589 results about "Geo spatial" patented technology

The invention relates to the technical field of spatial data analysis, and particularly provides a commercial vitality space-time prediction and dynamic evaluation method based on multi-modal feature fusion, which comprises the following steps: collecting geographic space, text semantics, real-time behavior and economic attribute multi-modal heterogeneous data, and cleaning and desensitizing to obtain a structured data set; features are extracted from the single-modal data, and commercial vitality core feature vectors are obtained through cross-modal attention fusion in combination with an emotion-vitality association model; inputting the result into a space-time sequence prediction model, and outputting a commercial activity prediction result in a future preset time period; combining real-time updated data to construct a quantization-quality two-dimensional evaluation system, and performing real-time calculation by using a stream processing framework to obtain a dynamic evaluation result; and displaying and outputting a structured evaluation report through a visual panel. According to the method, timely and reliable decision support is provided for scenes such as business district operation optimization, business investment decision and regional economy monitoring, and the precision and real-time requirements of business vitality analysis are comprehensively met.

The invention relates to the technical field of meteorological data processing, in particular to a training data acquisition method and system for a power transmission line fault prediction model. The method comprises the following steps: constructing a multi-source heterogeneous original data pool containing severe convection meteorological data, power transmission line operation and fault data and geographic space data; performing time-space reference unified processing and grid division on the data; mapping the line paragraph to a grid unit, identifying a severe convection event and dividing an influence area through a density clustering algorithm based on a multi-parameter collaborative threshold value, calculating a grid exposure degree, and combining a strength grade weight and a terrain correction weight to calculate a space correlation strength; setting a differentiation time association window to calculate a time factor according to the severe convection dominant type, calculating association credibility based on the space association strength and the time factor, and screening high-credibility association results; and performing attribution labeling on the fault information to obtain a training data set. The problem of low training data quality is solved.

The invention provides a power grid mountain fire prediction method based on causal driving and a space-time diagram convolutional network, and belongs to the technical field of mountain fire prediction. A dynamic feature encoder and a static feature encoder are designed to extract high-dimensional spatial-temporal features of meteorological time sequence information, geographic space environment and power transmission line distribution multi-source heterogeneous data, a causal discovery algorithm is adopted to construct a dynamically evolved causal graph topology, a real causal driven relationship between variables is identified, a causal intensity matrix is decoupled into positive and negative adjacent matrixes, and the dynamic evolved multi-source heterogeneous data is obtained. And designing a causal constrained graph convolution module to aggregate and propagate high-order information, and finally realizing accurate prediction of the power grid forest fire. According to the method, the PCMCI causal discovery algorithm is introduced, so that the real causal driven relationship among multivariate time sequence factors is effectively identified; a causal GCN layer in the CSTGCN model extracts spatial dependence features by using an adjacent matrix constrained by a causal structure, the CSTGCN model embeds causal structure information into a spatio-temporal feature learning framework, and higher prediction precision and generalization performance are achieved.

The invention discloses an intelligent alarm positioning method and system based on an unmanned aerial vehicle, and belongs to the technical field of unmanned aerial vehicle monitoring and geographic space information processing, and the method comprises the steps: obtaining a video stream in real time based on the unmanned aerial vehicle, and recognizing a risk point location in a video image; a three-dimensional space positioning model is constructed based on telemetry data and lens angle parameters acquired by the unmanned aerial vehicle in real time. And performing three-dimensional coordinate dynamic solution on the risk point location based on a three-dimensional space positioning model to obtain a world coordinate of the risk point location. And determining and outputting an alarm position of the risk point location based on the world coordinates. An automatic detection and artificial interaction dual-channel mechanism is adopted, an alarm target is accurately identified and positioned in an unmanned aerial vehicle real-time video, a corresponding relation between video pixels and geographic coordinates is established through real-time registration of an unmanned aerial vehicle image and the digital earth, a space coordinate conversion error caused by view angle difference is corrected by using a depth map, and an alarm target is accurately identified and positioned. Accurate conversion from video pixel coordinates to world coordinates is realized, and high-precision positioning support is provided for remote monitoring of the unmanned aerial vehicle.

The invention discloses a privacy protection track generation method based on track feature distribution modeling. A credible track is generated by extracting similarity of track positions in semantics, geography and directions. The method comprises the following steps: firstly, collecting original trajectory data, preprocessing, and extracting trajectory data in a specified longitude and latitude range as an original trajectory data set; then, geospatial discretization processing is carried out on the original trajectory data in a gridding mode, and the original trajectory is converted into a grid trajectory; and then, extracting features of the grid trajectory, constructing a global movement model, generating a synthesized grid trajectory based on the features, and converting the synthesized grid trajectory into a position trajectory to obtain a final synthesized trajectory. And finally, the final synthetic trajectory is used for replacing the original trajectory for publishing, so that privacy protection of the original trajectory is realized.

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 discloses an artificial intelligence-based agricultural waste carbon emission reduction scenario simulation method and system, and the method comprises the steps: obtaining multi-source geographic space data of a target region, uniformly fusing the multi-source geographic space data into a grid system with a preset resolution, and carrying out the calculation of the multi-source geographic space data based on a space weighted distribution model, the statistical yield of provincial agricultural products is distributed to each grid of a grid system, and the theoretical resource quantity and the high-precision spatial distribution diagram of the agricultural wastes are obtained in combination with a grass-grain ratio database. Method for determining carbon emission and carbon sink quantitative parameters of agricultural wastes under various utilization paths through dynamic accounting model, simulating carbon emission reduction potential and spatial distribution under different resource utilization scenes based on artificial intelligence model according to carbon emission and carbon sink quantitative parameters of agricultural wastes under various utilization paths, and planting front-end crops and structure and space layout is optimized. A quantifiable and visual scientific decision-making tool is provided for regional low-carbon agricultural planning, and the accuracy and feasibility of an emission reduction strategy are remarkably improved.

Techniques are disclosed relating to partitioning batch queries for multi-engine execution. A system receives a batch query specifying query data and including a request for geospatial data for regions corresponding to the query data. Based on locations corresponding to the query data, the system partitions the query data into subsets. The system may assign the subsets of query data to query engines corresponding to the locations of the subsets. The system may cause the engines to retrieve geographic region data corresponding to the locations included in the subsets, where the retrieving is performed by a given query engine for a corresponding subset by accessing an in-memory index of the given engine that stores geographic region data for a geographic partition within which the corresponding subset of query data is located. The system may store region data retrieved by the engines for the subsets in an aggregated data store.

The invention discloses an identification and treatment method for a water and soil loss priority treatment area of a small watershed slope in a hilly area. The method comprises the following steps: calculating a small watershed soil erosion modulus and a sediment connectivity index, and carrying out geospatial modeling to form a soil erosion and connectivity partition identification priority treatment area; determining the land utilization type and terrain status of water and soil loss, and dividing functional zones from the spatial angles of an upper slope, a middle slope and a lower slope; a water and soil conservation forest is built in a forest and grass vegetation area on the slope or an abandoned cultivated land above a reclamation prohibition slope and a water and soil loss treatment area on the barren mountain and slope according to the principle of suitable land and suitable trees, and water and soil loss is prevented and controlled through natural restoration; water and soil loss prevention and control measures of slope land reclamation-hydrological network optimization and node control-grassland soil fixation and fertility increasing are adopted in a slope water and soil loss treatment area below the reclamation forbidding gradient in the slope; the slope bottom is provided with a plant filter belt, a tail end grit chamber and an ecological ditch measure for desilting and pollution reduction; the problem that a water and soil conservation treatment system in a small watershed is low in treatment degree is solved.

The invention discloses a general model pre-training and self-adaptive optimization system and method for a remote sensing image in the technical field of remote sensing images. The method comprises the following steps: acquiring a structured data set containing multi-modal remote sensing core data and corresponding auxiliary geographic information; based on the structured data set, utilizing a two-channel feature extraction architecture to synchronously process visual spectral features and geographic spatial features, and performing model training in combination with innovative training tasks of geographic context contrast learning and multi-temporal mask reconstruction to construct a pre-training model with general characterization capability; and when the pre-training model is applied to a target domain, performing cross-domain feature distribution alignment by utilizing a dynamic gradient inversion layer, and selectively unfreezing part of model parameters by adopting a task perception unfreezing strategy to complete adaptive migration. According to the method, through a self-supervised pre-training architecture of geographic coordinate hash coding and multi-temporal mask reconstruction, the spatial-temporal feature extraction robustness is enhanced, and the problem of generalization of a small sample scene is solved at the same time.

The invention belongs to the technical field of knowledge maps, and discloses a next interest point recommendation method based on a cross-regional city space knowledge map. According to the method, on the basis of the geographic space knowledge graph and the user preference knowledge graph, a cross-regional relationship modeling mechanism is introduced, the spatial proximity and the reachability between the regions are comprehensively considered, and more comprehensive description of the cross-regional behavior of the user is realized. According to the method, a recommendation framework combining a geographic module and a sequence module is designed, spatial dependence between interest points can be captured, a high-order sequence mode in user sign-in behaviors can be mined, and recommendation accuracy and diversity are improved. Geographic representation and sequence representation are fused through a consistency learning framework, the robustness and generalization ability of the model can be enhanced, and the model can still keep stable performance in sparse data and cold start scenes. According to the method, the defects of insufficient region boundary perception, recommendation result centralization, poor cross-region prediction adaptability and the like of an existing method are overcome.

The invention provides a multi-target seamless tracking method and system based on active prediction and feature pre-fusion, and the method comprises the steps: obtaining real-time video streams of a plurality of intelligent cameras for registration, and obtaining a unified fusion video stream under a global geographic space coordinate system; a plurality of dynamic targets are recognized according to the fused video stream, a globally unique identity label is distributed to each dynamic target, a three-dimensional space prediction track and a virtual relay area of each dynamic target are determined by combining the fused video stream, and a relay intelligent camera is determined according to a shooting condition index; the relay intelligent camera is aligned with the corresponding virtual relay area, when the relay intelligent camera recognizes the dynamic target in the virtual relay area, the master control authority is switched to the relay intelligent camera for seamless tracking, and the virtual relay area where the dynamic target appears is actively predicted in a prospective mode for alignment of the relay intelligent camera; shooting view is ensured, and target losing is avoided.

The invention belongs to the technical field of energy planning, and discloses a coal mining area green electricity direct connection planning method and system, and the method comprises the steps: integrating geographic space information, obtaining a three-dimensional geographic space information database, carrying out the processing through a land recycling evaluation model, obtaining a thermodynamic diagram, and carrying out the quantitative analysis of the thermodynamic diagram, and obtaining the power generation amount. Processing and fusing by using a space-time convolutional neural network and a distributed intelligent sensing model to generate a meteorological database; analyzing by using a wind power and photovoltaic space-time output model to obtain a photovoltaic combined sample; processing by using the load prediction model to obtain a load prediction curve set; a source-load time sequence mismatch quantitative model is input for analysis, and an energy storage collaborative configuration scheme is obtained; inputting a multi-target line optimization model for analysis to obtain a direct connection line topological structure diagram; and performing data-driven optimization decision optimization to obtain a source storage load collaborative optimization model, and performing coal mining area green electricity direct connection planning through the source storage load collaborative optimization model. The green electricity is obtained through direct connection, and the cost is reduced.

The invention discloses a geographic space entity vectorization method and a geographic space entity vectorization system based on multi-modal fusion and metric learning, which are used for solving the problems that the traditional geographic information representation is single and multi-source data cannot be effectively fused. The method comprises the following steps: firstly, carrying out multi-modal feature extraction on text and coordinate information of a geographic space entity; then, through a neural network model with an independent coding stream and a deep fusion layer, intelligently fusing the multi-modal features and projecting the multi-modal features to a unified embedded vector space; and finally, optimizing the model by adopting a metric learning normal form through a hybrid triple sampling strategy combining semantic and spatial constraints. The invention also provides a system for realizing the method. The system comprises a feature engineering module, a training set generation module, a model training module and a reasoning application module. The system can automatically complete the whole process from data preprocessing, model training to vectorization reasoning. Compared with the prior art, the method has the advantages that the embedded vector with high semantic distinction degree and accurate spatial perception capability can be generated, and the computability and intelligence of geographic spatial data are effectively improved.

The invention relates to the technical field of intelligent traffic, and discloses a method and system for predicting traffic flow and a storage medium. The method comprises the steps of obtaining multi-source dynamic data of a mountainous area road network, constructing a road network connection relation model, extracting flow transfer characteristics and transfer dynamic fluctuation characteristics between nodes, performing time sequence trend analysis on a historical data sequence of a flow transfer path, and predicting a transfer influence range caused by road network state change; and when the transfer influence range exceeds a preset range threshold value, screening an affected road subset, generating a flow transfer spatial distribution map, updating weight parameters of the road network connection relation model, performing flow offset simulation, determining adjusted road network connection state characteristics, performing geospatial mapping on the road network connection state characteristics, and obtaining a flow transfer spatial distribution map of the road network connection relation model. And extracting a traffic flow quantitative index, and outputting a traffic flow prediction result when a preset congestion risk condition is satisfied. According to the invention, the accuracy and pertinence of mountain road network traffic flow prediction can be improved.

The invention relates to the technical field of ground penetrating radar data processing, in particular to a hidden disease-oriented ground penetrating radar space-time registration and detection method. Comprising the following steps: S1, establishing a continuous mapping relation between a radar signal and a geographic space; s2, constructing a local trajectory indexing mechanism based on anisotropy measurement; s3, constructing a space-time joint correction space based on error prior distribution; s4, performing parameter inversion based on maximum posterior probability estimation constraint; s5, establishing a registration quality evaluation and signal feature decoupling mechanism; and S6, constructing a dielectric priori driven dual physical attribute index and adaptive discrimination model. According to the method, the influence of GPS positioning errors and track swing is overcome, centimeter-level physical space alignment is realized, and false alarms caused by registration deviation are remarkably reduced; by introducing the physical constraint of the frequency dimension, the signal-to-clutter ratio under the complex city background is improved, and the false alarm rate is remarkably reduced.

The invention provides a low-altitude equipment state information processing method and system, and belongs to the technical field of low-altitude equipment state information processing, and the method comprises the steps: receiving the real-time state data of each low-altitude equipment state through a multi-protocol data access network; based on position data in the real-time state data, dividing a geographic space into grid units with set levels by using an airspace grid engine, and encoding the grid units to generate an airspace grid model; based on the airspace grid model, airspace occupation calculation and conflict detection are carried out, and early warning information is generated; and pushing the collision risk early warning information to a monitoring terminal for three-dimensional visual display through the message middleware, and / or sending the collision risk early warning information to a low-altitude equipment control system to generate a flight control instruction. According to the invention, real-time data is acquired through multiple protocols, the dynamic airspace grid model is constructed, conflicts are accurately detected, early warning is generated, the data are pushed to the monitoring terminal for visual display, and the flight safety and the operation efficiency are improved.

The invention provides a regional electromagnetic intelligent evaluation method and system based on spectral entropy weighting, and the method comprises the following steps: collecting electromagnetic spectrum data and the motion state of a patrol vehicle, and constructing a virtual aperture data matrix through the motion information; space covariance analysis and subspace projection are carried out on the matrix, multipath scattering interference is separated and eliminated, and a pure field intensity sequence is obtained through reconstruction. By calculating the normalized spectrum entropy of the Doppler power spectrum of the pure field intensity sequence, an inverse proportion weight coefficient representing the signal confidence is generated; and weighting the weight to a pure field intensity sequence, and mapping the pure field intensity sequence to a geographic space. And dynamically adjusting the density of the evaluation grid according to the field intensity gradient, and finally carrying out weighted centroid calculation based on inverse proportion weight to obtain a regional electromagnetic environment evaluation result. According to the method, the technical problem that a regional evaluation result is not accurate due to the fact that multipath components cannot be separated and single-point data quality cannot be evaluated in traditional mobile patrol is solved.

The invention relates to a lightweight multi-scene disease and pest detection method and system based on RT-DETR. According to the scheme, firstly, a standardized image processing link is constructed, multi-band feature extraction is carried out on an input image by using a convolutional backbone network introduced with wavelet transform, an effective receptive field is expanded in a frequency domain through wavelet decomposition and an inverse reconstruction mechanism, and feature capture of a tiny insect pest target is enhanced while calculation redundancy is reduced. Furthermore, a bidirectional feature pyramid network including global and local double-branch collaborative modeling is adopted, cross-level dynamic interaction and gating fusion are performed on multi-scale features, and environmental noise interference such as veins and illumination under a complex farmland background is effectively inhibited. And finally, establishing a homography mapping model from a pixel plane to a geographic space according to camera calibration parameters, converting a visual detection result into a spatial distribution diagram layer with latitude and longitude information, and realizing dimension crossing of pest and disease damage monitoring from single-point identification to region-level risk assessment.

The invention discloses a structured corpus generation method and device for geological map multi-modal large model training, and the method comprises the steps: preprocessing geological vector data, and constructing a semantic aligned geological data set; randomly selecting a data viewport, and setting the geographic space range and size of a map viewport; configuring a plurality of visual parameter items, performing controllable random adjustment on text labels, element symbols and map maps, and customizing and producing geological map samples as required; extracting multi-dimensional attribute information of the geological map sample to construct structured meta-information annotation data, and constructing a geological element mask annotation file to obtain a structured corpus for geological map multi-modal large model training. According to the method, large-scale production of the training samples with diversity, high quality and image-text alignment features is realized, the geological map corpus meets the requirements of multi-modal large model training on data scale, diversity, labeling precision and modal alignment, and the training efficiency and generalization ability of a geological map intelligent interpretation understanding model are remarkably improved.

The invention relates to a government affair item recommendation method, device and equipment based on multiple modalities and a medium, and belongs to the technical field of government affair item recommende.The method comprises the steps that non-geographic space data features are generated according to query information input by a user, generating geographic space data features according to the positioning track data of the user, the remote sensing image data of the positioning place and the map data; recall is carried out in a government affair item knowledge base according to the non-geospatial data features and the geospatial data features, and first candidate recommended government affair items are obtained; and generating a recommendation result according to the query information and the first candidate recommended government affair item through a large language model. According to the method, government affair items are recommended in combination with the non-geographic spatial data features and the geographic spatial data features, and multi-dimensional matching of user requirements and government affair items is achieved.

In an example system, within proximity of a geospatially stationary first device that is configured to output a primary content, the system detects a second device and a potential viewer of the geospatially stationary first device. The system selects supplemental content for output by the second device based on spatial arrangement of the first device, the second device, and the potential viewer and the metadata of the primary content displayed by the first device. The system causes the second device to output the supplemental content.

The present application relates to a kind of on-orbit disaster risk area lightweight extraction method and system, belong to the cross technical field of satellite remote sensing data processing and emergency disaster response.First, satellite remote sensing image is received and preprocessed on-orbit, then disaster risk area is extracted on-orbit, geographic spatial data conversion;Data lightweight: after the data of conversion format are respectively simplified using boundary simplification function, centroid is extracted using centroid calculation function, and simplified vector data is generated;Data download based on beidou short message, ground data receiving and visual display steps.The present application can realize "on-orbit complete core processing, lightweight data adapt beidou, minute level download to ground", break through the timeliness bottleneck of disaster emergency response.

The invention provides a space-time fusion coding method and a data relation generation method and system, and is applied to the technical field of physical world-oriented multi-source heterogeneous data organization management. Space information of a target data object is mapped to a geographic space subdivision grid coding system to generate a space code; the method comprises the following steps: standardizing time information according to preset time granularity (such as year, month, day and hour), mapping the time information to a layered time coding system to generate a time code, and fusing a space code, the time code and object identification information according to a preset rule to generate a unique space-time fusion code; and finally, the space-time fusion codes and attribute information are bound to form standardized data records, data objects of different sources and different formats are mapped to a unified space-time coding system, data islands are broken, and a data foundation is laid for constructing a unified space-time relation network.

The invention belongs to the technical field of nuclear power station site selection, and provides a nuclear power station site selection-oriented geographic knowledge graph construction method and system, and the method comprises the steps: obtaining related data of a nuclear power station site selection field, the related data comprising field knowledge data and geographic space data; a nuclear power station site selection geographic knowledge graph mode layer is constructed by adopting a top-down method, a nuclear power station site selection core ontology is determined, a hierarchical ontology system is defined, subordinate entities of the core ontology and attributes and data types of the subordinate entities are defined and constrained, a concept layer and the entities and an incidence relation between the concept layer and the entities are constructed, and the core ontology is established. A nuclear power station site selection geographic knowledge graph data layer is constructed by adopting a bottom-up method, entity and relation extraction is carried out respectively, and knowledge extraction is realized; and performing knowledge fusion on the nuclear power station site selection field entity, relationship and attribute information obtained by knowledge extraction to form a final geographic knowledge graph for nuclear power station site selection. According to the invention, the tightness of site selection and domain knowledge is improved.

The invention discloses an automatic import and export method for multi-source data integration, which comprises the following steps of: acquiring multi-source geographic space initial data through a preset multi-source data import interface, performing standardization processing on a coordinate system, a timestamp and an attribute code in the geographic space initial data to obtain standardized elements, generating a standardized element set, and storing the standardized elements into a database; a source mark and a version mark are added to each standardized element in a standardized element set, and the method relates to the technical field of geospatial data processing. The basic difference of multi-source geospatial initial data is eliminated through standardization, then a conflict boundary is obtained through a boundary conflict cluster, and the adjacent elements of the conflict boundary and a cross-boundary are positioned, so that the multi-source geospatial initial data is obtained. And then realizing automatic adsorption and cutting of adjacent boundaries based on a boundary handshake protocol, and performing vertex coordinate correction and topological edge reconstruction operation on adjacent elements crossing the boundaries to obtain a seamless boundary diagram, thereby eliminating boundary gaps of multi-source data fusion.

The invention relates to an airport site selection evaluation optimization method, which comprises the following steps of: inputting a candidate take-off and landing point set and a destination point set, acquiring geographic space data, airspace limitation data, population distribution data and meteorological environment data related to candidate take-off and landing points and flight paths, and carrying out standardization processing on the geographic space data, the airspace limitation data, the population distribution data and the meteorological environment data; for each candidate take-off and landing point, calculating a landform suitability score, a population density influence degree score, a flight efficiency score and a safety compliance score; allocating configurable weight coefficients for the four predetermined dimensions, and performing weighted summation to obtain a comprehensive score of each candidate take-off and landing point; and carrying out sorting according to the comprehensive scores of all the candidate rising and landing points, and outputting a site selection recommendation ranking. The technical defects that an existing low-altitude aircraft take-off and landing point site selection method is high in subjectivity, single in evaluation dimension, difficult to adapt to dynamic task requirements and neglect real three-dimensional airspace environment constraints can be solved, and an automatic, quantifiable and multi-constraint-condition comprehensive site selection decision is made.

The invention relates to the technical field of geographic multi-dimensional data management, and provides a rasterized geographic information construction method based on Gaussian projection, which comprises the following steps: carrying out zoning projection processing on the earth surface, and converting a geodetic coordinate system into a rectangular plane coordinate system by adopting Gaussian-Kruger projection; further dividing the projection zone into a plurality of longitude and latitude zones, and distributing a unique identifier EarthID for each longitude and latitude zone; in each latitude and longitude zone, taking the central point as a reference, and calculating the offset Axoffset and Ayoffset of the grid in the latitude and longitude directions; the position of each grid is uniquely determined according to the EarthID, the Axoffset and the Ayoffset; grid coordinates are converted into geodetic coordinates through an inverse calculation method, and visual display of the grids on the map is achieved. According to the method, the problems of unified expression, efficient management and flexible application of the multi-scale geographic space data are effectively solved, and powerful technical support is provided for space-time big data analysis in the fields of smart cities, traffic planning, emergency command and the like.

The invention discloses a soil nutrient inversion method based on multi-source physical constraint and a multi-scale attention network, and aims to solve the problems that a soil nutrient inversion method based on remote sensing lacks enough truth value marking data and does not consider spatial autocorrelation of soil nutrients in geographic space and a physical imaging rule influenced by a microenvironment and an earth surface structure. Therefore, the problems of deviation of the inversion value and lack of spatial texture constraint and geographical continuity of the soil nutrient content distribution map generated by inversion are solved. According to the method, the collaborative Kriging trend and soil microenvironment and earth surface structure characteristics are introduced as physical constraints, a sample expansion confrontation model is constructed, sample expansion is performed by a scientific method integrating a physical environment and a spatial law, the physical imaging law of soil nutrients is fully considered, and the accuracy of the sample expansion confrontation model is improved. In combination with a designed multi-scale residual attention inversion model for extracting key information from a spectrum, the generated soil nutrient content distribution map is relatively excellent in spatial texture constraint and geographical continuity.

The invention relates to the technical field of facility evaluation, in particular to an intelligent evaluation method for hydraulic engineering monitoring facilities in a flood storage and detention area, which comprises the following steps of: acquiring the operation duration of a hydraulic engineering node and performing segmented identification, adjusting a sampling period to acquire key parameters, calculating a change trend and setting an aging weight, identifying a critical risk node and extracting an evaluation factor. And constructing a feature vector group training model to predict a state level, and mapping a geographic grid to generate a risk map. According to the method, the characteristic aging weight is calculated through the periodic change trend of monitoring parameters, quantitative mapping of monitoring indexes along with time change is achieved, the characteristic vector is established according to the response amplitude, the displacement difference value and the change type and embedded into the sensing network training model, and the state level label with the geographic space mapping capacity is output; based on the fusion display of the risk level and geocoding, the visual display and dynamic risk control of the operation state and response capability of the monitoring facility are realized.