684 results about "Urban area" patented technology

The invention provides an urban functional area gridding identification and classification method based on multi-source spatio-temporal data, and relates to the technical field of area identification, and the method comprises the steps: carrying out the dynamic grid division of an urban area through employing a self-adaptive grid division algorithm, obtaining the multi-source spatio-temporal data of a gridding area, carrying out the space-time alignment, and generating a multi-dimensional time sequence feature set; extracting multi-dimensional feature vectors of the grid units through a multi-modal feature fusion algorithm, acquiring long-range time sequence features by using a bidirectional gating recursion unit, calculating spatial correlation features based on a multi-head attention mechanism, and inputting the two types of features into a feature fusion network with jump connection to generate enhanced spatial-temporal features; and an optimization strategy is adopted, a functional area clustering center point is determined through density peak value clustering in sequence to obtain an initial division result, the initial result is optimized by combining spectral clustering of spatial adjacency constraints, and the boundary of the functional area is optimized by using a simulated annealing algorithm, so that accurate identification and classification of the urban functional area are realized.

The invention relates to the technical field of data analysis, provides an urban meteorological disaster data identification method and system based on deep reinforcement learning, and is used for effectively improving the model performance so as to enhance the accuracy and real-time performance of meteorological disaster monitoring. The method comprises the steps of obtaining a meteorological monitoring data set of a target city area, executing meteorological data preprocessing operation on the meteorological monitoring data set to obtain a preprocessed meteorological spatial-temporal feature set, calling a trained deep reinforcement learning recognition model, and performing dynamic disaster mode matching processing on the meteorological spatial-temporal feature set to obtain a dynamic disaster mode recognition model. And generating a meteorological disaster recognition result set of the target city region, generating a disaster coping strategy set according to the meteorological disaster recognition result set, and performing dynamic strategy optimization processing on the deep reinforcement learning recognition model based on the disaster coping strategy set to obtain an optimized deep reinforcement learning recognition model. And deploying the optimized deep reinforcement learning recognition model to a meteorological disaster monitoring system.

The invention discloses a low-carbon city subjective and objective mixed planning interval entropy weighting evaluation method, and relates to the technical field of city management and low-carbon evaluation, and the method comprises the steps: collecting and preprocessing index data in real time, building a user behavior prediction model according to the index data, and predicting the index data of a user; dynamically adjusting weight distribution of subjective and objective evaluation indexes based on the predicted index data, and constructing a comprehensive evaluation model to calculate a comprehensive score of each region in the city; and formulating optimization measures of each region according to the comprehensive score. A prediction model is established by using a support vector machine, an evaluation result is highly matched with real-time data through dynamic weight adjustment, the hysteresis problem of traditional static evaluation is avoided, and more accurate and flexible low-carbon city evaluation is realized through dynamic adjustment of weight distribution and construction of a comprehensive evaluation model. And the comprehensive score of the urban area is calculated by adopting a nonlinear transformation function, so that the marginal effect change of the evaluation index under different load conditions can be better reflected.

The invention relates to the technical field of urban climatic science, in particular to an urban climatic environment rapid evaluation system and method. According to the technical scheme, the system comprises a data acquisition module, an index standardization and feature extraction module, an index weighting and dynamic parameter adjustment module and a climatic environment comprehensive index generation module. According to the method, based on meteorological and environmental multi-source data of an urban area, a multi-dimensional evaluation index set is firstly constructed through standardization and normalization processing, and then a fuzzy logic harmonic function and a non-linear harmonic function are utilized to identify the coupling relation and the cooperation strength among the indexes; and meanwhile, a dynamic optimization mechanism is introduced to adaptively adjust each weight coefficient according to the real-time index sensitivity, an urban climate environment index is finally generated, and spatial visual expression and grade early warning are realized. According to the method, rapid evaluation and trend judgment of the urban climate system state can be realized, and the method is suitable for urban planning, climate risk monitoring and toughness analysis.

The invention provides an urban inland inundation simulation method based on CPU-GPU heterogeneous parallel calculation, and aims to reduce the number of calculation grids by adopting a self-adaptive non-uniform grid, consider pipe network calculation and improve the calculation efficiency of low-fluctuation urban area flood simulation at the same time, and comprises the following steps: performing one-dimensional pipe network runoff CPU calculation, constructing a rainwater pipe network model, and constructing a rainwater pipe network model. Extracting an overflow process of an overflow node of the pipeline; dividing a non-uniform grid based on the original raster data and a gradient threshold; non-uniform grid parallel calculation is carried out; a CPU-GPU heterogeneous parallel one-dimensional and two-dimensional coupling model is constructed; and coupling water depth calculation. The method can flexibly adapt to topographic changes and reduce the number of computational grids, and solves the limitation that precision and efficiency are difficult to consider in waterlogging simulation of a uniform grid in a low-fluctuation area in combination with CPU-GPU heterogeneous parallel calculation.

The proposed invention relates to methods for controlling energy consumption by a motor vehicle, and can be used in transportation industry. The technical problem to be solved by the claimed invention is to provide a method, a device, a system, a motor vehicle, and a computer-read-able medium that do not possess the drawbacks of the prior art and thus make it possible to generate an accurate resource-efficient track for a motor vehicle that allows to reduce resource consumption by the motor vehicle moving along a portion of the route that contains a mandatory deceleration point, including portions of the route that are located in urban areas.

The invention relates to the technical field of traffic mode recognition, in particular to an urban traffic mode recognition system based on trajectory clustering, which comprises a trajectory credibility rejection module, a trajectory semantic clustering module, a trajectory evolution stage recognition module, a dynamic parking judgment module and an urban traffic mode generation module. According to the method, boundary dispersion rate difference analysis is carried out on traffic trajectory data, continuous cross-grid anomalies are identified, unstable sections in the trajectory are effectively eliminated, the spatial direction distribution concentration degree is taken as a basis, the trajectory coverage rate difference of the clustering center in adjacent time periods is calculated, and the spatial evolution trend of the trajectory is quantified; according to the method, dynamic description of a traffic mode in the time dimension is achieved, the misjudgment risk caused by a single time or space index is avoided in combination with double indexes of the space overlapping degree and the parking persistence between track fragments, and efficient identification of urban area travel function types is achieved through similarity matching between track features and travel features.

The invention discloses an urban area runoff pollutant transportation process simulation forecasting method, which is characterized in that a building area runoff production calculation method under the influence of a wind field is provided, and the building area runoff production calculation method is coupled with an SWMM rainfall flood model; secondly, constructing a two-dimensional hydrodynamic model based on an FVCOM and a Lagrange particle model, coupling the two-dimensional hydrodynamic model with the SWMM rainfall flood model, and driving the two-dimensional hydrodynamic model by taking the overflow flow and the pollutant concentration simulated by the SWMM rainfall flood model as boundary conditions; simulating a transportation process of pollutants along with evolution of surface ponding by establishing a'concentration-quantity 'conversion relation between the pollutants and particles at grid units and nodes, and obtaining a time sequence pollutant concentration data set; and training and establishing a pollutant concentration prediction model based on the obtained data, and then predicting the pollutant concentration of each prediction point after specific time in an actual rainfall event. According to the method, the runoff pollutant concentration can be predicted, and the urban non-point source pollution risk under the rainstorm weather is reduced.

The invention relates to the technical field of logistics distribution, and discloses a big data analysis method and system based on traffic transportation logistics, and the method comprises the steps: constructing a space-time diagram convolution network model, and predicting the traffic congestion propagation trend of an urban region; based on the congestion propagation trend, the order density and the distribution resource state, executing a space-time dynamic partitioning algorithm, and dividing the urban distribution region into a plurality of dynamic micro-regions; constructing a double-layer intelligent decision-making system, and generating a logistics fleet separation and combination decision and path planning; heterogeneous distribution resource dynamic marshalling is executed, and different types of distribution tools are combined into a miniature formation; using a multi-agent reinforcement learning algorithm to optimize the cooperation process in the micro formation; according to the invention, through fusion of congestion propagation prediction, dynamic micro-area division, double-layer intelligent decision and heterogeneous resource collaboration, key problems of traffic congestion, resource scheduling and'last kilometer 'distribution in urban logistics distribution are effectively solved.

The invention relates to the technical field of value accounting, and discloses an ecological system total production value accounting method based on GIS spatial analysis, and the method comprises the steps: carrying out the accounting of regional basic data and satellite image data, and constructing a bidirectional dynamic semantic mapping library corresponding to land change investigation land parcel classification-ecological system type; internet of Things monitoring data and statistical report data are obtained, and a standardized data set is obtained through space-time fusion of spatial interpolation-boundary correction and a sliding window smoothing method; constructing an urban area-typical district double-level accounting index system, and synchronously calculating a real object quantity and a value quantity; a model correction factor is called based on a GIS, indexes are quantified to grid units, a result is obtained through neighborhood analysis, and after parameters are calibrated, a multi-year ecological value trend map is generated in combination with the GIS; and outputting a customized result according to scenes such as ecological compensation, and outputting a final result after the customized result is qualified through three-level quality control verification. According to the method, the requirements of practical affairs such as ecological compensation and EOD projects on accounting precision and practicability can be met.

The invention relates to an unmanned aerial vehicle flight path planning method in a complex city low-altitude environment. The method comprises the following steps: S1, acquiring geographic information data of a target city area; s2, constructing a three-dimensional grid model of a target city region according to the geographic information data; s3, extracting obstacle boundary and no-fly zone information from the three-dimensional grid model, initializing the flight path of the unmanned aerial vehicle by adopting an A * algorithm, and obtaining a preliminary flight path planning result; s4, performing global optimization on the preliminary route planning result to obtain an optimized route planning result; and S5, if the optimized flight path planning result satisfies a preset condition, outputting the optimized flight path planning result, and if the optimized flight path planning result satisfies the preset condition, returning to S4 until the preset condition is satisfied. According to the invention, safe and efficient flight of the unmanned aerial vehicle in a complex dynamic urban low-altitude environment is realized.

The invention discloses an urban sound environment comprehensive treatment system based on the Internet of Things, which comprises a noise data monitoring and acquisition module, a noise data calculation and analysis module and a noise grading treatment module, and discloses an urban sound environment comprehensive treatment method. Real-time noise data monitoring and acquisition are carried out on specified urban areas, road sections and monitoring environments, and planning and layout can be carried out in the whole city; transmitting and preprocessing the real-time spectrum data by adopting a digital signal; determining the source of noise pollution on an established noise map by using a spatial positioning method according to the collected noise data through a set limit value; the method comprises the following steps: establishing a noise propagation mathematical model, analyzing a spatial relationship between noise data and a receptor, calculating and predicting noise levels of different areas, and comparing the noise levels with measured values; and different noise distributions are subjected to different-degree grading treatment according to the predicted value and the constructed noise map. According to the method, the performance of real-time noise monitoring and treatment feedback is improved.

The invention belongs to the field of urban ecological corridors, and discloses a construction method of a high-density urban area ecological-recreation function composite corridor network. The method comprises the following steps: inputting first phenological data and a first environmental factor into a maximum entropy model, and outputting grids of a suitable-to-grow area source ground of a target bird; adopting a natural breakpoint method to re-classify the grids of the source land of the suitable-for-growth area, and determining the grids as target ecological source land; determining a second environment factor greater than a preset correlation coefficient threshold and a contribution rate of the second environment factor by adopting a cutting method, and determining a landscape resistance surface of the target ecological source land based on the second environment factor and the contribution rate; calling a map software API interface, and screening out a target recreation space; constructing a cost grid surface of the target recreation space by adopting a spatial principal component analysis method and a cost factor; and determining an ecological corridor and a recreation type corridor by adopting a minimum path cost method, a landscape resistance surface and a cost grid surface. The urban ecological corridor with harmonious symbiosis of human and nature can be constructed.

The invention discloses a task allocation and optimization method, system and device based on a hierarchical task network, and the method comprises the steps: generating an unmanned plane task set through expert knowledge, and initializing task allocation parameters; then task level decomposition is carried out, a task allocation initial solution is established, and a to-be-executed sub-task set is formulated for each unmanned aerial vehicle; establishing a task constraint and an optimization target, and solving a task planning problem by adopting an improved genetic algorithm; and finally, a conflict-free task sequence is obtained through a conflict resolution and replanning algorithm. The system comprises an unmanned aerial vehicle cluster system, a ground station communication module, a central processing unit and a task planning solving module. An expert knowledge base for realizing task execution of the unmanned aerial vehicle cluster and a pre-execution instruction are stored in the device. By using the invention, the unmanned aerial vehicle cluster can efficiently execute the specified task set as required. The method can be widely applied to disaster rescue fields such as fields and urban areas.

The invention provides a wireless network coverage prediction method, which belongs to the technical field of wireless communication, and comprises the following steps: constructing a wireless signal prediction model, setting correction factors and loss parameters according to different geographical environments, and carrying out preliminary prediction on the network coverage condition; taking the test base station as a center, actually measuring base station wireless signal indexes of different distances and ranges according to a geographical environment, and fitting actual measurement data to obtain a network coverage condition; and performing iterative correction on the correction factor and the loss parameter of the prediction model for different geographical environments by combining the prediction data and the actual measurement data of the prediction model to obtain network coverage prediction data in different geographical environments. The method has the advantages that a prediction framework based on multi-model fusion effectively deals with the influence of a complex geographical environment, signal attenuation change caused by topographic relief and building shielding can be accurately quantified, and the pain point of prediction misalignment of a traditional method in variable terrains and dense urban areas is solved.

The invention discloses an urban construction intelligent monitoring system integrating geographic information and the Internet of Things technology, and belongs to the technical field of urban construction. In order to solve the problems that response to urban problems is lagged and development imbalance among urban areas is difficult to effectively reveal and solve, environment monitoring and infrastructure state evaluation are fused, the intelligent degree, real-time monitoring and quick response of urban management are remarkably improved, the processing speed of the urban problems is effectively improved, and the urban management system is suitable for urban management. The urban area is comprehensively evaluated through the evaluation index system, meanwhile, policy making is more refined through the area grading function, data support is provided for resource allocation, the timeliness of information is kept through continuous data updating, and the evaluation result is combined with a G IS system, so that the system is more efficient and reliable. The problem area and the dominant area are visually displayed, the balanced development of a city is promoted, and the overall living environment quality is improved.

The invention discloses an unmanned aerial vehicle cluster cloud side-end collaborative navigation control decision-making method, and belongs to the technical field of positioning navigation and control. The method comprises the following steps: an unmanned aerial vehicle cluster performs a task at a low altitude, and a ground operator performs real-time monitoring through a ground station cloud background; detecting the communication rate between the ground station cloud background and the unmanned aerial vehicle cluster; establishing a multi-target particle swarm optimization algorithm navigation decision evaluation function and constraint conditions to obtain a navigation decision solution of the unmanned aerial vehicle cluster; the unmanned aerial vehicle cluster flies according to the navigation decision solution; the whole-network communication rate between the unmanned aerial vehicles is monitored in real time in the unmanned aerial vehicle cluster; the unmanned aerial vehicle cluster carries out inter-vehicle communication interaction and carries out navigation flight according to a task allocation result; and the unmanned aerial vehicle individuals in the cluster carry out navigation decision solution calculation and autonomous navigation flight. The method can be effectively applied to unmanned aerial vehicle cluster cooperative task execution scenes with complex physical and electromagnetic environments such as urban space in the future, and the problem of accurate, reliable and robust navigation control capability of an unmanned aerial vehicle platform is solved.

The invention provides an urban area-oriented multi-source remote sensing data joint positioning method and system, and the method comprises the steps: obtaining a plurality of optical stereoscopic images and corresponding connection point data, constructing an optical stereoscopic block, and extracting laser elevation control point data and digital surface model data in the range of the optical stereoscopic block; calculating a ground coverage range of a plurality of optical stereoscopic images, mapping the ground coverage range to a grid unit by adopting a grid lookup table, obtaining a laser point accurate image space coordinate by combining the screened laser elevation control point data, and outputting a laser elevation control point set; calculating a laser elevation control point set based on a rational function model and affine transformation, and constructing a multi-source remote sensing data joint adjustment function model; and resolving the multi-source remote sensing data joint adjustment function model to form an observation error equation, performing heuristic search solution on the observation error equation, and outputting an adjustment model parameter result and a final connection point ground coordinate.

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 provides a multi-source rainfall data precision correction and downscaling method based on a causal relationship constraint, and relates to the field of meteorology and hydrology, and the method comprises the steps: building a feature causal relationship structure tree for multi-source rainfall data, and adding the constraint of a feature causal relationship on the basis of an LSTM deep learning correction model; a CLSTM rainfall data precision correction model under the causal relationship constraint is established, a GRASPS rainfall data downscaling method is fused, a rainfall correction and downscaling model Casual-GRASPS of the causal relationship constraint is generated, and a high-precision, high-resolution and high-time-space continuous fusion rainfall data set conforming to the statistical causal relationship is generated. The method has the advantages that a reliable method is provided for downscaling of meteorological data such as rainfall, and a reliable data basis is provided for fine-scale rainfall research of drainage basins and urban areas.

The invention discloses an intelligent management method and system for territorial space planning and a storage medium, and relates to the technical field of space planning, the method adopts a geographic information system (GIS) and multi-source information such as remote sensing data and satellite data to carry out three-dimensional modeling, and an efficient and accurate data support platform is provided for urban space planning. On the basis, environment data, traffic flow data and social influence data of urban areas are collected and integrated in real time by combining an API interface and an urban related system, and comprehensive real-time dynamic data support is provided for decision making. Through real-time simulation and data input, a planner can evaluate potential conflicts and problems among different urban space areas more visually and more accurately, a planning scheme is adjusted in time, and errors and low efficiency caused by information lag or incomplete data in traditional planning are avoided. By implementing the method, the urban planning cycle can be remarkably shortened, and the efficiency and scientificity of planning decision making are improved.

The invention discloses a smart city management system fusing artificial intelligence and big data, and relates to the technical field of smart city management, and the system comprises a data collection and feature extraction module which is used for obtaining and processing multi-source city data, extracting road network topological features, functional area distribution features and people flow dynamic features through a graph neural network, and obtaining a data collection and feature extraction module; generating and storing a structured feature vector; the cross-domain knowledge migration model construction module is used for constructing a city simulator, generating virtual data through a generative adversarial network and reinforcement learning, bridging a source domain and a target domain based on the virtual data, and realizing migration of source domain knowledge to the target domain by utilizing graph matching and an optimal transmission theory; and the model optimization and alignment module is used for aligning feature distribution of a source domain and a target domain through confrontation training of a domain discriminator and a gradient inversion layer, dynamically adjusting data weight, realizing seamless migration from virtual distribution to real distribution of the target domain, and realizing seamless knowledge migration from a mature urban area to a new urban area.

The invention relates to the field of risk assessment, and provides an overseas high-speed rail urban tunnel construction risk assessment method and system.The risk assessment method comprises the following steps that before tunnel construction, a risk plan is formulated, risk factors are decomposed, and a risk list is established; establishing an evaluation mechanism for the dynamic change of the risk factors, wherein the establishment method of the evaluation mechanism comprises the following steps: establishing a judgment matrix to obtain the weight of each factor, and performing dimension reduction processing and clustering on a risk factor matrix; and obtaining a comprehensive weight according to the cluster and the constructed judgment matrix, and calculating a risk assessment value after dynamically adjusting the comprehensive weight by adopting a reinforcement learning method. According to the overseas high-speed rail urban tunnel construction risk assessment method, targeted risk control measures are provided according to the risk levels and the risk types, and the risk occurrence possibility and consequences can be effectively reduced.

The invention relates to the technical field of image processing, in particular to an urban built-up area extraction method based on regional difference enhancement and local adaptive segmentation, which comprises the following steps of: preprocessing a regional remote sensing image data set, and constructing a luminous built-up area index image for multi-source data adjustment; optimizing the segmentation threshold of the segmented noctilucent built-up area index image by using an optimization algorithm; performing image processing on the obtained to-be-analyzed noctilucent built-up area index image by using the optimized segmentation threshold to obtain an urban built-up area image sequence; and performing time consistency processing on the obtained city built-up area image sequence to obtain a long-time sequence city built-up area result. According to the invention, the multi-source data is fused through the index image of the noctilucent built-up area, so that the difference between the urban area and the non-urban area is enhanced; and the segmentation threshold values of different cities are adaptively adjusted in combination with an optimization algorithm, so that the recognition accuracy is improved.

The invention relates to the field of urban public transport deployment, and provides a public transport network matching method and system based on an urban space structure, which divides urban areas by constructing a traffic model, identifies areas with dense demands on the basis of power consumption density, and provides a public transport network matching method and system based on the urban space structure. And the coverage condition of the existing public transport network on the areas with dense demands is evaluated, and uncovered areas are identified. For the uncovered areas, the information of stores and companies in the map data is used to subdivide the uncovered areas into a plurality of types of areas. A bus rapid transit system is additionally arranged in a commercial district to improve transportation efficiency, a peak period special line is additionally arranged in an office building area to meet commuting requirements, a customized bus special line is provided in an industrial district to meet requirements of specific periods and routes, and a conventional bus route is additionally arranged in a residential district to improve daily travel. Accurate matching between the urban space structure and the public transport demand is realized, and the coverage rate and efficiency of public transport service are improved.

The invention provides an urban blue-green infrastructure storage and drainage capability evaluation method, device, equipment and medium, and relates to the technical field of regulation and storage evaluation, and the method comprises the steps: constructing an urban blue-green infrastructure storage and drainage capability evaluation index system; the index system comprises a plurality of dimensions; the dimensions comprise a spatial pattern dimension, a safe operation dimension, a management and control support dimension and a storage and drainage effect dimension; screening evaluation indexes based on the evaluation index system and determining weights of the evaluation indexes; determining each first evaluation index value in the target area based on the evaluation index and the weight; performing three-level drainage partition division on the urban area, and determining a second evaluation index value on the drainage partition scale based on the first evaluation index value; and evaluating the storage and drainage capacity of the urban blue-green infrastructure based on the second evaluation index value. In the mode, the evaluation system is perfected, the evaluation dimension is increased, and the evaluation accuracy is improved.

The present invention relates to equipment that can realistically reproduce various types of airflow occurring in environments such as an urban area with tall buildings or regions with mountains and valleys, thereby enabling verification of the flight safety of a multicopter-type drone. The present invention comprises: a housing that provides a tunnel-shaped test space in which a plurality of blowers, together with controllers, are installed along the longitudinal direction of both sidewalls of the interior, the ceiling, and the floor, to generate airflows such as crosswinds, updrafts, downdrafts, and vortices; and an integrated control unit that selectively or simultaneously controls some or all of the plurality of controllers installed in the housing so as to form airflows in pattern corresponding to user input or settings.

The invention discloses an unmanned aerial vehicle inspection method and device based on a smart rod and a storage medium, and relates to the technical field of smart city infrastructure, and the unmanned aerial vehicle inspection method based on the smart rod comprises the steps that the smart rod determines a target unmanned aerial vehicle and an inspection scheme of the target unmanned aerial vehicle according to a triggered inspection task, the inspection scheme is sent to the target unmanned aerial vehicle; controlling the target unmanned aerial vehicle to execute the inspection scheme to collect inspection data, and returning the inspection data to the smart rod; the smart rod uploads the received inspection data to the cloud; the cloud carries out data comparison based on the inspection data and city reference data of an area where the intelligent rod is located, and determines a data comparison result; and if the data comparison result meets the abnormal condition, generating a recheck instruction and / or prompt information according to the data comparison result. The technical defect that the inspection efficiency is low due to the fact that the problems in the whole urban area cannot be responded in time is overcome, and the urban inspection efficiency is improved.

The invention provides a remote sensing building group depth detection and change intelligent evaluation method for urban planning, and the method comprises the steps: carrying out the multi-modal data collection of a target urban region, and carrying out the standardization processing, abnormal value elimination, multi-modal alignment and interference suppression processing, and obtaining a multi-modal data set; constructing a multi-branch hybrid network, and performing triplet feature extraction on the multi-modal data set through the multi-branch hybrid network to obtain a building group fusion feature map; performing change area detection and change type classification on the two building group fusion feature maps with different time phases to obtain building group change information; and constructing a dynamic evaluation index based on the urban planning core demand, and performing building group change evaluation in combination with the building group change information. According to the method, through multi-source and multi-modal data acquisition and processing, multi-branch hybrid network triplet feature extraction and change detection and dynamic evaluation index application, urban planning core requirements are adapted, and building group detection precision and change evaluation effectiveness are improved.

The invention discloses a geological disaster risk evaluation method and system based on big data, and the method comprises the following steps: S1, obtaining multi-source geological disaster data, including geological structure data, landform data, meteorological and hydrological data, human activity data and historical disaster data; s2, performing space-time alignment and standardization processing on the multi-source geological disaster data, and constructing a geological disaster space-time data set; s3, constructing a geological disaster risk evaluation model based on the deep learning network, wherein the geological disaster risk evaluation model comprises a feature extraction module, a space-time correlation module and a risk prediction module; s4, calculating a weight coefficient of each evaluation index by adopting a dynamic weight distribution method; s5, calculating a geological disaster risk index through the geological disaster risk evaluation model, and generating a risk grade zoning map; according to the invention, more intelligent geological disaster risk evaluation of the urban area is realized.