466 results about "Spatial heterogeneity" patented technology

The invention discloses a rock slope risk assessment method and system based on artificial intelligence, and belongs to the technical field of geological disaster risk assessment.The rock slope risk assessment method comprises the steps that a slope three-dimensional digital model is constructed, and geological-environment-monitoring data are integrated; extracting spatio-temporal characteristics, and predicting a landslide risk probability; quantifying a risk space diffusion path, and identifying a high-risk area; the early warning threshold value is dynamically adjusted, and accurate early warning is achieved; the problems that in the prior art, how to integrate multi-modal data (geological data, environment data and monitoring data) to achieve real-time sensing of the slope state and how to construct a deep learning model with high generalization ability to deal with risk prediction under the complex geological condition are solved. The problem of how to establish a risk transfer model considering spatial heterogeneity to improve high-risk area identification precision and how to realize dynamic adaptive adjustment of an early warning threshold to match personalized risk features of different slopes is solved.

The invention discloses a three-dimensional monitoring data time sequence difference compensation and dynamic threshold adaptive adjustment method. The method comprises the following steps: collecting three-dimensional point cloud data and environment sensor data; environment feature extraction is carried out by adopting regional adaptive partition PCA, and the problem of spatial heterogeneity is solved; by utilizing a physical information enhanced environment field reconstruction technology, the environment field distribution of the whole monitoring area is accurately reconstructed from limited environment sensor data; constructing a cognitive physical-data dual-drive compensation model, and embedding a physical model into a data drive architecture; calculating expected displacement caused by the environment based on the environment-structure response model and deducting the expected displacement from the actually measured displacement; and dynamically adjusting the change detection threshold. According to the method, the problem of'false change 'caused by environmental factors is solved, and the accuracy and reliability of three-dimensional monitoring data are improved.

The invention discloses a landslide susceptibility assessment method and system fusing feature interaction and dynamic weighting, and belongs to the field of geological disaster assessment, and the method comprises the steps: building an evaluation index system, and carrying out the factor classification; acquiring landslide and non-landslide point sample data, evaluating factor importance by adopting a random forest to obtain a preliminary weight, and preliminarily evaluating the susceptibility based on an I-RF model; detecting double-factor interaction by using a geographic detector, screening interaction factors for collaborative enhancement, and optimizing an expansion index system; retraining the random forest model, obtaining a new weight containing a single factor and an interaction factor, and constructing an I-RF optimization model; for spatial heterogeneity, sub-regions are divided by using spatial clustering, and the weight of each sub-region is dynamically corrected according to the sensitivity of a main control factor, so that self-adaptive adjustment is realized; and calculating susceptibility indexes of the sub-regions and the whole region, and grading to form spatialized and multi-level risk partitions. According to the method, the scientificity and accuracy of evaluation are effectively improved through feature interaction optimization and partition dynamic weighting.

Provided herein are methods of characterizing tumors or a region of interest in a biological sample.

The invention discloses an urban ecological unit scale water conservation function evolution simulation system, relates to the technical field of ecological hydrological simulation, and is technically characterized in that the system combines an InVEST model, a water balance method, a geographic detector and a Moran index to construct a multi-source data fused water conservation evaluation method. According to the system, key factors are obtained through remote sensing data and geostatistical data, a mapping model between water yield and actually measured runoff depth is established based on a regression fitting relation, and a spatial heterogeneity analysis and factor interaction detection mechanism is introduced, so that time sequence dynamic simulation and spatial pattern recognition of a water conservation function are realized. The system can be widely applied to the fields of urban water resource regulation and control, ecological space planning, hydrological safety evaluation and the like.

The invention discloses an underground-ground-sky combined rock burst and earth surface deformation early warning method, which comprises the following steps of: S1, arranging an underground monitoring system, and acquiring underground monitoring data; s2, setting a ground monitoring system, and collecting ground monitoring data; s3, setting a sky monitoring system, and collecting sky monitoring data; s4, calibrating the time scale difference of the underground monitoring data, the ground monitoring data and the sky monitoring data through a multi-scale time alignment algorithm; and S5, constructing a multi-stage joint probability assessment framework to carry out mining area rock burst and earth surface deformation risk assessment. According to the method, multi-source monitoring data are integrated through a multi-scale space-time fusion framework, and a full-chain association model of'microcosmic fracture-mesoscopic deformation-macroscopic accumulation 'is constructed. And on the basis of cross-scale time sequence alignment, spatial heterogeneity is optimized and eliminated, a multi-stage joint probability assessment framework quantifies a cross-scale collaborative risk, full-period intelligent management and control of'monitoring-early warning-feedback-optimization 'is realized, and early warning accuracy and timeliness are improved.

The invention discloses a method for evaluating the influence of black land ecological barrier pattern evolution on agricultural climate and yield, and relates to the technical field of agricultural ecological environment monitoring and remote sensing model coupling. Comprising the following steps: S1, acquiring a high-resolution satellite image, microwave remote sensing earth surface humidity data, optical remote sensing vegetation coverage data and thermal infrared remote sensing earth surface temperature data of a target area; according to the method for evaluating the influence of the black land ecological barrier pattern evolution on the agricultural climate and the yield, the problem of lack of ecological-climate-agricultural system collaborative modeling in the traditional technology is solved. A high-resolution satellite image is used for analyzing a network topology structure of the protection forest, microwave and thermal infrared remote sensing are fused to invert'water, soil and gas generation 'multi-element parameters, and dynamic quantitative evaluation of ecological barrier spatial heterogeneity evolution on local climate and crop growth is realized in combination with a dynamic weight distribution and two-way nesting technology.

The invention belongs to the technical field of landslide susceptibility analysis, and relates to a landslide susceptibility ensemble learning evaluation method considering spatial heterogeneity partitioning and factor feature screening, which comprises the following steps: generating a landslide sample based on historical landslide catalog data, and selecting a non-landslide sample through environmental factor frequency ratio analysis; the method comprises the following steps of: extracting static and dynamic environment factor data sets, realizing factor space interpretation force transformation by utilizing a t-SNE-ISO clustering algorithm and a feature screening strategy, eliminating high-correlation factors through a Pearson correlation coefficient method, quantifying interpretation force of each factor on landslide space differentiation by combining a geographic detector, screening optimal feature combinations under global and partition frameworks respectively, and performing landslide space differentiation on the landslide space. According to the method, a Stacking integrated learning framework is combined with CNN, DNN, MLP-based learners and LR element learners, a landslide susceptibility probability prediction model is formed, the generalization ability and prediction accuracy of the model are improved, and the method is especially suitable for landslide high-incidence areas with severe topographic relief and complex geological conditions.

The invention relates to the technical field of data twinning, in particular to a saline-alkali soil water-salt motion simulation method based on big data analysis. The method comprises the following steps: collecting saline-alkali soil multi-source monitoring data; based on the saline-alkali soil multi-source monitoring data, salt soil layer correlation feature analysis and water-salt driving factor coupling feature analysis are carried out, and salt soil layer correlation feature data and water-salt driving factor coupling feature data are obtained; simulation relation model design of saline-alkali soil water-salt movement is carried out through the saline-alkali soil level correlation feature data and the water-salt driving factor coupling feature data, and a saline-alkali soil water-salt movement simulation model is generated; based on the saline-alkali soil water-salt motion simulation model, designing saline-alkali soil farmland irrigation simulation parameters; and carrying out water-salt motion simulation prediction and feedback of the saline-alkali soil farmland irrigation on the saline-alkali soil farmland irrigation simulation parameters. According to the saline-alkali soil water-salt motion simulation analysis method, multi-driving-factor fusion and accurate spatial heterogeneity description are achieved.

The invention provides an urban ecological risk prediction system based on a cellular automaton. The system integrates and obtains remote sensing images, meteorological data and human activity data through a data acquisition module, and extracts green land coverage, land utilization types and species distribution information of cities. Thirdly, a space grid is divided through a space grid division module by means of a dynamic quadtree algorithm, ecological units are generated, and therefore microcosmic space heterogeneity is captured; then, the system utilizes a cellular automaton model to optimize a state transition rule through deep reinforcement learning, and couples a soil erosion model and a hydrological model to simulate the dynamic change of urban ecology. And finally, quantizing parameter uncertainty through a risk assessment module by using a Bayesian belief network and Monte Carlo simulation. According to the system, urban ecological risks can be effectively simulated and predicted, the spatial precision and scientificity of prediction are improved, and the coupling influence of multiple ecological factors is considered at the same time.

The invention discloses an intestinal cancer metastasis prediction method and device based on spatial omics, a medium and equipment, and the method comprises the steps: collecting original multi-omics data, and carrying out modal alignment and quality control processing to obtain pre-processed multi-omics data comprising second spatial transcriptome data, second single-cell RNA sequencing data and second pathological image data; performing cross-modal semantic embedding on the second spatial transcriptome data based on the second single-cell RNA sequencing data to generate a spatial enhanced expression profile; performing multi-scale graph construction on the second spatial transcriptome data and the second pathological image data, and extracting spatial heterogeneity features; inputting the spatial enhancement expression spectrum and the spatial heterogeneity features into a pre-trained metastasis risk prediction model, and outputting a liver metastasis probability spatial heat map and a key driving feature list; and finally generating a clinical prediction report containing high-risk area positioning. According to the method, through dynamic optimization of spatial resolution and multi-scale feature collaborative modeling, the sensitivity of early transfer detection is remarkably improved.

The invention relates to the technical field of water environment treatment, in particular to a water environment ecological treatment method and system based on a constructed wetland, and the method comprises the following steps: measuring the dissolved oxygen concentration, ammonia nitrogen concentration, total phosphorus concentration, water temperature and pH value of a root zone in the constructed wetland, and calculating the pollutant concentration distribution value and plant root system distribution density of each monitoring point; and obtaining a water quality root distribution characterization value. In an artificial wetland environment, the concentration of dissolved oxygen, the concentration of ammonia nitrogen, the concentration of total phosphorus, the water temperature and the pH value of a root zone are measured, the concentration distribution of pollutants is calculated, numerical calculation is carried out in combination with the distribution density of plant root systems, water quality root system distribution characterization is constructed, spatial heterogeneity analysis of wetland water quality is enhanced, and the targeting of pollutant degradation is optimized. Based on water quality root system distribution characterization, plant growth cycle parameters are calculated in combination with water quality parameters, association between water quality dynamic changes and plant growth states is constructed, and the adaptability of wetland ecological regulation and control is improved.

The invention discloses a multi-model coupled regional terrestrial ecosystem carbon reserve estimation method, which is suitable for the field of carbon cycle and carbon reserve estimation. The method comprises the following steps: firstly, collecting remote sensing data, and then estimating overground, underground and soil annual carbon density mean values of a regional terrestrial ecosystem by adopting an ARIMA model; the method comprises the following steps: clustering regions with different carbon densities by adopting a CatBoost model, dividing the carbon densities of a regional ecological system into low, medium and high levels, and avoiding the influence of spatial heterogeneity among different density levels on subsequent model training; an RNN model is constructed to evaluate the carbon density conditions of different levels of unit grids, and interannual dynamic carbon density parameters of different types of ecosystems are obtained based on multi-source data; and finally, calculating the carbon reserves of the unit grids, analyzing the annual average change trend and the spatial distribution condition of the carbon reserves, and realizing accurate evaluation of the regional terrestrial ecosystem carbon sink. The method is simple in step and good in using effect, and the change condition of the carbon sink capacity in the corresponding time can be obtained according to the sampling time interval of the input data.

The invention provides a cooperative early warning method, system, equipment and medium for deformation stress of soft soil around a pile, and relates to the technical field of underground engineering, and the method comprises the steps that monitoring data of the soft soil around the pile are obtained based on monitoring points, and the monitoring points comprise pile body monitoring points and soil body monitoring points; feature extraction is conducted on the monitoring data, and monitoring indexes reflecting pile periphery soft soil deformation and stress evolution are obtained based on information contribution degree difference and sensitivity selection; performing model construction based on the monitoring data and the monitoring indexes, and obtaining a deformation-stress collaborative map of the soft soil around the pile by considering risk assessment and spatial heterogeneity; and on the basis of the deformation-stress collaborative atlas, abnormal recognition of the soft soil around the pile is carried out, an abnormal area is obtained through embedded learning and clustering analysis, graded early warning is carried out according to the abnormal area, and an early warning result of the soft soil around the pile is obtained. The problem that an existing early warning method for the soft soil around the pile lacks dynamic sensing and combined early warning in the pile-soil interaction process is solved.

The invention relates to a regional distributed photovoltaic power prediction method and system based on multivariate data cross-modal fusion, and belongs to the technical field of photovoltaic power prediction. Firstly, adaptive extraction of meteorological data spatial features and power data spatial heterogeneity is realized through a dynamic perception convolution kernel of AG-CNN, and a high-quality spatial basis is provided for cross-modal fusion; secondly, utilizing a two-dimensional dynamic attention mechanism of a DR-Transform to deeply mine cross-modal association of'historical power time sequence-future weather driving ', adapting to fusion requirements in different scenes through dynamic weight distribution, and capturing association evolution in a long period; thirdly, designing a cross-modal feature bridging module; and finally, verifying the robustness of the model in a typical scene, and ensuring that the model meets the precision requirement of multi-scale scheduling of the power grid.

The invention belongs to the technical field of urban planning, and discloses an urban inland inundation toughness evaluation method and related equipment, rainfall event characteristic parameters are obtained through obtaining and analyzing rainfall early warning information, dynamic inland inundation toughness evaluation index weights are generated according to the parameters and a preset weight adjustment rule, and evaluation is executed according to the dynamic inland inundation toughness evaluation index weights. And finally, outputting an evaluation result containing weak link identification information, thereby effectively solving the problems of weight staticization and homogenization in the existing evaluation method. The evaluation index weight can be dynamically adjusted according to rainfall event characteristics and urban spatial heterogeneity, so that accurate evaluation of urban waterlogging toughness is realized, weak links are effectively identified, and a scientific basis is provided for accurate scheduling of emergency resources and effective implementation of disaster prevention measures.

The invention discloses a wetland carbon sink benefit evaluation method and system based on remote sensing data, and relates to the technical field of carbon emission evaluation. A wetland carbon sink benefit evaluation system based on remote sensing data comprises a carbon sink evaluation zoning module and a carbon sink benefit evaluation module. According to the invention, through multi-modal remote sensing data fusion and fine grid division, high-resolution analysis of spatial heterogeneity of a wetland region is realized, different types of carbon sink regions and distribution boundaries thereof can be accurately identified, and scientificity and particles of carbon sink partitioning are improved; a dynamic index characteristic graph layer is constructed through a remote sensing time sequence, a hydrological pulse response factor graph layer is constructed in combination with hydrological data, and clustering division is performed on the hydrological pulse response factor graph layer, so that dynamic identification and pixel level benefit calculation of a dynamic carbon sink area are realized.

The invention discloses a flood classification method based on a graph attention neural network. The method comprises the following steps: constructing a flood event data set; constructing a flood map set; based on a graph attention neural network GAT, graph-level features of a flood map corresponding to each flood event are extracted; and carrying out flood classification by using a K-means clustering method based on the graph-level features of the flood graph set. According to the method, the graph neural network is introduced into the field of flood classification, deep learning modeling of drainage basin flood space correlation features is realized, multi-site synchronous monitoring flow data in a drainage basin is integrated by using the graph attention network, space-time evolution features of flood in a drainage basin river network are extracted, and the limitation of a traditional single-site analysis method is broken through. According to the method provided by the invention, the spatial heterogeneity of flood is relatively considered, and the classification result contains richer information.

The invention relates to a grassland ecological degradation recovery effect dynamic evaluation method and device, equipment and a medium, and the method comprises the steps: obtaining interannual remote sensing data for a target region, carrying out the vegetation feature extraction of the interannual remote sensing data based on the sliding operation of a predetermined window on the interannual remote sensing data, and obtaining a reference time sequence; calculating the relative change rate of vegetation in the target area based on the reference time sequence; and according to the relative change rate and a preset threshold value, evaluating the degradation or recovery condition of the vegetation in the target area. According to the method, the optimal spatial analysis scale, namely the window, is determined according to spatial heterogeneity characteristics (semi-variable function range), adaptive conversion from the pixel to the landscape scale can be realized, subjective interference can be eliminated, a reference time sequence reflecting vegetation characteristics can be accurately determined on the basis of the window, and the method has the advantages of being simple in structure and convenient to operate. And the degradation or recovery condition of the target area can be accurately evaluated based on the reference time sequence.

The invention discloses an unconventional oil and gas yield prediction method based on space-time Transform, which is characterized in that geological parameters, fracturing construction parameters and production dynamic data are fused, and time sequence dependency and spatial heterogeneity of modeling parameters are synchronized by utilizing a space-time attention mechanism. Firstly, normalization and space-time alignment are carried out on multi-source data, and a joint input matrix is constructed; then, a dynamic change trend of fracturing construction parameters is extracted through a time attention module, a space contribution weight of geological parameters is quantified through a space attention module, and a space-time interaction matrix is introduced to capture a multi-dimensional coupling relation; and finally, outputting a yield prediction result through feature fusion and MLP. According to the method, the dependence of a traditional model on a single data source is broken through, the precision and interpretability of unconventional oil and gas yield prediction are remarkably improved, and reliable technical support is provided for oil and gas field development strategy optimization.

The invention discloses an intelligent temperature management method and system for an integrated circuit, which are used for performing different temperature control on functional modules in different areas, improving the heat distribution uniformity in the integrated circuit and reducing the aging condition caused by non-uniform heat. The method comprises the following steps: acquiring temperature measurement point data of a plurality of different positions of a to-be-measured chip; calculating temperature value change rates of a plurality of different temperature measuring points in a preset time according to the temperature measuring point data, and determining a high-dynamic hot spot region set in combination with a preset threshold value; calculating a temperature gradient value of each high-dynamic hot spot region and an adjacent region by utilizing spatial heterogeneity so as to generate a heat distribution path; controlling the heat dissipation equipment to execute a heat transfer action based on the heat distribution path, and calculating a heat transfer index of each high-dynamic hot spot region; determining the high-dynamic hot spot area with the heat transfer index not reaching the standard as a target area; and performing parameter adjustment on the function module in the target area to adjust the heating value of the function module.

The invention provides a regional new energy power prediction method and device. The method comprises the following steps: firstly, collecting static and dynamic data of a new energy station in a region; constructing a geographic climate feature vector based on the geographic position and the meteorological data, and dividing geographic climate sub-regions by using an HDBSCAN algorithm; constructing output feature vectors according to the dynamic data of the sub-regions, and screening agent station groups by using a K-means algorithm; calculating a dynamic weight in combination with a historical prediction error, an output characteristic and an installed capacity of the agent station; predicting the power of the proxy station by using the L-X hybrid model in combination with the dynamic weight, the weather and the power data; calculating the predicted power of each sub-region based on the predicted power of the proxy station and the dynamic weight; and finally, integrating the predicted powers and geographic positions of all the sub-regions to obtain region total power prediction. Spatial heterogeneity influence is reduced through hybrid clustering, weather and power association is captured by using an L-X model, and calculation complexity is reduced through a proxy station mechanism.

The invention discloses a black soil area ecological stability-restoring force coupling zoning method and system, and relates to the technical field of ecological environment protection. Collecting multi-source spatio-temporal data of a target black soil area, and analyzing and obtaining the ecological stability and ecological restorability of the area; according to the spatial heterogeneity of the ecological stability and the restoring force, spatial difference characteristics of ecological variables in different geographic units are mined, and spatial coupling analysis is carried out on the ecological stability and the ecological restoring force through restoring force prediction; and according to a coupling analysis result, performing ecological stability-restoring force coupling partition management on the target black soil area. According to the invention, space identification and differentiation management of the ecological system state of the black soil area are 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 tidal flat automatic extraction method fused with an irregular hexagonal grid. The method comprises the following steps: step 1, constructing a time sequence remote sensing data set; step 2, establishing a precision verification sample library; step 3, screening spectral indexes; 4, synthesizing an extreme tide level image; 5, generating an irregular hexagonal grid; step 6, carrying out adaptive threshold segmentation; step 7, secondary elimination of confused ground features; step 8, tidal flat range extraction and precision verification; according to the method, based on a time sequence remote sensing image set, pixels at all positions are screened through a specific spectral index maximum value, a new extreme tide level image is formed through combination, spatial heterogeneity of a complex coastal zone is adapted through an irregular hexagonal grid and a bottom-to-top hierarchical merging strategy, local self-adaptive threshold segmentation is achieved in cooperation with a local Otsu algorithm, and a new extreme tide level image is obtained. The optimal grid scale is automatically determined by means of an elbow method, and the confused ground features are secondarily eliminated through the time sequence standard deviation, so that the problem of miscarriage of the confused ground features is effectively solved, and high-precision full-automatic extraction of the tidal flat is realized.

The invention relates to the technical field of inversion systems, in particular to a coastal wetland forest ecosystem biomass and carbon sink rapid inversion system. According to the technical scheme, the system comprises a data acquisition and preprocessing module, an inversion module based on machine learning, a spatial heterogeneity adaptive module, a dynamic monitoring and updating module and a result visualization and output module. According to the method, a data basis is provided for accurate inversion through multi-source data fusion, and meanwhile, an inversion model combining the generative adversarial network and the long-short-term memory network realizes combination of a physical model and deep learning, so that the interpretability of the physical model is reserved, and the model performance is improved by utilizing the strong fitting capability of the deep learning; according to the invention, the adaptability of the system in a complex coastal wetland environment is improved through the spatial heterogeneity adaptive module, and the reliability of an inversion result is improved; according to the invention, real-time dynamic monitoring of biomass and carbon sink is realized through the dynamic monitoring and updating module, and rapid response of ecological system change is supported.

The invention provides a multi-source data fusion variable fertilization method and device, and belongs to the field of agricultural production technology and agricultural remote sensing application, and the method comprises the following steps: data acquisition and preprocessing: collecting historical data, ground sampling data, ground sensor data and remote sensing images; spatial data processing and consistency correction; variable fertilization model construction: constructing a fertilization amount and yield effect curve, constructing a yield and growth vigor index VI distribution diagram, and further constructing a variable fertilization model representing the relationship between the fertilization amount and the growth vigor index VI; the spatial heterogeneity quantification and zoning comprises the following steps: quantifying the spatial heterogeneity of environmental factors on fertilization requirements by using a geographic detector, and dividing a region into a plurality of sub-regions; and differential fertilization strategy optimization: generating a dynamic adjustment coefficient, and determining a final fertilization amount in combination with the fertilization amount recommended by the variable fertilization model. The crop growth condition can be described more comprehensively and accurately, and accurate agricultural management can be guided.

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 relates to a location recommendation method based on longitude and latitude perception and federated learning, which breaks through the limitation of semantic flattening of traditional geographical representation by decoupling independent self-attention coding of longitude and latitude sequences and explicitly capturing directional movement characteristics of a user. A federal dynamic clustering mechanism based on a behavior pattern is further designed, compatibility fusion of heterogeneous knowledge is achieved, the problems of gradient conflicts and long tail marginalization caused by spatial isomerism are effectively relieved, and therefore the problem of spatial heterogeneity in POI recommendation is solved, and the relevance and accuracy of recommendation are improved.

The invention relates to a multi-scale ecological system carbon metering method, a metering model establishing method and system, equipment and a medium, and belongs to the technical field of ecological environment monitoring. The establishment method comprises the following steps: collecting continuous data of the carbon flux of the net ecosystem with different scales, and corresponding environmental factors and spatial heterogeneity parameters; performing data preprocessing on the continuous data of the carbon flux of the net ecosystem with different scales; performing feature construction on the continuous data of the carbon flux of the net ecosystem with different scales after data processing, the corresponding environmental factors and the spatial heterogeneity parameters; performing training data set construction on the data after feature construction; and based on the constructed training data set and the integrated learning model, completing the establishment of the multi-scale ecological system carbon measurement model. According to the invention, fusion of continuous data of the carbon flux of the net ecosystem with different scales is realized, and refined monitoring of the carbon of the ecosystem at different spatial positions is realized.