Marine target object drift prediction and traceability analysis method

Abstract

The invention relates to the technical field of target object traceability, in particular to a marine target object drifting prediction and traceability analysis method, which comprises the following steps: acquiring the falling position and time of a marine target object and ocean and atmosphere numerical forecasting field data; constructing an FVCOM and SWAN combined wave power flow coupling mode, and according to the type of the target object, adopting a Monte Carlo method to put mass point set trajectory simulation at the last known position of the target object, and predicting the drift trajectory and the search and rescue range of the target object; the method comprises the following steps: collecting the position and time when a target object is found in an actual time-space and analysis data of a wind field and a flow field in the actual time-space during an accident, and performing reverse conversion of a time sequence and field data; and constructing a traceability model and assimilating the data, and converting a drift trajectory forecast result of the virtual space-time into a traceability analysis result of the actual space-time.

Description

Background Art

[0002] Ocean motion is affected by multiple factors such as atmospheric forcing, celestial body gravity, and baroclinic effects, making it difficult for traditional statistical models to accurately predict extreme ocean phenomena. With the progress of computer technology, ocean numerical forecasting has become the mainstream method by solving the dynamic equations. However, traditional vertical coordinates have calculation errors in the baroclinic gradient force in steep terrain areas, affecting the accuracy of flow field simulation. Statistical models cannot capture complex dynamic mechanisms and have insufficient prediction ability for extreme events. Early ocean models (such as two-dimensional models) ignored vertical turbulent dissipation and baroclinic effects and were difficult to simulate phenomena such as internal waves and thermoclines. Traditional backward Lagrangian tracking did not consider uncertainties such as wind-induced drift, differences in target types, and wind field randomness. There is a lack of a traceability model that couples multiple physical fields (wind, waves, tides), making it difficult to reflect the influence of the real ocean environment; in long-term traceability, model errors accumulate, resulting in results deviating from the actual trajectory. Summary of the Invention

[0003] The main object of the present invention is to provide a method for predicting the drift and traceability analysis of marine targets, effectively solving the above problems mentioned in the background art.

[0004] The technical solution of the present invention is as follows:

[0005] A method for predicting the drift and traceability analysis of marine targets is proposed, and the method includes the following steps:

[0006] S1. Obtain the falling water position, time of the marine target, and ocean and atmospheric numerical forecast field data;

[0007] S2. Construct a wave-current coupling model of FVCOM combined with SWAN, and use the Monte Carlo method to simulate the trajectory of a set of particles placed at the last known position of the target according to the target type, and predict the drift trajectory and search and rescue range of the target;

[0008] S3. Collect the position and time when the target is found in the actual space-time, and the analysis data of the actual space-time wind field and flow field during the accident, and perform reverse conversion of time series and field data;

[0009] S4. Construct a traceability model and assimilate the data, and convert the drift trajectory prediction result in the virtual space-time into the traceability analysis result in the actual space-time.

[0010] A further improvement of the present invention is that the S2 includes the following specific steps:

[0011] S21. Calculate the ocean current using the FVCOM model. Select the vertical coordinate as the bottom-following coordinate. Use the Mellor-Yamada 2.5-level turbulence closure sub-model to calculate the vertical mixing and the Smagorinsky turbulence closure sub-model to calculate the horizontal mixing. Adopt the inner-outer model separation algorithm and set the open boundary conditions. The tidal level and current data at the strait boundary are predicted from the harmonic constants of each tidal component, and the water level, flow velocity, temperature, and salinity data are provided by the daily-averaged HYCOM reanalysis data.

[0012] S22. Determine the sea surface driving conditions. Use the NCEP-CFSR reanalysis data with a spatial resolution of 0.5°×0.5° and a time interval of 6 hours to provide the 10-meter sea surface wind speed, shortwave radiation Q s , longwave radiation Q l , sensible heat flux Q h , latent heat flux Q e , evaporation and precipitation, and the net heat flux Q n = Q s - Q l ± Q h ± Q e , and set the model time and time step.

[0013] A further improvement of the present invention is that S2 further includes:

[0014] S23. Set the SWAN model. The calculation grid and water depth topography are the same as those of the FVCOM model. The forcing field is the wind field data at 10 m above the sea surface of the CFSR reanalysis data. The simulation calculation starts four days in advance, with a time step of 1 h and 5 iterations, and the result output step is 1 h.

[0015] S24. Perform coupled calculations to achieve two-way coupled simulation of FVCOM and SWAN, and calculate the influence of the Stokes drift and wave radiation stress on the flow field.

[0016] A further improvement of the present invention is that the Stokes drift calculation formula in S24 is:

[0017]

[0018] where U ss is the rate of Stokes drift on the ocean surface; is the unit wave number vector of the wave; k is the wave number of the wave; a is the amplitude of the wave; σ is the frequency of the wave; the Stokes drift calculation formula expressed by the significant wave height and the mean wave period T is:

[0019]

[0020]

[0021] The calculation formula of wave radiation stress is as follows:

[0022]

[0023] Among them, c is the wave speed, k is the wave number, is the wave energy, H s is the significant wave height, L is the wavelength, F SS , F CS , F CC is the vertical structure function, A R is the wave roller area, R z is the vertical distribution function, γ = H s / D.

[0024] A further improvement of the present invention is that the traceability model in S3 is to construct a traceability analysis model Leeway - trace based on the Leeway model, and use the wind field, flow field forecast data in the virtual space - time and the initial information of the target object to predict the drift trajectory in the virtual space - time.

[0025] A further improvement of the present invention is that the assimilation data in S3 uses the nudging method to assimilate the observation data. After nudging assimilation, the control equation of α(x, y, z, t) is:

[0026]

[0027] Among them, α o is the observed value, is the model prediction value, N is the number of observation points in the search area, γ i is the data quality factor of the i - th observation point, ranging from 0 to 1, G α is the nudging control factor, which satisfies the numerical stability criterion G α is set at the order of magnitude of the Coriolis force parameter; W i (x, y, z, t) is the weight coefficient W i (x, y, z, t) = w xy ·w σ ·w t ·w θ , where w xy , w σ , w t , w θ are the horizontal, vertical, time and direction weight coefficients respectively, and the expressions are:

[0028]

[0029]

[0030] where R is the search radius, is the distance from the calculation point to the observation point, and R σ is the vertical search radius, T w is the assimilation time window, Δθ is the angular difference, and c1 is a calculation constant with a value range of 0.05 - 0.5.

[0031] The technical effects of the present invention are as follows:

[0032] A method for predicting the drift and tracing the source of marine targets is constructed. By combining the SWAN wave model and considering the feedback of waves on ocean currents (such as Stokes drift and radiation stress), the present invention enhances the authenticity of the flow field prediction; decomposes the wind-induced drift into along-wind and cross-wind components, and considering the target type, wind field randomness, and stranding probability, improves the accuracy of drift prediction; transforms the source tracing problem into a forward prediction in virtual space-time, and processes the initial position uncertainty through the Monte Carlo method to expand the search range; introduces the nudging assimilation method to integrate observational data to correct the flow field simulation error, especially reducing the model cumulative error in long-term source tracing and improving the reliability of the results. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] Other features, objectives, and advantages of the present invention will become more apparent by reading the detailed description of the non-limiting embodiments with reference to the following drawings:

[0034] Figure 1 It is a schematic flowchart of a method for predicting the drift and tracing the source of marine targets according to Embodiment 1 of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0035] The present invention aims to propose a method for predicting the drift and tracing the source of marine targets. By combining the SWAN wave model and considering the feedback of waves on ocean currents (such as Stokes drift and radiation stress), the present invention enhances the authenticity of the flow field prediction; decomposes the wind-induced drift into along-wind and cross-wind components, and considering the target type, wind field randomness, and stranding probability, improves the accuracy of drift prediction; transforms the source tracing problem into a forward prediction in virtual space-time, and processes the initial position uncertainty through the Monte Carlo method to expand the search range; introduces the nudging assimilation method to integrate observational data to correct the flow field simulation error, especially reducing the model cumulative error in long-term source tracing and improving the reliability of the results.

[0036] Embodiment 1:

[0037] This embodiment proposes a method for predicting the drift and tracing the source of marine targets. As Figure 1 shown, it includes the following specific steps:

[0038] S1. Obtain the falling water position and time of the maritime target, as well as the ocean and atmospheric numerical prediction field data;

[0039] S2. Construct a coupled wave and current model of FVCOM combined with SWAN, and use the Monte Carlo method to simulate the trajectory of a set of particles released at the last known position of the target according to the target type, and predict the drift trajectory and search and rescue scope of the target;

[0040] S3. Collect the position and time when the target is found in the actual space-time, as well as the analysis data of the actual space-time wind field and current field during the accident, and perform inverse transformation of time series and field data;

[0041] S4. Construct a traceability model and assimilate the data, and convert the drift trajectory prediction result in the virtual space-time into the traceability analysis result in the actual space-time.

[0042] In this embodiment, S2 includes the following specific steps:

[0043] S21. Use the FVCOM model to calculate the ocean current. The vertical coordinate is selected as the bottom-following coordinate. Use the Mellor-Yamada 2.5-order turbulence closure sub-model to calculate the vertical mixing, use the Smagorinsky turbulence closure sub-model to calculate the horizontal mixing, adopt the inner-outer model separation algorithm, and set the open boundary conditions. The tidal level and current data at the strait boundary are predicted from the harmonic constants of each tidal component, and the water level, flow velocity, and temperature and salinity data are provided by the daily-averaged HYCOM reanalysis data;

[0044] S22. Determine the sea surface driving conditions, and use the NCEP-CFSR reanalysis data with a spatial resolution of 0.5°×0.5° every 6 hours to provide the 10-meter sea surface wind speed, short-wave radiation Q s , long-wave radiation Q l , sensible heat flux Q h , latent heat flux Q e , evaporation and precipitation, and net heat flux Q n = Q s - Q l ± Q h ± Q e , and set the model time and time step.

[0045] In this embodiment, S2 further includes:

[0046] S23. Set the SWAN model. The calculation grid and water depth terrain are the same as those of the FVCOM model. The forcing field is the wind field data at 10 m above the sea surface of the CFSR reanalysis data. The simulation calculation starts four days in advance, the time step is 1 h, the number of iterations is 5 times, and the result output step is 1 h;

[0047] S24. Perform coupling calculations to achieve two-way coupling simulation of FVCOM and SWAN, and calculate the influence on the flow field using Stokes drift and wave radiation stress.

[0048] In this embodiment, the Stokes drift calculation formula in S24 is:

[0049]

[0050] where U ss is the rate of Stokes drift at the ocean surface; is the unit wave number vector of the wave; k is the wave number of the wave; a is the amplitude of the wave; σ is the frequency of the wave; the Stokes drift calculation formula expressed by the significant wave height and the mean wave period T is:

[0051]

[0052] The wave radiation stress calculation formula is:

[0053]

[0054] where c is the wave speed, k is the wave number, is the wave energy, H s is the significant wave height, L is the wavelength, F SS , F CS , F CC is the vertical structure function, A R is the wave roller area, R z is the vertical distribution function, γ = H s / D.

[0055] In this embodiment, the traceability model in S3 is to construct a traceability analysis model Leeway-trace based on the Leeway model, and use the wind field, flow field forecast data in the virtual space-time and the initial information of the target object to predict the drift trajectory in the virtual space-time.

[0056] In this embodiment, the assimilation data in S3 uses the nudging method to assimilate the observed data. After nudging assimilation, the control equation of α(x, y, z, t) is:

[0057]

[0058] where α o is the observed value, is the model predicted value, N is the number of observation points in the search area, γ i is the data quality factor of the i-th observation point, ranging from 0 to 1, G α is the nudging control factor, which meets the numerical stability criterion G α Set at the order of magnitude of the Coriolis force parameter; W i (x, y, z, t) is the weight coefficient W i (x, y, z, t) = w xy ·w σ ·w t ·w θ , where w xy , w σ , w t , w θ are the horizontal, vertical, time, and direction weight coefficients respectively, and the expression is:

[0059]

[0060] where R is the search radius, is the distance between the calculation point and the observation point, R σ is the vertical search radius, T w is the assimilation time window, Δθ is the angle difference, and c1 is a calculation constant with a value range of 0.05 - 0.5.

[0061] Embodiment 2:

[0062] This embodiment provides an electronic device, including: a processor and a memory. Among them, a computer program that can be called by the processor is stored in the memory; the processor executes the above - mentioned method for predicting the drift and tracing the origin of maritime targets by calling the computer program stored in the memory.

[0063] This electronic device may have relatively large differences due to different configurations or performances, and can include one or more processors (Central Processing Units, CPU) and one or more memories. Among them, at least one computer program is stored in the memory, and this computer program is loaded and executed by the processor to implement the method for predicting the drift and tracing the origin of maritime targets provided by the above - mentioned method embodiment. This electronic device can also include other components for implementing the functions of the device. For example, this electronic device can also have components such as wired or wireless network interfaces and input - output interfaces for data input and output. This embodiment will not be elaborated here.

[0064] Those skilled in the art of the present technology know that the present invention can be implemented as a system, a method, or a computer program product. Therefore, the present disclosure can be specifically implemented in the following forms, namely: it can be entirely hardware, or entirely software (including firmware, resident software, microcode, etc.), or a combination of hardware and software, which is generally referred to as "circuit", "module", or "system" in this article. In addition, in some embodiments, the present invention can also be implemented in the form of a computer program product in one or more computer-readable media, which contain computer-readable program code.

[0065] Any combination of one or more computer-readable media can be adopted. The computer-readable media can be computer-readable signal media or computer-readable storage media. More specific examples (non-exhaustive list) of computer-readable storage media include: electrical connections with one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the above. In this document, the computer-readable storage media can be any tangible medium that contains or stores a program, and this program can be used by or in combination with an instruction execution system, apparatus, or device.

[0066] The present invention is described with reference to the flowcharts and block diagrams of methods, devices (systems), and computer program products according to the embodiments of the present invention. It should be understood that each flow or block in the flowcharts and block diagrams, as well as the combination of flows and blocks in the flowcharts or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing devices generate a device for implementing the functions specified in Figure 1 one flow or multiple flows and blocks Figure 1 one block or multiple blocks.

[0067] These computer program instructions can also be loaded onto a computer or other programmable data processing devices, so that a series of operation steps are executed on the computer or other programmable devices to generate a computer-implemented process, and thus the instructions executed on the computer or other programmable devices provide steps for implementing the functions specified in Figure 1 one flow or multiple flows and blocks Figure 1 one block or multiple blocks.

[0068] The embodiments of the present invention have been described above in conjunction with the accompanying drawings. However, the present invention is not limited to the above specific embodiments. The above specific embodiments are merely illustrative rather than restrictive. Under the inspiration of the present invention, those of ordinary skill in the art can also make many forms without departing from the spirit of the present invention and the scope protected by the claims. All of these fall within the protection scope of the present invention.

Claims

1. A method for predicting and tracing the drift of a marine target, characterized in that: The specific steps include: S1. Obtain the location and time of the target falling into the water at sea, as well as the numerical forecast field data of the ocean and atmosphere; S2. Construct the wave-current coupling model of FVCOM and SWAN, and use the Monte Carlo method to simulate the trajectory of a particle set at the last known position of the target according to the type of the target, and predict the drift trajectory and search and rescue range of the target; S3. Collect the location and time of the target object when it is discovered in real time and space, as well as the analysis data of the wind field and flow field in real time and space during the accident, and perform the reverse conversion between time series and field data; S4. Construct a traceability model and assimilate data to convert the drift trajectory prediction results of virtual space-time into traceability analysis results of actual space-time.

2. A method for predicting and tracing the drift of a marine target according to claim 1, characterized in that: The S2 comprises the following specific steps: S21. The FVCOM model is used to calculate the ocean currents. The vertical coordinates are selected as bottom coordinates. The Mellor-Yamada 2.5-order turbulence closed submodel is used to calculate the vertical mixing. The Smagorinsky turbulence closed submodel is used to calculate the horizontal mixing. The inner and outer model separation algorithm is used, and the open boundary conditions are set. The tidal current data at the strait boundary are obtained by the harmonic constant forecast of each tidal component. The water level, current velocity and temperature-salinity data are provided by the daily average HYCOM reanalysis data. S22. Determine the sea surface driving conditions using NCEP-CFSR reanalysis data with a 6-hour interval and a spatial resolution of 0.5°×0.5° to provide 10-meter sea surface wind speed, shortwave radiation Q s , long wave radiation Q l , sensible heat flux Q h , latent heat flux Q e , evaporation and precipitation, net heat flux Q n =Q s -Q l ±Q h ±Q e , set the mode time and time step.

3. A method for predicting and tracing the drift of a marine target according to claim 2, characterized in that: The S2 further includes: S23, set SWAN mode, the calculation grid and water depth terrain are the same as FVCOM mode, the forcing field is the wind field data at 10m above the sea surface from the CFSR reanalysis data, the simulation calculation starts four days in advance, the time step is 1h, the number of iterations is 5, and the result output step is 1h; S24. Carry out coupling calculation to realize bidirectional coupling simulation between FVCOM and SWAN, and use Stokes drift and wave radiation stress to calculate the impact on the flow field.

4. The method for predicting and tracing the drift of a marine target according to claim 3, characterized in that: The Stokes drift calculation formula in S24 is: Among them, U ss is the rate of Stokes drift on the ocean surface; is the unit wave number vector of the fluctuation; k is the wave number of the fluctuation; a is the amplitude of the fluctuation; σ is the frequency of the fluctuation; the Stokes drift calculation formula represented by the effective wave height and the average wave period T is: The calculation formula of wave radiation stress is: Among them, c is the wave velocity, k is the wave number, is the wave energy, H s is the effective wave height, L is the wavelength, F SS ,F CS ,F CC is the vertical structure function, A R is the wave water rolling area, R z is the vertical distribution function, γ=H s / D.

5. The method for predicting and tracing the drift of a marine target according to claim 4, characterized in that: The traceability model in S3 is a traceability analysis model Leeway-trace constructed based on the Leeway model, which uses the wind field and flow field forecast data of virtual space-time and the initial information of the target object to predict the drift trajectory of virtual space-time.

6. A method for predicting and tracing the drift of marine targets according to claim 5, characterized in that: The assimilated data in S3 adopts the nudging method to assimilate the observed data. After nudging assimilation, the control equation of α(x, y, z, t) is: where α o is the observed value, is the model prediction value, N is the number of observation points in the search area, γ i is the data quality factor of the i-th observation point, ranging from 0 to 1, G α is the nudging control factor, which satisfies the numerical stability criterion G α Set in the order of the Coriolis force parameter; W i (x,y,z,t) is the weight coefficient W i (x,y,z,t)=w xy ·w σ ·w t ·w θ , where w xy ,w σ ,w t ,w θ They are horizontal, vertical, time and direction weight coefficients, and the expressions are: Where R is the search radius, is the distance between the calculation point and the observation point, R σ is the vertical search radius, T w is the assimilation time window, Δθ is the angle difference, and c1 is the calculation constant, which ranges from 0.05 to 0.5.

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