1501 results about "Snowpack" patented technology

The invention provides a deep learning-based avalanche early warning model construction method and system, and the method comprises the steps: firstly obtaining multi-source environment monitoring data, including meteorological time sequence, topographic space and accumulated snow layer physical data, of a target region, carrying out the time dimension alignment of the meteorological time sequence data to generate a feature sequence, carrying out the meshing of the topographic space data to generate a feature set, and carrying out the construction of an avalanche early warning model; the method comprises the following steps: extracting parameters from accumulated snow layer physical data to generate a state vector, inputting a deep learning network model containing time sequence attention, spatial convolution and cross-modal interaction units, generating a fusion feature vector, constructing a training set based on historical avalanche event annotation data, performing dynamic weight optimization on the fusion feature vector, and generating an avalanche risk prediction model. And finally, receiving current monitoring data in real time, outputting a risk level and an early warning trigger threshold value by the avalanche risk prediction model, and generating a multi-level early warning signal when a real-time risk value exceeds the threshold value, thereby realizing accurate avalanche early warning.

The invention discloses a mountainous area accumulated snow area reconstruction method based on machine learning. The method comprises the steps that optical remote sensing data and topographic data are acquired and preprocessed; the optical remote sensing data comprises the MOD09GA data and the Landsat8OLI data; the preprocessing comprises terrain correction and accumulated snow recognition; taking an accumulated snow area generated by the Landsat8OLI data by using an accumulated snow recognition algorithm as a truth value label, and constructing a sample data set based on the preprocessed MOD09GA data and topographic data; dividing the sample data set into a training set and a verification set according to a proportion of 4: 1 so as to train and verify an XGBoost model; the XGBoost model improves the prediction performance by combining a plurality of decision trees; and performing accumulated snow area prediction on new input data by using the trained XGBoost model. According to the method, day-by-day 30-meter (m) accumulated snow area data can be produced, and the method has important research significance and value for mountainous area accumulated snow monitoring, hydrological process simulation and ecological system evolution.

The invention relates to the technical field of image processing, in particular to an automatic snow removal operation method and system based on image enhancement processing, and the method comprises the following steps: collecting an original accumulated snow image of a road surface, decomposing an image brightness component into a low-frequency component and a high-frequency component through a multi-scale Retinex algorithm, and generating an enhanced image; on the basis of the enhanced image, calculating a joint gradient of saturation and brightness in a pixel point HSV space, and generating a snow area binary segmentation image by adopting an improved Otsu dual-threshold method; the accumulated snow compactness grade is judged according to the texture uniformity; and driving the execution mechanism to spray the snow removing agent according to the path coordinates, and adjusting the spraying concentration of the snow removing agent in combination with the compactness grade. According to the method, the snow boundary definition and the plaque internal texture identifiability are improved, a high-quality image foundation is laid for subsequent segmentation and analysis, and the adaptive capacity and stability of the system under the natural light condition are enhanced.

The invention discloses a high-resolution peak accumulated snow depth inversion device and method based on a geographically weighted random forest, and the device comprises a data obtaining module which is used for obtaining the multi-source data of a research region, and the multi-source data comprise MODIS remote sensing accumulated snow data, Sentinel-1VV / VH backscattering data, topographic data, and meteorological station snow depth observation data; the high-resolution snow coverage data set construction module is used for processing the MODIS remote sensing snow data to obtain a high-resolution cloudless binary snow coverage data set; the feature construction module is used for constructing a comprehensive feature set containing a snow space-time process feature index, a back scattering feature index and a topographic feature index; and the model construction and analysis module is used for training a geographically weighted random forest model through the comprehensive feature set, and inverting the peak snow depth of the research area according to the optimal model. According to the invention, estimation of high-resolution peak snow depth can be realized.

The invention discloses a high-voltage switchgear for wind power generation, and relates to the field of high-voltage switchgear, the high-voltage switchgear comprises a mounting support frame, the bottom side of the mounting support frame is provided with two porcelain bottles, and the two porcelain bottles are respectively provided with a knife switch and a plug contact. It needs to be explained that during switching-on, the two wind shields conduct shielding, and the problem that a large number of electric arcs are generated during switching-on is avoided; in rainy and snowy days, the two wind shields are close to each other to seal the porcelain bottle, so that the problem that humid air corrodes the switch blade and the plug contact is effectively avoided, automatic lubrication can be performed, the lubricated part can be sealed, the lubricated switch blade and the lubricated plug contact can be effectively prevented from being exposed to rain or covered by accumulated snow, and the service life of the switch blade and the plug contact is prolonged. The lubricating liquid goes bad, so that the lubricating liquid loses efficacy. In addition, when a fire occurs, the two wind shields are folded, meanwhile, the fire extinguishing sand is continuously accumulated in the two wind shields, and the physical fire extinguishing function is achieved.

The invention relates to the technical field of accumulated snow remote sensing modeling, in particular to an accumulated snow black carbon concentration remote sensing inversion method. Acquiring remote sensing topographic data of the target area and performing topographic correction; the reflectivity of a snow area in the remote sensing topographic data after topographic correction is obtained; establishing a pure snow optical model by adopting an analytic asymptotic radiation transfer theory according to the reflectivity of the accumulated snow area; introducing an ice-black carbon composite particle absorption characteristic into the pure snow optical model, constructing a dirty snow optical model, and performing optimization solution on the dirty snow optical model by adopting a strict radiation transmission model fitting result; and according to the optimized dirty snow optical model, carrying out inversion on the black carbon concentration of the accumulated snow area. According to the snow remote sensing inversion method provided by the invention, the influence of terrain shadow can be effectively reduced, the black carbon absorption coefficient is modeled into a wavelength function based on the snow radiation transmission model, the calculation complexity is greatly reduced, and the calculation efficiency and precision are improved.

The embodiment of the invention provides a snow depth inversion method and system based on a random forest. The snow depth detection method is applied to the technical field of snow depth detection, and comprises the following steps: selecting an acquisition area, acquiring snow depth data in a predetermined acquisition area, and preprocessing to obtain processed data; selecting a prediction variable and a target variable, training the random forest model to obtain a trained random forest model, and performing precision verification; and inputting snow depth data acquired in real time into the trained random forest model, performing snow depth prediction, and outputting to obtain a prediction result. In this way, active microwave high-resolution snow depth data and passive microwave inversion of the mountain snow depth are combined, and Sentinel-1 inversion snow depth data are taken as a training target, so that the inversion precision of the mountain snow depth is improved.

The invention discloses a cold region land surface process river water and sediment model construction method, which comprises the following steps: selecting a target research drainage basin, extracting a drainage basin range ROI (Region of Interest), and collecting water and sediment data and related basic data in the drainage basin range; calculating a thermal erosion amount caused by temperature rise in the river reach, a snow melting erosion amount caused by snow melting, a water erosion amount induced by rainfall and a river ice erosion amount caused by ice running or flow ice scraping erosion of river ice in winter and spring; selecting landform characteristic parameters according to a river flow direction to construct a sediment transport coefficient of a river reach, establishing a cold region river water and sediment model according to the sediment transport coefficient and each erosion source item, and obtaining a sediment content simulation result based on the established cold region river water and sediment model; and evaluating the simulation performance of the model based on the established evaluation index. The method can dynamically respond to the erosion sand production and runoff sand transportation process of the cold region, provides timely and high-precision sand content calculation, and has important significance on water and soil conservation management and water and sand risk regulation and control of the cold region.

The invention belongs to the technical field of regional hydrological numerical simulation, and particularly relates to a runoff simulation method, system and application of a loose coupling hydrological model based on glacier and non-glacier zones.The method comprises the steps that firstly, a glacier area and a non-glacier area are divided based on glacier cataloguing data and accumulated snow covering products, gridding processing is carried out through a digital elevation model, and a runoff simulation result is obtained; and performing hydrological process simulation on the non-glacier area and the glacier area by adopting a VIC model and a DETIM model respectively to realize differential modeling, and obtaining a basin total runoff simulation process through result fusion. Through the loose coupling framework, the problems of poor model expansibility and high reuse difficulty in traditional integrated modeling are solved, the simulation precision and deployment efficiency are improved, and the method is suitable for water resource evaluation and climate response research in the cold and cold mountainous area.

The invention relates to the technical field of visual navigation, in particular to an intelligent logistics storage visual navigation path optimization method based on AI reinforcement learning, which specifically comprises the steps of environmental perception data acquisition, environmental feature extraction and modeling, wet and slippery area identification, state vector construction, reinforcement learning model construction, reward function design and multi-working-condition simulation training. Initial path planning, real-time state monitoring and dynamic path correction are carried out. According to the method, icing and snow accumulation areas are identified based on semantic segmentation and LBP texture features, a friction coefficient is output in combination with a convolutional neural network and a random forest regression model, pixel-level classification and physical parameter quantification of wet and slippery areas are realized, and high-precision risk state input is provided for path planning; a multi-dimensional state vector including a transportation equipment state, obstacle distribution, a target relative position and a risk state is constructed, environment dynamic characteristics are comprehensively described, and the environment characterization capability of a decision model is improved.

The invention provides photovoltaic line laying cable overhead equipment, and relates to the technical field of cable overhead, the photovoltaic line laying cable overhead equipment comprises a rack, and instantaneous vibration devices are arranged on two sides of a main wheel on the rack; two pin shafts of the instantaneous vibration device are jointly provided with a rotating frame in a rotating mode, an auxiliary wheel is arranged at the end of the rotating frame in a rotating mode, a first arc-shaped guide rod is fixedly arranged below a first seat stand on the rotating frame, the pin shafts are further provided with a pressing rotating rod in a rotating mode, the end of the pressing rotating rod is in sliding fit with the first arc-shaped guide rod, and the first arc-shaped guide rod is sleeved with a first arc-shaped spring. The locking block is provided with an unlocking inclined face making contact with the pushing block, a second arc-shaped guide rod is arranged in the middle of the pressing rotating rod in a sliding mode, the lower end of the second arc-shaped guide rod is arranged on a second seat stand, the second seat stand is fixedly arranged on the rack, and the second arc-shaped guide rod is sleeved with a second arc-shaped spring. When snow and ice are accumulated on the cable, downward bending of the cable is aggravated, the cable loses support of the auxiliary wheel instantly, and therefore the cable shakes up and down, and the accumulated snow and the ice on the cable are automatically removed through shaking.

The invention provides an ice and snow field snow melting early warning and intelligent snow removal scheduling method, which comprises the following steps: acquiring wet snow water content, snow layer density abnormity and field climate difference data through a multi-source sensor; the method comprises the following steps: performing primary processing on original data acquired by radar detection, infrared imaging and a micro weather station to obtain an initial distribution map of wet snow characteristics and local weather conditions; and according to the corrected wet snow characteristic distribution map, in combination with the initial distribution map and temperature influence factors and melting rate fluctuation characteristics obtained by data analysis of the micro meteorological station, aiming at equipment thrust loss and working surface friction loss, correcting acquisition deviation of multi-source sensor data, and obtaining a dynamic update map of the snow accumulation state in the risk area.

The present invention discloses a novel agricultural greenhouse, which relates to the technical field of agricultural greenhouses and includes a shed frame, a shed film, a dew removal mechanism, and a dirt cleaning mechanism. The present invention detects the light intensity inside the multi-span greenhouse through a photosensitive sensor, sets a dew removal mechanism at the side end of the shed frame, and sets a dirt cleaning mechanism above the shed film at the upper part of the shed frame, so that the internal space of the multi-span greenhouse automatically forms a humidity stratification with a low humidity upper layer and a high humidity lower layer, which not only avoids dew condensation and reduces the light transmittance but also does not affect the environmental humidity required for agricultural planting. Moreover, the top shed film in the low humidity upper layer environment will not immediately form dew again, and the light transmittance is maintained continuously. At the same time, under the synchronous rotation of Gear 1, Gear 2 is also driven to rotate synchronously through the bilateral racks, and the scraping member is driven to reciprocate in the groove path where the top of the shed film meets through the bidirectional screw, so as to achieve the purpose of automatically scraping the dust and snow at the groove where the top of the shed film meets, further ensuring the light transmission and heat preservation effects inside the shed.

According to the guano-removing photovoltaic cleaning system and method, guano pollution of a photovoltaic panel is detected through a sensing group, so that a scraper blade removes guano at a place with guano, continuous contact of the scraper blade with the photovoltaic panel is reduced, and the influence of automatic cleaning on the normal power attenuation speed of the photovoltaic panel is reduced; bird droppings are sprayed with clean water, then the surface of the photovoltaic panel is cleaned through the adhesive scraping strip when the adhesive force of the bird droppings is reduced, and the problems that the bird droppings on the photovoltaic panel are difficult to remove, and the photovoltaic panel is damaged by hard removal are solved. Furthermore, when bird droppings are not removed, the cleaning robot can sweep dust on the photovoltaic panel in a dry mode, and the cleaning robot of the system can also serve as a snow remover to remove loose accumulated snow with the thickness smaller than 4 cm in an area where the snowing amount is small. According to the technical scheme, bird droppings attached to the surface of the photovoltaic panel can be timely and automatically cleaned, the influence of the bird droppings on the service life of the photovoltaic panel is greatly reduced, and the service life of the photovoltaic panel is prolonged.

The invention belongs to the field of analog simulation, and particularly relates to a runway dynamic friction coefficient real-time simulation method, system and equipment based on multi-source data. The problem that taxiing simulation is insufficient in authenticity is solved. The method comprises the following steps: acquiring runway surface temperature data, accumulated water depth and / or accumulated snow depth; judging a runway type based on the runway texture features, matching a basic friction coefficient according to the runway type, and dynamically correcting the basic friction coefficient in combination with the accumulated water depth and / or accumulated snow depth and runway surface temperature data to obtain a corrected friction coefficient; acquiring a tire vertical load, an initial sideslip angle and tire pressure data of the aircraft, calculating a tire lateral force, and calculating a final friction coefficient in combination with the updated sideslip angle, the corrected friction coefficient and the sideslip attenuation coefficient; and inputting the data into a ground taxiing model of the flight simulator to realize real-time simulation. Through the attenuation effect of the dynamic process on the friction coefficient, the simulation result better fits the real runway state.

The invention relates to the technical field of highway tunnel photovoltaic snow protection shed tunnels, in particular to a highway tunnel photovoltaic snow protection shed tunnel self-heating regulation and control method which comprises the steps that the actual value of the accumulated snow thickness is collected, the estimated accumulated snow thickness value and the expected value are obtained, and when the actual expected value is smaller than the estimated expected value, the photovoltaic snow protection shed tunnel self-heating regulation and control method is started. And carrying out heating power adjustment on the photovoltaic panel partition based on the difference value. Through combination of multi-dimensional accumulated snow detection and regional accurate heating, the accumulated snow removing efficiency is improved, the heating energy consumption is optimized, stable operation in extreme weather is guaranteed, and the complex working condition requirements are met. In addition, the invention also provides a storage medium, an electronic device and a computer program product to implement the method.

The invention discloses photovoltaic solar multi-angle energy storage equipment, and relates to the technical field of energy storage equipment.The photovoltaic solar multi-angle energy storage equipment comprises a base, a storage battery is fixedly installed at the bottom of the base, a servo motor is fixedly installed at the bottom of the base, and a supporting rod is fixedly installed at the output end of the servo motor; a support is fixedly installed at the end, away from the servo motor, of the supporting rod in a penetrating mode, a photovoltaic panel is fixedly installed on the inner wall of the support, a snow removing mechanism is arranged on the support, and a deicing mechanism is arranged on the snow removing mechanism. The accumulated snow on the surface of the photovoltaic panel can be swept off through rotation of the threaded rod in cooperation with the threaded block, the I-shaped rod and the brush, the situation that the illumination effect of the photovoltaic panel is affected by accumulated snow accumulation is avoided, the photovoltaic panel can conduct photovoltaic conversion in winter, and the power generation efficiency of the photovoltaic panel is fully exerted.

The invention discloses a method for identifying high-risk avalanche trenches, which relates to the technical field of avalanche identification and comprises the following steps of: 1, calculating the snow collecting area of each trench; 2, the average gradient of the snow accumulation area of each groove and the area of the snow accumulation area of each groove are calculated, and the area ratio of the snow collection area to the snow accumulation area in each groove is obtained; and 3, when the area ratio is greater than or equal to a set ratio and the average gradient is greater than or equal to a set gradient, determining that the corresponding groove is a high-risk avalanche groove. According to the method, the avalanche groove is identified by combining the slope of the snow accumulation area and the area ratio of the snow collection area to the snow accumulation area, the geomorphic main control factor of the groove type avalanche is directly considered, and the risk of the groove type avalanche in the to-be-identified drainage basin can be identified through high-precision remote sensing data; the target area is determined for subsequent installation of the avalanche monitoring instrument, and the technical problems that the landform master control indexes are not comprehensive, the identification difficulty is large, the cost is high and subsequent installation of the monitoring instrument is inconvenient in the existing identification method are solved.

The invention belongs to the technical field of inspection robots, and particularly relates to an electric inspection robot dog with an inspection mechanical arm, the electric inspection robot dog comprises a robot dog body, the mechanical arm is mounted on the rear side of the top of the robot dog body, and an electromagnetic sensor probe assembly is mounted at the free end of the mechanical arm. The electromagnetic sensor probe assembly comprises a detection head, a detection contact piece, a fixing plate, a carrier roller, a telescopic rod, a supporting spring, a U-shaped frame and a cleaning assembly. Through the arrangement of the cleaning assembly, when accumulated snow on the surface of the cable needs to be cleaned and a mechanical arm drives cleaning plates to make contact with the surface of the cable, a driving motor can drive a driving screw to rotate through a transmission belt, so that the two cleaning plates move in the direction away from each other along the surface of the cable; therefore, accumulated snow attached to the surface of the cable is scraped, and the detection operation of the detection head is prevented from being blocked by the accumulated snow.

The utility model discloses a low-noise high-humidity skiing land radial tire which comprises tire shoulder pattern blocks and central pattern blocks, the tire shoulder pattern blocks and the central pattern blocks are radially arranged on a tire tread, the tire tread adopts a symmetrical design, and the tire shoulder pattern blocks and the central pattern blocks are arranged from outside to inside from the tire shoulder pattern blocks to the central pattern blocks. According to the utility model, by optimizing the shape and the arrangement of the patterns, the noise of the tire is effectively reduced, the noise problem of the snow tire is improved, the risk that the wet and slippery performance of the tire is reduced and the tire slips under the condition that accumulated water is accumulated on a road surface after accumulated snow is melted in the patterns of the snow radial tire in the prior art is solved, and the snow performance is ensured on the basis of ensuring the snow performance.

The invention discloses a plateau mountain area avalanche susceptibility intelligent assessment method, and relates to the technical field of high mountain ice and snow disaster space early warning and risk mapping. According to the method, the influence of the microtopography is captured by adopting high resolution, the evaluation accuracy is improved, the control effect of the slope microtopography (the scale of 30-50 m) on the accumulated snow stress field can be accurately captured by adopting a factor map layer (based on data such as 12.5 mALOSPALSARDEM and Landsat-8OLI images) with the spatial resolution of 12.5 m, and compared with traditional 30mDEM and hectometer-level meteorological reanalysis data, the method has the advantages that the evaluation accuracy is greatly improved, and the evaluation efficiency is improved. Spatial differences of key factors such as terrains, earth surfaces and meteorology can be depicted more carefully, more accurate basic data support is provided for avalanche susceptibility assessment, and missing of key microtopography influence factors due to insufficient resolution is avoided.

The invention discloses a runoff forecasting method of an EMD-BiLSTM model based on a snow melting multi-source factor, and the method comprises the steps: firstly building a prediction data set based on multi-source accumulated snow remote sensing data in combination with a hydrological station control basin and a snow melting confluence production process in an alpine region, and carrying out the preprocessing of the data set, performing time-frequency analysis on the runoff sequence by adopting an empirical mode decomposition method, decomposing a complex runoff signal into a plurality of basic mode components, and averaging the mode components to obtain an interpretable runoff change signal; a BiLSTM model is utilized to train a data set, meanwhile, an attention mechanism is introduced, a Bayesian optimization algorithm and a gradient cutting technology are adopted in the training process to determine an optimal hyper-parameter, and the problem of gradient explosion in the back propagation process is prevented. According to the method, high-precision prediction of the daily runoff of the alpine hydrological control basin is achieved through the model, and the method has high stability and generalization ability and is suitable for runoff prediction and water resource management in alpine regions.

A method of snow removal for a plurality of solar tracker assemblies. The steps of the method include: a) determining at least one current snow presence variable value associated with the solar tracker installation; b) determining at least one other current variable value associated with the solar tracker installation selected from one or more of the following variable categories: 1) a weather condition variable category; 2) a temporal variable category; 3) a weather prediction variable category; and 4) a snow removal routine variable category; c) applying the decision rule to determine if the at least one current snow presence variable value and the least one other current variable value correspond to a snow removal condition; and d) if the decision rule determines that the current variable values correspond to a snow removal condition, executing the snow removal routine for a set of the plurality of solar tracker assemblies.

The invention provides a multi-robot cooperative photovoltaic panel cleaning control method and system, and relates to the technical field of automatic cleaning robots, and the method comprises the steps: receiving a cleaning task request of a monitoring end, and calling a virtual photovoltaic space of an actual photovoltaic field station; an actual surface image of the photovoltaic panel is collected and input into the accumulated snow detection model, an actual accumulated snow area is recognized, characteristic parameters are determined, and meteorological data are obtained through meteorological monitoring equipment; judging whether an extreme accumulated snow cleaning mode is triggered or not according to the characteristic parameters and the meteorological data; if so, dividing the actual photovoltaic station into a plurality of sweeping sub-areas and distributing the sweeping sub-areas to a plurality of robots, and determining accumulated snow sweeping paths and parameters; the robot executes a task according to the accumulated snow cleaning path and the parameters and monitors the actual surface cleanliness of the photovoltaic panel; and the accumulated snow cleaning parameters are adjusted based on the surface cleanliness, and the virtual photovoltaic space is updated, so that the efficiency of multi-robot cooperative cleaning of the photovoltaic panel in extreme weather can be improved, and accurate adjustment and optimization of the cleaning mode are realized.

The invention belongs to the cross technical field of disaster emergency management, remote sensing information processing and intelligent traffic scheduling, and particularly relates to a snow disaster emergency snow removal path intelligent planning method fusing a multi-target ant colony algorithm, and the method comprises the steps: S1, inputting multi-source heterogeneous data: receiving and integrating the input data, weight parameters such as operation workload, operation difficulty, passing urgency and rescue urgency are set; s2, constructing a road network line segment structure: based on the road vector data, generating basic unit road network line segments by extracting cross points and carrying out topology cutting; s3, calculating the snow removal volume and the operation time consumption: constructing a line segment buffer area according to the snow depth grid data and the road snow removal width, obtaining snow depth slices, calculating the volume, estimating the snow removal time consumption of each line segment in combination with the snow removal capacity of the rescue team, and calculating the snow removal volume and the operation time consumption by adopting the fusion of the road buffer area and the snow depth grid slices; and the accumulated snow volume and the operation time consumption of each road segment are accurately estimated.

The invention relates to the technical field of photovoltaic supports, in particular to an adjustable photovoltaic support for a distributed photovoltaic power generation panel, which comprises a base, two supporting rods are symmetrically fixed at the top of the base, and a photovoltaic panel fixing frame for fixing a photovoltaic panel is rotatably mounted at the tops of the two supporting rods. A reset mechanism for cleaning accumulated snow on the surface of the photovoltaic panel is arranged outside the photovoltaic panel fixing frame, the reset mechanism can clean the accumulated snow on the surface of the photovoltaic panel, a storage mechanism for collecting or heating the accumulated snow is arranged below the photovoltaic panel fixing frame, and the storage mechanism can collect the accumulated snow sliding from the photovoltaic panel. Through cooperation of the reset mechanism, the storage mechanism, the lifting mechanism, the turnover mechanism, the lifting plate, the scraper and other structures, accumulated snow on the surface of the photovoltaic panel is effectively removed, it is ensured that the photovoltaic panel can stably operate according to a preset angle adjustment plan, and therefore the solar radiation receiving amount of the photovoltaic panel is maintained, and the power generation efficiency is improved.

The snow sweeping robot and the snow sweeping system disclosed by the application, wherein the snow sweeping robot comprises: a snow sweeping device, a vehicle body fixedly connected with the snow sweeping device for carrying the snow sweeping device to move, the vehicle body is provided with a battery mounting position for fixing a battery, and the snow sweeping robot further comprises a charging module for charging the battery, and the charging module is arranged at the bottom of the vehicle body to charge the snow sweeping robot from the bottom to the top. The snow sweeping robot disclosed by the application is provided with the charging module on the lower surface of the chassis, so that the whole vehicle body plays a shielding role, and snowflakes cannot fall on the surface of the charging module when it snows, so that the charging module is prevented from being hindered by accumulated snow on the surface.

The invention discloses an accumulated snow coverage analysis method and system and a medium, and the method comprises the steps: obtaining a plurality of pieces of to-be-processed satellite data of a to-be-analyzed region; performing feature extraction on the to-be-processed satellite data to obtain multi-dimensional feature data of each preset pixel; analyzing the multi-dimensional feature data, and determining a first accumulated snow identification result; respectively determining a plurality of target preset pixels geographically covering each preset geographic grid; carrying out statistics on distribution of the first accumulated snow identification results of a plurality of target preset pixels in each preset geographic grid, and determining a state statistical index; constructing a time data sequence of a preset geographic grid; analyzing the time data sequence of each preset geographic grid, and determining a second accumulated snow identification result of each preset geographic grid; and generating an accumulated snow coverage data set of the to-be-analyzed area based on the second accumulated snow identification result. The precision of the accumulated snow coverage data is improved.

The invention relates to the technical field of data processing, and discloses an accumulated snow depth detection method and system based on satellite navigation signals. The method comprises the steps that GNSS multi-band signals are collected through a multi-angle fusion antenna array, and direct and reflected signal components are obtained; performing phase tracking on the signal component to obtain an instantaneous phase difference sequence and a Doppler compensation parameter; analyzing the phase difference sequence by using an improved Lomb-Scargle algorithm, and extracting accumulated snow characteristic frequency and amplitude parameters; establishing a double-layer medium reflection model based on the characteristic parameters and performing terrain correction to obtain a theoretical phase difference curve; and through a multi-satellite collaborative inversion algorithm matching theory and an actually measured phase difference, outputting an accumulated snow depth measurement value. According to the invention, the problems of difficult multipath signal separation, poor terrain adaptability and unstable inversion precision in the existing GNSS reflected signal accumulated snow detection technology are solved, and the accuracy and reliability of accumulated snow depth detection under complex terrain conditions are improved.

An indoor simulation device and method for a wheel rolling on an icy and snow-covered road surface. The temperature, humidity, and the surface temperature of the test piece in the experimental chamber are respectively collected by a temperature sensor, a humidity sensor, and an infrared temperature sensing device. Then, the start and stop of the snow-making system are controlled through a control box, so as to adjust the experimental environment to meet the conditions of simulating harsh winter driving conditions such as snowfall and freezing rain. Finally, the start and stop of the wheel rolling device are carried out. The snow-making system of this device has the advantages of good refrigeration effect, fast cooling speed, and can reach -196°C. At the same time, this device can simulate the process of a wheel rolling on the road surface during actual driving, and is used to evaluate various fast, effective, and environmentally friendly snow removal methods and the anti-slip performance of the road surface after the wheel rolls on the road surface indoors.