256 results about "Data model (GIS)" patented technology

The present application provides a method and system for intelligent monitoring and early warning of meteorological disasters in power grids based on GIS, the method comprising: obtaining historical multi-source data including power grid equipment data, meteorological data and equipment operation data; standardizing the historical multi-source data to obtain a four-dimensional data model including longitude, latitude, altitude and time dimensions; constructing a disaster risk assessment model based on the four-dimensional data model using a deep learning method; inputting real-time multi-source data into the disaster risk assessment model to obtain disaster risk assessment results; making early warning decisions based on the disaster risk assessment results to generate early warning information; and generating emergency response plans based on the early warning information. The system comprises a data processing module, a model building module, a risk assessment module, an early warning decision module and an emergency response module. The technical solution of the present application realizes the effective fusion of multi-source heterogeneous data, improves the accuracy and real-time performance of early warnings, and improves the efficiency of emergency response.

The invention discloses a mountainous area high pier cast-in-place safety detection method based on multi-source data fusion, and relates to the technical field of intelligent building safety monitoring, and the method comprises the steps: deploying a sensor network, a multi-angle holder video monitor and an unmanned aerial vehicle, covering a mountainous area high pier for comprehensive monitoring, and collecting multi-source data; transmitting the multi-source data to an edge computing node in real time through a wireless network, and preprocessing the multi-source data to form a standardized multi-source data set; applying a spatio-temporal data fusion method, combining historical data and a comprehensive data model to predict a security risk, and generating a risk factor; threshold and rule abnormity identification is carried out on the risk factors, the safety condition is evaluated in real time, early warning is triggered in real time, and disposal suggestions are provided. According to the method, the safety detection efficiency and accuracy in the mountainous area high pier cast-in-place process are remarkably improved through multi-source data fusion and a quantum enhancement multi-modal algorithm, the triggering speed of safety early warning is effectively increased, and the safety guarantee of a construction site is remarkably enhanced.

The invention relates to the technical field of emergency command, and discloses an emergency command system and method based on a BIM digital base, and the method comprises the steps: building a unified data model through expanding an IFC standard, carrying out the semantic mapping and entity alignment of BIM building information, GIS geographic space data, monitoring data and an emergency plan document, and carrying out the construction of a unified data model. The method comprises the steps of generating an emergency knowledge graph with BIM building information as nodes, coupling physical simulation and an LSTM prediction algorithm, injecting monitoring data into a BIM model to deduce a disaster process, outputting a risk thermodynamic diagram and an evacuation forbidden zone, and calculating space-time accessibility of emergency resources through a reinforcement learning model according to a deduction result. An optimization scheme including an unmanned aerial vehicle path, a personnel evacuation route and an equipment scheduling instruction is generated, the instruction is synchronized to an augmented reality individual soldier terminal and a mobile command shelter through an edge computing node, a resource deployment state is labeled in real time in a BIM model, multi-terminal operation is synchronized, the timeliness and accuracy of disaster situation sensing are remarkably improved, and the disaster situation sensing efficiency is improved. And disaster loss is effectively reduced.

The invention relates to the technical field of mineral resource prediction and analysis, and discloses a regional mineral resource prediction and analysis method and system, and the method comprises the steps: carrying out the collection and standardization, and constructing a standardized physical data set; organizing the physical parameters in a spatial dimension, and constructing a multi-dimensional tensor structure; tensor evolution modeling is carried out to generate tensor time sequence data; obtaining an optimal weight combination according to geological unit division; calculating the mineralization probability of each space unit; evaluating the credibility of the mineralization probability result, and outputting a mineralization probability graph and a credibility scoring graph; the system comprises a data acquisition and standardization module, a tensor modeling module, an evolution modeling module, a feature optimization module, a probability prediction module, a credibility evaluation module and a visual output module. According to the method, a space-parameter joint expression mode based on a four-order tensor structure is introduced, various kinds of physical field data are subjected to integrated processing in a unified space framework, and a data model reflecting multi-dimensional geological response characteristics is effectively constructed.

The invention relates to a method and a system for determining the elevation of an offshore wind turbine foundation scouring analysis datum plane, and the method comprises the steps: carrying out three-dimensional modeling through seabed three-dimensional point cloud data, analyzing the thickness of shallow sludge through high-frequency and low-frequency sounding data, and obtaining stratum distribution through combining with shallow profile data; calculating a theoretical scouring depth based on the water flow data, and reconstructing a three-dimensional grid data model; calculating and screening a suitable area through a terrain gradient factor, and constructing a seabed base plane calculation data set; and calculating the average elevation of each fan basic area as the datum level elevation of scouring analysis. According to the method, refined reconstruction of the regional landform of the offshore wind turbine foundation is realized, the accuracy and scientificity of determining the elevation of the reference surface are improved, reliable data support is provided for stability evaluation and design optimization of the offshore wind turbine foundation, the structural safety risk caused by scouring is remarkably reduced, and the economic benefit is improved. And the overall safety and economic benefits of the offshore wind power plant are improved.

Embodiments of the present disclosure provide a three-dimensional scene reconstruction method and apparatus, a device, a medium, and a program product. The three-dimensional scene reconstruction method comprises: acquiring a scene image collected for a three-dimensional scene; on the basis of the scene image, determining sparse point cloud data and camera parameter information corresponding to the scene image; performing model initialization on the basis of the sparse point cloud data to obtain a current three-dimensional data model; performing rendering on the basis of the camera parameter information and the current three-dimensional data model to obtain a current color map, a current depth map and a current normal map from the perspective of the scene image, and determining a pseudo normal map from the perspective of the scene image on the basis of the current depth map; and on the basis of the current color map, the current normal map, the pseudo normal map, and an actual color map, training the current three-dimensional data model to obtain a trained target three-dimensional data model. By means of the technical solution provided by the embodiments of the present disclosure, higher-quality automatic reconstruction of three-dimensional scenes can be achieved, reducing reconstruction costs, and improving the level of detail and rendering quality of scene models.

The invention relates to the technical field of underwater concrete three-dimensional reconstruction, in particular to an underwater concrete three-dimensional reconstruction method and system based on linear structured light and stereoscopic vision fusion. The method comprises the steps of obtaining a depth map and a texture map of underwater concrete based on a line structured light module and a stereoscopic vision module, and obtaining a preliminary depth data sequence and a surface texture sequence; performing environment compensation and image enhancement on the depth data and the texture data to generate data after environment correction; based on the corrected data, performing registration and matching on the depth and texture data by using a multi-modal fusion algorithm to generate a fusion point cloud data model; motion compensation and deformation repair are carried out on the point cloud model, deformation and structure offset caused by water flow are corrected, and a repaired point cloud model is generated; the repaired point cloud is input into a three-dimensional model reconstruction engine, a high-precision underwater concrete three-dimensional model is generated, intelligent analysis is conducted on the surface of the model, and damage and structural integrity of the model are evaluated. The underwater concrete three-dimensional reconstruction method effectively improves the precision of underwater concrete three-dimensional reconstruction, solves the problem of model distortion in complex environments such as water flow and turbid water quality, and has efficient and accurate structure detection and maintenance functions.

According to the movie and television game animation point cloud three-dimensional curved surface reconstruction hole repairing method, an optimized Poisson curved surface reconstruction method is established, noise reduction processing is carried out on wrong point cloud data, abnormal points are removed, if data density is sparse, bicubic spline interpolation is adopted to improve the point cloud density, and the point cloud reconstruction efficiency is improved. Establishing a transfer low-square optimization method to accurately calculate point cloud normal vectors, adjusting the directions of the point cloud normal vectors to be consistent, improving the calculation precision of the normal vectors, and redirecting the normal vectors to ensure the consistency of the point cloud normal vectors; aiming at the fact that holes influencing a data model exist in a triangular mesh model, a triangular mesh optimization, adjustment and repair method based on a radial basis model is adopted, finally, good continuity can be kept between a repair result and a triangular mesh curved surface around the holes, good geometric consistency is kept between a repaired triangular mesh and a triangular mesh on the surface of an original model, and the repair accuracy is improved. The method is high in curved surface reconstruction speed, high in quality and suitable for being applied to multiple industries such as movie and television game cartoons.

The invention relates to the technical field of computer aided design, in particular to a BIM-based garden landscape optimization method and system, and the method comprises the following steps: extracting a spatial data model in a BIM based on the spatial feature data of garden landscape elements, carrying out the sight tracking of garden viewing nodes, capturing the visual data of different angles, and carrying out the calculation of the visual data. And analyzing the visual area range and the visual focus attraction to obtain the garden landscape sight line access coverage rate. According to the invention, through landscape data acquisition, sight tracking and illumination analysis, improvement of visual accessibility identification, quantitative evaluation of footpath environment factors, improvement of path experience judgment precision, path structure combination intersection relation and substitutability optimization, improvement of connectivity and viewing continuity, and facility layout fusion interference identification and function coordination; the space partition reasonability is improved, path and element matching is adjusted, the space response capability is enhanced, conversion from static analysis to dynamic scheduling is achieved, and the layout adaptability and the touring integrating degree are improved.

The embodiment of the invention provides a high-energy charged particle environment model construction method and device based on machine learning, and the method comprises the steps: obtaining detection data, carrying out the baseline construction of a high-energy charged particle environment model based on the detection data, and determining an environment model baseline; performing geomagnetic coordinate conversion based on the environment model base line, and determining geomagnetic coordinates; performing model construction based on the geomagnetic coordinates, and determining an environment baseline model; performing data extraction based on the environment baseline model, determining environment fluctuation data, and performing correlation analysis based on the environment fluctuation data and the spatial environment factors to determine a directed graph; building a data model based on a directed graph and a machine learning model, and determining an environment fluctuation model; and determining a high-energy particle environment model based on the environment fluctuation model and the environment baseline model. The characterization of multiple environmental factors on the data causal relationship is constructed by utilizing machine learning, so that the speed is high, and the comparison consistency with real data is good.

The invention discloses a complex terrain-oriented mobile geological survey data real-time acquisition and analysis system, and relates to the technical field of geological survey, and the acquisition and analysis system comprises a mobile acquisition terminal, a multi-platform acquisition device and a sensor integrated with a self-adaptive complex terrain, and a historical data template library. The storage module is used for storing multi-dimensional historical geological data subjected to standardization processing, standardization processing comprises data cleaning, feature extraction and space-time coordinate calibration, template retrieval and reuse based on feature similarity and a historical data reuse module are supported, and the historical geological data are stored through template matching, data fusion and incremental updating. And multiplexing of a historical template, semantic association of multi-source data and real-time adjustment of new data of a basic template are realized. According to the method, redundant analysis of newly-collected geological data is reduced by multiplexing the overlapped part of the historical geological survey data, and more efficient geological survey data collection and analysis can be completed only by matching the multiplexing data model and combining the new data to update the model.

The invention relates to the technical field of geomechanical data research, in particular to a geomechanical data visualization method and system based on a mixed data model. According to the technical scheme, the method comprises the steps of data collection and arrangement, mixed data model construction, visual algorithm design and application and visual result display and interaction. The data collection and arrangement comprises the step of collecting geomechanical data including geologic structure data, rock mechanics parameter data and ground stress data. According to the method, geologists can be assisted to deeply understand the complex relationship between geologic structure evolution and rock mechanical behaviors, and powerful data visualization support is provided for development of geologic theories; geological risks can be predicted in advance, an engineering design scheme is optimized, and engineering safety and economic benefits are improved; through accurate visualization and deep analysis of geomechanical data, the possibility and the potential influence range of disaster occurrence can be evaluated more accurately, and key technical support is provided for disaster early warning and prevention and control measure formulation.

The invention relates to the technical field of water network intelligent management and control, and discloses a water network management and control multi-source space digital intelligent fusion base construction and decision optimization method, which comprises the following steps: cooperatively obtaining multi-source space data; format conversion, coordinate system unification and systematic semantic annotation are carried out through a data preprocessing platform, and a normalized spatial data set is formed; constructing a data fusion model, quantitatively evaluating the quality of a data source by adopting a fuzzy comprehensive evaluation method so as to determine a fusion weight, and performing weighted fusion by adopting a specific algorithm aiming at different data types such as vectors, grids and three-dimensional models so as to generate a unified spatial data model; and finally, a decision optimization system performs decision scheme generation and iterative optimization based on the model in combination with a hydrodynamic model and a genetic optimization algorithm. By establishing a standardized data processing and targeted weighted fusion process, the problems that multi-source heterogeneous data is difficult to integrate and the fusion precision is not high are effectively solved, and a reliable data base is constructed.

The invention discloses a hydrological data model fusion analysis method based on water resource scheduling decision support, and relates to the field of water resource scheduling, and the method comprises the steps: loading an HMM-BP noise detection model, obtaining a multi-source data fusion model, and outputting a space-time grid data cube; constructing a water consumption behavior prediction unit through a TPB decision tree model, adding a water right transaction engine, and outputting a water consumption prediction value, a behavior of an intelligent contract protocol and a transaction cooperation model; an incremental method is adopted to generate N combined scene matrixes of rainfall and temperature rise correlation adjustment, an ecological stability evaluator is combined, a climate and ecological coupling model is constructed, and a scene and risk mapping table and a resource re-allocation weight are output; and constructing an intelligent rule base platform to obtain an executable decision logic tree. The system has the advantage that full-chain intelligentization of water resource scheduling is realized through modular integration. Therefore, social and market collaborative prediction, climate and ecological risk quantification and adaptive decision support are achieved.

The invention discloses a forest resource investigation method based on a laser radar and visible light synchronous photography technology, and belongs to the technical field of forestry. The method comprises the following steps: selecting a forest stand to be measured, and setting a scanning route; scanning the forest stand by using a laser radar and visible light synchronous photography technology; in the forest stand model, directly identifying tree species, and counting forest stand density; exporting point cloud data, performing individual tree segmentation according to a forest tree sequence, and generating DBH data and position coordinate data in batches; calculating the stand volume and tree species composition of the forest stand; determining a space structure of the forest stand according to the diameter at breast height data and the position coordinate data of the forest, and calculating a competition index of each tree; and linking the database with the forest stand model, establishing an augmented reality forest stand model, and realizing visualization of the forest resource data model. According to the method, the three-dimensional visual model of the forest stand can be established, the tree species can be directly identified, the forest resource survey data can be obtained through calculation, the survey efficiency is high, the cost is low, the data storage amount is small, and the later data processing difficulty is low.

The invention discloses a spatial multi-omics data integration method based on graph attention and a multivariate loss function. Comprising the following steps that spatial multi-omics data and spatial position coordinates corresponding to the spatial multi-omics data are obtained, a preset spatial multi-omics data model is input, and the model comprises a graph attention encoder and a decoder; the spatial multi-omics data model constructs a graph structure according to spatial multi-omics data and spatial position coordinates, a graph attention encoder performs cell spatial multi-omics data fusion based on the constructed graph structure, and a decoder performs data reconstruction based on fused data to complete cell spatial multi-omics data integration; and constructing a multivariate loss function to carry out iterative optimization on the cell space multi-omics data integration process of the space multi-omics data model, and outputting an optimal cell space multi-omics data integration result. According to the method, the batch effect is solved, the smoothness of a final result in space and the accuracy of spatial analysis are improved, and the integration of multi-omics data of the cell space is realized.

The invention discloses a knowledge-data dual-drive urban inland inundation simulation method considering uncertainty. The method comprises the following steps: acquiring basic hydrological elements and geographic information data to construct a feature input set; constructing an urban inland inundation physical model based on a hydrological hydrodynamic principle; the physical model parameters are iteratively corrected based on actually measured rainfall-inundation data, and a water depth data set with parameter uncertainty constraints is output; correcting data uncertainty, and constructing an urban waterlogging data set; generating and training a sub-model group through self-service sampling, and fusing structural uncertainty and weight uncertainty by adopting an integration strategy; and quantifying the uncertainty of integrated prediction, outputting a prediction result with a confidence interval, and verifying the precision. According to the method, the interpretability of a physical model and the feature learning ability of a data model are effectively fused, high-resolution and interpretable waterlogging water depth prediction in a complex urban scene is realized through a multi-source uncertainty quantification mechanism, and reliable support is provided for early warning and intelligent scheduling of urban flood.

The invention provides a marginal and coast defense big data fusion method, and belongs to the technical field of data fusion, and the method comprises the steps: collecting basic data from various data sources, carrying out the preprocessing of the basic data, carrying out the type division of a preprocessing result, and extracting data features from various types of data; performing multi-dimensional analysis on the data features, and creating a multi-dimensional data model in combination with multi-dimensional analysis results; fusing the preprocessing results based on a multi-dimensional data model to obtain a fusion result, and performing credibility judgment on the fusion result; the comprehensive view of the marginal defense and coast defense is obtained based on the credibility judgment result, the problem that the formats, the precision and the time spans of the multi-source data are different is effectively solved, the compatibility and the integration efficiency of the data are improved, and more comprehensive situation awareness and strategy support are provided for marginal defense and coast defense.

The invention discloses a complex system data visualization modeling method based on GIS and topological graph fusion, and relates to the technical field of data visualization modeling, and the method comprises data fusion modeling, space-time anomaly reasoning, state propagation modeling and multi-dimensional visualization enhancement. The method comprises the following steps: performing unified access, cleaning and standardization processing on operation data, topological relation data, geographic space data, function configuration data and environment data of a complex system, and constructing a unified data model fusing geographic attributes and topological attributes; realizing abnormal root cause reasoning and spatial constraint propagation modeling of a system state by using a convolutional time sequence modeling method combined with a graph neural network; a dynamic evolution rule of a node state is described through a time gating mechanism, and interactive display is realized; according to the method, global visualization of the operation state of a complex system, dynamic backtracking of an abnormal propagation path and hierarchical analysis of key nodes can be realized, and the intelligent monitoring, situation awareness and decision support capabilities of the system are improved.

The invention discloses a radix astragali seu hedysari origin traceability system and algorithm. The system comprises a data acquisition module, a quality evaluation module, a risk early warning module, an algorithm analysis module and a block chain evidence storage module. According to the method, geographical indication data, biodiversity characteristic analysis and the Internet of Things technology are integrated, data collection of the whole life cycle of astragalus mongholicus is achieved, and the problems that a traditional method depends on manual recording, tampering is prone to occurring, and the cost is high are solved; a weighted random data model is adopted to perform multi-source data fusion, and the weight is dynamically adjusted through a machine learning algorithm, so that the production area discrimination accuracy is remarkably improved, and the production area discrimination accuracy can reach more than 95%.

The present invention discloses a target detection method based on deep learning, comprising the following steps: S1: classification, deep generative model, establishing the probability distribution of model data, by sharing parameters at multiple spatial positions, CNN can extract common features and generate new data samples, which is particularly suitable for processing structured data, such as images and videos; S2: positioning, building a deep neural network DNN architecture, extracting spatial features, identifying key points, and recursive neural network RNN ​​is used to process time series data, capture motion patterns, and simulate the dependencies between data; S3: detection, using the data model of positioning to obtain a detection model of deep learning, combined with region nomination and convolutional neural network, it can automatically learn and extract image features and obtain an accuracy data model. The present invention maintains a high accuracy, and it effectively captures the detailed information of the object by predicting bounding boxes and classification confidence on feature maps at different levels.

The invention discloses a multi-modal map data-oriented network architecture search synchronous transfer learning method, and the method comprises the steps: carrying out the preprocessing operation of a collected near infrared spectrum, a collected Raman spectrum and a collected microscopic image, carrying out the feature extraction, and carrying out the fusion of the extracted features through a cross attention mechanism, and forming a new optimal fusion feature; in the constructed SSLNST module, a fused data set is divided into a source domain and a target domain through a transfer learning technology, and neural network architecture search NAS adopts a three-stage optimization strategy including network search, fine tuning and feedback; a loss weighting optimization strategy is introduced into the obtained target domain model, the proportion between the classification loss value and the sample weight is adjusted, and the training process of the fusion data model is optimized. The technical problems that in the prior art, the map fusion efficiency is low, the method is poor, and an effective model cannot be constructed under the condition that labeled sample data is insufficient are solved.

The invention discloses an AI-based household condition gridding six-dimensional element holographic display system and method, and relates to the technical field of gridding social governance information, and the system comprises a data aggregation module which integrates data across departments and carries out cleaning processing; the data modeling module is used for constructing a space-time model based on a GIS (Geographic Information System) and integrating a multi-source map, and automatically generating and realizing dynamic division of various types of grids for social governance based on the multi-source map; the social governance module is used for collecting data through an AR mobile terminal, processing the data through a clustering algorithm and arranging events according to priorities; the user condition grid module is used for dividing grids, associating six-dimensional data and dynamically associating with workers at all levels; the six-dimensional element module is used for carrying out multi-dimensional statistics on six elements; the holographic display module is used for realizing grid drilling and information visualization; and the intelligent AI module analyzes the data and generates early warning. According to the invention, intelligent analysis and dynamic display of grid information are realized through AI and holographic technologies, and an AI intelligent scheme and an efficient digital scheme are provided for social governance.

The invention provides a GIS-based intelligent livestock breeding full-chain visualization method and system, and the method comprises the steps: collecting the basic information of a farm, livestock information, production process data and other multi-source data through a standardized data interface, and carrying out the correlation mapping of business data and GIS spatial data based on a geographic coordinate system; constructing a three-dimensional data model containing spatial information, attribute information and relation information; three-dimensional scene rendering is realized by utilizing a WebGL technology, a macroscopic visual engine, a mesoscopic visual engine and a microcosmic visual engine are constructed, and multi-scale visual display is realized; constructing a full-life-cycle data model of the livestock products, and realizing visual display of full-chain traceability of the livestock products; based on a visual data analysis method, a decision support system for industrial layout analysis, breeding scale prediction, epidemic disease risk assessment and market supply and demand analysis is constructed. Visual management of the whole industry chain of livestock breeding is achieved, the industry management efficiency is improved, the epidemic disease risk prevention and control capacity is enhanced, and data support is provided for decision making of the livestock industry.

The invention discloses a city update project management platform and a control method thereof, and the method comprises the steps: obtaining the current construction data of a city update project at the current moment when the update of the city update project is detected; according to the current situation construction data, based on a preset space management coding system, constructing a three-dimensional data model of the city update project at the current moment; according to the three-dimensional data model, performing simulation display on the current situation real scene and the planning virtual scene of the city updating project at the current moment; and storing all the three-dimensional data models of the city updating project, and tracking the updating implementation progress of the city updating project according to all the three-dimensional data models. And based on the current situation construction data of the city update project at the current moment, constructing a three-dimensional information model of the city update project at the current moment by adopting an unreal engine and a GIS technology so as to display the current situation scene and the planning design scene of the city update project, and dynamically tracking the periodic construction result and data information of the city update.

The invention discloses a photovoltaic power station abnormal data reconstruction method and system based on a feature weighted local abnormal factor, and the method comprises the steps: obtaining the historical data of a photovoltaic power station, and constructing and training a feature weighted local abnormal factor model; inputting power grid data into the model for identification, deleting abnormal data and obtaining a missing data set; normalizing the data, and dividing the normalized data into a training set and a test set; training the reconstruction missing data model by using the training set, and judging the reconstruction performance according to the test set; and acquiring photovoltaic power station data in real time, acquiring a missing data set, inputting the missing data set into the trained missing data reconstruction model, and reconstructing missing data. According to the method, different importance is given to different features when the local abnormal factors are calculated, so that the overall abnormal point detection accuracy is improved, the gradient stability is remarkably improved, and the data reconstruction accuracy is improved.

The invention discloses a method for establishing a channel element spatial database, which comprises the following steps of: determining a data object of channel element data acquisition so as to extract feature information to establish a channel element data model; based on the channel element data model, determining spatial geometric information of the data object, and establishing a channel element spatial data model; analyzing the logic relation between the data objects on a model level to establish a channel element spatial data logic model; and on the basis of a Geodatabase spatial database, carrying out physical conversion on the channel element spatial data logic model, and establishing a channel element spatial database so as to realize the same standard for channel element data and facilitate maintenance of related data. The invention also discloses a navigation channel element data acquisition system, which is based on the navigation channel element spatial database, develops the navigation channel element data acquisition system integrating data acquisition, data quality inspection and result display by adopting a B / S architecture, and realizes standardization, digitization and spatialization of data acquisition.

The invention discloses an intelligent dispensing program planning method for a dispensing system. The method comprises the following steps: identifying an imported product drawing and generating a drawing information graph; establishing an element dispensing library; a dispensing coordinate graph is generated according to the drawing information graph and the element dispensing library; establishing a motion data large model; generating a path planning graph according to the dispensing coordinate graph and the motion data large model; a dispensing track diagram is generated in combination with a path optimization algorithm; a dispensing program is generated according to the dispensing track diagram; the method comprises the following steps: identifying an imported product drawing, generating a drawing information graph, generating a dispensing coordinate graph according to the drawing information graph and an element dispensing library, generating a path planning graph through a motion data large model, and performing path optimization in combination with a path optimization algorithm to generate a dispensing track graph. Therefore, the dispensing path with the shortest motion path and the highest dispensing efficiency is obtained, dispensing program editing of complex products can be rapidly, accurately and efficiently carried out, and the problems that an existing program editing mode is low in efficiency and quality are solved.

This invention relates to the field of short-term meteorological forecasting technology, specifically to a forecasting model architecture based on point cloud data and a spatiotemporal frequency domain joint attention mechanism. This forecasting model architecture includes a Z-coordinate system three-dimensional point cloud visualization data model and a P-coordinate system four-dimensional spatiotemporal frequency domain joint attention model. The three-dimensional point cloud visualization data model directly represents the three-dimensional distribution of meteorological elements. Combined with the DEM trust propagation completion algorithm, the observation coverage of a 1km resolution area is increased by 40%, significantly improving the forecast accuracy in complex terrain areas (such as mountainous regions). Then, a decision tree model and satellite observation data are used to fuse precipitation data, filling in the gaps in precipitation observation data for areas without monitoring stations. Finally, based on the Earth's rotation period and P-coordinate system four-dimensional spatiotemporal time domain data, a Fourier transform + phase modulation module enhances the frequency domain capture capability of precipitation cycle signals, significantly improving the TS score for severe convective weather.

The invention discloses an automatic map drawing method and system, and the method comprises the steps: obtaining farmland multi-source data containing multispectral sensor data and a high-resolution image, and generating a first farmland data set through time sequence fusion; according to a pre-established unified data model, performing standardization processing on the first farmland data set to obtain a second farmland data set; extracting farmland boundary and topographic features from the second farmland data set, and if the complexity of the topographic features exceeds a preset threshold, introducing a digital elevation model to supplement topographic data to generate a third farmland data set; obtaining dynamic change characteristics according to the third farmland data set, judging crop growth and soil state change trends by adopting a time sequence analysis algorithm, and generating a fourth farmland data set; and extracting update features from the fourth farmland data set, fusing preset high-precision map data by adopting an incremental update algorithm, and generating a dynamic farmland map. The map updating efficiency and precision are improved, and reliable data support is provided for agricultural management and decision making.