1523 results about "Satellite remote sensing" patented technology

The invention relates to the field of unmanned ship path planning, in particular to an autonomous water quality monitoring unmanned ship navigation control method and system based on multimode communication. The method comprises the following steps: collecting regional water quality monitoring parameters based on a shipborne water quality monitoring sensor, carrying out space pollution distribution excavation, and constructing a regional pollution concentration distribution diagram; obtaining a regional satellite remote sensing map, carrying out water flow distribution dynamic evolution, and constructing a water flow path distribution network; carrying out pollutant migration logic evolution and multi-region gradient diffusion prediction on the water flow path distribution network according to the regional pollution concentration distribution map, and constructing a water quality pollution diffusion prediction situation map; and according to the water quality pollution diffusion prediction situation map, carrying out highest diffusion progressive gradient identification, carrying out optimal navigation monitoring sequence analysis, and constructing an optimal navigation monitoring sequence. According to the invention, through dynamic and real-time unmanned ship water quality cruise path adjustment, the accuracy and efficiency of water quality monitoring are improved.

The invention provides a flood control project virtual simulation and risk rehearsal method based on digital twinning, and the method comprises the following steps: obtaining drainage basin multi-source data collected by a space-air-ground integrated monitoring network, the multi-source data comprising a satellite remote sensing image, an unmanned aerial vehicle LiDAR point cloud and ground sensor monitoring data; inputting the multi-source data into a pre-constructed digital twin model, wherein the model comprises a spatio-temporal data fusion layer, a physical process modeling layer, a multi-scale simulation deduction layer and a risk assessment decision-making layer which are connected in sequence; the spatio-temporal data fusion layer is used for performing spatio-temporal registration and feature fusion on input multi-source data, and constructing a total element digital backplane; through the space-air-ground integrated monitoring network and the spatio-temporal data fusion technology, high-precision digital mapping of basin total elements is realized. Compared with a traditional single data source, time-space consistency of flood routing simulation is remarkably improved through multi-source data fusion, and prediction errors of key physical processes such as river channel scouring are greatly reduced.

The invention provides a river water quality monitoring method based on multi-source remote sensing data, and belongs to the technical field of river water quality monitoring. The method comprises the following steps: establishing an inherent optical characteristic model of a river water body based on a Hybrid water body radiation transmission model, optimizing a calculation path by adopting a shortest path algorithm, and carrying out water body optical component inversion by adopting a quasi-analysis algorithm and a generalized inherent optical characteristic algorithm in combination with a multispectral characteristic enhancement model to obtain absorption coefficients of all components; and constructing a multi-band combination index to realize optical coupling effect decoupling, and applying the fine-tuned water quality parameter inversion basic model to a target river area to output a suspended matter concentration distribution diagram, a chlorophyll concentration distribution diagram and a transparency distribution diagram. The technical problem of low precision of remote sensing inversion of water quality parameters caused by mutual coupling of multiple optical active components in a river water body is solved.

The invention provides a multi-source heterogeneous data fusion side slope digital twin modeling method, which comprises the following steps of: acquiring side slope multi-dimensional monitoring data by arranging a GNSS (Global Navigation Satellite System) sensor, a multi-point displacement meter, a distributed optical fiber strain sensor, an accelerometer, an osmometer, a monocular camera and satellite remote sensing image equipment; the collected data is converted into a unified format through time alignment, space registration and standardization processing and serves as modeling input; the method comprises the following steps: constructing an initial digital twinborn model reflecting the real form and physical characteristics of a slope by utilizing a three-dimensional modeling and finite element simulation technology; in combination with real-time sensing data, model evolution is dynamically driven based on a space-time fusion algorithm, boundary conditions and material parameters are automatically corrected through actual measurement deviation feedback, and continuous twin iteration updating of the model is achieved; and finally, extracting a landslide risk index to realize real-time early warning of the side slope. According to the invention, multi-source sensing and digital twinborn fusion is realized, and the accuracy, real-time performance and intelligent level of slope monitoring are improved.

The invention provides a multi-modal remote sensing and knowledge graph-based urban planning decision-making method and system, and the method comprises the steps: integrating multi-source heterogeneous data, achieving the feature alignment and fusion of an optical image and SAR data in a satellite remote sensing image through a deep learning technology, and generating an urban ground feature feature vector; associating the urban ground feature feature vector with an urban planning policy database, outputting a structured early warning report of an illegal construction early warning event set and a policy compliance label, and forming a dynamic policy constraint condition for subsequent multi-objective optimization; processing historical traffic flow data based on the dynamic graph model, and outputting a time-space distribution prediction result of future traffic conditions; and generating a Pareto optimal city planning scheme by combining multi-objective optimization with a spatial-temporal distribution prediction result of a future traffic condition. According to the method, high-precision urban surface feature classification, real-time violation extension early warning and traffic flow accurate prediction are realized through multi-modal remote sensing data fusion and a space-time knowledge graph technology, and multi-target optimization and digital twinborn verification are combined, so that the planning efficiency is improved, and extension applications such as carbon neutralization are supported.

The invention discloses a high-temporal-spatial-resolution refined flow field reconstruction method, device and equipment and a medium, and relates to the technical field of ocean current reconstruction, and the method comprises the steps: carrying out the normalization and temporal-spatial alignment of satellite remote sensing, buoy observation and numerical simulation data; based on the alignment data, performing rehearsal on the unstructured nested grid through an FVCOM model, and then dynamically encrypting the grid according to the flow field gradient and generating a background flow field; inputting the background flow field into a PINN-GAN combined framework, and outputting a refined flow field through physical constraint loss and double-discriminator adversarial training; and scheduling a calculation task by adopting a heterogeneous accelerator, verifying the reconstructed refined flow field in real time, and performing feedback optimization. Through generation of the background flow field and refinement reconstruction, the ocean current flow field with high temporal-spatial resolution can be reconstructed efficiently and accurately.

The invention relates to a coastal wetland ecological restoration effect evaluation method and system, and belongs to the technical field of ecological monitoring and restoration. Aiming at the problems of static evaluation index lag, insufficient data fragmentation integration and lack of causal diagnosis mechanism in the prior art, multi-source time sequence ecological data are acquired by constructing a three-dimensional monitoring network consisting of satellite remote sensing, an unmanned aerial vehicle, an Internet of Things sensor and an underwater robot; a fusion data set is generated based on space-time alignment and tidal phase marking, dynamic evolution characteristics are extracted by using an LSTM network, and a spatial distribution difference chart of ecological parameters before and after restoration is generated in combination with a GAN; and furthermore, through hot area clustering and historical data association mining, a degradation driving factor is positioned, and an optimization suggestion is generated. The method breaks through the space-time limitation of a traditional evaluation method, remarkably improves the degradation diagnosis precision and the regulation and control efficiency of restoration engineering, and is suitable for various ecological restoration scenes such as estuary delta and coral reefs.

The invention discloses a method for simulating and forecasting flood in a cold highland area based on hydrological and hydrodynamic coupling, and belongs to the technical field of disaster forecasting. The method specifically comprises the following steps: S1, multi-source basic data collection and preprocessing: collecting multi-type and multi-scale basic data for a target cold and cold mountainous area drainage basin; and S2, deep learning correction and fusion of the satellite rainfall data: aiming at the local overestimation and underestimation problems of the satellite rainfall data, a deep learning algorithm is adopted to carry out hour scale correction and fusion. Four types of core data of satellite remote sensing, reanalysis, ground observation and geographic space are collected, total factors of'rainfall-runoff-terrain-underlying surface 'required by flood simulation in the cold and cold mountainous area are covered, simulation one-sidedness caused by lack of data types in traditional modeling is avoided, rainfall and runoff abnormal values are eliminated by adopting a 3-sigma criterion, data formats and spatial-temporal scales are unified, and the modeling efficiency is improved. A standardized data set is formed, and interference of abnormal values, format incompatibility and space-time mismatching on subsequent model input is avoided.

The invention relates to the technical field of remote sensing data processing and glacier area calculation, in particular to an unmanned aerial vehicle and satellite remote sensing data fused glacier area calculation method, which comprises the following steps of: cooperatively acquiring a satellite multispectral image and unmanned aerial vehicle high-resolution optical and LiDAR data, performing time synchronization, high-precision space registration and data enhancement processing, and calculating the glacier area through the unmanned aerial vehicle and satellite remote sensing data fusion. A satellite image glacier macroscopic feature and an initial mask are extracted by using a convolutional neural network and an NDSI / NDWI algorithm, and unmanned aerial vehicle image microscopic texture, edge and topographic features are acquired through a local binary pattern, edge detection and LiDAR point cloud; based on pyramid layering and a conditional random field, adopting a variance weighting algorithm to realize multi-scale feature level fusion; and after segmentation through an Otsu algorithm, calculating the area through a pixel counting method and introducing gradient correction, and evaluating the reliability through three types of precision. The method breaks through the limitation of a single data source, fuses macroscopic and microscopic features, improves the boundary positioning precision and calculation efficiency, and is suitable for glacier dynamic monitoring in a complex environment.

The invention discloses a forestry ecological environment real-time monitoring and management method based on big data, and belongs to the technical field of forestry ecological environment management, and the method comprises the following steps: S1, multi-source heterogeneous data collection and sensor network deployment; s2, multi-modal data fusion and intelligent transmission: developing an adaptive communication protocol dynamic switching module, and automatically switching to a satellite communication link in a 4G / 5G network blind area; s3, mass data storage and distributed calculation: constructing a hybrid cloud storage architecture, and storing real-time monitoring data into a Redis cache queue; s4, intelligently extracting ecological environment indexes; s5, dynamically evaluating the ecological bearing capacity; s6, performing multi-level early warning and emergency response; s7, making a precise forestry management decision; and S8, carrying out system self-optimization and closed-loop management. According to the method, the air-space-ground integrated monitoring network is constructed, so that the comprehensiveness and the real-time performance of forestry ecological environment perception are remarkably improved; multi-source data of the unmanned aerial vehicle, the satellite remote sensing and the ground sensor are complementary.

The invention discloses a forest steppe fire risk assessment method and system, and belongs to the technical field of forest steppe fire prevention. The problem of high false alarm and missing report rate caused by lagging fire risk identification, weak multi-source data fusion and insufficient dynamic response in the prior art is solved. According to the technical scheme, the method comprises the following steps: deploying a ground sensing node array to obtain real-time environment data of a grassland region, fusing high-resolution satellite remote sensing and regional weather forecast data, and constructing a space-time aligned risk assessment data cube; establishing a grassland fire risk factor dynamic coupling model, and dynamically allocating factor weights in combination with the adaptive weight decision tree; generating a comprehensive risk index, carrying out nonlinear mapping to five risk levels, and linking a visual engine to generate a dynamic thermodynamic diagram and push the dynamic thermodynamic diagram to a command terminal; the system supports online updating of the model, and weight parameters are automatically optimized based on a new fire event. According to the invention, high-precision, real-time and spatialized evaluation is realized, the early warning capability and prevention and control decision efficiency are improved, and ecological and economic losses are reduced.

The invention discloses a storm surge water increase prediction method and device, electronic equipment and a storage medium, and relates to the technical field of seawater monitoring, and the method comprises the steps: obtaining multi-source observation data of a target sea area, the multi-source observation data comprising satellite remote sensing data, near-shore monitoring data, meteorological mode data and drainage basin data; performing space-time alignment and exception processing on the multi-source observation data to generate standardized space-time grid data; performing hybrid prediction modeling on the standardized space-time grid data through a physical data dual-drive modeling layer to obtain a target probability water increasing field; and dynamically correcting the target probability water increasing field by using real-time observation data to obtain a storm surge water increasing predicted value. A physical mechanism model and a probability generation model are combined, limitation of a single model is overcome, and the nonlinear evolution process of the storm surge is effectively captured; the time-space dual-drive architecture is adapted to complex coast terrains and changeable meteorological conditions, and the risk of missing report or false report is reduced.

The invention discloses a valley-type tailing pond flood runoff prediction method based on underlying surface parameter dynamic correction, which comprises the following steps: S1, acquiring valley-type tailing pond multi-source data for preprocessing, and constructing a basic database; s2, underlying surface parameters of the valley-type tailings pond are obtained, and the initial value range of each parameter is determined; s3, establishing an underlying surface parameter dynamic correction model, setting differentiated production and confluence parameters for different areas, and performing dynamic correction; s4, constructing a coupled hydrological-hydrodynamic model, simulating a runoff forming process and time-varying characteristics, outputting predicted values of runoff flow, flood peak time and flood peak water level, and comparing the predicted values with actual measured values of a historical flood area of a satellite remote sensing image for verification and calibration; and S5, inputting the real-time data into the hydrological-hydrodynamic model, and predicting the submerging range, submerging time and submerging degree of the flood runoff based on a geographic space analysis method. According to the method, the timeliness and the accuracy of runoff prediction of small watershed areas without runoff data such as valley type tailings ponds are improved.

The invention relates to the technical field of topographic survey, in particular to an automatic surveying and mapping method and system for a complex topographic land parcel for urban and rural planning. The method comprises the following steps: acquiring satellite remote sensing data and a digital elevation model corresponding to an urban and rural planning survey area, and performing survey area terrain pre-detection to generate an urban and rural planning complex terrain survey area plot; the method comprises the following steps: carrying out element semantic recognition and segmentation on an urban and rural planning complex terrain plot to obtain terrain obstacle distribution corresponding to each building, vegetation and water body element in the urban and rural planning complex terrain plot; obtaining a corresponding three-dimensional surveying and mapping boundary constraint of the complex terrain land parcel and carrying out surveying and mapping path obstacle planning to generate a terrain constraint surveying and mapping planning path corresponding to the urban and rural planning complex terrain land parcel; and performing automatic navigation surveying and mapping processing on the urban and rural planning complex terrain plot to generate urban and rural planning complex terrain plot surveying and mapping data. According to the invention, the efficient, accurate and automatic navigation surveying and mapping process of the plot under the complex terrain condition can be realized.

The invention relates to the technical field of ecological environment monitoring, in particular to a multi-dimensional data analysis-based water body pollution monitoring and processing system, which comprises a dynamic data cube construction module, a pollution event slice extraction module, a multi-dimensional feature fingerprint coupling module, a pollution source reverse traceability module and a self-adaptive early warning trigger module. The method comprises the following steps: receiving water quality parameters of an internet of things sensor, meteorological data of a meteorological hydrological station and satellite remote sensing spectral data, and constructing a dynamic data cube of unified space-time coordinates and feature axes; identifying pollution event slices by using entropy change analysis and a self-adaptive threshold method; carrying out characteristic coupling analysis on the pollution event, and fusing a spectrum fluctuation mode, a physicochemical gradient and toxicity response to generate a pollution characteristic fingerprint; pollution source positioning and contribution weight estimation are realized by adopting a dynamic time warping algorithm, and corresponding disposal equipment is driven to start based on an early warning level, so that intelligent closed-loop control of pollution identification, source tracing and treatment is realized.

The invention relates to the technical field of atmospheric environment monitoring, in particular to an atmospheric pollution source tracking and early warning system based on deep learning, which comprises a multi-source data acquisition layer for acquiring multi-dimensional data of atmospheric pollutant concentration, weather, geography and the like through multi-element equipment such as satellite remote sensing and ground sensors; the data preprocessing layer completes data cleaning by using a statistical method and a machine learning algorithm, and realizes heterogeneous data standardization processing through feature extraction, multi-source integration and space-time alignment, and the deep learning model layer realizes heterogeneous data standardization processing by means of a space-time convolutional neural network, a graph neural network and a long-short term memory network in combination with a space-time attention mechanism. The traceability analysis layer is combined with a back diffusion algorithm and a geographic information system to position a pollution source and generate a thermodynamic diagram. And the early warning decision-making layer sets threshold early warning through transfer learning of the adaptive area, and generates an optimal pollution control scheme based on reinforcement learning.

The invention relates to the field of real-time visualization of road conditions, in particular to a data simulation visualization rendering method and system for urban traffic road conditions. The method comprises the following steps: extracting a real-time satellite streetscape image based on urban satellite remote sensing scanning, and performing scene pixel-level segmentation to obtain scene texture rendering parameters; scene illumination visual identification is carried out according to the real-time satellite streetscape image, traffic scene background modeling is carried out based on scene texture rendering parameters, and a real-time scene background model is constructed; the method comprises the following steps: acquiring urban-level multi-source traffic monitoring data flow, performing vehicle state sensing, and constructing a multi-dimensional particle feature matrix; road network topological correlation analysis and global traffic network state perception are carried out according to the real-time satellite streetscape images, and a road network state perception model is constructed. According to the invention, a real real-time traffic environment is visualized, scene effects in different traffic states are presented, the current road condition can be rapidly evaluated, and the traffic control decision efficiency is improved.

The invention provides a hydro-junction water level adjusting method and system. Wherein the sediment deposition space-time atlas containing the sedimentary gradient is constructed and periodically corrected through scanning of an underwater robot and synchronous acquisition of satellite remote sensing data. A radar array is deployed at a bend section of a flood discharge channel, and vector distribution of a three-dimensional sediment flow velocity field is analyzed by using multi-frequency pulse signals. Through correlation analysis of the dynamic coupling sedimentary gradient and the flow velocity field, a correlation area of flow velocity sudden change and sediment deposition is accurately identified, and a constraint condition set including coordinate positioning and a flow-sediment matching rule is established. And in combination with real-time water level monitoring data, calculating and generating a dynamic adjustment parameter sequence of the opening degree of the flood discharge gate by using a constraint condition set, thereby realizing accurate regulation and control of the water level. According to the technical scheme, the flood discharge parameters are intelligently optimized, the water level of the hydro-junction can be accurately regulated and controlled, the sediment deposition problem of a high-sediment-content water area is effectively relieved, and safe operation and flood control efficiency of the junction are guaranteed.

The invention relates to the technical field of intelligent environment monitoring and early warning, and discloses an intelligent environment monitoring and early warning system based on multi-sensor fusion, which comprises an intelligent environment monitoring and early warning system. The intelligent environment monitoring and early warning system comprises an intelligent sensing layer, an edge computing layer, a network transmission layer, a data storage layer, an intelligent analysis layer, an application service layer and a system support layer. According to the intelligent environment monitoring and early warning system based on multi-sensor fusion, multi-source monitoring means such as a fixed monitoring station, mobile monitoring equipment, a wearable terminal and satellite remote sensing are integrated through the intelligent sensing layer, and a multi-sensor fusion technology is matched, so that the dimension limitation of a traditional single monitoring mode is broken through; environmental parameters (pollutant concentration, meteorological factors, ecological indexes and the like) can be captured in all directions, and multi-dimensional and high-precision data acquisition in a complex environment is realized.

The invention relates to the technical field of wind field simulation, and provides a wind field numerical simulation method based on multiple meteorological data sources. The objective of the invention is to solve the problems of large simulation error, rough terrain boundary processing and poor turbulence model parameter adaptability caused by non-uniform coverage of a single data source, insufficient precision and unscientific multi-source fusion. The method is characterized by comprising the following steps: step 1, constructing a CFD three-dimensional grid based on a geometric model of a target area; 2, collecting original data of a ground station, satellite remote sensing, numerical forecasting and the like; 3, performing standardized preprocessing (abnormal value elimination, missing interpolation, radiation / geometric correction and resampling), determining a multi-source fusion weight by combining historical data analysis, and generating comprehensive meteorological data by adopting a weighted average method; and 4, inputting the comprehensive data into a CFD-RANS model, dynamically adjusting turbulence parameters, accurately setting terrain boundary conditions, and obtaining a wind field space-time distribution result through numerical solution. According to the invention, through combination of multi-source data fusion and CFD simulation, the wind field simulation precision and stability are improved.

The invention provides an ultra-low-altitude-domain three-dimensional radio map generation system, and the system comprises a three-dimensional grid division and data preprocessing module which is used for dividing a target region into voxel grids containing latitude and longitude and height coordinates; the satellite remote sensing image feature extraction module is used for generating environment high-dimensional image features of the target area; the communication base station parameter encoder is used for generating base station space propagation characteristics of the target area; the two-dimensional feature migration and three-dimensional feature generation module is used for generating initial three-dimensional feature representation; the three-dimensional interpolation and height correction module is used for outputting corrected three-dimensional RSRP features; and the multi-modal fusion and decoding module is used for fusing the corrected three-dimensional RSRP features, the environment high-dimensional image features and the base station space propagation features, and inputting a fusion result into a three-dimensional decoding network to generate a final three-dimensional radio signal power distribution diagram. The three-dimensional radio map is generated for the urban low-altitude scene.

The invention relates to the technical field of ecological environment monitoring, and discloses a coastal wetland intelligent monitoring method and system based on artificial intelligence, and the method comprises the steps: collecting unmanned plane data, satellite remote sensing data, Internet of Things sensor data and water quality monitoring buoy data of a coastal wetland; the method comprises the following steps: processing satellite remote sensing data by adopting a wavelet threshold denoising algorithm based on an attention mechanism, calibrating Internet of Things sensor data by adopting an LSTM network, and carrying out data space-time alignment based on a space-time attention fusion model to obtain preprocessed data; inputting the preprocessed data into a Transform-ResNet hybrid model to carry out environmental change evaluation, and outputting an ecological health index; when the predicted ecological health index is lower than a threshold value, a PPO algorithm is adopted to dynamically adjust a monitoring strategy according to the early warning level, and an early warning report is pushed; the whole process is intelligent, manual intervention is greatly reduced, and support is provided for coastal wetland ecological protection.

The invention discloses an ocean three-dimensional temperature and salt field prediction method based on remote sensing data driving, and the method comprises the steps: firstly constructing a satellite remote sensing sea surface temperature data set, a sea surface height anomaly data set and an ocean three-dimensional reanalysis data set which are needed by an experiment; then, on the basis of a convolutional neural network and a Transform module, a basic framework of the model is constructed according to the temporal and spatial variation characteristics of the remote sensing data and the temporal and spatial variation characteristics of the three-dimensional temperature and salt field; the model adopts an end-to-end design mode, and adapts to planar spatial-temporal variation of historical remote sensing data through a two-dimensional convolutional coding structure; the three-dimensional spatial-temporal change of a future three-dimensional temperature and salt field is concerned through a three-dimensional convolution decoding structure; an attention mechanism and a feature evolution module are designed according to the spatial-temporal features of the two kinds of data, and the change relation of the remote sensing data and the temperature and salt field data under different spatial-temporal dimensions is learned. And finally, inputting a mode of combining satellite remote sensing temperature of historical n days and sea surface height anomaly into the trained model to obtain three-dimensional temperature and salt field data of future n days.

The invention discloses a soil water and salt dynamic monitoring and quality evaluation method based on multispectral remote sensing, and relates to the field of geological monitoring, and the method comprises the steps: carrying out the scattering deviation correction and roughness correction of an observation spectrum through a satellite remote sensing image, an unmanned aerial vehicle multispectral image, a ground sample and meteorological driving data; the soil intrinsic reflectivity, the vegetation coverage component and the salt crusting component are obtained; a water-salt inversion model is established by combining thermal infrared and red edge wave band information, and multi-temporal soil volumetric moisture content and surface salinity index are inverted; by introducing meteorological conditions and vegetation dynamic characteristics, a water-salt coupling partial differential equation and a graph space-time constraint model are constructed, and continuous space-time distribution of soil water content and salinity is obtained; extracting salinity, moisture, vegetation response and soil health indexes, establishing a soil quality comprehensive evaluation model, and outputting high-risk plaques and a treatment priority sequence. The method realizes dynamic monitoring and quality grading evaluation of soil water and salt, and is suitable for saline-alkali soil treatment and ecological restoration.

The present invention relates to the technical field of algae content monitoring. Disclosed is an algae content information monitoring method based on multi-source remote sensing data. The present invention comprises: acquiring multi-source remote sensing data comprising satellite remote sensing data, unmanned aerial vehicle remote sensing data and ground monitoring data, and preprocessing the data, involving radiation correction, geometric correction and noise elimination; and fusing the multi-source data into a trained algae monitoring model for prediction, and using Kalman filtering to assimilate observation data and model prediction data. The present invention fuses observation data and model prediction data by means of Kalman filtering technology, so as to dynamically adjust the model state, such that the model can more accurately reflect the actual observation situation. The Kalman filtering optimizes the real-time prediction capability of models by balancing the uncertainties between observation data and prediction data, thereby ensuring the accuracy and real-time performance of monitoring results, and also ensuring the reliability and adaptability of models under various environmental conditions.

The invention discloses an enteromorpha green tide fine drift forecasting method considering an obstacle blocking effect, and belongs to the field of enteromorpha green tide drift forecasting methods. The method comprises the following steps: a, selecting a forecast area, and obtaining obstacle distribution conditions and environmental elements in the forecast area; b, acquiring a historical satellite remote sensing image of the forecast area, and acquiring the green tide distribution position, shape and coverage area of enteromorpha on the basis of the satellite remote sensing image; c, constructing an enteromorpha green tide fine drift forecasting model considering the obstacle blocking effect; d, training the constructed enteromorpha green tide fine drift forecasting model considering the obstacle blocking effect; and e, determining an enteromorpha green tide distribution position as an initial drift position through a satellite remote sensing image, and carrying out drift forecasting by adopting the trained refined drift forecasting model to obtain enteromorpha green tide particle positions at different moments. The method can provide accurate drifting early warning results for raft frame culture areas and key disaster protection areas.

The invention relates to the technical field of geographic space intelligence, and discloses a territorial space purpose control intelligent analysis system, which comprises the following modules: a multi-source data acquisition module, which is based on satellite remote sensing and an IoT sensor, plans vector data, adopts a spatio-temporal data fusion algorithm, and integrates territorial, ecological and economic field heterogeneous data through a distributed crawler technology; generating a territorial space total element data set; the multi-source data acquisition module comprises a remote sensing acquisition sub-module, an Internet of Things access sub-module and a planning data analysis sub-module. Through a distributed data crawling and real-time stream fusion technology and spatio-temporal data modeling, rapid integration of multi-source heterogeneous information is realized, low-efficiency delay of traditional manual acquisition is eliminated, high-precision deformation monitoring and land use change identification are synchronously completed, the violation behavior discovery timeliness is remarkably improved, and the method is suitable for large-scale popularization and application. The three-dimensional space analysis algorithm accurately quantifies the above-ground and underground space element interaction relation, and the engineering conflict risk is effectively avoided.

The invention relates to a mineral reserve dynamic monitoring method and system based on satellite remote sensing and a block chain. The method comprises the following steps: acquiring original remote sensing data and metadata of a mining area, executing hash operation on the data by utilizing a satellite-borne trusted execution environment to generate integrity proof, and performing digital signature to construct a trusted data packet; transmitting the data packet to a ground station to verify the validity of the signature, extracting verified data, and submitting the verified data to a block chain for evidence storage; based on the on-chain evidence storage data, using a deep learning model to extract earth surface features and calculating reserves changes; and finally, a reserve change result is returned to the block chain, and dynamic supervision and early warning are realized through an intelligent contract, so that a technical system from data source credible guarantee, transmission process tampering prevention, reserve intelligent calculation to supervision automatic execution is constructed. The problems that in traditional mineral product monitoring, the authenticity of a data source is lack, the processing process is not traceable, and supervision response is lagged are solved, and the reliability, auditing performance and supervision timeliness of a mineral product reserve dynamic monitoring result are improved.

The invention relates to the technical field of intelligent forestry management, and particularly discloses a forest farm intelligent comprehensive management and control system, which comprises a space-air-ground collaborative sensing module, a fire danger dynamic modeling module and an intelligent decision execution module. The method comprises the following steps: acquiring temperature field data through multispectral satellite remote sensing, constructing a high-precision terrain model through a laser radar, extracting vegetation features through hyperspectral imaging, extracting a thermal anomaly structure by adopting topological continuous coherence analysis and a Morse-Smal complex method, and establishing an adaptive fire danger model in combination with a quantum annealing optimizer. The system can generate a fire danger thermodynamic diagram in real time and divide risk levels, and drives the unmanned aerial vehicle to perform priority patrol and obstacle avoidance path planning. The problems of difficulty in pseudo hot area identification, low fire danger prediction precision, non-intelligent path planning and the like in a complex environment are solved, and the intelligent level of forest farm fire prevention and control is improved.

The invention relates to a coal mining subsidence area plant foundation monitoring method and system based on multi-source data fusion, and the method comprises the steps: obtaining multi-source monitoring data of a target coal mining subsidence area plant area, the multi-source monitoring data comprising satellite remote sensing data, ground measurement data and interference radar data; performing time synchronization and space unification on the multi-source monitoring data to obtain monitoring data with unified space; performing fusion processing on the monitoring data with uniform space to obtain comprehensive deformation data; constructing a three-dimensional settlement deformation field according to the comprehensive deformation data, and extracting deformation rate and acceleration information of each monitoring point through a gradient calculation method; and according to the deformation rate and the acceleration information, obtaining risk assessment of the plant foundation in the target coal mining subsidence area. The technical problems of multi-source heterogeneous data fusion and real-time settlement monitoring and early warning are effectively solved, and the precision and timeliness of foundation settlement monitoring are improved.