982 results about "Snow" patented technology

The invention relates to the technical field of highway traffic safety guarantee, and particularly discloses a highway early warning system and an early warning equipment layout method under ice and snow conditions. The problems that recognition lags, the early warning response has the regional splitting phenomenon and the early warning information has deviation in the common technology are solved. Comprising a data acquisition layer which is used for acquiring meteorological data, road surface data, traffic flow data and vehicle behavior data in real time and eliminating abnormal data through a cross validation mechanism; the analysis decision-making layer analyzes the fused multi-dimensional data based on a multi-factor coupling dynamic risk assessment model, and generates a blue / yellow / orange / red four-level early warning signal; and the early warning issuing layer is used for issuing early warning information according to the early warning grade matching equipment combination. Through multi-source data cross validation and feature fusion, the false report and missing report rate is remarkably reduced, and the risk identification accuracy is improved.

The invention discloses a hybrid model power prediction method fusing an improved snow ablation optimization algorithm and CNN + KAN + LSTM + MSA. The method comprises the following steps: constructing a snow ablation optimization algorithm according to a fusion good point set strategy and a periodic oscillation mutation strategy; and a CNN-KAN + BiLSTM-MSA hybrid model fused with the GVSAO algorithm and a hyper-parameter learning process are constructed. Through a good point set strategy, the initial solution distribution is more uniform, the coverage solution space is wider, and the global search capability is enhanced; the periodic oscillation mutation strategy prevents premature convergence of the solution, keeps diversity of the solution, and ensures exploration of the globally optimal solution; in the local development stage, candidate solutions are refined step by step, and the understanding quality and convergence efficiency are improved; the performance of a snow ablation optimization algorithm is greatly improved, and a global optimal solution can be better found; spatial feature extraction and time sequence learning are combined, so that the problem of time sequence prediction is effectively solved.

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.

Particulate matter, such as fog, snow, rain, steam, vehicle exhaust, debris (plastic bags), etc. may cause one or more sensor types to generate false positive solid surface detections. In particular, various depth measurements may be impeded by particulate matter. Identifying false positive return(s) may comprise clustering lidar points, determining differences in range indicated by two different lidar devices having lidar points in the cluster, determining first differences that are more negative than a negative difference threshold and second differences that are more positive than a positive difference threshold, determining a first portion of lidar data in the cluster associated with the first differences and the second differences is associated with particulate matter or debris, and controlling a vehicle based at least in part on suppressing the first portion of the lidar data or indicating that the first portion of the lidar data is associated with particulate matter or debris.

The embodiment of the invention provides an oxidation kettle temperature self-adaptive control system and a control method. The control system comprises a temperature detection module; the self-adaptive control algorithm module is used for dynamically adjusting the output of the heating or cooling device through data provided by the temperature detection module based on a self-adaptive control algorithm of a snow ablation algorithm; the feedback control module compares the output of the temperature detection module with a set temperature, the feedback control module adjusts control parameters and ensures that the temperature reaches a set value, the feedback control module adopts a double-closed-loop PID control system, and the double-closed-loop control structure comprises a self-adaptive PID controller of an outer loop and a rapid PID controller of an inner loop which are combined; and the man-machine interaction module allows an operator to set a target temperature and a control parameter, and displays a temperature change curve in real time. Through dynamic adjustment of PID controller parameters combined with real-time monitoring, intelligent analysis and an intelligent optimization algorithm and design of a double-closed-loop feedback system, quick response and stable control of temperature control are realized.

The invention relates to the technical field of snowstorm disaster estimation, in particular to a complex terrain snowstorm disaster estimation method, and the method comprises the steps: obtaining high-temporal-spatial-resolution weather forecast data provided by an intelligent grid forecast product; acquiring historical snow disaster data, landform data and social economic data in the snow disaster risk census database; based on the weather forecast data and the snow disaster risk census data, performing data cleaning, fusion and feature extraction by using a machine learning algorithm; according to the extracted features, constructing an evaluation model reflecting a complex relationship between the snowfall and the snow disaster risk; according to the method, the intelligent grid forecasting product and the snow disaster risk census data are fused, so that the risk estimation with high temporal-spatial resolution is realized. The data fusion not only improves the precision of estimation, but also enables the estimation result to reflect the differentiated risk of different areas.

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 relates to the technical field of meteorological prediction and artificial intelligence, in particular to an icing thickness prediction method, system and device based on gray correlation and a medium, comprising: collecting icing multi-dimensional data, and dynamically screening key meteorological features through gray correlation; constructing a data set subjected to cleaning and normalization processing and dividing the data set; a bidirectional gating circulation unit and a multi-head self-attention mechanism are fused to design a hybrid model; performing parameter optimization and test verification based on the training set; redundant interference is eliminated through dynamic feature screening, long-term and short-term icing evolution laws are analyzed bidirectionally, extreme meteorological event feature focusing is enhanced through an attention mechanism, cross-climate-region migration deployment is achieved in combination with a parameter self-adaptive compensation module, prediction precision and ice and snow sudden change working condition early warning capacity are remarkably improved, and intelligent decision support is provided for power grid disaster prevention.

The invention discloses a multi-sensor fusion unmanned vehicle detection method suitable for a complex environment. The method comprises the following steps: obtaining road surface data through a laser radar and an infrared thermal imaging camera; detecting rain and snow weather conditions in real time, and calculating weather environment factors based on rainfall data and snowfall data; dynamically adjusting data weight coefficients of the laser radar and the infrared thermal imaging camera based on the weather environment factors; using a computer vision algorithm to identify an obstacle area, and extracting the distance and height of the obstacle according to the corresponding data weight coefficient; and the obstacle type is analyzed, the minimum safe distance or the minimum torque between the unmanned vehicle and the obstacle is calculated, and a driving decision is provided for the unmanned vehicle. According to the invention, the self-adaptive capability and safety of the unmanned vehicle in a complex environment are improved, the accident risk caused by sudden obstacles is effectively reduced through an intelligent early warning and intervention mechanism, and a solid safety foundation is laid for the wide application of an unmanned driving technology.

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 relates to the technical field of ice and snow environment simulation, and discloses an ice and snow environment simulation test method based on multi-field coupling. The method comprises the following steps: collecting a dynamic multi-field data set in a target ice and snow environment, wherein the dynamic multi-field data set covers a periodic temperature change sequence, a relative humidity fluctuation sequence and surface ice layer thickness real-time monitoring data; carrying out multi-physics field coupling numerical simulation processing to generate coupling field distribution characteristics including an axial heat conduction coefficient gradient, a radial fluid velocity cumulant and a tangential ice layer stress fluctuation coefficient; calling a pre-configured ice and snow growth simulation model to execute ice layer evolution analysis to obtain an ice layer growth rate prediction value and a high-risk icing area position identifier; performing environment dynamic compensation correction on the ice layer growth rate prediction value by using the correlation characteristic of the relative humidity fluctuation sequence and the material heat capacity attribute; based on the high-risk icing area position identification, scientific guidance is provided for related protection in the ice and snow environment.

The invention discloses an automatic cleaning device for photovoltaic panel operation and maintenance, and relates to the technical field of photovoltaic panel operation and maintenance. A cleaning mechanism is arranged on the mounting frame, and a first driving mechanism for driving the mounting frame to slide along the surface of the photovoltaic panel is arranged on the photovoltaic panel; the cleaning mechanism comprises a circulating cleaning mechanism which is mounted on the mounting frame; and a first auxiliary cleaning mechanism. According to the photovoltaic panel cleaning device, by arranging the circulating cleaning mechanism and the first auxiliary cleaning mechanism, an airflow cleaning structure is formed when the photovoltaic panel is cleaned, so that airflow penetrates through a gap between the circulating cleaning mechanism and the photovoltaic panel to be guided out; ice layers and snow clusters condensed on the photovoltaic panel are cleaned through the wind action of the airflow, and the purpose of rapid and efficient cleaning is achieved; and meanwhile, snow particles and other sundries adhering to the circulating cleaning mechanism are cleaned through the wind action, and the continuous cleaning effect of the circulating cleaning mechanism is guaranteed.

The invention relates to a self-adaptive control method for a rain and snow mode of rail transit, electronic equipment and a medium, and the method comprises the steps: comprehensively studying and judging a rain condition based on multi-sensor data and numerical weather forecast data, and calculating a weather grade in the rain and snow mode, the multi-sensor data including video detection data, laser radar data and rainfall data; whether a rain and snow mode is set or not is judged according to the weather level, if yes, the current track slip degree is evaluated according to the multi-sensor data, and the GEBR value is adaptively updated based on the slip degree; the CC calculates the running safety braking envelope of the train by using the updated GEBR value, and feeds back a slip detection result and a dynamic parameter adjustment result in real time; and the ATS dynamically adjusts the operation plan of the train according to the slip detection result reported by the CC, and optimizes the operation scheduling of the whole train. Compared with the prior art, the method has the advantages that self-adaptive management of the rain and snow mode is achieved based on the dynamically-adjusted GEBR, and the running safety and reliability of the train in the rain and snow weather are effectively improved.

The invention discloses a quantitative analysis method for an artificial rain and snow enhancement effect based on fine calculation of an influence zone, which comprises the following steps: acquiring the influence zone in an artificial spreading catalyst diffusion zone according to artificial ice crystal concentration or silver iodide concentration; carrying out extraction and qualitative analysis on the radar products in the influence area; performing space-time matching on the influence area and the radar product, and calculating a mean value and distribution of radar parameters in the influence area; performing spatial distribution and time sequence change analysis on the radar parameters, and calculating parameter change and change rate of the radar product in the influence area; and quantitatively analyzing the parameter change and the change rate, and giving a rainfall enhancement operation effect. The method not only can improve the precision of quantitative analysis of the artificial rain and snow enhancement effect based on fine calculation of the influence area, but also has good interpretability, and can be directly applied to a quantitative analysis system of the artificial rain and snow enhancement effect.

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 relates to the technical field of power system safety monitoring, and provides a millimeter wave radar power transmission line icing monitoring system and method, which can perform all-weather icing monitoring under complex meteorological conditions (such as rain, snow and fog). Frequency domain features are extracted through fast Fourier transform (FFT), inversion of the icing thickness is carried out in combination with algorithms such as support vector regression (SVR), and the icing thickness on the power transmission line can be accurately obtained in real time. Through the wide coverage characteristic of the millimeter wave radar, a large-range power transmission line can be monitored. Compared with a locally installed optical fiber sensor, the system can cover a wider line area by arranging a small amount of radar equipment, the monitoring range is expanded to tens of kilometers, and the installation and maintenance cost of the equipment is greatly reduced; when the icing thickness of the power transmission line and the icing thickness increasing speed of the power transmission line exceed a set safety threshold value, an alarm is triggered to remind a maintainer to clean the line.

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 discloses a heat dissipation opening and closing type power cable which comprises a cable insulating layer, an external sleeving layer, a cable shell and a heat dissipation opening and closing assembly. The dual-operation mode of heat dissipation and closed protection can be achieved, adjustment is conducted through different temperatures, flexible heat dissipation can be conducted at high temperature in summer, closed protection can be conducted at low temperature in winter, and it is avoided that rain and snow enter and freeze the interior; the adjusting ring rotates and drives the multiple movable battens to move in the heat dissipation cavity in the radial direction, when the adjusting ring rotates and enables the inner side of the adjusting ring to abut against the multiple movable battens to move inwards in the radial direction, the opening and closing blocks can be disengaged from the opening and closing holes, and heat dissipation is conducted through the heat dissipation holes, the heat dissipation cavity and the opening and closing holes; when the adjusting ring rotates and enables the closed spring column to eject the movable batten to move towards the outer side, the multiple opening and closing blocks are inserted and closed in the opening and closing holes one by one, so that closed protection is achieved, and flexible adjustment can be conducted.

In order to solve the problem that the snow depth predicted by a sentinel No.1 data set is difficult to meet high spatial resolution and high temporal resolution at the same time, the invention provides a snow depth prediction method, a storage medium and electronic equipment, and the method comprises the steps: obtaining sentinel No.1 data, auxiliary data and station data about the snow depth, constructing a geographic time weighted snow depth parameter based on the site data; carrying out resampling and space-time matching on the data to obtain a first data set; inputting the first data set into an integrated deep learning model to obtain a preliminary snow depth; obtaining time sequence climate data and geographical time data of the research area, performing resampling, and converting the preliminary snow depth data into time sequence data; the resampled time sequence climate data and geographical time data and the time sequence data of the initial snow depth form a second data set; and inputting the second data set into the ResNet-BiLSTM time sequence model to obtain snow depth data. According to the invention, snow depth prediction data with high temporal-spatial resolution can be generated.

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 utility model discloses a rigid-flexible composite honeycomb long-life pavement structure capable of melting snow and ice, which comprises a roadbed layer, a cushion layer, a base layer, a rigid layer and a flexible layer from bottom to top; the rigid layer is composed of honeycomb prefabricated blocks; geothermal heat pipes are placed in the gaps and concave surfaces between the honeycomb prefabricated blocks of the rigid layer itself, the geothermal heat pipes are placed in the width direction of the road surface, the geothermal heat pipes themselves are located at both ends in the width direction of the road surface and are aligned with the positions on both sides of the width of the road surface itself, the geothermal pad pipes themselves are located at both ends in the width direction of the road surface and extend downward to the ground to be connected to the pipe mouth of an air power pump; the flexible layer comprises the concave surfaces in which the geothermal heat pipes are placed between the honeycomb prefabricated blocks of the rigid layer, a flexible filling layer in the gap and a flexible surface layer arranged on the rigid layer; the geothermal heat pipes are made of metal; the utility model ensures that the rigid asphalt pavement can be used for a long time while taking into account the ability to melt snow and ice.

The invention discloses a traffic sign recognition and lane line detection method and system for extreme weather, and relates to the technical field of computer vision and artificial intelligence. According to the method, an efficient image restoration algorithm is designed, the influence of haze, rain and snow and other weather noise on the image quality is eliminated, and key detail information of traffic signs and lane lines is reserved; secondly, a YOLOv8 target detection network is improved, the problems of missing detection of small targets and false detection of complex backgrounds are solved, and the detection accuracy of the multi-scale traffic signs is improved; furthermore, a lightweight lane line detection algorithm is designed based on an improved line anchor mechanism, and the detection precision and real-time performance of the lane line in a complex scene are improved; and finally, an image restoration module, a traffic sign recognition module and a lane line detection module are integrated, multi-modal support of images, videos and real-time camera data is realized, an engineering solution adaptive to extreme weather is formed, and practical application of an intelligent traffic system in a complex environment is promoted.

The invention belongs to the technical field of artificial intelligence, and particularly relates to an AI-based snowfield trajectory and extreme environment intelligent identification method, which comprises the following steps: a multi-modal data acquisition module; a fusion type deep learning model; a confrontation generation data enhancement unit; an adaptive control subsystem; and an edge calculation node deploys a lightweight model inference engine to realize low-delay trajectory feature extraction and classification decision. According to the method, a multi-scale feature pyramid structure and a cross-modal attention mechanism are adopted, the convolutional neural network and the recurrent neural network are combined, and spatial features and modeling time sequence dynamics can be extracted at the same time. The synthetic data of the extreme snowfield scene is generated by using the conditional generative adversarial network, and the physical constraint generator and the multi-scale discriminator are combined, so that the real snow particle motion trail can be simulated, and the global consistency and the local texture authenticity of the generated data are ensured.

The invention relates to the field of transportation, and provides an icebreaking test method and system for an electric pedal, and the method comprises the steps: firstly obtaining test environment information of the electric pedal to analyze a test application scene, configuring test operation parameters according to the test environment information, and constructing an icebreaking test simulation environment; analyzing running state data of the electric pedal in a simulation environment, calculating a power loss rate, adjusting a power supply strategy according to the power loss rate, then carrying out multiple times of icebreaking tests, obtaining an icebreaking record set, calculating an icebreaking efficiency index, constructing an evaluation framework, predicting icebreaking effects under different ice layer hardness by using the framework, and analyzing stress change and key influence factors; and finally, connecting the road surface detection module according to the key factors to obtain a test pedal, collecting real-time icebreaking data, detecting optimization points, and generating an icebreaking optimization report. The use quality of the electric pedal in the cold ice and snow environment can be improved.

The invention relates to the technical field of hydrogen exchange robots, and discloses a hydrogen fuel heavy truck movable hydrogen exchange robot which comprises a movable truss, movable tables are fixed to the two ends of the movable truss, first movable wheels are installed below the movable tables, a sliding rail is arranged below the movable truss, the first movable wheels are located on the sliding rail, and the first movable wheels are located on the sliding rail. The movable truss is provided with a [-shaped seat, the two sides of the [-shaped seat are each provided with a second movable wheel, the movable truss is provided with two strip-shaped grooves, and the second movable wheels are located in the strip-shaped grooves; in the hoisting and replacing process of the hydrogen tank module, rain and snow attached to the top of the hydrogen tank module can be blown to the two sides through the first blowing nozzles fixed to the mounting plate in rainy and snowy weather, so that the replaced hydrogen tank module can reduce the problems of short circuit and damage caused by contact between the rain and snow and electronic equipment in the hydrogen charging equipment in the charging process of the hydrogen charging equipment.

The present invention provides a quantitative analysis method on influence of black carbon emission to glacier change. The method includes describing a process of dry and wet snow deposition influencing albedo change by taking a specific surface area as a description parameter, calculating an specific surface area increase within a step length at the current moment according to the specific surface area at the previous moment in combination with the change of water content, and deducing to obtain a target specific surface area; establishing a light-absorbing impurity-albedo dynamic evolution model according to the target specific surface area and black carbon concentration; further coupling to an enhanced temperature index model by taking the albedo outputted by the light-absorbing impurity-albedo dynamic evolution model as a link; and calculating mass balance on a surface according to an enhanced temperature index model, and taking the mass balance as an input of a glacier dynamic model to obtain glacier change amount. The established light-absorbing impurity-albedo dynamic evolution model is coupled with the glacier dynamic model containing the enhanced temperature index model and the dynamic process to evaluate the quantitative influence of black carbon emission in history and future on the glacier change.

The invention provides an expressway bridge deformation detection early warning method based on a cold region environment, which relates to the technical field of bridge structure health monitoring and comprises the steps of monitoring point arrangement and equipment installation, real-time data acquisition and transmission, data processing and analysis, early warning threshold setting and early warning grading, early warning response and disposal, and maintenance and updating. Through the temperature compensation and protection device, the influence of severe environments such as low temperature, wind and snow in the cold region on the performance of the sensor is effectively solved, long-term stable operation of the monitoring system is ensured, and the micro deformation of the bridge in the complex environment of the cold region can be comprehensively and accurately captured by adopting multi-source sensor data fusion and a deep learning algorithm. Through scientific and reasonable early warning threshold setting and a grading early warning mechanism and in combination with the bridge deformation prediction model, potential safety hazards of the bridge can be found in time, early warning signals are accurately sent out, and powerful guarantee is provided for safe operation of the bridge.

The invention relates to the technical field of ice bridges, and discloses an ice bridge for river-crossing transportation in a cold region and a construction method. The pine rods perpendicular to the water flow direction are placed perpendicular to the water flow direction and used for enhancing the ice bridge structure; the pine rods parallel to the water flow direction are placed parallel to the water flow direction and are alternately matched with the pine rods perpendicular to the water flow direction to form a stiffened framework of the ice bridge; the natural ice surface serves as a foundation of the ice bridge and bears transportation loads. The designed width of the ice bridge is 6-8 m, and the snow cleaning width of the ice surface at the ice channel is 20-25 m. The bearing capacity and stability of the ice bridge can be greatly improved through a reasonable construction method such as multi-layer ice layer reinforcement and wooden pole framework supporting. The basic layer of the ice bridge adopts the basic ice layer with the thickness of 50cm, and the strength of the second reinforcing ice layer is further enhanced, so that the bearing capacity of the ice surface is enhanced, and heavy vehicles and mechanical equipment can be supported.

The invention discloses a ventilation type anti-radiation device, and relates to the technical field of anti-radiation covers, the ventilation type anti-radiation device comprises an upper cover body and a lower cover body, the upper cover body and the lower cover body are detachably connected, the upper cover body and / or the lower cover body are / is provided with flow guide grids for airflow circulation and spiral rising, no gap exists in the overlook direction of the flow guide grids, and gaps exist in the side view direction of the flow guide grids. A meteorological sensor is installed in the upper cover body. The ventilation grid has the capabilities of guiding flow and enhancing airflow circulation, accelerates air circulation, further effectively reduces the temperature in the cover body, is simple in structure, low in cost and convenient to produce and maintain, fully considers radiation and rain and snow protection, and is superior to an existing protective cover framework in ventilation efficiency.