723 results about "Land use" patented technology

A 100-meter gridded spatialization method for carbon emissions of different land use types based on multi-source heterogeneous data is provided, including the following steps: S1, fitting Luojia-1A Satellite nighttime light data year by year with defense meteorological satellite program-operational linescan system (DMSP-OLS) and national polar-orbiting partnership-visible infrared imaging radiometer (NPP-VIIRS) fused 1-kilometer gridded nighttime light data, performing additive fusion on previous light data and current light data, introducing a time inertia weight factor to improve particle swarm optimization-back propagation (BP) neural network algorithm, and forming 100-meter nighttime light data correctable on a long time series; and S2, simulating a complex nonlinear dynamic changing relationship between multilevel 100-meter gridded data of different land use types in different industries and energy carbon emissions in different industries based on the 100-meter nighttime light data, and establishing a 100-meter spatialization inversion model for energy carbon emissions of different land use types in different industries.

The invention proposes a land utilization monitoring method and system based on remote sensing and big data, and relates to the technical field of land monitoring, and the method comprises the steps: dividing sub-regions, and obtaining the multi-temporal and multi-resolution remote sensing data of the sub-regions; performing feature extraction and classification on the remote sensing data based on a deep learning model to generate a land utilization classification map; based on the dual-temporal difference attention network, identifying a change area and constructing a change driving factor library fusing meteorological data and human activity data; detecting an abnormal area based on the driving factor library, and generating an abnormal type label and an attribution analysis report in combination with a dynamic early warning threshold; performing visual rendering on the monitoring result, and outputting an abnormal region early warning map and a disposal suggestion; high-precision feature extraction and classification are realized, change areas and driving factors are deeply analyzed, abnormal areas are effectively detected and early warning is performed, and the accuracy, timeliness and practicability of land utilization monitoring are improved.

The invention discloses a soil organic matter prediction mapping method, device and equipment based on deep learning and a medium, and relates to the field of digital soil mapping, and the method comprises the steps: obtaining a remote sensing image of target region soil, and setting a preset number of soil sampling points; comprehensively acquiring environmental data indexes such as terrain factors, vegetation factors, climate factors and land utilization factors; constructing three-dimensional environment tensor data in a preset range of each sampling point, and inputting the three-dimensional environment tensor data into the soil organic matter content prediction model to obtain an organic matter content prediction value set of the soil sampling points in the target area; and generating a spatial distribution prediction map of the soil organic matter content of the target area by combining the prediction value set and the spatial coordinates of the soil sampling points, thereby improving the accuracy of soil organic matter prediction and the mapping efficiency, and providing technical support for soil resource management and agricultural sustainable development.

The invention relates to the technical field of space planning, and discloses a territorial space planning multi-source data fusion processing method and system, and the method comprises the steps: carrying out the format analysis and semantic annotation of multi-source territorial space data, and obtaining the standard territorial space data of territorial planning; performing hierarchical feature extraction on the standard territorial spatial data to obtain spatial features and attribute features of territorial planning; performing adaptive feature fusion on the spatial features and the attribute features to obtain a multi-semantic fusion feature map of territorial planning; constructing a spatial constraint rule base of territorial planning based on a use control rule and an ecological protection red line of territorial planning; based on a spatial constraint rule base, resolving conflict pattern spots in the multi-semantic fusion feature map, and performing semantic enhancement on the resolved multi-semantic fusion feature map to obtain an optimized fusion result; performing visual rendering on an optimization fusion result to obtain an auxiliary decision graph; according to the invention, the efficiency of data fusion processing in territorial space can be improved.

The invention discloses a water and soil loss dynamic risk assessment system based on a remote sensing image, and relates to the field of water and soil loss risk assessment. The invention discloses a water and soil loss dynamic risk assessment system based on remote sensing images. The system comprises a data acquisition and preprocessing module, a dynamic feature extraction and analysis module, a hierarchical risk assessment model and a risk grading and modeling module, the method further comprises the following steps: acquiring a remote sensing image and historical soil erosion map data of a specific administrative region in a specific period through a data acquisition and preprocessing module; the dynamic feature extraction and analysis module is used for extracting land utilization types, vegetation coverage and quarterly change features of topographic factors through the acquired data; according to the method, a three-layer model architecture is arranged, static historical data, human activity disturbance and social exposure are decoupled and quantified, and the evaluation dimension is improved, so that the accuracy of an evaluation conclusion is improved.

The invention relates to a land utilization type intelligent identification method and system based on remote sensing data. The method comprises the following steps: acquiring multi-source remote sensing data; based on a preset land utilization characteristic model, analyzing and processing the multi-source remote sensing data to obtain corresponding land utilization characteristics in the land utilization characteristic model; based on a land utilization characteristic knowledge base, matching and analyzing the land utilization characteristics to obtain a matching result; and according to a matching result, determining a land utilization type corresponding to the matching result, and outputting the land utilization type as an identification result. By adopting the method, multi-dimensional information complementation can be realized, space-time coverage is enhanced, and data reliability is improved; a high-precision classification result is provided, dynamic monitoring is supported, and an application scene is expanded; the defect of a single data source is overcome, manual dependence is reduced, the operation period is shortened, the cost is reduced, the complex scene recognition capability is improved, and space-time continuous dynamic monitoring and fine management support is provided for the fields of ecological assessment and the like.

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.

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 the field of natural resource right confirmation, and discloses a live-action three-dimensional construction method for right confirmation of a complex terrain area, and the method comprises the following steps: S1, carrying out the collaborative collection of multi-source data; s2, fusing the three-dimensional model and cadastral data; s3, automatically and intelligently extracting ownership boundaries; s4, dynamically binding and visualizing the ownership information; according to the method, the point cloud data and the image data are subjected to registration, de-noising and semantic segmentation processing, a real scene three-dimensional model with a layered cadastre label is constructed in combination with land block boundaries, ownership persons and land use type attributes in cadastre ownership investigation information, registration of the point cloud data and the image data is achieved through an improved ICP algorithm, and the registration accuracy of the point cloud data and the image data is improved. A weight coefficient, a regularization factor and a penalty matrix of point cloud curvature similarity are considered in a registration error function, the method is used for constraining registration distortion of a topographic relief area, and the improved registration algorithm can better adapt to characteristics of a complex topographic area and can effectively reduce registration errors especially in a place with large topographic relief.

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 relates to the technical field of data analysis, in particular to a territorial space planning data analysis system for ecological restoration, which comprises an interference parameter acquisition module, an ecological disturbance calculation module, a restoration area identification module, a priority level judgment module and a site selection stability screening module. According to the method, the accuracy and efficiency of ecological restoration are improved by capturing various disturbance factors of an ecological sensitive area and dynamically evaluating ecological degradation, and the signal of ecological degradation can be recognized earlier by comprehensively monitoring the change trend of earth surface coverage and the dynamic state of a water body and quickly responding to the subtle change of an ecological environment; ecological service function indexes are adopted for inter-year comparison, a long-term view angle about the health state of an ecological system is provided, prediction accuracy is improved, ecological restoration work can be implemented in a more targeted mode, and the lasting effect of the ecological restoration work is ensured through continuous monitoring of land utilization changes.

A water and soil loss pattern spot landing system based on land utilization information weak constraint comprises a land utilization information acquisition module, a factor evaluation and analysis module, a time sequence analysis and pattern spot landing module and a data integration and visualization module. The factor evaluation and analysis module is used for calculating a water and soil loss erosion modulus and evaluating and analyzing influence factors, the time sequence analysis and pattern spot landing module is used for analyzing water and soil loss changes and generating landing pattern spots, and the data integration and visualization module is used for data integration and pattern spot visualization display. According to the water and soil loss pattern spot landing system based on the land utilization information weak constraint, an erosion modulus calculation algorithm based on data fusion and pixel unmixing is proposed to calculate an erosion modulus, and a water and soil loss pattern spot change analysis algorithm based on time sequence classification is proposed to analyze pattern spot change.

The invention discloses a power transmission channel vegetation carbon sink recovery potential assessment method and a system with the power transmission channel vegetation carbon sink recovery potential assessment method, and the power transmission channel vegetation carbon sink recovery potential assessment method comprises the following steps: collecting and fusing multi-aspect data of a power transmission channel planning target area; and establishing a detailed and highly-targeted base database for carbon sink recovery of the to-be-constructed target area of the power transmission channel. The method comprises the following steps: comprehensively considering the change of land use before and after power transmission channel construction, and formulating a typical tree species distribution pattern scheme according with a specific scene and corresponding accounting factors by combining land use types of areas to which land blocks belong; and a power grid safe operation constraint and a carbon sink metering method are combined. The comprehensive influence of power transmission channel construction on vegetation carbon sink can be accurately mastered, reasonable measures are taken to realize maximization of carbon sink benefits, and coordinated development of power transmission channel construction and ecological environment protection can be realized.

The invention discloses a water quality prediction method based on machine learning and an SWAT model, and belongs to the technical field of water environment simulation and water quality early warning. The method comprises the steps that S1, hydrology, water quality, meteorology, land utilization types, pollution sources and spatial elevation multi-temporal data are collected from multiple channels in a drainage basin, and noise reduction, normalization and missing value filling preprocessing methods are adopted for original data; s2, performing sensitivity analysis on the nonlinear relationship between the SWAT model parameters and the output by adopting SVR, and screening out key parameters with remarkable influence; s3, on the basis of the water quality monitoring data, constructing a water quality time sequence prediction model by adopting an LSTM model; and S4, constructing a self-learning mechanism based on the LSTM and the SVR model, and retraining the model by adjusting a learning time window and regularly utilizing latest data. The method is excellent in the aspects of model efficiency, prediction precision and application adaptability, and has remarkable engineering application value and popularization potential.

The invention relates to the field of city updating, and discloses a city updating decision-making method based on public participation, which comprises the following steps: acquiring city space basic data including a vector map, remote sensing image data and historical, cultural and geographic information data of a city; on the basis of the acquired spatial data, a public participation base map is constructed, and the base map comprises information of a road network, land use types and historical and cultural areas of the city; collecting opinion data of the public on facility site selection through an interactive platform on the public participation base map; and according to the collected public opinion data, constructing a multi-subject scoring model. According to the method, a decision-making method based on public participation is adopted, space basic data and public opinions of cities are combined, facility site selection is evaluated through a multi-subject scoring model, and the opinions of different subjects can be integrated by the system by setting weight values of the public, experts and the government, so that the diversity and fairness of site selection schemes are ensured.

The invention relates to the technical field of urban space information processing, in particular to an urban functional area fine classification method based on multi-modal data collaboration. According to the method, POI data, AOI data, land utilization data, remote sensing images and other multi-source heterogeneous data are fused, a multi-dimensional feature system is constructed, and an urban functional area classification model covering a single functional area, a composite functional area, a mixed functional area and a non-functional area is provided. According to the method, firstly, block-level basic units are constructed based on an open street map (OSM), then coordinate transformation and function classification are carried out on POI and AOI data, features such as POI density, AOI area proportion and land utilization structure are extracted respectively, and finally, function type identification is carried out on urban block units according to set classification rules. Compared with the prior art, the method has the advantages that through multi-source information fusion and discrimination logic optimization, the accuracy and applicability of urban functional area recognition are remarkably improved, and the method is suitable for scenes such as urban planning, land utilization evaluation and urban management.

The invention discloses an ecological system monitoring method based on an INVEST model, and belongs to the technical field of ecological system monitoring, and the method comprises the steps: S1, building a unified ecological monitoring data frame through satellite remote sensing, an Internet of Things sensor and a ground survey multi-source data collection and cleaning algorithm; s2, dynamically adjusting INVEST model parameters based on regional ecological characteristics and a machine learning algorithm, according to the method, ecological system services are quantitatively evaluated by using an INVEST model core module, key ecological parameters are extracted in combination with a remote sensing inversion technology, the problems that traditional ground survey is high in cost and limited in coverage range are solved, and the method is suitable for large-scale popularization and application. The remote sensing data preprocessing and classification algorithm improves the efficiency and precision of surface feature information extraction, the PLUS model simulates the land utilization change scene and CMIP6 climate scene input, the INVEST model is combined to quantify the future ecosystem service function, and the limitation that the traditional static assessment cannot cope with the dynamic environment change is solved.

The invention relates to the technical field of environmental monitoring, and discloses a method for reconstructing carbon dioxide concentration enhancement based on multi-source satellite data, which comprises the following operation steps: collecting environmental information such as carbon dioxide dry air column concentration, nitrogen dioxide total column concentration, population density, land utilization, weather and the like to construct an environmental database; pre-processing the collected multi-source data to ensure time-space consistency; according to the population density of the region, the total column concentration of nitrogen dioxide and the land utilization type, setting a threshold value, and dividing the target region into a discharge region and a background region; constructing a background concentration reconstruction model, inverting the carbon dioxide background concentration, and calculating a residual error with actually measured carbon dioxide to obtain a carbon dioxide concentration enhanced sample; based on the carbon dioxide concentration enhanced sample, a concentration enhanced reconstruction model is constructed, and high-resolution carbon dioxide concentration enhanced spatial-temporal distribution is obtained. According to the method, high-frequency monitoring of the carbon source in the region can be realized spatially, and powerful support is provided for dynamic tracking of carbon emission.

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 soil heavy metal distribution prediction method and system based on machine learning, and the method comprises the steps: obtaining topographic factor land use industrial activity data, carrying out the fusion remote sensing information processing, and determining a multi-source data set; performing standardization processing according to the multi-source data set, and performing space-time registration if the scale difference after standardization processing exceeds a preset threshold value to obtain data in a unified format; key features are extracted according to the unified format data, and a dimension reduction feature set is obtained through principal component analysis; a random forest model is constructed according to the dimension reduction feature set, parameters are optimized, and a heavy metal content prediction model is determined; inputting the sampling finite point location data into a heavy metal content prediction model, judging an industrial activity influence area, and obtaining a preliminary distribution estimation result; based on the preliminary distribution estimation result, high-resolution grid data are obtained by fusing topographic factors for the space complex region; and generating a pollution distribution diagram according to the high-resolution grid data, judging a low-prediction-precision region, and obtaining a final optimized layer.

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

The invention discloses an urban and rural land dynamic evaluation system based on satellite remote sensing monitoring, relates to the technical field of land monitoring, solves the problem of low accuracy recognition when a change area is recognized during dynamic evaluation of an original remote sensing image, and provides a dynamic evaluation method for the urban and rural land based on satellite remote sensing monitoring through feature vector comparison (standard vector and abnormal vector distinguishing). The contour partition corresponding to the abnormal vector is marked as a'change partition ', the partition disappearance interference caused by natural factors (such as cloud layer coverage) can be eliminated in combination with the limitation of an image acquisition period within three days, and a real land use change area is accurately positioned. A standard partition is introduced as a reference, and quantitative analysis of spatial position and morphological difference of a change area is realized through vector set translation coincidence verification of front and back images.

The invention discloses a flood season water environment pollution prevention and control method, and relates to the technical field of water pollution prevention and control, and the method comprises the following specific steps: data collection and preprocessing: collecting related data covering hydrology, water quality, land utilization, population distribution and industrial layout from each information department, according to the method, the influence of climate change on flood season water pollution is considered, historical meteorological data of a target area is collected and analyzed, a long-term climate change trend is accurately identified by using a meteorological analysis model, a climate background basis is provided for correlation analysis, and on the basis, a water pollution diffusion prediction model and a hydrological simulation model are combined, so that the flood season water pollution prediction accuracy is improved. The water regimen change characteristics and the water pollution risk degree of the flood season in the future specific prediction period are analyzed, the current flood season water pollution problem can be effectively solved, the adaptability to future climate change challenges can be improved, and a scientific basis is provided for making a long-term water pollution prevention and control strategy.

The invention provides a farmland plot identification method, device and equipment based on multi-source data, and the method comprises the steps: obtaining first remote sensing data and second remote sensing data of a research region; wherein the resolution of the second remote sensing data is higher than that of the first remote sensing data; based on the first remote sensing data and a first target model, dividing the research area into at least one type of first agricultural plots; the at least one type of target area represents land parcel distribution of different land utilization types of the research area; based on the at least one type of target area, the second remote sensing data and a second target model, generating a target type block corresponding to the research area; the target category blocks represent land parcel distribution of the target land utilization type in the research area. By adopting the method, the precision of agricultural plot identification is improved.

The invention relates to the technical field of natural gas pressure regulating station automatic site selection, and provides a natural gas pressure regulating station automatic site selection method, system, equipment and medium, and the method comprises the steps: precisely extracting a water system contour boundary through employing an edge detection algorithm, generating a water system distribution diagram, and building a terrain constraint condition through combining with a gradient grading mask; the gradient attenuation range of the water system safety buffer area is dynamically adjusted based on the river channel bending curvature characteristic and the branch intersection density, and water body protection and land use requirements are effectively balanced; recognizing boundaries of a building area and a forest protection area through color and shape features of the remote sensing image, and performing superposition to generate a composite security constraint layer; and screening low-gradient contiguous effective candidate blocks after excluding the protected area, simulating a feasible path through a depth-first search algorithm, and selecting an area which is high in connectivity with a main road and small in topographic relief as a final site. The ecological environment damage risk is reduced, the engineering implementation feasibility is ensured, and the optimal balance of ecological protection, safety protection and construction cost is achieved.

The invention discloses a territorial space planning stock land management and control early warning system based on multi-dimensional analysis, and relates to the related field of territorial space management, and the system comprises a data association module which is used for collecting territorial space planning constraint data and regional multi-dimensional land data of a target region, and carrying out the spatial association superposition, and forming a stock land database; the partition development module is used for carrying out stock unit identification and regional aggregation analysis to obtain a partition development stock land; the multi-dimensional constraint analysis module is used for constructing a multi-dimensional constraint system, performing multi-dimensional constraint analysis on the partition development stock land, and determining a partition stock land volume threshold value; and the land use management and control early warning module is used for monitoring and obtaining stock land use development planning volume data according to a partition stock land use volume threshold value, and performing land use management and control early warning based on a grading early warning mechanism. The technical problem that existing stock land management and control early warning is insufficient in accuracy and timeliness is solved, and the technical effect of improving the stock land management and control early warning accuracy and timeliness is achieved.

The invention relates to a land site selection recommendation method and system fusing constraint conditions and an optimization algorithm, and the method comprises the steps: obtaining a constraint feature set, dividing the constraint feature set into strong constraint features and weak constraint features, and constructing a site selection knowledge graph; obtaining project demand information, and screening a strong constraint set and a weak constraint set based on the site selection knowledge graph; obtaining a compliance land use information set based on the strong constraint set, extracting fuzzy terms in the weak constraint set, and generating auxiliary decision suggestions based on the site selection knowledge graph; solving the compliance land use information set and the weak constraint set, generating a site selection recommendation solution set, and outputting the site selection recommendation solution set to the user side; according to the method, the planning provisions and the spatial data are automatically analyzed through the multi-channel feature extraction model, the site selection knowledge graph is constructed to achieve intelligent classification of constraint conditions, the recommendation solution set is generated in combination with the multi-target optimization algorithm, and the method has the advantages that the automatic processing degree of the complex planning provisions is improved, and multi-target optimization decision making is achieved.

The invention discloses an underground water phosphorus enrichment area space identification method based on deep learning. The method comprises the following steps: S1, constructing a carbon-iron-phosphorus coupling knowledge graph; s2, carrying out graph structure expansion on the carbon-iron-phosphorus coupling knowledge graph; s3, using the high-dimensional feature set as input to initialize the position of the improved war eagle optimizer population, and outputting a feature weight vector set; s4, constructing a saliency guidance multi-scale graph neural network model structure; s5, generating a voxel-level phosphorus enrichment risk field; and S6, generating land use limited batch division, treatment priority ranking and an early warning time curve. According to the method, the nodes and the paths which contribute to the phosphorus enrichment risk can be automatically highlighted under the significance scoring mechanism, and the model interpretability and the risk mechanism transparency are remarkably improved.