29 results about "Topographic factor" patented technology

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.

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.

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 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 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 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.

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 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.

The invention discloses a coal rock distribution prediction method and device, electronic equipment and a storage medium. The coal rock distribution prediction method comprises the following steps: determining target topographic factor information of a micro-ancient landform formed by a target coal rock stratum, the topographic factor information being a micro-ancient landform shape represented by a stratum inclination angle and a stratum curvature, the topographic factor information can be used for representing peat marsh facies characteristics which are required by the coal rock stratum in a sedimentary environment and have a positive promotion effect on coal rock stratum deposition; determining seismic wave impedance corresponding to the target coal rock stratum; and carrying out seismic inversion based on the target terrain factor information and the seismic wave impedance corresponding to the target coal rock stratum, and predicting to obtain a coal rock distribution result of the target coal rock stratum. According to the scheme, the seismic inversion is constrained and verified by the topographic factor information of the micro-ancient landform and the seismic wave impedance of the target coal rock stratum, and the accuracy of a coal rock distribution prediction result is improved.

The invention discloses a karst region soil thickness mapping method based on a stacked integrated model, and relates to the technical field of soil mapping, and the method comprises the following steps: taking climate, terrain and stony desertification elements as spatial information; obtaining optical remote sensing images of the research area in recent ten years, processing the remote sensing images to obtain vegetation, soil and stony desertification indexes, and generating various topographic factors through topographic software; performing correlation analysis on environment covariables to eliminate multiple collinearity, converting qualitative variables and unifying resolution; dividing the data into two groups containing and not containing stony desertification information, and eliminating and screening an optimal variable by using six models through recursive features; taking the six models as base models, and taking prediction results as input to construct three meta-models to realize stacking integration; and finally, soil thickness prediction mapping is completed, and the uncertainty is predicted through quantile regression quantification. According to the method, the stacked integrated model and stony desertification information are combined, and the precision and resolution of soil thickness prediction are improved.

This invention relates to the field of ecological environment monitoring and carbon cycle assessment technology, and discloses a forest carbon sink prediction method based on spatial heterogeneity decoupling and hierarchical distributed autoregressive meta-learning fusion. The method first integrates ground survey data, remote sensing temporal features, meteorological driving factors, topographic factors, and soil attribute data to construct a multi-source heterogeneous feature system. Then, it decouples and models different categories of features, and divides ecologically homogeneous clusters based on a self-organizing map neural network. For each ecological cluster, it constructs an independent nonlinear autoregressive model for distributed prediction. Based on this, a meta-learning fusion model is constructed, and the outputs of each distributed model are dynamically weighted and biased to obtain the predicted forest vegetation carbon density, and the forest vegetation carbon storage and annual carbon sink are calculated. This method can effectively handle spatial heterogeneity and temporal lag effects, constructs a progressive error convergence structure, and improves the accuracy, stability, and cross-regional generalization ability of forest carbon sink prediction.

This invention discloses a remote sensing-based method for estimating farmland slope, relating to the field of farmland slope technology. The method includes the following steps: acquiring image data and coarse-resolution digital elevation model (DEM) data; processing the image data to obtain a digital terrain model and orthophotos; visually interpreting the orthophotos to extract farmland distribution and shape indices; extracting topographic factors based on the DEM and filtering key factors using the variance inflation factor method; weighting the key factors using the entropy weighting method to obtain a comprehensive membership degree, combining a threshold to filter target slope points and overlaying farmland to obtain a preliminary farmland slope; performing a weighted average of the coarse-resolution DEM data to obtain the topographic slope; calculating the main slope aspect correction coefficient using the K-means clustering method on the DEM; fitting the preliminary farmland slope, topographic slope, main slope aspect correction coefficient, and shape index using a power function model to obtain a farmland slope estimation model, thus achieving the estimation of farmland slope.

The invention relates to the technical field of water supply transformation, and discloses a water supply dispatching frequency conversion regulation and control strategy optimization method based on pressure balance, which comprises the following steps of: obtaining terrain elevation data and pipe network topology data of a construction area, performing data preprocessing and generating a terrain gradient feature vector; calculating gravitational potential energy distribution by using a terrain flow field coupling analysis algorithm, generating a terrain influence coefficient matrix, analyzing a pipe network segmentation condition caused by construction based on a graph cut algorithm, calculating topological change characteristics of an affected sub-network, and integrating the terrain influence coefficient matrix and the topological change characteristics. According to the method, a quantitative relation between gravitational potential energy distribution and pressure propagation is established through a terrain flow field coupling analysis algorithm, so that the influence of terrain factors on pressure can be accurately quantified, and pressure compensation requirements at different elevation positions are differentially processed by introducing a terrain influence coefficient matrix; the defect that the terrain influence is simplified into a single parameter by a traditional method is overcome.

The invention discloses a slope deformation intelligent interpretation method and system based on an InSAR time sequence image, and belongs to the technical field of geological disaster monitoring, and the method comprises the steps: obtaining a multi-temporal SAR image, and generating a deformation rate field and a time sequence; the deformation signal quality of a low-coherence region is improved by adopting a space-time double-domain adaptive enhancement algorithm; based on a multi-scale space-time diagram attention-Transform coupling network, extracting space-time features of deformation; fusing the deformation characteristics, the geological and topographic factors and the environment trigger factors to perform multi-modal risk intelligent assessment; according to the system, through deep coupling and closed-loop feedback among the modules, collaborative improvement of deformation signal quality, a feature extraction effect and risk assessment accuracy is realized, and the risk assessment accuracy is improved. And a high-precision and high-reliability technical means is provided for large-range slope monitoring and geological disaster prevention and control.

The application discloses a kind of urban flood mapping and driving factor analysis method based on multi-source remote sensing data, comprising the following steps: based on global surface water data set and Sentinel-1 image, respectively extract optical and SAR semi-permanent water body, take the intersection of both to generate enhanced semi-permanent water body mask;Water body distribution is obtained by extracting flood water body using Sentinel-1 image, while combining Sentinel-2 image for water body extraction accuracy verification;The accuracy of SRTM DEM is improved based on the DEM error prediction model of the Transformer network, to generate the corrected DEM;Based on the elevation, slope and surface cover type of the corrected DEM, a multi-dimensional spatial analysis model is constructed to identify the spatial distribution characteristics of flood water body and its topographic driving mechanism.The application can improve the spatial accuracy and timeliness of flood risk assessment by combining with the comprehensive analysis of topographic factors.

The application discloses a slope deformation intelligent interpretation method and system based on InSAR time sequence images, and belongs to the technical field of geological disaster monitoring. The method comprises the following steps: acquiring multi-temporal SAR images and generating a deformation rate field and a time sequence; adopting a space-time dual-domain self-adaptive enhancement algorithm to improve the deformation signal quality of a low-coherence region; extracting the space-time features of deformation based on a multi-scale space-time graph attention-Transformer coupling network; fusing deformation features, geological and topographic factors and environmental trigger factors to perform multi-modal risk intelligent evaluation; and reversely optimizing the preposed module parameters according to the evaluation confidence, so as to realize closed-loop collaborative optimization. Through the deep coupling and closed-loop feedback among the modules, the method realizes the collaborative improvement of the deformation signal quality, the feature extraction effect and the risk evaluation accuracy, and provides a high-precision and high-reliability technical means for large-range slope monitoring and geological disaster prevention.

The present application relates to a kind of spatial interpolation method based on fuzzy theory and integrated learning to geological exploration, belong to geological spatial data interpolation field.The spatial interpolation method based on fuzzy theory and integrated learning to geological exploration provided by the present application, by utilizing fuzzy theory, two kinds of spatial interpolation methods are effectively integrated, by selecting multiple central observation points as interpolation reference, effectively block the influence of the data with strong spatial variability on interpolation method, and by constructing the functional relationship between observation distance and attribute, the correlation between attribute information and topography in space is better reflected.The present application is based on fuzzy theory, by establishing integrated learning spatial interpolation prediction model, provide a kind of higher precision, lower time cost solution for spatial three-dimensional data expansion, the present application can solve the problem that spatial three-dimensional data points obtained due to topographic factor, instrument failure and human factor are sparse and unevenly distributed.

The present application provides a kind of river type narrow foundation pit anti-heave stability calculation method, by considering the blocking effect of circular sliding heave on side soil and supporting structure, introduce topographic related factors slope angle and river distance, and river level variation produces four parameters of force and seepage force to traditional calculation method, obtain the calculation result of river type narrow foundation pit anti-heave stability.The upper part of the soil in the soil outside the pit slope angle and river distance, and river level variation produces force and seepage force to pit.The upper part of the soil in the soil outside the pit slope angle and river distance two parameters are to reflect the uneven terrain problem of calculating the river type narrow foundation pit outside the pit soil, make up the defect that traditional method does not consider the pit outside terrain factor.The river level variation produces force and seepage force two parameters to pit in order to reflect the influence problem of calculating the river type narrow foundation pit outside the river, make up the advantage that traditional method does not consider the pit outside river influence factor.