821 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 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.

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 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 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 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 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 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 relates to the technical field of ecological environment protection, in particular to a mangrove forest carbon sink monitoring and metering method based on an unmanned aerial vehicle, a radar and an AI technology, and the method comprises the steps: 1, obtaining the laser radar data of a mangrove forest through a laser radar carried by the unmanned aerial vehicle; step 2, acquiring elevation data in a mangrove forest vegetation layer area, and obtaining point cloud data after topographic error correction; 3, obtaining a multispectral image of the mangrove forest, and obtaining a multispectral data matrix; 4, identifying forest growth data features, constructing a mangrove forest carbon sink prediction model, and predicting the mangrove forest carbon sink amount; step 5, marking the image region with the NDVI value higher than a preset NDVI threshold value as a blade over-dense region; and according to the area of the overdense leaf region and the multispectral data matrix, calculating a light depression factor by using a photosynthetic depression factor formula, determining the carbon sink deviation of the overdense leaf region by using a regional carbon sink deviation formula, and obtaining a real carbon sink value of the mangrove forest according to a carbon sink calculated value obtained by prediction.

The invention provides a weak current pipeline settlement monitoring system based on multi-source data fusion. The system comprises a ground sensor array which comprises a plurality of fiber bragg grating static level gauges, a strain sensing optical cable and a plurality of MEMS triaxial accelerometers and is used for collecting horizontal displacement, deformation stress and vertical settlement in sequence; the satellite remote sensing module comprises a synthetic aperture radar module and a GNSS module and is sequentially used for acquiring earth surface deformation data and elevation data of a monitoring area; and the data analysis platform is used for carrying out feature extraction on the data acquired by the ground sensor array, carrying out multi-source data fusion on the extracted features and the satellite remote sensing data acquired by the satellite remote sensing module by adopting a multi-source data fusion algorithm, and carrying out settlement monitoring and early warning based on a fusion result. According to the scheme, the problems that traditional single monitoring precision is insufficient and response lags are solved, the pipeline settlement early warning reliability can be remarkably improved, and the pipeline breakage risk is effectively prevented.

The invention discloses an overhead transmission line mechanical construction three-map one-table intelligent generation method, which comprises the following steps of: fusing a digital orthoimage model, elevation data and a power transmission and transformation project model through a coordinate transformation matrix, and establishing a three-dimensional scene with a terrain curvature attribute; based on the terrain curvature and equipment coordinates of the three-dimensional scene, a multi-objective optimization algorithm is adopted to synchronously plan a composite path of a cableway, a track and a road, and a road network list map containing an obstacle avoidance topology is generated; recognizing a tower footing projection polygon in the three-dimensional scene, segmenting the types of surface attachments in combination with a digital orthoimage model image, and generating a surface feature list map with a water and soil conservation identifier; and establishing a mapping function of the terrain curvature and the environment water conservation rule, outputting equipment types and spatial poses according to the hoisting safety margin and the earth surface disturbance threshold value, and generating an equipment list map. According to the method, a unified space reference and multi-system dynamic cooperation mechanism is constructed, and the planning precision and construction compliance of three graphs and one table are remarkably improved.

The invention provides a flood simulation method driven by physical and data fusion, and belongs to the technical field of flood simulation, the method comprises the following steps: pre-training to obtain a qualified urban flood simulation model, a target loss function comprising a data supervision loss item and a physical constraint loss item, the physical constraint loss item is composed of a mass conservation residual error loss item, a momentum conservation residual error loss item and an energy conservation residual error loss item; inputting ground elevation data and historical meteorological data of a target area in a city into the hydrological model to obtain water depth grid data and flow velocity grid data, and inputting the water depth grid data and the flow velocity grid data into the city flood simulation model for prediction processing to obtain water depth grid data and flow velocity grid data of the target area at different time points in the future; and based on the data at different time points, generating an urban flood ponding dynamic evolution graph of the target area. The invention aims to improve the efficiency and accuracy of urban flood scene prediction.

The invention relates to the technical field of remote sensing measurement, in particular to a power transmission line tree high-risk assessment method and device based on satellite remote sensing and a medium. And based on the result and line data, calibrating a safety distance, combining terrain elevation correction, identifying the tree, calculating the height of the tree and the clearance distance of the line, comparing the clearance distance with a safety standard, dividing high, medium and low risk levels, and generating a high-risk assessment result of the power transmission line tree. According to the method, the vegetation distribution is described by extracting the spectral features of the remote sensing image, the dynamic safety interval is constructed in combination with the position of the power transmission line and the vegetation distance, the model is established by fusing terrain and crown elevation data, clearance parameters are quantitatively calculated to generate the risk level, the complex terrain adaptability and the risk point automatic identification precision are improved, and the early warning reliability is improved. And intelligence and decision-making accuracy of power transmission line inspection are promoted.

The invention discloses a slope-to-terrace erosion gully treatment method and device, computer equipment and a storage medium, and the method comprises the steps: obtaining the surface elevation data of a to-be-slope-to-terrace region, and obtaining first topographic data; simulating a runoff path and a runoff depth of a terrain corresponding to the first terrain data under a rainstorm condition by adopting a preset water erosion simulation model, and predicting a development site of the erosion gully; and laying an erosion gully treatment scheme for the development site. The method comprises the following steps: acquiring first topographic data of a to-be-changed-to-terrace area, simulating rainstorm weather for the first topographic data by using a preset water erosion simulation model, scouring the first topographic data to form a runoff path and a runoff depth, further predicting that erosion gullies are possibly formed at positions, and then arranging erosion treatment schemes for the positions, the problem that the erosion gully is not effectively treated in the prior art is solved.

The invention relates to the technical field of bridge swivel construction monitoring and early warning, in particular to a BIM-based intelligent real-time visual monitoring system for a bridge swivel process, and the system comprises an intelligent sensing module which collects actual measurement point cloud data, stress data and elevation data of two sides of a bridge body in the bridge swivel process; the BIM monitoring module specifically comprises the following steps: distinguishing each actual measurement point cloud data corresponding to a bridge body and each actual measurement point cloud data corresponding to an environment; calculating the relative deviation of the bridge body at the current acquisition moment; acquiring a risk point cloud set; obtaining a point cloud distribution density, a collision risk value, a first inclination risk value and a second inclination risk value at the current acquisition moment; the state early warning module is used for carrying out real-time early warning on the risk in the bridge turning process; and the traction control module is used for controlling the working state of the bridge turning process according to the early warning result. The accuracy of monitoring the risk state of collision or inclination of the bridge body is improved, and the safety of bridge construction is improved.

The invention discloses a water area and resource quantity monitoring and predicting method and system based on a remote sensing technology, and belongs to the technical field of water resource monitoring. The method comprises the following steps: determining a water area range according to historical data of a research area and an on-site survey condition, and determining a research area range; the method comprises the following steps: respectively acquiring ground data and water depth and water surface elevation data by using an unmanned aerial vehicle and an unmanned ship, respectively processing to obtain a land DEM and a water area DEM, and fusing to form an all-terrain DEM; downloading satellite image data of the research area, and performing geographical registration on the satellite image data; in the geographically registered satellite image data, describing the boundary of the water area through visual interpretation; superposing the water area boundary to an all-terrain DEM, obtaining the elevation of each point on the water area boundary line, calculating the average elevation of the water area boundary, and calculating the water area and the water resource amount to obtain the historical water area and the water resource amount; and on the basis of the obtained historical water area and water resource quantity, utilizing an ARIMA model to predict future water resource quantity and water area.

The invention relates to the technical field of digital twinning, in particular to a flood disaster evolution method and system based on the digital twinning technology, and the method comprises the steps: collecting all point cloud data of a river basin terrain, constructing a three-dimensional terrain model, obtaining the reflection intensity and elevation data of each point cloud, clustering all point clouds corresponding to the ground, and obtaining a three-dimensional model; recording the areas where all point clouds corresponding to the ground in each cluster are located as local areas; and calculating a coplanar characterization value, an erosion evaluation value, an instability risk value, a soil fixation evaluation value and a terrain evolution factor of each local area, and constructing a digital twinborn simulation platform of the river basin terrain in combination with the three-dimensional terrain model to perform analog simulation on flood disasters. The defect that a static terrain model cannot respond to terrain evolution caused by flood disasters can be avoided, simulation errors generated by a digital twin simulation platform in the flood disaster evolution process are effectively reduced, and the accuracy of risk assessment of the flood disasters is improved.

The invention relates to the technical field of agricultural data analysis, in particular to a land reclamation suitability evaluation and cultivated land quality improvement method based on big data, which comprises the following steps of: acquiring and superposing land utilization, soil and elevation data to construct a multi-source land attribute evaluation data set, calculating the distance from a pattern spot to a water source, reconstructing a weight through Gaussian attenuation, and calculating the land utilization, soil and elevation data; the method comprises the following steps: forming a spatial distance weighted feature matrix, extracting a gradient and an irrigation guarantee rate, performing weight reduction on an overrun part of a conflict threshold value, performing linear weighting calculation on a comprehensive score after normalization, dividing a regulation priority area, matching quality improvement measures, and forming a cultivated land quality improvement project layout map. A pattern spot level evaluation basis is formed through multi-source attribute space superposition, water source distance attenuation calculation is introduced to enable weight to change along with reachability, irrigation difference response is enhanced, conflict constraint correction is carried out on gradient and irrigation guarantee, adverse combination influence is inhibited, stable sorting is formed through uniform scale weighting, and the method is applied to the field of irrigation. And improvement priority area identification and quality improvement measure directional configuration are supported.

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 relates to the technical field of automatic control, in particular to a depth control system and method for a seeder in a hilly and mountainous area, and the system comprises a visual module which is configured to collect a visual image of a to-be-sown area according to a first preset frequency; a data processing module configured to: convert the visual image into a depth map using an image processing model; performing gridding processing on the visual image; for each column of grids, determining at least one piece of target surface elevation data corresponding to the grid based on the depth map; determining at least one first adjustment instruction based on the preset seeding depth and the at least one piece of target surface elevation data; and the control instruction output module is configured to issue at least one first adjusting instruction corresponding to each column of grids to the corresponding depth limiting adjusting mechanism. According to the system, independent and accurate control over the ditching depth of each ditcher can be achieved through the visual preview and intelligent control technology, so that the seedling emergence uniformity and the crop yield are improved.

The invention provides an SOC prediction method and system. The method comprises the following steps: acquiring historical driving behavior characteristics, historical driving mileage, historical SOC, historical GPS coordinates and historical elevation data; acquiring historical road slope feature data; constructing an SOC original prediction model, and obtaining an SOC prediction model based on the historical data training model; acquiring real-time driving behavior characteristic data, real-time driving mileage data, real-time GPS coordinate data and real-time elevation data; acquiring real-time road slope feature data; and inputting the real-time data into the model for prediction, and obtaining real-time SOC prediction data. According to the SOC prediction method and system provided by the invention, the constructed SOC prediction model can comprehensively consider the influence of the road gradient, the driving behavior and the environmental factor on the SOC change, capture the time sequence characteristics, improve the prediction precision, and provide data support for optimizing the battery management system and improving the cruising ability of the electric vehicle.