105 results about "City region" patented technology

The invention discloses an urban heat island effect decision-making method based on satellite data and artificial intelligence, and relates to the technical field of artificial intelligence, and the method comprises the steps: obtaining satellite remote sensing data and urban basic data of a target urban region, the urban basic data comprising population density distribution, historical intervention effects, infrastructure information and urban function partition data; preprocessing the satellite remote sensing data to generate a standardized surface temperature distribution image, and based on the surface temperature distribution image, identifying an urban heat island area and dividing the intensity grade of the urban heat island area; the method has the advantages that satellite remote sensing data and urban multi-dimensional basic data are fused, dynamic scoring and optimization algorithms are combined, the heat island area is accurately recognized, the optimal intervention strategy is generated, the problems that a traditional method is insufficient in data utilization and poor in decision-making scientificity are solved, and the method has the advantages of improving treatment accuracy and resource utilization efficiency.

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 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 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 provides an urban area dynamic representation learning method based on context representation learning, and belongs to the field of urban function and spatio-temporal data mining, and the method comprises the steps: S1, constructing a continuous dynamic graph according to public transport data; s2, an urban area dynamic representation extractor under multiple time dimensions is constructed, the extractor is used for extracting structure information and semantic information in the continuous dynamic graph, nodes in the continuous dynamic graph are converted into representation vectors of fixed dimensions, and the representation vectors are dynamically updated along with time; s3, pre-training the extractor by using a plurality of prediction tasks, including designing a corresponding loss function to guide the extractor to learn more universal urban region characterization; and S4, utilizing the trained plurality of extractors to obtain the dynamic representation of the urban area, and combining the dynamic representation of the urban area with context representation learning to be applied to a plurality of downstream tasks. According to the method, comprehensive space-time semantic information can be captured, and the dynamic region representation of the city is dynamically captured from multiple time granularities.

The invention provides an urban facility updating method and device based on a large language model, equipment and a medium, and relates to the technical field of urban planning, and the method comprises the steps: inputting a division cue word into a plan large language model, and obtaining a plurality of communities corresponding to a to-be-planned urban region outputted by the plan large language model; the division prompt words are used for indicating the plan big language model to divide the to-be-planned city area into a plurality of communities; the plan big language model is constructed based on a general multi-modal big language model; for each community, determining a target facility updating sub-scheme corresponding to the community through a plan big language model; and based on the target facility updating sub-schemes corresponding to all communities, determining an urban facility updating scheme corresponding to the to-be-planned urban area. According to the technical scheme, the urban facility updating scheme is constructed through the large language model, participation of experts and residents is not needed, negative influences caused by human factors are eliminated, and the efficiency and accuracy of planning the urban facility updating scheme 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 invention provides an urban area attribute determination method and device, equipment, a medium and a program product, relates to the technical field of artificial intelligence, and aims to solve the problem that urban area characterization methods in related technologies lack fine-grained sensing ability. The method comprises the following steps: acquiring first group movement data, wherein the first group movement data is used for representing position change information of a target user group in a plurality of areas within a preset time period; performing state updating on initial state vectors of a plurality of regions based on the first group mobile data to obtain region dynamic representations of the plurality of regions at each moment in the preset time period; and performing region attribute prediction based on the region dynamic representation of the plurality of regions at the target moment to obtain target attribute values of the plurality of regions at the target moment. According to the method, fine-grained urban region representation can be generated, and the accuracy of region attribute prediction is improved.

The invention provides an urban multi-modal large model training method, device and equipment and a medium, and relates to the technical field of artificial intelligence. Constructing an image-text interlaced data set according to the satellite image description text corresponding to each satellite image and the streetscape image description text corresponding to each streetscape image; and training the city multi-modal large model based on the image-text interlaced data set, and adjusting perceptron parameters corresponding to the perceptron in the city multi-modal large model and large language model parameters corresponding to the large language model to obtain a trained target city multi-modal large model. According to the technical scheme, the problems that in the prior art, a training data source is single, and images and texts are independently processed are solved, the trained target city multi-mode large model has good understanding on the city environment, and the space structure and function characteristics of a city can be comprehensively captured.

The invention provides an urban extreme rainfall risk assessment and drainage optimization method fusing a composite threshold and a Chicago method, and relates to the field of power grid disaster prevention and reduction planning and meteorological and hydrological analysis, and the method comprises the following steps: employing a quartile method and a composite threshold method to recognize and classify extreme rainfall events; constructing an extreme rainfall pattern model, and determining a minute-level rainfall intensity curve by using the extreme rainfall pattern model; generating an extreme rainfall spatial-temporal distribution characteristic spectrum according to the extreme rainfall event set in combination with a spatial interpolation technology fused with a deep learning model; the drainage capacity of the urban drainage system is evaluated, and the urban drainage system is optimized according to the evaluation result. The method has high adaptability, can be popularized to urban areas with different terrains and climate conditions, provides an efficient technical tool for coping with extreme weather events under the climate change background and improving the disaster prevention and reduction capacity of a power system, and has important social and economic values.

The present invention provides a sponge city design method and system, relating to the field of sponge city data processing technology. The method comprises obtaining information; simulating the waterlogging situation in the urban area using an agent-based model based on the information to obtain a waterlogging distribution map; constructing a graphical model based on the drainage system data; and constructing a drainage pipeline layout optimization model based on the graphical model and the waterlogging distribution map. The drainage pipeline layout optimization model includes an objective function and constraints, the objective function including minimizing the sum of the construction cost of the drainage pipeline and a penalty for insufficient drainage capacity. The constraints include flow balance constraints at the drainage points, capacity constraints of the drainage pipelines, drainage capacity constraints in the flood-prone areas, and drainage capacity constraints in the drainage bottleneck areas. The method then solves the drainage pipeline layout optimization model based on hypergeometric distribution sampling. The present invention takes into account the differences between different urban areas and significantly improves the design results of sponge cities.

The invention discloses a vehicle insurance quotation optimization method and device, equipment and a storage medium. The method comprises the steps that first evaluation data are acquired, and the first evaluation data at least comprise vehicle basic data, personnel basic data and behavior basic data corresponding to a target vehicle, and regional risk data and historical compensation data of a city region to which the target vehicle belongs; and according to the first evaluation data and a pre-trained target fusion pricing model, performing comprehensive modeling by combining supervision of the prefecture and city golden supervision bureau, insurance industry collaboration, a prefecture and city active adjustment business structure, previous three to five-year historical compensation payment data of the prefecture and city, head guarantee pricing data and third-party prefecture and city same-industry settled compensation payment data, and determining a quotation evaluation factor. And determining a vehicle insurance quotation corresponding to the target vehicle according to the quotation evaluation factor. The vehicle insurance fine pricing system is optimized, the accuracy, flexibility and efficiency of vehicle insurance pricing are improved, the method is more suitable for second and third companies, and better-quality services are provided for clients.

The invention discloses an anti-seismic toughness evaluation method and system oriented to an urban area. The method comprises the following steps: obtaining a structure vulnerability function strength median variable under each classification; obtaining a median of a structure vulnerability function intensity median value variable; constructing an empirical vulnerability function of the single target structure, and determining damage states of all single structures in the urban area under the action of a preset earthquake; obtaining toughness levels corresponding to all the monomer structures of the urban area; and respectively calculating a region toughness index based on the individual number ratio and a region toughness index based on the building area ratio, and obtaining the seismic toughness level of the urban region. According to the scheme of the invention, two indexes of different dimensions, namely the number of the building individuals and the building area, are combined, and a comprehensive regional toughness level index is formed through weighted summation, so that the overall anti-seismic toughness condition of the urban region is reflected more comprehensively and more accurately, and the evaluation result is systematic and has operability.

The present invention discloses a travel scheduling optimization method based on the rigid demands of residents, comprising the following steps: S1: Obtain administrative division boundary data and the AMap_Adcode_citycode urban coding table of Amap; S2: Determine the required Point of Interest (POI) data based on administrative regions and urban coding; S3: Use Pandas to quickly preprocess the POI data; S4: Construct an average accessible distance model between regions; S5: Classify various types of POIs; S6: Process the obtained SDE results through ImageJ; S7: Use Matlab to construct a difference equation to calculate the equilibrium point; S8: Perform a weighted average on the equilibrium point values; S9: Determine a travel optimization scheduling plan for the result obtained from the weighted average. Based on more objective POI data of various types of transportation, life, and address facilities, the present invention conducts SDE analysis in a selected urban area, calculates the weight coefficient of the travel intensity generated by the rigid demands of residents in the research area, so as to formulate a travel scheduling optimization method suitable for the residents in the research area.

The invention relates to the technical field of spatial and temporal distribution reconstruction and early warning of VOCs, in particular to an urban area VOCs spatial and temporal distribution reconstruction and early warning method, which comprises the following steps: S1, collecting and preprocessing an urban area VOCs original data set; s2, according to the pollution type of the VOCs in the urban area; s3, constructing a regional VOCs reconstruction and prediction model based on the hybrid CNN-LSTM; s4, setting initial parameters of the model, and training the constructed model to obtain optimal model parameters; s5, performing precision verification on 10 DeepAir models generated after training by using the overall verification set in the step 2; s6, deploying the trained regional VOCs reconstruction and prediction model into the urban region; s7, using the regional VOCs reconstruction and prediction model to generate a complete VOCs concentration distribution reconstruction map in the region; according to the method, accurate reconstruction and prediction of the regional VOCs distribution diagram are realized, and urban regional VOCs spatial and temporal distribution monitoring and early warning required by current practical application can be met.

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 belongs to the technical field of smart cities, and particularly relates to a smart city environment management method and system based on digital twinning, and the method achieves the high-precision collection of species distribution, vegetation coverage, surface temperature and wind speed data in a city region. By constructing a dynamic space model containing a species-environment interaction relationship, the dynamic change of surface thermal radiation absorption and release caused by increase and decrease of different species is successfully simulated; furthermore, by calculating hourly influence coefficients and iteratively deriving a ten-year microclimate evolution trend, a priority ranking scheme for species protection or regulation is finally generated. According to the method, the understanding depth and response speed of urban managers for ecological environment changes are greatly improved, real-time calibration based on latest monitoring data becomes possible, and scientificity and foresight of urban management decisions are ensured.

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 provides an interpretable multi-mode urban area representation method. The method comprises the following steps: extracting POI features, remote sensing image features, OD inflow features and parking lot features as node features, and extracting OD flow interaction features and distance features as edge features; the correlation between the regions is described through a graph structure, the dependency relationship between different dimensions in the regions is captured through a multi-head attention mechanism, and processed node features and edge features are input into a variational graph auto-encoder VGAE to be processed to generate representation vectors; reconstructing node features and edge features from the representation vector using a decoder; an interpretation module is used to interpret graph substructures and node features that each dimension representing the vector contributes most. According to the method, the high-dimensional input data can be extracted to generate the low-dimensional representation vector, the characteristics of the urban area can be captured more comprehensively, a large amount of label data is not needed, the diversity and precision of characteristic representation are improved, and the interpretability and transparency of the representation vector can also be improved.

The present invention discloses a method for optimizing the layout of urban LID facilities based on SOBOL sensitivity analysis. The method is characterized by first establishing a SWMM model of the urban area under study, constructing a functional relationship between node overflow and LID scheme construction costs within the SWMM model, and then using the SOBOL method to conduct a global sensitivity analysis of the LID facility layout scale parameters for each sub-catchment area. Based on the results of the parameter sensitivity analysis, the LID facility layout area for the selected sensitive variables is adjusted and optimized. The Pareto optimal solution sets before and after parameter optimization are compared and analyzed to obtain a more optimal urban LID facility layout scheme. Compared to existing technologies, this method improves the reduction effect of node overflow by increasing the LID layout area in sensitive areas and reduces the LID layout area in insensitive areas to reduce scheme construction costs. Consequently, the present invention has higher computational efficiency and makes the LID spatial layout more rational.

The invention discloses an urban low-carbon cooperative regulation and control method and system based on multi-source data, and relates to the technical field of low-carbon cooperative regulation and control, and the main scheme is as follows: judging whether the comprehensive carbon emission of an urban region meets the standard or not according to the comprehensive carbon emission, and executing a regulation and control strategy in the energy field when the comprehensive carbon emission does not meet the standard; judging whether the annual runoff control rate of the urban area meets the standard or not according to the annual runoff control rate, and executing a regulation and control strategy of the building field if the annual runoff control rate does not meet the standard; calculating full life cycle carbon emission of different types of LID facilities according to the carbon emission data of the LID facilities; judging whether the LID facilities of the urban area meet the standard or not according to the full-life-cycle carbon emission, and executing an LID full-cycle regulation and control strategy if the LID facilities of the urban area do not meet the standard; after the corresponding regulation and control strategy is selected, effect evaluation of the regulation and control strategy is carried out regularly, and the execution effect of the regulation and control strategy is judged. The problem that a traditional method is single in data dimension is solved.

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 present invention discloses an automatic urban morphology measurement method based on a spatial entropy fourth-order matrix model, which relates to the field of urban design technology. The method performs fourth-order matrix modeling based on the relative morphological information of the target building and surrounding buildings, and then classifies the buildings according to the clustering algorithm of the fourth-order matrix. The city is partitioned according to the spatial entropy value measurement, thereby identifying problematic buildings and urban areas. The present invention can meet the work needs of large-scale urban renewal and automatically generate a morphological transformation plan specific to each building. It has the characteristics of large scale and high precision, thereby improving the work efficiency of urban renewal.

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 present invention discloses a method for evaluating low-carbon cities using mixed planning interval entropy weighting, which relates to the fields of urban management and low-carbon evaluation technology. The method includes real-time collection and preprocessing of indicator data, establishing a user behavior prediction model based on the indicator data to predict the user's indicator data; dynamically adjusting the weight distribution of subjective and objective evaluation indicators based on the predicted indicator data, constructing a comprehensive evaluation model to calculate the comprehensive score of each area in the city; and formulating optimization measures for each area based on the comprehensive score. A support vector machine is used to establish the prediction model, and dynamic weight adjustment is used to ensure that the evaluation results are highly consistent with real-time data, avoiding the lag problem of traditional static evaluation. By dynamically adjusting the weight distribution and constructing the comprehensive evaluation model, a more accurate and flexible low-carbon city evaluation is achieved. The comprehensive scores of urban areas are calculated using a nonlinear transformation function, which can better reflect the changes in the marginal effects of evaluation indicators under different load conditions.

The present invention relates to the technical field of takeaway order scheduling management, and particularly relates to a method and system for instant delivery management of takeaway orders, including: an information acquisition module for acquiring store location information and order information within a city area; a coordinate construction module for constructing store location coordinates and order location coordinates; an order statistics module for analyzing the number of orders to be delivered by a store, the number of orders already delivered by a store, and the order delivery interval; a distribution analysis module for analyzing store distribution parameters; a delivery analysis module for analyzing delivery distribution parameters and store delivery parameters; an order analysis module for analyzing the distance of cloud orders, and also for analyzing order allocation parameters according to the cloud order distance, delivery distribution parameters, store delivery parameters, and store distribution parameters; and an order allocation module for allocating order information to each store. The present invention realizes precise allocation management of takeaway orders.

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.