1166 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 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 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 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 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 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.

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 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.

In various examples, systems and methods are disclosed relating to transforming sensor measurements according to configurable atmospheric conditions. One or more circuits can identify a point cloud comprising a plurality of points and a parameter of a weather condition to simulate and modify an intensity of at least one of the plurality of points according to the parameter of the weather condition. The one or more circuits can update, based at least on a subset of the plurality of points and the parameter of the weather condition, the point cloud to include one or more additional points.

The present invention discloses a method and an apparatus for obstacle detection under complex weather. The method includes: obtaining an image under a complex weather condition; performing enhanced preprocessing on the image by using a multi-scale retinex with color restoration MSRCR algorithm; inputting the preprocessed image into a trained obstacle detection model based on an improved YOLOv3 network; and according to output of the obstacle detection model based on the improved YOLOv3 network, determining an obstacle detection result under the complex weather; replacing a Leaky-ReLU activation function in convolutional layers in the original YOLOv3 network with an ELU activation function; and training the obstacle detection model with the processed data set to obtain a trained obstacle detection model based on the improved YOLOv3 network.

The invention relates to the technical field of hydrometeorological monitoring, in particular to a rainstorm flood water line early warning and monitoring method. According to the invention, the consistency and timeliness of real-time water level data from different data sources are ensured through a multi-source hydrometeorological data acquisition and time synchronization technology. Through accurate time synchronization processing, deviation caused by data time lag is avoided, accurate butt joint and fusion of various water level data are guaranteed, and subsequent flood risk assessment and early warning are more reliable. Particularly, under sudden weather conditions such as rainstorm and the like, quick response can be realized, and accurate risk assessment can be provided; according to the method, the noise in the water level data of each data source is effectively removed by combining a combined preprocessing algorithm of moving average filtering and Kalman filtering, and weights of different data sources are weighted according to prediction errors, so that comprehensive accurate water level data is obtained. According to the fusion method, the smoothness and accuracy of the data are improved, and high-quality input data are provided for subsequent risk assessment.

According to the photovoltaic power generation prediction system and method based on the smart power grid provided by the invention, the efficient fitting advantage of a gradient boosting decision tree model on high-dimensional static characteristics is utilized under a long-period stable weather condition, and the capturing capability of a long-short-term memory network on time sequence nonlinear dynamics is fully exerted in a weather sudden change period; weather categories are judged in real time and weights are dynamically adjusted through an environmental condition classifier, so that the RMSE of a final prediction result in four typical scenes of sunny days, cloudy days, cloudy days and rainy days is obviously reduced, and the RMSE is obviously superior to that of an existing fixed weight combination model; meanwhile, through an automatic data cleaning chain formed by an isolation forest and Lagrange interpolation, the data anomaly rate is controlled to be 0.1% or below, and the dimensional difference is eliminated by adopting minimum-maximum standardization, so that the model training stability is ensured; the parallel hybrid architecture realizes minute-level rolling prediction under the acceleration of the GPU, and the real-time requirement of a provincial power grid dispatching center is met.

The invention relates to the field of intelligent monitoring of electric power facilities, in particular to a power transmission line foreign matter detection method based on multi-component semantic segmentation and hybrid Transform-CNN (Convolutional Neural Network). According to the method, the Transform module is combined with the CNN model, multi-component semantic segmentation is performed on the to-be-analyzed image through the decoder based on the UNet network, and the method can be applied to rapid detection under complex conditions and numerous interference environments. Therefore, the foreign matter detection of the power transmission line for performing high-precision segmentation and real-time detection on the attached foreign matters on multiple parts such as a lead, an insulator and a fitting of the power transmission line in a complex scene is realized, the safety level alarm is performed when an abnormal trigger mechanism is reached, and the test shows that the IoU is improved by 15% and the false detection rate is reduced by 40% in rainy and foggy weather, so that the safety of the power transmission line is improved. The FPS reaches 32 frames per second (real-time edge calculation is met), the accuracy and robustness of foreign matter detection of the power transmission line are greatly improved, and the timeliness of safety early warning of the power transmission line is also greatly improved.

The invention discloses a power distribution network risk management method, system and device in extreme weather and a medium, and the method comprises the following steps: obtaining meteorological data through detecting weather conditions, and predicting a weather development trend; analyzing the fault risk of each node of the power distribution network according to the meteorological data and the weather development trend; and generating a decision scheme according to the analysis result of the fault risk, and coordinately executing power dispatching and emergency disposal by combining the decision scheme with an emergency communication network for power supply guarantee. According to the invention, the comprehensive management method integrating meteorological monitoring, risk assessment, emergency communication, power dispatching and power supply guarantee is constructed, so that the risk management capability of the power distribution network under the extreme weather condition is remarkably improved; and the precision and timeliness of weather prediction are improved.

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

The invention discloses a building optical intelligent lighting and lighting optimization management system, and relates to the technical field of building energy saving and intelligent control, and the system comprises the following components: a data collection module, a data processing and storage module, an illumination demand prediction module, a control decision module and an equipment execution module. By integrating the data acquisition module, the data processing and storage module, the illumination demand prediction module, the control decision module and the equipment execution module, accurate prediction and optimal management of the indoor illumination demand of the building are realized, and the system constructs an illumination demand prediction model by using a machine learning algorithm. According to the model, multi-dimensional factors such as time, weather conditions, building energy consumption historical data and personnel behavior habit data are comprehensively considered, accurate prediction of indoor illumination requirements in different time periods and under different weather conditions is achieved, and based on the prediction result, the control decision module makes an optimal control strategy and adjusts lighting and lighting equipment.

The invention discloses a safety fence protection method and device based on the Internet of Things technology, and belongs to the technical field of safety alarm and Internet of Things application, and the method comprises the following steps: respectively collecting the distance, heat source distribution and movement track information of a target protection area through an ultrasonic sensor, an infrared sensor and a millimeter wave radar; generating a virtual fence model with an initial alarm boundary by adopting a confidence weighted fusion algorithm based on environmental condition dynamic adjustment; environmental change parameters such as obstacle distribution and meteorological conditions are monitored in real time, when the change exceeds a preset threshold value, a dynamic boundary adjustment strategy is triggered, and the fence alarm boundary shape is optimized through a boundary node position redistribution mechanism; a distributed computing framework is adopted to decompose boundary detection tasks, and the tasks are dynamically allocated according to the real-time resource utilization rate of edge computing nodes. According to the invention, the problems of insufficient real-time performance, limited dynamic adjustment capability and low system integration degree in a complex environment in the prior art can be effectively solved.

The invention discloses a flood inundation range dynamic simulation method and system based on multi-source data fusion. The method comprises the following steps: S1, collecting multi-source original data of a to-be-simulated target drainage basin; s2, extracting an initial submerging range grid according to the multi-source original data; s3, according to the initial submerging range grid, generating a river channel-beach area coupled section terrain file; s4, obtaining the dynamic water level process of each downstream section according to the section topographic file and the weather forecast rainfall field; and S5, obtaining a dynamic simulation result of the flood inundation range according to the dynamic water level process. According to the invention, through continuous driving of the distributed hydrological model and the one-dimensional hydrodynamic model, full-chain simulation from rainfall forecast to submerging dynamic state is realized, so that the submerging range can be updated in real time along with meteorological conditions and riverway water levels.

A method for virtual evaluation of on-road energy consumption of a new energy vehicle, comprising: constructing an evaluation route on the basis of cities where a vehicle is used and typical routes of said cities; generating an on-road working condition library on the basis of the evaluation route, a month, a weather condition, a road condition and a vehicle usage period; acquiring test data of the vehicle under different working conditions; determining evaluated energy consumption on the basis of the test data; determining a nominal energy consumption discrete coefficient on the basis of the evaluated energy consumption and nominal energy consumption; determining average comprehensive energy consumption on the basis of the evaluated energy consumption; and determining an energy consumption level of the vehicle on the basis of the nominal energy consumption discrete coefficient and the average comprehensive energy consumption. Also disclosed is an apparatus for virtual evaluation of on-road energy consumption of a new energy vehicle. Even without large-scale road testing, on-road energy consumption can still be efficiently and conveniently evaluated in numerous cities and diverse vehicle usage scenarios.

The invention relates to a meteorological disaster early warning system based on C-V2X and edge calculation, and belongs to the technical field of traffic meteorological monitoring. The system comprises an edge calculation module and a C-V2X communication module. The edge calculation module collects video image information and vehicle state information and carries out multi-mode perception fusion, meteorological disaster identification is carried out on fused data through an embedded artificial intelligence model, and then direct communication between vehicles is realized based on a C-V2X technology. Early warning information is accurately delivered to subsequent vehicles which are on the same route and run in the same direction through direct communication between the vehicles, a driver is reminded to drive carefully or take necessary measures in advance, and the road traffic safety is improved. The vehicle-mounted edge calculation and artificial intelligence technology is utilized, real-time monitoring and early warning of the road weather condition are achieved, the safety and passing efficiency of road traffic are improved, and the method has wide application prospects.

The invention discloses an intelligent identification and counting method, system and equipment for an industrial elevator and a medium, and the method comprises the steps: collecting an interference target negative sample, generating a diversified illumination scene sample, combining the interference target negative sample with the illumination scene sample, training and optimizing a detection model, deploying the trained and optimized detection model, and processing a video stream in real time. And judging the effective lifting behavior of the elevator, and intelligently counting the operation frequency of the elevator. According to the method, an adversarial training strategy and negative sample database construction are adopted, the discrimination capability of the model for an interference target is remarkably improved, the stable recognition performance of the system under the all-weather condition is ensured through developed illumination enhancement simulation, and the intelligent counting algorithm based on the state machine is used for realizing the intelligent counting of the interference target through a rigorous condition judgment mechanism. Accurate statistics of the work cycle of the elevator is achieved, a reliable automatic counting solution is provided for an industrial site, and the intelligent level of production management is greatly improved.

The invention discloses a visual audio multi-mode monitoring system and method for identifying fan blade defects. A panoramic video monitoring mechanism can obtain a back panoramic image of a blade of a current wind driven generator and a surrounding environment image of the wind driven generator; a blade root pickup is arranged on the panoramic video monitoring mechanism, and a blade tip pickup is arranged on a tower drum of the wind driven generator, so that blade tip section audios and blade root section audios of the wind driven generator can be obtained. Firstly, the environment visibility under the current weather condition is recognized through the panoramic video monitoring mechanism, and then different real-time monitoring strategies are executed according to the environment visibility, so that the blade defect monitoring is not limited by the weather environment any more, the application scene is improved, and the blade defect monitoring accuracy is improved no matter in the high-visibility environment or the low-visibility environment. The false alarm risk can be reduced as much as possible in a visual and audio mutual verification mode, and the accuracy of blade surface defect identification is greatly improved.

The invention belongs to the technical field of driving safety, and provides a driver fatigue risk dynamic assessment method, system and device and a medium, and the method comprises the steps: obtaining an infrared image of a driver at a current moment, and carrying out the detection to obtain a current face key point; based on a historical face key point set in a historical time period including the current face key points, obtaining a basic fatigue risk at the current moment; the average eye closing time ratio, the blinking frequency and the yawning frequency in the historical time period are counted, and fatigue risk excitation is obtained through calculation according to the historical average eye closing time ratio, the blinking frequency and the yawning frequency; and carrying out weighted fusion on the basic fatigue risk and the fatigue risk excitation to obtain a comprehensive fatigue risk. The method does not need to increase too much equipment cost to collect information such as the driving track and the steering wheel correction frequency, does not need to recognize sensing information such as weather conditions, road states and motion situations with high requirements for computing power, saves a large amount of time cost and hardware cost, and is high in efficiency.

The invention relates to the technical field of computers, in particular to a municipal environmental sanitation cleaning task scheduling method and system based on big data analysis, and the method comprises the steps: obtaining the historical garbage amount data of an urban traffic station, and carrying out the garbage amount partitioning through a K-means + + clustering algorithm according to the historical garbage amount data; acquiring urban meteorological data, and predicting an urban weather condition through a wavelet transform and LSTM neural network hybrid model according to the urban meteorological data; the state of the sanitation vehicle is monitored in real time through a vehicle-mounted sensor, and the position and the driving track of the sanitation vehicle are tracked in real time through the GPS positioning technology; according to the garbage quantity partition, the predicted urban weather condition, the environmental sanitation vehicle state and the position and the driving track of the environmental sanitation vehicle, a cleaning task is distributed to the environmental sanitation vehicle; and real-time traffic conditions are obtained, and the sanitation vehicles are scheduled to go to the garbage quantity subareas to execute cleaning tasks in combination with the real-time traffic conditions. The environmental sanitation management efficiency can be improved.

A system for generating customized alerts for a user may (1) receive weather data from a database; (2) determine, from the weather data, weather conditions indicative of a predicted weather event beginning within a predefined time period; (3) receive a location of the user; (4) receive information relating to one or more protection policies of the user; (5) determine that the predicted weather event is relevant to the user based upon the location of the user and the information relating to the one or more protection policies of the user for protecting at least one of the user or property of the user; and (6) automatically initiate an action to mitigate a potential impact of the predicted weather event on the user responsive to the determination that the predicted weather event is relevant to the user.

The invention relates to the technical field of ocean monitoring and data processing, in particular to a sea condition estimation method based on multi-source information fusion. The method comprises the following steps: emitting electromagnetic waves to a target sea area by using a radar, receiving a sea clutter reflection signal and converting the sea clutter reflection signal into a digital image signal to obtain a sea clutter image, and after preprocessing and sea wave edge feature extraction, calculating to obtain a first meaningful wave height parameter of sea waves in the sea area through a preset formula based on extracted sea wave edge discrete points; collecting the surging data of the ship in the target sea area in real time through RTK GPS equipment, performing preprocessing and spectral analysis to extract frequency components related to the sea waves, and calculating a second meaningful wave height parameter of the sea waves in the sea area based on the frequency components; and effectively fusing the two types of parameters, performing deviation correction on the first meaningful wave height parameter by using a fusion result, and outputting a sea condition estimation result. The method can improve the accuracy and reliability of sea condition estimation, can adapt to different marine environments and weather conditions, and is wide in application prospect.