127 results about "Land based" patented technology

The invention relates to landslide risk assessment, which is used for solving the problem that landslide risk assessment focuses on single data, neglects the comprehensive influence of related factors, and does not consider the influence of vegetation landslide resistance and rock strength, so that the real-time assessment of landslide risks is inaccurate. The fourth system covering layer slope landslide risk real-time evaluation system based on the Internet of Things comprises a slope monitoring module; according to the method, the vegetation coverage is calculated, the height and crown breadth change rate of the vegetation are analyzed, the influence of the growth condition of the vegetation on the slope stability is evaluated, and the effect of the vegetation on the landslide risk is more accurately judged through the comprehensive vegetation data processing mode; after land types are distinguished, risk judgment is carried out on the rock land based on rock stress related indexes, errors caused by unified standard evaluation are avoided through the distinguishing treatment mode, and the evaluation result better conforms to the actual situation; and the rock strength in the rock land is analyzed in detail, so that the stability of the rock land is mastered more accurately.

The invention belongs to the field of urban ecological corridors, and discloses a construction method of a high-density urban area ecological-recreation function composite corridor network. The method comprises the following steps: inputting first phenological data and a first environmental factor into a maximum entropy model, and outputting grids of a suitable-to-grow area source ground of a target bird; adopting a natural breakpoint method to re-classify the grids of the source land of the suitable-for-growth area, and determining the grids as target ecological source land; determining a second environment factor greater than a preset correlation coefficient threshold and a contribution rate of the second environment factor by adopting a cutting method, and determining a landscape resistance surface of the target ecological source land based on the second environment factor and the contribution rate; calling a map software API interface, and screening out a target recreation space; constructing a cost grid surface of the target recreation space by adopting a spatial principal component analysis method and a cost factor; and determining an ecological corridor and a recreation type corridor by adopting a minimum path cost method, a landscape resistance surface and a cost grid surface. The urban ecological corridor with harmonious symbiosis of human and nature can be constructed.

The invention discloses an urban reserve land intelligent classification method and system based on spatial data fusion, and relates to the technical field of data processing, and the method comprises the steps: carrying out the formalized expression of a land use rule of a preset urban region, and constructing a land domain knowledge graph; collecting multi-source spatial data, performing feature extraction and alignment, and generating a high-dimensional feature vector of each plot; and performing hybrid classification evaluation based on the high-dimensional feature vector of each land parcel, synchronously outputting land use types, development potential levels and land reference value intervals of the land parcels, and calling a land domain knowledge graph to perform compliance verification and correction. The technical problems that an existing urban reserve land classification method depends on artificial experience and lacks accurate and multi-dimensional data fusion and compliance check, so that the accuracy and practicability of a classification result are low are solved, and the purpose that the classification accuracy and practicability of the urban reserve land are improved through multi-source spatial data fusion and an intelligent classification framework is achieved. And the accuracy and practicability of urban reserve land classification are improved.

The invention provides a saline-alkali soil improvement data analysis method and system based on remote sensing data, and relates to the technical field of land scientificity, and the method comprises the steps: collecting ground monitoring data and remote sensing data, and generating a comprehensive feature vector and a spectrum feature value; then, outputting a saline-alkali soil improvement grade by using a convolutional neural network model; a correction coefficient is generated through the soil salinization index, the spectral index and the pH value, an upgrading and downgrading mechanism is set in combination with a correction threshold interval, and the improvement grade is dynamically adjusted to make up for the model error and improve the evaluation accuracy. According to the method, the saline-alkali soil quantitative analysis model based on the convolutional neural network is constructed, and the ground monitoring data and the remote sensing spectrum characteristic data are combined, so that the improvement grade of the saline-alkali soil is scientifically evaluated.

The invention discloses an abandoned land extraction method based on multi-scale multi-time sequence and contrast loss, and the method employs the introduction of a dual-feature extraction module and multi-scale feature fusion, enables a network to learn local features and global features from abundant input data distribution, and improves the network feature learning capability. Secondly, a self-attention module is added behind the feature extraction module, and global features are further enhanced; and finally, the idea of comparative learning is innovatively introduced into a supervised learning process, and a special method for dividing positive and negative samples is defined according to task characteristics of abandoned land segmentation, so that the segmentation accuracy is greatly improved. The abandoned land semantic information can be effectively segmented from the cultivated land range, and the result of the ablation experiment further explains the effectiveness of each module.

The invention relates to the technical field of land utilization change evaluation, in particular to a land change degree evaluation method and system based on gradient division and two-dimensional analysis and a storage medium. Dividing the to-be-evaluated land into a plurality of target sub-regions with different target gradients by adopting topographic position indexes; wherein the terrain position index is obtained by calculating an elevation value and a slope value of the land; and analyzing the target sub-regions from two evaluation dimensions including a terrain gradient effect dimension and a land gradient utilization characteristic dimension, and determining a land change degree evaluation result of the to-be-evaluated land based on the obtained terrain gradient effect evaluation result and the land gradient utilization characteristic dimension evaluation result of the plurality of target sub-regions. The objective of the invention is to solve the problem of how to carry out land utilization change evaluation on areas with obvious vertical zone performance and altitude gradient performance.

The invention provides an unmanned aerial vehicle take-off and landing control system based on UWB base station positioning, comprising: take-off and landing base stations arranged on a take-off and landing plane, the take-off and landing base stations including a first UWB base station, a second UWB base station, a third UWB base station and a fourth UWB base station arranged at four corners of the take-off and landing plane, the first UWB base station being set as a reference base station, the second UWB base station the GPS latitude and longitude information of the first UWB base station is synchronized to the first UWB base station, and two-dimensional positioning of the second UWB base station, the third UWB base station and the fourth UWB base station is carried out according to the GPS latitude and longitude information of the first UWB base station; the first relay beacon, the second relay beacon and the third relay beacon are arranged in the take-off and landing direction of the unmanned aerial vehicle, the first relay beacon, the second relay beacon and the third relay beacon are arranged in the take-off and landing direction in the direction away from the first plane, and the heights of the first relay beacon, the second relay beacon and the third relay beacon are gradually increased. And when the unmanned aerial vehicle is far away from the third relay beacon and within a preset distance, the unmanned aerial vehicle navigates according to the position information of the third relay beacon and the GPS information of the unmanned aerial vehicle with a preset first weight.

The invention discloses a modeling method and device of a velocity model, computer equipment and a storage medium, and belongs to the technical field of seismic exploration. The method comprises the following steps: determining a first optimized speed model corresponding to land according to first seismic data, and determining a second optimized speed model corresponding to ocean according to second seismic data; part of data is intercepted from the first optimization speed model and the second optimization speed model to serve as a third initial speed model corresponding to the sea-land transition zone, and after the third initial speed model is optimized, it can be ensured that the accuracy of the obtained third optimization speed model corresponding to the sea-land transition zone is high; and therefore, the imaging effect is relatively good when earthquake imaging is carried out on the sea-land transition zone subsequently.

The invention relates to the field of image equipment, in particular to a region intrusion judgment method, system and device for an outdoor complex scene and a medium. Comprising the following steps: receiving abnormal monitoring information sent back by an outdoor terminal; obtaining a corresponding reserve land first-level supervision area based on the abnormal monitoring information; analyzing an abnormal intrusion behavior in the first-level supervision area of the reserve land, and judging whether the abnormal intrusion behavior causes a dangerous event to the current reserve land or not; if yes, starting an early warning processing plan; and if not, performing secondary judgment on the current abnormal intrusion behavior, constructing an overlapped supervision area based on the current reserve land area, and performing a temporary intrusion supervision plan on the abnormal intrusion behavior of the overlapped supervision area for judgment. And the supervision precision of the complex outdoor scene reserve land is effectively improved.

The embodiment of the invention provides a greenbelt arbor configuration method and device based on multi-objective optimization. The method comprises the following steps: repeatedly executing the following steps to obtain a plurality of arbor configuration schemes of the green land: generating an arbor combination which comprises individuals of each kind of arbor at different ages in a plurality of kinds of arbor; calculating the sum of the carbon sequestration amount, the sum of the maintenance cost and the sum of the vertical projection areas of all the individuals in the arbor combination in the whole life cycle of the green land to obtain three target values; if the area-related parameters including the total sum of the vertical projection areas of the arbors meet preset constraint conditions, determining the arbors as arbors configuration schemes, and performing multi-target optimization on the plurality of arbors configuration schemes according to three target values by taking the carbon sink amount, the maintenance cost and the vertical projection areas of the arbors as optimization targets, and an optimized arbor configuration scheme is obtained, so that the optimized arbor configuration scheme better meets the layout requirements of the greenbelt in the aspects of carbon sink amount, maintenance cost and arbor vertical projection area.

The invention discloses an unmanned aerial vehicle landing method, system and device based on multi-stage control, and the method comprises the steps: determining a dynamic model based on the state data of an unmanned aerial vehicle; determining a first control parameter of the first stage based on the kinetic model, and controlling the movement of the unmanned aerial vehicle in the first stage based on the first control parameter; determining a second control parameter of a second stage based on first end point state data of the unmanned aerial vehicle at the end point of the first stage, and controlling the movement of the unmanned aerial vehicle in the second stage based on the second control parameter; and determining a third control parameter of a third stage based on second end point state data of the unmanned aerial vehicle at the end point of the second stage, and controlling the unmanned aerial vehicle to land based on the third control parameter. Through a three-stage control strategy, in combination with a wind field model, a ground contact force compensation model, ground contact impact force analysis and the like, accurate and stable landing of the unmanned aerial vehicle in a complex environment is achieved, and landing safety and stability are improved.

The invention discloses a planting field weed identification method and system based on machine vision and a medium, and the method comprises the steps: obtaining a plurality of sample images, carrying out the feature extraction of the sample images, and obtaining a plant actual feature set and a weed actual feature set; constructing a primary network model by using a generative adversarial network, dividing the plurality of sample images into a training set and a test set, and training and testing the primary network model by using the training set and the test set to obtain a feature recognition model; acquiring a to-be-measured planting field image, and performing segmentation processing on the to-be-measured planting field image to obtain a to-be-measured leaf image; inputting a to-be-detected leaf image into the feature recognition model, and outputting a to-be-detected feature set of the to-be-detected leaf image by the feature recognition model; and weed identification is carried out according to the feature difference between the to-be-detected feature set and the plant actual feature set and the weed actual feature set. According to the invention, the weed identification precision and accuracy are improved.

The present invention pertains to optical wave guides (ie structures that guide waves by restricting the transmission of energy to one direction) buoys, anchors, and climate change. The Algal Wave Guide directs energy to Algae in maritime and lake environments in order to expand the viable range of highly productive carbon sequestering environments such as the Brown Algae among others. Sequestering carbon at sea has several advantages over terrestrial methods such as lower cost, surface availability compared to land based real estate, reduced infrastructure, increased fishery quotas, commercial chemical and lubricant production, tourism, fertilizer inputs, etc.

The invention discloses a graph database construction method for construction land full-life-cycle management, and belongs to the technical field of GIS data storage and retrieval. According to the invention, by utilizing the flexible data model of the graph database and the analysis capability of a complex relation network, the graph database association relationship is established for the planning, batching, storage, supply and registration stages of the land parcels by modeling the land parcels and the land parcel association relationship, so that the full-life-cycle management of the land based on the graph database is realized. According to the method, a plot entity and an association relation (vertexes and edges of a corresponding graph) of the plot entity are expressed by utilizing the strong modeling capability of a graph database, and a flexible data model can adapt to continuously changing business requirements and analysis of a complex relation network, so that the purposes of full-life-cycle management of the construction land and visual, rapid and flexible display are achieved.

This invention involves a machine-learning model that can assess the amount of foliage covering a specific area of land based on a satellite image. The model can be trained using various methods, including the use of physical features found on farms utilising virtual fencing. By utilising pseudo-labels obtained from wearable devices worn by livestock, such as virtual fence boundaries and grazing events, the training methods can be improved. Current training methods for satellite image models may rely on user entered pasture cover data alone.

The invention discloses a land reserve utilization benefit evaluation method, relates to the technical field of land utilization management, and uses a data collection module to collect and extract basic information of land, determines the development cost and environmental influence of the land based on the basic information of the land, and evaluates the development efficiency. The comprehensive evaluation module evaluates the basic value, development potential and comprehensive benefits of the land, analyzes the evaluation result, proposes adjustment suggestions, compiles and outputs an evaluation report by using a report output module, and the comprehensive evaluation module comprises a comprehensive land value evaluation unit, a unit for quantifying economic benefits brought by land development and a unit for comprehensively reflecting land utilization benefits. According to the invention, comprehensive, systematic, scientific and flexible evaluation of land reserve utilization benefits is realized.

The invention discloses a low-efficiency land use redevelopment function prediction method based on a graph neural network, and the method comprises the steps: S1, obtaining the land use benefit of each space unit in a learning region, dividing each space unit into a high land use benefit type, a middle land use benefit type and a low land use benefit type, selecting a space unit with a high land utilization benefit type as an urban land use configuration case library; s2, taking the urban land use configuration case library obtained in the step S1 as a sample set, and constructing and training a functional feature learning model based on a graph neural network according to the sample set; and S3, obtaining each space unit in the prediction area, inputting the space units into the functional feature learning model obtained in the step S2, and performing low-efficiency land use redevelopment function prediction. By simulating urban space function interaction and mechanism innovation, a traditional land utilization prediction normal form based on static rules and spatio-temporal evolution is broken through, and scientific support is provided for land benefit improvement and function replacement decision in urban updating.

The utility model discloses an efficient and energy-saving aquaculture tail water land-based circulating purification treatment system which comprises a floating object and silt separation device, a nano ozone bubble oxidation sterilization device, a nitrogen and phosphorus removal separation device, a microbiological treatment device, a sedimentation tank and a pipeline. The floating object and silt separation device comprises an outer barrel, an inner container barrel, an inner barrel cover, a support, an aeration device, a first water inlet pipe, a first water outlet pipe, a first waste discharge pipe, a second waste discharge pipe, a first collection cup and a base, the outer barrel is installed on the base, the inner container barrel is arranged in the outer barrel, and a precipitation cavity is formed between the bottom of the inner container barrel and a bottom plate of the outer barrel; an interlayer cavity is formed between the outer wall of the inner container barrel and the inner wall of the outer barrel and is communicated with the precipitation cavity; a flow guide hole is formed in the center of a bottom plate of the inner container barrel and communicates with the inner container barrel and the outer barrel. The first water inlet pipe horizontally penetrates through the outer barrel and extends into the inner container barrel in a beveling mode, and an aeration disc on the aeration device is arranged in the lower portion of the inner container barrel. Due to the adoption of the structure, the system realizes high efficiency and energy conservation.

The present invention provides a bottom-up intelligent identification method for land space based on grid, including: step 1, obtaining study area data and reclassifying point of interest data; step 2, identifying the initial land use type based on remote sensing image features; step 3, using the kernel density estimation algorithm to perform spatial estimation on the reclassified point of interest data to obtain the subdivided land use type; step 4, constructing a main function grid based on the subdivided land use type and determining the optimal scale of the grid, identifying the main function of the grid, and obtaining an estimated main function grid; step 5, using an unsupervised classification algorithm to obtain the initial land space pattern; step 6, performing spatial overlay analysis on the estimated main function grid and the initial land space pattern to obtain the final land space pattern. This application effectively solves the current problem of difficulty in unifying land space identification, monitoring and management, and lays the foundation for digital governance of land space.

A composite optimization positioning method of a multi-buoy combined land base station is based on an RTK technology, constructs double-difference baselines for a plurality of buoys, and constructs a closed composite optimization solution scheme by using a plurality of baselines, and comprises the following specific steps: (1) baseline construction: obtaining observation data between the plurality of buoys and a land reference station by using the RTK technology, constructing a double-difference base line between every two buoys to obtain a base line resolving result; (2) calculating a closing error; (3) carrying out composite solution; (4) result screening and determination: calculating difference values among multiple groups of combined observation values; eliminating values with difference values exceeding 95% confidence interval of the difference value sequence; calculating an arithmetic average value of multiple groups of observation values of which the difference values are within 95% confidence interval, and taking the arithmetic average value as a final positioning result of the buoy; and (5) selecting a buoy closed ring. The method aims at solving the problem that the ocean buoy positioning result is unreliable and even fails under the complex sea condition, the availability rate of the positioning result can be improved, and the buoy positioning precision can be improved to a certain degree.

The invention relates to a soft soil foundation drainage system, and relates to the technical field of building construction, the soft soil foundation drainage system comprises a soft soil foundation, drainage ditches are formed in the periphery of the soft soil foundation, a plurality of plastic drainage plates are arranged in the soft soil foundation in a penetrating mode, and a layer of waterproof plate is laid on the upper end face of the soft soil foundation and the upper end face of the drainage ditches at the same time; the periphery of the waterproof plate is arranged in a downward inclined mode, a fill soil layer is arranged above the waterproof plate, a plurality of installation pipes are arranged on the waterproof plate in a penetrating mode, and a sealing structure for fixing and sealing the installation pipes and the waterproof plate is arranged between the installation pipes and the waterproof plate. The mounting pipes are in one-to-one correspondence with the plastic drainage plates, the plastic drainage plates and the mounting pipes are fixed and connected in a sealed mode, the upper ends of the mounting pipes are provided with plugging barrels for preventing the heap fill stratum from entering the mounting pipes, and gaps for water flow to pass through are reserved between the lower ends of the plugging barrels and the mounting pipes. The method has the effect of improving the drainage efficiency in the soft soil foundation.

The invention relates to an ecological corridor identification method and system based on machine learning. The method comprises: extracting ecological source land based on land utilization data; training a machine learning classification model by using the ecological system service data and the multi-source space impact factors, inputting the multi-source space impact factors into the trained machine learning classification model, predicting ecological resistance values of all space units in the research area, and outputting an ecological resistance surface; and inputting the ecological source land and the ecological resistance surface into an ecological connectivity analysis model based on a circuit theory, simulating an ecological flow process, identifying space channels connected between different ecological source lands according to an ecological flow probability or accumulated resistance, and determining an ecological corridor in the research area. Compared with the prior art, the method has the advantages of improving objectivity and accuracy of an ecological corridor identification result and the like.

A vehicle periscope assembly provides for real time augmentation of viewing objects viewable within an optical viewing path. A display is mounted proximate a defined optical viewing path and is configured to generate images that correspond to objects viewable along the optical viewing path.

The present invention relates to the technical field of data processing, and particularly relates to a real-time acquisition and processing method and system for hydrological data based on the Internet of Things. The method includes: taking reservoirs, irrigated lands, and water channels in water conservancy projects as physical entity objects, and taking historical monitoring data in water conservancy projects as digital inputs to construct a digital twin model of the water conservancy project. Determining the functional relationship between the aridity from the reservoir to the irrigated land and the seepage loss rate; predicting the first aridity of the irrigated land in a future period, substituting the first aridity into the functional relationship to obtain the seepage loss rate of the irrigation water flow from the current reservoir to the irrigated land; determining the relative irrigation optimality of the current reservoir for the irrigated land; determining the water volume allocation rate for dispatching from the current reservoir to each irrigated land, and dispatching water resources from the current reservoir to each irrigated land based on the water volume allocation rate. Thus, the present invention improves the scheduling and allocation efficiency of water resources and solves the problem of water resource waste.

The utility model discloses a subsurface underground water level descending drainage structure which comprises a land base layer. The preliminary filtering assembly comprises a water collecting well and a rectangular filtering net; the self-rotating cleaning assembly comprises a bearing cylinder, a concealed pipe, a circular filter screen, a rotating rod, a metal plate, a rectangular strip and bristles; a circular filter screen is movably connected to the middle of the bearing cylinder, a rotating rod is movably connected to the middle of the concealed pipe, metal plates are obliquely and fixedly installed on the two sides of the rotating rod, a rectangular strip is fixedly connected to the top of the rotating rod, and bristles are fixedly connected to the top of the rectangular strip. The obliquely-installed metal plate can drive the rotating rod to rotate under the impact of water flow, so that the rectangular strip and the bristles are driven to rotate, the circular filter screen can be cleaned, the blocking phenomenon is avoided, meanwhile, the water flow is adopted for generating force on the whole, the follow-up disassembling and battery replacing steps are avoided, and the working efficiency is improved. And the convenience of subsequent use is improved.

A forest land landslide collapse area detection method based on multi-view unmanned aerial vehicle remote sensing is characterized by comprising the following steps: step 1, constructing a forest land landslide collapse area detection system based on multi-view unmanned aerial vehicle remote sensing; 2, an air route planning module performs aerial photography route planning on the forest land target area; 3, the unmanned aerial vehicle aircraft carries out image shooting on the forest land target area to obtain an image data set; 4, performing sparse point cloud reconstruction on the image data set by the three-dimensional model construction module to obtain sparse point cloud; 5, performing high-precision three-dimensional reconstruction on the sparse point cloud by a three-dimensional model construction module to obtain a three-dimensional model; step 6, segmenting a forest land landslide collapse area in the three-dimensional model by an area identification calculation module, and determining forest land landslide area data; and 7, the area identification calculation module calculates the triangular area of each forest land landslide area and accumulates the triangular areas to obtain total area data. The method has the advantages that automatic recognition and area calculation of the forest land landslide collapse area are achieved.

The invention discloses a construction land soil characteristic pollutant intelligent identification system and method based on multi-source information fusion, and belongs to the field of pollutant identification. The invention discloses a multi-source information fusion-based intelligent identification system for specific pollutants of soil of a construction land. The system comprises a multi-source information acquisition module, a multi-source information fusion module, an intelligent identification model and an identification early warning module. The method solves the problems that in the prior art, in the detection process, a single data source is depended, and identification is one-sided due to narrow data coverage and insufficient information dimension, the pollution area can be locked through remote sensing, the concentration can be calibrated through sensor data, misjudgment caused by single data abnormity is avoided, data continuity is ensured, and the detection accuracy is improved. According to the method, the recognition precision is remarkably improved, the pollution state can be represented more comprehensively, the pollutant recognition accuracy is greatly improved, the time and cost of large-range preliminary screening are greatly reduced, interference is eliminated through multi-data cross validation in multi-source fusion, and misjudgment caused by scene interference is avoided.

RADAR DEVICE, ECHO IMAGE GENERATION METHOD AND ECHO IMAGE GENERATION PROGRAM For the purpose of generating an echo image showing a more accurate detection result of an object, a radar device (101) is provided with: an acquisition unit that acquires echo data indicating a correspondence relationship between a position in a detection object area and the level of a reflected wave reflected at the position by an electromagnetic wave transmitted via an antenna (11); a detection unit that detects an object based on the echo data; a land determination unit that determines whether or not the object is on land based on the area of the object; a port determination unit that determines whether or not the position of the vessel (1) is in a port based on the distance (D) between the vessel (1) and the land; a threshold determination unit that determines a threshold for the level of the reflected wave according to the determination result of whether or not the vessel (1) is in a port; and a generation unit that generates an echo image based on the threshold and the echo data. (FIG. 1)

The invention discloses a social media data fused urban inefficient land intelligent identification method, which comprises the following steps of: realizing multi-source data space fusion by acquiring a social media geographic marking text, an urban updating policy file, a remote sensing image and basic geographic data; a natural language processing and topic modeling method is used for extracting keywords from the city update text, a topic-feature word mapping table is constructed, a RoBERTa-l arge model and a semantic rule are combined to calculate a text emotion score, and a land use negative emotion map is generated; and constructing a multi-dimensional evaluation system including indexes such as plot ratio and road network density, calculating a composite index by adopting an AMP algorithm, dynamically allocating weights through an entropy weight method, and judging the low-efficiency land. And a white list area is introduced for verification and parameter iterative optimization, and finally, an identification result is visually displayed in WebG IS and AR platforms, so that the accuracy and decision support capability of urban low-efficiency land identification are improved.

The invention discloses a multi-objective optimized urban development boundary land intelligent configuration simulation method, which comprises the following steps: S1, acquiring multi-source original data of a to-be-simulated city, and preprocessing the multi-source original data; s2, based on the preprocessed data, dividing urban land basic units, extracting feature information of each unit, and constructing an urban development boundary configuration basic data set; and S3, building a city development scene generation model of a generative model architecture, and taking the basic data set as input. The invention relates to the technical field of city planning and simulation. According to the multi-objective optimized intelligent configuration simulation method for the urban development boundary land, a generative model and a deep learning technology are adopted, a virtual development boundary scheme is generated according to multi-source data, the virtual development boundary scheme and actual urban land data are compared and analyzed, a deviation value is calculated, model parameters are automatically adjusted, iterative optimization is carried out, and the intelligent configuration simulation method for the urban development boundary land is achieved. And the development scheme gradually tends to be optimal.