CN116797756A - Holographic ocean background field implementation method - Google Patents
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Abstract
The invention discloses a method for realizing a holographic ocean background field, which relates to the field of marine instrument and equipment testing and ocean science experiments. The invention can accurately describe the medium and small scale environmental parameter change of the test sea area, solves the problems of accurate perception and advanced prediction of the original background field of the sea area in the process of the offshore test by devices, instruments and equipment, and provides reliable basic data, process input and verification/check conditions for obtaining scientific and correct test performance evaluation; the method can provide a complete background field with high resolution and visualization, solves the problems that the field observation is only relied on, the point can not be used for replacing the surface, and the cost is high, and provides a solution for the visualization and the visualization reproduction of the background field environment element change in the network space.
Description
Technical Field
The invention relates to the field of marine instrument and equipment testing and marine science testing, in particular to a method for realizing a holographic marine background field.
Background
Data perception and prediction of historical, current, and future environmental background fields are the supporting basis for offshore testing. At present, the ocean observational network initially constructed in China mainly aims at large-scale research, but the observation of the local process of the small-scale and medium-scale offshore test is not enough, and the data cannot be transmitted in real time; the stereoscopic observation system covering sea surfaces, water bodies and seafloors is still in an exploration stage, and the traditional observation means and technology are mainly adopted; the space layout is mainly based on points and lines in the region, and data islands exist, so that the full water depth, integrated, real-time and high space-time resolution observation capability is lacked; most of the devices are not fully fused with the digital technology, and related data is only given according to the requirements of the test objects, so that the data volume is not rich enough and the intelligent performance is not realized; the background field information has poor perceptibility, and the data accumulated by the observation system and the working state of the object to be researched cannot be effectively shared and comprehensively presented.
On the other hand, the ocean power environment relates to multisource power elements with different time-space scales and interaction among the elements, a power mechanism is complex, nonlinearity is remarkable, and the difficulty in improving the arrangement density, numerical modeling precision, calculation accuracy and effectiveness of observation equipment is high. With the emergence of new technologies such as data mining and artificial intelligence, the problems have new solutions. Compared with the traditional model based on the physical process, the artificial intelligent model based on the data characteristics has the advantages of data hiding characteristics, mobility, cooperativity and flexibility, is high in calculation efficiency, can be used as an optimization supplement for the existing observation and calculation method, and therefore achieves faster, more accurate and more convenient ocean information space-time expansion and perception prediction.
Disclosure of Invention
The invention aims to solve the defects in the prior art, and provides a method for realizing a holographic ocean background field.
In order to achieve the above purpose, the present invention adopts the following technical scheme:
a method for realizing holographic ocean background field comprises the following steps:
s1, extracting effective real-time observation data through an offshore observation module, performing data assimilation treatment, and importing the real-time observation data into a high-precision numerical calculation module;
s2, carrying out data calculation through a high-precision numerical calculation module, verifying the accuracy of the data, if the error of a calculation result is smaller than 10%, importing the result into a three-dimensional digital mapping module and an artificial intelligent big data processing module, if the error of the calculation result is larger than 10%, carrying out secondary data assimilation through a result model parameter debugging module, and carrying out comparison verification on real-time observation data and result data;
s3, analyzing and mining the real-time observation data and the result data through an artificial intelligent big data processing module, learning training data and updating a database;
s4, correcting the model parameter debugging module through the artificial intelligent big data processing module,
s5, analyzing the result data through the three-dimensional digital mapping module, and constructing a time-space continuous digital background field.
Preferably, the marine observation module in S1 includes four contents including an aerospace observation, a water body observation, a submarine observation, and a data preprocessing, where the aerospace observation, the water body observation, and the submarine observation form an air-sea-land-sea stereoscopic observation data set.
Preferably, the basic model of the high-precision numerical calculation module in the step S2 is a marine-atmosphere-wave-silt water ecological mathematic physical coupling model, and the marine-atmosphere-wave-silt water ecological mathematic physical coupling model is built on the real-time observation data and the learning training data.
Preferably, the calculation principle of the high-precision numerical calculation module is a complex physical process based on ocean power, and the bidirectional coupling effect among multiple power elements needs to be comprehensively considered, and the high-precision numerical calculation module comprises a meteorological module, a hydrodynamic module and a wave module.
Preferably, the model base of the artificial intelligent big data processing module in the step S3 is based on data, image characteristics and an associated neural network model, and the model is cross-fused with a marine dynamic physical process model of high-precision numerical calculation to form an intelligent marine numerical calculation model.
Preferably, the data processed by the artificial intelligence big data processing module comprises real-time observation data, historical data and learning training data, and the functions of the artificial intelligence big data processing module comprise CNN-LSTM deep neural network and accumulated real-time observation data and historical data.
Preferably, the model parameter debugging module in S4 calculates a model based on the high-precision numerical value.
Preferably, the core technology of the three-dimensional digital mapping module in S5 is a digital twin technology, and the mapping form of the three-dimensional digital mapping module is a one-dimensional time sequence of environmental elements of a background field, a two-dimensional plane field, each depth profile and a three-dimensional space-time animation.
The invention has the following beneficial effects: the method can accurately describe the medium-small scale environmental parameter change of the test sea area, solve the problems of accurate perception and advanced prediction of the original background field of the sea area in the process of offshore test by devices, instruments and equipment, and provide reliable basic data, process input and verification/check conditions for obtaining scientific and correct test performance evaluation; the method can provide a complete background field with high resolution and visualization, solves the problems that the field observation is only relied on, the point can not be used for replacing the surface, and the cost is high, and provides a solution for the visualization and the visualization reproduction of the background field environment element change in the network space.
(1) Based on the optimal offshore observation distribution points, an air-sea-land offshore stereoscopic observation data set is formed, and the multi-element, multi-scale and all-weather data verification problem of a background field is solved;
(2) Based on high-precision numerical calculation, a fine-scale and high-precision calculation model of ocean-atmosphere-wave bidirectional coupling is built, and the simulation precision problem of a small-scale range of a region is solved;
(3) Based on artificial intelligence big data processing, valuable assimilation data is provided, and optimization problems of computing resources, simulation reliability and prediction aging are solved;
(4) Based on the three-dimensional digital mapping, complete and reliable ocean background field information formed by historical data, real-time data and derivative data is established.
Drawings
Fig. 1 is a flowchart of an implementation method of a holographic ocean background field according to the present invention.
Fig. 2 is a schematic diagram of the implementation method of the holographic ocean background field provided by the invention.
Fig. 3 is a schematic diagram of the structure of the marine observation content according to the present invention.
Fig. 4 is a schematic structural diagram of a high-precision numerical calculation model framework according to the present invention.
Fig. 5 is a schematic diagram of the structure of the CNN-LSTM neural network according to the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments.
1-5, a holographic marine background field implementation method comprises the following steps:
s1, extracting effective real-time observation data through an offshore observation module, performing data assimilation treatment, and importing the real-time observation data into a high-precision numerical calculation module;
s2, carrying out data calculation through a high-precision numerical calculation module, verifying the accuracy of the data, if the error of a calculation result is smaller than 10%, importing the result into a three-dimensional digital mapping module and an artificial intelligent big data processing module, if the error of the calculation result is larger than 10%, carrying out secondary data assimilation through a result model parameter debugging module, and carrying out comparison verification on real-time observation data and result data;
s3, analyzing and mining the real-time observation data and the result data through an artificial intelligent big data processing module, learning training data and updating a database;
s4, correcting the model parameter debugging module through the artificial intelligent big data processing module,
s5, analyzing the result data through the three-dimensional digital mapping module, and constructing a time-space continuous digital background field.
In the invention, the marine observation module in S1 comprises four contents of aerospace observation, water body observation, submarine observation and data preprocessing, and as shown in figure 3, the function of the marine observation module is to establish a multi-element comprehensive three-dimensional observation network, and provide valuable actual measurement data for marine background field element calibration, verification, assimilation and the like for the next high-precision numerical calculation.
(1) The space observation content is real-time observation data of meteorological environment elements such as wind speed, wind direction, air temperature, air pressure, precipitation, visibility and the like in a region above the sea surface by using satellites, unmanned aerial vehicles and offshore platforms as observation instrument carriers. The satellite observation can build a large-range full-coverage data platform; the unmanned aerial vehicle observation is used for directly calibrating and supplementing satellite data on the surface scale; the offshore platform observation is used for high-precision tracing, calibration and inspection of in-situ data.
(2) The water body observation content is real-time observation data of water body environment elements such as sea water temperature, salinity, water level, waves, ocean currents, turbidity, dissolved oxygen, chlorophyll, noise, spectrum, water quality and the like by taking a buoy and a submerged buoy as an observation instrument carrier. Buoy observation generally meets basic observation requirements and can be moved according to observation precision; the submerged buoy is not easily affected by sea environment, and is additionally arranged when actual test requirements are met, such as deep sea detection, long-term fixed point and the like.
(3) The submarine observation content is to take a seabed base as an observation instrument carrier, carry various detection equipment instruments such as power, ecology, acoustics and electromagnetism, construct a submarine reference and positioning navigation system, a submarine acoustics monitoring system and a submarine non-acoustics detection system, and acquire submarine activity and information of a region in real time.
(4) The data preprocessing content is to collect the multi-source heterogeneous actual measurement original data of each observation module by utilizing wired communication or 4G/5G wireless communication, and perform preprocessing work such as integration, cleaning, transformation, reduction and the like.
The aerospace observations, water observations and subsea observations form an air-sea-land-sea stereoscopic observation dataset.
In the invention, the basic model of the high-precision numerical calculation module in the S2 is a marine-atmosphere-wave-silt water ecological mathematic physical coupling model, and the marine-atmosphere-wave-silt water ecological mathematic physical coupling model is established on the real-time observation data and the learning training data, and has the functions of realizing the simulation of the spatial-temporal distribution characteristics and the physical evolution rules of the full coverage, multiscale and continuity of the target sea area to describe the marine power elements, improving the resolution of the marine background field data information, and realizing the three-dimensional analysis and the twin reproduction of the one-dimensional point position time sequence data obtained by the offshore observation unit to the multidimensional field information.
The calculation principle of the high-precision numerical calculation module is based on a complex physical process of ocean power, and the bidirectional coupling effect among multiple power elements needs to be comprehensively considered, the high-precision numerical calculation module comprises a meteorological module, a hydrodynamic module and a wave module, and various variables such as a wind field, a water level, a wave field, a flow field, a sea water temperature salt field and the like of a target sea area are obtained through simulation calculation; in addition, the hydrodynamic module further comprises two submodules of sediment and water ecology, and the sediment submodule can be called to simulate sediment transportation and seabed evolution according to the requirements of the problem to be solved, and the water ecology submodule is called to simulate power processes such as diffusion, propagation and sedimentation of biochemical elements.
The logic framework of the model module for high-precision numerical calculation is shown in fig. 4, and bidirectional parameter transmission and physical quantity exchange exist among the three modules. The meteorological module provides high-precision meteorological power elements for the model, and the calculation results mainly comprise wind fields, atmospheric layer air pressure, relative humidity, temperature, sea surface heat radiation flux, precipitation and the like, so that the required sea surface boundary conditions are provided for calculation of the hydrodynamic module and the wave module; the sea surface temperature result calculated by the hydrodynamic module provides an atmospheric bottom boundary condition for the meteorological module, and the results of the calculated sea surface height, the three-dimensional flow field, the bottom surface roughness and the like are transmitted to the wave module; the calculation results of the wave module mainly comprise wave parameters such as wavelength, wave age, wave height, wave period, wave breaking, energy dissipation and the like, the calculation of the shear stress at the atmospheric boundary layer of the meteorological module is affected, and the wave-flow coupling effect with the hydrodynamic module is quantified through the parameterization expression of the wave vorticity force and the wave radiation stress.
The convergence and accuracy of the high-precision numerical calculation model are based on the training correction of actual measurement data observed at sea and historical data processed by artificial intelligence big data, and the latest actual measurement data is continuously introduced to optimize and adjust the calculation model.
In the invention, the model foundation of the artificial intelligent big data processing module in S3 is based on data, image characteristics and an associated neural network model, the model is cross-fused with a marine dynamic physical process model of high-precision numerical computation to form an intelligent marine numerical computation model, the data processed by the artificial intelligent big data processing module comprises real-time observation data, historical data and learning training data, the functions of the artificial intelligent big data processing module comprise CNN-LSTM deep neural network and accumulated real-time observation data and historical data, the model uses the CNN-LSTM deep neural network to perform characteristic training learning on long time sequence computation results of different precision grids in the high-precision numerical computation model from space and time latitude as shown in FIG. 5, a statistical mapping relation of each power element under different time and space precision conditions is established, nested encryption of the numerical grids is replaced, information capture and short-time prediction of a regional high-precision element field by the neural network model are realized, thereby computing resources are saved, and computing efficiency is improved.
Along with the continuous accumulation of actual measurement data and historical data observed at sea, database samples are continuously updated, and through rapid reading and accurate mining of data, the data are fed back into a high-precision numerical calculation model, valuable assimilation data are provided for the numerical calculation model based on a physical process, so that the reliability of numerical simulation is improved, and simulation errors are reduced.
In the invention, the model parameter debugging module in S4 is based on the industrial intelligent big data processing module.
In the invention, the core technology of the three-dimensional digital mapping module in S5 is a digital twin technology, a real-time observation data set observed at sea, a derivative data set calculated by high-precision numerical value, a history data set and a prediction data set which are subjected to artificial intelligence big data processing, learning and prediction are taken as input conditions, a multi-dimensional data relation mapped with the history, real-time and future states of a target sea area background field is constructed, a time-space continuous numerical background field which is completely mapped with a real physical field is formed, and the functions of management analysis, holographic reconstruction, visual display and prediction decision-making of multi-source data elements such as submarine topography, ocean currents, tides, waves, temperature, salinity, gravitational field, magnetic field, sea surface weather and the like are realized.
The mapping form of the three-dimensional digital mapping module is one-dimensional time sequence, two-dimensional plane field, each depth profile and three-dimensional space-time animation of background field environment elements.
The invention may be put into effect by way of the following operative principles thereof,
the invention provides a holographic ocean mode, which is an implementation method for realizing three-dimensional, continuous, fine and visual ocean background fields. The ocean background field refers to the environmental basic physical information of the space range of the target sea area under the influence of no test object, including but not limited to the elements of submarine topography, water level, ocean current, waves, wind conditions, precipitation, temperature, salinity, visibility, turbidity and the like; "holographic" refers to the continuous, numerical representation of the above-described background field physical variables in space stereo and time course; the method is characterized in that an optimal marine observation point layout mode is utilized, and a data set for continuously describing a field variable process is constructed by combining a high-precision marine numerical mode and big data-based neural network learning.
The foregoing is only a preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art, who is within the scope of the present invention, should make equivalent substitutions or modifications according to the technical scheme of the present invention and the inventive concept thereof, and should be covered by the scope of the present invention.
Claims (8)
1. The implementation method of the holographic ocean background field is characterized by comprising the following steps of:
s1, extracting effective real-time observation data through an offshore observation module, performing data assimilation treatment, and importing the real-time observation data into a high-precision numerical calculation module;
s2, carrying out data calculation through a high-precision numerical calculation module, verifying the accuracy of the data, if the error of a calculation result is smaller than 10%, importing the result into a three-dimensional digital mapping module and an artificial intelligent big data processing module, if the error of the calculation result is larger than 10%, carrying out secondary data assimilation through a result model parameter debugging module, and carrying out comparison verification on real-time observation data and result data;
s3, analyzing and mining the real-time observation data and the result data through an artificial intelligent big data processing module, learning training data and updating a database;
s4, correcting the model parameter debugging module through the artificial intelligent big data processing module,
s5, analyzing the result data through the three-dimensional digital mapping module, and constructing a time-space continuous digital background field.
2. The method according to claim 1, wherein the marine observation module in S1 comprises four contents including an aerospace observation, a water body observation, a submarine observation, and a data preprocessing, and the aerospace observation, the water body observation, and the submarine observation form an air-sea-land-sea stereoscopic observation data set.
3. The method for realizing the holographic ocean background field according to claim 1, wherein the basic model of the high-precision numerical calculation module in the step S2 is an ocean-atmosphere-wave-silt water ecological mathematic physical coupling model, and the ocean-atmosphere-wave-silt water ecological mathematic physical coupling model is established on the real-time observation data and the learning training data.
4. The method for realizing the holographic ocean background field according to claim 3, wherein the calculation principle of the high-precision numerical calculation module is a complex physical process based on ocean power, and the bidirectional coupling effect among multiple power elements needs to be comprehensively considered, and the high-precision numerical calculation module comprises a meteorological module, a hydrodynamic module and a wave module.
5. The method for realizing the holographic ocean background field according to claim 1, wherein the model base of the artificial intelligent big data processing module in the step S3 is based on data, image characteristics and an associated neural network model, and the model is cross-fused with a high-precision numerical calculation ocean dynamic physical process model to form an intelligent ocean numerical calculation model.
6. The method according to claim 5, wherein the data processed by the artificial intelligence big data processing module comprises real-time observation data, historical data and learning training data, and the functions of the artificial intelligence big data processing module comprise CNN-LSTM deep neural network and accumulated real-time observation data and historical data.
7. The implementation method of the holographic ocean background field according to claim 1, wherein the model parameter debugging module in S4 is based on an industrial intelligent big data processing module.
8. The method for realizing the holographic ocean background field according to claim 1, wherein the core technology of the three-dimensional digital mapping module in the step S5 is a digital twin technology, and the mapping form of the three-dimensional digital mapping module is a one-dimensional time sequence, a two-dimensional plane field, each depth profile and a three-dimensional space-time animation of background field environment elements.
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