50 results about "Metocean" patented technology

The invention provides an automatic early warning method for sudden weather in a target area, which belongs to the technical field of weather early warning, and comprises the following steps of: establishing a primary dense matrix by adopting adaptive filtering processing and a frequency domain signal separation technology, and generating a secondary dense matrix by applying a marine meteorological recognition model of a spiral progressive network structure; a dynamic statistical equation is used to calculate the physical coupling relationship of each parameter to establish a multi-scale weather process balance matrix, a maximum flow and minimum cut algorithm is used to optimize a weather system coupling relationship network to calculate a coupling degree matrix, and a dynamic threshold adjustment mechanism is established according to coupling strength parameters to adjust the early warning detection frequency. And based on a comparison result of the coupling degree moment order maximum characteristic value and a preset risk threshold value, establishing a grading early warning system and outputting a corresponding early warning signal to control an offshore oil and gas platform emergency response system. The technical problem that a multi-time scale weather process coupling relationship cannot be effectively processed is solved.

The invention discloses a dynamic feature and physical gating fused wave height prediction system and method, belongs to the technical field of artificial intelligence and machine learning, is used for marine environment monitoring, and comprises a data acquisition and preprocessing module, a feature screening module and a model prediction module. The method comprises the steps of obtaining and preprocessing marine meteorological variable data, constructing a Shapley additive interpretation lightweight gradient elevator regression model to screen a core variable set, constructing a physical guidance gating feature perception Transform prediction model, constructing a mixed loss function constraint prediction model, and outputting a sea wave significant wave height prediction value after prediction model training is completed. According to the method, through a feature screening and dynamic weight distribution mechanism, the utilization efficiency and scene adaptability of multi-source marine meteorological features are remarkably improved, the method can be flexibly expanded to sea wave prediction tasks of different sea areas and spatial-temporal scales, and high-precision and high-reliability technical support is provided for ocean engineering, shipping safety and disaster early warning.

The invention provides a wind power prediction method based on marine meteorological characteristics and wake flow interference, and aims to solve the problem that the existing wind power prediction does not fully consider the thermal and dynamic characteristics of a marine boundary layer, the wake flow interference effect of a fan group and the attenuation of a salt mist environment. According to the method, power prediction is realized by constructing a multi-source marine meteorological characteristic field, a wake flow interference field and an environment attenuation compensation mechanism. Specifically, a meteorological characteristic matrix is generated by combining an integral norm of wave height change, a position and wind direction characteristic matrix is constructed based on a fan position and a prevailing wind direction, wake flow interference intensity is quantified through a Gaussian kernel function, and meanwhile, an attenuation compensation item containing an error function is designed to process a salt spray corrosion effect. Establishing a bimodal wind speed probability distribution model, and combining a space-time adaptive quantile regression output power prediction interval; the method effectively solves the problems of marine meteorological dynamic characterization, wake flow interference quantization and environmental attenuation compensation, and significantly improves the precision and reliability of offshore wind power prediction.

The invention discloses a sea temperature prediction method and system based on long-time modeling and cross-branch fusion, and belongs to the technical field of marine meteorological intelligent prediction and deep learning modeling. The method comprises the following steps: carrying out spatial feature coding on a standardized sea temperature time sequence image sequence by using a deep convolution encoder to obtain multi-level spatial features; performing two-dimensional discrete wavelet decomposition on the multilevel spatial features to obtain low-frequency approximate representation, horizontal singular features, vertical singular features and diagonal edge features, and inputting the features into a wavelet attention module to obtain short-term time sequence enhancement features; inputting the multi-level spatial features and the low-frequency approximate representation into a ConvLSTM module to obtain high-frequency dynamic enhancement features; and inputting the short-term time sequence enhancement feature and the high-frequency dynamic enhancement feature into a cross-branch attention fusion module to obtain a fusion feature, and sequentially inputting the fusion feature into an Informer time sequence encoder and a decoder to obtain a prediction result.

This invention provides a method, apparatus, and device for generating marine meteorological data, relating to the field of data processing technology. The method for generating marine meteorological data includes: acquiring identification observation data and numerical simulation data of marine meteorological observation data; interpolating the numerical simulation data onto a preset target grid to generate interpolated wind field data; generating corrected wind field data based on the interpolated wind field data and the identification observation data; generating simulated ocean wave data based on the corrected wind field data; generating corrected ocean wave data based on the simulated ocean wave data and the identification observation data; and encoding the corrected wind field data and the corrected ocean wave data into target marine meteorological data. This invention achieves the fusion of autonomous observation data and numerical simulation data, and efficiently generates high-precision auxiliary meteorological data that meets the input requirements of marine satellites.

The invention belongs to the technical field of ship tracking and positioning, and discloses a ship dynamic tracking method and system based on Beidou positioning, and the method comprises the steps: employing a ship-borne Beidou satellite receiver to determine the position in real time, judging whether to trigger a ship dangerous case alarm, marking a ship in distress, and transmitting the dangerous case alarm information to a Beidou comprehensive application service platform; positioning a ship in distress on an electronic chart, marking a dangerous case range, querying surrounding ships or a global marine meteorological model to generate a vector marine field of the dangerous case range, predicting a drifting high-probability area of the ship in distress, searching a prepared rescue ship for the ship in distress, generating a minimum-cost path, and calculating predicted arrival time; the method comprises the following steps: acquiring the position and state of a ship in real time, generating a pre-rescue ship list, sending a rescue request to the pre-rescue ship, marking an actual rescue ship according to a reply, and carrying out parallel obstruction detection and obstruction coordination on the actual rescue ship. And full-process tracking rescue from dangerous case alarm to rescue execution is realized.

The invention is suitable for the field of marine meteorology, and provides a three-dimensional sea temperature space-time prediction method, which comprises the following steps: obtaining reanalysis data and numerical mode prediction data; and inputting the reanalysis data and the numerical mode prediction data into a pre-trained three-dimensional sea temperature intelligent prediction model to obtain a three-dimensional sea temperature space-time prediction result. According to the method, the spatial-temporal characteristics of the sea temperature change can be effectively captured, so that the prediction precision is improved, a high-precision sea temperature prediction result can be provided, good interpretability is achieved, and more powerful support is provided for scientific research and climate change response.

The invention relates to the technical field of deep learning and ocean engineering meteorology, and particularly provides an industrial ocean meteorological disaster forecasting method based on deep learning. The method comprises the following steps: acquiring real-time meteorological data, and constructing a dynamic text prompt set; performing semantic-physical feature joint modeling on the dynamic text prompt set by using a multi-modal fusion expert module to obtain a real disaster sample; constructing a dream learning framework, entering a dream stage, and obtaining a virtual disaster dream sample; in an awakening stage, high-dimensional potential representation is obtained through a real disaster sample and a virtual disaster dream sample; according to the high-dimensional potential representation, a forecast result is output, a full-closed-loop industrial meteorological disaster prediction system is formed, and the method improves the accuracy, robustness and interpretability of meteorological disaster prediction in a marine industrial scene.

The invention discloses a numerical weather forecast cold start error suppression method and device, and relates to the technical field of offshore wind power, and the suppression method comprises the steps: S1, collecting initial data through a marine meteorological sensor array, including sea wave height, period, wave direction and corresponding wind field initial observation data, and constructing a multi-dimensional data set after preprocessing; s2, on the basis of the data set and historical sea wave-wind field time series data, a physical mechanism and data driving combination method is adopted to construct a sea wave-wind field coupling model, and the nonlinear coupling relation between sea waves and the wind speed and the wind direction is quantified. According to the method, a physical mechanism and data driving fusion modeling is adopted, so that the meteorological rule conformity is guaranteed, the nonlinear coupling relationship is accurately captured, the limitation of a pure physical and pure data driving model is avoided, and the prediction core support is tamped; based on neural network mining time sequence and mode characteristics, a lattice point relaxation algorithm parameter strategy is dynamically adjusted, and the problem that traditional fixed parameters are poor in adaptability is solved.

This invention discloses a wave height prediction system and method integrating dynamic features and physical gating, belonging to the field of artificial intelligence and machine learning technology, for marine environmental monitoring. The system includes a data acquisition and preprocessing module, a feature selection module, and a model prediction module. The method includes acquiring and preprocessing marine meteorological variable data, constructing a Shapley additive interpretation lightweight gradient booster regression model to select the core variable set, constructing a physically guided gating feature-aware Transformer prediction model, constructing a hybrid loss function-constrained prediction model, and outputting significant wave height prediction values ​​after the prediction model is trained. This invention significantly improves the utilization efficiency and scenario adaptability of multi-source marine meteorological features through feature selection and dynamic weight allocation mechanisms. It can be flexibly extended to wave prediction tasks at different sea areas and spatiotemporal scales, providing high-precision and highly reliable technical support for marine engineering, shipping safety, and disaster early warning.

The application discloses a kind of long time dependence modeling and cross-branch fusion sea temperature prediction method and system in the technical field of marine weather intelligent prediction and deep learning modeling.The method comprises the following steps: using deep convolutional encoder to encode the spatial features of standardized sea temperature time series image sequence, obtaining multi-level spatial features; the multi-level spatial features are two-dimensionally discrete wavelet decomposed, to obtain low-frequency approximation representation, horizontal singular feature, vertical singular feature and diagonal edge feature, then input into wavelet attention module, to obtain short-term time series enhanced features;Multi-level spatial features and low-frequency approximation representation are input into ConvLSTM module, to obtain high-frequency dynamic enhancement features;Short-term time series enhanced features and high-frequency dynamic enhancement features are input into cross-branch attention fusion module, to obtain fusion features, then input into Informer time series encoder and decoder in turn, to obtain prediction results.

The application relates to the technical field of marine meteorological data assimilation, in particular to a marine meteorological data assimilation method based on self-adaptive scale decomposition. An original data set is acquired and preprocessed to obtain a two-dimensional matrix. Based on an empirical orthogonal function analysis method EOF, dimension reduction is carried out on the initial matrix to obtain a spatial mode which does not change with time. A spatial self-adaptive scale reduction model is constructed by using the correlation of points on the spatial mode. Grid setting is carried out on the spatial self-adaptive scale reduction model to obtain an optimized multiple network. A target functional is determined and scale-by-scale assimilation is carried out to obtain an analysis field. An accuracy calculation formula and an accuracy threshold value are determined. Sample observation values and sample analysis values of a sample to be analyzed are acquired, and the accuracy is calculated. The two-dimensional matrix is extracted and processed by the EOF, the spatial self-adaptive scale reduction model is obtained, and scale-by-scale assimilation of adaptive matching of spatial characteristics is realized.

The disclosure relates to a computer-implemented method for optimizing a layout of a subsea oil & gas field on a portion of the seabed, comprising providing input specifying locations unsuitable for well location and injection or production targets, determining optimal locations each of an injection well or of a production well by optimizing a well location and trajectory, determining optimal locations by performing an optimization that rewards every well is connected to exactly one drill center and the overall drilled length and number of drill centers is minimized, determining a location and orientation for the surface facility or processing facility based on metocean data, marine regulation constraints and the determined locations each of a drill center. The method also comprises determining a layout of flowlines by performing an optimization process including iterations of computing a solution to the optimization and defining mechanical constraints on the flowlines to find another solution.

The invention belongs to the technical field of marine weather forecast, and particularly relates to an atmosphere structure stability correction method, system and device and a medium, and the method comprises the steps: analyzing the incidence relation between the atmosphere structure stability and the wind wave growth rate, and determining a core evaluation parameter; collecting a plurality of marine meteorological observation data, and preprocessing the plurality of marine meteorological observation data to construct a standardized training data set; constructing a wind wave growth rate initial correction model based on physical constraints; integrating the initial correction model into a wind energy input source item of a sea wave numerical mode so as to correct the stability of the atmosphere structure in real time and obtain correction stability; verifying the correction effect through long-term numerical simulation, and performing model performance verification on the initial correction model by adopting multi-index statistical evaluation to obtain a model verification result; and performing parameter sensitivity analysis and optimization on the initial correction model based on a model verification result to obtain an optimized correction model. According to the invention, the accuracy of wave numerical forecasting is improved.

This invention relates to the field of marine meteorological observation equipment technology, specifically a shipborne high-precision rainfall measurement device and method based on video image and attitude synchronization. The device includes a rain collection head, a main body, a multi-tube array rain gauge, an optical observation module, a main control module, and a temperature and humidity sensor. The main body, rain collection head, and bottom plate form a Faraday cage. The rain gauge is equipped with a damping and liquid-sealing structure to suppress liquid surface oscillations, and a variable diameter design amplifies liquid level changes. The measurement method synchronizes ship data with the attitude sensor, triggers image acquisition at the alignment moment, extracts the water level line using an algorithm, and calculates the rainfall using a variable diameter formula. The temperature and humidity sensor is linked to achieve temperature compensation and snow melting. This invention integrates attitude calibration and video image technology to form a closed-loop measurement process, possesses electromagnetic shielding and full-scene adaptability, and can effectively improve the accuracy and stability of rainfall measurement in shipborne environments. It is suitable for various ship observation scenarios in nearshore and ocean-going environments.

The utility model provides a marine meteorological monitor, which relates to the technical field of marine monitoring and comprises a monitoring body, a positioning sleeve is fixed on the top surface of the monitoring body, and a group of photovoltaic panels fixed on the top surface of the monitoring body are arranged outside the positioning sleeve. And a cleaning assembly for cleaning the surface of the photovoltaic panel is arranged above the monitoring body. According to the marine meteorological monitor, transmission wings are blown by sea wind, so that the transmission wings drive a moving frame to move on the surface of a positioning sleeve through a rotating rod and drive a cleaning rod to clean the surface of a photovoltaic panel, sea birds can be repelled, the sea birds or other sundries are prevented from staying on the surface of the monitoring device, and the use efficiency of the photovoltaic panel is improved; the transmission plate is driven by the transmission rod to rotate around the rotating shaft, the transmission wing is driven by the rotating rod to rotate, the use angle of the transmission wing can be adjusted according to the sea surface wind speed or wind direction, the rotating speed of the transmission wing is kept within a certain range, and the service life of the detector is prolonged.

The invention is suitable for the field of marine meteorology, and provides an error correction method, device and equipment for El Nino prediction, and the method comprises the steps: obtaining sea temperature field post-report data in a target time period; extracting surface layer sea temperature post-report data from the sea temperature field post-report data, and performing sample averaging operation on the surface layer sea temperature post-report data to obtain surface layer sea temperature feature data; acquiring surface layer sea temperature label data in a target time period; training a random forest model according to the surface layer sea temperature feature data in the target time period, the surface layer sea temperature label data in the target time period and a preset model training algorithm to obtain a trained random forest error correction model; and receiving an El Nino real-time prediction result, and inputting the El Nino real-time prediction result into the random forest error correction model to obtain an El Nino final prediction result. The method can adapt to different climate conditions and marine environments, and systematic deviation in real-time prediction can be corrected.

The invention provides a rain and fog simulation cabin for acquiring rain and fog reference data and an acquisition method thereof. The rain and fog simulation cabin comprises a closed cabin body, a water supply system, a rain and fog system, a controllable light source system, a target trolley, a six-degree-of-freedom mechanical arm camera platform deck, a data acquisition system, a controller and an upper computer. According to the invention, rainfall, fog making, illumination control, ship motion simulation and rainwater closed-loop recovery are integrated, and a complex marine meteorological scene can be reproduced truly and repeatedly in a controlled indoor environment. Motion data of a real unmanned ship under different sea conditions are input into a mechanical arm, and simulation shooting of a camera under heaving, pitching and rolling is achieved; meanwhile, key parameters such as the rainfall range, the number and arrangement of nozzles and the color temperature range of a light source are scientifically demonstrated in the design of the simulation cabin, and it is ensured that experimental conditions are consistent with statistical characteristics of a real natural environment; a whole set of scientific sampling process is provided, and multi-source synchronization of the camera, the mechanical arm, the light source and the sensor is achieved.

The application provides a target area burst weather automatic early warning method, and belongs to the technical field of weather early warning.The application adopts adaptive filtering processing and frequency domain signal separation technology to establish a first-level dense matrix, uses a marine weather identification model with a spiral progressive network structure to generate a second-level dense matrix, uses a dynamic statistical equation to calculate the physical coupling relationship of each parameter to establish a multi-scale weather process balance matrix, adopts a maximum flow minimum cut algorithm to optimize the weather system coupling relationship network to calculate a coupling degree matrix, establishes a dynamic threshold adjustment mechanism according to the coupling strength parameter to adjust the early warning detection frequency, and establishes a hierarchical early warning system based on the comparison result of the maximum eigenvalue of the coupling degree matrix and a preset risk threshold to output corresponding early warning signals to control the emergency response system of the offshore oil and gas platform, thereby solving the technical problem that the coupling relationship of the multi-time scale weather process cannot be effectively processed.

The application discloses a ship carbon dioxide emission prediction method based on an ANN model and multi-source data fusion, acquires ship AIS data and marine meteorological observation data and performs pretreatment; through a double fusion strategy of a time stamp matching and a space index matching algorithm, the processed AIS data and the meteorological data are spatio-temporally fused to generate a multi-source feature database; based on ship static parameters in the multi-source feature database, ship main engine power and auxiliary engine power are estimated through a segmented function, baseline carbon dioxide emission is calculated in combination with sailing state discrimination and emission factor selection, and the baseline carbon dioxide emission is abnormally corrected; taking ship speed, wind speed and air temperature as input variables and taking the corrected baseline carbon dioxide emission as a target variable, an artificial neural network prediction model ANN is trained, and the trained artificial neural network prediction model ANN is used to predict ship carbon dioxide emission.

This application provides a cloud measurement system and method based on a wave glider. By integrating a laser cloud-measuring radar with a marine meteorological observation instrument onto a wave glider platform with ultra-long endurance, autonomous navigation, and virtual mooring capabilities, a sea-based mobile cloud observation system is constructed. This system overcomes the inherent shortcomings of existing satellite remote sensing observations, such as discontinuity and lack of vertical structure information, as well as the high cost, poor maneuverability, and limited coverage of ship and buoy observations. It achieves long-term, continuous, and mobile observation of cloud systems over vast ocean areas and can acquire high-precision cloud base height, vertical structure, and conventional meteorological elements from the sea surface, significantly improving the spatiotemporal continuity and data quality of marine meteorological observation.

The FVCOM-SWAVE-based marine meteorological numerical forecasting method disclosed by the invention aims to deeply explore the influence process and the action mechanism of wave-induced Stokes drifting on the temperature of the upper layer of the ocean under extreme weather conditions, explore and compare the sea wave action before and after typhoon passing through a series of sensitivity experiments by utilizing a coupling numerical mode FVCOM-SWAVE, and finally predict the sea weather. And the influence mechanism and contribution to the sea temperature are realized.

This invention relates to the fields of marine meteorology and navigation, and discloses a method for predicting meteorological factors for ship navigation based on marine meteorological information. This method is used to predict meteorological factors for ship navigation, assess navigation risks, and plan alternative routes. The method includes processing meteorological data and ship data for a target sea area to obtain historical meteorological time series and ship motion response sequences; constructing state vector trajectories of meteorological factors using historical meteorological data, establishing a dynamic model library, and obtaining the ship motion response function to meteorology based on meteorological and motion response data; generating deterministic meteorological factor prediction sequences by embedding the current meteorological state into the dynamic model; and constructing stochastic differential equations to generate probabilistic prediction distributions of meteorological factors. This invention integrates data-driven and physical constraints, providing more comprehensive navigation decision support.

The invention discloses a mesoscale analysis method for marine meteorology, and relates to the technical field of mesoscale analysis for meteorology. According to the mesoscale analysis method for marine meteorology, marine meteorology observation data of a plurality of time periods in a set marine operation platform area are continuously acquired, meteorological disturbance aggregation indexes of the corresponding time periods are analyzed, meteorological image data of each time period are acquired, and a pre-trained image disturbance identification model is combined, so that the mesoscale analysis of marine meteorology is realized. Analyzing a meteorological structure evolution index of a corresponding time period, analyzing a meteorological evolution risk perception index in combination with a meteorological disturbance aggregation index of the corresponding time period, and predicting a meteorological evolution risk perception index of a next time period; according to the method, corresponding response measures are taken for the set marine operation platform area through the meteorological evolution risk perception index of the next time period, so that the accuracy of risk perception is improved, continuous identification of meteorological risks in the operation area is further facilitated, and the operation platform has higher prospective pre-judgment ability.

The application belongs to the cross technical field of artificial intelligence and marine weather prediction, and discloses a sea wave probability prediction method and system. The method obtains historical wind field data and sea wave spectrum data of a target sea area, pre-processes and organizes the data, and constructs a training data set. A hybrid expert probability model is constructed, which includes a gating network and multiple expert networks. The hybrid expert probability model is trained using the training data set. The trained model receives input wind field data and outputs a hybrid probability distribution through the synergistic effect of the gating network and the expert networks. The hybrid probability distribution is sampled to obtain a predicted sea wave spectrum set, realizing sea wave risk probability prediction. The application can obtain complete distribution information through one forward calculation, does not require a numerical mode set, significantly reduces the computing power and energy consumption overhead, and facilitates high-frequency updating and quasi-real-time business application in resource-limited environments such as shipborne, buoy and near-shore sites.

The application discloses a ship speed prediction and optimization method and device and a readable storage medium, and relates to the field of ship speed prediction and optimization.The method comprises the following steps: establishing a ship speed prediction model by taking a main engine power, a ship heading, a flow speed, a relative flow direction, a wind speed, a relative wind direction, an effective wave height, a relative wave direction, and a combination period of wind waves and swell waves as input and taking a ship speed as output; obtaining ship route planning information and marine weather forecast information; dividing a ship planned route into a plurality of planned segments; obtaining relevant information of each planned segment in combination with the marine weather forecast information, and then predicting real ship speeds of each planned segment under corresponding weather sea conditions and different main engine powers in combination with the ship speed prediction model; and establishing a cost function according to the main engine power and the corresponding real ship speed, and constructing a ship speed optimization model in combination with constraint conditions, so that optimized speeds of each planned segment are obtained. The application can predict and optimize ship speeds.

The application discloses a marine weather monitoring device based on a ship and relates to the technical field of ship marine monitoring. The mounting table is provided with a mounting hole. The mounting table can be fixed on the ship through the mounting hole. A base is arranged on the mounting table, a fixing seat is arranged on the base, a damping piece is arranged above the fixing seat, a horizontal adjusting mechanism is arranged above the damping piece, a balance mechanism is arranged in the horizontal adjusting mechanism, a wind speed and direction monitor, a temperature and humidity monitor and an atmospheric pressure monitor are arranged above the horizontal adjusting mechanism. When the ship encounters large waves, the wind speed and direction monitor, the temperature and humidity monitor and the atmospheric pressure monitor keep the horizontal state of the device through the horizontal adjusting mechanism, and the vibration amplitude of the whole device after encountering the waves is reduced through the damping piece.

The invention discloses a meteorological marine data cleaning processing method and device, and relates to the technical field of meteorological data fusion processing, and the method comprises the following steps: S1, building a marine meteorological data set, S2, preprocessing data, S3, screening abnormal data, S4, building an abnormal data processing model, and S5, analyzing meteorological marine data. Abnormal data screening: the preprocessed data are screened, the screened data comprise change time abnormal data, peak-valley value abnormal data, abnormal data higher than an average value, abnormal data lower than the average value and fluctuation abnormal data, and thus the abnormal data can be accurately cleaned; according to the method, the abnormal data processing model is constructed, the screened abnormal data is processed, and the accuracy of the data abnormity is judged through the abnormal data processing model, so that the accurate processing of the meteorological ocean data is realized, and the reliable measurement of the ocean abnormal weather is realized.

The invention belongs to the technical field of sea surface forecasting, and particularly relates to a multi-source heterogeneous data-driven air-sea interface collaborative observation and short-term prediction method, which is characterized in that a self-attention mechanism in an Informer model is utilized to establish correlation modeling between an input sequence and an output sequence, obtaining a global feature matrix; according to the method, a BiGRU model is used for capturing local time sequence dependence features in sequence data to obtain a local feature matrix, and the method has the advantages that a parallel prediction model based on Informer and BiGRU-GlobalAttention is used for training and reasoning fused data, and short-time prediction of key marine meteorological elements in a period of time in the future is achieved.

The invention discloses a distributed double-loop near-infrared link seaborne visibility monitoring system and method, and relates to the technical field of marine meteorological monitoring, and the system comprises a shore-based receiving station, a seaborne observation station, a satellite relay station, and a data processing module. The sea observation station is provided with an attitude sensor, a GNSS receiver, at least two near-infrared links, a data acquisition module and an environmental parameter sensor, data acquired by the data acquisition module of the sea observation station is transmitted to the shore-based receiving station through the satellite relay station, and the shore-based receiving station unpacks the received data and then transmits the data to the data processing module; and the data processing module processes and calculates the data to obtain a seaborne visibility value. According to the invention, limitation of single-point data is avoided, the problem that transmission of visible light in fog and haze environments is blocked is overcome, measurement errors caused by heating and movement of an offshore platform can be reduced, and the device is more reliable compared with equipment with a single observation channel.