48 results about "Topographic factor" patented technology

The invention discloses a soil organic matter prediction mapping method, device and equipment based on deep learning and a medium, and relates to the field of digital soil mapping, and the method comprises the steps: obtaining a remote sensing image of target region soil, and setting a preset number of soil sampling points; comprehensively acquiring environmental data indexes such as terrain factors, vegetation factors, climate factors and land utilization factors; constructing three-dimensional environment tensor data in a preset range of each sampling point, and inputting the three-dimensional environment tensor data into the soil organic matter content prediction model to obtain an organic matter content prediction value set of the soil sampling points in the target area; and generating a spatial distribution prediction map of the soil organic matter content of the target area by combining the prediction value set and the spatial coordinates of the soil sampling points, thereby improving the accuracy of soil organic matter prediction and the mapping efficiency, and providing technical support for soil resource management and agricultural sustainable development.

The invention discloses a water and soil loss dynamic risk assessment system based on a remote sensing image, and relates to the field of water and soil loss risk assessment. The invention discloses a water and soil loss dynamic risk assessment system based on remote sensing images. The system comprises a data acquisition and preprocessing module, a dynamic feature extraction and analysis module, a hierarchical risk assessment model and a risk grading and modeling module, the method further comprises the following steps: acquiring a remote sensing image and historical soil erosion map data of a specific administrative region in a specific period through a data acquisition and preprocessing module; the dynamic feature extraction and analysis module is used for extracting land utilization types, vegetation coverage and quarterly change features of topographic factors through the acquired data; according to the method, a three-layer model architecture is arranged, static historical data, human activity disturbance and social exposure are decoupled and quantified, and the evaluation dimension is improved, so that the accuracy of an evaluation conclusion is improved.

The invention discloses a soil heavy metal distribution prediction method and system based on machine learning, and the method comprises the steps: obtaining topographic factor land use industrial activity data, carrying out the fusion remote sensing information processing, and determining a multi-source data set; performing standardization processing according to the multi-source data set, and performing space-time registration if the scale difference after standardization processing exceeds a preset threshold value to obtain data in a unified format; key features are extracted according to the unified format data, and a dimension reduction feature set is obtained through principal component analysis; a random forest model is constructed according to the dimension reduction feature set, parameters are optimized, and a heavy metal content prediction model is determined; inputting the sampling finite point location data into a heavy metal content prediction model, judging an industrial activity influence area, and obtaining a preliminary distribution estimation result; based on the preliminary distribution estimation result, high-resolution grid data are obtained by fusing topographic factors for the space complex region; and generating a pollution distribution diagram according to the high-resolution grid data, judging a low-prediction-precision region, and obtaining a final optimized layer.

The invention discloses a meteorological data assimilation method fusing topographic features, and relates to the technical field of meteorological data assimilation, and the method comprises the steps: combining a meteorological variable-topographic factor correlation map with an atmospheric state numerical field, and obtaining a multivariable initial meteorological field model fusing topographic factors; identifying space-time dynamic deviation distribution characteristics of the multivariable initial meteorological field model by using real-time multi-source environment observation data, and generating a terrain disturbance response matrix; carrying out dynamic correction and space-time error reconstruction based on a terrain disturbance sensitive weight on the terrain disturbance response matrix and the multivariable initial meteorological field model through an assimilation optimization algorithm to obtain a meteorological assimilation result; according to the method, dynamic correction and space-time error reconstruction are carried out on the initial meteorological field through the terrain disturbance sensitive weight, directional perception and intelligent correction of observation deviation are realized, and the response capability and the error control level of the model in a dynamic change scene are effectively enhanced.

The invention relates to a soil erosion amount dynamic monitoring method and system based on multi-source remote sensing data fusion. The method comprises the following steps: acquiring multi-source remote sensing data and auxiliary data, and performing geometric fine correction and atmospheric correction on an image based on high-resolution digital elevation data to obtain a data set with consistent space; performing inversion based on the data set to obtain a corrected terrain factor and a water and soil conservation measure factor; generating a time sequence rainfall erosivity factor and a time sequence vegetation coverage factor in combination with the multi-temporal satellite rainfall data; and calculating a preliminary soil erosion modulus in combination with the soil type map, and correcting by using multi-stage high-resolution digital elevation data to obtain a dynamic monitoring result of the soil erosion amount. By adopting the method, the problem of multi-source data fusion matching can be solved, and the factor inversion precision is improved, so that the accuracy of a dynamic monitoring result of the soil erosion amount is improved.

The invention discloses a parallel modeling method for simulating propagation of electric waves in irregular terrains, and relates to the field of radio. The method comprises the following steps: constructing a terrain-related first parabolic equation about radio wave propagation; introducing an auxiliary function, and determining a second parabolic equation; writing the second parabolic equation into a solution in an exponential function form, performing approximate transformation on an exponential term containing a topographic factor, and performing high-order fractional approximation on an exponential term containing a pseudo differential factor; based on a finite difference method, solving an intermediate variable of a high-order fraction in parallel, and further solving a field function subjected to terrain translation; and restoring the field function in combination with the auxiliary function and the terrain profile. Compared with the prior art, the method has the advantages of high calculation precision and high calculation efficiency, and is particularly suitable for predicting radio wave propagation loss on irregular terrains.

The invention provides an under-cloud / in-cloud removal inversion method based on daily scale nitrate wet sedimentation, which belongs to the field of remote sensing atmospheric environment application science, and is characterized in that a cloud pollutant concentration parameter before a rainfall event is calculated based on satellite data in combination with a backward trajectory model so as to quantify a remote pollution transport effect; a random forest machine learning model is constructed based on multi-source data of satellite observation, ground observation and the like, and daily scale settlement estimation is performed by integrating meteorological factors, pollutant concentration parameters, cloud attribute parameters, social economic factors, terrain factors and the like; a cross validation optimization model is adopted, and the space generalization ability is improved through deviation correction; and analyzing the contribution of each factor, and outputting daily scale wet settlement flux gridding data. According to the under-cloud / in-cloud removal inversion method based on daily scale nitrate wet sedimentation, daily resolution wet sedimentation estimation is achieved for the first time, the short-term sedimentation dynamic state of a summer heavy rainfall event is accurately captured, and the contribution of the in-cloud removal effect to a clean area is quantified.

This invention provides a landslide change detection sample enhancement method considering topographic factors, belonging to the field of image processing technology. The method includes: cropping landslide samples to obtain pixel clusters with the ground truth label "landslide" as landslide mask samples, and performing random X and Y axis flipping and / or random angle rotation; selecting scene samples without landslide labels, calculating the normalized mutual information between the post-landslide image corresponding to the landslide mask sample and the post-temporal image of the scene sample, obtaining candidate scenes and calculating the slope aspect and slope of each pixel to obtain slope areas prone to landslide development; obtaining mosaic candidate areas in the slope areas prone to landslide development, calculating the average slope aspect, determining the slope aspect, and mosaicking the transformed landslide mask sample onto the mosaic candidate area to obtain a new landslide change detection sample. This invention rapidly generates landslide change detection samples by mosaicking landslide patches onto slope areas with significant differences, ensuring reasonableness.

The invention discloses a full-irradiation aviation electromagnetic observation method. The method comprises the following steps: S1, scanning survey line topographic data of a target survey area; s2, dynamically controlling postures of a transmitting coil and a receiving coil based on the survey line topographic data in the S1, and collecting electromagnetic data of a target survey area; and S3, processing the electromagnetic data collected in the step S2, and carrying out random cross gradient constraint inversion to obtain geological structure information of the underground target in the target measurement area. The detection effect of an aviation electromagnetic method in a steep fluctuating environment is remarkably improved for mineral resources, engineering geophysical prospecting and hydrogeological prospecting in a complex terrain area through a full-irradiation aviation electromagnetic observation system in a complex fluctuating terrain and an isotropic resistivity cross constraint inversion method. According to the method, the interference of topographic factors on electromagnetic signals is effectively reduced, and the limitation of insufficient signal coverage in complex topographies in a traditional method is overcome, so that the data processing precision is improved, and richer underground information is obtained.

This invention discloses a fully automated method for identifying and classifying deep-sea landforms based on isobath morphological entropy, relating to the fields of marine geological exploration and topographic analysis. The method includes: extracting isobaths from a digital elevation model of the target area at predetermined intervals and constructing an isobath topology map to identify closed loops and bifurcation points; and calculating multi-dimensional isobath morphological entropy sub-factors based on the isobath topology map, including isobath topological complexity index, isobath geometric entropy, isobath density gradient anomaly, and isobath curvature spectrum characteristics. This invention, by calculating multi-dimensional morphological entropy sub-factors encompassing isobath topological complexity, geometric entropy, density gradient anomaly, and curvature spectrum characteristics, provides a quantitative description of the topological structure, spatial distribution, and frequency domain characteristics of deep-sea landforms. Compared to traditional single-topographic factor analysis methods, it can capture multi-level topographic information from microscopic morphology to macroscopic patterns, significantly improving the comprehensive characterization ability of complex landform structures.

This invention discloses an ecological pollution assessment method and visualization system that integrates hydrogeological factors, relating to the field of ecological and environmental assessment technology. The method includes constructing potential ecological risk indices for soil and water bodies and performing spatial interpolation, then performing three-dimensional coupling and fusion based on topographic and hydrological connectivity. Topographic factors such as slope, runoff direction, and surface runoff are incorporated into the pollution migration weight model. The spatial correlation structure of the risk index at sampling points is characterized based on a semi-variogram, ensuring that the contribution weight of each sampling point to the interpolation point has a clear statistical basis. Based on the three-dimensional coupled pollution model, geographical attributes and ecological sensitivity levels are introduced for weighted correction, so that the assessment results not only reflect pollution intensity but also simultaneously reflect the regional ecological carrying capacity, thus providing systematic support for the refined pollution assessment and management of ecological and environmental zones.

The invention discloses a blasting warning range generation and alarm method and system based on a three-dimensional geological model. The method comprises the steps of generating a three-dimensional visual model of a muck pile in a three-dimensional geologic model of a target mining area according to acquired parameters and latitude and longitude coordinates of the muck pile; the parameters of the muck pile comprise parameters of each blast hole and arrangement parameters of a plurality of blast holes; determining a plurality of edge blast holes according to the parameters and latitude and longitude coordinates of the muck pile; generating a blasting warning range of the muck pile based on the blasting vibration distance and the blasting shock wave distance of the plurality of edge blast holes and the blasting flying stone distance combined with the influence of a down-slope or down-slope topographic factor; generating a warning time period of the blasting warning range according to the obtained warning time parameter; and monitoring whether an object enters the blasting warning range or not in real time within the warning time period, and giving an alarm when finding that the object enters the blasting warning range. According to the invention, scientific, automatic generation and intelligent monitoring alarm of a blasting warning range can be realized.

This disclosure relates to a data processing method, specifically a regional data processing method, apparatus, storage medium, and electronic device. The regional data processing method includes: responding to a user's input operation, acquiring regional data of a target region corresponding to the input operation; the regional data includes topographic data of the target region and soil data of sample plots within the target region; rasterizing the target region to obtain raster points, and determining factor data of the dominant micro-site features corresponding to the raster points based on the regional data; the dominant micro-site features include topographic factors and soil factors; determining the micro-site classification result corresponding to the raster points based on the factor data, and drawing a spatial distribution map of micro-site types in the target region based on the raster points and the micro-site classification result. The regional data processing method provided by this disclosure can conveniently, accurately, and intuitively obtain regional micro-site type classification results.

This application provides a method and system for early debris flow identification based on SAR technology combined with a 3D model. Interferometric SAR data is selected as the wide-area coverage data for the target area, and fully polarimetric SAR data is selected as the local refined data for the target area. Phase unwrapping is performed on the wide-area coverage data to generate a wide-area digital elevation model (DEM). Polarization decomposition and phase optimization are performed on the local refined data to generate a local DEM. The wide-area DEM and the local DEM are fused to obtain a hybrid DEM. Based on the hybrid DEM, topographic factors are extracted to generate DEM spatial data to support debris flow susceptibility analysis. Early debris flow identification is performed based on historical debris flow data, historical precipitation data, regional soil moisture content, and DEM spatial data, yielding identification results. A differential monitoring and early warning system based on SAR technology and a 3D model is established. Remote sensing data generated by SAR technology is applied to debris flow disaster identification, improving the early identification capability of debris flow hazards and conducting debris flow hazard deformation prediction analysis.

The invention provides an automatic identification method for a black tide three-dimensional structure based on a flow velocity gradient, and the method comprises the steps: calculating the flow velocity gradient line by line according to latitude and layer by layer according to depth, and precisely determining the boundary points of the black tide three-dimensional structure in each layer and each line through combining with the longitude difference constraint and candidate point screening technology. In order to adapt to different marine environments, the method supports flexible selection of whether the influence of topographic factors is included or not, a two-dimensional field obtained by subtracting an average value from a resultant velocity is constructed, the bottom boundary depth is determined by using a zero isoline, finally, distribution of a black tide structure is visualized in a three-dimensional scatter diagram form, and the three-dimensional form of the black tide structure is comprehensively presented. According to the method, the limitation that a traditional method is limited to surface analysis and a black tide structure is difficult to accurately extract in real time is broken through, the defects of forced division of water area features and fixed depth setting in a clustering method are overcome, real-time structural form acquisition based on the data time resolution is realized, and meanwhile, the calculation complexity is remarkably reduced.

The invention discloses a rainfall landslide risk evaluation sampling optimization method based on distribution area expansion, and belongs to the field of risk evaluation of geological disasters. The method comprises the following steps: S1, when a regression coefficient of a rainfall factor in a logistic regression model is less than 0, expanding a sampling region to a rainfall gradient region; s2, collecting samples in the expanded sampling area, and dividing the samples into landslide samples and non-landslide samples according to whether the landslide occurs or not; s3, according to a rainfall landslide formation mechanism, respectively extracting influence factors related to landslide occurrence from the landslide sample and the non-landslide sample, including a topographic factor, a geological factor, a landform factor and a rainfall factor; s4, inputting the extracted influence factors into a logistic regression model for training, calculating a regression coefficient of each influence factor, and calibrating a relationship between rainfall and landslide; when the regression coefficient of the rainfall factor is positive, entering S5, otherwise, returning to S1; and S5, inputting a new sample into the trained logistic regression model, and outputting a prediction result.

The invention discloses a global scale strong earthquake induced landslide space distribution probability intelligent prediction method. The prediction method comprises the following steps: S1, selecting x times of earthquake induced landslide events under a global scale as a main target, and artificially constructing an accurate landslide label; s2, selecting four types of feature factors including a seismic oscillation factor, a geological factor, a topographic factor and an environmental factor, enabling the four types of feature factors to be in one-to-one correspondence with landslide labels in geographic spatial positions, and converting all the feature factors into a TIFF format by using a GDAL open source library; according to the method, topographic factors, geological factors, environmental factors and seismic oscillation factors are comprehensively considered, and a novel full-convolution regression neural network model is adopted, so that the spatial distribution probability of landslide induced by each region in the global under the condition of an earthquake can be efficiently and accurately predicted; and all correlation factors can be automatically processed quickly when an earthquake occurs, so that the model achieves nearly real-time prediction performance.

The present invention provides a method for automatically identifying the three-dimensional structure of the Kuroshio based on the velocity gradient, including: calculating the velocity gradient row by row according to latitude and layer by layer according to depth, and precisely determining the boundary points of the three-dimensional structure of the Kuroshio in each layer and each row by combining the longitude difference constraint and the candidate point screening technique. To adapt to different ocean environments, this method supports the flexible selection of whether to incorporate the influence of topographic factors. By constructing a two-dimensional field of the combined velocity minus the average value and using the zero isocline to determine the bottom boundary depth, the distribution of the Kuroshio structure is finally visualized in the form of a three-dimensional scatter plot, comprehensively presenting its three-dimensional shape. The present invention breaks through the limitation of the traditional method which is limited to surface analysis and difficult to accurately extract the Kuroshio structure in real time, avoids the deficiencies of forced division of water area characteristics and fixed depth setting in the clustering method, realizes the acquisition of real-time structural morphology based on the time resolution of data, and significantly reduces the computational complexity.

The invention discloses a regional debris flow residual slope product slope source identification model determination method, an identification method and a related device, and relates to the field of geological disaster identification and risk assessment. The method comprises the following steps: firstly, acquiring a multispectral optical satellite image, a high-precision digital elevation model, an unmanned aerial vehicle aerial orthoimage and unmanned aerial vehicle live-action three-dimensional image data of a debris flow gully in a target area; dividing a debris flow gully in a target area based on a high-precision digital elevation model to obtain a plurality of slope units; calculating a topographic factor, an average vegetation coverage and an effective migration space of each slope unit; a target region is determined through a residual slope product slope source target region judgment model and the parameters, a typical debris flow gully is selected in the target region, and then residual slope product and non-residual slope product slope sources are screened out in combination with obtained data; and finally, constructing a sample library based on the comprehensive remote sensing data and the screening result, and training a machine learning model to obtain a regional recognition model. According to the invention, automatic identification of the residual slope product slope material source can be realized.

The present invention provides a method for inverting cloud / in-cloud clearance based on daily-scale nitrate wet deposition, which belongs to the field of remote sensing atmospheric environment applications. Based on satellite data combined with a backward trajectory model, pollutant concentration parameters in the cloud before a precipitation event are calculated to quantify the long-range pollution transport effect; a random forest machine learning model is constructed based on multi-source data such as satellite observations and ground observations, integrating meteorological factors, pollutant concentration parameters, cloud attribute parameters, socioeconomic factors, and topographic factors to estimate daily-scale deposition; a cross-validation optimization model is used and bias correction is used to improve spatial generalization ability; the contribution of each factor is analyzed, and daily-scale wet deposition flux gridded data is output. The present invention adopts the above-mentioned inverting method for cloud / in-cloud clearance based on daily-scale nitrate wet deposition, and for the first time achieves daily-resolution wet deposition estimation, accurately captures the short-term deposition dynamics of heavy precipitation events in summer, and quantifies the contribution of cloud clearance effects to clean areas.

The invention relates to the field of remote sensing charting, and discloses a farmland plough layer loss remote sensing charting method based on multi-source remote sensing features and improved PSO-RF, and the method comprises the steps: 1, obtaining remote sensing image data, and carrying out the preprocessing; step 2, coupling DEM derived topographic factors and multispectral sensitive indexes, and constructing a multidimensional feature set; step 3, making a plough layer loss label data set by using the image and a field sampling result; step 4, taking a random forest RF as a base classifier, introducing particle swarm optimization PSO global search key parameters, fusing a dynamic Kappa weighted loss function of category imbalance perception, and constructing an improved PSO-RF model; 5, outputting a plough layer loss spatial distribution diagram by taking the overall precision OA and the Kappa coefficient as evaluation indexes; the method provided by the invention solves the problems of high misjudgment rate in a bare soil period, poor adaptability of a spectral index region, unbalanced sample types and difficulty in manual adjustment and optimization of model parameters, and provides an efficient and accurate general technical path for black land protection and regional scale soil plough layer loss monitoring.

The invention relates to the technical field of erosion gully space prediction, in particular to a method and related equipment for predicting the development space of an erosion gully from a shallow gully to a cutting gully, and the method comprises the steps: obtaining the spatial distribution sample data of the shallow gully and the cutting gully of a target region, and constructing a multi-factor database containing the landform, climate, soil and management measure factors; and establishing an A-S critical model taking the terrain as a core based on the terrain factors, and re-fitting the A-S critical model in a classification combination mode by coupling the non-terrain factors and the terrain factors to optimize model parameters. And finally, generating a spatial prediction result of development from the shallow trench to the cutting trench by using the coupled model parameters. According to the method, the prediction precision is improved through multi-factor coupling, and a scientific basis is provided for water and soil loss prevention and ecological restoration.

The invention provides a soil organic matter content inversion method based on machine learning and macrotopographic factors, and belongs to the field of soil quality evaluation.The soil organic matter content inversion method comprises the steps that soil organic matter data are collected, remote sensing data are obtained, and spectral indexes are constructed for the reflectivity of all wavebands in multispectral data; collecting topographic data including elevation, microtopographic factors and macrotopographic factors; and performing inversion on the soil organic matters by utilizing multiple modeling methods, performing comparative analysis on inversion results of different models, and screening out an optimal model. The current mainstream machine learning algorithms (including LightGBM, XGBoost and other gradient boosting tree models, a random forest and a neural network) are systematically compared. The cross-type model comparison and evaluation not only verifies the applicability of an advanced algorithm in soil attribute inversion, but also screens out an optimal model for a research region through demonstration, and provides a reliable and reproducible technical path for realizing high-precision and high-efficiency regional soil mapping.

This invention discloses a device for precise location of geological hazard hazard points by combining intelligent remote sensing interpretation with topographic factors. It relates to the field of geological hazard location technology and includes: a data acquisition terminal for acquiring multi-temporal synthetic aperture radar interferometric image sequences and digital elevation model data, performing spatiotemporal baseline registration to generate a temporal interferometric phase dataset, and calculating a set of topographic factors; a data processing center for separating weak deformation signals through variational mode decomposition and independent component analysis, fusing denoised deformation phase sequences and topographic factors to generate a multi-dimensional spatiotemporal feature tensor, constructing a topographic topology map, inputting it into a graph attention network model to output hazard probability values ​​and boundary confidence, determining the location of hazard points, and tracking the boundaries of hazard bodies; and a remote access terminal for performing visualization and spatial database storage. This invention effectively improves the early and accurate identification capability of large, deep landslide hazards with strong concealment and the accurate delineation capability of hazard body boundaries.

The invention discloses a soil water erosion feature extraction method based on image segmentation, and relates to the technical field of water erosion feature extraction. The method comprises the steps that an original soil remote sensing image of a target area is acquired and preprocessed, and a preprocessed image is obtained; spectral features, texture features and exposure features of the soil are extracted from the preprocessed image and fused to generate a water erosion sensitivity layer; collecting digital elevation model data of a target area, calculating topographic factors, dividing different topographic function units according to the topographic factors, and presetting a differentiated layered image segmentation rule base for each unit; segmenting the water erosion sensitivity layer based on the rule base to obtain a water erosion area segmentation map; and constructing a water erosion contour extraction model based on an energy minimization theory, optimizing the segmented image, and finally outputting accurate soil water erosion characteristics. According to the method, the precision of soil water erosion feature extraction is effectively improved by fusing multiple features and a layered segmentation strategy.

The invention discloses a method for quickly investigating and evaluating an ecological geologic background in a high mountain and valley region based on remote sensing and geological information, and belongs to the technical field of ecological geologic investigation and evaluation. According to the method, through information integration of multi-source remote sensing and geology, landform, soil and vegetation databases, and through radiometric calibration, atmospheric correction and spatial registration processing, topographic factors and landform structural features are accurately extracted. And carrying out landform unit subdivision by combining fractal dimensions and landform morphological indexes, introducing a geology-ecology coupling self-supervised graph neural network model, and carrying out spatial heterogeneous graph expression on different geology and ecology attributes. Spatial statistics and clustering analysis are utilized to realize automatic zoning evaluation of regional ecological sensitivity and geological stability, and model parameters are optimized through field sample plot check. According to the method, the ecological geologic background unit identification of the alpine and valley complex area can be efficiently realized, and a scientific support is provided for ecological protection and geological disaster risk management.

The invention relates to a data processing method, in particular to a regional data processing method and device, a storage medium and electronic equipment. The regional data processing method comprises the following steps: in response to an input operation of a user, obtaining regional data of a target region corresponding to the input operation; the area data comprises topographic data of the target area and soil data of quadrat points in the target area; rasterizing the target area to obtain grid points, and determining factor data of micro site dominant factors corresponding to the grid points according to the area data; the micro site dominant factors comprise terrain factors and soil factors; and determining a micro-site classification result corresponding to the grid points based on the factor data, and drawing a micro-site type spatial distribution diagram of the target area based on the grid points and the micro-site classification result. According to the regional data processing method provided by the invention, the regional micro site type division result can be conveniently, accurately and intuitively obtained.

This application provides a method, system, and device for tropospheric error correction based on BeiDou satellite signals. The method includes: deploying multiple reference stations within a preset coverage area to receive pseudorange and carrier phase observation data from BeiDou satellites, performing gross error elimination and cycle slip detection on the observation data, and constructing an ionospheric-free combined observation equation; combining real-time precise ephemeris and satellite clock bias, calculating the total zenith tropospheric delay of each reference station using precise single-point positioning technology, and obtaining the zenith wet delay using barometric pressure data and an empirical model; further summarizing the wet delay information from multiple reference stations, and establishing a regional wet delay model by combining topographic factors such as altitude, and converting the zenith delay into a tropospheric correction in the satellite line-of-sight direction using a mapping function. This invention can achieve high-precision tropospheric error modeling and correction under high-altitude and complex terrain conditions, and is applicable to power grid inspection, transmission line monitoring, and high-precision positioning services for BeiDou terminals.

The invention discloses a differential processing design method for residual soil in power transmission line construction, and belongs to the technical field of water and soil conservation of electric power engineering. The method comprises the following steps: collecting multi-source data of a tower footing construction area; microtopographic factors such as gradient, slope direction and topographic relief are extracted based on a digital elevation model; constructing an evaluation index system comprising a terrain factor, an ecological sensitive factor, an engineering constraint factor and a dynamic risk factor; on the basis of the system, the weight of each index is calculated through an entropy weight method, an evaluation model is established, and a microtopography sensitivity grading result is output; according to grade I to grade IV sensitive area grading, differentially designing residual soil treatment measures, including combined application of natural sloping, novel blocking measures, novel soil fixation materials and ecological restoration technology; and finally, carrying out three-dimensional dynamic simulation and multi-scheme comparison and selection on a stacking scheme by utilizing a digital twinning technology, and optimizing and determining a final treatment scheme. According to the invention, the precision and ecologicalization of residual soil treatment are realized, and the stability of the pile body and the ecological restoration efficiency are effectively improved.

The invention provides a power distribution facility rush-arrangement scheduling method and system based on risk early warning, and the method comprises the steps: obtaining a corrected rainfall forecast field based on the observation data of a rainfall station in a region to be subjected to rush-arrangement scheduling, the grid forecast data and a topographic factor; constructing a local flood forecasting model and inputting a corrected rainfall forecasting field on the basis of topographic and geomorphic characteristics and drainage system layout in a local area of the power distribution facility to obtain water depth data, water flow velocity and submerging duration of the position of the power distribution facility, and calculating an equipment risk score; and determining a candidate power distribution facility based on the equipment risk score, distributing a corresponding robbing scheduling resource through a preset optimization algorithm, generating a robbing scheduling sequence, distributing the robbing scheduling sequence to the power distribution facility, and executing a corresponding robbing scheduling action on the to-be-robbing scheduling region. According to the method, the equipment and regional risks are quantified after the extreme environment of the power distribution facility region is predicted, and the power distribution facility emergency scheduling is optimized in combination with risk assessment, so that the reliability of the power distribution facility emergency scheduling is improved.