran index; s3, constructing a clean energy base output project weight matrix; s4, constructing a clean energy base carbon emission spatial feature change prediction model considering spatial dependency; and S5, predicting the future carbon emission of the target area by using the prediction model, and judging the spatial autocorrelation of the target area by using the Moran index. According to the invention, prediction can be carried out based on the carbon emission spatial characteristic change of multi-dimensional data, the carbon emission change trend of the clean energy base under different working conditions can be accurately predicted, and the spatial distribution characteristics of carbon emission in different areas and different scenes can be comprehensively analyzed.

The invention discloses a micro-motion multi-scale layer-by-layer inversion method and device for shallow stratum structure analysis, and the method comprises the steps: determining the coverage range of a target region based on geological conditions, and setting a plurality of arrays in the coverage range according to a preset standard; radius units with different scales are arranged in the same array; the method comprises the following steps: collecting surface micro-motion data, and preprocessing the surface micro-motion data to obtain to-be-inverted data; performing spatial autocorrelation calculation on the data to be inverted in the different radius units to obtain a frequency dispersion curve corresponding to each radius unit; performing layer-by-layer inversion based on the frequency dispersion curve corresponding to each radius unit to obtain a geologic model corresponding to each radius unit; and performing fusion optimization on the geologic models corresponding to the radius units to obtain a complete stratum model and performing inversion to obtain an inversion result, and realizing comprehensive analysis of the geologic structure from a shallow layer to a deep layer by setting the radius units of different scales in the same array and combining layer-by-layer inversion and model fusion. And the precision and the accuracy of an inversion result are improved.

The invention discloses a basin human settlement system ecological quality space-time evaluation method, system and device and a medium, and relates to the technical field of ecological quality evaluation. The method comprises the steps of obtaining target area data of a human settlement system with a Yellow River source area as a typical drainage basin; calculating an ecological quality index (EQI) based on the target area data; replacing an idealized large-scale grid with a 2m-resolution pattern spot based on a real ecological boundary to serve as a target area evaluation unit, and integrating an ecological quality index (EQI) and collected potential influence factor data into the target area evaluation unit to generate an ecological pattern spot unit; based on the ecological pattern spot unit, determining an ecological quality space-time evaluation result by using a Moran's I index and an ecological quality change matrix; the ecological quality spatio-temporal evaluation result is used for describing spatial autocorrelation and spatio-temporal change of the ecological quality. The method can improve the accuracy of space-time evaluation of the ecological quality of the basin human settlement system.

The invention relates to the technical field of data processing, in particular to a topological consistency risk analysis and monitoring early warning method for a tower inclination state, and the method carries out the time alignment and quality control of multi-modal data, such as an inclination angle, a wind speed and direction, temperature and humidity, positioning and an equipment state. Constructing a time-varying adjacency matrix based on line topology, geographical adjacency and wind field elements, and normalizing the time-varying adjacency matrix to form a graph model; the inclination angle or the trend of the inclination angle serves as a graph signal, space consistency measurement is calculated, and linkage with sliding window trend detection of a single rod and EWMA and ADWIN drift detection is carried out; identifying segment-level or region aggregation anomalies by synthesizing image smoothing energy, adjacent rod difference and spatial self-correlation; and outputting early warning grades under wind direction, wind speed, duration time window and data quality gating, and adaptively adjusting a threshold value, a time window and a model weight during drifting. The method can reduce false alarm and missing alarm, improves early warning accuracy and interpretability, and is suitable for centralization or edge cloud collaborative deployment.

The invention discloses a tourism rail transit passenger flow characteristic analysis method based on mobile phone signaling data, and the method comprises the following steps: S1, collecting and cleaning the mobile phone signaling data of multiple operators, and obtaining the cleaned mobile phone signaling data; s2, performing spatial registration and reconstructing a track according to a user identifier and a timestamp to form a user track sequence; s3, counting passenger flow indexes, constructing a spatial weight matrix and calculating spatial autocorrelation factors; s4, introducing the space factors into a hidden Markov model and training parameters; s5, complementing a missing track, and generating a complete travel state sequence; and S6, analyzing passenger flow indexes, identifying a gathering area, an abnormal area and a diffusion path, and outputting monitoring information. According to the method, the spatial autocorrelation factor is introduced into the hidden Markov model, spatial dependence modeling and state completion are performed on the rail transit passenger flow based on mobile phone signaling data, and accurate identification and monitoring of passenger flow distribution, hot spot areas and abnormal events are realized.

The invention relates to the technical field of environmental science and engineering, and discloses a water body sediment heavy metal pollution diagnosis and risk zoning method, which comprises the following steps of: firstly, performing functional zoning and differentiated point distribution sampling based on hydrodynamic topographic features; secondly, carrying out multi-dimensional index detection on the sediment sample to obtain a multi-dimensional data set containing a chemical occurrence form; secondly, combining a positive definite matrix factorization model with bivariate local space autocorrelation analysis, quantitatively analyzing the contribution of the pollution source and verifying the space fate of the pollution source; then, constructing a two-dimensional risk assessment system based on the chemical occurrence form, and calculating a migration release risk index and a bio-availability risk index; and finally, according to source-sink positioning and risk assessment results, a differentiated partition control strategy is generated. According to the invention, through coupling chemical morphology and spatial analysis, the concealment risk that the total amount does not exceed the standard but the bioavailability is high can be accurately identified, and accurate positioning and migration path verification of the composite pollution source are realized.

The invention relates to the technical field of land utilization change evaluation, in particular to a carbon emission spatial correlation evaluation method, model and system based on a land gradient utilization model and a medium. Calculating the carbon absorption amount of the to-be-evaluated area from the natural vegetation dimension, the crop dimension and the water area dimension; calculating the carbon emission of the to-be-evaluated area from the energy consumption dimension, the industrial production process dimension, the waste treatment dimension, the agricultural dimension, the respiration dimension and the water area carbon volatilization dimension; determining a carbon emission net value of the to-be-evaluated area according to a difference value between the carbon absorption amount and the carbon emission amount, and calculating a spatial autocorrelation index of the to-be-evaluated area according to the carbon emission net value; determining a carbon emission space change direction of the to-be-evaluated region; and determining a carbon emission spatial correlation evaluation result of the to-be-evaluated region according to the spatial autocorrelation index and / or the carbon emission spatial change direction. The objective of the invention is to solve the problem of how to carry out spatial correlation evaluation on areas with obvious vertical zone performance and altitude gradient performance.

The invention relates to a tunnel portal road icing early warning method based on digital twinning, and the method comprises the steps: generating a clean data set through integrating rock mass fracture, underground water level and meteorological monitoring data; filling the monitoring blind area based on the spatial self-correlation weight to form a dynamic water seepage distribution diagram; combining laser radar point cloud data to construct a freezing early warning digital twinborn body, and dynamically mapping a rock mass fracture network and water seepage space distribution; and finally, inputting the real-time weather and the road surface temperature field into the twinborn body, quantifying the icing probability of the tunnel portal pavement microporous structure through a thermodynamic model, and generating an icing risk spatial distribution diagram. The method solves the core defects of geology and tunnel portal road monitoring splitting, three-dimensional space perception deficiency, response lag and insufficient probability quantification in traditional early warning, realizes whole-process dynamic early warning from rock mass water seepage migration to road surface ice coagulation, improves the identification precision and response speed of tunnel portal road sudden ice coagulation risk, and improves the early warning efficiency. And a reliable basis is provided for intelligent traffic decision making.

The invention discloses a territorial space planning current situation evaluation method, and belongs to the technical field of territorial space planning. Comprising the following steps: step 1, constructing an initial evaluation unit data set adapted to geographic features; 2, constructing a multi-level index system covering the land use structure, the spatial form and the ecological environment dimension, setting scale sensitivity parameters and threshold intervals for the indexes, and quantitatively analyzing the correlation among the indexes under different scales; step 3, calculating dispersion and spatial heterogeneity of internal indexes of the evaluation units, and realizing self-adaptive segmentation and combination of the evaluation units in combination with association strength between adjacent units; step 4, realizing cooperative calculation of a multi-scale evaluation result through a scale conversion matrix and spatial autocorrelation analysis; 5, optimizing an evaluation result through parameter adjustment; and step 6, displaying the multi-scale evaluation result, the dynamic evolution process of the evaluation unit and the spatial distribution characteristics of each evaluation index.

The invention discloses a soil nutrient inversion method based on multi-source physical constraint and a multi-scale attention network, and aims to solve the problems that a soil nutrient inversion method based on remote sensing lacks enough truth value marking data and does not consider spatial autocorrelation of soil nutrients in geographic space and a physical imaging rule influenced by a microenvironment and an earth surface structure. Therefore, the problems of deviation of the inversion value and lack of spatial texture constraint and geographical continuity of the soil nutrient content distribution map generated by inversion are solved. According to the method, the collaborative Kriging trend and soil microenvironment and earth surface structure characteristics are introduced as physical constraints, a sample expansion confrontation model is constructed, sample expansion is performed by a scientific method integrating a physical environment and a spatial law, the physical imaging law of soil nutrients is fully considered, and the accuracy of the sample expansion confrontation model is improved. In combination with a designed multi-scale residual attention inversion model for extracting key information from a spectrum, the generated soil nutrient content distribution map is relatively excellent in spatial texture constraint and geographical continuity.

The invention provides a livestock building temperature data fusion monitoring method, a livestock building temperature data fusion monitoring device and electronic equipment. The livestock building temperature data fusion monitoring method comprises the steps of acquiring temperature data acquired by a multi-source sensor network, heating equipment power and nursing period animal space density in real time; and constructing a topological network based on the obtained animal husbandry building planar graph, taking each temperature sensor as a node in the topological network, and calculating an effective path distance between all temperature sensor node pairs. Dynamically calculating a heat source influence factor based on the power of heating equipment and the space density of the animals in the nursing period; and inputting the effective path distance and the heat source influence factor into an improved geographically weighted regression model to obtain a composite weight. And performing fusion processing on the temperature data by using the composite weight to generate a temperature distribution diagram. And local space autocorrelation analysis is carried out on the temperature distribution map, and a temperature anomaly aggregation area in the livestock building is positioned as a temperature data fusion monitoring result. The defect that a traditional temperature monitoring method is insufficient in precision in a spatial heterogeneity environment is effectively overcome.

The embodiment of the invention discloses an air quality improvement evaluation method and device based on assistance intensity and a medium. The method comprises the following steps: acquiring the atmosphere supervision assistance intensity and various meteorological indexes of a to-be-evaluated area, and the pollutant concentration in the air around an enterprise before and after atmosphere supervision assistance; establishing a multiple linear regression model by taking the assistance strength, each meteorological index and the pollutant concentration before atmospheric assistance as independent variables and taking the pollutant concentration after atmospheric assistance as a dependent variable; taking data of each region in the to-be-evaluated region as a unit sample, and carrying out bivariate spatial autocorrelation analysis on assistance strength and pollutant concentration improvement before and after assistance; and according to the local space self-correlation characteristics of the assistance intensity and pollutant concentration improvement of each region and the regression coefficient of the assistance intensity, performing evaluation and assistance strategy recommendation on the air quality improvement effect of enterprises in each region. According to the embodiment of the invention, atmosphere supervision assistance intensity data is fused, and refined evaluation of the air improvement effect is realized.