606 results about "Snow" patented technology

The invention provides a snow eave growth monitoring system based on terrain coupling and a dynamic evolution model, and relates to the technical field of mountain snow disaster monitoring. A sensitive area identification module is used for obtaining environment data of ridge terrain and a wind field, constructing a wind and terrain micro-scale coupling tensor field according to the environment data, and sending the wind and terrain micro-scale coupling tensor field to a dynamic evolution module; identifying a potential formation sensitive area of the snow eave according to a wind and terrain micro-scale coupling tensor field; and the dynamic evolution module is used for establishing a dynamic evolution model fusing pneumatic jump accumulation and a microcosmic sintering mechanism according to the identified snow eave potential formation sensitive area, simulating a snow eave generation and growth process according to the dynamic evolution model, and obtaining a structure expansion result generated by the snow eave according to simulation. According to the invention, a sensitive area identification module fusing a wind field and a terrain micro-scale coupling tensor field is constructed, and a snow eave structure evolution model, a multi-dimensional sensing data fusion mechanism and a zoning risk output module are combined, so that the growth process of a snow eave under specific terrain and meteorological conditions is dynamically described.

The invention relates to the technical field of intelligent early warning, and discloses a risk early warning method and system for low-temperature rain, snow and freezing meteorological disasters, and the method comprises the steps: collecting multi-source data, and constructing a heterogeneous graph; preprocessing the data to obtain a training and reasoning sample; a graph neural network is constructed on the heterogeneous graph, joint learning is carried out on node time sequence features and edge relations, and graph representation representing cold air invasion, rain and snow zone movement and risk propagation delay is obtained; outputting road surface temperature evolution, icing threshold arrival time and wire icing growth rate based on the physical guidance neural network; fusing the features obtained by the graph neural network and the features obtained by the physical guidance neural network, outputting an asset-level risk score and a confidence interval thereof, and obtaining a predicted arrival time; and establishing early warning according to the prediction result and the observation result. According to the invention, finer and more reliable grading risk early warning is provided, and the accuracy, interpretability and practicability of early warning are improved.

The invention discloses a glacier area surface water resource distribution image extraction method based on deep learning, and belongs to the field of surface water resource image extraction, and the method comprises the steps: obtaining a multispectral remote sensing image covering a glacier area and digital elevation model data, and carrying out the data preprocessing; building a YOLOv12 instance segmentation model comprising a backbone network, a neck network and a detection head, and constructing an improved YOLOv12 segmentation model; carrying out model training and optimization; segmenting and extracting surface water resources through the optimized segmentation model; processing is performed after segmentation is completed, and finally an optimized multi-class surface water resource distribution diagram is generated. According to the method, based on an improved YOLOv12 network architecture, the glacier area surface water resources in the remote sensing image are comprehensively and automatically extracted by fusing vision, a convolution block attention module and a space-to-depth convolution technology, the problems of high reflection of ice and snow, ice cracks, complex stone glacier textures and fuzzy edges are effectively solved, and the segmentation precision is improved.

The invention discloses a method for testing weather resistance of a material based on a low-temperature ice and snow environment, which belongs to the technical field of material testing, and comprises the following steps: generating a dynamic environment parameter set according to an environment simulation script, applying dynamically changing ice and snow loads and multiple physical and chemical environment stresses in a preset composite environment stress cabin, constructing a coupling environment field acting on the to-be-tested sample; and acquiring the state of the to-be-tested sample in the coupling environment field in real time, generating a multi-dimensional monitoring data set, performing comparative analysis on the multi-dimensional monitoring data set and a preset material aging characteristic template, generating material response data representing the current state of the to-be-tested sample, and constructing an environment parameter adjustment instruction set to regulate and control the coupling environment field in the composite environment stress cabin in real time. According to the method, a closed-loop feedback control strategy combining construction of a dynamic coupling environment field and real-time monitoring of material response is adopted, intelligent acceleration of the testing process can be achieved on the premise that the authenticity of a failure mechanism is guaranteed, and the testing efficiency and the accuracy of a result are remarkably improved.

The invention discloses an intelligent control system and method based on an intelligent indication board, and relates to the technical field of intelligent traffic and Internet of Things control, and the method comprises the following steps: building a unified time mark baseline, collecting an exposure sequence, a saturation threshold sequence and a track residual error, and generating an exposure-track coupling spectrum for calibrating a rain and snow triggering time window; and calculating phase residual mapping under the constraint of an exposure-trajectory coupling spectrum, identifying an exposure oscillation root cause, extracting a saturated source region and a trajectory breakpoint set, and generating a fault anchor point. According to the method, rain and snow interference is identified through a time mark base line and a coupling spectrum, a fault anchor point is constructed, an optical path is played back to assess risks, multi-source signals are fused to generate a steady track, conjugate correction and feedforward constraint are adopted to suppress oscillation, a time reversal closed-loop instruction and multi-strategy joint debugging are combined, and self-adaptive optimization of exposure and induction information is achieved. And the stability and the control precision of the intelligent indication board in extreme weather are improved.

The invention discloses a power transmission line insulator dual defense system and method, and belongs to the technical field of power transmission line maintenance, in the system, a power supply subsystem generates, stores and conveys high-pressure gas, and a primary defense subsystem and a secondary defense subsystem are respectively communicated with the power supply subsystem through high-pressure conveying hoses to obtain a high-pressure gas source; the first defense subsystem is arranged around an insulator of the power transmission line and forms an annular air curtain to prevent rain and snow from being attached to the surface of the insulator; the secondary defense subsystem detects and removes rain and snow breaking through the annular air curtain; the photovoltaic power supply subsystem is used for providing power for the whole defense system; all the subsystems are electrically connected with the intelligent control subsystem, and the intelligent control subsystem is used for coordinating starting, operation and parameter adjustment of all the subsystems. The problems that an existing anti-icing technology is high in energy consumption and slow in response, an icing micromechanism is difficult to capture, and protection is insufficient due to the fact that extreme working conditions are difficult to reproduce are solved, and efficient blocking and removing of rain and snow are achieved.

The invention relates to sensing and tracking of intelligent driving in rain, snow, fog, night and large shielding scenes. In order to solve the problems of target invisibility, track interruption and misconnection caused by low visibility, a shielded dynamic target identification and tracking method based on deep learning is provided; according to the method, under a unified spatial index, sparse point cloud and road topology, passable and traverse areas, static shielding volume coding, ray marking visibility boundary and shielding entrance and exit are carried out; generating a motion voting field with consistent visibility through multi-agent situation reasoning, performing normal directional enhancement according to a passable boundary, and extracting a risk corridor; voxelization is carried out on the multi-frame point cloud in the corridor, a dynamic sparse voxel map is constructed, a voting field is used for gating cross-frame edge connection, occupation changes and infinitesimal displacement are aggregated, and three-dimensional verification candidates, limited state estimation and a time continuous track are obtained; outputting the target and the corridor to which the target belongs, and forming a potential conflict zone according to the intersection of the target and the own vehicle path; the reproduction rate and the advance are improved, and cross-lane misconnection is reduced.

The invention relates to the technical field of remote sensing image recognition, in particular to a method for determining a moso bamboo forest snow disaster affected area based on a GEE platform. The method mainly solves the problems of low efficiency, high cost and difficulty in realizing large-range rapid evaluation caused by dependence on manual field investigation in the prior art. According to the technical scheme, the method comprises the following steps: firstly, obtaining a multi-temporal Sentinel-2 image before and after a snow disaster, and carrying out the preprocessing of the multi-temporal Sentinel-2 image; extracting spectral bands, vegetation indexes and texture features to form an initial feature set; then, a key feature variable combination is determined through statistical significance filtering and machine learning optimization; on this basis, establishing a logistic regression discrimination model and determining an optimal classification threshold; and finally, carrying out snow disaster state classification on the moso bamboo forest region by utilizing the trained model, and generating a disaster region spatial distribution diagram.

The invention discloses a sudden snow disaster risk degree probability prediction method oriented to insufficient historical time sequences. The method comprises the following steps: step 1), constructing a multi-index risk index based on related data such as historical snow disasters; 2) performing time sequence modeling on the violent snow disaster risk index by adopting an autoregressive integral moving average model, and capturing trend and periodic characteristics of the violent snow disaster risk index; 3) discretizing a continuous risk index of modeling production into a plurality of risk levels, and constructing a state transition probability matrix; step 4), introducing space neighborhood influence and carrier exposure constraint, and constructing a Markov space weighted state transition model; 5) coupling CMIP6 climate scene data, and dynamically adjusting future state transition probability, and 6) fusing ARIMA trend prediction and Markov state transition probability, and outputting future multi-period snowstorm disaster risk level probability distribution.According to the method, the accuracy of snowstorm disaster risk prediction and the modeling ability and prediction adaptability of future climate change scenes are improved.

Three-dimensional target detection in complex weather is a key problem in intelligent driving, and extreme weather is easy to interfere with a sensor, so that the detection precision is reduced. The invention provides a three-dimensional target detection method in complex weather based on millimeter wave radar and vision fusion, and designs an image generation module for simulating complex environment images such as rainfall, snowfall and the like aiming at the problem of scarcity of bad weather image data in a public data set. And by adopting a self-distillation mechanism, multi-modal knowledge learned by the teacher model in normal weather is migrated to the student model trained in bad weather, so that the detection performance in a complex environment is improved. Besides, in order to enhance the multi-modal information fusion capability, gating fusion modules are introduced into the teacher model and the student model, and a learnable gating fusion mechanism is utilized to dynamically adjust weight distribution of multi-modal feature fusion. According to the method, the robustness and the detection precision of the model under the complex weather condition are effectively improved.

The invention discloses an artificial snow increasing effect evaluation method, belongs to the technical field of meteorological engineering, combines MOS correction, radar assimilation, micro-physical diagnosis and cold cloud catalysis simulation to construct a simulation-catalysis-verification full-chain artificial snow increasing evaluation system, and is suitable for artificial snow increasing operation optimization and effect verification in a complex terrain area. According to the method, radar data is assimilated through a mesoscale mode to optimize forecast, catalytic simulation and actual operation comparative analysis are combined, the snow increasing effect is obtained through quantification, the artificial snow increasing efficiency is further improved, and the method can be expanded to other artificial influence weather scenes such as rain increasing and hail suppression.

The invention relates to the technical field of power transmission lines, in particular to a power transmission line icing galloping multi-mode prediction method fusing wire dynamic tension and meteorological data, and aims to solve the problems of low prediction precision and non-dynamic risk assessment in the prior art. The method comprises the following steps: by constructing a multi-source data acquisition module, acquiring data such as frequency, amplitude, acceleration and angular velocity in dynamic tension, weather and galloping characteristics of a lead, and lead current and video monitoring data; performing cleaning, synchronization and feature extraction on multi-source data, such as icing thickness and snow form; the method comprises the following steps: constructing a multi-modal prediction model, carrying an attention mechanism, and outputting a galloping amplitude, frequency, mode and dynamic tension prediction result; and in combination with the safety threshold and the equipment parameters, intelligent identification alarm and dynamic risk assessment are realized. According to the invention, icing galloping prediction precision and risk assessment timeliness are improved, and safe operation of the power transmission line can be effectively guaranteed.

The invention discloses an integrated photovoltaic support wind and snow coupling testing device and method.The testing device comprises a box body and a measuring assembly, a snowfall simulation mechanism is arranged at the top of the box body with an air inlet and an air outlet formed in the left side and the right side, and an aeroelastic model is arranged at the bottom of the box body and comprises a supporting plate, a supporting frame and a photovoltaic panel; the supporting plate is connected with a bottom plate of the box body through an angle adjusting mechanism; the method comprises the following steps: manufacturing a testing device; the inclination angle of the photovoltaic panel is adjusted, and the dynamic response of the photovoltaic panel in the wind environment and the wind and snow environment is tested; and obtaining a load combination effect value of the photovoltaic panel under the wind and snow coupling effect according to the test data. A photovoltaic support and an integrated photovoltaic module are simulated by making an aeroelastic model, the size of a wind load and a snow load of a photovoltaic panel in a wind and snow coupling environment and parameters under the wind load are obtained, and a wind and snow coupling load combination effect value is obtained. According to the method, the fluid-solid coupling effect between the wind and snow and the photovoltaic module can be comprehensively considered, and the actual behavior of the photovoltaic module under the wind and snow effect is truly reflected.

The invention relates to the technical field of atmospheric monitoring, and discloses an atmospheric dry-wet deposition automatic sampling and monitoring device, which comprises: a dry hopper for collecting atmospheric dry deposition; the wet hopper is used for collecting atmospheric wet deposition; the shielding mechanism can shield the wet hopper when the dry hopper collects the atmospheric dry deposition, can also monitor whether rain and snow exist in the environment, and collects the atmospheric wet deposition through the wet hopper and shields the dry hopper at the same time after the rain and snow are monitored; the driving mechanism can drive the shielding mechanism to be switched between the dry hopper and the wet hopper; the at least two vibration mechanisms can detect whether animals stay on the dry hopper or the wet hopper or not, and the vibration mechanisms can drive the animals staying on the dry hopper and the wet hopper in the process that the driving mechanism drives the shielding mechanism to be switched between the dry hopper and the wet hopper. According to the automatic sampling and monitoring device for atmospheric dry and wet deposition, animal staying can be detected through the vibration mechanism with the built-in pressure sensor, and then the driving mechanism drives the vibration mechanism to shake and expel.

A snow drop prevention tool (1) includes: a first member (10) having a first upright wall (11), a first securing section (12) extending from an upper end of the first upright wall (11), a first coupling section (13) extending toward an opposite side from the first upright wall (11), and a snow stopper (16) protruding upward from the first securing section (12); a second member (20) having a second upright wall (21), a second securing section (22) extending from an upper end of the second upright wall (21), and a second coupling section (23) extending above the first coupling section (13) from the second upright wall (21); and a bolt (30), the second coupling section (23) being provided with a long hole (24) through which the bolt (30) is inserted, the first coupling section (13) being provided with a female threaded hole (14) to which the bolt (30) is screwed.

The invention discloses a train safe driving guarantee control system based on the Internet of Things, and particularly relates to the field of train safe driving. Comprising a multi-node data interaction screening module, a data space-time alignment association module, an ice and snow environment comprehensive influence quantification module, a wheel-rail contact stability dynamic evaluation module, a safe driving boundary dynamic calculation module, a multi-dimensional control strategy execution module and a dynamic closed-loop feedback optimization module. The multi-node data interaction screening module is used for constructing a multi-node interaction link through a wireless communication technology to obtain multi-dimensional data and screening effective parameters; according to the method, the multi-dimensional space-time association data matrix is constructed through multi-node real-time interaction and in combination with the space-time alignment technology, the problems that data fragmentation is caused, space distribution monitoring is insufficient, and the global state is deduced only depending on single-point data are solved, the ice and snow environment assessment accuracy is improved, and reliable data support is provided for accurate control of a train.

The invention belongs to the technical field of point cloud data noise reduction, and particularly discloses a point cloud data noise reduction method and system, a terminal and a medium. Comprising the steps of obtaining original point cloud data of a target scene; for any point in the point cloud, calculating the local point cloud density based on the spatial distance from the point to a plurality of nearest neighbor points in the neighborhood of the point, and determining the neighborhood search radius of the point according to the local point cloud density; constructing a neighborhood point set according to the neighborhood search radius, and extracting local structure consistency features in the neighborhood point set; based on the local structure consistency feature, generating a judgment condition for judging a noise point, and judging whether the target point is the noise point or not according to the judgment condition; and filtering the points which are judged to be noise points from the point cloud data to obtain the point cloud data after noise reduction. Through adaptive neighborhood construction, local structure consistency feature modeling and dynamic generation of noise discrimination conditions, robust recognition of point cloud noise points under complex weather conditions such as rain and snow is realized, and improvement of the overall quality of point cloud data is facilitated.

The invention discloses a power transmission line insulator severe weather defect detection method based on a YOLOv10-MPM algorithm, and the method comprises the steps: designing different image enhancement strategies to simulate a natural scene based on an original visible light image, and constructing a synthetic data set containing three typical complex weather scenes, namely fog, rain and snow; sequentially dividing the synthetic data set into a training set, a verification set and a test set according to a proportion, and dividing data set labels into flashover, defect and insulator according to defect types; a designed multi-shape coordination attention mechanism MSSA is used for replacing an original point mode space attention module PSA in the YOLOv10n model; a C2f module is improved by using an MOCAA module; pIOU2 is adopted to replace a traditional loss function; carrying out lightweight design on the model by using LAMP pruning; therefore, a YOLOv10-MPM model is constructed; and training the YOLOv10-MPM model based on the training set, and detecting the model by the verification set. According to the method, the detection precision is improved, and the size of the algorithm model is greatly compressed, so that the algorithm model can be conveniently deployed on edge equipment such as an unmanned aerial vehicle.

The invention discloses a target image recognition and target detection method based on a video enhancement algorithm, and relates to the technical field of image processing. The method comprises the following steps: firstly, receiving a rain, snow and fog scene video stream through a visual sensor, extracting a video frame target image, performing video enhancement processing, eliminating rain and snow shielding, fog blurring and noise, and generating an effectively enhanced image which is complete in target contour, clear in details and adaptive to subsequent detection; inputting the image into an improved YOLO model of the rain, snow and fog scene, completing feature extraction and category recognition through an optimized feature extraction network, outputting a preliminary target bounding box and a category label, and judging whether a target to be detected and a specific category exist or not; and finally, if the target exists, counting the detection data and carrying out validity verification, thereby realizing high precision, low misjudgment and strong real-time performance of target detection in severe weather of rain, snow and fog, and further effectively solving the problem of high detection result misjudgment rate caused by parameter adjustment lag of adaptive filtering in the prior art.

The invention discloses a three-dimensional vehicle tracking method based on multi-modal fusion neural integrated Kalman filtering, belongs to the field of intelligent traffic and automatic driving perception, and aims to solve the problems of insufficient target detection robustness and the like caused by extreme weather (rain and snow), illumination variation, rapid vehicle turning and long-term shielding in a highway scene. The method comprises the following steps: constructing a multi-scene vehicle tracking data set; a cross-modal interaction module is designed, and the point cloud and the image features are fused through BEV-to-Image and a Projection Transform; a neural integrated Kalman filtering module is provided, and a nonlinear state transition matrix is learned in combination with LSTM and an attention mechanism; constructing a trajectory life cycle optimization module, and predicting a target survival probability by using historical motion features; a frame sensing multi-similarity cascade association module is designed, and identity switching is reduced through differential matching. The method can deal with sparse point cloud, fast acceleration and long-term shielding scenes, improves the tracking precision and robustness, meets the real-time requirements, and provides support for the perception of automatic driving expressways.

The invention relates to the technical field of industrial equipment intelligent monitoring, in particular to a power transmission cable monitoring image enhanced recognition and false alarm prevention method under ice and snow conditions, which comprises the following steps: S1, constructing a training data set, and preprocessing images in the training data set; the training data set comprises an image pair composed of a degraded image and a corresponding real clear image, namely a degraded-clear image pair; s2, constructing an improved SRGAN model for image enhancement, wherein the improved SRGAN model comprises a generative network and a discrimination network; s3, training the improved SRGAN model by using the training data set constructed in the step S1, and performing parameter optimization by using an RMSProp optimizer in network training; s4, performing enhancement processing on the input degraded image through the network model obtained through training in the S3; the problems that the efficiency is low and potential faults cannot be found in time in two existing modes for checking freezing of glaze, rime or wet snow of the camera are solved.

The invention provides an intelligent digital platform based on cold region highway disaster emergency disposal, and relates to the technical field of cold region highway emergency management. Comprising a cold region highway disaster database and knowledge graph module, an intelligent emergency disposal scheme recommendation module, a dynamic resource allocation module, a data updating and maintenance module, a platform iterative optimization module and a man-machine interaction module. According to the method, by combining the geographical environment and the knowledge graph of the cold region, the recommendation scheme considers the particularity of disasters such as ice and snow, frost heaving and the like, such as snow-melting agent type selection at low temperature, avoiding blind application of a general scheme, improving the disposal accuracy by more than 40%, and by priority ranking and path optimization, the resource arrival time is shortened by 30-50%, resource idling and waste are avoided, and the method is suitable for popularization and application. The emergency cost is reduced by 20%, the efficiency of resource allocation of the platform is improved, and based on user feedback and new technology iteration, the platform can adapt to new disaster types in cold regions, such as road surface frost cracks caused by extreme cold waves, and the practicability is kept for a long time.

The invention discloses a power transmission line icing prediction method, system and device based on satellite data assimilation and a medium, and relates to the technical field of ice and snow monitoring, and the method comprises the steps: obtaining satellite microwave radiance observation data, fusing the observation data and a numerical mode background field through data assimilation, generating grid meteorological data, and carrying out the prediction of the icing of a power transmission line. The method comprises the following steps: processing gridding meteorological data by using a spatial-temporal feature joint modeling technology, reconstructing meteorological data of continuous time steps into a spatial-temporal coupled four-dimensional tensor, synchronously extracting spatial-temporal dimension dynamic evolution features through a three-dimensional convolution kernel, and fusing historical states and current features based on a gating mechanism to obtain a spatial-temporal feature fusion model; and outputting a spatial distribution prediction result of the icing thickness of the power transmission corridor. According to the method, the high-precision meteorological field is obtained, the time-space dynamic characteristics of icing evolution are synchronously extracted, the key meteorological factors are screened in combination with an improved gating mechanism and attention, and accurate closed-loop simulation of power transmission corridor icing is realized based on iterative prediction of physical constraints, so that the accuracy and reliability of icing early warning are remarkably improved.

The invention discloses a polar region daytime sea fog / low cloud detection method and device based on multi-source polar orbit satellites and a medium. The method comprises the steps that remote sensing image data collected by the multi-source polar orbit satellites are acquired and preprocessed; calculating a sea surface identification index by using data of two preset wave bands in the preprocessed remote sensing image data, and jointly removing areas related to seawater and sea ice / snow in the preprocessed remote sensing image data according to the sea surface identification index and near-infrared wave band reflectivity data in the remote sensing image data; for each pixel of a third preset wave band in the remote sensing image data from which the seawater, the sea ice and the snow are removed, calculating a gray-level co-occurrence matrix of the pixels, and counting a plurality of texture features based on the gray-level co-occurrence matrix; and inputting the statistical texture feature values corresponding to the remote sensing images corresponding to all polar orbit satellites into the trained random forest model, identifying and removing medium and high clouds in the random forest model, and obtaining sea fog / low clouds as the remainder. The influence of satellite difference and solar altitude angle change on the method is small.

The invention discloses an ice and snow airport runway flatness measuring system and method based on point cloud, and the system comprises an unmanned plane platform which is used for carrying a data collection module; the data acquisition module is used for synchronously acquiring a runway binocular image and sparse LiDAR point cloud data; the image-to-point cloud module is used for converting the binocular image into dense visual point cloud based on an improved MonSter algorithm; the point cloud fusion module is used for carrying out registration and fusion on the sparse LiDAR point cloud and the dense visual point cloud to generate three-dimensional point cloud data; the data processing module is used for processing the fused three-dimensional point cloud data; and the flatness analysis module is used for calculating an international roughness index and a flatness index of the runway based on the processed three-dimensional point cloud data, and performing automatic evaluation and visual display. The method has stability and measurement precision in a low-temperature, high-reflection and snow-ice covering environment, and quantitative basis and scientific decision support are provided for snow-ice airport runway maintenance.

The invention relates to the technical field of computer vision and image processing, in particular to a container detection method and system oriented to cross-modal adaptive fusion. The method comprises the following steps: acquiring a visible light image, an infrared thermal imaging image and millimeter wave radar point cloud data in a port scene; constructing a multi-modal adaptive fusion deep learning model containing an environmental perception gating network; the environment sensing gating network analyzes the current environment parameters in real time, and dynamically calculates and outputs the fusion weight of each mode; based on the fusion weight, adaptively fusing the features extracted from each modal; and synchronously identifying the container number and detecting the physical state, the safety state and the operation state of the container by using the fused features. According to the method, the accuracy and robustness of port container identification and state detection under severe weather conditions such as fog, rain, snow and night are remarkably improved, and the safety and efficiency of port automatic operation are guaranteed.

The invention discloses a cold region expressway tunnel group collaborative speed limiting method based on variable cells, relates to the technical field of traffic safety, and aims to solve the problem that the congestion risk of a tunnel group is increased due to the fact that a driver cannot accurately limit the vehicle speed due to deterioration of a driving environment under the condition of rainy and snowy weather in a cold region. And various traffic flow indexes under the actual weather influence are calculated. And by utilizing the tunnel group speed limit control target function, based on the traffic condition and weather condition of the tunnel group, the tunnel group cooperative speed limit is dynamically adjusted, and a driver is assisted to limit the vehicle speed, so that the tunnel group congestion risk caused by the fact that the driver cannot accurately limit the vehicle speed under the cold region rain and snow weather condition is reduced.

The utility model discloses a double-sided solar crystalline silicon cell module which comprises a bottom plate and further comprises an adjusting mechanism, the top of the bottom plate is provided with the adjusting mechanism, and the adjusting mechanism comprises a supporting rod, a connecting plate, a rotating shaft A, a frame, an inserting hole, a spring A, an inserting rod, a back plate, a crystalline silicon cell piece, an EVA film, tempered glass and a polycarbonate plate. The two sides of the top of the bottom plate are both vertically and fixedly connected with supporting rods, the tops of the supporting rods are both provided with connecting plates, when a user is about to enter severe weather environments such as sand, dust, wind and snow, inserting rods can be pulled out of the surfaces of the connecting plates, then the frame is turned over along a rotating shaft A, one side of a polycarbonate plate faces upwards, a photovoltaic module on the other side is used for power generation, and although the light transmittance is reduced, the photovoltaic module is not damaged. However, the photovoltaic module has high strength and excellent impact resistance, can reduce the influence of the external environment on the photovoltaic module in the frame, protects the normal use of the photovoltaic module, adopts a double-sided structure, adapts to different working environments by different surface layers, and reduces the maintenance of the photovoltaic module.

The utility model belongs to the technical field of coal bunker retaining walls, and particularly relates to a large-diameter round coal bunker retaining wall structure which comprises a coal retaining wall, buttresses and a foundation, the coal retaining wall is round, the buttresses are distributed on the outer side wall of the coal retaining wall in a round array shape, the top end of the foundation is connected with a round limiting block, and the round limiting block is connected with the coal retaining wall. A group of drainage holes are formed in the surface of the round limiting block in a round array shape in a penetrating mode, and a water collecting groove is reserved between the round limiting block and the coal blocking wall. Therefore, the problem that the rigidity of the flexible steel beam is gradually worse due to the influence of complex weather conditions such as strong wind and rain and snow is effectively reduced. According to the design, the durability of the retaining wall structure of the large-diameter circular coal bunker is improved, accumulated water in the water collecting tank can be conveniently discharged to the outside through the arranged drainage holes, the water dredging efficiency is improved, and the corrosion risk of residual water to the coal retaining wall is further reduced.

The invention discloses a rapid deployment method and system for a mobile high-voltage chamber, and relates to the technical field of high-voltage chamber deployment. According to the method, preliminary exploration is completed by establishing an exploration machine group composed of a plurality of unmanned aerial vehicles, and a deployment area is formed in combination with ground personnel checking and site optimization; after being preassembled in a factory, the mobile high-pressure chamber is transported by a heavy truck with a hydraulic suspension system and is matched with laser positioning and AR alignment to realize high-precision hoisting; electrical equipment adopts an automatic docking mechanism composed of a prefabricated cable primary and secondary connector and an electric push rod to complete docking, and is equipped with an annular air bag to deal with rain and snow weather. The problems of low investigation efficiency, poor positioning precision, insufficient electrical docking reliability and poor protection in severe weather in traditional deployment are solved, the mobile high-voltage chamber deployment efficiency and stability are remarkably improved, and the mobile high-voltage chamber deployment method is suitable for electric power emergency guarantee, electric power construction in remote areas and temporary power utilization scenes.