67 results about "City region" patented technology

The invention discloses an autonomous decision-making system of a multi-level intelligent agent in an urban area and an operation method thereof. Each end-side decision-making unit broadcasts an equipment state variable to a side-side decision-making unit; the side decision-making unit determines a global reference state and generates a control instruction of each end decision-making unit; after the equipment executes the control instruction, each end-side decision-making unit updates an equipment state variable according to the real-time operation data of the equipment, and when an abnormal event is judged to occur, a side decision-making unit predicts a decision-making variable of each piece of equipment based on a collaborative optimization model of an equipment target and an urban area target; the cloud regulation and control platform predicts a global consistency variable based on the collaborative optimization model of all the side decision units; predicting a regulation and control instruction of each side decision-making unit according to a global dynamic optimization target under a system operation constraint; and the side decision-making unit decomposes the regulation and control instruction into the control instruction of each end decision-making unit, so that the problems that the global cooperative capability of the urban regional integrated energy system is weak and cooperative scheduling is not timely under extreme events are solved.

The invention discloses an urban scale rainfall absorption and vegetation configuration collaborative optimization evaluation method, which belongs to the field of urban rainfall flood management optimization, and comprises the steps of performing integration and rainfall event identification on basic data of an urban region; physical mechanism inference is carried out by constructing multiple physical gating such as energy consistency, wet surface dissipation and geometric radiation sensitivity, and underlying surface hydrological response characteristics are obtained; constructing and solving a robust optimization model in combination with the underlying surface hydrological response characteristics and a preset planning target, and determining an optimal vegetation configuration scheme; and carrying out back substitution on the optimal vegetation configuration scheme to carry out closed-loop check and effect evaluation. According to the method, the hydrological response characteristics of the urban underlying surface can be accurately quantified, the vegetation configuration scheme with the definite achievement probability and the controllable risk is formulated under the uncertain condition, and the scientificity and the reliability of urban water resource toughness planning are 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 belongs to the technical field of three-dimensional modeling, and discloses a city building three-dimensional model refined construction method based on oblique photography. Comprising the steps of collecting inclined image data of a to-be-modeled urban area and performing data cleaning to obtain a modeling image data set; carrying out projection consistency analysis, identifying a boundary dislocation region and removing the boundary dislocation region to obtain a dislocation removal data set; shielding area identification is carried out, shielding compensation is carried out based on an identification result, and an enhanced three-dimensional point data set is output; executing detail reservation to obtain a triangular facet data set; normal optimization is carried out, plane adjustment is carried out on the triangular surface data set based on an optimization result, and a three-dimensional grid data set is obtained; constructing an initial three-dimensional model, and performing map mapping on the initial three-dimensional model to obtain a detail map set; constructing a fine building three-dimensional model in combination with the detail map set and the initial three-dimensional model; and the boundary definition, the structural integrity and the texture fineness of the urban building three-dimensional model are improved.

The application provides a kind of urban ecological restoration priority order identification method, device and electronic equipment, the method comprises: obtaining the spatial distribution data of multiple ecosystem service indicators in different years in target urban area and carrying out standardization processing;According to the spatial distribution data of multiple ecosystem service indicators in different years after standardization processing, determine the ecosystem service cluster value ESB of each pixel corresponding to the preset grid scale in different years in target urban area;According to the ESB of each pixel in different years and impervious surface index value ISA, determine the ecological restoration target year period;For any target pixel, according to its ISA change rate in the ecological restoration target year period, and the ESB change rate of its neighborhood pixel in the ecological restoration target year period, determine its ecological restoration priority order. Prior area of urban ecological restoration area can be divided, improve the efficiency and economy of urban area ecological restoration.

The invention discloses a path planning method based on a city engine and an unreal engine. The method comprises the following steps: completing basic data preparation, rule definition, model generation and visual presentation and analysis, and establishing a city model; secondly, constructing a vehicle motion model in an unreal engine, and simulating weather; the three-dimensional static model and a dynamic model in an unreal engine are fused to generate an urban environment; carrying out geographical gridding on the urban area; introducing a trafficability map and physical restrictions of the vehicle, and setting the trafficability map of the vehicle; an improved A * algorithm based on geographic gridding is introduced, path planning is conducted, the improved A * algorithm is used, and path optimization is achieved through a heuristic function and a penalty function; and outputting the generated path plan.

The invention discloses an adaptive adjustment method and system for urban lighting under digital twinning, and relates to the technical field of urban intelligent lighting, and the method comprises the steps: carrying out the twinning reconstruction of a lighting equipment network in an urban region, and determining a lighting twinning body; through urban distributed sensing, state initialization is carried out on a lighting twinborn body, lamp control constraint elements are introduced, each lighting device is used as a light environment intelligent agent, local game decision is executed, a lighting strategy is determined, and the lamp control constraint elements at least comprise comfort income, energy-saving income and glare punishment; and performing multi-thread instruction generation according to the lighting strategy, and performing lighting management and control on the lighting equipment network. According to the invention, the technical problems that the traditional urban illumination regulation and control are difficult to accurately match the actual demand and cannot balance the multi-aspect targets are solved, and the technical effects of adaptively adjusting the urban illumination, balancing the multi-aspect targets and improving the accuracy and effectiveness of illumination management and control are achieved.

The invention relates to the technical field of data evaluation, and discloses an urban vegetation cooling effect evaluation method and system based on multi-source remote sensing data, a terminal and a storage medium, and the method comprises the steps: obtaining urban region data of a city, and carrying out the preprocessing and feature extraction to obtain a feature extraction result; inputting the feature extraction result into a vegetation cooling effect evaluation training model, outputting a feature sorting result, a feature analysis result and an ecological threshold recognition result, obtaining a vegetation cooling effect evaluation prediction result, and adjusting the vegetation cooling effect evaluation training model to obtain a vegetation cooling effect evaluation model; and acquiring to-be-evaluated city data of a target city, performing preprocessing and feature extraction to obtain a target feature extraction result, and inputting the target feature extraction result into the vegetation cooling effect evaluation model to obtain a vegetation cooling effect evaluation result. According to the method, through multi-source remote sensing data fusion, the urban vegetation cooling effect is accurately depicted, so that the accuracy of a vegetation cooling effect evaluation result is improved.

The invention discloses a city expansion mode recognition method based on algebraic topology and a morphological measure theory, and relates to the technical field of crossing of city planning, geographic information systems and mathematics. Comprising the following steps: constructing a spatio-temporal data cube and a landscape height function, extracting topology invariant features of an urban area by using a continuous coherence algorithm, and generating a persistent graph; then, through calculating generalized curvature measurement and generating a distribution histogram, extracting geometric features of the urban plaque boundary; the topological features and the geometric features are fused to construct a topological-measurement fingerprint; and finally, using a k-nearest neighbor algorithm to classify the city expansion modes in the metric space. According to the method, the dependence of a traditional method on empirical parameters is fundamentally overcome, the method has extremely high tolerance on data noise and form irregularity, and a more powerful and reliable analysis tool is provided for urban expansion mode recognition.

The invention discloses a regional scale city form multi-objective optimization method and system based on proxy model assistance, and the method comprises the steps: carrying out the discretization of a city region, obtaining a plurality of grid units, and obtaining a global gene vector and a local gene vector of each grid unit; decoding the gene vector to obtain an urban morphological feature matrix; obtaining the total energy demand of the region by using the proxy model; constructing a multi-objective optimization model by taking minimization of the total energy demand of the region and maximization of the development intensity as objective functions; on the basis of a multi-objective evolutionary algorithm, population individuals are converted into a feature matrix, the individual fitness is evaluated by utilizing an agent model, iterative search is performed on gene vectors, and a Pareto optimal solution set is obtained and serves as an urban form optimization scheme. According to the method, through an innovative hierarchical gene coding technology and an efficient proxy model evaluation technology, on the premise that the planning feasibility is guaranteed, the tradeoff relation between the regional scale urban form and the energy performance is rapidly, accurately and widely explored.

The invention provides an urban heat island effect space-time evolution mode mining method and system fusing time sequence clustering and trend extraction. The method comprises the following steps: acquiring remote sensing image data of a research region in a research year section; dividing urban areas in the research area based on the land coverage data; dividing a rural reference area based on the urban area in the research area, the land coverage data, the normalized vegetation index and the elevation data; calculating the surface urban heat island intensity of each pixel in the urban area in the research year section in combination with the surface high temperature data and the country reference area; performing clustering processing on each pixel in the urban area based on a calculation result of the surface urban heat island intensity by adopting a time sequence clustering algorithm to obtain a plurality of space partitions; and constructing an average earth surface urban heat island intensity time sequence of each space partition and carrying out trend analysis to obtain urban heat island effect space-time evolution mode characteristics, including earth surface urban heat island intensity evolution trends and significance levels of the earth surface urban heat island intensity evolution trends of each space partition.

Embodiments of the present application provide a method and device for quantifying the stress effect of urban expansion on green space. The method comprises: obtaining urban expansion data and green space data of a plurality of urban areas; the urban expansion data comprises a plurality of urban expansion characteristics, and the green space data comprises a plurality of green space characteristics; clustering the plurality of urban areas according to various green space characteristics in the green space data of the plurality of urban areas; for any one cluster, training a machine learning model using the urban expansion data and the green space data of the urban areas in the cluster to obtain a green space data prediction model corresponding to the cluster; and using an interpreter model to interpret the prediction model to determine the positive and negative and strength of the stress effect of various urban expansion characteristics on green space characteristics under the cluster. In this way, the stress effect of urban expansion on green space can be effectively quantified by combining machine learning and an interpreter.

The invention discloses an urban area traffic road network subarea outer boundary drawing method, which comprises the following steps of: based on intersection position information and road section information which are preset on a map, slicing and refining the whole road network according to a topological relation by utilizing a slicing principle, and taking two adjacent intersections with the topological relation as minimum topological units; taking a boundary external expansion range as a limiting condition for drawing a region boundary, and obtaining a region external boundary of a minimum topological unit by using an envelope principle and a Convex Hull convex hull algorithm; and performing de-duplication and union on the plurality of minimum topological units according to the road network topological relation, and finally forming a crawler-type boundary set of the traffic road network of the whole urban area. According to the method, the situation that different region boundaries are overlapped due to the fact that the region boundaries are caught in concave packet traps during region boundary drawing can be avoided, and high adaptability and flexibility are achieved; and meanwhile, different scene requirements are met by setting different boundary limit thresholds.

The invention discloses an urban building three-dimensional model construction method and system, and relates to the technical field of three-dimensional modeling, and the method comprises the following steps: S1, dividing a target urban region into a plurality of modeling sub-regions, and obtaining first building point cloud data, first image data and material reflectivity data; s2, after a scanning prediction evaluation model is established and trained, calculating a shielding effect coefficient and a reflection absorption effect coefficient of the rth modeling sub-region, evaluating to output a first classification result and a second classification result, and generating a corresponding strategy; and S3, after the corresponding strategy is implemented, extracting a dynamic target feature and a repeated pattern region feature of the rth modeling sub-region to calculate and obtain a dynamic target interference coefficient and a repeated pattern region coefficient, and evaluating to output a third classification result and a fourth classification result.

The invention relates to the technical field of urban intelligent management and control, and provides an urban composite environment health risk assessment and intervention method and system, and the method comprises the steps: obtaining the geographic space data and ground observation data of an urban region; constructing a collaborative environmental stress index, and calculating an SESI predicted value; performing grid construction on the urban area to construct a graph structure, performing feature learning by using a GNN algorithm, predicting a node SESI value, and generating a collaborative stress map; constructing a comprehensive vulnerability evaluation model, calculating a vulnerability index, and identifying a high-vulnerability region; fuzzy comprehensive evaluation and TOPSIS methods are fused, intervention measures of the high-fragility area are sorted, and an intervention scheme is generated. According to the method, the nonlinear interaction modeling and the high-resolution graph neural network are fused to realize accurate recognition of the composite environment health risk, and the intervention strategy is optimized through the multi-criterion decision model, so that scientific regulation and dynamic optimization of the urban intervention scheme are realized.

The invention relates to big data processing, and particularly discloses an urban spatial layout planning auxiliary method and system based on big data mining, which deeply integrates static function attributes represented by urban point of interest (POI) data and a dynamic use mode disclosed by rasterized time sequence population activity data. And constructing a static function-dynamic activity joint coding feature for each urban grid unit. On the basis, the baseline level of each unit feature is learned by further utilizing spatial dynamic and static aggregation analysis, and intelligent weight distribution and information aggregation are performed according to the dynamic change and spatial proximity relation, so that the complex association and dynamic influence among urban space units are effectively captured; and aggregated coding representation capable of macroscopically representing the overall spatial features of the target city region is generated. And finally, based on the highly-condensed and information-rich aggregation coding representation, realizing accurate classification of the urban functional areas.

The application discloses a kind of urban electric logistics truck team's cooperative scheduling optimization method and system, equipment, medium, it is based on Markov decision model to construct truck team cooperative scheduling model, by charging, distribution, transport capacity scheduling and receiving order are included into the action space of agent, comprehensively consider the influence of path planning, charging, freight demand, transport capacity scheduling and other factors to vehicle scheduling strategy, greatly improve the cooperative scheduling optimization effect;And, by the vehicle scheduling action selection mechanism in each region is converted into 0 / 1 programming problem, greatly improve the flexibility of action real-time selection, can be well adapted to the scheduling scene of delivery demand and charging cost change;Meanwhile, each grid area of city distribution area is taken as object and is scheduled and optimized, it is favorable to alleviate city area power load imbalance phenomenon.

The invention discloses a city-level parking resource optimization method and system, and relates to the technical field of intelligent transportation. The method comprises the following steps: acquiring real-time operation data and a geographic position of a parking lot in a target city area; constructing an urban parking topological graph which takes each parking lot as a node and takes a connecting road as a side; obtaining the real-time weight of each edge based on the real-time traffic data; calculating a real-time parking pressure value; nodes with the real-time parking pressure values larger than or equal to a first preset pressure value are high-pressure nodes, and nodes with the real-time parking pressure values smaller than the first preset pressure value are judged as potential nodes; performing pressure conduction simulation to obtain a simulated parking pressure value; the potential nodes and the high-pressure nodes with the simulated parking pressure values larger than the second preset pressure value and smaller than the first preset pressure value are determined as potential congestion clusters; and for each potential congestion cluster, calculating a price adjustment coefficient based on the simulated parking pressure value, obtaining a real-time parking rate, and generating a drainage instruction. According to the invention, the configuration accuracy of city-level parking resources can be improved.

The invention relates to the technical field of carbon ecological compensation, in particular to a city area carbon ecological compensation optimization method based on multi-scenario simulation, which comprises the steps of generating two groups of basic data sets based on geographic information data, respectively and dynamically simulating industrial and agricultural activity changes, and calculating carbon emission and absorption net increment values by combining system dynamics modeling. And determining a carbon deficit sensitivity coefficient and a carbon surplus stability coefficient, and generating a carbon ecological compensation factor to formulate an optimization scheme. The method can comprehensively quantify the carbon ecological compensation requirements of each city region, remarkably shortens the simulation period, solves the problem that a single model is difficult to reflect the regional heterogeneity and is insufficient in representativeness, and improves the scientificity and practicability of a simulation result.

The invention discloses a city update intelligent monitoring method, system and platform based on double-branch cross fusion and multi-view dynamic attitude, and the method comprises the steps: generating and obtaining first data corresponding to a target city region, and generating corresponding second data in combination with cube projection; creating a first model corresponding to the first data, and based on the first model, generating corresponding third data in combination with a Cross-MTF feature fusion operator; establishing a second model corresponding to the first data, and generating corresponding fourth data; constructing a third model corresponding to the city, and generating corresponding fifth data based on the third model; based on the third data, the fourth data and the fifth data, performing fusion processing on the multi-view dynamic attitude index, and generating corresponding sixth data; wherein the sixth data is urban update thermodynamic diagram data, and the system and the platform corresponding to the method can realize automatic and refined perception and analysis of urban facade changes.

This invention pertains to computational fluid dynamics and machine learning technologies, specifically a physical mechanism-guided heterogeneous extrapolation method for high-resolution, meter-level dynamic fields of urban buildings. The method includes the following steps: selecting representative urban blocks to form a sample block set; using a WRF model to provide a regional-scale meteorological background field for the target city; driving a LES model to perform flow field calculations on the sample block set, generating high spatiotemporal resolution flow field data for the sample blocks, and pairing this data with corresponding meteorological background data to generate a training database; constructing and training a deep learning model for latent space injection of meteorological information, establishing an end-to-end nonlinear mapping relationship from the coupling characteristics of urban geometry and meteorological boundaries to the LES fine flow field through a dual-path encoding path and latent space injection mechanism; and dividing the target urban area into multiple local computational units to extrapolate the high-resolution dynamic field of the entire urban area. This invention balances computational efficiency, prediction accuracy, and physical consistency, enabling rapid response to urban dynamic fields.

The invention provides a ticket amount distribution optimization method and system under a city region and suburban railway mixed ticket system, and belongs to the technical field of traffic operation management. According to the invention, the limitation of a single ticket system is broken through, online and offline dual-system cooperation is realized through a dynamic ticket amount conversion mechanism, the problem that commuting passenger flow is extruded is effectively solved, and the passenger flow satisfaction rate is obviously improved; the problem of solving a high-dimensional optimization problem of ticket allocation is solved, an improved particle swarm algorithm (fusing dynamic parameter adjustment and a diversity restart mechanism) is designed, the calculation efficiency is greatly improved, and minute-level optimization of a hundred-station-level road network can be supported; the passenger flow characteristics are accurately matched, a dual-system demand coupling model is constructed based on OD passenger flow distribution, the seat utilization rate and the transportation income are remarkably improved, and a four-network integration ticket system intercommunication project is directly supported.

The application belongs to the technical field of data processing, and discloses a time-aware urban area representation generation method, system, terminal and storage medium based on dynamic condition modulation, which comprises the following steps: acquiring static urban data and OD flow data of an urban area; performing static feature extraction on the static urban data and fusing the extracted static features to obtain a regional static semantic representation; constructing a regional dynamic state sequence according to the OD flow data and encoding the regional dynamic state sequence to obtain a regional dynamic condition; generating scaling parameters and offset parameters according to the regional dynamic condition, and performing dimension-by-dimension modulation on the regional static semantic representation according to the scaling parameters and offset parameters to obtain a time-aware urban area representation. The regional static semantic representation is modulated from static to dynamic by using the regional dynamic condition, so that the generated time-aware urban area representation can reflect the long-term stable attributes of the region and the dynamic evolution law of the region.

The invention discloses a method for generating city-level basic geographic entity data, and relates to the field of geographic information engineering and city spatial data processing. According to the method, aiming at the problems of single data source, insufficient multi-source data fusion, topological relation missing, extensive quality control and the like in the existing basic geographic entity data generation, high-precision and high-integrity data generation is realized through multi-source data fusion and a standardized process. The method comprises the specific steps of data source collection and screening, multi-source data preprocessing, entity attribute assignment, topological relation construction, quality inspection and correction, data standardization and storage and the like. Meanwhile, embodiment cases in different scenes such as a high-density urban area, a new urban area and a hilly urban area are provided, it is verified that the method can effectively improve data precision, ensure attribute integrity and topology consistency, can provide a reliable spatial information basis for multiple fields such as smart city construction, and is suitable for urban areas with different terrains and building densities.

The application discloses a kind of quick cross path scheduling methods of inter-city area customized public transport system, the shortest distance method between regions is used to build start point area end point set and end point area start point set, to generate start point area travel sequence Yo by insertion method, to generate end point area travel sequence Yd by insertion method, then the Yo and Yd are optimized by quick exchange method, finally the minimum value of start point × Yo × Yd is solved, which is the final inter-city area customized public transport system target path, with the shortest bus travel distance as the target;The method does not use probability distribution, effectively avoids the fluctuation of the final path result, according to the characteristics that the distance between nodes in different regions is much larger than the distance between nodes in the same region, the inter-regional traffic node set is established, and it is used as the end point of the start point region and the start point of the end point region, respectively, to maximize the range of optimal solution, effectively reducing the overall calculation scale.

The invention discloses an urban area change detection method fusing multi-modal data and a knowledge graph, and relates to the technical field of area change detection, and the method comprises the steps: obtaining multi-source geographic data, such as a remote sensing image, a streetscape image, an interest point and the like, and carrying out the preprocessing; inputting data such as streetscape image texts obtained through preprocessing into a knowledge graph ontology structure, and constructing a multi-mode dual-temporal city knowledge graph; constructing a multi-modal knowledge representation framework by using a bidirectional cross attention mechanism to obtain knowledge graph vector features; and constructing a regularized cosine similarity index by using the vector features of the knowledge graph, and analyzing a city region change detection result. According to the method, the multi-mode double-time-phase city knowledge graph is constructed, and the change condition of the city region is accurately sensed; a multi-modal knowledge representation learning framework based on a bidirectional cross attention mechanism is designed, and deep fusion of features between image information and text information is promoted.

The invention discloses an urban inland inundation prediction method based on multi-source sensor fusion, and the method comprises the steps: collecting multi-source original time sequence observation data in a target urban region, and carrying out the preprocessing of the observation data, and obtaining a preprocessing time sequence data set; calculating the reliability weight of each sensor in combination with historical observation data, and performing weighted correction on the observation value of each sensor based on the reliability weight; splicing the corrected observation values of the sensors according to a unified time axis vector, constructing a multi-source joint observation vector, and inputting the multi-source joint observation vector into a pre-training neural network time sequence prediction model; in the model training process, an innovative loss function is adopted, the sensor reliability weight is introduced into loss calculation, and weighted optimization of key sensor data is achieved. According to the method, multi-source sensor information can be fully utilized, the influence of abnormal data of a single sensor is weakened, high-precision and real-time prediction and early warning of urban inland inundation are realized, and scientificity and reliability of urban disaster prevention decision making are improved.

The present application belongs to the technical field of urban computing and data mining, and in particular to a multi-view urban area embedding method based on spatial function consistency. The method comprises the following steps: S1: multi-view data collection and processing; S2: spatial function consistency intra-view representation learning; S3: intra-regional cross-view interaction; S4: inter-regional cross-view interaction; S5: double-feature attention fusion; and S6: downstream task application. The present application can effectively suppress information interference caused by noise edges and weakly related connections, and improve the accuracy and stability of regional local structure modeling. At the same time, the present application enhances the global semantic connectivity and cross-view information propagation capability of the regional representation. On this basis, through a double-layer attention fusion mechanism, adaptive integration of multi-view features and inter-regional associated features is realized, further improving the discriminability, robustness and generalization capability of the final regional embedding representation.

The present application belongs to the technical field of smart city, and particularly relates to a smart city environment management method and system based on digital twinning. The method not only realizes high-precision collection of species distribution, vegetation coverage, ground temperature and wind speed data in the urban area, but also successfully simulates the dynamic changes of ground thermal radiation absorption and release caused by the increase and decrease of different species by constructing a dynamic spatial model containing species-environment interaction. Further, by calculating the hourly influence coefficient and iteratively deriving the microclimate evolution trend in a ten-year period, a priority ranking scheme for species protection or regulation is finally generated. This method greatly improves the understanding depth and response speed of urban managers on ecological environment changes, makes real-time calibration based on the latest monitoring data possible, and ensures the scientificity and foresight of urban management decisions.

The invention discloses an urban digital twinning online channel modeling method, and relates to the technical field of cross fusion application of a digital twinning technology and wireless channel modeling. Comprising the following steps: 1) acquiring geographic information system data of a target city area, establishing a city three-dimensional static model through Unity software, and performing equal-proportion reconstruction and reflector parameter addition, so that electromagnetic characteristics of the model are matched with a real physical world; 2) acquiring satellite remote sensing images, constructing a data set of various urban scenes, and training the data set by adopting Swin Transform to realize identification and segmentation of urban communication scenes; 3) based on a scene identification and segmentation result, completing electromagnetic parameter matching of a communication scene in combination with a 6G universal channel model, and performing static electromagnetic reconstruction by using a ray tracing technology of Wireless Insite software; and 4) integrating the scene segmentation module, the channel characteristic prediction module and the three-dimensional city model to a Unity Platform.According to the method, the channel characteristic change in a complex city scene can be accurately captured.