1655 results about "Weather condition" patented technology

A system and method of calculating an estimated time of arrival for a traveler is provided, along with a notification of this estimated time that is sent to a third party. The traveler is equipped with a navigation unit having a global positioning sensor and a wireless communications device. The navigation unit communicates with a service center equipped with a server capable of generating a preferred navigational route for the traveler. The service center also includes a traffic database for maintaining current traffic information. Once the traveler has selected a predetermined time and frequency of notifications based upon a selected criterion, the navigation unit transmits the traveler's position and velocity to the service center. The service center then calculates the estimated time of arrival based upon the traveler's position, velocity, distance to be traveled and traffic and weather conditions. Once this time is calculated, the service center sends a notification to the third party.

The invention relates to a low-altitude logistics unmanned aerial vehicle path planning method, which comprises the following steps of 1, discretizing an urban low-altitude region into computable grid units, a dynamic-static obstacle classification modeling technology is combined to endow a static obstacle with a basic risk degree, a height attenuation factor is introduced to quantify the influence of a high-altitude obstacle on a low-altitude path, and a collision risk of a dynamic obstacle is dynamically evaluated through a time dimension risk degree prediction model; 2, based on an improved genetic algorithm, comprehensively considering flight time, height change, risk degree and dynamic risk cost through a multi-objective optimization objective function, and generating a globally optimal task allocation scheme; 3, generating a local optimal three-dimensional path considering meteorological conditions and dynamic obstacle influence by adopting an improved particle swarm algorithm; and 4, performing path planning to feed back actual flight time and energy consumption to task allocation for optimization.

The invention relates to a multi-scene self-adaptive high-speed obstacle early warning method and device, equipment and a medium. The method comprises the following steps: firstly, acquiring multi-source data of traffic sensing equipment, and carrying out space-time calibration and semantic analysis on the data to obtain a vehicle track feature vector set; thirdly, calculating real-time traffic flow parameters of the gridding area according to the feature vector set, and further generating a traffic feature database; secondly, modeling is carried out on the traffic characteristic database according to time periods and weather, and theoretical speed baseline parameters of all scenes are obtained; and finally, constructing a multi-dimensional feature matrix according to the theoretical speed baseline parameters, inputting the multi-dimensional feature matrix into the classification model to calculate an abnormal probability value, and if it is detected that a continuous abnormal probability value exceeds a dynamic threshold value in a preset time window, generating an early warning signal. By adopting the method, real-time monitoring and early warning of obstacles in multiple scenes of the expressway can be realized, and more effective guarantee is provided for driving safety of the expressway.

The invention provides an aircraft digital twinning situation deduction and risk assessment method in a complex meteorological environment, and belongs to the technical field of data processing, the aircraft digital twinning situation deduction and risk assessment method in the complex meteorological environment comprises the following steps: collecting and processing weather data covering parameters such as wind direction and wind speed; obtaining aircraft performance parameters and building a database; and inputting and analyzing the flight plan. By establishing a three-dimensional deduction model including dynamics, weather influence and environment models, aircraft situations under different weathers are deduced, and flight safety, performance and task risks are evaluated. The method has the advantages that weather factors are comprehensively and accurately analyzed, and influences are quantified through data acquisition and processing and a meteorological model; the system is suitable for various aircrafts, can integrate navigation and control systems, and realizes remote monitoring management; risk assessment is scientific and reliable, multiple factors are integrated, powerful support is provided for flight decision making, and aircraft safety and task success rate in complex weather are effectively improved.

The invention discloses an unmanned aerial vehicle three-dimensional path planning method based on an improved artificial travel mouse optimization algorithm, belongs to the technical field of unmanned aerial vehicle path planning methods, and aims at enabling an unmanned aerial vehicle to autonomously navigate, avoid obstacles, find an optimal path and improve task execution efficiency and safety in a complex environment. A constraint condition and a target function of unmanned aerial vehicle three-dimensional path planning are mapped into a search space and a fitness function of an optimization algorithm, an optimal flight path is searched by adopting an artificial travel mouse optimization algorithm, and optimization calculation is performed on the path in combination with unmanned aerial vehicle performance constraints, topographic features and meteorological conditions; aiming at a three-dimensional path planning problem of an unmanned aerial vehicle under a complex condition, an artificial travel mouse algorithm is adaptively improved, and algorithm parameters and structures are respectively adjusted according to mathematical characteristics of a problem model in the aspects of initialization, exploration and development; the flight path is adjusted based on environment change and self state information sensed by a sensor in real time, and the safety and task requirements are met.

The invention discloses a ship management method and system based on digital twinborn scene construction. The method comprises the following steps: acquiring multi-source scene data of a target water area in real time, and constructing a corresponding ship management digital twin model; performing integrity evaluation on the constructed digital twinborn model, and performing defect compensation according to an evaluation result to obtain an optimized digital twinborn model; further collecting ship density and weather condition data of each sub-region of the target water area, constructing a network transmission delay prediction model, analyzing related data based on the model, and generating a network delay coefficient distribution diagram of the target water area; and according to the delay distribution diagram, determining the real-time updating lag amount of each subunit of the optimization model, and correcting the ship position information in the digital twin model. According to the method, the real-time performance and accuracy of the digital twin model can be remarkably improved, and the ship operation management efficiency and safety in a complex water area environment are enhanced.

The invention provides a meteorological-power collaborative prediction method and device, and belongs to the field of new energy power systems. The method provided by the invention comprises the steps of determining condition parameters of a prediction task and a prediction site, wherein the condition parameters at least comprise multi-source meteorological observation data and new energy station operation data; according to the multi-source meteorological observation data and a pre-trained meteorological prediction large model, generating a future time sequence meteorological prediction result of the prediction site in a future time period; according to the time scale of the prediction task and historical weather conditions, adaptively matching a corresponding parameter set of the pre-trained power prediction large model; and inputting the future time sequence weather prediction result into the large power prediction model to generate a new energy power prediction result of the new energy station. According to the meteorological-power collaborative prediction method and device provided by the invention, the technical problems that the collaboration of meteorological prediction and power prediction in a new energy power system is insufficient, the precision is limited, and a complex scene is difficult to adapt can be solved.

The invention discloses an unmanned aerial vehicle obstacle avoidance method based on sensing fusion and reinforcement learning, and belongs to the technical field of aviation flight control, and the method comprises the following steps: S1, sensor fusion time-space synchronization; s2, sensing fusion obstacle recognition and positioning; s3, obstacle avoidance route planning based on reinforcement learning; the low-resolution global map and a local occupation grid map with the unmanned aerial vehicle as the center are superposed to serve as multi-channel input, and the current state and the historical action sequence of the unmanned aerial vehicle are combined to be input into an intelligent agent model fused by a trained convolutional neural network and a long-short-term memory network; and an obstacle avoidance decision is output through a reward function of the agent model, and the airborne computer executes the obstacle avoidance decision to control the unmanned aerial vehicle to complete an obstacle avoidance action. Through sensor fusion, the obstacle sensing capability under different weather conditions and different flight environments is enhanced, the sensing distance is also increased, and the flight safety under extreme conditions can be ensured.

The invention relates to the technical field of safe flight of unmanned aerial vehicles, in particular to an air-ground multi-mode bird monitoring and early warning method and system for guaranteeing low-altitude safety, and the method comprises the steps: obtaining radar, visual, infrared and other multi-sensor data, and determining a first bird recognition probability, a second bird recognition probability and a third bird recognition probability through the data; and determining a final bird recognition probability based on the basic weight matrix, when the final bird recognition probability is greater than a preset threshold value, obtaining a relative distance and an included angle between the unmanned aerial vehicle and the bird, and establishing a multi-stage early warning mechanism according to the relative distance and the included angle to perform bird early warning. According to the invention, the contradiction between load limitation of small and medium-sized unmanned aerial vehicles and wide-area monitoring requirements is solved, all-weather, all-weather, all-direction, non-blind-area, all-route and real-time bird monitoring, recognition and tracking under a complex urban environment are realized, the real-time monitoring accuracy of birds is improved, early warning is performed in combination with distance and included angle information, and the real-time monitoring accuracy of the birds is improved. The collision between the unmanned aerial vehicle and birds can be more accurately and effectively avoided, and the low-altitude flight safety is guaranteed.

The invention discloses a foggy day driving augmented reality auxiliary method and system based on multi-mode perception, and relates to the technical field of intelligent driving. The method comprises the following steps: constructing a three-dimensional environment map to carry out real-time driving risk prediction, and carrying out preliminary adjustment on data source weight distribution in the three-dimensional environment map according to a set rule; the PPO reinforcement learning algorithm is adopted to readjust data source weight distribution, a new three-dimensional environment map is reconstructed based on the readjusted data source weight, and the risk probability, predicted in real time, of sudden deceleration of the front vehicle and the reconstructed three-dimensional environment map are projected to a windshield through a layered projection strategy to prompt a driver. According to the method, accurate and comprehensive multi-mode sensing data can be obtained in a foggy day environment, the accuracy of decision making and risk prediction is ensured, quick response can be made according to environment changes, the safety of a driver under complex weather conditions is improved, and the method can adapt to more changing environments in practical application.

The invention discloses a severe weather image sharpening method based on continuous learning, and the method comprises the steps: 1, carrying out the preprocessing of an image data set under various severe weather conditions, including image pairing and multi-task division; 2, dividing the image restoration network into two parts of feature projection and output projection, designing a degradation sensing module for extracting degradation specific information output by the feature projection under different weather conditions, and establishing background supervision and degradation supervision by adopting a multi-teacher model for guiding learning of a student model; and 3, establishing respective optimization targets for the student model and the teacher model, inputting pairing data of different weather removal tasks, and optimizing the student network by a multilateral distillation normal form, thereby obtaining an optimal model with integrated image severe weather removal capability. According to the method, different types of severe weather images continuously acquired in a real scene can be dealt with, and the implementation effect of an image restoration network in a resource-limited scene is improved.

The invention belongs to the technical field of aviation meteorological monitoring, and relates to an airport dominant visibility automatic measurement method and system based on a three-dimensional laser radar, and the method comprises the steps: carrying out the full-space scanning of an airport through the laser radar, so as to collect echo signals; preprocessing and calibrating the echo signal; performing atmospheric extinction coefficient inversion on the preprocessed echo signal based on an inversion algorithm to obtain an atmospheric extinction coefficient, and then converting the atmospheric extinction coefficient into meteorological visibility; carrying out quantile statistics on all meteorological visibility in a period, judging a dominant visibility value, and carrying out adaptive weighted fusion on the dominant visibility value, the visibility value measured by the point-type visibility meter and the visibility value observed manually to obtain a fused dominant visibility value. The system can break through the limitation of traditional manual and point type visibility meter observation, and realizes accurate and automatic measurement of all-region, all-weather and unattended dominant visibility of an airport.

The invention discloses a vehicle door and window state monitoring and early warning method and system, and belongs to the technical field of vehicle monitoring. The method comprises the steps that when doors and windows are not closed and a vehicle locking signal is received, the parking environment of a vehicle is graded, and an environment risk index is determined; historical behavior data of a vehicle owner is obtained, behavior modeling is carried out, a parking duration level is determined, screening is carried out in a preset parking duration level table, and a duration risk index is determined; acquiring weather information, and determining a weather risk index based on a screening result of the weather information in a preset weather condition grading table; determining a comprehensive risk value according to the environment risk index, the duration risk index and the weather risk index; and according to the comprehensive risk value, determining an early warning level and pushing early warning information to the user side in a corresponding early warning form. More flexible, accurate and humanized prompts can be provided according to different scenes, user responses and personalized requirements; the problem of low interaction efficiency in the prior art is solved.

The invention provides an intelligent path planning method and system based on dynamic road condition prediction. The method comprises the steps of firstly obtaining multi-source dynamic data of a target area; then, constructing a weather influence prediction model to predict weather influence parameters in a future time period; secondly, constructing a weather-traffic coupling model, and respectively establishing correlation models of corresponding precipitation, traffic flow density and average vehicle speed according to road types through historical data analysis, so as to estimate the traffic efficiency of each road section under a dynamic weather condition; and finally, generating a plurality of candidate paths according to the passing efficiency, screening out an alternative path set meeting a multi-target optimization condition from the candidate paths, performing simulation evaluation on the alternative path set, and determining an optimal path according to a simulation result. Compared with a traditional static path planning method, the method has the advantages that the responsiveness of a traffic system to meteorological disasters is remarkably improved, and predictable navigation service is provided for intelligent network connection vehicles.

The invention provides a ship image target detection method, system and device based on infrared and visible light and a medium, and belongs to the technical field of ship image detection. Infrared and visible light ship images are input into a YOLOv10 model; separating a kernel attention mechanism to extract texture and contour features contained in the infrared and visible light ship images; calculating an attention weight by using a multi-scale expansion attention mechanism, and paying attention to ship surrounding environment information in the infrared and visible light ship images; and analyzing and detecting target information in the infrared and visible light ship image based on the loss function of the bounding box shape, and outputting and displaying the target information. The infrared image and the visible light image are processed at the same time, and effective target information can be provided at night or under complex meteorological conditions based on the sensitivity of the infrared image to the temperature; the visible light image provides texture and color information in the daytime or under the condition of good illumination, and the accuracy and robustness of detection are improved.

The disclosure relates generally to methods and systems for ground water prediction using integration of satellite and auxiliary observations. Conventional techniques in the art for the ground water level prediction are not accurate and efficient for predicting the ground water level. The present disclosure combines time-series archived remote sensing data with a wide array of past auxiliary datasets specific to a given region for precise prediction of future ground water condition, to predict the ground water situation i.e. rising or declining for a given region. These encompass diverse information such as weather conditions, soil properties, agricultural data, population statistics, water resources information, industrial details, drought records, seismic data, and surface deformation patterns. The present disclosure also considers different future scenarios which can be built on future cropping patterns, forecasted weather conditions, use of irrigation system in future, and future population growth of the given region.

The invention discloses a troposphere delay model construction method based on a numerical meteorological model and an unmanned aerial vehicle, and the method comprises the steps: obtaining meteorological parameter values in the numerical meteorological model for a target region; a troposphere observation value obtained by the target unmanned aerial vehicle through the airborne GNSS receiver and the airborne multi-source meteorological equipment in the process of moving along the preset path in the target area is obtained; determining first troposphere delay data according to the meteorological parameter values and a corresponding preset oblique troposphere delay model; determining second troposphere delay data by adopting a preset ambiguity fixing algorithm based on the satellite observation value; and according to the first troposphere delay data and the second troposphere delay data, determining troposphere delay data of the target area. According to the method, accurate estimation of troposphere delay in a complex region and a meteorological condition rapid change region is realized, and a reliable troposphere delay model is provided for realizing high-precision monitoring of InSAR (Interferometric Synthetic Aperture Radar) deformation monitoring.

The invention discloses an indoor gas leakage monitoring and intelligent joint control system, and belongs to the technical field of intelligent safety monitoring systems. Comprising a sensing layer, a network layer, a control layer and a platform layer, the sensing layer comprises a multi-sensor array and an edge computing node, the multi-sensor array is combined with a catalytic combustion sensor, an infrared sensor, a semiconductor sensor and a temperature and humidity sensor, and sensor data are fused through a weighting algorithm so as to reduce the false alarm rate; a temperature and humidity sensor dynamically adjusts a detection threshold value to adapt to different climate conditions, and an edge computing node is deployed with a lightweight machine learning model. Through multi-sensor data fusion and a weighting algorithm, the false alarm rate is reduced to be smaller than 0.1% per year, environment self-adaptive calibration and AI auxiliary decision making are performed, interference of alcohol, perfume and the like is effectively eliminated, different climate conditions are adapted, leakage judgment is completed at an equipment end through edge calculation, cloud delay is avoided, a multi-stage emergency strategy is provided, and intelligent safe ecology driven by the Internet of Things is constructed.

Systems and methods for energy-aware flight mission planning and control in unmanned aerial vehicles. The system includes a flight control center with machine learning-based modules for path planning and weather analysis, ensuring optimal routes considering energy consumption and weather conditions. Dynamic programming allows for the calculation of energy-efficient 3D paths. This approach allows for adaptable mission planning, offering alternative paths, and real-time adjustments based on telemetry data and weather predictions. UAV mission efficiency and safety is enhanced, improving unmanned aerial vehicle operations.

The invention relates to the technical field of thermal management optimization, and discloses an active cooling and power optimization system of a plateau turbine generator, which comprises a multi-physics field modeling module, a reinforcement learning decision module, a strategy optimization training module and an edge execution control module, and is used for resolving a temperature field / flow field / stress field coupling state in real time, providing environment dynamic characterization, and optimizing the power of the plateau turbine generator. According to the method, multi-source sensing data is processed, a cooling liquid flow rate and cooling fin angle control instruction is generated, strategy iteration optimization is carried out in a digital twin environment, the physical rationality and energy efficiency optimality of a control algorithm are ensured, a lightweight strategy network is deployed, and sub-second-level real-time response and fault safety protection are achieved. Through intelligent control and multi-physical field cooperation, the temperature adjusting precision and the system response speed are improved, efficient power output of the turbine is kept under different altitudes and climate conditions through a self-adaptive strategy, and a reliable clean energy solution is provided for high-altitude areas in combination with multiple safety protection mechanisms.

The invention provides a train operation control method and device under severe weather, and the method comprises the steps: obtaining train idling slip information sent by a train automatic protection subsystem or a train control and management system, and determining a rail slippery state according to the train idling slip information; determining a severe weather mode instruction of the target train based on the rail slippery state and the weather state; and sending a severe weather mode instruction to the train automatic protection subsystem or the train control and management system, wherein the severe weather mode instruction is used for indicating a target train in the target operation area to enter a severe weather mode. According to the train operation control method and device in the severe weather, the severe weather mode instruction of the target interval is determined according to the wet and slippery state of the rail and the weather state, safety protection can be carried out on the guaranteed emergency braking rate in the severe weather, and the situation that due to the fact that the train braking adhesion coefficient is reduced to break through the existing design boundary, the train operation efficiency is improved is avoided. Therefore, the safety of train operation is improved.

The invention discloses a path planning method for a new energy logistics vehicle in a zero-carbon park, and the method comprises the following steps: generating an initial distribution path and a charging demand node based on the real-time remaining electric quantity, the cargo load capacity and a dynamic energy consumption prediction model of the new energy logistics vehicle; performing charging station priority optimization on the initial path according to the real-time occupancy state of the charging facility in the zero-carbon park and a vehicle remaining electric quantity threshold value, and generating a dynamic adjustable path set; based on historical distribution data, real-time traffic flow and weather condition input, performing congestion probability evaluation and energy consumption correction on the dynamic adjustable path set, and outputting a space-time optimal path scheme; and according to the logistics task allocation weight, the vehicle load capacity constraint and the cargo distribution characteristics, performing multi-target collaborative scheduling on the space-time optimal path scheme, generating a final path planning instruction, and updating a vehicle task queue in real time. According to the invention, new energy vehicle path dynamic optimization is realized, charging efficiency and transport capacity configuration are improved, and environmental adaptability is enhanced.

A method for processing image data includes receiving sensor data generated by one or more sensors of an autonomous vehicle and determining a weather condition based on the received sensor data. The method also includes identifying one or more adapter matrices of a plurality of adapter matrices integrated within one or more layers of a machine learning model based on the determined weather condition; and processing the received sensor data, using the one or more identified adapter matrices, to identify and / or track one or more objects in the received sensor data.

The invention relates to the technical field of construction site monitoring, in particular to a construction site monitoring system and equipment based on a remote sensing image technology, and the system comprises a remote sensing image collection module, an unmanned aerial vehicle platform, a low-orbit satellite platform and a ground mobile platform which all acquire remote sensing images of a construction site through sensors; the multi-modal fusion module is used for performing data fusion processing according to the remote sensing image to obtain multi-modal data; when the system is used, the unmanned aerial vehicle platform, the low-orbit satellite platform and the ground mobile platform are used for collecting remote sensing image data, various targets on a construction site are conveniently and accurately recognized, misrecognition is reduced, and the system is beneficial for images under complex environments such as different illumination and weather conditions and images under the complex background of urban buildings. The construction site target is identified and positioned, and in a data-deficient and new construction site environment, the self-supervised learning and transfer learning technology can effectively improve the learning ability of the model, reduce the dependence on annotated data, and quickly adapt to new task requirements.

The invention relates to the technical field of deep learning, in particular to a new energy photovoltaic power prediction system and method based on deep learning. Comprising the steps of obtaining historical power data and meteorological data of a photovoltaic station, and performing power prediction of a current time sequence through a single-station power prediction model; building a multi-site joint prediction model to capture the spatial correlation between sites so as to predict the power of the photovoltaic sites; establishing a weather mode identification method according to the meteorological data to distinguish a normal weather mode and a sudden weather mode; and establishing an adversarial weather-power combined prediction model to process multi-site power collaborative prediction. According to the method, single-station power prediction and multi-station joint prediction are carried out through online learning and transfer learning, so that the station power prediction efficiency is improved; by simulating extreme and violently changing weather conditions, adversarial training is carried out to establish an adversarial weather prediction network, and then weather and power combined prediction is carried out, so that the accuracy of the prediction model in a severe environment is improved.

The invention relates to an automatic driving equipment monitoring system on an expressway, and belongs to the technical field of intelligent traffic. The sensor collects road data; the anti-interference module is used for performing space-time alignment on data acquired by the sensors and dynamically adjusting the fusion weight of the sensors according to weather conditions; the edge calculation layer is used for performing real-time processing and dynamic task scheduling on the data and balancing calculation load and response speed; and the cloud decision-making layer is used for carrying out global integration and decision-making optimization on the data, triggering a multi-level early warning protocol when an emergency event or a road condition is detected, and broadcasting early warning information and vehicle passing suggestions through road test equipment. Manual work is replaced by automatic inspection, the disease identification accuracy is greatly improved, the inspection efficiency is improved, the emergency response time is shortened through real-time data uploading and global scheduling, inspection can be completed through a common vehicle-mounted terminal, special equipment does not need to be leased, and the daily cost is reduced.

The invention belongs to the field of condensation prevention, and discloses an electrical cabinet condensation risk assessment method and related equipment based on time-space characteristics, and the method comprises the steps: collecting outdoor meteorological data of a transformer substation, data in a power distribution room, data in an inner wall cabinet of an electrical cabinet and cable trench data in real time, and outputting a condensation occurrence probability through a pre-constructed condensation occurrence probability model; according to the method, the limitation of response lag of a traditional dehumidification method is overcome, the weight can be dynamically adjusted according to the time-space characteristics at the current moment, and therefore the condensation risk index is calculated, and the condensation risk of the electrical cabinet is evaluated in time. According to the method, the condensation risk prevention capability is remarkably improved, electric power safety accidents caused by the condensation problem are effectively avoided, and the economic loss of electric power enterprises is reduced. The method is more flexible and efficient in energy consumption control, meets the requirements of energy conservation and emission reduction, can better adapt to diversified climate conditions of different areas, and is more remarkable in dehumidification effect especially in high-humidity areas or areas with large day and night temperature difference.

The invention discloses a vehicle detection method under complex weather conditions based on CTI-YOLO, and aims to improve the detection precision and real-time performance under the complex weather conditions. The method is realized by the following steps: step 1, acquiring original images of road vehicles in complex weather, and preprocessing the original images to obtain a preprocessed vehicle image data set in complex weather; 2, a YOLOv8 model is improved, and a CTI-YOLO detection model is constructed by fusing an MSRAM, a GLSA and a TADHead; 3, training the model to obtain an efficient training model, and detecting the preprocessed road vehicle image data set by using the trained CTI-YOLO detection model; and step 4, realizing deployment and visualizing the actual application effect of the method. According to the method, the vehicle detection precision is guaranteed, meanwhile, the model parameter quantity is small, the detection speed is high, and the requirement for the performance of hardware equipment is lowered.

The invention relates to a vehicle cruise control method and device, vehicle-mounted computing equipment and a vehicle, relates to the technical field of intelligent driving, and can improve the driving safety and stability of the vehicle under ice and snow weather conditions. The method comprises the following steps: predicting a future road surface state of a driving road surface of a vehicle according to meteorological data and road surface state information; under the condition that the future road surface state is the ice and snow road surface state, actual friction information of the driving road surface is obtained; determining a target driving state of the vehicle according to the actual friction information and a vehicle cruise condition, controlling the driving of the vehicle according to the target driving state, and obtaining an actual driving state in the driving process; and according to the actual driving state and a braking compensation strategy corresponding to the ice and snow pavement state, braking control information is determined, and driving of the vehicle is adjusted according to the braking control information.

The invention provides a radar flowmeter with environment compensation and a compensation method, and relates to the technical field of measurement, the radar flowmeter comprises an environment compensation module and a monitoring module; the environment compensation module is arranged on the upper shell and is used for monitoring rainfall and wind speed; the monitoring module is arranged on the lower shell and used for monitoring the water level and the flow. The upper shell and the lower shell are fastened through bolts. The compensation method comprises the steps of obtaining a rainfall signal and a humidity value; judging whether the current environment is rainfall or local dripping based on the rainfall signal and the humidity value; and when it is judged that rainfall exists, the section flow of the river channel is dynamically compensated. According to the radar flowmeter with the environment compensation function and the compensation method, high-precision and real-time water flow monitoring can still be achieved under the complex weather conditions such as rainfall and strong wind, and the radar flowmeter with the environment compensation function and the compensation method have remarkable technical advantages and application prospects.