981 results about "Elevation data" patented technology

The invention relates to the technical field of wind power prediction. The invention provides a wind power prediction method and system. The method comprises the following steps: acquiring multi-dimensional meteorological time series data, three-dimensional elevation data and unit operation data of a target wind power plant; constructing a spatial-temporal feature fusion network, extracting time sequence dynamic features, and performing weighted fusion on the spatial correlation features and the time sequence dynamic features to obtain a fusion feature vector; establishing a hybrid prediction model, and taking the fusion feature vector as input to obtain a wind power initial prediction result; introducing a terrain correction factor, constructing a turbulence intensity compensation function, and performing micro-terrain disturbance correction on the wind power initial prediction result; and outputting a final power prediction curve and a confidence interval. The problems that in an existing wind power prediction method, a physical model is insufficient in complex terrain microclimate modeling precision, high in calculation complexity and difficult to meet the real-time requirement, a statistical learning method is limited in high-dimensional nonlinear time sequence feature expression capacity, and prediction errors are remarkably increased under the abnormal working condition are solved.

The invention provides a terrain surveying method and device based on unmanned aerial vehicle cooperation and electronic equipment, and relates to the technical field of engineering surveying and complex terrain surveying and mapping. The method comprises the following steps: determining a first surveying flight route of an unmanned aerial vehicle in combination with ground elevation data of a to-be-surveyed area, controlling the unmanned aerial vehicle to collect initial area topographic data, identifying and classifying the initial area topographic data, and determining a weak texture false topographic area; re-planning a second survey flight route of the unmanned aerial vehicle according to the weak texture false terrain area, starting the millimeter wave radar, and scanning and collecting supplementary terrain survey data at different angles; fusing the supplementary topographic survey data at different angles to obtain local topographic survey data; and splicing and fusing the initial regional topographic data and the local topographic survey data to obtain real topographic survey data. According to the technical scheme, the measuring precision and the surveying efficiency of the topographic surveying data of the complex topographic region can be improved, and the reliability and the stability of the real topographic surveying data are improved.

The invention discloses a multi-modal remote sensing intelligent identification method for hidden geological disasters. The method comprises the following steps: carrying out normalization processing on multi-modal remote sensing data; constructing a pixel-level cross-modal visual converter; constructing a pixel-level cross-modal attention module, wherein the pixel-level cross-modal attention module comprises a pixel self-attention sub-module, a pixel mutual-attention sub-module and a layer adaptation mixing module; a layer adaptive noise mechanism is introduced, and the weight ratio of intra-modal self-attention and inter-modal mutual attention in different levels is adjusted in a self-adaptive mode; constructing a relation discriminator, and modulating the attention calculation process by evaluating the difference between modals of corresponding points in the space; training the model, adopting a multi-task loss function including semantic segmentation loss and boundary perception loss to perform joint optimization, and generating a concealment geological disaster segmentation result. According to the method, complementary information of an RGB optical image, an InSAR deformation rate image and DEM elevation data is fully utilized, and accurate recognition of the hidden geological disaster is realized through an efficient pixel-level cross-modal fusion mechanism.

Provided are a method, system and device for global path planning for an unmanned vehicle in an off-road environment. The method includes: obtaining satellite elevation data and a satellite remote sensing image of a current off-road environment; constructing a digital elevation model (DEM); determining slope and land surface relief of each grid in the current off-road environment; performing gray processing on the satellite remote sensing image to obtain grayscale values of the grids; determining traversal costs of the grids corresponding to different ground types; constructing a global grid map based on the slope and the land surface relief of each grid, as well as the traversal costs corresponding to the different ground types; determining a rugged terrain potential field and path costs; and searching for paths using a Bresenham's line algorithm and Theta* algorithm based on the rugged terrain potential field and the path costs, to generate a global path.

The invention provides a hydraulic engineering information management method and system based on big data. The method comprises the following steps: firstly, uploading flood flow velocity dynamic data, terrain elevation data and spectral feature data to a cloud server, then carrying out multi-dimensional superposition on the flood flow velocity dynamic data, associating with a historical inundation path, and generating a superposition association result; constructing a three-dimensional geographic model based on terrain elevation data and spectral feature data, generating a spatial superposition map according to boundary features of a water body and a terrain abrupt change area and a flood routing path, performing position association with a material reserve position, and performing dynamic priority ranking according to a submerging probability gradient, and finally, according to the resource scheduling instruction sequence, correcting the flight path of the unmanned aerial vehicle cluster and the data acquisition area to form a closed-loop feedback mechanism. According to the technical scheme provided by the invention, the accuracy and timeliness of real-time data updating are ensured.

The invention relates to the technical field of glacier information processing, in particular to a glacier surface moraine identification method based on multi-source remote sensing parameters, which comprises the following steps: performing radiation-atmosphere joint correction and geometric-reprojection on optical, thermal infrared, synthetic aperture radar and digital elevation data to generate physical consistency data; deducing the surface temperature under the driving of short-wave albedo, downward radiation and turbulent flux by using a cross-modal visual conversion network and a symmetric neural operator, and obtaining a thickness-existence degree posterior through a normalized flow; an uncertainty field is constructed according to information entropy and variance, a representative pixel unmanned aerial vehicle radar sampling and value measurement backflow fine tuning model is selected by means of quantum annealing, and then a spatial continuous thickness field is output through anisotropic diagram diffusion. According to the method, the future thickness is integrated according to mass conservation in combination with day-by-day weather forecast, and the future thickness is coupled with the existence degree index to form a burst risk grid, so that high-precision and iterable moraine dam early warning is realized.

The invention provides a large-scale water body underwater terrain inversion method and device and a medium, and the method comprises the steps: carrying out the partitioning of a to-be-inverted water body region, and obtaining a deep water region and a land-water conversion shallow water region; based on a spatial interpolation method, carrying out inversion on the land-water transformation shallow water area to obtain a land-water transformation shallow water area topographic image; a deepwater area water depth inversion model constructed based on the wave band reflectivity information and the underwater topographic elevation data performs inversion on the deepwater area to obtain a deepwater area underwater topographic image; and carrying out image splicing fusion on the land and water transformation shallow water region topographic image and the deep water region underwater topographic image to obtain the underwater topography of the water body to be inverted. In the face of a complex and changeable water body environment, a water body area to be inverted is divided into a deep water area and a land and water conversion shallow water area, and different and more targeted inversion methods can be adopted according to the characteristics of water bodies in different areas.

The invention discloses a flood detention area emergency evacuation early warning method based on environmental perception and intelligent analysis, and relates to the technical field of flood control and disaster mitigation, and the method comprises the steps: collecting the original data of a flood detention area site, and forming a monitoring array; a monitoring array is divided into a plurality of monitoring units according to geographic grids, a rainfall permeation model is established according to rainfall data and soil humidity data, a surface runoff field is constructed in combination with terrain elevation data, and the confluence direction of the runoff field is calibrated through water level data; based on a rainfall penetration model and a surface runoff field, generating a flood detention area inundation evolution sequence, segmenting into an inundation grade map according to a time step length, planning a flood detention area emergency evacuation path, matching with a road network in a geographic information system, generating an optimal evacuation path, and issuing graded early warning information to an early warning area. And sending a withdrawal instruction to residents in the flood detention area through the emergency broadcasting system. According to the invention, by establishing a partition early warning mechanism and a differential early warning strategy, accurate delivery and efficient transmission of early warning information are realized.

The invention relates to the technical field of engineering data analysis, in particular to an unmanned 3D (three-dimensional) paving and compacting cooperative construction method, which comprises the following steps of: acquiring the surface temperature of a paved mixture in real time by integrating an advanced temperature detection device; and an effective compaction time window is calculated by combining the elevation data, the wind speed acquired by the environment monitoring module and a preset compaction aging model, so that the surface temperature of the asphalt mixture is automatically analyzed to determine the optimal compaction time. According to the method, the uncertainty and subjectivity of estimating the proper compaction time of the mixture by depending on artificial experience or a simple temperature monitoring means in a traditional method are overcome, the compaction operation can be carried out under an ideal temperature condition, the construction quality is greatly improved, and the service life of a pavement is greatly prolonged.

The invention relates to the technical field of pollution analysis, in particular to a construction method and device of an agricultural watershed non-point source pollution decision support system.The construction method of the agricultural watershed non-point source pollution decision support system comprises the steps that firstly, the nitrogen and phosphorus loss amount of each pollution source is calculated; acquiring digital elevation data; determining an intermediate water body between each pollution source and the target water body according to the digital elevation data, and obtaining a migration path corresponding to each pollution source; when the absorption amount of each migration path is determined, the water body type is considered, and the influence of the difference between the hydraulic characteristics and the physicochemical properties of the water body on the absorption amount can be reflected through the water body type, so that the calculated absorption amount of each migration path is more accurate; furthermore, the target water body considers the complex communication relation and topological relation among the ditch, the pond, the reservoir and the river, and the simulated water body load capacity is more accurate.

The invention relates to the field of intelligent operation and maintenance of a high-speed railway overhead line system, provides a machine learning-based optimization method and system for dropper arrangement of the high-speed railway overhead line system, and aims to solve the problems of insufficient pantograph-catenary coupling stability and relatively low abrasion uniformity of the overhead line system. The method comprises the steps that contact force space distribution characteristics of a pantograph and a contact net are collected, and contact force balance constraint conditions are generated; constructing a line parameter compensation mechanism by combining the track ultrahigh value, the vertical curve curvature and the elevation data of the suspension point acquired by the laser scanner, and outputting a compensation three-dimensional coordinate of the suspension point of the overhead line system; a programmable gate array is adopted to implement parallel acceleration on neural network weight calculation and space coordinate transformation, contact force constraint and compensation coordinates are iteratively processed, and dropper arrangement parameters are generated; based on the segmented tension value and the node offset compensation value, driving equipment to finish accurate adjustment and positioning installation of the dropper. According to the technical scheme provided by the invention, the pantograph-catenary coupling stability and the wear uniformity of the catenary are improved.

The invention discloses a hydrological data prediction method based on a convolutional neural network and a physical information neural network. Comprising the following steps: 1) utilizing an HEC-RAS hydrological model to generate cross sectional area, flow and water level elevation data of a key section of a target river channel in a preset time period as training reference truth values; 2) constructing space-time coordinates corresponding to the data as an input data set; 3) space-time input is processed through a physical information neural network model based on a convolutional neural network, the convolutional neural network extracts local heterogeneity features of a spatial position of a river channel through one-dimensional convolution, and a continuity equation and a momentum equation of a Saint-Venant equation set are embedded into a training process in a residual form through physical information constraint; and 4) finally, directly outputting a hydrological parameter prediction result of the target river channel. Therefore, under the environment that the observation data is deficient or missing, the hydrological parameter prediction precision and the model generalization ability are remarkably improved by fusing the simulation data and the physical mechanism constraint.

The invention provides an intelligent irrigation control method and system for a high-standard farmland. The method comprises the following steps: acquiring crop canopy image data and farmland terrain elevation data of different crop planting areas in the high-standard farmland; determining a crop growth stage based on morphological characteristic data in the crop canopy image data, and constructing a soil hydrodynamic simulation model according to a root distribution depth interval and a theoretical water absorption rate coefficient corresponding to the crop growth stage; and based on the farmland terrain elevation data and the soil porosity parameter, generating a soil moisture migration parameter by using a soil hydrodynamic simulation model, generating a pipe network pressure regulation and control instruction in combination with the soil moisture migration parameter and a preset irrigation strategy, and adjusting the water outlet pressure distribution of the intelligent irrigation pipe network according to the pipe network pressure regulation and control instruction. Generating an irrigation control instruction; accurate space-time matching of irrigation of the multi-crop rotation area is achieved, the water utilization efficiency is improved, and the problem of irrigation uniformity at the terrain difference and the rotation boundary is solved.

The invention provides a rainfall data processing method and device based on multi-source data fusion, and the method comprises the steps: inputting multi-satellite fusion remote sensing rainfall observation data, terrain elevation data and latitude and longitude data of a target region at a target time interval into a rainfall data downscaling model, carrying out feature fusion, channel attention enhancement and space attention enhancement processing on the model, and outputting downscaling image data of which the resolution is higher than that of remote sensing rainfall observation data; and correcting the downscaling image data according to the site rainfall observation data corresponding to the plurality of observation sites in the target area at the target time interval to obtain target rainfall observation data of the target area at the target time interval. According to the method, the capturing capability and the multi-modal fusion capability of the model for rainfall distribution information in remote sensing data can be improved, the capturing capability of the model for complex terrains and rainfall characteristics can be improved, and then the expression precision and the application reliability of the rainfall distribution information in the generated high-resolution image can be improved.

The invention discloses a tidal reach water level and flow relation analysis method based on tidal level harmonic analysis. The method comprises the steps of collecting actually measured water level data, basin river topography and digital elevation data of a tidal reach hydrologic station; characteristic parameters and water surface elevation change characteristics of all partial tides of the tide level of the tidal river reach are obtained on the basis of tide level harmonic analysis, and then independent tide flow and river runoff flow are obtained; establishing a tidal reach one-dimensional hydrodynamic model, and analyzing a flow change process and a water level flow change relation under different water level change scenes; and analyzing the contribution degree of the tidal flow and the river runoff to the actual flow and the change process by utilizing a multivariate regression analysis and drift diameter analysis method to obtain the change characteristics of the water level and flow relationship of the tidal reach. The method has the advantages that the water level and flow relation of the tidal reach can be rapidly analyzed, complex estuary hydrodynamic process simulation is not needed, and the method is also applicable when actually measured flow data is lacked.

The embodiment of the invention provides a surface reflectance inversion method and device for optical satellite remote sensing data, and the method comprises the steps: obtaining sensitive parameter data corresponding to pixels, the pixels are all pixels in to-be-inverted optical satellite remote sensing data, and the optical satellite remote sensing data are collected by an optical satellite sensor, the sensitive parameter data comprises observation geometric data, atmosphere auxiliary data and elevation data; inputting the sensitive parameter data into an atmospheric correction neural network model to obtain an atmospheric correction coefficient of the pixel, the atmospheric correction neural network model being obtained by pre-training based on an atmospheric correction coefficient lookup table of the optical satellite sensor; and calculating according to the atmospheric correction coefficient and the radiation brightness of each pixel to obtain the surface reflectance of the optical satellite remote sensing data. According to the method, the inversion of the surface reflectance is carried out pixel by pixel based on the neural network model, and the method has the characteristics of high universality, high inversion precision and high calculation speed.

The invention provides a BIM technology and project amount intelligent verification method and system, and the method comprises the steps: carrying out the word segmentation and part-of-speech tagging of a progress log, extracting an excavation region, a completion amount and a timestamp of the day, and obtaining a construction progress information set; according to the construction progress information set, identifying a semantic relationship of an excavation area, a completion volume and a time triple, and determining time-space semantic representation; extracting geometric information of each target earthwork logic body from the set of earthwork logic bodies, and calculating a design engineering quantity of each associated logic body in a corresponding preset elevation range in real time in combination with corresponding elevation data extracted from the BIM model to generate a design quantity data set; and according to the early warning result, updating the state attribute of the earthwork logic body in the BIM model, generating a dynamically updated BIM model state in combination with the deviation occurrence timestamp and the difference value, extracting the spatial distribution of the earthwork logic body in the BIM model, and generating a construction path.

The invention relates to the field of aided design of construction roads, and discloses a planning method for a temporary construction road of a power transmission line, which comprises the following steps: acquiring elevation data, topographic parameters, tower position data, existing road information and raster data of a construction area of the power transmission line; constructing an initial grid network based on a grid method, converting the elevation data into grid elevation values, and storing the grid elevation values into the initial grid network; constructing a final grid network with a communication relationship by taking a slope angle between adjacent grids of the initial grid network and the communication cost of the topographic parameters corresponding to the grids as constraint conditions; constructing a moving window to search the final grid network, and constructing a weighted directed graph; and determining a starting point and an end point of the power transmission line, and searching the weighted directed graph based on an improved Dijkstra algorithm to obtain a design scheme of an optimal temporary construction road with the minimum total construction cost between the starting point and the end point. The accuracy and reliability of temporary construction road planning of the power transmission line can be improved.

The invention discloses an icing prediction method and system based on terrain potential energy field and multi-scale meteorological coupling, relates to the technical field of power transmission line icing prediction, and solves the problems that a traditional icing thickness prediction model is split and traditional static terrain parameters cannot represent dynamic terrain potential energy. According to the technical scheme, the method comprises the following steps: acquiring meteorological data and high-resolution digital elevation data; calculating a topographic potential energy tensor; establishing a multi-meteorological coupling model, and correcting the multi-meteorological coupling model through the topographic potential energy tensor; carrying out meteorological element correction on the corrected multi-meteorological coupling model to obtain a corrected meteorological field; and obtaining a first icing thickness prediction value and a second icing thickness prediction value based on a pre-constructed icing growth physical model and a pre-trained machine learning model, and carrying out weighted calculation on the first icing thickness prediction value and the second icing thickness prediction value to obtain a final icing thickness prediction value. And the effect of improving the prediction precision under complex terrain and extreme meteorological conditions is achieved.

The embodiment of the invention provides a ground surface settlement and crack monitoring method, device and equipment for a high and cold mining area and a storage medium. The method comprises the following steps: acquiring original time sequence image data, point cloud data, multi-stage elevation data ground surface radar image data and an online map of a research area; determining a first planar settlement amount based on the original time sequence image data; determining a second planar settlement amount based on the point cloud data; carrying out fusion treatment on the first planar settlement volume and the second planar settlement volume to obtain an initial planar overall settlement volume; comparing the multi-stage elevation data to obtain a point settlement amount; performing data verification on the initial planar overall settlement volume based on the point settlement volume and preset settlement volume data to obtain a target planar overall settlement volume; performing combined processing on the target planar overall settlement volume and an online map to obtain a two-dimensional settlement image; extracting surface morphological features from the surface radar image data; and based on the surface morphological characteristics and the two-dimensional settlement image, determining a geological disaster-prone risk area.

The invention discloses a spatial planning three-dimensional visualization method based on geographic information, which relates to the field of spatial planning and comprises the following steps: acquiring and analyzing remote sensing image data to obtain attribute data and digital elevation data; obtaining and analyzing an actually measured topographic map to obtain vector data; establishing a three-dimensional terrain database by combining the digital elevation data, the vector data and the remote sensing image data; collecting ground building data, generating a building model, and establishing a three-dimensional building model library; acquiring public facility information, and establishing a basic service facility model library; combining the three databases with attribute data to establish a three-dimensional visual GIS database; according to the planning and design scheme, obtaining corresponding three-dimensional visual GIS database data, and establishing a three-dimensional visual area model; a three-dimensional surface dynamic model is generated through a three-dimensional visualization GIS database, and a three-dimensional visual area model is deduced; and based on the deduction result, comparing and optimizing the planning and design scheme.

The invention belongs to the field of space earth observation technology and application, and discloses a remote sensing image stony desertification grading method, device and equipment based on a feature space, and the method comprises the steps: obtaining a remote sensing image, elevation data DEM and land utilization data of a research area, and carrying out the preprocessing; terrain correction is carried out on the area with the gradient larger than 25 degrees through an SCS + C terrain correction model; constructing a rock index RCRI and a vegetation index NDRE, and performing normalization; an RCRI-NDRE feature space is constructed; constructing a stony desertification index according to a distance formula from a point to a straight line; high-resolution remote sensing images are used for assisting visual interpretation of stony desertification sample points of different degrees, and grading thresholds between stony desertification of different degrees in the RCRI-NDRE feature space are calculated; and stony desertification grading is carried out based on the grading threshold value, and distribution conditions of stony desertification of different degrees are obtained. According to the method, remote sensing image data are extracted, and the RCRI-NDRE feature space of the rock index RCRI and the vegetation index NDRE is constructed, so that different degrees of stony desertification grading of the target area can be realized.

The invention discloses an SAR image mountain shadow dynamic threshold removal method based on an inverse exponential function, and relates to the technical field of remote sensing image data processing, and the method comprises the steps: preliminarily extracting a water body region based on an SAR image through employing a dual polarization water body index SDWI, and randomly generating sample points in the range of the water body region; assigning corresponding elevation and gradient attributes to each sample point through digital elevation data SRTM, and performing artificial interpretation by using an optical image to obtain a sample data set containing two types of sample points of a mountain shadow and a water body; and removing abnormal values of a sample data set through a quartile distance method, performing thinning processing on sample points to calculate a demarcation point of the two types of sample points, and constructing a demarcation function model of an inverse index for distinguishing a mountain shadow and a water body. Therefore, by adopting the SAR image mountain shadow dynamic threshold removal method based on the inverse exponential function, the false water body region caused by the misjudgment of the terrain shadow is effectively eliminated, and the authenticity and the consistency of the water body space distribution are improved.

The invention discloses a frozen soil roadbed hot melt settlement laser scanning measurement method and system, and relates to the technical field of measurement, the method comprises the following steps: obtaining gridding surface data, and obtaining an elevation data sequence of each grid point; acquiring an elevation change index of each grid point according to the elevation data sequence of each grid point; acquiring a plurality of grid points adjacent to the ith grid point and taking the plurality of grid points as a plurality of reference grid points of the ith grid point, acquiring each relative difference quantity of the ith grid point, and acquiring a correction proportion of the ith grid point according to the plurality of relative difference quantities of the ith grid point; and obtaining target elevation data of each grid point according to the correction proportion of each grid point and the measured elevation data at the current moment, and forming a measurement result including the gridding surface data according to the target elevation data of each grid point. The method has the advantages of identifying abnormal values, dynamically correcting measurement and improving data reliability.

The invention relates to the technical field of water conservancy projects, in particular to a water conservancy earthwork dynamic monitoring method and system based on multi-source point cloud data fusion, and the method comprises the steps: carrying out the identification and standardization processing of airborne LiDAR, ground scanning and oblique photography point clouds, and generating a standard point cloud data set with uniform spatial positions and balanced density; dividing an abrupt slope area, a gentle slope area and a flat area based on topographic gradients, and constructing a grid framework adaptive to topographic features; carrying out weighted fusion by combining the point cloud coverage rate and the normalized terrain gradient to generate high-precision grid elevation data; calculating an earthwork variation distribution matrix by comparing historical elevation models; dynamically analyzing the change trend of the excavation and filling area, and synchronously evaluating the construction progress deviation and the geological disaster risk. According to the method, the problems of insufficient multi-source point cloud fusion precision, poor terrain adaptability and dynamic evaluation splitting in the prior art can be solved, and the monitoring precision and the engineering safety early warning efficiency under the complex terrain condition are remarkably improved.

The invention provides a multi-source environment interpretation method and device based on linear frequency modulation analysis, and belongs to the field of space-based remote sensing intelligent process.The method comprises the steps that a hyperspectral image and laser radar digital surface model data of a target area are obtained, local neighborhood windows with pixels as the centers are extracted respectively, and the local neighborhood windows are obtained; generating a spatial-spectral data cube and elevation data; performing fractional domain feature extraction on the spatial spectrum data cube and the elevation data in sequence, and obtaining reconstruction features based on linear frequency modulation basis function mapping and cross-modal fusion; and identifying and classifying the reconstructed features to obtain a target area ground feature classification result. According to the method, through cascade design of fractional domain transformation, frequency modulation modeling and cross-modal attention, multi-source distribution alignment, dynamic feature enhancement and efficient fusion and generalization are realized, and an interpretable and low-dependence solution is provided for multi-source cross-regional ground feature classification.

The invention provides a permafrost roadbed disease early warning method, and relates to the technical field of permafrost roadbed disease evaluation. The method comprises the following steps: performing deformation laser scanning detection on the surface of the permafrost roadbed to obtain road surface deformation point cloud elevation data of the permafrost roadbed; and preprocessing the road surface deformation point cloud elevation data to obtain a surface elevation two-dimensional grid of the permafrost roadbed. And calculating a deformation gradient value of each grid unit in the elevation two-dimensional grid by adopting an eight-neighborhood gradient algorithm to obtain a surface deformation gradient two-dimensional grid of the permafrost roadbed. And classifying the surface deformation gradient two-dimensional grids based on a preset deformation gradient threshold value, identifying a standard-exceeding area of the permafrost roadbed, and determining a roadbed disease index based on the area of the standard-exceeding area. And determining the disease early warning level of the permafrost roadbed according to the roadbed disease index. According to the method, the development process of frozen soil roadbed diseases can be effectively predicted, and the timeliness and reliability of freeze-thaw disaster early warning are remarkably improved.

The invention discloses a flood disaster scene-oriented urban power distribution network toughness evaluation method and system, and belongs to the technical field of power distribution network toughness evaluation, and the method comprises the steps: obtaining rainfall data, land utilization data, elevation data and the like, and inputting the data into a waterlogging simulation module; the waterlogging simulation module simulates rainfall runoff production and runoff convergence to generate ponding distribution; the disaster judgment module obtains the fault probability of the power distribution equipment and the line in combination with the power distribution equipment and line disaster fault model, and generates a system disaster scene under the flood disaster; the load reduction optimization module constructs a power distribution system load reduction optimization model, and takes the minimization of the total load reduction amount of the power distribution system as a target; and the system toughness evaluation module measures the overall toughness of the power distribution system in the flood disaster scene. According to the method, the overall toughness level is quantitatively evaluated based on the system load reduction amount, the node load importance degree and the ponding condition are combined to recognize the weak links of the system, reference is provided for differential reinforcement and operation and maintenance strategies, and subsequent waterlogging prevention planning and operation and maintenance management are guided.

The invention discloses an off-line and on-line combined unmanned aerial vehicle simulation flight path planning method, which belongs to the field of unmanned aerial vehicle flight path planning, and specifically comprises the following steps: obtaining a series of insertion waypoint longitudes and latitudes through a waypoint interpolation algorithm according to a selected route before a flight task is executed in an off-line mode; then altitude information stored in an elevation database is obtained in an elevation query mode, the target height of each waypoint of the unmanned aerial vehicle is calculated, and then route planning in the whole task process is completed; in the online mode, in the flight mission execution process, a real-time to-ground laser ranging system is adopted to obtain real-time to-ground height information of the unmanned aerial vehicle, after a measurement error is calibrated by combining flight attitude information of the unmanned aerial vehicle, the measurement error is compared with the actual flight height of the unmanned aerial vehicle, and the to-ground height of the unmanned aerial vehicle is calibrated on the basis of a pre-planned route; and the ground-imitating flight precision of the unmanned aerial vehicle is improved. According to the invention, high-precision ground-imitating equal-altitude flight route planning of the long-endurance unmanned aerial vehicle in a complex environment is realized.

The invention provides a slope deformation monitoring method based on image point cloud modeling, and the method comprises the steps: 1, obtaining an image; 2, carrying out the point cloud conversion, and generating point cloud data through a shot picture; and step 3, carrying out point cloud processing, wherein the point cloud processing comprises preprocessing, point cloud filtering and point cloud fine processing. Step 4, DEM generation: generating a DEM according to the obtained slope point cloud elevation data; and step 5, visualization processing: reading multi-stage DEM data and difference data, and carrying out visualization processing on the DEM data after difference. The portable device is used for shooting the image and processing the image to generate the high-precision point cloud data, so that efficient, real-time and low-cost slope deformation monitoring is realized, comprehensive deformation information and an intuitive visual chart are provided, meanwhile, an automatic early warning function is achieved, and the accuracy and timeliness of slope safety management are remarkably improved.