25 results about "Urban surface" patented technology

The invention provides a high-temporal-spatial-resolution surface temperature reconstruction system for urban thermal environment refined monitoring, and relates to the field of electric digital data processing. Comprising a multi-source heterogeneous data intelligent acquisition and preprocessing module, an urban thermal environment multi-dimensional feature knowledge modeling module, a physical constraint deep learning surface temperature reconstruction module and an intelligent monitoring early warning and decision support module. The multi-source heterogeneous data intelligent acquisition and preprocessing module is responsible for collecting and preprocessing various remote sensing and ground observation data, and the urban thermal environment multi-dimensional feature knowledge modeling module constructs a knowledge system of an urban underlying surface, a three-dimensional form and a thermal process. The physical constraint deep learning surface temperature reconstruction module is used for realizing accurate reconstruction of high temporal-spatial resolution surface temperature, and the intelligent monitoring early warning and decision support module converts a reconstruction result into visual display, risk early warning and regulation and control decision suggestions; according to the system, high-precision, physically consistent and interpretable urban surface temperature reconstruction can be realized.

The embodiment of the invention discloses an urban surface feature three-dimensional model simplification processing method, system and device and a storage medium, and realizes efficient simplification of urban surface features and accurate reservation of structural information. Aiming at the problems of key feature loss, low semantic segmentation precision, easy model structure deformation and the like caused by a unified simplification strategy in a traditional method, a semantic segmentation technology based on deep learning is proposed, and a city scene is segmented into categories of buildings, vegetation and the like in combination with multi-feature fusion (geometry, color and topology) and a graph convolutional network, so that the segmentation accuracy is remarkably improved; a personalized simplification strategy is adopted, the edge collapse sequence of the building is restrained through an improved QEM algorithm, the simplification rate reaches 90% or above, a facade structure is reserved, the vegetation simplification rate exceeds 76%, and the overall form is maintained; and in combination with grid filtering and region growth optimization, noise is effectively eliminated, and the boundary is smoothed.

The application provides a method for determining inflow hydrograph of urban stormwater inlet, which is in line with actual urban runoff conditions and can effectively reduce the error of calculation results. The urban area is divided into multiple sub-regions according to the road network, and the net rainfall is discharged from the stormwater inlet, and the single stormwater inlet in the sub-region is calculated respectively; the urban surface runoff is calculated by using the comprehensive runoff coefficient method, and the runoff coefficient not greater than 1 is used to represent the runoff after the rainfall is intercepted and lost on the ground; for the rainfall in the small urban area, the ground water flow is in the form of slope flow and shallow concentrated flow; the catchment time is the sum of the two, the slope flow with a flow line not greater than 90m is calculated by using the Manning dynamics method to calculate the surface runoff flow time; after the slope flow stops flowing for 90m, the shallow concentrated flow with a water depth greater than 3cm appears at this time, and the stormwater inlet design flow inflow calculation formula is: Q = F * (P - S) / (T + 90) wherein Q is the stormwater inlet inflow, L / min; F is the average catchment area controlled by each stormwater inlet, m 2 .

The invention discloses a method for regulating and controlling a thermal environment by an urban form under coupling of human eyes and a bird's-eye view angle. The method comprises the following steps of: inverting a surface temperature based on a radiation transfer equation method and a Landsat-8 remote sensing image; carrying out fine extraction on urban ground feature information fused with multi-modal data; constructing a three-dimensional comprehensive representation system of the urban form under the coupling of a bird's-eye view angle and a human eye view angle; establishing an urban surface temperature prediction model fusing an urban two-dimensional form, an urban three-dimensional form in a bird's-eye view angle and an urban three-dimensional form in a human eye view angle under different spatial scales; and an optimal configuration method for regulating and controlling the surface thermal environment by the urban form under the fine scale and the rough scale is explored. According to the method, a scientific basis can be provided for quantitative regulation and control and space planning optimization of the urban thermal environment, and the effects of refined cooling and thermal environment improvement under urban space structure optimization and greening configuration adjustment are achieved.

The application provides a city surface area classification method and related equipment. The city surface area classification method comprises: obtaining a first target parameter of a to-be-determined city surface area, the first target parameter comprising a characteristic building height, an average building height, a building height standard deviation, a building coverage rate, an impervious surface ratio, a characteristic vegetation canopy height, an average vegetation canopy height, and a vegetation coverage rate of the to-be-determined city surface area; inputting an electronic map corresponding to the to-be-determined city surface area and the first target parameter into a city surface area classification model to obtain a city surface area type corresponding to the to-be-determined city surface area output by the city surface area classification model, the city surface area type being used for analysis or simulation of heat waves, air quality, and extreme weather of the to-be-determined city surface area. In the application, the fineness and determination accuracy of the city surface area type are improved.

This invention discloses a DEM data correction method that takes into account the characteristics of surface runoff routes, belonging to the field of digital elevation model construction technology. It solves the problem that in some scenarios, region-based DEMs can no longer accurately simulate the surface runoff process, thus preventing DEMs from playing their due role in urban surface runoff simulation. The key technical points are that the method includes the following steps: S1, data preparation: acquiring DEM data of the analysis area and marking the outlet and boundary positions in the DEM raster; S2, marking the flow direction of the raster cell at the outlet location as 0, indicating that the water flow cell of that raster does not flow to other raster cells; S3, finding the raster cells adjacent to the outlet cell and calculating the flow direction of each raster cell according to the principle of maximizing elevation difference. The effect is that this invention covers the key technology of raster elevation value correction based on surface flow direction characteristics, which can improve the application value of DEM data in the field of hydrological analysis.

The invention provides an urban surface and underground space flood simulation method based on a hydrodynamic model, and belongs to the technical field of urban flood disaster prevention and control and hydrological hydraulic engineering. The method comprises the steps of urban surface and underground space generalization processing, model element coupling association establishment, model element algorithm setting and model result calibration verification. The model established by the method has high-performance and high-precision computing power, can efficiently and accurately simulate the flowing form of flood in the urban complex underground space, and provides support for urban flood early warning.

This invention relates to a method for predicting urban surface temperature by combining LSTM and geographic information, belonging to the field of spatiotemporal modeling technology for urban surface temperature. It includes the following steps: S1: Acquire temporal LST and geospatial feature data to construct a spatiotemporal sequence input; S2: Based on geospatial features, generate the initial hidden state and cell state of a Long Short-Term Memory (LSTM) network through linear mapping; S3: Introduce spatial weights based on spatial distance to spatially weight and adjust the hidden state of the LSTM, modeling spatial heterogeneity; S4: Employ a spatial cross-attention mechanism to model the correlation between geographic location information and the hidden state, fuse spatial information, and output the prediction result. By sequentially introducing the above mechanisms, this invention achieves collaborative modeling of the temporal dependence and spatial non-stationarity of surface temperature without interfering with the LSTM temporal modeling process, significantly improving prediction accuracy and stability, especially in complex urban environments and cross-regional applications.

The invention discloses a high-resolution city high-temperature monitoring method coupled with a city climate model and deep learning, and belongs to the field of high-temperature monitoring and early warning. Aiming at the problems of low spatial resolution, insufficient utilization of multi-source information, untight fusion of a physical mechanism and deep learning and the like in the prior art, multi-source information such as high-resolution remote sensing images, ground meteorological observation, urban surface attributes and the like is combined, and urban climate physical modeling and deep learning algorithms are creatively integrated; and high-precision inversion and dynamic monitoring of the urban high-temperature spatial pattern are realized. According to the method, comprehensiveness, timeliness and scientificity of data can be considered, the monitoring precision and prediction capability of urban high temperature distribution are effectively improved, and powerful data support is provided for early warning, risk assessment and scientific management of urban high temperature disasters.

The invention relates to an experimental device and an experimental method for urban surface-underground space flood simulation, the experimental device comprises an underground space model, a surface model and a rainfall simulation device, and the underground space model is internally provided with a plurality of underground building models; the ground surface model is located above the underground space model and comprises a ground model and a channel model, a river channel model, a pipe network model and a ground facility model which are located on the ground model, and the channel model is communicated with the ground model and the underground building model; the rainfall simulation device is configured to spray water drops to the earth surface model and simulate extreme rainfall. When the experimental device is in a working state, the rainfall simulation device simulates extreme rainfall to the earth surface model, the pipe network model collects and discharges the rainfall to the river channel model, and the river channel model discharges water; after the drainage capacity of the pipe network model and the river channel model reaches a threshold value, the ground model and the ground facility model cooperatively simulate the evolution process of waterlogging on the ground surface, and after waterlogging accumulation is higher than a preset value, the waterlogging enters the underground building model from the channel model.

The application discloses a kind of high-resolution urban high temperature monitoring methods coupled with urban climate model and deep learning, belong to high temperature monitoring and early warning field.Aiming at the problems such as low spatial resolution of prior art, insufficient use of multi-source information and not close fusion of physical mechanism and deep learning, combined with high-resolution remote sensing image, ground meteorological observation and urban surface attribute and other multi-source information, innovatively integrate urban climate physical modeling and deep learning algorithm, realize the high-precision inversion and dynamic monitoring of urban high temperature spatial pattern.The application can consider comprehensiveness, timeliness and scientificity of data, effectively improve the monitoring precision and prediction ability of urban high temperature distribution, and provide strong data support for urban high temperature disaster warning, risk assessment and scientific management.

The invention discloses a surface temperature inversion method and system based on an urban complex geometric structure, and the method comprises the steps: obtaining a thermal infrared remote sensing image, an atmosphere contour line, WVC, DSM, land utilization data, and a spectrum library of a certain city; the thermal infrared reflection wave band data, the atmospheric profile bottom layer temperature, the atmospheric parameters, the sky visual factor and the urban surface material emissivity are obtained by preprocessing the thermal infrared reflection wave band data, the atmospheric profile bottom layer temperature, the atmospheric parameters and the urban surface material emissivity; introducing a sky visual factor to quantify an urban geometric structure, and providing an urban pixel thermal radiation model; calculating a surface temperature data set, an effective emissivity average value data set, an effective emissivity difference value data set and an on-satellite brightness temperature data set, constructing a simulation data set together with a sky visual factor, and dividing the simulation data set into a training data set, a test data set and a verification data set; fitting a local regression coefficient of the GWRSW model by using the training data set and the test data set; and inputting the verification data set into the GWRSW model to estimate the surface temperature of the urban pixel. According to the method, the land surface temperature inversion precision in a complex urban environment is effectively improved.

The invention belongs to the technical field of mountainous city heat island effect, and discloses a mountainous city heat island surface space-time evolution analysis method based on a local climate region, comprising the following steps: S1, data acquisition; s2, LCZ partition grid scale selection is carried out; s3, LCZ parameter selection is carried out; s3, LCZ classification is carried out; s4, LCZ internal type conversion is carried out; s5, inverting the surface temperature and calculating the heat island intensity; s6, analyzing the surface spatial form and space-time evolution characteristics; based on a local climate region theory, the mountain city surface heat island intensity and LCZ space-time evolution are analyzed and studied by utilizing remote sensing and geographic information system combined mapping and surface temperature inversion, so that the dynamic balance between urban sustainable development and ecological safety is maintained while the mountain city heat island effect is relieved, and the economic benefit is improved. And powerful support is provided for adaptation and mitigation measure planning of climatic changes in cities.

The application discloses an environment-friendly emission auxiliary system and implementation method for urban surface drilling, which comprises a collecting head and a grading bin, the collecting head is used for collecting the mixture of sludge and water discharged by an orifice anchoring pipe and introducing the mixture into the grading bin; wherein the grading bin comprises a main bin, a flow guide plate one, a flow guide plate two and an overflow brush plate; the front end side wall of the main bin is connected with the collecting head; the flow guide plate one and the flow guide plate two are vertically inserted into the main bin near the front end and the rear end, and the lower ends are not in contact with the bottom plate of the main bin; the overflow brush plate is vertically inserted into the main bin and is located between the flow guide plate one and the flow guide plate two, and the lower end is tightly attached to the bottom of the grading bin; the flow guide plate one, the flow guide plate two and the overflow brush plate divide the main bin into three spaces connected with each other from front to back, so that the water flow forms an S-shaped flow path. By using the auxiliary system, the mixture of sludge and water discharged by drilling is collected, and the sludge and water are quickly separated through two-stage filtration and two-stage sedimentation.

The invention discloses an urban surface deformation analysis method based on multi-scale blind source decomposition. The method specifically comprises the following steps: S1, obtaining a time sequence surface deformation field and various deformation inducement data; s2, performing spatial multi-scale decomposition on the time sequence earth surface deformation field, and separating the time sequence earth surface deformation field into a large-scale deformation component and a small-scale deformation component; s3, performing preliminary correlation analysis on the large-scale deformation component and the first type of deformation incentive data; s4, performing blind source separation on the large-scale deformation component confirmed by the preliminary correlation analysis, and decoupling the large-scale deformation component into a plurality of statistical independent deformation source signals; s5, performing deep correlation analysis on each decoupled deformation source signal and the corresponding first type of deformation inducement data; s6, performing association analysis on the small-scale deformation component and the second type of deformation inducement data; and S7, comprehensively analyzing the driving mechanism of the ground surface deformation of the research area. According to the method, the multi-source and multi-scale superposition earth surface deformation signals can be effectively decoupled, and the scale dependence driving mechanism of the signals is disclosed.

The invention discloses an estimation method for predicting urban surface temperature based on urban green land information acquired by remote sensing. The method is characterized by comprising the following steps: acquiring remote sensing image data of a target city; preprocessing the remote sensing image data, constructing a label data set, calculating the surface temperature of the target city, and extracting the built-up area range of the target city; urban green land is extracted in the built-up area range; calculating a vegetation index and a landscape index based on the urban green land distribution range; the vegetation index and the landscape index serve as independent variables, the surface temperature serves as a dependent variable, an optimal independent variable is obtained through screening, three machine learning models are constructed through the optimal independent variable to serve as prediction models, and the prediction precision of the three prediction models is compared to obtain an optimal prediction model; the method has the advantages that the surface temperature is reflected quantitatively through the urban green land information, a basis for green land planning is provided for livable environment construction of a city, and the stability of a prediction model is kept.

The invention discloses an urban surface runoff pollution cascade ecological interception and purification system and an application method thereof, and relates to the technical field of ecological slope protection and rainwater management. An included angle exists between the bottom of a buffer module and the horizontal plane, and a first side wall and a second side wall are oppositely arranged and located on the two sides of a bottom water guide element correspondingly; in the direction from the first side wall to the second side wall, the height of the bottom water guide element is gradually reduced, the upper portion of the bottom water guide element is a water permeable layer capable of permeating rainwater, the permeated water enters the bottom water guide element, and the multi-stage filter layer in the bottom water guide element can treat the water entering the bottom water guide element. The multiple buffer modules are sequentially arranged in the first direction, the first direction is parallel to the first side wall, the adjacent bottom water guiding elements are communicated, in the first direction, the heights of the multiple bottom water guiding elements are gradually reduced, and the bottom water guiding element located at the lowest end is communicated with a rainwater collecting module. Construction is convenient, the rainwater retention time can be prolonged, and the rainwater purification efficiency is improved.

This invention relates to the field of intelligent computing technology for urban hydrology and hydrodynamics, and discloses a method for coupled modeling of urban flooding surface and drainage pipe network based on a surrogate model. The method first constructs a surface-pipe network coupled hydrodynamic numerical model based on topographic data, drainage pipe network structure data, and rainfall input, generating training data such as node head, pipe segment flow, and surface water depth. Based on this, a surrogate model for the underground pipe network and a surrogate model for the surface are constructed respectively, achieving efficient prediction of the pipe network hydrodynamic state and surface water depth distribution. Furthermore, a feature modulation coupling mechanism driven by the pipe network state is proposed. By establishing a spatial mapping relationship between pipe network nodes and surface grids, the pipe network hydrodynamic state is transformed into rasterized features, and modulation parameters are generated to linearly modulate the intermediate features of the surface model. This achieves implicit coupled modeling of the underground drainage process and surface flood evolution without explicitly solving the water exchange equation. Finally, through serial coupling of the joint training and inference stages, a fast, stable, and physically consistent simulation of the urban surface-pipe network system is achieved. This method effectively reduces computational complexity and improves simulation efficiency and stability, and can be applied to scenarios such as urban flooding early warning, drainage scheduling, and risk assessment.

The method is mainly used for carrying out in-situ determination on the infiltration characteristics of the urban hardened underlying surface, and can be suitable for all hardened underlying surfaces with weak water permeability and partial water permeability. The instrument is based on the principle of measuring soil infiltration through a constant water head method, the instrument is fixedly sealed in a to-be-measured area through a sealing material, then a Markov bottle injected with water is placed in a limiting area of a bottom infiltration disc, the Markov bottle and the bottom infiltration disc are connected through a rubber pipe, and the experiment process is controlled through a valve. The infiltration rate and the accumulated infiltration amount are calculated by recording the change condition of the liquid level in the Markov bottle along with time, so that the infiltration characteristic is determined. The instrument can clearly judge the change condition of the water head height in the infiltration process at any time; the double-motor bottle circularly and alternately supplies water, so that the measuring process is more convenient, and the result is more accurate; a thin water layer at the bottom enables the result to be more accurate; the measured data is free from correction, and the test data can be compared and analyzed with the data measured by the traditional infiltration instrument; the whole instrument is small in size, light and convenient to carry.

This application provides a machine learning-driven method for analyzing the intensity mechanism of urban surface heat islands, integrating a local climate zone framework. The method includes: acquiring geospatial data of an urban agglomeration region; dividing the urban agglomeration region into multiple LCZ type units based on the geospatial data under the LCZ1-LCZ10 / LCZA-LCZG classification system to obtain LCZ classification results; determining SUHII based on the geospatial data and LCZ classification results to obtain SUHII spatial distribution data; extracting two-dimensional and three-dimensional urban morphology factors based on multi-source geospatial data; training an XGBoost model based on the SUHII spatial distribution data and urban morphology factors to obtain an optimized XGBoost model; performing a two-scale analysis on the optimized XGBoost model using the SHAP analysis method to obtain two-scale analysis results; and generating thermal environment regulation strategies for the entire urban agglomeration and various LCZ units based on the two-scale analysis results.

The invention relates to the technical field of surface temperature downscaling methods, and discloses an urban high-resolution surface temperature downscaling method driven by multi-source data and GEE cloud, and the core idea of the method is to construct a universal LST downscaling model which can be suitable for urban high-resolution LST cartographic tasks of various satellite data sources. A fine-scale LST model suitable for an urban complex surface structure is constructed, and multi-scale time sequence urban LST monitoring and mapping can be achieved. According to the method, for a coastal city region with a large-area water body, the accurate water body temperature can be inverted, the method has good applicability, and the precision of coastal city LST downscaling is greatly improved; the key technical problem of ground surface temperature information extraction in the urban complex ground surface environment is solved. Based on a powerful GEE cloud platform, various data are directly obtained, processed and shared in an online programming mode, the tedious links such as traditional remote sensing image downloading and preprocessing are effectively avoided, and the time and economic cost is remarkably reduced.

The application provides a kind of urban vegetation aboveground biomass accurate inversion method and system considering vegetation type, carry out research area vegetation aboveground biomass calculation, including corresponding biomass calculation after plot sampling according to different research areas;High-resolution optical data and spaceborne photon counting lidar data preprocessing, fine extraction of urban vegetation information, high-resolution remote sensing image data spectral feature extraction and optical-biomass inversion sub-model construction, vegetation height data extraction of spaceborne photon counting lidar data and spaceborne LiDAR-biomass inversion sub-model construction;Under the support of fine classification information of urban surface vegetation, integrate biomass inversion sub-model, obtain integrated inversion model by training urban vegetation biomass sample data, which fuses urban surface vegetation horizontal information and three-dimensional structure information, and carry out large-scale urban vegetation aboveground biomass accurate mapping.

The application discloses a kind of based on SAR and optical image fusion's timing urban surface change detection method, comprising: step 1, with optical image and SAR image as data source, through SAR image auxiliary filling and spatiotemporal kriging interpolation realizes optical image cloud processing, combined with DEM topographic correction, orthorectification and dynamic time warping realizes multi-source image spatiotemporal alignment, and constructs data set.Step 2, constructs heterogeneous branch Transform deep learning model, extracts optical and SAR heterogeneous features by multi-scale time series convolution, realizes adaptive fusion using channel attention mechanism, and outputs change detection result after long time series dependence is captured by encoder.Step 3, the final change mask map and change time map are obtained by cascading spatial denoising.The present application makes full use of the complementary advantages of SAR and optical image, and realizes high-precision, high-timeliness building construction process detection in complex surface scene.

This invention provides a method for predicting urban surface water accumulation points based on large-scale model technology, comprising: collecting facility data corresponding to each drainage facility in the city to be tested; establishing an urban drainage model of the city to be tested by combining the city's topographic data; training the urban drainage model with precipitation to obtain urban drainage volume information and urban water accumulation information corresponding to different precipitation intensities; analyzing the regional water accumulation corresponding to each urban area of ​​the city to be tested based on the urban drainage volume information and the urban water accumulation information; determining the water accumulation distribution information of the city to be tested; identifying several water accumulation points corresponding to different precipitation intensities in the city to be tested; and generating a precipitation-water accumulation point distribution map of the city to be tested. This invention can accurately predict the entire process of urban surface water accumulation, helping emergency personnel to take preventive measures in advance, thereby effectively reducing the losses caused by urban rainfall and water accumulation.

This application proposes a machine learning-based method for assessing urban surface flooding risk, comprising: determining the surface characteristics of the target geographic area based on high-resolution remote sensing imagery, lidar point cloud data, and drainage network data; using the surface characteristics, precipitation time series, and initial surface water depth before precipitation as input information for a water depth prediction model, outputting the first predicted water depth for each location unit within the target geographic area; simulating the water diffusion process in the target geographic area to obtain the second predicted water depth for each location unit at a specified prediction time; and determining the flooding risk level of the target geographic area based on the second predicted water depth. This method achieves real-time, efficient, and accurate prediction of urban surface flooding risk for complex underlying surfaces.