2560 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 relates to the technical field of environment monitoring and early warning, in particular to a road and bridge pavement environment monitoring and early warning method and system. The method comprises the following steps: arranging acquisition equipment for acquiring pavement environment data, wherein the pavement environment data comprises pavement ice and snow state data; the collected road surface environment data are preprocessed; training an environment monitoring and early warning model based on the road historical environment data to obtain a trained environment monitoring and early warning model, and inputting the preprocessed environment data into the environment monitoring and early warning model for monitoring and early warning; comparing a monitoring and early warning analysis result with a preset threshold value, and determining whether early warning needs to be sent out according to a comparison result; meanwhile, when ice and accumulated snow are detected to appear on the road surface, the position and the size of the ice and the accumulated snow are marked in visual equipment. The system comprises a data acquisition module, a model training module and an early warning judgment module. According to the invention, a comprehensive and efficient road and bridge pavement environment monitoring and early warning system is formed.

The invention discloses a mixed forecasting method and system for snow-melting runoff, relates to the technical field of hydrological forecasting, and solves the technical problem of insufficient reliability of snow-melting runoff forecasting under complex terrain and meteorological conditions in the prior art. The method comprises the steps that a long-short-term memory network model, namely a DS-LSTM model, based on dynamic-static hybrid driving is constructed, training verification is conducted on the DS-LSTM model through a data set generated by research area data, and the DS-LSTM model captures the influence of early-stage meteorological elements on accumulated snow characteristics through long-period memory characteristics; describing the effect of a local static underlying surface on accumulated snow features through a multi-dimensional learning ability, replacing a snow accumulation and melting calculation module in a snow melting runoff model with a verified DS-LSTM model to obtain a snow melting runoff mixed forecasting model, and adopting the snow melting runoff mixed forecasting model to realize snow melting runoff mixed forecasting; according to the method, snow accumulation and melting calculation data of multi-factor dynamic correction can be provided for the snow melting runoff model.

The invention relates to the field of turnout snow melting control, and particularly discloses a turnout snow melting energy-saving control module system, which is characterized in that whether each heating loop of a turnout snow melting device is started or not is judged by monitoring the accumulated snow depth and temperature of a turnout section corresponding to each heating loop of the turnout snow melting device in real time; collecting multi-dimensional meteorological data of turnout sections corresponding to heating loops of the turnout snow melting device, and analyzing suitable heating power of the heating loops; tracking and monitoring the heating effect of each heating loop of the turnout snow melting device, judging whether the heating power of each heating loop of the turnout snow melting device needs to be adjusted or not, and obtaining adjustment information; the temperature of the turnout road section corresponding to each heating loop of the turnout snow melting device is monitored in real time, whether each heating loop of the turnout snow melting device is closed or not is judged, then intelligent control over the turnout snow melting device is achieved, self-adaptive adjustment is conducted on the heating power of the turnout snow melting device, the heating effect of the turnout snow melting device is guaranteed, and energy is saved.

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 utility model relates to the technical field of new energy charging piles, and discloses a light storage and charging integrated new energy charging pile which comprises a charging pile body, a top plate is fixedly installed on the top of the charging pile body, a supporting rod is fixedly installed on the top of the top plate, and a cover plate is fixedly installed on the top of the supporting rod. According to the light storage and charging integrated new energy charging pile, the opening and retracting assembly is arranged, after the opening and retracting assembly is used, under mutual matched transmission of a gear, an inner gear ring and a rack, after a servo motor is started, a plurality of photovoltaic panels are driven to move towards the center of the bottom of a cover plate, and the photovoltaic panels can be blocked by the cover plate after moving; according to the charging pile, the photovoltaic panels are prevented from being damaged by severe weather such as accumulated snow and hail, the service life of the photovoltaic panels is prolonged, and in addition, the multiple photovoltaic panels can be driven to move towards the outer side of the cover plate, extend out of the cover plate, receive sunlight and convert solar energy into electric energy to be stored in the charging pile body.

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 glacier information extraction method based on deep learning, and relates to the technical field of information extraction, and the method comprises the following steps: collecting required remote sensing image data according to a research area, constructing a sample library, preprocessing the collected remote sensing image data, and obtaining a pre-processed remote sensing image data; glacier features of the preprocessed remote sensing image data are extracted, abnormal conditions in the extraction process are analyzed, an abnormal feature analysis table is obtained, the remote sensing image data in the sample library are labeled, and glacier boundaries and classification labels are determined. Through optimization and customization of the deep learning model, interference caused by natural factors such as cloud and mist shielding, accumulated snow coverage and shadow change is reduced, the model can learn specific texture, shape and context information of the glacier, the glacier area and the non-glacier area can be accurately distinguished even in a complex and changeable environment, and the method has the advantages of being high in practicability and the like. Therefore, the glacier information extraction accuracy is remarkably improved.

A solar charging system for electric vehicles that includes high-efficiency solar panels mounted on the outer surface of an electric vehicle is disclosed. The system comprises photovoltaic cells for converting a broad spectrum of sunlight into electrical energy, even in low-light conditions with up to 25% efficiency. The solar panels are linked to the vehicle's charging port via an electrical conduit, and a charging module regulates the energy for charging both the main traction and auxiliary batteries. The system can be designed for easy retrofitting, utilizing an adhesive rear surface for secure attachment. In some embodiments, the solar panels include a hydrophobic coating for water repellence and debris removal, and heating elements that actuate automatically under a temperature of zero degrees centigrade to melt ice and snow.

The invention discloses an optical power prediction method and system considering photovoltaic panel snow covering, and the method comprises the steps: 1, determining influence factors which cause the photovoltaic power generation power loss of a photovoltaic cell panel under a snow covering condition, and building a relation model between each influence factor and a photovoltaic power generation power loss rate; 2, building a prediction model of the photovoltaic power generation power under the snow covering condition based on a relation model between each influence factor and the loss rate of the photovoltaic power generation power; and step 3, predicting the photovoltaic power generation power of the photovoltaic cell panel under the snow covering condition based on the constructed photovoltaic power generation power prediction model. Therefore, the snow accumulation influence on the photovoltaic panel is particularly considered, multiple key influence factors of the photovoltaic power generation power loss rate under the snow covering condition are fully considered, the prediction precision is high, the power prediction accuracy of the photovoltaic power station under the snow covering environment is remarkably improved, and the power prediction accuracy of the photovoltaic power station under the snow covering condition is improved. And the risk of insufficient large-scale photovoltaic power supply caused by snow covering is effectively reduced.

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 relates to the technical field of power distribution cabinets, and discloses a high-protection outdoor power distribution cabinet which comprises a cabinet body and a cavity formed in the cabinet body. A heat dissipation opening is formed in the side face of the cabinet body, the shielding assembly is arranged in the cavity and at the top of the cabinet body and used for protecting the cabinet body, the driven part is arranged in the cavity and at the top of the cabinet body and used for removing accumulated snow, and the deicing assembly is arranged in the cabinet body and the cavity and used for deicing the heat dissipation opening and preventing water from entering the heat dissipation opening. Through arrangement of a shielding assembly and a driven part, when accumulated snow on the top of a baffle is too much and under the pressure effect of the accumulated snow, the baffle, a frame and a sleeve rod move downwards, and after the baffle completely falls down, a driving rod can be controlled to rotate through a rotating rod, so that the driving rod drives an abutting protruding block to extrude a matched protruding block; the baffle is controlled to swing back and forth at the position hinged to the frame, so that accumulated snow on the top of the baffle is shaken off, and the situation that the accumulated snow is accumulated too much, extrusion is caused, and overall pressure is too large, and damage is caused is avoided.

The invention provides a deep learning-based method and device for evaluating the influence of an avalanche disaster on a road, and relates to the technical field of disaster coping, and the method comprises the steps: obtaining the historical avalanche monitoring data of a monitoring region; establishing an avalanche disaster influence prediction model according to the topographic parameters and the physical parameters of the snow body before avalanche; according to the avalanche disaster influence characteristics, the road positioning data and the post-avalanche road influence parameters, establishing an avalanche disaster road influence prediction model; acquiring current data of the monitoring area; obtaining current avalanche disaster influence characteristics according to the avalanche disaster influence prediction model; and predicting the influence of the avalanche disaster on each road section according to the avalanche disaster road influence prediction model. According to the method, key parameters of an avalanche occurrence area can be acquired in real time, and the occurrence probability of an avalanche disaster and the influence degree of the avalanche disaster on a road can be accurately predicted and early warned by means of a deep learning algorithm, so that the safety and traffic capacity of the road are effectively improved.

The invention discloses an accumulated snow prevention tracking type photovoltaic support which comprises a rotating base, a photovoltaic assembly, a supporting device, a wind power and wind direction detection system, an accumulated snow monitoring system, a snow removing device, a tracking device and a control device. The bottom end of the telescopic support is fixedly connected with the rotating base, the top end of the telescopic support is fixedly connected with the bottom end of the solar panel, and a limiting device used for limiting the telescopic length is fixed to the telescopic support. The accumulated snow monitoring system comprises optical sensors which are arranged at four corners and the center of the top surface of the frame; the snow removing device comprises air injection devices and heating devices, the air injection devices are arranged above the two sides of the solar panel, and the heating devices are fixed to the periphery of the top end of the frame. When the accumulated snow monitoring system detects that accumulated snow reaches a certain degree, the snow removing device is started, and the situation that the power generation efficiency is reduced due to long-time coverage of the accumulated snow is avoided.

The invention discloses a photovoltaic control system and method for coping with extremely cold wind and snow weather. The method comprises the following steps: acquiring data of an inverter and a photovoltaic panel; judging the freezing condition of the cooling fan in combination with the data of the inverter and the cooling fan; if it is judged that the cooling fan is in the frozen state, a control loop corresponding to the inverter is adjusted to enable the inverter to work in an SVG mode; judging the snow accumulation condition of the photovoltaic panel by combining the environment data and the state data of the photovoltaic panel; if the photovoltaic panel is in the snow accumulation state, a heating device installed on the photovoltaic panel is controlled to heat the photovoltaic panel. The system is used for implementing the method. The photovoltaic system has the advantages that the cooling fan can be unfrozen, accumulated snow of the photovoltaic panel can be removed, and it can be guaranteed that the photovoltaic system can better cope with extremely cold weather. And in addition, the unfreezing process of the cooling fan and the snow removing process of the photovoltaic panel are simple and safe, excessive hardware structures are not added, and it can be guaranteed that excessive extra cost is not increased.

The invention relates to the technical field of photovoltaic devices, in particular to a new energy photovoltaic device and a method thereof.The new energy photovoltaic device comprises a photovoltaic air outlet mechanism, the photovoltaic air outlet mechanism comprises a photovoltaic component, an air outlet storage component and a feeding component, the air outlet storage component is arranged at the top of the photovoltaic component, and the feeding component is arranged at the top of the air outlet storage component; when the storage gas outlet component is heated to generate gas, the first cleaning component is made to move left and right repeatedly, under the operation of the first cleaning component, the surface of the photovoltaic panel is cleaned through the second cleaning component, water or a snow removing agent is discharged through the collecting component, and the surface of the photovoltaic panel can be washed or accumulated snow can be melted; the flow of discharged water or a snow removing agent is controlled through the air outlet intercepting part, meanwhile, air can be blown to the surface of the photovoltaic panel, the cleaning effect is improved, the collecting part can be controlled to discharge the water or the snow removing agent through the adjusting part, accumulated snow adsorbed on the surface of the photovoltaic panel can be scraped through the scraping part, and therefore the use effect of the photovoltaic panel in snowy days is kept.

The anti-freezing device comprises a lower cylinder body and an upper cylinder body, the top face and the bottom face of the lower cylinder body are open, the bottom face of the upper cylinder body is open, the top face of the upper cylinder body is sealed, the upper cylinder body is movably installed at the upper end of the lower cylinder body, and the upper cylinder body and the lower cylinder body are connected in a sliding mode. A plurality of first air holes penetrating through the lower cylinder body are formed in the middle lower position of the lower cylinder body; and a plurality of second air holes penetrating through the upper cylinder body are formed in the middle upper position of the upper cylinder body. According to the utility model, the lower cylinder body and the upper cylinder body are entirely irradiated on the wolfberry seedlings, the lower ends of the insertion columns are inserted into soil, then the pointed-end sticks are inserted into the insertion holes, and the pointed-end sticks are inserted into the soil, so that the lower cylinder body and the upper cylinder body are firmly fixed on the surface of the soil and cover the wolfberry seedlings; accumulated snow and freezing rain can only cover the surface of the upper barrel body, and the situation that the surfaces of the Chinese wolfberry seedlings are bent and broken due to the fact that the accumulated snow and the freezing rain cover the surfaces of the Chinese wolfberry seedlings is avoided.

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.

To provide a snow accumulation determination device, a snow accumulation determination method, and a snow accumulation determination program which highly accurately determine a snow accumulation state on a solar panel for images of a camera view point not included in learning data.SOLUTION: According to one embodiment, a snow accumulation determination device includes an acquisition unit that acquires an image including a solar panel at a first time. Additionally, the snow accumulation determination device includes a learning model that uses the image including the solar panel at the first time as input and uses a result obtained by identifying a region where snow accumulation exists on the image including the solar panel at the first time as output. Furthermore, the snow accumulation determination device includes a learning unit that learns the learning model by using pseudo-snow accumulation images in which pixels of the image including the solar panel at a second time are converted into pixels corresponding to snow accumulation and pseudo-class label images applied with corresponding object labels. The snow accumulation determination device also includes a determination unit that determines the state of snow accumulation on the solar panel on the basis of the result obtained by identifying the region.SELECTED DRAWING: Figure 3

The invention provides a snowfield path planning method and system for a ski field transport vehicle, and relates to the technical field of vehicle navigation, and the method comprises the steps: obtaining a departure coordinate and a destination coordinate of a to-be-planned path; generating a basic path based on the starting point coordinates and the destination coordinates according to a basic terrain model of the ski field; acquiring topographic information and accumulated snow information of the basic path approach area; according to the terrain information and the accumulated snow information, obtaining a prohibited area in which a ski field transport vehicle cannot pass; and adjusting the basic path according to the prohibited area to obtain an actual planned path. According to the method, the influence of accumulated snow on normal driving of the ski field transport vehicle can be obtained according to the accumulated snow condition of each area in the ski field obtained in real time, so that a reasonable and safe driving path is planned for the driving of the ski field transport vehicle on the snowfield in the ski field.

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 utility model provides an intelligent snow and ice melting system for a steel structure roof. Comprising an intelligent control system (1), a snow melting and deicing system (2), an environment monitoring system (3), a first connecting line (4) and a second connecting line (5), the environment monitoring system acquires the roof state and transmits a signal to the intelligent control system, the intelligent control system judges whether to start the snow and ice melting system or not, and heating is started when the temperature reaches the lower limit value and the accumulated snow or ice thickness reaches the upper limit value. Stopping heating when the temperature reaches an upper limit value and the thickness of accumulated snow or ice reaches a lower limit value; after the snow and ice melting system is started for heating, the heating power of the adjustable infrared heating film is changed along with the change of the environment state of the roof, the heating power is reduced when the outdoor temperature is high and the snow body thickness and the ice body thickness are small, and the heating power is increased when the outdoor temperature is low and the snow body thickness and the ice body thickness are large. And the most ideal snow melting and deicing effect can be achieved with the minimum power consumption.

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 utility model provides a roof snow melting and deicing device which solves the problems that it is troublesome to manually clean snow on a roof, and potential safety hazards are likely to happen. Comprising electric heating cloth which is laid on the surface of a snow accumulation layer, a waterproof layer is arranged on the side, facing the snow accumulation layer, of the electric heating cloth, and the electric heating cloth is electrically connected with a power supply system; edge baffles are detachably and fixedly arranged at eaves on the left side and the right side of the roof, and each edge baffle comprises a vertically-arranged vertical plate. Left-right through open slots are formed in the vertical plates, and the left end and the right end of the electric heating cloth penetrate through the adjacent open slots; a first steering frame is fixedly arranged on the side, away from the roof, of the edge baffle and located above the open groove, a vertically-through steering groove is formed in the first steering frame, and the end of the electric heating cloth upwards penetrates through the steering groove and then bypasses the end, away from the edge baffle, of the first steering frame to perpendicularly extend downwards. The vertical downward end of the electric heating cloth is connected with an automatic tensioning device used for tensioning the electric heating cloth to be attached to the top of the accumulated snow layer.

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 relates to the technical field of snow depth prediction, and discloses a snowfall process snow depth prediction method based on accumulated snow remote sensing and meteorological intelligent grid fusion, which comprises the following steps: constructing rain and snow phase state separation schemes under three time scales by using a dynamic threshold parameterization scheme and a frequency threshold scheme; analyzing a relationship between meteorological factors such as snow depth and precipitation in a snowfall process by using ground observation data so as to construct a water-snow quantitative conversion relationship model; remote sensing monitoring snow depth data fused by H8, MODISFSC and CheAMSR2 is used as a current snow distribution situation, meteorological factors such as meteorological intelligent grid weather forecast precipitation and air temperature are used as a future scene, and a'water-snow 'quantitative conversion relation mode is used for predicting the depth of accumulated snow which is gridded every 3 hours in the future snowfall process. Quantitative conversion between precipitation and actual ground snow depth in a point-to-surface weather forecast product is realized, the forecast accuracy of the snow depth is improved, and a basis is provided for high-temporal-spatial-precision quantitative snow depth forecast service on a regional surface.

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 relates to the technical field of railway turnout snow melting, in particular to railway turnout active snow melting maintenance equipment and an intelligent control method.The railway turnout active snow melting maintenance equipment comprises a turnout, an electrical cabinet, an air pump, a distribution pipeline at the output end of the air pump and induction heating equipment; the electrical cabinet performs electric control and transmits related signals, the multiple induction heating devices are continuously arranged at the rail web of the inner side of the turnout stock rail and used for heating and melting accumulated snow between the induction heating devices and a switch rail, and meanwhile cold air is input into the two ends through the air pump to cool an internal coil, then secondary heating is performed in a gap between a heat insulation layer and a steel plate, and the melted snow is discharged. According to the invention, turnout snow melting is carried out based on combination of electromagnetic heating and hot air, the snow melting efficiency is high, the energy consumption is low, no influence is caused on a steel rail, the preheating time is short, traffic is opened immediately after snow melting, and meanwhile, intelligent control of self-starting, self-monitoring and self-judging can be carried out according to environment and scheduling information, so that the energy consumption is further reduced.