229 results about "Ocean observations" patented technology

The invention provides an ocean intelligent forecasting large model construction method, and relates to the field of ocean forecasting, and the method specifically comprises the following steps: obtaining multi-source ocean observation data, and constructing a high-resolution ocean analysis data set which is subjected to quality control, space-time registration, standardization and data set division processing through multi-source observation data fusion and numerical mode assimilation; constructing a basic prediction model, and performing multi-scale fusion on the frequency domain enhanced features and the spatial local features by using the basic prediction model; the trained basic prediction model is operated in a set area range, and an output result of the basic prediction model is recovered to an original physical quantity value through inverse standardization; and comparing the rolling output of the basic prediction model with observation data or a high-resolution mode result through a correction module, learning an error, outputting a correction quantity, and superposing the correction quantity with an original prediction result to obtain a prediction field. According to the technical scheme, the problem that the ocean forecasting model in the prior art cannot meet the requirement of a complex application scene is solved.

The invention provides an ocean wind field prediction method based on a neural network, and belongs to the technical field of ocean wind field prediction.The method comprises the steps that sparse ocean observation data are collected, a spatial covariance matrix is established, the spatial covariance matrix is converted into a graph structure, and then multi-hop neighborhood feature aggregation is conducted through a graph convolutional network; a tensor decomposition algorithm is combined for modeling high-order feature interaction to generate a gridding wind field, a bidirectional long-short-term memory network encoder is used for extracting space-time invariant features, a multi-layer perceptron predictor is used for directly mapping a future multi-step wind field, and a course learning strategy and a Shenchang differential equation boundary layer are matched for correction. The technical problem that sparse ocean observation data are difficult to accurately reconstruct into a high-resolution gridding wind field is solved.

The invention discloses an ocean three-dimensional temperature field reconstruction method and system, and relates to the technical field of data processing, and the method comprises the steps: obtaining multi-source heterogeneous ocean observation data and a numerical model background field, and generating an input feature group; performing feature extraction on the input feature group by using a double-branch encoder; the extracted features are input to a multi-head space-time channel attention fusion module for dynamic calibration and fusion, and deep fusion hidden variables are obtained; jointly inputting the deeply fused hidden variables and the numerical model background field into a decoder based on a conditional variation auto-encoder, generating high-resolution three-dimensional temperature field grid data, and synchronously outputting a three-dimensional uncertainty field; according to the method, the continuous, complete and high-precision ocean three-dimensional temperature field in the whole research area is reconstructed through a mathematical method and a physical method by utilizing limited, sparse, multi-source and heterogeneous ocean observation data, and the problem that the ocean three-dimensional temperature field generated in the prior art is not accurate enough is solved.

The invention provides an automatic processing method for real-time observation data of an ocean station, and belongs to the technical field of ocean observation data processing. A wavelet packet decomposition multi-scale noise separation algorithm is established to distinguish environmental noise and real signals, dual-sensor redundancy configuration is combined with Bayesian inference to identify sensor drift, and a calibration coefficient is updated in real time through a recursive least square method. A one-dimensional time sequence is mapped to a high-dimensional phase space by utilizing a phase space reconstruction algorithm to realize high-precision prediction of a chaotic signal, a hierarchical data storage architecture is established, and a data migration strategy is iteratively optimized through a hierarchical correlation degree function; the technical problem that time synchronization signals are difficult to reconstruct accurately according to asynchronous sampling data of multiple sensors of an ocean station is solved.

The invention belongs to the technical field of ocean observation, and discloses an underwater sound velocity distribution super-resolution construction method based on multi-modal data fusion. According to the method, a sound velocity distribution forecasting model is constructed, the model carries out temperature preliminary reconstruction on original space area temperature data through a Unet neural network, and sea surface temperature features are extracted through a Mama neural network; correcting the preliminarily reconstructed temperature data through sea surface temperature features to obtain high-spatial-resolution temperature data; and finally, obtaining high-spatial-resolution sound velocity distribution data through a sound velocity distribution calculation module. According to the method, the underwater temperature and the sea surface temperature are subjected to data fusion, so that the sound velocity distribution of the whole space area is quickly and accurately forecasted under the condition of original low-spatial-resolution data, and the problem of insufficient spatial resolution of ocean sound velocity distribution estimation is solved.

The invention provides a coastal water level prediction method based on multi-modal observation data, and belongs to the technical field of coastal water level prediction.The coastal water level prediction method comprises the steps that multi-modal ocean observation data of different sensors are collected, and a multi-scale space-time registration matrix is established to achieve space-time alignment of heterogeneous data; an ocean dynamic process feature extractor is constructed to recognize astronomical storm surge and wave features and calculate nonlinear coupling parameters, an adaptive space-time fusion algorithm is adopted to dynamically adjust weights according to data quality to generate a space-time consistency data set, and an ocean dynamic coupling strength discrimination model is established to determine a modeling strategy. And finally generating a coastal water level prediction product containing a predicted value confidence interval and risk early warning. The technical problem of insufficient coastal water level prediction precision caused by difficult effective fusion of multi-modal ocean observation data under the condition of spatial-temporal scale mismatching is solved.

The invention discloses an XBT-T5 observation data deviation analysis and temperature correction method, and relates to the technical field of ocean observation, and the method comprises the steps: obtaining the original temperature observation data of a target sea area based on an XBT-T5 front-end temperature sensor, calculating the depth, generating a temperature-depth profile, and constructing a temperature-depth database in combination with historical depth observation archive data; the method comprises the following steps: synchronously acquiring temperature and depth data by using a CTD temperature-salinity-depth instrument, and carrying out association pairing with a temperature-depth database to generate an XBT-T5 observation data deviation field under the same spatial-temporal scale; based on generation of an XBT-T5 observation data deviation field under the same spatial-temporal scale, designing a dual-channel adaptive encoder, and constructing a temperature-depth collaborative correction model; and according to the temperature-depth collaborative correction model, coupling the temperature-depth database and generating an XBT-T5 observation data deviation field under the same spatial-temporal scale, thereby realizing XBT-T5 observation data deviation analysis and temperature correction. The method improves the quality and application value of observation data.

The invention provides a multi-source data fusion correction method for a marine observation station, which belongs to the technical field of marine observation, and comprises the following steps of: establishing a hierarchical interpolation correction model comprising a weighted moving average algorithm and a space collaborative interpolation algorithm, and constructing a marine environment adaptive threshold adjustment mechanism based on a closed-loop feedback system; a Kalman filter is combined with a multi-factor coupling drift prediction model of a long-short-term memory network, a progressive degradation fusion strategy switched according to environmental conditions is designed, and a biological pollution gradual change detection correction model adopting a liquid neural network structure is constructed. A multi-source data quality evaluation system based on data continuity, numerical stability, consistency and historical reliability is established, high-precision self-adaptive fusion correction of the marine environment multi-source sensor data is achieved, and the technical problem that high-precision self-adaptive fusion correction of the marine environment multi-source sensor data is difficult to achieve is solved.

The invention relates to a wave height parameter correction method and system for low-angle laser radar observation data, and relates to the technical field of ocean observation data processing, and the method comprises the steps: carrying out the smoothing and data cleaning processing of original wave height data, so as to construct a fitting model of polynomial and Gaussian global trend analysis; and then, through double-model dynamic fusion, polynomial short-term fluctuation capture and Gaussian long-term trend capture are deeply combined, so that the generalization ability of the model is improved. And finally, intelligent reference interval positioning is introduced to adapt to wave height distribution changes in different periods and positions, excessive correction is avoided in combination with introduced correction factors, accurate correction data are obtained, the problem that the wave height is attenuated along with the distance is solved, and the efficiency and reliability of correction processing are remarkably improved.

The invention relates to the technical field of ocean observation data, in particular to an offshore ocean observation data quality control method based on time-frequency domain characteristics, which comprises the following steps: extracting the characteristics of first ocean data, including time sequence characteristics, frequency domain characteristics, inner time sequence characteristics and outer time sequence characteristics; fusing the internal time sequence features and the external time sequence features to obtain internal and external time sequence features; fusing the internal and external time sequence features and the frequency domain features to obtain time-frequency domain features; inputting a preset anomaly detection model to obtain abnormal first ocean data; and correcting the abnormal first ocean data to obtain the ocean data after quality control. According to the method, the time sequence features, the frequency domain features and the fusion of the internal and external time sequence features are introduced, the multi-dimensional features of offshore ocean observation data are comprehensively described, and the accuracy of anomaly detection is remarkably improved. A preset anomaly detection model is adopted to identify the time-frequency domain features, and an abnormal data correction mechanism is combined, so that the reliability and stability of observation data are effectively improved.

The invention relates to the technical field of ocean observation, in particular to an intelligent quality control system and method for ocean survey data, which takes whole-process data quality closed-loop management and control as a core, proposes an element-standard-process three-dimensional quality control framework, constructs a real-time and timing dual-mode architecture, and integrates a whole-chain visualization and non-intervention automation technology. By extracting more than 40 differentiated quality control elements of more than 10 types of data, a four-layer quantitative standard of equipment state-parameter setting-data integrity-operation compliance is established, and closed-loop control of the whole process is standardized. The error condition detection rate of the system reaches 89% or above, the manual quality control time cost is reduced by 90% or above, the quality control time efficiency is compressed to the real-time / timing hour level, paradigm innovation from post-event error correction to real-time intelligent management and control is achieved, core support is provided for high-quality collection and high-reliability application of ocean survey data, and the method is suitable for various ocean comprehensive scientific investigation scenes.

The invention provides a parameter self-adaptive adjustment method for a semi-automatic calibration system of a thermohaline depth gauge, and belongs to the technical field of marine survey. The parameter self-adaptive adjustment method comprises the following steps: determining the length of a calibration window by self-adaptive clustering; acquiring original data of the sensor and extracting features; calculating a measurement deviation value; establishing an association data set of the environmental factors and the deviations; constructing and training a multilayer neural network model; dynamically optimizing the calibration window length; verifying and optimizing the parameter adaptive adjustment model; applying an error decomposition function to separate different types of errors; generating compensation parameters by using a deep residual attention network model; and deploying the optimization model into the system to realize real-time adaptive adjustment. According to the method, adaptive data processing and deep learning technologies are combined, the problem that the measurement precision of the temperature-salinity-depth instrument is reduced in a complex and changeable environment is effectively solved through sensor parameter real-time optimization and error accurate compensation, and the reliability and accuracy of ocean observation data are improved.

The invention discloses a land-based data automatic receiving and preprocessing management system and method, and relates to the technical field of ocean observation data processing, and the system comprises a communication service subsystem, a data preprocessing subsystem, an intelligent perception linkage subsystem, and a data management subsystem. And full-process automatic processing of underwater acoustic real-time communication subsurface buoy observation data is realized through modular integration. Automatic receiving, safe storage and efficient preprocessing of deep sea power and biogeochemical element data are achieved, reliable data support is provided for marine scientific research and environment monitoring, and the long-term observation requirement of a 6000-meter water depth subsurface buoy system is met.

The invention relates to the technical field of international marine scientific investigation collaborative technology and mutual identification, and discloses an international marine observation data fusion and achievement mutual identification system suitable for joint scientific investigation. Comprising a multi-source data standardization access module, a polar region adaptive intelligent fusion module, an international achievement mutual recognition standard and verification module, a hierarchical sharing and mutual recognition process management module, a collaborative iteration and operation and maintenance management module, a block chain full-process traceability system and an international joint collaborative operation and maintenance mechanism. According to the system, the problem of international data heterogeneity is solved through multi-source data standardization preprocessing, accurate data fusion under polar region / open sea complex working conditions is realized by utilizing an intelligent fusion technology, a mutual recognition system of bidirectional verification is established, and the traceability of the whole process is ensured by adopting a block chain. The system supports hierarchical sharing and dynamic iterative optimization, is cooperatively operated and maintained by two international parties, guarantees long-term adaptation to standard updating and equipment iteration of the two parties, and effectively improves the joint scientific investigation data sharing efficiency and the result mutual recognition credibility.

The invention relates to the technical field of ocean observation, in particular to an ocean power and biochemical element observation system aiming at different observation requirements, which comprises a submersible specimen body, an intelligent sensing module, a multi-mode communication module, a multi-element observation module and a main control module, the technical problems that an existing observation system is incomplete in communication architecture, lack of intelligent perception, insufficient in multi-element synchronization, low in data transmission reliability and poor in adaptability are solved, and full-water-depth, multi-scene and high-precision ocean power and biochemical element collaborative observation is achieved.

The invention relates to the technical field of ocean observation, in particular to a hydrate reservoir information in-situ monitoring device. Comprising a monitoring box body; a metal grating plate is arranged at the bottom of the monitoring box body, and hydrate reservoir sediments can enter the monitoring box body through grating holes in the metal grating plate; a dissolved oxygen sensor, a pH sensor, a Raman probe rod and a resistivity probe rod are sequentially arranged in the monitoring box body from top to bottom, and a water tank camera is arranged on the side wall of the monitoring box body; a plurality of annular electrodes are arranged on the resistivity feeler lever in the circumferential direction of the feeler lever at intervals, and each annular electrode is connected with an annular electrode distance adjusting mechanism and an annular electrode aperture adjusting mechanism. And in-situ monitoring of the hydrate reservoir can be realized.

The invention discloses an ocean mesoscale vortex three-dimensional temperature field inversion method, device and equipment and a medium, and relates to the field of ocean observation and data inversion, and the method comprises the steps: obtaining multi-source ocean observation data; based on the multi-source ocean observation data, performing mesoscale vortex identification, and determining a mesoscale vortex three-dimensional structure; constructing a mesoscale eddy three-dimensional temperature field inversion model by adopting a regression analysis method according to the mesoscale eddy three-dimensional structure based on the multi-source ocean observation data; solving a regression coefficient in the mesoscale eddy three-dimensional temperature field inversion model by adopting a hierarchical least square method; and according to the regression coefficient, the mesoscale eddy three-dimensional temperature field inversion model is adopted to invert a mesoscale eddy three-dimensional temperature field, and dynamic constraints are embedded in the inversion process so as to obtain the temperature of each standard layer of the mesoscale eddy. According to the invention, the precision and reliability of the temperature inversion result of each standard layer of the mesoscale vortex are improved.

The invention relates to the technical field of ocean observation, in particular to a CTD observation data anomaly detection and correction method, which comprises an anomaly detection stage and an anomaly correction stage, in the anomaly detection stage, an RS-MLP combined mixed detection model is adopted to carry out anomaly identification on original CTD data; and in the abnormity correction stage, time sequence prediction correction is carried out on the detected abnormal data based on the STL-LSTM model. In the aspect of detection precision, the RS-MLP method integrates domain knowledge and a data driving method, the recall rate is improved while the high precision rate is ensured, and the omission ratio is effectively reduced; in the aspect of correction quality, the STL-LSTM method extracts internal structure features of data through time sequence decomposition, and realizes high-quality anomaly correction in combination with the time sequence modeling capability of LSTM; in the aspect of calculation efficiency, the two-stage design of the RS-MLP not only ensures the detection precision, but also improves the calculation efficiency; the rapid screening of RS reduces the amount of data that needs to be processed by MLP.

The invention provides a profile data set construction method and system based on multi-source heterogeneous ocean observation, and the method comprises the steps: obtaining multi-source heterogeneous original ocean observation profile data and description information of the data from a plurality of target ocean data centers / observation mechanisms in a target time period; performing version cleaning on the original ocean observation profile data according to the unique identifier determined by the metadata and the description information of the original ocean observation profile data, and performing high-frequency cleaning on the original ocean observation profile data based on the spatio-temporal joint features to obtain multiple pieces of target ocean observation profile data; and performing matrix processing on the target ocean observation profile data to obtain corresponding normalized data so as to construct a profile data set in the target time period. Therefore, the multi-source heterogeneous profile data can be subjected to data cleaning and format normalization in sequence, so that the consistency, accuracy and availability of the data are improved.

The invention provides a temperature grid point data generation method and system for ocean profile observation, and the method comprises the steps: obtaining a plurality of pieces of ocean temperature observation profile data from multiple sources; for each piece of ocean temperature observation profile data, respectively interpolating the observation temperature included in the ocean temperature observation profile data to a corresponding target standard depth in the standard depth set according to an interpolation strategy to obtain a first observation temperature sequence of the ocean temperature observation profile data; carrying out the averaging of the first observation temperature sequence of the ocean temperature observation profile data of which the observation time falls into the target time period and the geographic position falls into the target horizontal grid, and obtaining a second observation temperature sequence; and according to a preset time window and a space window, successively carrying out moving average processing on the second observation temperature sequence to obtain temperature grid point data. Therefore, through vertical interpolation and space-time smoothing, ocean observation profile data with uneven distribution and different depths are finally processed into high-quality ocean temperature grid point data with uniform space coverage.

The application relates to an active profile display device and method of a marine observation buoy, which is composed of an embedded microprocessor, an LED driving module, an illumination sensor, an LED lamp strip, a 485 bus protocol module, an AIS receiving module and a positioning module, wherein the embedded microprocessor is connected with the LED driving module, the illumination sensor, the 485 bus protocol module, the AIS receiving module and the positioning module respectively, and the LED driving module is connected with the LED lamp strip. The embedded microprocessor performs data compliance discrimination inspection every second, and performs active profile display analysis control module in a timing mode. According to the weighted calculation of the distance normalization result of the ship and the buoy, the LED driving power, the light duration and the extinguishing duration are adjusted. The beneficial effect of the application is that the LED lamp strip power and the light and extinguishing duration are dynamically adjusted according to the distance of the surrounding ships, so that better marker profile display effect is realized with less energy consumption.

The invention relates to the technical field of deep sea communication and ocean observation, in particular to a long-term stable real-time communication system based on domestic satellites under high sea conditions. Comprising a submerged buoy body, the submerged buoy body is provided with a communication floating body module, a multi-source domestic satellite communication module, a low-power-consumption control module and a data processing module which are electrically connected in sequence, and an intelligent sensing module and an underwater data access module which are respectively connected with the data processing module; the device further comprises a main floating body. The system can be widely applied to deep-sea subsurface buoy platforms, marine environment monitoring stations, deep-sea scientific investigation equipment and other scenes, realizes real-time transmission of hydrodynamic and biogeochemical multi-element synchronous observation data at the water depth of 0-6000 meters, and supports research on the dynamic process of isolated waves, near-inertia internal waves and the like in the ocean and safety guarantee of the marine environment.

The invention provides a geographic element spatio-temporal topology establishment method for a large ocean forecast disaster reduction model, and belongs to the technical field of ocean observation forecast. Multi-source heterogeneous ocean data is fused and cleaned in real time, sparse data complementation of physical constraints is realized by using a temperature-salt flow collaborative interpolation model and a graph neural network, and the real-time fusion and cleaning of the multi-source heterogeneous ocean data are realized. A spatial index is established based on quadtree Z curve coding, a main filter and an auxiliary filter are executed in parallel through a GPU double-layer sub-thread grid to achieve millisecond-level query response, and local increment updating is conducted on a topological relation through spatial partitioning and an event-driven mechanism. Bayesian Kriging interpolation and Monte Carlo simulation are introduced to add confidence labels for topological nodes to quantify uncertainty, and finally standardized spatio-temporal topological relation data are output, so that the technical problem that a large ocean forecast disaster reduction model is difficult to understand and a geographic element spatio-temporal topological relation under the condition of observation forecast data is efficiently constructed is solved.

The invention discloses a self-defined ocean observation data storage method, device and equipment and a medium, and relates to the technical field of data processing, and the method comprises the steps: creating and maintaining an ocean environment element data label library which can be edited by a user in a database; the element data label library is used for storing and managing predefined marine environment element data labels; based on the label library, responding to the selection operation, and constructing a database table template of a set structure through the selected data labels and the self-defined fields; the ocean observation equipment to be accessed is associated and bound with the constructed database table template; and in response to the association binding, automatically generating a data storage table corresponding to the marine observation equipment according to the associated database table template so as to receive and store observation data of the marine observation equipment and form a marine environment data knowledge base decoupled from the marine observation equipment. According to the method, the storage capacity of the multi-source heterogeneous data of the marine environment data management system is improved.

The invention relates to the technical field of ocean observation, in particular to an air-sea flux observation system platform attitude monitoring and correcting system which comprises a buoy platform composed of a buoy body and a buoy anchoring system, and a buoy platform attitude monitoring system used for conducting attitude monitoring on the buoy platform is arranged on the buoy body. And the attitude correction system is used for performing attitude correction on the buoy platform and comprises a coordinate system rotating module, a shake correction module and a tilt correction module. The problem that the accuracy of data observed by an air-sea flux observation system is difficult to guarantee by buoy attitude change in the prior art is solved.

The invention discloses a control method of a sea-air cross-medium aircraft, and belongs to the field of aircrafts. The control method comprises the following steps that (1) the laid sea-air cross-medium aircraft is in a vertical state in water, the sea-air cross-medium aircraft dives vertically by adjusting a buoyancy bin, and after the sea-air cross-medium aircraft reaches a preset position, the sea-air cross-medium aircraft is maintained at a set depth underwater through control of the buoyancy bin, so that the sea-air cross-medium aircraft dives vertically; external information is collected through the camera shooting bin, and ocean observation is carried out; (2) after observation of the sea-air cross-medium aircraft is finished, the sea-air cross-medium aircraft floats to the water surface under the control of the buoyancy cabin; then, arms in the wing folding mechanisms are controlled to be unfolded, blades rotate to generate lift force, and the sea-air cross-medium aircraft is made to get out of water and fly in the air; and (3) after the sea-air cross-medium aircraft flies to a preset position, the aircraft arms are controlled to retract, the aircraft dives again and reaches the preset position, and the aircraft is kept silent and waits for next water outlet. According to the invention, the reciprocating cross-domain flexibility and stability of the aircraft are improved.

The invention provides a multi-fusion ocean salinity observation method, system, equipment and medium, and belongs to the technical field of ocean salinity observation.The method comprises the steps that an observation request instruction is received and responded, and an ocean observation area is determined; analyzing and processing the ocean observation data set by using an ocean salinity observation data analysis model; determining a target ocean observation area; according to the specific observation demand index, one or more target data acquisition terminals capable of meeting the demand index are matched and determined from a preset available data acquisition terminal resource library; creating a specified observation task for the target ocean observation area based on a scheduling instruction set; and sending the observation result information to a corresponding observation terminal. According to the method, the response speed of the marine environment change is improved, the observation cost is reduced through resource dynamic scheduling, and technical support is provided for scenes such as marine ecological monitoring and disaster early warning.

A method includes converting, via a wavelet transform, (i) data associated with a prior ocean state forecast to wavelet space prior ocean state data and (ii) ocean observations to wavelet space observation data, and then filtering the wavelet space observation data. The method includes generating a correction value based on a difference between the wavelet space prior ocean state data and the filtered observation data, and determining a wavelet space increment value based on (i) the generated correction value, (ii) an error covariance associated with the prior ocean state forecast, and (iii) an error covariance associated with the ocean observations. The method includes converting, via an inverse of the wavelet transform, the wavelet space increment value to a physical space increment value, and generating a current ocean state forecast based on (i) the converted physical space increment value and (ii) a background state associated with the prior ocean state forecast.

The invention belongs to the technical field of ocean engineering and ocean observation, and relates to an ocean sensor long-term test device and method based on an autonomous underwater vehicle. The method comprises the following steps: executing a marine environment observation task, and storing an observation data sequence in a second AUV in real time; continuously monitoring the electric quantity state and the storage space occupancy rate, and when the electric quantity state is lower than a preset electric quantity threshold value or the storage space occupancy rate reaches a preset storage threshold value, generating a corresponding energy supplement demand signal or a data transmission demand signal; analyzing and judging the generated energy complementing demand signal or data transmission demand signal to determine an energy complementing mode or a data transmission mode; performing joint optimization on the energy complementing path or the data transmission path and the observation path after energy complementing or data transmission is completed, and generating a path planning result of multi-device cooperative operation and a corresponding scheduling strategy; and based on the path planning result and the scheduling strategy, related equipment is controlled to execute corresponding energy complementing operation or observation data transmission operation.

The application discloses a kind of multi-group one-dimensional frequency energy density spectrum ratio measurement precision evaluation method and system, belong to marine observation technical field, comprising: the training data sample of establishing including M group synchronous observation sea wave one-dimensional frequency energy density spectrum sequence;21 similarity evaluation value sequences are constructed;21 similarity evaluation value sequences are sorted according to the order of evaluation value from big to small, and at least two dimensions in sequence segmented average, overall average, fluctuation amplitude, fluctuation rate, distribution concentration and dispersion degree are analyzed, to determine the comprehensive weight coefficient of 21 similarity evaluation values;The one-dimensional frequency energy density spectrum sequence of two synchronous observations to be evaluated is obtained, and the corresponding 21 similarity evaluation values thereof are calculated;The final similarity evaluation value NRD is calculated, to evaluate the ratio measurement precision of two to be evaluated sequences.The application constructs a multidimensional evaluation system, can comprehensively reflect the similarity of two sea wave spectrum sequences.