CN108829918B - Intelligent buoy networking simulation method and system for ocean phenomenon - Google Patents
Intelligent buoy networking simulation method and system for ocean phenomenon Download PDFInfo
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Abstract
The invention provides an intelligent buoy networking simulation method and system facing ocean phenomena, which comprises the following steps: ocean phenomenon recognition: establishing a marine three-dimensional temperature and salinity environment and a marine three-dimensional fluid environment, and identifying a marine phenomenon by combining sea surface height abnormity measured by a marine altimeter according to flow field change information, temperature and salinity profile information and information in the drift characteristics of the intelligent buoy which are monitored in the motion process of the intelligent buoy; networking simulation aggregation of the intelligent buoy: the method comprises the steps of taking recognized ocean phenomena as objects, inputting required parameters according to a dynamic model of an intelligent buoy to obtain total time and total energy consumption in the whole observation process, and respectively establishing a time shortest model and an energy consumption optimal model for networking simulation observation of the intelligent buoy according to an observation path, a shortest principle and an energy consumption observation mechanism of the intelligent buoy. The invention has more practical motion track, high networking simulation efficiency, high utilization rate of ocean data obtained by the buoy and low energy consumption in the simulation observation process.
Description
Technical Field
The invention relates to the technical field of computers, in particular to an intelligent buoy networking simulation method and system for ocean phenomena.
Background
Ocean phenomena including tides, sea waves, vortexes, fronts, and the like are links for transporting and mixing energy and substances in the ocean, and have important roles in distribution of energy of ocean substances and energy exchange between the ocean and the atmosphere.
With the development of satellite technology and ocean exploration technology, people can observe these ocean phenomena through various means, such as buoys, satellites, remote sensing, observation stations and the like; the remote sensing satellite observation has high speed and wide area, but can only acquire the information of the ocean surface layer. Therefore, the comprehensive utilization of the on-site observation data to analyze the three-dimensional structure of the mesoscale phenomenon is still a research hotspot of the current marine phenomenon. The main modes are a subsurface buoy observation net, an Argo buoy and a Glider. The Argo buoy is difficult to realize real-time tracking observation, the Glider observation process is high in cost, and long-period observation is difficult to realize.
Disclosure of Invention
Aiming at the defects in the prior art, the invention aims to provide an intelligent buoy networking simulation method and system for ocean phenomena.
The invention provides an intelligent buoy networking simulation method facing ocean phenomena, which comprises the following steps:
marine phenomenon identification: establishing a marine three-dimensional temperature and salinity environment and a marine three-dimensional fluid environment, and identifying a marine phenomenon by combining sea surface height abnormity measured by a marine altimeter according to flow field change information, temperature and salinity profile information and information in the drift characteristics of the intelligent buoy which are monitored in the motion process of the intelligent buoy;
networking simulation aggregation step of the intelligent buoy: the method comprises the steps of taking recognized ocean phenomena as objects, inputting required parameters according to a dynamic model of an intelligent buoy to obtain total time and total energy consumption in the whole observation process, and respectively establishing a time shortest model and an energy consumption optimal model for networking simulation observation of the intelligent buoy according to an observation path, a shortest principle and an energy consumption observation mechanism of the intelligent buoy.
Preferably, the method further comprises a networking simulation optimization step:
on the basis of the established shortest time model and the energy consumption optimal model, the shortest time and minimum energy consumption optimization scheme for the intelligent buoy networking observation facing the ocean phenomenon is respectively planned through two optimization models according to the ocean phenomenon, the current position and the evolution trend which need to be simulated and observed.
Preferably, the marine phenomenon identifying step specifically includes:
extracting and visualizing the three-dimensional flow field environment: constructing a marine three-dimensional thermohaline environment by using thermohaline data in the HYCOM data, wherein the HYCOM data is longitude and latitude grid data, and extracting thermohaline information of each layer of the HYCOM data by using Python to establish the marine three-dimensional thermohaline environment;
extracting and visualizing the environment of the warm salt field: calculating ocean flow fields of different depths in HYCOM data from a vertical layer, calculating the ocean current speed and direction of each grid point according to the warp speed and the weft speed of the grid point, and establishing ocean flow field environments of different depth levels, thereby constructing a global ocean three-dimensional fluid environment
Marine phenomenon identification: in the motion process of the intelligent buoy, the ocean phenomenon is identified according to the monitored information and the sea surface height abnormity measured by the ocean altimeter.
Preferably, the power model of the intelligent buoy comprises:
the total energy consumption of the glider is the energy consumption of the actuator, and the formula is E-UIT, wherein U is voltage, I is current, T is total time, time T is calculated by sections, and each section is calculated by dividing the distance by the speed in the distance direction.
Preferably, the first and second liquid crystal display panels are,
CD＝Vcz*T；Vcz＝Vc*sinα
AD＝(Vc*cosα+V*cosθ)*T
AC＝S＝2d/tanθ
AD2+CD2＝AC2
the submergence speed V of the intelligent buoy is decomposed into Vx in the heading direction and Vy in the submergence direction; the velocity of the ocean current, Vc, is decomposed into an offset velocity in the heading Vcz, and a component, Vcx, in the heading of the smart buoy; alpha is the included angle between the ocean current and the navigation direction, theta is the pitch angle, and the submergence depth of the intelligent buoy each time is d;
under the action of no ocean flow field, the ocean current reaches a point D from the point A, the ocean current deviates to a point C, the time from a starting point A to a first water outlet point C is T, iteration is carried out by taking a first water outlet level as an initial point, in the iteration process, alpha 1 is alpha + beta, alpha 1 is an included angle between the heading of the point C and the direction of the ocean current, alpha is an included angle between the heading of the point A and the ocean current, beta is an included angle between AB and CB, and CD, AD and AC are linear distances between the two points.
The invention provides an intelligent buoy networking simulation system facing ocean phenomena, which comprises:
ocean phenomenon recognition module: establishing a marine three-dimensional temperature and salinity environment and a marine three-dimensional fluid environment, and identifying a marine phenomenon by combining sea surface height abnormity measured by a marine altimeter according to flow field change information, temperature and salinity profile information and information in the drift characteristics of the intelligent buoy which are monitored in the motion process of the intelligent buoy;
networking simulation of intelligence buoy gathers module: the method comprises the steps of taking recognized ocean phenomena as objects, inputting required parameters according to a dynamic model of an intelligent buoy to obtain total time and total energy consumption in the whole observation process, and respectively establishing a time shortest model and an energy consumption optimal model for networking simulation observation of the intelligent buoy according to an observation path, a shortest principle and an energy consumption observation mechanism of the intelligent buoy.
Preferably, the system further comprises a networking simulation optimization module:
on the basis of the established shortest time model and the energy consumption optimal model, the shortest time and minimum energy consumption optimization scheme for the intelligent buoy networking observation facing the ocean phenomenon is respectively planned through two optimization models according to the ocean phenomenon, the current position and the evolution trend which need to be simulated and observed.
Preferably, the marine phenomenon identifying step specifically includes:
extracting and visualizing the three-dimensional flow field environment: constructing a marine three-dimensional thermohaline environment by using thermohaline data in the HYCOM data, wherein the HYCOM data is longitude and latitude grid data, and extracting thermohaline information of each layer of the HYCOM data by using Python to establish the marine three-dimensional thermohaline environment;
extracting and visualizing the environment of the warm salt field: calculating ocean flow fields of different depths in HYCOM data from a vertical layer, calculating the ocean current speed and direction of each grid point according to the warp speed and the weft speed of the grid point, and establishing ocean flow field environments of different depth levels, thereby constructing a global ocean three-dimensional fluid environment
Marine phenomenon identification: in the motion process of the intelligent buoy, the ocean phenomenon is identified according to the monitored information and the sea surface height abnormity measured by the ocean altimeter.
Preferably, the power model of the intelligent buoy comprises:
the total energy consumption of the glider is the energy consumption of the actuator, and the formula is E-UIT, wherein U is voltage, I is current, T is total time, time T is calculated by sections, and each section is calculated by dividing the distance by the speed in the distance direction.
Preferably, the first and second liquid crystal display panels are,
CD＝Vcz*T；Vcz＝Vc*sinα
AD＝(Vc*cosα+V*cosθ)*T
AC＝S＝2d/tanθ
AD2+CD2＝AC2
the submergence speed V of the intelligent buoy is decomposed into Vx in the heading direction and Vy in the submergence direction; the velocity of the ocean current, Vc, is decomposed into an offset velocity in the heading Vcz, and a component, Vcx, in the heading of the smart buoy; alpha is the included angle between the ocean current and the navigation direction, theta is the pitch angle, and the submergence depth of the intelligent buoy each time is d;
under the action of no ocean flow field, the ocean current reaches a point D from the point A, the ocean current deviates to a point C, the time from a starting point A to a first water outlet point C is T, iteration is carried out by taking a first water outlet level as an initial point, in the iteration process, alpha 1 is alpha + beta, alpha 1 is an included angle between the heading of the point C and the direction of the ocean current, alpha is an included angle between the heading of the point A and the ocean current, beta is an included angle between AB and CB, and CD, AD and AC are linear distances between the two points.
Compared with the prior art, the invention has the following beneficial effects:
1. the influence of an ocean current field is considered for the motion process of the intelligent buoy in the prior art, and the motion track is more practical;
2. networking simulation of the multi-intelligent buoy on ocean phenomena is more efficient, and the availability of ocean data obtained by the buoy is higher;
3. the energy consumption in the intelligent buoy movement process is effectively quantified, so that the energy consumption in the buoy networking simulation observation process is calculated, and then the buoy simulation observation route can be intelligently set, so that the energy consumption in the simulation observation process can be reduced.
Drawings
Other features, objects and advantages of the invention will become more apparent upon reading of the detailed description of non-limiting embodiments with reference to the following drawings:
FIG. 1 is a flow chart of the present invention;
FIG. 2 is a flow chart of the intelligent buoy movement process;
FIG. 3 is a diagram of a smart buoy movement route;
FIG. 4 is an exploded view of the speed of movement of the intelligent buoy;
FIG. 5 is a diagram of an intelligent buoy movement analysis;
FIG. 6 is an analysis diagram of the angle between the heading and the ocean current.
Detailed Description
The present invention will be described in detail with reference to specific examples. The following examples will assist those skilled in the art in further understanding the invention, but are not intended to limit the invention in any way. It should be noted that it would be obvious to those skilled in the art that various changes and modifications can be made without departing from the spirit of the invention. All falling within the scope of the present invention.
As shown in fig. 1, the method for simulating networking of an intelligent buoy facing to ocean phenomena provided by the invention comprises the following steps:
marine phenomenon identification: establishing a marine three-dimensional temperature and salinity environment and a marine three-dimensional fluid environment, and identifying a marine phenomenon by combining sea surface height abnormity measured by a marine altimeter according to flow field change information, temperature and salinity profile information and information in the drift characteristics of the intelligent buoy which are monitored in the motion process of the intelligent buoy;
networking simulation aggregation step of the intelligent buoy: as shown in fig. 2, the identified marine phenomena are used as objects, required parameters are input according to a dynamic model of the intelligent buoy to obtain total time and total energy consumption in the whole observation process, and a time shortest model and an energy consumption optimal model for intelligent buoy networking simulation observation are respectively established according to an observation path, a shortest principle and an intelligent buoy observation energy consumption mechanism.
Networking simulation optimization: on the basis of the established shortest time model and the energy consumption optimal model, the shortest time and minimum energy consumption optimization scheme for the intelligent buoy networking observation facing the ocean phenomenon is respectively planned through two optimization models according to the ocean phenomenon, the current position and the evolution trend which need to be simulated and observed.
The intelligent buoy networking simulation observation time is shortest, and for a certain intelligent buoy appearance, the horizontal direction movement speed of the intelligent buoy is related to net buoyancy and glide angle. Therefore, under the condition that the initial position and the target point position of the intelligent buoy are determined, the scheme for obtaining the shortest simulation observation time of the intelligent buoy can be used as a scheme for solving the shortest path and time for the intelligent buoy to reach the target point. The intelligent buoy networking simulation observation path and the shortest time scheme can be calculated through the intelligent buoy dynamic model, and the optimal parameters are set to complete the construction of the intelligent buoy shortest time model.
The energy consumption of the intelligent buoy mainly comes from the energy consumption required by the working of the sensor in the intelligent buoy sampling process, the energy consumption of the intelligent buoy is related to the number of sampling profiles in the intelligent buoy observation process, and the energy consumption of the intelligent buoy is the minimum, namely the energy consumption of the intelligent buoy is the minimum of the sampling profiles. According to the intelligent buoy power model, an optimal scheme is calculated and parameters are set through the current position, the target position and the ocean flow field of the intelligent buoy, and the glide angle of the intelligent buoy is set to achieve the minimum number of sampling sections, so that the minimum energy consumption is achieved.
The marine phenomenon identification step specifically comprises:
extracting and visualizing the three-dimensional flow field environment: the ocean three-dimensional thermohaline field environment is constructed by using thermohaline data in HYCOM (hybrid Coordinate ocean model) data, the HYCOM data are longitude and latitude grid data, the horizontal resolution is 1/12 degrees, the vertical direction is distributed at unequal intervals from the depth of 0m to 5000m, and the total number of the layers is 40. Extracting temperature and salt information of each layer of HYCOM data by utilizing Python to establish a marine three-dimensional temperature and salt environment;
extracting and visualizing the environment of the warm salt field: calculating ocean flow fields of different depths in HYCOM data from a vertical layer, calculating the ocean current speed and direction of each grid point according to the warp speed and the weft speed of the grid point, and establishing ocean flow field environments of different depth levels, thereby constructing a global ocean three-dimensional fluid environment
Marine phenomenon identification: in the motion process of the intelligent buoy, the ocean phenomenon is identified according to the monitored information and the sea surface height abnormity measured by the ocean altimeter.
Taking the mesoscale vortex as an example, tracking and observing the vortex by using a single buoy; and setting a corresponding target position according to the current position of the single buoy, and calculating the required optimal scheme for observing the vortex by the single buoy based on a buoy power model and setting parameters such as a buoy glide angle under the ocean flow field environment. The invention uses multi-buoy networking simulation, wherein the movement locus of the vortex is judged according to the identified vortex data, and a plurality of intelligent buoys near the vortex are mobilized to carry out vortex-chasing observation. Wherein the optimal solution of the whole intelligent buoy system is generated by the time and energy consumption of a single buoy from the current position to the target position.
The power model of intelligence buoy includes:
the total energy consumption of the glider is the energy consumption of an actuating mechanism, and the formula is E-UIT, wherein U is voltage and is a fixed value U-14.7V; i is current, the current and the distance are in direct proportion relation after research, and when S is 1km, I is 1A; when S is 4km, I is 4A; the calculation formula is that I is S/1000, and the unit of S is m; t is the total time, because the motion track of the intelligent buoy is a broken line formed by connecting a plurality of water inlet points and water outlet points, the motion route is shown in figure 3. Is performed in segments, so that the time is calculated in segments, each segment being calculated from the distance divided by the velocity in the direction of the distance, the calculation of the velocity and time being explained in detail below.
The power model parameters are set as follows:
CD＝Vcz*T；Vcz＝Vc*sinα
AD＝(Vc*cosα+V*cosθ)*T
AC＝S＝2d/tanθ
AD2+CD2＝AC2
the submergence speed V of the intelligent buoy is decomposed into Vx in the heading direction and Vy in the submergence direction; the velocity of the ocean current, Vc, is decomposed into an offset velocity in the heading Vcz, and a component, Vcx, in the heading of the smart buoy; alpha is the included angle between the ocean current and the navigation direction, theta is the pitch angle, and the submergence depth of the intelligent buoy each time is d;
under the action of no ocean flow field, the ocean current reaches a point D from the point A, the ocean current deviates to a point C, the time from a starting point A to a first water outlet point C is T, iteration is carried out by taking a first water outlet level as an initial point, in the iteration process, alpha 1 is alpha + beta, alpha 1 is an included angle between the heading of the point C and the direction of the ocean current, alpha is an included angle between the heading of the point A and the ocean current, beta is an included angle between AB and CB, and CD, AD and AC are linear distances between the two points.
Firstly, according to the current position of the intelligent buoy, the position of an observation target point, an ocean flow field environment, an intelligent buoy power model and the like, respectively establishing a time shortest model and an energy consumption optimal model for networking simulation observation of the intelligent buoy according to an observation path, a shortest principle and an intelligent buoy observation energy consumption mechanism. On the basis, according to the ocean phenomenon, the current position and the evolution trend which need to be simulated and observed, an optimal scheme with the shortest time and the least energy consumption for observing the intelligent buoy networking facing the ocean phenomenon is respectively planned through two optimization models. The module can enable a user to manually set and adjust the generated observation scheme to obtain the final whole scheme of the buoy networking observation process. And the networking observation process control is used for checking various settings of the observation simulation experiment by a user, starting the simulation experiment, and performing control functions of networking simulation, including suspending, stopping and continuing the current simulation observation task.
Based on the above intelligent buoy networking simulation method facing the ocean phenomenon, the invention also provides an intelligent buoy networking simulation system facing the ocean phenomenon, which comprises:
ocean phenomenon recognition module: establishing a marine three-dimensional temperature and salinity environment and a marine three-dimensional fluid environment, and identifying a marine phenomenon by combining sea surface height abnormity measured by a marine altimeter according to flow field change information, temperature and salinity profile information and information in the drift characteristics of the intelligent buoy which are monitored in the motion process of the intelligent buoy;
networking simulation of intelligence buoy gathers module: the method comprises the steps of taking recognized ocean phenomena as objects, inputting required parameters according to a dynamic model of an intelligent buoy to obtain total time and total energy consumption of the whole observation process, and respectively establishing a time shortest model and an energy consumption optimal model for networking simulation observation of the intelligent buoy according to an observation path, a shortest principle and an energy consumption observation mechanism of the intelligent buoy;
networking simulation optimizing module: on the basis of the established shortest time model and the energy consumption optimal model, the shortest time and minimum energy consumption optimization scheme for the intelligent buoy networking observation facing the ocean phenomenon is respectively planned through two optimization models according to the ocean phenomenon, the current position and the evolution trend which need to be simulated and observed.
The marine phenomenon identification step specifically comprises:
extracting and visualizing the three-dimensional flow field environment: constructing a marine three-dimensional thermohaline environment by using thermohaline data in the HYCOM data, wherein the HYCOM data is longitude and latitude grid data, and extracting thermohaline information of each layer of the HYCOM data by using Python to establish the marine three-dimensional thermohaline environment;
extracting and visualizing the environment of the warm salt field: calculating ocean flow fields of different depths in HYCOM data from a vertical layer, calculating the ocean current speed and direction of each grid point according to the warp speed and the weft speed of the grid point, and establishing ocean flow field environments of different depth levels, thereby constructing a global ocean three-dimensional fluid environment
Marine phenomenon identification: in the motion process of the intelligent buoy, the ocean phenomenon is identified according to the monitored information and the sea surface height abnormity measured by the ocean altimeter.
The power model of intelligence buoy includes:
the total energy consumption of the glider is the energy consumption of the actuator, and the formula is E-UIT, wherein U is voltage, I is current, T is total time, time T is calculated by sections, and each section is calculated by dividing the distance by the speed in the distance direction.
CD＝Vcz*T；Vcz＝Vc*sinα
AD＝(Vc*cosα+V*cosθ)*T
AC＝S＝2d/tanθ
AD2+CD2＝AC2
The submergence speed V of the intelligent buoy is decomposed into Vx in the heading direction and Vy in the submergence direction; the velocity of the ocean current, Vc, is decomposed into an offset velocity in the heading Vcz, and a component, Vcx, in the heading of the smart buoy; alpha is the included angle between the ocean current and the navigation direction, theta is the pitch angle, and the submergence depth of the intelligent buoy each time is d;
under the action of no ocean flow field, the ocean current reaches a point D from the point A, the ocean current deviates to a point C, the time from a starting point A to a first water outlet point C is T, iteration is carried out by taking a first water outlet level as an initial point, in the iteration process, alpha 1 is alpha + beta, alpha 1 is an included angle between the heading of the point C and the direction of the ocean current, alpha is an included angle between the heading of the point A and the ocean current, beta is an included angle between AB and CB, and CD, AD and AC are linear distances between the two points.
Those skilled in the art will appreciate that, in addition to implementing the system and its various devices, modules, units provided by the present invention as pure computer readable program code, the system and its various devices, modules, units provided by the present invention can be fully implemented by logically programming method steps in the form of logic gates, switches, application specific integrated circuits, programmable logic controllers, embedded microcontrollers and the like. Therefore, the system and various devices, modules and units thereof provided by the invention can be regarded as a hardware component, and the devices, modules and units included in the system for realizing various functions can also be regarded as structures in the hardware component; means, modules, units for performing the various functions may also be regarded as structures within both software modules and hardware components for performing the method.
In the description of the present application, it is to be understood that the terms "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", and the like indicate orientations or positional relationships based on those shown in the drawings, and are only for convenience in describing the present application and simplifying the description, but do not indicate or imply that the referred device or element must have a specific orientation, be constructed in a specific orientation, and be operated, and thus, should not be construed as limiting the present application.
The foregoing description of specific embodiments of the present invention has been presented. It is to be understood that the present invention is not limited to the specific embodiments described above, and that various changes or modifications may be made by one skilled in the art within the scope of the appended claims without departing from the spirit of the invention. The embodiments and features of the embodiments of the present application may be combined with each other arbitrarily without conflict.
Claims (6)
1. An intelligent buoy networking simulation method for ocean phenomena is characterized by comprising the following steps:
marine phenomenon identification: establishing a marine three-dimensional temperature and salinity environment and a marine three-dimensional fluid environment, and identifying a marine phenomenon by combining sea surface height abnormity measured by a marine altimeter according to flow field change information, temperature and salinity profile information and information in the drift characteristics of the intelligent buoy which are monitored in the motion process of the intelligent buoy;
networking simulation aggregation step of the intelligent buoy: the method comprises the steps of taking recognized ocean phenomena as objects, inputting required parameters according to a dynamic model of an intelligent buoy to obtain total time and total energy consumption of the whole observation process, and respectively establishing a time shortest model and an energy consumption optimal model for networking simulation observation of the intelligent buoy according to an observation path, a shortest principle and an energy consumption observation mechanism of the intelligent buoy;
the power model of intelligence buoy includes:
the total energy consumption of the glider is the energy consumption of an actuating mechanism, and the formula is that E is UIt, wherein U is voltage, I is current, t is total time, time t is calculated by sections, and each section is calculated by dividing the distance by the speed in the distance direction;
CD＝Vcz*T；Vcz＝Vc*sinα
AD＝(Vc*cosα+V*cosθ)*T
AC＝S＝2d/tanθ
AD2+CD2＝AC2
the submergence speed V of the intelligent buoy is decomposed into Vx in the heading direction and Vy in the submergence direction; the velocity of the ocean current, Vc, is decomposed into an offset velocity in the heading Vcz, and a component, Vcx, in the heading of the smart buoy; alpha is the included angle between the ocean current and the navigation direction, theta is the pitch angle, and the submergence depth of the intelligent buoy each time is d;
under the action of no ocean flow field, the ocean current reaches a point D from the point A, the ocean current deviates to a point C, the time from a starting point A to a first water outlet point C is T, iteration is carried out by taking a first water outlet level as an initial point, in the iteration process, alpha 1 is alpha + beta, alpha 1 is an included angle between the heading of the point C and the direction of the ocean current, alpha is an included angle between the heading of the point A and the ocean current, beta is an included angle between AB and CB, and CD, AD and AC are linear distances between the two points.
2. The intelligent buoy networking simulation method for the marine phenomenon as claimed in claim 1, further comprising a networking simulation optimization step of:
on the basis of the established shortest time model and the energy consumption optimal model, the shortest time and minimum energy consumption optimization scheme for the intelligent buoy networking observation facing the ocean phenomenon is respectively planned through two optimization models according to the ocean phenomenon, the current position and the evolution trend which need to be simulated and observed.
3. The intelligent buoy networking simulation method for marine phenomena as claimed in claim 1, wherein the marine phenomena identification step specifically comprises:
extracting and visualizing the ocean three-dimensional temperature and salt field environment: constructing a sea three-dimensional thermohaline field environment by using thermohaline data in the HYCOM data, wherein the HYCOM data is longitude and latitude grid data, and extracting thermohaline information of each layer of the HYCOM data by using Python to establish the sea three-dimensional thermohaline field environment;
extracting and visualizing the ocean three-dimensional flow field environment: calculating ocean flow fields of different depths in the HYCOM data from a vertical layer, calculating the ocean current speed and direction of each grid point according to the warp speed and the weft speed of the grid point, and establishing ocean flow field environments of different depth levels, thereby constructing a global ocean three-dimensional flow field environment;
marine phenomenon identification: in the motion process of the intelligent buoy, the ocean phenomenon is identified according to the monitored information and the sea surface height abnormity measured by the ocean altimeter.
4. The utility model provides an intelligent buoy networking simulation system towards ocean phenomenon which characterized in that includes:
ocean phenomenon recognition module: establishing a marine three-dimensional temperature and salinity environment and a marine three-dimensional fluid environment, and identifying a marine phenomenon by combining sea surface height abnormity measured by a marine altimeter according to flow field change information, temperature and salinity profile information and information in the drift characteristics of the intelligent buoy which are monitored in the motion process of the intelligent buoy;
networking simulation of intelligence buoy gathers module: the method comprises the steps of taking recognized ocean phenomena as objects, inputting required parameters according to a dynamic model of an intelligent buoy to obtain total time and total energy consumption of the whole observation process, and respectively establishing a time shortest model and an energy consumption optimal model for networking simulation observation of the intelligent buoy according to an observation path, a shortest principle and an energy consumption observation mechanism of the intelligent buoy;
the power model of intelligence buoy includes:
the total energy consumption of the glider is the energy consumption of an actuating mechanism, and the formula is that E is UIt, wherein U is voltage, I is current, t is total time, time t is calculated by sections, and each section is calculated by dividing the distance by the speed in the distance direction;
CD＝Vcz*T；Vcz＝Vc*sinα
AD＝(Vc*cosα+V*cosθ)*T
AC＝S＝2d/tanθ
AD2+CD2＝AC2
the submergence speed V of the intelligent buoy is decomposed into Vx in the heading direction and Vy in the submergence direction; the velocity of the ocean current, Vc, is decomposed into an offset velocity in the heading Vcz, and a component, Vcx, in the heading of the smart buoy; alpha is the included angle between the ocean current and the navigation direction, theta is the pitch angle, and the submergence depth of the intelligent buoy each time is d;
under the action of no ocean flow field, the ocean current reaches a point D from the point A, the ocean current deviates to a point C, the time from a starting point A to a first water outlet point C is T, iteration is carried out by taking a first water outlet level as an initial point, in the iteration process, alpha 1 is alpha + beta, alpha 1 is an included angle between the heading of the point C and the direction of the ocean current, alpha is an included angle between the heading of the point A and the ocean current, beta is an included angle between AB and CB, and CD, AD and AC are linear distances between the two points.
5. The intelligent buoy networking simulation system for marine phenomena as claimed in claim 4, further comprising a networking simulation optimization module:
on the basis of the established shortest time model and the energy consumption optimal model, the shortest time and minimum energy consumption optimization scheme for the intelligent buoy networking observation facing the ocean phenomenon is respectively planned through two optimization models according to the ocean phenomenon, the current position and the evolution trend which need to be simulated and observed.
6. The intelligent buoy networking simulation system for marine phenomena as claimed in claim 4, wherein the marine phenomena identification step specifically comprises:
extracting and visualizing the three-dimensional flow field environment: constructing a marine three-dimensional thermohaline environment by using thermohaline data in the HYCOM data, wherein the HYCOM data is longitude and latitude grid data, and extracting thermohaline information of each layer of the HYCOM data by using Python to establish the marine three-dimensional thermohaline environment;
extracting and visualizing the environment of the warm salt field: calculating ocean flow fields of different depths in HYCOM data from a vertical layer, calculating the ocean current speed and direction of each grid point according to the warp speed and the weft speed of the grid point, and establishing ocean flow field environments of different depth levels, thereby constructing a global ocean three-dimensional fluid environment
Marine phenomenon identification: in the motion process of the intelligent buoy, the ocean phenomenon is identified according to the monitored information and the sea surface height abnormity measured by the ocean altimeter.
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| CN110008509B (en) * | 2019-03-01 | 2020-07-07 | 中国海洋大学 | Method for analyzing internal solitary wave action force characteristics under consideration of background flow field |
| CN110222138B (en) * | 2019-06-11 | 2021-08-17 | 上海彩虹鱼海洋科技股份有限公司 | Buoy searching method and system based on airline |
| CN111551972B (en) * | 2020-05-15 | 2022-10-18 | 中国科学院空天信息创新研究院 | Argo track processing method and system |
| CN112905686B (en) * | 2021-03-24 | 2022-08-05 | 中国海洋大学 | Python program-based ocean profile water body data extraction method |
| CN113297810B (en) * | 2021-05-13 | 2022-10-11 | 中国海洋大学 | Method and system for arranging field observation equipment for inspecting sea surface height |
| CN114459485A (en) * | 2021-11-29 | 2022-05-10 | 湖北中南鹏力海洋探测系统工程有限公司 | Sea surface layer drifting buoy autonomous navigation method based on weak power |
| CN116858290B (en) * | 2023-09-04 | 2023-12-08 | 中国海洋大学 | Deep open sea surface height observation and calibration method and system based on large unmanned plane |
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