205 results about "Ground surface temperature" patented technology

The invention discloses a land degradation supervision method based on remote sensing monitoring, and relates to the technical field of land degradation monitoring. The method is used for solving the problems of hidden degradation risk identification and dynamic propagation simulation. According to a multi-temporal thermal infrared remote sensing image, daily variation characteristics of surface temperature are extracted through spectral analysis, a recessive salinization risk distribution map is constructed by combining measured data of soil salinity, and interference of vegetation coverage is overcome. And multi-source remote sensing and geographic data are fused, dominant driving factors significantly associated with the salting risk are screened, and the degeneration cause is clarified. And in combination with ecological restoration potential and treatment resource constraints, calculating an antagonism index of a driving factor and the restoration potential, and dividing degradation threat levels. And a natural hydrology and human activity dual-path propagation model is constructed, a risk space-time evolution process is simulated, and a future hotspot migration path and an optimal blocking window are predicted. According to the method, accurate early warning and active prevention and control of land degradation are realized through a technical chain of risk identification, driving analysis, threat grading and dynamic simulation.

The invention provides a plateau lake agricultural non-point source pollution treatment method which comprises the following steps: acquiring vegetation indexes and surface temperature field data by using a remote sensing satellite, and generating a pollution source space thermodynamic diagram in combination with water quality and soil parameters of ground sampling points; performing space-time alignment and data fusion on the thermodynamic diagram and real-time runoff and soil permeability data acquired by the hydrological sensor network, and constructing a structured pollution migration database; on the basis of the database, a hybrid neural network model embedded with physical constraints is utilized to predict pollutant concentration distribution within 72 hours in the future; inputting the predicted value into a multi-stage optimization controller, and generating a control parameter set comprising treatment intensity, engineering parameters and a fertilization ratio; generating a treatment strategy map covering the drainage basin through a GIS; and deploying an Internet of Things monitoring node to collect the treated water quality data to form a closed-loop control link. The treatment efficiency and effect can be improved, the treatment cost is reduced, and the negative influence on the ecological environment is reduced.

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 invention relates to the technical field of alpine sand soil moisture leakage estimation technologies, and particularly discloses an alpine sand soil moisture leakage estimation method which comprises the following steps: S1, multi-source data acquisition: acquiring 0-110cm layered soil moisture content and temperature data by using a cold-resistant soil hydrothermal sensor array, and calculating the moisture content and temperature data of the layered soil; synchronously acquiring Sentinel-2 remote sensing data to extract vegetation coverage and land surface temperature, and recording regional freezing and thawing cycle times and biological crust coverage types; s2, key parameter inversion: calculating a permeability coefficient correction factor kFTC = alpha * N (1 / 2) through a multi-expression programming model based on the freezing and thawing cycle number N. According to the method for estimating the soil moisture leakage amount of the high and cold sand land, the limitation of a traditional model based on steady-state water flow and empirical parameters is broken through by quantifying the dynamic effect of freezing and thawing cycles on the porosity of a 30-80 mm soil layer and the water blocking effect of 23.41%-30.39% when the biological crust coverage degree exceeds 30%; and the influence of the low-temperature freezing and thawing cycle and the biological crust on the leakage mechanism of the high and cold sand land is accurately depicted.

The invention provides an interpretable random forest surface temperature downscaling method coupled with meteorological factors, and relates to the technical field of space downscaling. The method comprises the following steps: acquiring low-resolution surface temperature remote sensing data and multi-source feature data of a target area, and performing radiometric calibration, unified projection and normalization to form a preprocessed data set; dividing earth surface type subareas according to the earth surface classification map, solving a radiation correction coefficient by combining historical meteorological statistics, and performing subarea-level correction to obtain a subarea correction data set; training a random forest regression model by using the partition correction data set, and establishing a nonlinear mapping relationship between the surface temperature and the multi-source features; inputting high-resolution feature data to deduce a preliminary high-resolution surface temperature, and performing residual interpolation correction to obtain a final result; and the SHAP algorithm is adopted to explain feature contributions, so that the result precision, stability and interpretability are improved.

A method for spatial reconstruction and downscaling of all-sky surface temperature belongs to the field of remote sensing science and technology. It contains the following main steps: acquisition and preprocessing of multi-source remote sensing data and atmospheric driven data, spatial reconstruction of all-sky surface temperature data, and spatial downscaling of all-sky surface temperature data. The operability and universality of the present invention in practical applications will promote the popularization and application of surface temperature data in the fields of climate change, dynamic monitoring and assessment of hydrological and ecological environment, land surface radiation budget and energy balance, and further promote the development of thermal infrared remote sensing science.

The invention provides a water and soil loss early warning method and device based on climate change, and relates to the technical field of machine learning, and the method comprises the steps: collecting the surface temperature data and freeze-thaw state data of a target region in real time, and determining the freeze-thaw cycle state of a surface active layer based on a set freeze-thaw state criterion; calculating the melt water runoff generation amount of the surface active layer based on the freeze-thaw cycle state, and calculating the comprehensive runoff yield by combining rainfall forecast data; dynamically calculating the unit surface scouring force based on the comprehensive runoff runoff yield, and judging whether the unit surface scouring force reaches an erosion starting state or not based on a soil instability limit shear threshold value under the freezing-thawing interface; and triggering water and soil loss early warning when the superposing effective melt water runoff volume of the earth surface freeze-thaw conversion state reaches a preset percentile and the earth surface unit scouring force exceeds a soil instability limit shear threshold value. According to the invention, early warning can be carried out according to climate change for water and soil loss in alpine regions.

The invention relates to a land surface temperature refined downscaling method combined with space-time hysteresis, and belongs to the technical field of remote sensing image processing. The method comprises the following steps: firstly, carrying out preprocessing and LST inversion on Landsat8TIRS data, and calculating MODIS LST and DEM data; then, constructing a time sequence data set, and establishing a non-linear relationship between MODIS LST and Landsat8NDVI, NDBI, MNDWI and SRTM DEM data and a time-space lag relationship between two continuous time periods of LST based on a time-space weighted lag random forest model; and finally, downscaling the LST by using a common Kriging interpolation method to obtain high-resolution surface temperature distribution. According to the method, the precision and reliability of the LST downscaling result are effectively improved, and a scientific basis is provided for climate regulation, ecological protection, thermal environment management and the like of mountainous cities.

The invention provides a drought monitoring method and device for a target area, electronic equipment and a storage medium, and relates to the technical field of drought monitoring, and the method comprises the steps: for each pixel corresponding to the target area, obtaining meteorological reanalysis data, a normalized vegetation index and a target land surface temperature corresponding to the pixel in a first preset time period; for each pixel, determining a hot boundary and a cold boundary corresponding to the pixel based on the meteorological reanalysis data and the normalized vegetation index corresponding to the pixel; and determining a drought monitoring result corresponding to the target area based on the hot boundaries, the cold boundaries and the target surface temperature corresponding to all the pixels. According to the technical scheme, for the target area, the hot boundary and the cold boundary are determined pixel by pixel through the meteorological reanalysis data, the situation that the weather conditions of the large area with the complex terrain are different is considered, accurate drought monitoring can also be carried out in the large area with the complex terrain, the applicability of drought monitoring is widened, and the drought monitoring accuracy is improved. Moreover, the method does not need to divide the region, and is simpler in steps.

The invention relates to the technical field of remote sensing technology and ecological environment restoration evaluation, in particular to a mine ecological restoration project effect evaluation method based on multi-source remote sensing data. The method comprises the following steps: carrying out multi-source remote sensing data collaborative acquisition and preprocessing on an acquired mine restoration area to obtain a noise reduction data set; performing multi-dimensional cooperative sensing processing on the noise reduction data set to obtain a multi-dimensional cooperative sensing data set; performing soil spectral feature separation and extraction on the multi-dimensional collaborative sensing data set to obtain soil spectral feature data; calculating a surface temperature distribution diagram according to the soil spectral characteristic data; performing surface soil water content inversion according to the soil spectral characteristic data and the surface temperature distribution diagram to obtain a surface water content distribution diagram; estimating the water content of the deep soil according to the surface water content distribution diagram to obtain soil water profile data. According to the method, the remediation input benefit is improved by quantitatively inverting the physical parameters of the soil and dynamically analyzing the rainfall response process.

The invention provides a surface water heat flux integrated estimation method, system and equipment, and relates to the technical field of hydro meteorological remote sensing, and the method comprises the steps: according to the target surface temperature data, target net radiation data, target air temperature data, target vegetation coverage data and target soil moisture data of a target region, calculating the target surface temperature data; constructing to obtain target surface environment key parameters corresponding to the target region; and inputting the target earth surface environment key parameters into the surface water heat flux integrated estimation model to obtain target earth surface sensible heat, latent heat and soil heat flux estimation values which are simultaneously output by the surface water heat flux integrated estimation model and correspond to the intra-day time scale of the target region, the surface water heat flux integrated estimation model is obtained by training a physical information neural network machine learning model based on the sample surface water heat flux observation data corresponding to the sample surface water heat flux key parameters and the sample surface water heat flux key parameters. According to the method, the surface water heat flux estimation accuracy is improved.

The invention discloses a method for evaluating rock mass fracture freeze-thaw fracture damage risk based on surface temperature, which comprises the following steps: S1, carrying out data acquisition on a target area based on a multi-source sensor carried by an unmanned aerial vehicle platform, and obtaining fracture geometric parameters, daily surface minimum temperature, daily surface maximum temperature, daily temperature difference and fracture moisture conditions; s2, on the basis of the geometric parameters of the crack, constructing a crack frost heaving force analytical model; obtaining a quantitative relation between the stress intensity factor and the environment temperature; s3, the I-type fracture toughness of the target rock mass is measured, and the critical crack initiation temperature of the rock mass is determined; determining the number of critical freezing and thawing cycles; s4, on the basis of the daily earth surface minimum temperature, the daily temperature difference, the fracture moisture condition and the critical crack initiation temperature, the annual accumulated effective freezing and thawing cycle number is obtained; and calculating a freeze-thaw damage ratio, and carrying out risk quantification and grading. According to the invention, the problem of inaccurate evaluation caused by limitation to local monitoring and lack of an environment factor and material parameter integrated evaluation framework in the prior art is solved.

The invention belongs to the field of meteorological data processing, and provides a surface temperature space-time fusion method and system based on a conditional generative adversarial network, and the method only needs to predict a coarse-resolution image of a date and another fine-resolution reference image at any time in the same region for model input, and does not limit the reference acquisition time. Therefore, the influence of time inconsistency on a prediction result can be greatly reduced, and meanwhile, a multi-scale mechanism is integrated to adapt to the input of reference image pairs with different resolutions. From the perspective of quantitative indexes and visual evaluation, the CGAN-LSTFM has robustness for reference images at different moments and resolution ratios of different scales, and meanwhile, due to less input and limitation, the model also has more flexibility.

The invention discloses a surface temperature medium and long term forecasting method based on a multi-scale TransFuse pyramid network. The surface temperature medium and long term forecasting method comprises the steps of S1, acquiring original LST time sequence data and performing preprocessing; s2, constructing a three-dimensional time delay embedded feature tensor for the data preprocessed in the S1 by adopting a stepped cross-node tensor feature reconstruction method; s3, on the basis of the three-dimensional time delay embedded feature tensor constructed in the S2, meteorological features of different time scales are extracted in parallel through a dynamic multi-resolution convolution kernel group; s4, performing five-stage cascade operation on the meteorological features of different time scales extracted in the S3 to realize deep fusion of cross-scale features, and performing high-level semantic information integration through a global feature pyramid; and S5, finally outputting a multi-step prediction result through a regression prediction framework based on the fusion features obtained in the S4. According to the invention, an extensible technical normal form is effectively provided for high-precision meteorological prediction in a limited computing power scene, and important application potential is shown in actual business deployment.

The invention belongs to the technical field of data processing, and discloses a Mars rover non-contact trafficability estimation method and system based on thermal inertia, and the method comprises the steps: obtaining a ground surface temperature time sequence and environment parameters of a target region; constructing a thermal inertia estimation model, and inputting the earth surface temperature time sequence and the environmental parameters into the thermal inertia estimation model to obtain a thermal inertia estimation value of the fire soil; constructing a thermal inertia and trafficability index relation model, inputting the thermal inertia estimation value of the fire soil into the thermal inertia and trafficability index relation model, and outputting a predicted trafficability index; and determining a trafficability state grade according to the predicted trafficability index, and generating a navigation control suggestion, so that the Mars rover passes through the target area according to the Mars rover navigation control suggestion. According to the method, the trafficability index of the Mars rover is predicted by extracting the thermal inertia estimation value of the fire soil in the target area, the evaluation precision of the Mars rover passing through the target area can be effectively improved, and the driving safety of the Mars rover is ensured.

The invention provides a high-resolution surface soil moisture estimation method and device, a terminal and a medium. The method comprises the following steps: obtaining space-time seamless coarse resolution moisture data and space-time seamless surface temperature data according to obtained soil moisture data, brightness temperature data and downscaling factor data; processing the obtained downscaling factor data and the space-time seamless surface temperature data through space aggregation resampling to obtain coarse resolution downscaling factor data and high resolution downscaling factor data; inputting the space-time seamless soil moisture data, the coarse resolution scale factor data and the high resolution downscaling factor data into various machine learning models to obtain various high resolution space-time seamless moisture estimation data; and performing fusion processing on the multiple high-resolution space-time seamless moisture estimation data through a Bayesian triangular cap fusion method to obtain high-resolution space-time seamless surface soil moisture estimation data. And high-resolution and time-space seamless estimation of the surface soil moisture is realized.

The invention discloses a land utilization change comprehensive feature analysis method based on multi-source heterogeneous data fusion, and relates to the technical field of remote sensing and geographic information processing. Building volume, building density, night light, surface temperature and population density are aligned under unified coordinates and resolution; constructing an average building height according to volume / density and coding and recording quality; uniform noctilucence is realized through partition three-order mapping; interpolation is carried out by adopting fractional learning under the missing measurement mask, and pixel-level uncertainty is output; uniformizing the annual temperature; extrapolation is carried out through multi-scale space-time small block coding-decoding containing flux conservation and Laplace constraint, and uncertainty is given; implementing uncertainty weighted principal components according to the five indexes to obtain a comprehensive strength index; concentric buffering, set splitting regression and partial response confidence band recognition threshold values are combined. The method has the advantages of cross-period comparability, physical consistency, traceable uncertainty and the like.

The invention discloses a debris flow prediction method based on multi-source remote sensing, and the method comprises the following steps: 1, data acquisition and preprocessing: obtaining target region data through satellite remote sensing, and eliminating terrain shadow interference; step 2, feature extraction: extracting vegetation coverage and surface temperature from the optical image, extracting deformation magnitude and time sequence from InSAR data, and extracting gradient, slope direction, curvature and terrain humidity index from DEM terrain data; 3, performing multi-dimensional feature fusion, and constructing a multi-dimensional feature vector; 4, model construction: establishing a debris flow susceptible area identification model, and fusing the vegetation-terrain coupling characteristics of the optical image, the deformation dynamic characteristics of the InSAR and the terrain stability characteristics of the terrain data; 5, carrying out susceptibility partitioning and early warning, dividing risk grades based on probability values output by the model, and triggering graded early warning responses; according to the method, the multi-dimensional feature space is constructed through the collaborative interaction of the multi-source remote sensing data and the fused data.

The invention develops a surface temperature intelligent downscaling seasonal characterization and analysis method based on GeoAI machine learning. According to the method, interpolation and downscaling can be carried out on a surface temperature missing area influenced by cloud layer factors through urban landscape characteristic index products and incomplete satellite surface temperature inversion data. Through a specific stratified sampling mechanism, the problems of training deviation and over-fitting caused by insufficient representativeness of a data training set are solved, and a surface temperature product which takes seasons as units and has a 10m resolution ratio and a complete space-time sequence is generated. Verification shows that the method can accurately compensate for surface temperature data loss caused by factors such as cloud cover, ensures space integrity and time continuity of the surface temperature data, truly reflects the surface temperature of a research area when a satellite passes, and effectively represents seasonal space-time evolution of an urban thermal environment. Besides, the invention further develops a GeoAI-based feature importance intelligent evaluation framework, landscape features strongly related to the surface temperature can be analyzed and screened, scientific support and reference are provided for accurate characterization, modeling and optimization of the urban thermal environment, and the method has important application value.

The invention discloses a space-time analysis method and device for an urban thermal environment, relates to the field of urban thermal environment space-time change and driving mechanism research, and aims to solve the problem of poor accuracy of deducing a dynamic evolution rule of the urban thermal environment in the prior art. The method comprises the following steps: acquiring target surface temperature data and human activity data of a target city, and based on the target surface temperature data, performing regional division on the target city according to a preset thermal environment division rule to obtain a plurality of target islands for representing temporal and spatial change conditions of the target city in daytime and night thermal environments; the surface temperature grades of the target islands in the plurality of target islands are different; further determining human activity intensity index features according to the human activity data; and finally, based on the target surface temperature data, the human activity intensity index characteristics and the plurality of target islands, determining a space-time relationship between the human activity and the target surface temperature data. And the accuracy of deducing the dynamic evolution rule of the urban thermal environment is improved.

The invention provides an urban surface heat island intensity evaluation method, which comprises the following steps of: acquiring a satellite multi-band data image of a target region in a research period, dividing a water body and an urban built-up region on the basis, and selecting part of the water body as a reference station region; extracting seasonal and tendency factors according to the long-term observed surface temperature data of the reference base station area, correcting the surface temperature data of the reference base station area to obtain corrected temperature, and comparing the corrected temperature with the surface temperature of the urban built-up area to obtain the surface heat island intensity of the urban built-up area. Therefore, the fluctuation trend of seasons to the temperature of the reference station area is quantified, the spatial distribution of the urban thermal environment and the thermal risk is clearer and more visual and is convenient to position, and evaluation errors caused by seasons and background climate changes can be reduced.

The invention discloses a method for confirming the width of a heat island corridor of an urban heat island network, and belongs to the technical field of urban ecological intelligent analysis. The method comprises the following steps: inverting the surface temperature of a research area; in combination with morphological spatial pattern analysis, landscape connectivity indexes and circuit theories, screening a heat island source ground and generating a resistance surface, and constructing a multi-grade urban heat island corridor network; constructing a multi-level buffer zone for different levels of galleries, and respectively extracting surface temperature mean values; extracting a buffer zone temperature extreme value of each grade of gallery, calculating a temperature difference parameter, and quantifying the heat exchange intensity; calculating heat transfer efficiency per unit area by combining the area of the buffer zone; analyzing the nonlinear attenuation characteristics of the heat transfer efficiency along with the offset distance of the buffer zone, extracting the inflection point of the heat transfer efficiency, and taking the offset distance of the buffer zone corresponding to the inflection point as the space action width of the heat island gallery, thereby realizing the scientific definition of the space action boundary of the heat island network. The method can also provide a scientific basis for relieving the urban heat island effect, optimizing the green land layout and the like.

The invention relates to the technical field of surface temperature measurement based on an infrared radiation principle, and discloses a non-contact surface temperature measurement device which realizes surface temperature measurement through cooperation of a plurality of components. The optical system is located in front of the infrared detector, the field angle is determined, infrared radiation is focused, and a 3D printing black sealing shell of the optical system is rainproof and windproof. The infrared radiation detector of the MLX90614 chip converts infrared energy into a voltage signal and processes data, the data is sent to the main processor through the signal conditioning circuit for analog-to-digital conversion and other operations, and the sensor is endowed with digital and intelligent characteristics. The communication module comprises an RS-232 serial port and a ZigBee wireless communication module and is used for data transmission and connection with an upper computer, and the state monitoring module monitors the work of the processor. Hardware design is completed by using an embedded system and a ZigBee technology, software design is realized by IAR and Z-Stack protocol stacks, and the surface temperature can be accurately measured. The device also has two-way communication and online upgrading functions, the emissivity and the distance are corrected through an algorithm, the efficiency is improved, the standard requirements are met, and meteorological observation and ground temperature measurement are assisted.

The invention discloses a marking method and system for a geothermal abnormal target area, and the method comprises the steps: generating a surface temperature distribution diagram according to the remote sensing thermal infrared data of a target area, and marking a first-stage geothermal abnormal target area; a temperature measurement network is arranged in the first-stage geothermal abnormal target area, ground temperature measurement data are collected, and a shallow ground temperature distribution diagram is generated; marking a secondary geothermal abnormal target area by combining the shallow ground temperature distribution diagram and the ground surface temperature distribution diagram; and obtaining geothermal information of the second-stage geothermal abnormal target area, marking a drilling position according to the geothermal information, drilling the drilling position, collecting deep geological data, and inputting the deep geological data into a pre-trained geothermal geological model to obtain a target geothermal abnormal target area output by the geothermal geological model. According to the method, through comprehensive analysis and processing of multiple data, the accuracy and reliability of marking of the geothermal abnormal target area are improved.

The invention discloses a space remote sensing image analysis method for detecting surface water and underground water exchange in a river region based on surface temperature, and belongs to the field of remote sensing detection surface temperature anomaly analysis. According to the method, background field construction is carried out on pixels in a space by combining single-scene high-resolution surface temperature data and land cover classification data, and temperature thermal anomaly of a river main stream is extracted by adopting an adjacent pixel temperature anomaly analysis method, namely, the temperature thermal anomaly of the river main stream is extracted by analyzing subtle change of surface temperature in a remote sensing image; and identifying abnormal pixels which are obviously different from the temperature characteristics of the surrounding area. The abnormal pixels often correspond to areas where surface water and underground water exchange is active, such as permeable zones, spring water exposure points and the like. The method can be applied to drainage basin water resource management and ecological protection, and the conversion path and intensity between surface water and underground water can be deduced by further analyzing and processing the abnormal pixels extracted by the method and combining background information such as geology and hydrology.

The invention discloses a land surface temperature and angle collaborative normalization method based on spatial neighborhood constraint, and the method comprises the steps: taking a multi-temporal multi-pixel multi-angle land surface temperature observation value as an input, and coupling an angle effect nuclear drive model with a time angle collaborative normalization model of a land surface temperature daily variation curve model of a time effect; a least square mathematical method is adopted, parameters of a time angle collaborative normalization model are determined, space neighborhood information is added, finally, the unknown number of the surface temperature is reduced, the ill-conditioned problem is solved, and collaborative normalization of a surface temperature product is achieved; in addition, time-angle collaborative normalization of the surface temperature product can be realized under a small amount of observation.

The present invention discloses a surface temperature estimation method, system, device and storage medium, and relates to the technical field of surface temperature estimation. The method comprises the following steps: obtaining a daily surface temperature dataset with low spatial resolution and an underlying surface dataset with high spatial resolution; reprojecting the daily surface temperature dataset with low spatial resolution to obtain a new daily surface temperature dataset; using a sliding window clipping method to process the spatial resolutions of the new daily surface temperature dataset and the underlying surface dataset to be consistent; and inputting the processed new daily surface temperature data and the underlying surface data as feature data into a surface temperature estimation model to obtain daily surface temperature data with high spatial resolution. The model can provide reliable long-time series, high-spatial-resolution surface temperature data when performing resource and environmental monitoring.

The application discloses a kind of urban heat island network construction method based on multilevel thermal field recursive decomposition in the technical field of urban climate analysis, including obtaining seamless summer average ground surface temperature grid data of initial area, the multidimensional environmental driving factor data required for building thermal resistance surface;Based on mean-standard deviation threshold method, the continuous ground surface temperature field of the initial area is multilevel recursive decomposition, and the main level heat island source is identified, and the nested secondary heat island source is recursively identified inside the main level heat island source, and the hierarchical nested heat island network structure is constructed.The application realizes the accurate decomposition of multilevel heat island source by using mean, standard deviation threshold value for recursive calculation of continuous ground surface temperature field, can effectively identify cross-scale heat island connection pinch point and heat island connection barrier, can realize the hierarchical construction of urban heat island network, and is conducive to improving the management efficiency of urban heat island.

The embodiment of the application discloses a dynamic current-carrying capacity prediction method of a direct-buried cable and related devices, and the method comprises the following steps: obtaining historical weather data and historical cable load current of a direct-buried cable in a historical time period in a preset area; the historical weather data comprises historical ground temperature and historical precipitation; determining soil dynamic characteristic parameters based on the historical ground temperature and the historical precipitation; determining the historical dynamic current-carrying capacity of the direct-buried cable based on the soil dynamic characteristic parameters and the historical cable load current; obtaining future weather data and future cable load current of the preset area in a future time period; determining a target quantile point; determining a regression coefficient vector corresponding to the target quantile point based on the target quantile point, the historical weather data, the soil dynamic characteristic parameters, the historical dynamic current-carrying capacity and the historical cable load current; and determining the future dynamic current-carrying capacity of the direct-buried cable corresponding to the target quantile point in the future time period based on the future weather data, the future cable load current and the regression coefficient vector.

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.