1236 results about "Geo information" patented technology

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 discloses an intelligent method and system for multi-source heterogeneous data fusion management, and belongs to the field of data processing, and the intelligent method for multi-source heterogeneous data fusion management comprises the following steps: deploying a sensor network in a historical ponding area based on historical ponding point data and urban hydrological geographic information, acquiring real-time monitoring data, wherein the real-time monitoring data comprises rainfall intensity, road waterlogging depth, community waterlogging condition and drainage pipe network water level / flow; compared with the prior art, the method has the beneficial effects that sensors are arranged in the historical ponding area to obtain real-time monitoring data, and weather forecast, a terrain model and a pipe network model are combined to construct a waterlogging prediction model to predict whether a ponding risk exists in the historical ponding area in future set time (such as 0.5-3 hours in the future); the ponding risk is treated in advance, so that the problems of traffic interruption caused by ponding, citizen travel obstruction and the like are solved.

The invention belongs to the technical field of air pollution monitoring, and particularly relates to an air pollution monitoring system and method based on big data. According to the method, the pollutant diffusion topological structure is constructed through collection degree fusion of the multi-source environmental data, the prediction accuracy of air pollution monitoring is effectively improved, and when the pollutant diffusion topological structure is constructed, the influence of meteorological parameters and geographic information on a pollutant transmission path is comprehensively considered, so that the prediction accuracy of the air pollution monitoring is improved. The method enables a corresponding prediction mechanism to more accurately describe a pollutant space-time evolution rule in an air environment, introduces a prediction error-based iterative optimization mechanism after a prediction result is output, and compares the pollutant prediction concentration with the actual monitoring concentration in real time, thereby achieving the real-time prediction of the pollutant. And a diffusion weight coefficient and a diffusion probability matrix in the pollutant diffusion topological structure are adjusted, so that a corresponding prediction mechanism can maintain high-precision prediction capability, and relatively accurate data support is provided for early warning of an air environment.

The present invention relates to the technical field of algae proliferation risk prediction. Disclosed is a machine learning model-based algae proliferation risk prediction method. The present invention comprises collecting water quality data, geographic information and historical algae proliferation event data and preprocessing the collected data. In the present invention, a graph structure between monitoring points is constructed on the basis of spatial features, each monitoring point is represented as a node in a graph, and a relationship between nodes is represented as an edge, so that spatial dependency between monitoring points can be effectively captured. The weight of each edge is defined as the reciprocal of the distance between the nodes, so that the model can more accurately reflect the effect of adjacent monitoring points on each other. By constructing the graph structure to represent the relationship between the monitoring points, large-scale spatial data can be efficiently processed. A graph neural network is capable of performing a convolution operation on a graph structure, effectively aggregating information of adjacent nodes, thereby improving the spatial relationship expression capability and the model prediction accuracy.

The invention discloses a real-time multi-dimensional sensitivity evaluation and self-adaptive safety prevention and control method and system for natural resource geographic information. The method comprises the following steps: acquiring real-time natural resource geographic information data; obtaining a preset behavior graph model; updating a preset behavior graph model according to the real-time natural resource geographic information data so as to obtain an updated behavior graph model; acquiring an updated node risk vector according to the updated behavior graph model; obtaining a trained Bayesian risk prediction model; inputting the updated node risk vector into a trained Bayesian risk prediction model so as to obtain a real-time abnormal behavior identification result; and generating a personalized prevention and control strategy scheme according to the real-time abnormal behavior recognition result. According to the method, intelligent identification, dynamic evaluation and active protection of natural resource geographic information in a full life cycle are realized by constructing a multi-dimensional sensitivity quantitative model, a dynamic risk perception mechanism based on a graph structure and a safety prevention and control strategy capable of being updated in real time.

The invention belongs to the technical field of remote sensing image intelligent processing and agricultural information extraction, and particularly relates to a remote sensing large model pre-training and multi-granularity boundary supervision natural field type extraction method, which comprises the following steps: firstly, pre-training a model on a large-scale space-time spectrum remote sensing data set, and combining anchor point sensing mask and geographic information coding; secondly, inputting the multi-scale features into a multi-branch structure sensing network, and outputting a semantic segmentation prediction map through high and low resolution double input and dynamic attention fusion; generating a multi-scale field boundary label through morphological operation, and outputting a boundary prediction map through multi-task supervision after domain enhancement of features by a frequency space double-domain enhancement module; and finally, aligning the two images and performing pixel-level operation to obtain a high-precision extraction result. Through the method, the global classification error during cross-region migration is greatly reduced, the method is adaptive to a low-pixel wide ridge, the boundary detection value and the closure rate of a small-scale field are improved, and the extraction precision of a large field and a small field is considered.

The invention belongs to the technical field of intelligent traffic and new energy power supply, particularly relates to a traffic equipment solar power supply fault diagnosis system, and aims to solve the problems that a solar power supply system is not timely in fault diagnosis, low in precision and difficult to distinguish instantaneous interference and continuous faults. The system collects multi-source data through an environment sensing and electrical parameter monitoring module, generates a power deviation sequence and extracts time sequence characteristics by combining dynamic expected power modeling with actual output comparison; the fault identification module adopts a multi-level logic discrimination and 12-hour continuous verification mechanism, accurately identifies photovoltaic panel pollution, storage battery aging, poor line contact and controller faults, and distinguishes instantaneous interference; and the decision alarm module generates graded alarms according to fault types and grades, and realizes accurate positioning and operation and maintenance scheduling in linkage with geographic information. The system also has the functions of internal resistance pulse detection, dual-channel redundancy sampling, adaptive threshold adjustment and model self-learning, and the diagnosis accuracy and the operation and maintenance efficiency are significantly improved.

The invention relates to the technical field of atmospheric dust pollution prevention and environmental protection, and discloses a multi-objective optimization layout method for dust monitoring points of a bulk cargo port, which comprises the following steps: processing historical meteorological data and port geographic information, and calculating the comprehensive evaluation concentration of each candidate grid point in combination with a pollutant diffusion model; constructing a multi-target nonlinear mixed integer programming model taking maximization of environment sensitive point coverage and minimization of concentration interpolation deviation as target functions, wherein the model comprehensively considers multiple reality constraints; solving the model by adopting an improved genetic algorithm to obtain an optimal solution set capable of forming tradeoff among different targets; and carrying out quantitative evaluation on the solution set through preset monitoring area coverage rate and monitoring accuracy indexes, and determining an optimal monitoring point position layout scheme. According to the invention, a scientific and quantitative optimized layout scheme can be provided for bulk cargo port dust monitoring, and the reliability and spatial representativeness of a monitoring network are improved.

The invention discloses a drainage pipeline multi-parameter sensing trenchless repair dynamic regulation and control method and system. Through a fusion technology path of multi-source comprehensive data acquisition, edge calculation real-time cross validation of abnormity, pipeline state simulation analysis of defect levels, intelligent algorithm dynamic adjustment of repair parameters and geographic information visualization platform full-process monitoring, closed-loop management from abnormity identification to repair regulation and control is realized. A comprehensive data set is formed through multi-source data acquisition, an edge computing technology is utilized to quickly identify abnormities, a simulation technology is combined to evaluate the severity of defects, then process parameters are optimized and repaired based on defect levels, and a regulation and control instruction is generated through linkage of a visual platform and a pump station dispatching system. And finally, a pipeline repair and operation regulation and control scheme is formed through integration, and the repair effect and the system stability are ensured. According to the method, the pipeline abnormity processing accuracy and the repairing efficiency are remarkably improved, and a technical guarantee is provided for safe and stable operation of an urban drainage system.

The invention provides an image data and pattern spot management system and method and electronic equipment, and relates to the technical field of geographic information data processing, and the image data and pattern spot management system comprises an image data indexing module which is used for carrying out three-dimensional indexing on a remote sensing image to obtain a space-time-data source association chain of the remote sensing image, the remote sensing image contains pattern spot data with an identifier; the pattern spot complete cycle construction module is used for performing multi-temporal analysis on the pattern spot data to obtain an analysis result, and constructing a pattern spot change life cycle map of the pattern spot data in combination with the association chain; and the tracing module is used for positioning the target pattern spot when the target pattern spot needs to be traced to obtain a change node of the target pattern spot and a space-time-data source index corresponding to the change node, and searching in the association chain according to the change node and the space-time-data source index to obtain a traceable data chain. According to the invention, the storage and management of the geographic image data and the pattern spot data are more systematized and refined.

The invention relates to the technical field of forest fire management, in particular to an integrated forest fire intelligent analysis method and system. The system comprises a background service system, a central intelligent analysis system and a mobile intelligent analysis system. The background service system is deployed in a cloud computing server cluster and is used for uniformly storing personnel information, fire scene information and geographic information data and realizing multi-source data fusion and authority management through a standardized API (Application Program Interface); the central intelligent analysis system is deployed in a high-performance computer, integrates a forest fire danger auxiliary decision-making model, a forest fire spreading trend prediction model, a force distribution dynamic display module and a collaborative plotting module, and supports fire spreading prediction and real-time decision-making instruction issuing based on multi-dimensional data such as weather, terrain and vegetation; the mobile intelligent analysis system realizes fire spreading trend analysis, mobile plotting and instruction receiving in an offline environment, supports offline data caching and calling in an emergency scene, and effectively improves forest fire emergency response efficiency and resource scheduling accuracy.

The invention relates to the technical field of agricultural informatization and intelligent decision making, and discloses a vegetable growth cycle pest control decision making system and method based on a knowledge graph. According to the method, an unstructured agricultural data stream including vegetable varieties, growth stages, environmental parameters, pest and disease records and geographic information is collected from an original database. A data tuple set with a time stamp is generated by performing scanning identification and content extraction on a data stream. Then, clustering is carried out according to the growth stages to which the tuples belong, and stage feature clusters sorted according to the growth cycle are formed; and performing multi-dimensional feature correlation analysis on the stage feature cluster, and establishing a dynamic coupling relationship among the environmental parameters, the disease and pest history and the geographic space features under the growth cycle dimension. And based on the dynamic coupling relationship, generating disease and pest control decision parameters of the vegetables in the specific planting area in each growth cycle stage. According to the invention, accurate matching between the control decision and the crop physiological time sequence and regional characteristics is realized.

The invention discloses a power failure event cooperative processing method, system and device based on geographic information and a medium, and relates to the technical field of power system power distribution network fault processing and informatization. The method comprises the following steps: when a power failure event occurs, acquiring multi-source heterogeneous data from an associated system; performing fault analysis on the multi-source heterogeneous data through a fault diagnosis model, and outputting fault positioning information and a fault type; obtaining associated maintenance resource state data and real-time traffic road condition data according to the fault positioning information; the maintenance resource state data comprises a rush repair team position, a skill level and a vehicle state; and based on the fault positioning information, the fault type, the user attribute, the historical work order data, the maintenance resource state data and the real-time traffic road condition data, generating an optimal maintenance scheduling scheme through a reinforcement learning scheduling model. The cross-department and cross-business cooperative processing is realized, and the first-aid repair efficiency and the resource utilization rate are remarkably improved.

The invention belongs to the technical field of surveying and mapping and geographic information processing, and discloses a geographic entity intelligent identification and reconstruction system based on multi-source surveying and mapping data. The system is composed of a multi-source data acquisition and preprocessing module, a cross-modal feature coding and fusion module, a structural atlas construction and spatial logical reasoning module, a deformable neural modeling module and a physical prior guided collaborative prediction and closed-loop optimization module. According to the method, a cross-modal feature coding and fusion module is arranged, and a modal attention mechanism is introduced to dynamically weight multi-source data, so that heterogeneous information such as a laser point cloud, an inclined image and a multispectral image is fused into a unified coding vector in a high-dimensional space; compared with feature extraction performed by using a static deep network in a comparison file, the method of the invention adopts a minimum residual function to perform modal weight training, has an adaptive feature integration capability, and effectively improves the accuracy of geographic entity recognition and the robustness of boundary segmentation in different scenes.

The invention relates to the technical field of satellite hydrological data processing, and particularly discloses a GRACE data super-resolution network space downscaling method fusing geographic information and environmental variables, which comprises the following steps: S1, acquiring original resolution GRACE data and original GLDAS data of a research area, and preprocessing the data; s2, dividing the data obtained by preprocessing in the step S1 into a training set and a test set, and training the GRACE data space downscaling model by using the training set to obtain a trained discriminator and a trained generator; and S3, inputting the GRACE low-resolution data in the test set and the high-resolution environment variable at the moment corresponding to the data into the generator trained in the step S2, and finally obtaining a downscaled high-resolution GRACE image. The method not only can be used for dynamic monitoring of regional scale underground water reserves and flood risk assessment, but also can be expanded and applied to scenes such as agricultural drought monitoring and ecological hydrological process simulation.

The invention discloses a forest resource map modeling method and system, and belongs to the technical field of forestry information. The method comprises the following steps: capturing multi-source heterogeneous original observation data through a sensing unit group deployed in a forest region; performing space-time alignment and quality evaluation on the data by a map generation engine to generate an original observation sequence; calling and analyzing auxiliary geographic information in the environment context library to set prior configuration parameters of the atlas reckoning device; and finally, a map reckoning device is driven to perform fusion reckoning on the observation sequence, and a structured forest resource semantic network is output. The system correspondingly comprises a sensing unit group, an environment context library, an atlas generation engine and an atlas reckoning device. According to the method, full-chain intelligent management of forest resources from precise perception and intelligent cognition to prospective planning is realized through space-based collaborative intelligent perception, a depth generation model of historical knowledge injection and operation simulation based on space-time prediction, and the precision, efficiency and decision support capability of forest resource monitoring are greatly improved.

The invention discloses a scene generation method and device based on multi-source GIS data fusion, and relates to the technical field of digital twinning and programmed generation. The method comprises the following steps: acquiring GIS data, preprocessing the GIS data, and storing the preprocessed GIS data in a geographic information resource library; a structured resource library is constructed, semantic parameters are added to the three-dimensional model in the structured resource library through the configuration file, and three-dimensional model resources with structured semantics are constructed; on the basis of the three-dimensional model resources with structured semantics and GIS data in a geographic information resource library, building and road generation and terrain processing are carried out in a programmed modeling engine through a configuration file, and scene data are generated; and importing the generated scene data into a real-time rendering engine, carrying out dynamic environment interaction and biocenosis simulation, and generating a city scene. The problems that in the prior art, an urban three-dimensional modeling method is low in efficiency and insufficient in environment interaction reality sense are solved.

The invention relates to the technical field of electric vehicle charging pile planning, in particular to an intelligent charging pile layout optimization method based on a multi-scale space-time diagram neural network. Comprising the following steps: S1, constructing a heterogeneous dynamic graph which represents a potential charging pile position node set, epsilon t represents a time-varying edge set, At represents a time-varying adjacent matrix, and Xt represents a node feature matrix; s2, calculating a self-adaptive adjacency matrix with a specific relationship, and calculating a multi-type dynamic relationship between capture nodes of the self-adaptive adjacency matrix based on the constructed heterogeneous dynamic graph model; s3, updating a structure bias matrix, and capturing dynamic change characteristics of the network; s4, calculating distance measurement between nodes, and fusing geographic information and semantic information; s5, through graph neural network learning, based on the constructed heterogeneous dynamic graph and the calculated adaptive adjacency matrix, executing a graph neural network learning process, and extracting node space-time representation; and S6, predicting a future charging demand based on node representation obtained by graph neural network learning, and generating based on the future charging demand.

The invention discloses a highway accident scene sensing system based on AI identification and unmanned aerial vehicle cooperation, and relates to the technical field of intelligent traffic. Comprising a central planning node for fusing geographic information, meteorological information and road obstacle information of a target site with the real-time state of an unmanned aerial vehicle, constructing a digital twin scene model, generating an initial four-dimensional collaborative awareness route plan, and dividing sub-airspaces; the distributed airborne intelligent agent obtains peripheral multi-source data of the unmanned aerial vehicle, constructs a surrounding environment real-time situation map through an AI recognition algorithm, and determines a flight instruction of the unmanned aerial vehicle according to the surrounding environment real-time situation map and the divided sub-airspace; and the reconfigurable intelligent metasurface is used for adjusting surface electromagnetic characteristics according to the divided sub-airspaces and sending directional beams to the unmanned aerial vehicle. Therefore, the problems of cooperative scheduling, obstacle avoidance and communication guarantee of multiple unmanned aerial vehicles in a complex accident scene are effectively solved, and the efficiency and safety of emergency disposal are remarkably improved.

The embodiment of the invention provides an address data matching method and related equipment, and belongs to the technical field of geographic information services. The method comprises the following steps: constructing an address annotation corpus according to input address information data and a preset address database; the method comprises the following steps: generating a geographic information embedding vector according to a preset geographic information knowledge graph, performing address element analysis in combination with an address annotation corpus to obtain an address element sequence so as to construct a dictionary tree, and performing similarity screening through a spatial hierarchical matching algorithm to obtain a similar address set; generating an address embedding vector matrix through a preset word embedding vector model, and performing feature extraction through a preset semantic feature extraction model to obtain semantic-level similar features; according to input address information data, multi-dimensional character similarity matching is carried out to obtain character-level similar features, then weighted fusion is carried out in combination with semantic-level similar features, and target matching address data is determined according to a weighted fusion result. According to the embodiment of the invention, the address data matching accuracy and efficiency can be improved.

The invention discloses a big language model dialogue recommendation method based on multi-modal geographic information fusion and context modulation, which comprises the following steps of: firstly, coding and fusing a text address, geographic coordinates and numerical attributes to obtain a geographic context matrix; then intention modeling is carried out to generate user intention representation, then context feature modulation is utilized, dynamic modulation is carried out on the user intention representation by utilizing an asymmetric fusion mechanism, a context guiding signal containing geographical constraints is generated, and finally, the context guiding signal is spliced before the user intention representation. The big language model input to the parameter freezing obtains user preference vectors and calculates recommendation scores with the candidate positions, the candidate positions are sorted according to the recommendation scores, and the candidate positions with high scores are recommended to the user. According to the method, on the premise that internal parameters of the large language model are not changed, the geospatial reasoning ability is effectively given to the large language model, and the accuracy and interpretability of dialogue recommendation are remarkably improved.

The invention belongs to the technical field of geographic information technology and remote sensing image processing and service, and discloses a slice-free image service system and method based on dynamic projection and real-time mosaic, which abandons the traditional pre-slicing mode, directly provides online service based on original image data, and improves the service efficiency. The problem of redundant storage caused by pre-generation and storage of massive tiles is fundamentally eliminated, and storage space is saved by up to 90%. Meanwhile, when the original data is updated, the system does not need to carry out a time-consuming re-slicing process, real-time updating and publishing of services can be realized, and the core pain points of long data updating period and high delay in the traditional technology are thoroughly solved. Through the integrated dynamic projection engine and the real-time mosaic module, the on-demand service request of the client for any coordinate system, any spatial range and any resolution can be responded.

The invention discloses a smart park security system based on a GIS technology, and belongs to the technical field of security protection. Comprising a GIS basic service module, a security element plotting module, a basic element visualization module, a multi-source heterogeneous security information integration module, a data storage and management module, a depth feature extraction module, a model training and real-time reasoning module and a control response and verification module. And the GIS basic service module is used for providing a unified space reference and geographic information service and supporting coordinate conversion, space analysis and map rendering. Based on the unified space reference capability of the GIS basic service module and in combination with the GPS RTK positioning and Beidou dual-mode positioning of the security element plotting module, precise space association of the park security equipment is realized, the plotting position is precise, the real-time operation state of the equipment can be deeply bound with GIS coordinates, and a manager can intuitively master the dynamic state of the equipment through a map, so that the management efficiency is improved. The problems of fuzzy plotting and state disjunction of a traditional system are solved.

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 field of geographic information mutual analysis, and discloses a multi-source dynamic collaborative geographic information mutual analysis method and device, equipment and a medium. The method comprises the following steps: collecting time-synchronized static geographic information, dynamic GPS positioning data and a plurality of pairs of starting and ending point unstructured text data sets, and constructing a dynamic spatio-temporal data pool; high-quality spatio-temporal data is obtained through dual anomaly verification and Kalman filtering cleaning correction; optimizing an address longitude and latitude mapping table in combination with a three-level administrative range and a dynamic high-frequency word; a non-standard address is analyzed into a structured text through a BERT-CRF model, and a precise coordinate and a standardized text are obtained through a hierarchical dynamic matching algorithm. By adopting the method, meter-scale positioning is realized, the non-standard address resolution accuracy is greatly improved, the duplicate name address mismatching rate is reduced, a dynamic scene is adapted, the resolution efficiency and reliability are considered, and an efficient technical support is provided for bidirectional accurate mutual resolution of geographic coordinates and text addresses.

The invention discloses a virtual accompanying test system for an unmanned surface vehicle, and relates to the field of unmanned surface vehicle testing, and the system comprises an integrated case, and a task scene simulation module, a sensor simulation module, a pose mapping module, a collision avoidance safety evaluation module and a test display control interface which are integrated in the integrated case. The task scene simulation module constructs a three-dimensional virtual test water area scene based on the static geographic information data, and is provided with a dynamic obstacle and a virtual unmanned ship; the sensor simulation module generates simulation data of the detected unmanned ship environment sensing sensor in the scene; the input end of the pose mapping module is connected with a position and attitude measuring unit which is externally arranged on the case and is arranged on the measured unmanned ship through a data bus, receives real-time position and attitude data of the position and attitude measuring unit, and drives the pose of the virtual unmanned ship to change; the collision avoidance safety evaluation module calculates a total safety evaluation score according to interaction data of the virtual unmanned ship and the dynamic obstacle; and the test display control interface is used for man-machine interaction, test process display and result display.

The invention relates to the field of low-altitude economy and geographic information, in particular to a low-altitude scene-based twinborn data rapid updating method, which comprises the following steps of: acquiring multi-source sensing data of a target low-altitude area in real time; comparing with a historical twinborn model, and identifying a change area and evaluating a priority by using an artificial intelligence algorithm; dynamically generating an optimal unmanned aerial vehicle data acquisition route by combining the airspace information and priority of the change area; then the unmanned aerial vehicle is controlled to collect updated data along the route; performing lightweight real-time three-dimensional reconstruction on the updated data to form a local updated model; and finally, fusing the model with a historical twinborn model to generate a final target twinborn model. Through intelligent change identification, dynamic route planning, lightweight reconstruction and incremental updating, the updating efficiency is significantly improved, the resource consumption is reduced, the real-time accuracy of the model is ensured, and the method is suitable for multiple fields such as low-altitude economy and urban governance.

The invention belongs to the technical field of geographic information, and discloses a city update planning scheme optimization method and system driven by a large language model, a terminal and a storage medium, and the method comprises the steps: obtaining multi-source heterogeneous data, carrying out site context interpretation according to the multi-source heterogeneous data, and generating site semantic representation; according to a semantic text input by a user, converting the semantic text into a planning parameter vector set by utilizing regulation constraints and the site semantic representation; based on the regulation constraints, the site semantic representation and the planning parameter vector set, generating a space planning scheme conforming to a city updating target; and performing multi-dimensional index detection, semantic interpretation and parameter feedback optimization on the spatial planning scheme based on an index evaluation and semantic feedback optimization large language model to obtain an optimized city update planning scheme. According to the method, the problems of disjunction between scheme generation and optimization and insufficient interactive interpretation in the prior art are effectively solved.

The invention discloses a regional photovoltaic power prediction method based on a dynamic graph neural network, and the method comprises the steps: remarkably improving the quality and consistency of model input data through fusing real-time meteorological data, historical power generation power, an equipment state and geographic information; a time-varying adjacency matrix is periodically and dynamically updated, and the real-time dynamic space relation of each power station in the area is accurately captured; a collaborative modeling method of dynamic graph convolution and a time sequence network is utilized to realize accurate prediction of complex space-time coupling dependence; meanwhile, through introduction of the hidden Markov state transition model, fine description of a power prediction sequence and reasonable classification of power levels are achieved, the problem that a traditional prediction method is insufficient in adaptability to real-time dynamic working conditions is effectively solved, and finally comprehensive improvement of regional photovoltaic power prediction precision and stability is achieved.

The invention relates to the technical field of three-dimensional map recognition, and discloses a semantic segmentation-based low-altitude three-dimensional map element autonomous recognition method and system. The method comprises the following steps: acquiring a low-altitude remote sensing image and preprocessing to generate a multi-channel image matrix containing geographic coordinates and spectral characteristics; extracting multi-scale features through a pyramid feature extraction network in combination with cavity convolution, and obtaining an adaptive weighted feature tensor through a cascade attention mechanism fusion channel and a spatial weight; adopting a bidirectional feature fusion strategy to generate fusion features, and outputting an initial category probability distribution diagram by a semantic segmentation header network; obtaining a refined mask through edge perception optimization and superpixel segmentation correction, and mapping the refined mask to a three-dimensional coordinate system to generate a vector layer with a semantic tag; a constraint rule is deduced through a topological relation inference engine, logic conflicts are eliminated through rule-driven post-processing, finally, a standardized three-dimensional map element database meeting the geographic information standard is generated, and efficient, accurate and autonomous recognition of low-altitude three-dimensional map elements is achieved.