529 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 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 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 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.

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 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 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.

The utility model relates to the technical field of conveying pipeline protection, and discloses a pipeline external protection mechanism in an extreme environment, which comprises a pipeline, two groups of mounting flanges and a protection component. According to the pipeline external protection mechanism in the extreme environment, the outer side of the pipeline is sleeved with a set of sleeves, the two ends of the sleeves achieve sealing of the contact portions of the pipeline and the sleeves through sealing assemblies, temperature loss caused by air convection is reduced, meanwhile, through the multiple sets of annular pipes in the pipeline and argon filled in the annular pipes and the heat preservation cavity, the temperature loss caused by air convection is reduced; meanwhile, through supporting and shaping of the annular pipe, it is guaranteed that the whole sleeve is not prone to deformation, the annular groove formed in the outer side of the sleeve can guide rain and snow accumulated at the top end of the sleeve to flow downwards, the rain and snow are prevented from being accumulated at the top of the pipeline, and the service life of the sleeve is prolonged. And therefore, the safe use performance of the whole pipeline under extreme climate conditions is improved.

The application discloses a lake expansion contribution decomposition calculation method, extracts lake area and reverses water quantity change through remote sensing image and digital elevation model, discriminates precipitation phase state by using a temperature threshold method, estimates ice and snow ablation amount by combining a degree-day factor method, and calculates total glacier runoff; a single-layer evaporation model is constructed, net radiation, sensible heat, latent heat and water body heat storage and other fluxes are calculated based on a lake surface energy balance equation, and daily lake surface evaporation is obtained; quantile mapping and Bayesian fusion are combined to correct lake surface precipitation; non-glacier runoff is calculated according to a lake water balance equation; the stable period and the expansion period are divided according to the lake area trend, the stable period hydrological mean value is taken as a benchmark to calculate the expansion period abnormal value, and finally, the expansion contribution of each hydrological factor is determined according to the proportion of the abnormal value in the total water quantity increment. Through the method, the contribution intensity of different driving factors is clear, scientific support is provided for simulating lake change and predicting evolution, and ecological protection and management strategies are more targeted.

The invention discloses an intelligent networking driving recording system based on multi-mode perception. The intelligent networking driving recording system comprises a perception layer, a processing layer, an application layer and a block chain evidence storage module. The invention further discloses a data tamper-proofing method of the intelligent networked driving recording system based on multi-mode perception, which comprises the following steps: S1, synchronously acquiring thermal radiation data, radar point cloud data and video image data in a driving environment through the infrared thermal imaging sensor, the 4D millimeter wave radar and the high-definition optical camera; s2, infrared thermal imaging data are optimized, radar point cloud clustering is optimized through a DBSCAN algorithm, and target tracking is achieved based on UKF. The system can cover the whole temperature range of-30 DEG C to 85 DEG C and severe weather such as rain, snow and haze, effectively reduces the false alarm rate of infrared thermal imaging and the missed detection rate of a millimeter wave radar static target, avoids the loss of accident key evidence caused by environmental factors, greatly improves the evidence obtaining reliability at night and in severe weather, and reduces traffic accident disputes.

The application relates to the technical field of road snow-melting and ice-melting, in particular to a snow-melting and ice-melting system and a construction method, the snow-melting and ice-melting system comprising a temperature measuring optical fiber, a DTS host, a control end, a temperature raising device and a water storage device; the temperature measuring optical fiber is laid on the upper layer of a road surface to realize real-time detection of the temperature of the road surface on the path; the temperature raising device is arranged between the road surface and a road surface heat preservation layer, the temperature raising device comprises a water inlet and a water outlet which are communicated with an inner chamber of the temperature raising device; the water storage device is connected with the water inlet and the water outlet; a signal input end of the DTS host is connected with the temperature measuring optical fiber, and a signal output end is connected with the control end; when the temperature value of the temperature measuring optical fiber obtained by the DTS host reaches an icing temperature boundary value, a temperature raising signal is sent to the control end, and the control end controls the water storage device to circulate and pass in hot conductive water into the chamber according to the temperature raising signal. The snow-melting and ice-melting system and the construction method solve the problems that the existing road snow-melting and ice-melting mode has poor effect and causes environmental pollution.

The utility model relates to the technical field of monitoring equipment, in particular to video monitoring equipment with good weather resistance, which comprises a mounting bracket and a monitor body, a protective cover is fixedly mounted on the outer side of the monitor body, a driving motor is fixedly mounted at the middle end of the top of the protective cover, the output end of the driving motor is fixedly connected with a threaded rod, and the threaded rod is fixedly connected with the mounting bracket. A movable plate is in threaded connection with the left end of the outer surface of the threaded rod, guide pin shafts are fixedly connected to the front side and the rear side of the movable plate, limiting bases are movably connected to the outer sides of the guide pin shafts, and a solar cell panel is fixedly installed at the tops of the limiting bases. According to the utility model, under the action of the protective cover, the solar cell panel and the side baffle plates, the monitoring equipment has the advantage of good weather resistance, and the problem that the normal work of the equipment is influenced because the outdoor video monitoring equipment is easy to erode by rain, snow and sand after being used for a long time, especially by the irradiation of hot sunlight in summer is solved.

The utility model discloses an assembly type building pile foundation for a polar region environment, which comprises four pile bodies, a pile platform is movably connected among the tops of the four pile bodies, the outer surface of each pile body is fixedly connected with a stable frame, the inner cavity of each pile body is fixedly connected with a heat conduction pipe, and the heat conduction pipe is fixedly connected with the bottom of each pile body. A protective blanking cap is movably connected between the middle end of the top of the pile body and the upper end of an inner cavity of the heat conduction pipe. Through the arrangement of the heating belt and the heat conduction pipe, ice and snow or an ice cover around the pile body can be heated and melted in the overall installation process, and the ice and snow or the ice cover can be frozen again in the subsequent process of freezing the melted ice and snow or the ice cover. The spiral stable frame and the ice and snow geological structure around the pile body can be distributed and combined again, so that the use stability of the pile body is greatly improved, and the pile platform and the pile body are conveniently locked and fixed by personnel through the arrangement of the mounting studs and the mounting nuts.