12 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 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 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 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.

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.

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.