677 results about "Land utilization" patented technology

The invention relates to the technical field of natural resource monitoring and spatial information processing, in particular to a method and a system for feeding back land utilization change based on a land spatio-temporal model. The method comprises the following steps: deploying multi-source land monitoring equipment, carrying out collaborative data acquisition and standardization processing, and constructing a land space-time reference data set; performing triple mapping on the land space-time reference data set to obtain a land semantic association graph; constructing a land utilization knowledge graph based on the land semantic association graph; constructing a land change detection initial model by using the land utilization knowledge graph; meanwhile, in a high-frequency change scene, such as an urban and rural ecologic zone or an ecological sensitive area, a traditional model is slow in response to short-term land utilization disturbance, automatic adjustment cannot be carried out through deviation feedback between historical errors and model output, and the reliability of the model in actual application scenes such as resource regulation and control is limited.

The invention relates to the field of geographic information technology and natural resource management, and discloses an automatic analysis system for land utilization flow change in land change investigation. The system comprises an object-level spatiotemporal spectrum tree reconstruction and data initialization module, a topological potential energy field construction module based on evolution entropy, a multi-scale geometric difference extraction and morphological analysis module, a variable stiffness topological adsorption and decoupling module based on a stiffness ratio, and a flow damping filtering and event serialization module driven by semantic rules. The system constructs a topological potential energy field by using a historical state vector and semantic stiffness, performs one-way adsorption, adjustment or retention on a difference polygon according to relative stiffness, and cleans invalid traffic based on a damping matrix. According to the method, through physical field simulation and semantic logic verification, high-frequency disturbance misjudgment is effectively eliminated, pattern spot boundary correction is ensured to conform to the legal weight, and the authenticity and logic self-consistency of change survey data are improved.

A dynamic real estate valuation system based on multiparametric market indicators, which includes the following: a valuation engine configured to generate real-time results for property valuation; a multitude of distributed data ingestion and processing units configured to capture heterogeneous data sources, including historical property transaction data, real-time property listings, zoning and land use records, macroeconomic indicators, geospatial data, environmental sensor outputs, and sentiment-derived metrics; a model orchestration control unit comprising a stack of machine learning models, wherein the models include at least a gradient boosting decision tree model, a long-short-term memory (LSTM) time series forecaster, and an enhancement learning module that iteratively optimizes the model parameters based on the observed evaluation accuracy; a data contextualization controller configured to apply dynamic weighting to each input parameter based on the geographic, temporal, and market context by executing decay functions and location-specific rule matrices; a physical property valuation terminal (PVT) that includes an edge processing unit (EPU), geolocation circuitry, secure communication interfaces and a touch-based user interface; a valuation book subsystem configured to hash the valuation output, timestamp, and signatures of the input record into a blockchain-based distributed ledger; wherein the system is designed to continuously recalibrate its valuation results by comparing the predicted valuations with the actual sales or rental prices, and wherein the physical terminal is designed to produce a legally certifiable valuation document with embedded provenance data.

The invention discloses a multi-dimensional rural settlement space reconstruction service system and method, and the system comprises a sensing layer which monitors environment parameters in real time, and develops a mobile terminal APP to collect subjective sensing data; performing cleaning, fusion and standardization processing on multi-source heterogeneous data, constructing a rural settlement digital twin base of the heritage area, and performing traceability and tamper-resistant processing on data acquisition; the collaboration layer is used for generating a land utilization conflict degree thermodynamic diagram and evaluating the business state symbiosis degree of the business; quantifying the collaboration degree of each dimension, and identifying protection and development collaboration regions; simulating spatial evolution trends under different reconstruction schemes, and generating a candidate scheme library; the optimization layer is used for constructing a settlement three-dimensional digital twinborn body based on a BIM + GIS technology, designing a'protection-development-sharing 'three-gear adjusting knob, and generating a seasonal adaptability space configuration scheme by applying a genetic algorithm; and a continuous layer: establishing a five-dimensional monitoring index library, predicting a reconstruction effect by applying an LSTM neural network, and dynamically adjusting an optimization strategy according to a monitoring result.

The invention provides a remote sensing-based land utilization carbon emission evaluation method, device and equipment and a medium, and the method comprises the steps: carrying out the radiometric calibration and spatial registration of obtained multi-source remote sensing data, and obtaining a standardized raster data set; generating a land utilization classification grid map based on the standardized grid data set; generating a land change vector layer through a continuous multi-stage land utilization classification grid map; generating a dynamic evaluation unit vector boundary according to the land utilization classification grid map and the land change vector layer; correcting a vegetation carbon absorption coefficient and an industrial emission coefficient in real time based on remote sensing inversion data, and constructing a spatial carbon transfer model; and inputting the land utilization classification grid map, the dynamic evaluation unit vector boundary and the spatial carbon transfer model into a layered mixed carbon emission evaluation model to obtain a carbon emission intensity distribution map. By adopting the scheme, the carbon emission under different land utilization modes can be accurately evaluated, and the scientificity and policy applicability of regional carbon balance evaluation are improved.

The invention discloses an agricultural land utilization monitoring management system and method based on remote sensing, and belongs to the technical field of remote sensing image processing. Multi-temporal remote sensing images are acquired, and a land surface energy fluctuation spectrogram is constructed; extracting disturbance characteristics through small-scale grid slices to form a multi-dimensional disturbance characteristic tensor; identifying an abnormal evolution region by using a sparse volume accumulation algorithm, and outputting a preliminary screening identification graph; inputting the region with the continuous evolution characteristic into a time sequence attention mechanism inversion network, estimating a crop growth state trajectory, matching an agricultural planting mode library, and generating a candidate land utilization behavior probability distribution diagram; in combination with regional consistency optimization, a historical planting period and meteorological disturbance data, calculating a purpose change confidence score, and outputting early warning information and a monitoring report; the agricultural land dynamic change identification precision and management capability can be effectively improved.

The invention discloses a drainage basin water quality pollution source flux identification method based on an SWAT model, computer equipment and a storage medium, and the method comprises the steps: obtaining topographic data, land utilization data, soil type data and meteorological data, and enabling the topographic data, the land utilization data, the soil type data and the meteorological data to form a drainage basin spatial database; based on the preprocessed digital elevation model, determining a water flow direction and setting a reasonable water collection area threshold value, and dividing sub-basins to construct a basin calculation unit network topology structure; obtaining pollution source data, and constructing an SWAT-CUP model according to the pollution source data; performing system calibration and verification on the watershed hydrological process by adopting an SUFI-2 algorithm in the SWAT-CUP model; carrying out system calibration and verification on the total nitrogen load of the drainage basin by adopting an SWAT-CUP model; and based on the calibrated SWAT hydrological model, the input condition of the pollution source is changed. The method can cover the whole watershed and quantify the contribution rate of different pollution sources; cost is high, operation is simple, and long-term change can be dynamically simulated.

The invention discloses a flood disaster scene-oriented urban power distribution network toughness evaluation method and system, and belongs to the technical field of power distribution network toughness evaluation, and the method comprises the steps: obtaining rainfall data, land utilization data, elevation data and the like, and inputting the data into a waterlogging simulation module; the waterlogging simulation module simulates rainfall runoff production and runoff convergence to generate ponding distribution; the disaster judgment module obtains the fault probability of the power distribution equipment and the line in combination with the power distribution equipment and line disaster fault model, and generates a system disaster scene under the flood disaster; the load reduction optimization module constructs a power distribution system load reduction optimization model, and takes the minimization of the total load reduction amount of the power distribution system as a target; and the system toughness evaluation module measures the overall toughness of the power distribution system in the flood disaster scene. According to the method, the overall toughness level is quantitatively evaluated based on the system load reduction amount, the node load importance degree and the ponding condition are combined to recognize the weak links of the system, reference is provided for differential reinforcement and operation and maintenance strategies, and subsequent waterlogging prevention planning and operation and maintenance management are guided.

The invention discloses an irrigation area ditch drainage-farmland waterlogging simulation evaluation method. The method comprises the following steps: S1, obtaining meteorological and hydrological data, DEM data, drainage facility vector data and land utilization classification data of a target area; s2, generalizing a drainage system of the irrigation area, and generating nodes, drainage channels and sub catchment area. Shp files; s3, converting the. Shp file into an. Inp file, constructing a parameterized model, and performing parameter calibration and verification on the model; s4, simulating waterlogging conditions under different rainfall and channel siltation situations; s5, extracting overflow node data, and performing flood inundation analysis; and S6, generating a flood risk density map according to the overflow node data. The invention further discloses an irrigation area ditch drainage-farmland waterlogging simulation evaluation system. According to the irrigation area ditch drainage-farmland waterlogging simulation evaluation method and system disclosed by the invention, the problem that ditch drainage simulation and farmland waterlogging risk evaluation cannot be carried out under an irrigation area waterlogging disaster in the prior art is solved.

The invention relates to a remote sensing image segmentation method and system based on land utilization, and the method comprises the steps: collecting continuous periodic remote sensing images of the same region, marking a land type and a seasonal factor vector, and determining whether to carry out zoning or not according to a latitude span; then identifying the boundary of the miniature land parcel, calculating the boundary identification degree, delimiting a protection area, respectively calculating the seasonal factor vectors of the non-protection area and the protection area, and fusing the seasonal factor vectors into a global seasonal factor; secondly, preliminarily segmenting the image by adopting a fuzzy C-means clustering model in combination with a global seasonal factor, and finally carrying out image re-segmentation and verifying the accuracy in combination with a plot seasonal feature and boundary recognition degree correction model; according to the method, through operations such as fine seasonal factor vector construction, latitude zoning, protection area delimiting and multi-feature fusion, the shadow area land type classification accuracy is effectively improved, the influence of seasonal variation and boundary blur in a complex earth surface scene can be reduced, and the robustness and adaptability of the model are enhanced.

The invention discloses a soil attribute intelligent prediction method and system based on a multi-dimensional dynamic scale, and relates to the technical field of soil prediction.The method comprises the steps that remote sensing images, terrain factors, meteorological factors and soil actual measurement sample data are collected and stored in a multi-source raster data bin in a unified mode; preprocessing the multi-source data, and constructing raster data in a unified format; combining terrain difference, land utilization and climate fluctuation, dynamically dividing a spatial scale, a time scale, a terrain layering scale and a land utilization pattern scale, and constructing a multi-dimensional dynamic scale feature expression vector; carrying out soil attribute modeling and training based on a multi-task deep neural network model; and applying the model to a non-actually-measured area, and outputting a prediction result and a spatial distribution map layer. The system comprises a multi-source raster data construction and management module, a multi-dimensional dynamic scale feature construction module and a soil attribute modeling and prediction module. The method can be used for forestry management and protection, resource management, ecological monitoring, resource evaluation and other scenes.

The invention provides a coastal wetland ecological corridor identification and optimization method and system, and relates to the technical field of ecological environment monitoring. The method comprises the following steps: integrating optical, radar, terrain, night light and road data on an earth engine cloud platform, and unifying time and space references to form an initial data set; extracting a normalized vegetation index and a vegetation coverage degree, and combining with a water body frequency to identify an ecological source land; generating a comprehensive resistance surface according to the land utilization and coverage type and the road distance; and performing minimum cost path and circuit connectivity analysis in a geographic information system to obtain potential galleries, key nodes and barrier regions, and outputting gallery grades and optimization suggestions in a grading manner according to a cost threshold, thereby realizing high-precision identification and optimization of the coastal wetland ecological network.

The invention discloses an ecological safety early warning evaluation system based on three-generation space coupling and multi-source model fusion. The system comprises an index framework construction module, a weight determination module, an ecological safety level evaluation module, a three-generation space coupling evaluation module and an ecological safety early warning evaluation module. The invention provides an ecological safety early warning evaluation system, which masters the possibility of occurrence of ecological environment problems and spatial and temporal distribution characteristics, and provides a scientific basis for management of governments and environmental protection departments in the region. According to the method, indexes are selected according to historical spatial data and various statistical yearbook, an ecological safety evaluation system is constructed based on a three-generation space theory, three-generation coupling coordination is calculated according to different land utilization functions, and finally an ecological safety early warning area is determined according to the space-time relationship between the two.

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

The invention relates to the technical field of water resource management, in particular to a flow hydrograph simulation method based on coupling of a Xinanjiang model and a depression, and the method comprises the steps: obtaining the geographic space data and meteorological data of a target drainage basin; the geospatial data at least comprises a digital elevation model, land utilization data and soil type data; and carrying out sub-drainage-basin division on the target drainage basin based on the digital elevation model, identifying depressions in the digital elevation model by adopting an improved priority flood algorithm, and constructing a spatial topological relation of the depressions. According to the method, the hydrological characteristics of the depression are fused into the Xinanjiang model, the regulation and storage function of the depression group on the drainage basin runoff is quantified, the hydrological simulation precision of the depression dominant drainage basin is remarkably improved, and by improving the depression recognition algorithm, pre-construction of the slope confluence time matrix based on the cells and rapid judgment of the depression merging mechanism, the stability of the depression confluence time matrix is improved. The calculation speed of the distributed hydrological model is greatly improved, and the efficiency requirement of the real-time hydrological model can be met.

The invention relates to an unbalanced data-oriented electric power system lightning disaster risk detection method, which comprises the following steps of: S1, gridding a target detection land parcel to obtain a plurality of grid units; S2, based on latitude and longitude coordinates of each grid unit, obtaining line parameters and environment characteristics of each grid unit in combination with a line database and a geographic information base, wherein the line parameters comprise tower height, span, grounding resistance, loop number, lightning arrester number and transformer capacity, and the environment characteristics comprise soil conductivity, building height and land utilization type; and S3, inputting the line parameters and the environment characteristics of each grid unit into a trained first detection model to obtain the thunder and lightning probability and the maximum peak current of each grid unit. Compared with the prior art, the multi-source data such as the tower height, the span, the grounding resistance and the soil conductivity are comprehensively considered, and the risk assessment result has higher regional adaptability and accuracy.

The invention relates to the technical field of remote sensing image processing, in particular to a remote sensing driven land utilization monitoring system. According to the invention, by extracting a ground feature boundary contour in a remote sensing image and constructing a morphological parameter set with geometric structure proportion characteristics, a path category data system for dividing a land spatial form based on structural characteristics is established, and normalization processing and a Gaussian mixture model are introduced to realize ground feature aspect ratio clustering. Constructing a structural difference index set in combination with the distribution concentration ratio, clearly identifying the path type to track the land parcel direction or the structure change trajectory, and calculating a disturbance factor in the multi-temporal remote sensing image based on the land type number change and the connection direction number of the patch adjacent region; and the main direction, the boundary contour similarity and the spatial disturbance of the land parcel in the specified period are comprehensively judged, so that the dynamic change identification of the land utilization state is realized, and the land utilization form change amplitude and the time period characteristics can be accurately reflected by setting a judgment reference and a change state marking mechanism.

The invention discloses a salinization remote sensing inversion method fusing crop information and data preprocessing. A land utilization data set is subjected to splicing, cutting and other processing to obtain a high-confidence-coefficient cultivated land range; point distribution sampling is carried out according to a regular grid method, and soil conductivity is measured; preprocessing the remote sensing image; dividing crop growth stages and constructing a multi-temporal spectral feature set; constructing an NDVI time sequence data set and extracting phenological characteristics; extracting planting structure information based on the multi-temporal spectrum and phenological characteristics; a planting structure is converted into crop type data through one-hot coding, and the crop type data is fused with multi-temporal spectrum and phenological characteristics to construct a crop information data set; the method comprises the following steps: constructing a salinity inversion model through data preprocessing and combining with ground actually measured salinity data, and verifying the salinity inversion model; evaluating the cross-year generalization ability of the model; and finally, the inversion result is visualized to generate a salinization map. According to the invention, the system integrates the multi-dimensional information of crops and optimizes the data preprocessing process, and significantly improves the salinization monitoring precision and the cross-space-time adaptability.

The invention discloses a multi-scenario land utilization carbon reserve simulation method based on combination of deep learning and a PLUS-InVEST model. The method comprises the following steps: collecting and preprocessing historical land utilization, natural environment and social economic data; analyzing land utilization change characteristics; optimizing an LEAS module of the PLUS model by using a deep neural network, excavating a nonlinear relationship between land utilization expansion and driving factors, and generating an expansion probability graph; configuring a neighborhood weight and a transfer matrix of the CARS module; predicting future land utilization requirements under different scenes by adopting a long-short-term memory neural network; inputting the demand into a CARS module, and simulating and generating a future land utilization space pattern under multiple scenes in combination with differential constraint conditions; based on the InVEST model carbon reserve module and the carbon density data, calculating, comparing and analyzing the carbon reserve of each scene; and outputting a result, carrying out precision verification, and proposing a land utilization planning and carbon regulation strategy. According to the method, the land utilization simulation precision and the carbon reserve evaluation scientificity are improved, and decision support is provided for regional low-carbon land utilization.

The invention belongs to the technical field of environment monitoring, and particularly relates to a quantitative recognition method for contribution of autogenous source organic carbon of a surface water body. Based on the stable isotope ratio and the three-dimensional fluorescence spectrum, the water DOM source analysis method is constructed, the source and characteristics of DOM are comprehensively analyzed, and the characteristic information and contribution of autogenous source organic carbon are specifically recognized. The three-dimensional fluorescence spectrum has the advantages of being rapid in detection, small in sample use amount and the like, DOM sources can be rapidly and qualitatively identified, different DOM sources can be quantitatively calculated through stable isotope ratio analysis, and meanwhile the research area land utilization, hydrological water environment and water quality parameters are combined, so that the DOM sources can be rapidly and qualitatively identified. And the source and migration rule of the DOM in the water body can be determined more comprehensively. The quantitative identification method provided by the invention has the advantages of more comprehensive water DOM research, more complete identification system and more reliable traceability result.

The invention discloses an intelligent redevelopment research method for low-efficiency land use, which comprises the following steps of: acquiring multi-source data including land change survey data, topographic data, urban planning data, remote sensing data, point-of-interest data, mobile signaling data and social economic data in a research area, and inputting the multi-source data into a land use identification model; obtaining a spatial distribution diagram of low-efficiency land in the research area and corresponding low-efficiency factors; inputting the spatial distribution diagram and the low-efficiency factors into a redevelopment potential calculation model to obtain a redevelopment comprehensive potential value of the low-efficiency land; a plurality of redevelopment schemes are obtained based on the redevelopment comprehensive potential value, the rigid constraint of the low-efficiency land during redevelopment and the redevelopment mode library; and obtaining respective corresponding benefit index difference values after the low-efficiency land is redeveloped by using the plurality of redevelopment schemes, and obtaining an optimal redevelopment scheme from the plurality of redevelopment schemes by using a multi-attribute decision algorithm and the benefit index difference values.

The invention discloses a county planting and breeding space collaborative optimization method, which comprises the following steps of: constructing an economic-ecological-planting and breeding coupled multi-target model, setting dynamic constraint conditions such as water resources and cultivated land, generating a Pareto non-inferior solution set by combining an NSGA-III algorithm, performing optimization such as chromosome coding and fitness calculation, and simulating a future land utilization pattern by utilizing a PLUS model. The method integrates multiple benefits, breaks through the limitation of a single target, and overcomes the defect that a traditional method is difficult to balance multiple targets; an optimization algorithm and a simulation model are deeply fused, a historical data trend is mined, and the problem of simulation and optimization disjunction is solved; through a fine constraint mechanism and optimization design, the method adapts to resource changes, improves accuracy, and provides scientific support for county planting and breeding space layout.

The invention relates to an urban non-point source pollution identification method and system based on rainfall events. The method comprises the following steps: step S100, decomposing rainfall data of a continuous time sequence into independent rainfall events and snowfall events; step S200, dividing grid units, and performing runoff calculation in each unit so as to obtain runoff information of spatial distribution; wherein the rainfall event runoff is estimated by adopting a runoff curve hydrological model method; calculating snow melting runoff by adopting a temperature index snow melting model; s300, estimating the average event concentration of the rainfall event by adopting an accumulation and scouring formula, and estimating the average event concentration of the snow melting event by using average observation values in different land utilization types; and step S400, realizing refined identification and accurate estimation of urban non-point source pollution load by integrating urban functional area characteristics, surface runoff simulation and event average concentration simulation of pollutants. The method is suitable for estimating the rainfall runoff pollution load in a large-scale region with relatively limited water quality monitoring data.

The invention provides a cultivated land non-cultivation data processing and analysis system and method. The system comprises a non-cultivation rate influence factor analysis module and a non-cultivation rate prediction module. The non-farming rate influence factor analysis module integrates a geographic detector model, a panel model, a space model, a space-time geographic weighted regression model and other modules to obtain a significant influence factor result of non-farming in a target area; and the non-farming rate prediction module comprises a panel model prediction module and a time t prediction module to predict the non-farming rate corresponding to each administrative region in the next year. Based on the technical scheme of the invention, multi-source heterogeneous data such as land utilization, climate environment and the like are integrated; index classification is more detailed and comprehensive, so that an analysis result is more effective; time and space factors are fused, and variables of different regions are analyzed more accurately; the factor identification accuracy is remarkably improved, the space prediction precision is remarkably improved, and the time sequence prediction error is remarkably reduced.

The invention relates to the technical field of land planning analysis, in particular to a coastal zone land utilization space planning method based on big data analysis, which comprises the following steps: acquiring a multi-time-sequence remote sensing image and environment monitoring data of a planning area, and calculating an ecological safety index of each space unit in the planning area; obtaining economic activity big data and infrastructure data of a planning area, and calculating a land development suitability index of each space unit in the planning area; and establishing a multi-target collaborative game model taking the land utilization demand and the occupancy identifier as constraint conditions, and finally generating and outputting an optimal coastal zone land utilization space planning scheme. According to the method, the real interpretability of the model is improved, rapid adaptation can be conveniently realized through parameter configuration in different regions and different policy tendencies, and the method has high transportability and engineering deployment value.

The invention belongs to the technical field of inter-basin underground water circulation flux estimation, and particularly relates to an infiltration and lateral inflow separated underground water flux remote sensing estimation method and system. The method includes: acquiring a multi-source remote sensing data set of a research area; performing partition correction on evapotranspiration data of different land utilization type areas in the research area; the method comprises the following steps: dividing a research region into equal-area units, identifying a vertical dominant unit and a lateral dominant unit, and further determining a cross-basin underground water exchange zone; and solving a local vertical infiltration amount by using the vertical dominant unit and interpolating to form a background field, establishing a combined equation set which takes the background field as an initial value and takes the dynamic characteristic parameters of the underground water as additional constraints for the lateral dominant unit, solving to obtain an external lateral inflow amount, and further obtaining a cross-basin underground water circulation flux. According to the method, the vertical dominant unit and the lateral dominant unit are identified, and the cross-basin groundwater circulation flux is quantitatively estimated.

The invention discloses a satellite hyperspectral remote sensing image-based adaptive random forest land utilization classification and identification method, which comprises the following steps of: data preprocessing: loading a vector boundary of any predetermined administrative region, the method comprises the following steps: acquiring surface reflectance data of a certain satellite hyperspectral image in any preset time period from a natural resource satellite remote sensing cloud service platform, and preprocessing; making a sample set; parameter definition and remote sensing index selection: calculating a group of representative remote sensing indexes by using a specific wave band combination of the hyperspectral image; a remote sensing index and an original wave band are used as input feature vectors together to form a complete feature space, and in the training process of the random forest, part of features are randomly extracted from the feature space during construction of each decision tree for node division; performing cross validation and parameter tuning; the visualization of the optimal parameter result is selected; according to the invention, adaptive parameter tuning based on random forest algorithm classification is realized.

The invention discloses a mining area surface environment monitoring method, aiming at the current situation of surface ecological environment damage and prevention of a plain mining area, and researching a mining area dynamic change monitoring benchmark; for surface movement and deformation caused by mining area disturbance, a D-InSAR technology is applied to perform surface subsidence monitoring on a deformation block. According to the invention, technical means such as InSAR, GPS, remote sensing satellite images and the like are comprehensively utilized to obtain multi-source data of the mining area surface environment, key technologies of integration and application of the multi-source monitoring data are researched, a mining area surface environment multi-source monitoring technical system is established, and on the basis of multi-source high-resolution remote sensing data, technical means such as image processing, mode recognition and the like are adopted, so that the mining area surface environment multi-source monitoring system is established. Land utilization changes and ecological influence conditions are studied, InSAR and GNSS technologies are applied to perform high-precision monitoring analysis research, the overall monitoring capability, reliability and precision are improved, the technical defects of single technologies for mining area geological environment monitoring are overcome, and the mining area geological environment monitoring system has wide popularization and application prospects.

The invention discloses an urban heat island intensity prediction method based on a physical mechanism guided recurrent neural network. The method comprises the following steps: S1, constructing a multi-source fusion data set including land utilization cover data and meteorological data; and S2, constructing a PGRNN fusion model fusing a recurrent neural network RNN and a long short-term memory network LSTM, inputting the urban heat island intensity sequence data or the multi-source fusion data set and the urban heat island intensity matching combination sequence data into the PGRNN fusion model to carry out prediction training, and constructing an urban heat island intensity prediction expression to carry out heat island intensity prediction processing. The PGRNN fusion model realizes long-time-sequence prediction training of a multi-source fusion data set and urban heat island intensity, a physical guidance mechanism taking urban canopy energy balance as a core is creatively introduced and constructed in the PGRNN fusion model, and an urban canopy energy balance physical rule plays a key constraint role in a prediction process of long-time-sequence data; and the consistency and the precision of the urban heat island intensity under a long time sequence are improved.