203 results about "Land parcel" patented technology

The invention relates to the technical field of land reserve, in particular to a multi-source integration and dynamic updating method and system for land reserve data, and the method comprises the steps: obtaining multi-source land data, constructing a unified space-time database through hierarchical coordinate fusion, and achieving the data integration. And then, land parcel ownership characteristics are extracted, compliance evaluation is completed, and policy change data are synchronously analyzed to generate planning constraints. Idle risk assessment is carried out based on spatio-temporal data and policy constraints, land value fluctuation is predicted, and accurate valuation data is obtained through idle risk weighted correction. And finally, according to risk assessment and policy constraints, a low-efficiency land taking strategy is generated, all data and strategies are uploaded to a management platform in real time, value abnormal block marking and land reserve dynamic optimization tasks are executed respectively, and full-process closed-loop management from data integration to intelligent decision making is realized.

The invention belongs to the field of land parcel information extraction technology, and provides an agricultural machinery operation suitability evaluation method comprising the following steps: S1, based on a remote sensing image and a digital elevation model, extracting field parcel geometric features, road features, field obstacle features and terrain parameters, and calculating field parcel indexes; and S2, a multi-dimensional environment correction factor is calculated, the obstacle influence function and the agricultural machinery operation efficiency loss are integrated, a multi-parameter weighted agricultural machinery operation suitability evaluation model is constructed, and an agricultural machinery operation suitability index is calculated. The invention also correspondingly provides an agricultural machinery operation suitability evaluation system. The method not only reflects the direct influence of field obstacles on agricultural machinery operation, but also objectively quantifies the efficiency loss in actual operation through path planning simulation, and provides a scientific basis for precise reconstruction of farmland and intelligent scheduling of agricultural machinery.

The invention discloses a space-time knowledge graph-based facility layout scheme generation method. The method comprises the steps of S1, constructing an urban facility layout knowledge base; s2, acquiring a reference case place set of the target grid to be optimized; s3, extracting a facility significant co-occurrence mode in the reference case place set, and taking the facility significant co-occurrence mode as a recommended facility configuration mode of the target grid to be optimized; and S4, according to the feature vector of the city built environment of the target to-be-optimized grid obtained in the S1, inputting the feature vector and the recommended facility configuration mode of the target to-be-optimized grid obtained in the S3 into the constructed facility layout generation model, and obtaining an output result which is a facility layout scheme. According to the method, the facility distribution rule knowledge and the facility layout optimization intelligence are integrated, and the facility configuration recommendation and the facility layout optimization scheme can be automatically output for a specific land parcel.

The invention relates to a two-dimensional and three-dimensional boundary point association method based on improved boundary point coding and a medium, and the method comprises the steps: carrying out the preprocessing of original land parcel red line data, sequentially numbering land parcels, converting all the land parcels into plane boundary point elements, determining a first boundary point in each plane boundary point element, processing each land parcel into a clockwise land parcel, and carrying out the coding of the clockwise land parcels. Performing two-dimensional boundary point continuous numbering on all vertexes of the land parcel, taking the land parcel after encoding the two-dimensional boundary points as a two-dimensional surface element, and converting the land parcel into a land parcel three-dimensional stereoscopic model comprising three-dimensional boundary points, boundary lines and boundary surfaces; and continuously numbering all the three-dimensional boundary points in sequence by using a cross section analysis method to obtain a land block after coding the three-dimensional boundary points, and establishing an association relationship between the three-dimensional boundary points with successfully matched coordinates and the corresponding two-dimensional boundary points. Therefore, automatic coding of the three-dimensional boundary points of the land parcel red line data and automatic association between the two-dimensional boundary points and the three-dimensional boundary points are realized, and the three-dimensional boundary point management efficiency in land parcel management is improved.

The invention discloses a high-precision remote sensing image cultivated land boundary extraction method, which comprises the following steps of: acquiring high-resolution remote sensing image data, preprocessing the data, and obtaining cultivated land parcel vector data through manual annotation; constructing a multi-task depth semantic segmentation model based on a coding and decoding structure; constructing a mixed loss function; acquiring a high-resolution remote sensing image of a cultivated land area to be detected and performing data preprocessing; inputting a high-resolution remote sensing image of a cultivated land area to be detected into the trained high-resolution remote sensing image cultivated land extraction model based on multi-task learning; and converting the cultivated land semantic segmentation result into cultivated land parcel vector data containing latitude and longitude coordinates. According to the method, through staged Transform coding based on overlapping patches and efficient sequence reduction, efficient multi-scale coding is realized, the problem of insufficient remote dependence capture is solved, and the computing power bottleneck of high-resolution self-attention is relieved.

A method for identification of land improvements in a given parcel of land that includes generating a first training data set by clipping a large image of a parcel of land into individual parcel images each including images of improvements, and each improvement being labeled with an improvement type, providing the individual parcel images to a first classification model, training the first classification model based on the individual parcel images to identify unlabeled improvements in a parcel image and to obtain a multi-label classifier, generating a second training data set of images, and training a second, semantic segmentation model based on the second training data set.

The invention discloses a corn yield estimation method based on multispectral remote sensing and a three-dimensional convolutional neural network, and the method comprises the following steps: obtaining a multi-temporal remote sensing image of the whole growth period of corn in a target region, and obtaining a multi-temporal remote sensing image of land parcels by land parcels; generating a yield grid map in the target area by adopting an inverse distance weight method based on ground actual measurement yield data; segmenting the yield grid map by taking the boundaries of the land parcels as masks, and taking the average value of all pixels in each land parcel as a yield value; pairing the yield value of each land parcel with the multi-temporal remote sensing images one by one, and constructing an image-yield pairing sample set; matching a sample set by using an image-yield, and training a three-dimensional convolutional neural network model; identifying a to-be-estimated multi-temporal multispectral remote sensing image by using the trained three-dimensional convolutional neural network model to realize plot-level corn prediction; according to the method, high-precision plot-level corn yield prediction is realized by fusing the multi-temporal multispectral remote sensing image and the three-dimensional convolutional neural network.

The invention provides a vehicle turning path planning method and device for irregular land parcels, and is applied to the technical field of path planning, and the method comprises the steps: carrying out the initialization of a pheromone field based on the boundary data of a geographic information system and internal obstacle data, and constructing a rasterized map for representing a passable region; based on the current state of the vehicle, determining the minimum distance of obstacles in the surrounding environment of the target vehicle; based on the real-time movement speed, the minimum turning radius of the target vehicle is determined; determining a dynamic corridor parameter of the target vehicle based on the minimum distance and the minimum turning radius of the surrounding obstacle; based on the dynamic corridor parameters and the pheromone field of the rasterized map, generating a turning point probability density field of the target vehicle, and sampling the turning point probability density field to obtain a turning point set; and constructing a turning path of the target vehicle based on the turning point set, and determining the turning cost of the turning path. According to the invention, Pareto optimization of energy consumption and efficiency in a complex scene can be realized.

The invention belongs to the technical field of data analysis and processing, and particularly relates to a joint review and judgment method for territorial space planning, compiling and reviewing full-chain business, which comprises the following steps of: 1, reading a project declaration text, a reply text, a planning layer and a management and control layer, planning polygon elements and control polygon elements are read from the planning layer and the control layer, and corresponding enclosing rectangles are generated; 2, inputting a project application text marking sequence into the pointer network model, and performing polygon superposition operation on a project plot polygon and a retrieval result to form a spatial superposition unit set; and step 3, reading the territorial space planning management and control rule base, and generating a project joint review opinion list including a modified point location list and a review conclusion. According to the method, the examination efficiency can be remarkably improved, the manual operation amount is reduced, the examination consistency is improved, and high-precision and high-reliability intelligent support is provided for territorial space planning management.

The invention discloses a farmland plot remote sensing automatic extraction method and system based on an improved SAM model, and relates to the field of agricultural remote sensing image intelligent interpretation, and the method comprises the steps: obtaining a target remote sensing image, and carrying out the automatic blocking processing, normalization processing and coordinate registration processing, and obtaining a target remote sensing processing image; reasoning the target remote sensing processing image according to the target detection model to obtain a candidate bounding box; performing feature extraction on the target remote sensing processing image to obtain a pixel-level cultivated land probability graph; and constructing an RSParcel-SAM model according to a convolutional low-rank adaptive fine tuning strategy, a sparse hybrid expert network and a composite prompt injection mechanism, and inputting a target remote sensing image, a candidate bounding box and a pixel-level cultivated land probability graph to obtain a cultivated land parcel. According to the method, the problems of insufficient field adaptation, lack of semantic understanding and contradiction between strong prompt dependence and automation are solved, and high-precision, high-efficiency and high-robustness full-automatic extraction of the cultivated land parcels is realized.

The invention discloses a land area measurement method and system for land planning and design, and the method comprises the steps: collecting a sequence aerial image of a target land parcel, and synchronously recording the positioning and attitude determination data of each frame of image; edge sharpening processing and multi-view three-dimensional reconstruction are carried out on the sequence aerial image, and a digital elevation model with texture features is generated; performing terrain gradient analysis and edge contour extraction based on a digital elevation model, and identifying a land block boundary through an adaptive threshold segmentation algorithm and generating an initial vector boundary diagram; performing topological relation verification and fragment pattern spot fusion on the initial vector boundary diagram, and outputting an optimized plot vector boundary; and carrying out coordinate registration on the optimized plot vector boundary and a geographic information system base map, calculating a projection area through integral operation, and generating a final measurement report containing a topographic relief coefficient. According to the embodiment of the invention, the operation efficiency, boundary recognition precision and result reliability of land area measurement under complex terrains can be improved.

The invention relates to the technical field of intelligent agriculture, and discloses a traditional Chinese medicine organic planting management method based on big data analysis. A space coordinate system is established in a planting area, regular partitioning is carried out, a unique land parcel identifier is formed, a soil humidity sensor is arranged in the center of each land parcel, meteorological observation points are matched to continuously collect air temperature, wind speed and illumination, and an evapotranspiration potential index is generated through sliding window statistics and normalization. The system performs dynamic balance prediction and state correction according to the soil moisture content, the irrigation volume and the evapotranspiration loss, extracts a reference evapotranspiration rate by using an unirrigated sample, determines a threshold interval and a real-time deviation in combination with a historical quantile, and smoothly improves the stability through a sliding window. The threshold value is updated hour by hour within a feasible range, irrigation is triggered when the water content is lower than the threshold value, and the irrigation amount is limited within a reasonable range through depth and flow conversion. Structured logs are generated in the whole process, and irrigation whole-process trace reserving is achieved.

The invention relates to the technical field of agricultural planting, in particular to an intelligent field patrol system of an unmanned aerial vehicle, and the system comprises a land parcel modeling module which is used for collecting farmland boundary and crop distribution information and generating a farmland digital model; the dynamic path planning and task scheduling module is used for generating a cruise path based on the digital model, adjusting the flight height, course and speed according to environment information, dividing sub-paths under the condition of multiple unmanned aerial vehicles, distributing tasks and setting a collision avoidance and power scheduling strategy; and the multi-source data acquisition and fusion module is used for controlling the unmanned aerial vehicle to fly along the cruise path. According to the invention, through the plot modeling module, the dynamic path planning and task scheduling module, the multi-source data acquisition and fusion module, the identification and review module, the time sequence analysis module and the result output and task generation module, the problem that manual field inspection is mostly adopted in traditional manual field inspection; therefore, the problems of low patrol efficiency and incomplete data are solved.

The invention discloses a land type intelligent identification and updating method and system for land investigation, and relates to the technical field of land investigation and remote sensing land type identification, and the method comprises the steps: carrying out the unified coding and weight fusion of optical, radar, terrain and auxiliary features on a standardized land block image stack through a feature fusion and dimension reduction model; generating plot-level fusion feature representation; jointly inputting the land block level fusion feature representation and the pixel statistical features in the land block into a land class recognition model, outputting a land block level land class category and confidence through a double-layer fusion structure, and generating a multi-temporal land class recognition result; and calculating a change credibility score of the land parcel based on a multi-temporal land class identification result and a time sequence observation index, and determining whether the land parcel belongs to a stable state, a suspected change or a confirmed change by comparing an index trend, class stability and space consistency. According to the method, collaborative identification of macroscopic structure information and microcosmic detail information is realized, and the accuracy and the spatial integrity of regional land class classification are improved.

A system and method are provided for simulating the sizing and siting of energy generation facilities to supply electricity for a recommended industrial load. The method begins by accessing specific criteria for each land parcel in a portfolio and filtering out unviable parcels based on factors such as existing wind or solar installations, protected areas, or parcel size. An objective function is calculated to represent the overall cost of generating electricity to meet the load. The system executes multiple design simulations, generating random placements of energy facilities based on criteria like industrial load size and location, renewable energy resource availability, and transmission line proximity. For each simulation, the cost of construction and electricity delivery is calculated to identify the lowest possible cost. Using a probabilistic technique, the method approximates the global minimum cost and ranks each simulation by cost-efficiency, helping identify optimal land parcels for energy facility placement.

The invention discloses a GIS (Geographic Information System)-based urban and rural planning management system, which relates to the technical field of land planning and is characterized by acquiring multi-source data, preprocessing, standardizing and spatio-temporal indexing the multi-source data, evaluating a planning scheme, generating a comparison and improvement scheme, recharging actual construction data and public opinions and correcting a model. According to the method, multi-source data fusion and standardized processing are realized, multi-dimensional evaluation can be performed on travel convenience, land utilization efficiency, environmental toughness and law and regulation compliance, an optimization scheme is automatically generated based on an evaluation result, a problem land parcel is positioned through a spatio-temporal index, and the method is high in practicability and high in practicability. A plurality of sets of comparable adjustment schemes are provided, a feedback correction mechanism is combined, actual construction data and public opinions can be recharged, the model precision is continuously improved, finally, through visual display and permission generation, it is ensured that a planning result is legal, compliant, executable and transparent, and urban and rural planning scientificity and public participation degree are improved.

The invention belongs to the technical field of wind power plants, and discloses a point location information determination method and device, equipment, a storage medium and a product. The method comprises the following steps: clustering a plurality of to-be-queried point locations in an available land parcel of a wind power plant to obtain a target clustering center, then constructing a triangulation network by taking the target clustering center as a vertex, and determining an affiliation relationship between the plurality of to-be-queried point locations and triangles in the triangulation network; and determining wind speed and wind direction information corresponding to the plurality of to-be-queried point locations according to the target wind speed and wind direction information corresponding to the target clustering center and the affiliation relationship. The multiple to-be-queried points are clustered, the number of the points needing to be queried through the wind resource interface is reduced, then the wind speed and wind direction information corresponding to the multiple to-be-queried points is determined according to the target wind speed and wind direction information corresponding to the target clustering center and the affiliation relation, and therefore under the condition that the number of query times of the wind resource interface is limited, the query efficiency of the wind resource interface is improved. The wind speed and wind direction information of a large number of to-be-queried point locations in available plots is effectively and accurately determined through a clustering center query method.

The invention relates to the technical field of agricultural engineering, and discloses a grain machinery operation area accurate extraction method based on visual remote sensing and AI, and the method comprises the following steps: constructing a multi-source spatio-temporal data set of land parcel level alignment; reconstructing a normalized vegetation index time sequence for each plot by adopting an interpolation method and a filtering method, and generating a plot scale crop growth curve; generating a hourly improved vegetation index image through calculation, and extracting texture features in combination with a crop growth curve; constructing a change atlas based on the normalized vegetation index time sequence; inputting the remote sensing image and the crop growth curve into a semantic segmentation model; performing fusion classification on the texture features and the change atlas by using a random forest algorithm; and converting the probability graph and a binary mask result into a vector boundary, and completing precision verification by calculating an intersection-to-union ratio. Accurate data support is provided for agricultural production monitoring, agricultural machinery scheduling and the like.

The embodiments disclose a database and file management system for real estate transactions including a real estate database comprising real estate information stored within files therein relating to a plurality real estate parcels, a server coupled to the plurality of real estate databases configured to receive the real estate information, a mobile automobile having an integrated navigation system coupled to the server, a graphical user interface having geographical mapping data displayed on the graphical user interface, wherein the graphical user interface is configured to allow a user to search and collect the real estate database with voice commands for real estate parcels of interest to the user that are in close proximity to a real-time location of the mobile automobile, and wherein the navigation system is further configured to compare and analyze the collected real estate parcels and display recommended real estate parcels for the user to select.

A system and a computer-implemented method for training a learning algorithm for siting a new energy generation facility comprises executing simultaneous simulations over multiple electrical power generation capacities by accessing land parcel characteristics, including electricity transmission and market demand data. A clustering algorithm produces a first training set with expected electricity output for subsets of land parcels. Combined scores are calculated based on cumulative land size, transmission length, and projected market revenue, ranked by highest score, and stored by lowest construction cost. The method identifies changes in land parcel characteristics, determines if the changes exceed a threshold, and, if so, re-trains the clustering algorithm by re-executing the steps using the updated characteristics as a second training set.

The system clearly displays the fields corresponding to the land parcel numbers entered by the field managers, etc. [Solution] This is a program to be executed by a computer equipped with a processor and memory. The program causes the processor to perform the following steps: acquire text information relating to the land number of a field; compare the acquired text information with the land number in the land register to identify the official land number of the field; associate the identified official land number with a polygon representing the field; and display the land number, the official land number, and the identification information of the polygon associated with the official land number in association with map data.

The application belongs to the field of land development management, and discloses a land development right management method and system. The method comprises the following steps: acquiring geographic information of original land blocks and absolute restriction areas, accurately identifying land fragments available for development and geometric properties thereof; identifying connection properties such as spatial distance, traffic accessibility and visual corridor between the land fragments according to the geometric properties of the land fragments, and constructing a correlation network; matching the constructed correlation network with a preset distributed function mode library to determine an applicable distributed function mode combination; generating a distributed function integration scheme according to the matching result, evaluating the overall value of the distributed function integration scheme, and finally outputting land development scheme suggestion information. Through the above technical scheme, the problem of land fragmentation caused by multiple restrictions can be effectively addressed, and a new and feasible development scheme is created for all parties in a complex land environment with fundamental interest conflicts, thereby significantly improving land utilization efficiency and project success rate.

The invention discloses an engineering immigrant land investigation method, system and device, and relates to the technical field of land investigation, and the method comprises the steps: obtaining a land orthoimage and land three-dimensional point cloud data obtained by photographing a land in a land acquisition range through an unmanned aerial vehicle, and taking the land orthoimage and the land three-dimensional point cloud data as input, identifying land types of the land in the land acquisition range by using the trained land type identification model, generating a GIS file, obtaining a land position input instruction, identifying the land of the ownership in the GIS file based on the land position input instruction, obtaining an identified GIS file, and storing the identified GIS file in a database; based on the identified GIS file, a land survey table of the land of the ownership is generated, and the ownership carries out electronic signature in the land survey table. According to the method, the engineering immigrant land investigation work can be efficiently and accurately carried out.

This application relates to the field of land use selection technology, and provides a land use selection method and related equipment. The method includes: acquiring a regional map of a target area and extracting multiple land parcels from the regional map; aggregating all land parcels to obtain multiple candidate land parcels, and acquiring spatial information for each candidate land parcel; calculating a selection score for each candidate land parcel based on user location requirements and the spatial information of all candidate land parcels; and determining the final selected land parcel from all candidate land parcels based on all selection scores. The method of this application can improve the rationality of land use selection.

The present invention relates to the field of intelligent power distribution network planning evaluation. Disclosed are a scene view digital map-driven intelligent evaluation method and system for a power distribution network planning solution. The method comprises: acquiring a scene view digital map image; using a stacked autoencoder-based U-Net convolutional neural network land parcel identification policy to acquire land parcel information; and, on the basis of an indicator hierarchical structure and data-driven evaluation, selecting a candidate planning solution. The present invention can accurately infer the area and load curve of each land parcel, thereby improving the accuracy of evaluation; not only improves training efficiency, but also reduces the complexity of manually adjusting network structures, thus rendering evaluation processes more efficient and automated; can more comprehensively consider the requirements of power distribution systems in various aspects; provides clear bases and rankings for decision-making; and not only technically improves the accuracy and efficiency of evaluating power distribution network planning solutions, but also provides important support and guidance for the optimization and future development of power distribution systems.

The application discloses a risk assessment method and system for a high-risk area of land approval but non-supply, and belongs to the field of land resource management.The method is as follows: determining the plot classification results of a target area at each historical time point according to historical geospatial data of the target area; obtaining the supply-demand change trend of each plot type according to the plot classification results to calculate the demand intensity of each plot type at a current time point; taking the plots to be approved or approved but non-supplied in the target area at the current time point as first plots, and predicting the plot type evolution trend of the first plots according to the surrounding plot information of the first plots and the policy text data of the target area at each historical time point; and evaluating the risk of approval but non-supply of the target area according to the plot type evolution trend of each first plot and the demand intensity of each plot type at the current time point. The application can improve the accuracy of identifying the high-risk area of approval but non-supply.

The present application relates to the technical field of land ownership change management, and in particular to a blockchain smart contract storage and risk early warning method for land ownership change, which acquires land ownership change request data packets and generates data fingerprints, calls ownership verification smart contracts to complete ownership consistency verification. After verification, a space-time heterogeneous graph data is constructed by fusing space topology and time sequence information, a risk quantification proof is generated by a space-time graph convolution risk quantification model, and a risk warning level is output after zero-knowledge verifiable calculation verification. Low-risk changes automatically complete on-chain storage and network state synchronization, and high-risk changes automatically generate warning instructions and push them to the supervision node and freeze the request. The present application realizes tamper-proof storage and pre-risk early warning of the whole process of land ownership change, improves the processing efficiency and supervision ability of ownership change, and effectively prevents ownership fraud and illegal trading behavior.

A system and method are provided for simulating the sizing and siting of energy generation facilities to supply electricity for a recommended industrial load. The method begins by accessing specific criteria for each land parcel in a portfolio and filtering out unviable parcels based on factors such as existing wind or solar installations, protected areas, or parcel size. An objective function is calculated to represent the overall cost of generating electricity to meet the load. The system executes multiple design simulations, generating random placements of energy facilities based on criteria like industrial load size and location, renewable energy resource availability, and transmission line proximity. For each simulation, the cost of construction and electricity delivery is calculated to identify the lowest possible cost. Using a probabilistic technique, the method approximates the global minimum cost and ranks each simulation by cost-efficiency, helping identify optimal land parcels for energy facility placement.

The application belongs to the technical field of deep learning and remote sensing image processing, and specifically discloses a city land use mapping method based on multi-modal data collaborative perception, which comprises the following steps: performing parcel division on high-resolution remote sensing images of a research area to obtain irregular city parcels and remote sensing spectral features thereof; setting street sampling points along a road network, extracting street perception features and extracting interest point semantic features; constructing a heterogeneous graph structure based on the street sampling points, the street perception features and the interest point semantic features falling into the same parcel, and then extracting parcel-level multi-source geographic features; inputting the remote sensing spectral features and the parcel-level multi-source geographic features into a full sparse topic model for semantic alignment and fusion to generate fused parcel feature representations; and performing classification based on the fused parcel feature representations to output city land use mapping results. The application can realize high-precision and high-robustness city land use recognition under the condition that multi-source data is unevenly distributed or sparse.

The utility model relates to the technical field of risk management, control and repair of polluted land parcels, in particular to a barrier joint protection device for a vertical barrier ultra-deep flexible membrane joint, which comprises a fore shaft pipe and a flexible membrane, the fore shaft pipe is arranged in a cut-off wall grooving foundation pit, and the bottom of the fore shaft pipe is anchored into the cut-off wall grooving foundation pit; an interlocking buckle is arranged at the end, facing the locking opening pipe, of the flexible film, a protection assembly is arranged on the locking opening pipe and used for protecting the interlocking buckle, the protection assembly comprises protection plates arranged on the locking opening pipe, the protection plates are symmetrically arranged on the two sides of the interlocking buckle, and flexible rubber strips are arranged on the protection plates on the two sides of the interlocking buckle. The flexible rubber strips are provided with mud separating pieces used for separating mud, the mud separating pieces comprise flexible mud separating brushes arranged on the flexible rubber strips, and the flexible mud separating brushes on the two flexible rubber strips abut against each other. The method has the effects of reducing blockage of the flexible film joints and improving the supporting strength of the cut-off wall grooving foundation pit.