A small self-powered wave observation intelligent monitoring buoy system

Abstract

The present invention proposes a small self-powered wave observation intelligent monitoring buoy system. The geographic coordinate parameters of the first target buoy, the first wave sensor parameters, and the first battery power percentage parameters obtained by the satellite are received, and the historical effective wave height at this time is obtained. The wave correction monitoring model is constructed using the geographic coordinate parameters of the target buoy, the wave sensor parameters, the battery power percentage parameters, and the historical effective wave height. The constructed wave correction monitoring model generates the current effective wave height according to the received geographic coordinate parameters of the second target buoy, the second wave sensor parameters, and the second battery power percentage parameters, and turns on the navigation light and controls the power switch to warn of the danger. The present invention realizes high-precision error correction and improves the measurement accuracy of waves. And the power supply itself is controlled to achieve long-term wave detection and achieve navigation safety.

Description

Technical Field

[0001] The present invention belongs to the technical field of wave intelligent monitoring, and in particular relates to a small self-powered wave observation intelligent monitoring buoy system. Background Art

[0002] Observing waves through buoys is the most traditional and basic observation method for wave observation. Since wave buoys are the most direct measurement method for waves, wave buoy wave observation is considered to be the most accurate wave observation method. As the most basic means of ocean observation, the observation data of wave buoys are often used as the true value of the actual wave situation, used to calibrate ocean remote sensing observations such as ocean satellites, or as true values ​​to adjust wave numerical models or as standards to verify the level of wave forecasting. However, in fact, the specifications of wave buoys are not the same, and there are obvious differences in the size and weight of the buoy body. For example, the buoys deployed by the National Data Buoy Center (NDBC) of the United States, the Met Office of the United Kingdom, and the Meteo France of France mainly use buoy specifications with a diameter of 3m, which is significantly different from the buoy specifications with a diameter of 10m used by the operational wave observation deployment of the Ministry of Natural Resources of China. The most direct difference is the huge difference in the weight of the buoys. The 3m wave buoy generally weighs 10 tons or less, while the weight of China's 10m operational wave buoy is more than 50 tons. Naturally, buoys of different specifications and weights will produce different dynamic responses to basically the same waves, thus affecting the consistency of wave observations by different buoys.

[0003] It should be noted that since wave buoy observations are often used as standards to calibrate remote sensing or forecasts, differences in the consistency of their observations will lead to deviations or errors in wave remote sensing algorithms, wave numerical models or wave forecasting research, which may lead to a series of serious problems later.

[0004] Therefore, people have carried out several buoy cross-calibration works to address this important issue. However, the approach of the above work is to place wave buoys of different specifications to be calibrated at the same location for synchronous observation for a period of time, thereby forming a cross-calibration data set and completing the calibration.

[0005] This method has certain limitations, mainly in that a special synchronous observation experiment must be designed when using this cross-calibration method, especially for cross-calibration of wave buoy systems in different countries. It is necessary to obtain buoys of corresponding specifications in the corresponding countries and conduct special synchronous observation experiments, which will consume a lot of manpower and material resources. Therefore, there are great difficulties in actual implementation. Although cross-calibration using different buoys can improve the consistency of effective wave height observation of wave buoys to a certain extent and improve the application effect of buoy observation data, the high-precision positioning system is not used for error correction during the calibration process. The buoy will move repeatedly under the action of waves at a certain reference position. When the buoy changes position, the measurement accuracy will inevitably be affected to a certain extent. In addition, most buoys cannot effectively control their own power supply to achieve long-term wave detection and achieve navigation safety.

[0006] Therefore, it is necessary to design and implement a real-time monitoring method for the wave conditions at sea and an intelligent detection method that can effectively and accurately avoid navigation accidents and improve the accuracy of ocean monitoring. Summary of the invention

[0007] In order to solve the above technical problems, the present invention proposes a small self-powered wave observation intelligent monitoring buoy system.

[0008] In a first aspect of the present invention, a small self-powered wave observation intelligent monitoring buoy method is provided, the method comprising:

[0009] Obtain geographic coordinate parameters of the first target buoy, first wave sensor parameters, and first battery power percentage parameters, and obtain corresponding historical effective wave heights;

[0010] Constructing a wave correction monitoring model according to the geographic coordinate parameters of the first target buoy, the first wave sensor parameters, the first battery power percentage parameters, and the historical effective wave height;

[0011] The wave correction monitoring model receives the geographic coordinate parameters of the second target buoy, the second wave sensor parameters, and the second battery power percentage parameters to generate the current effective wave height;

[0012] According to the current effective wave height, the navigation light is turned on and the power switch is controlled to issue a danger warning.

[0013] Further, the geographic coordinate parameters of the first target buoy or the geographic coordinate parameters of the second target buoy are obtained by Beidou terminal positioning, and the geographic coordinate parameters of the first target buoy or the geographic coordinate parameters of the second target buoy are set in three dimensions with the original placement position as the origin;

[0014] The first wave sensor parameter or the second wave sensor parameter is obtained by a sensor fixed at the center of the floating body, and the first wave sensor parameter or the second wave sensor parameter includes the wave height, wavelength, and wave speed of the sea wave;

[0015] The first battery power percentage parameter or the second battery power percentage parameter is obtained through an internal data acquisition instrument of the buoy.

[0016] Furthermore, the wave correction monitoring model is constructed using a machine learning algorithm, a classifier or a SVM model based at least on the Fisher criterion, and is trained by using the geographic coordinate parameters of the first target buoy, the first wave sensor parameters, the first battery power percentage parameters to obtain the first wave correction characteristics and the historical effective wave height.

[0017] Furthermore, the wave correction monitoring model adopts a classifier based on the Fisher criterion, and the calculation formula is as follows:

[0018] F(S)=KQ T T

[0019] T is the first wave correction feature, F(S) is the current effective wave height output, Q T is the normal vector perpendicular to the hyperplane, and K is the correction coefficient.

[0020] Furthermore, the correction coefficient K is calculated as follows:

[0021]

[0022] Where: Representing a function equation The period value, i and j represent different observation buoys, s and m represent different Beidou navigation satellites, is the multi-difference measurement value at time t, h represents the satellite frequency, c represents the average velocity of the ocean waves, represents the integer ambiguity after the second difference, represents the atmospheric ionospheric residual, represents the tropospheric delay residual.

[0023] A small self-powered wave observation intelligent monitoring buoy system is also provided, the system includes a target buoy, a second buoy, a first satellite terminal, and a second satellite terminal, and is characterized in that: the target buoy includes a wave data acquisition module, a wave correction monitoring model generation module, a wave correction monitoring model processing module, and a data storage and early warning module,

[0024] The wave data acquisition module is connected to the first satellite terminal or the second satellite terminal, and is used to acquire the geographic coordinate parameters of the first target buoy or the second target buoy sent by the first satellite terminal or the second satellite terminal, and is also used to acquire the first wave sensor parameters or the second wave sensor parameters and the first battery power percentage parameter or the second battery power percentage parameter, and is also used to acquire the corresponding historical effective wave height and send it to the wave correction monitoring model generation module or the wave correction monitoring model processing module;

[0025] The wave correction monitoring model generation module is connected to the wave data acquisition module, obtains and combines the geographic coordinate parameters of the first target buoy, the first wave sensor parameters and the first battery power percentage parameters, and the corresponding historical effective wave height to obtain the first wave correction feature, uses the first wave correction feature and the corresponding historical effective wave height to train a wave correction monitoring model, and sends the wave correction monitoring model to the wave correction monitoring model processing module;

[0026] The wave correction monitoring model processing module receives the wave correction monitoring model transmitted by the wave correction monitoring model generating module, receives the geographic coordinate parameters of the second target buoy, the second wave sensor parameters and the second battery power percentage parameters transmitted by the wave data acquiring module to obtain the second wave correction feature, and inputs the second wave correction feature into the wave correction monitoring model to generate the current effective wave height;

[0027] The data storage and warning module is used to store the current effective wave height and turn on the navigation light and control the power switch to warn of danger;

[0028] Further, the geographic coordinate parameters of the first target buoy or the geographic coordinate parameters of the second target buoy are obtained by Beidou terminal positioning, and the geographic coordinate parameters of the first target buoy or the geographic coordinate parameters of the second target buoy are set in three dimensions with the original placement position as the origin;

[0029] The first wave sensor parameter or the second wave sensor parameter is obtained through a sensor fixed at the center of the float, and the first wave sensor parameter or the second wave sensor parameter includes the wave height, wavelength, and wave speed of the waves; the first battery power percentage parameter or the second battery power percentage parameter is obtained through an internal data acquisition instrument of the buoy.

[0030] Furthermore, the wave correction monitoring model is constructed using a machine learning algorithm, a classifier or a SVM model based at least on the Fisher criterion, and is trained by using the geographic coordinate parameters of the first target buoy, the first wave sensor parameters, the first battery power percentage parameters to obtain the first wave correction characteristics and the historical effective wave height.

[0031] Furthermore, the wave correction monitoring model adopts a classifier based on the Fisher criterion, and the calculation formula is as follows:

[0032] F(S)=KQ T T

[0033] T is the first wave correction feature, F(S) is the current effective wave height output, Q T is the normal vector perpendicular to the hyperplane, and K is the correction coefficient.

[0034] Furthermore, the correction coefficient K is calculated as follows:

[0035]

[0036] Where: Representing a function equation The period value, i and j represent different observation buoys, s and m represent different Beidou navigation satellites, is the multi-difference measurement value at time t, h represents the satellite frequency, c represents the average velocity of the ocean waves, represents the integer ambiguity after the second difference, represents the atmospheric ionospheric residual, represents the tropospheric delay residual.

[0037] Beneficial effects of the present invention:

[0038] Using different buoys and different satellites, multi-directional full-state calibration is carried out, which improves the consistency of effective wave height observation of wave buoys to a certain extent, improves the application effect of buoy observation data, uses high-precision positioning system to correct errors during the calibration process, and takes into account that the buoy will move repeatedly under the action of waves at a certain reference position, which improves the measurement accuracy. In addition, the power supply is effectively controlled to achieve long-term wave detection and ensure navigation safety. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] Figure 1 It is a flow chart of a small self-powered wave observation intelligent monitoring buoy method of the present invention;

[0040] Figure 2 This is a structural schematic diagram of a small self-powered wave observation intelligent monitoring buoy system of the present invention;

[0041] Figure 3 It is a schematic diagram of the appearance of the buoy used in the present invention. DETAILED DESCRIPTION

[0042] The invention is further described below in conjunction with the accompanying drawings and specific implementation methods.

[0043] A buoy is a kind of navigation mark floating on the water surface. It is anchored at a designated location and used to mark the scope of a waterway, indicate shoals, obstructions, or indicate a special purpose surface navigation mark. Buoys are the most numerous and widely used among navigation marks. They are set in places where it is difficult or inappropriate to set up fixed navigation marks. The function of a buoy is to mark shoals in a waterway or obstacles that endanger navigation safety. A buoy equipped with a lamp is called a light buoy, which is used as a signal buoy to aid navigation in navigable waters day and night. Some buoys are also equipped with radar transponders, radio beacons, fog warning signals, oceanographic survey instruments and other equipment.

[0044] In this embodiment, the buoy adopts the HBF1.25 monitoring buoy configuration, and its configuration list is shown in the following table:

[0045]

[0046]

[0047]

[0048]

[0049] The first embodiment of the present invention:

[0050] In a first aspect of the present invention, a small self-powered wave observation intelligent monitoring buoy method is provided, the method comprising:

[0051] Obtain geographic coordinate parameters of the first target buoy, first wave sensor parameters, and first battery power percentage parameters, and obtain corresponding historical effective wave heights;

[0052] Constructing a wave correction monitoring model according to the geographic coordinate parameters of the first target buoy, the first wave sensor parameters, the first battery power percentage parameters, and the historical effective wave height;

[0053] The wave correction monitoring model receives the geographic coordinate parameters of the second target buoy, the second wave sensor parameters, and the second battery power percentage parameters to generate the current effective wave height;

[0054] According to the current effective wave height, the navigation light is turned on and the power switch is controlled to issue a danger warning.

[0055] Further, the geographic coordinate parameters of the first target buoy or the geographic coordinate parameters of the second target buoy are obtained by Beidou terminal positioning, and the geographic coordinate parameters of the first target buoy or the geographic coordinate parameters of the second target buoy are set in three dimensions with the original placement position as the origin;

[0056] The first wave sensor parameter or the second wave sensor parameter is obtained by a sensor fixed at the center of the floating body, and the first wave sensor parameter or the second wave sensor parameter includes the wave height, wavelength, and wave speed of the sea wave;

[0057] The first battery power percentage parameter or the second battery power percentage parameter is obtained through an internal data acquisition instrument of the buoy.

[0058] Furthermore, the wave correction monitoring model is constructed using a machine learning algorithm, a classifier or a SVM model based at least on the Fisher criterion, and is trained by using the geographic coordinate parameters of the first target buoy, the first wave sensor parameters, the first battery power percentage parameters to obtain the first wave correction characteristics and the historical effective wave height.

[0059] In this embodiment, the first wave correction feature is obtained by a combination of parameters, specifically (X, Y, H, h, l, v, p), where X, Y, H represent three-dimensional coordinates, h, l, v represent the wave height, wavelength and wave speed obtained by the sensor, and p represents the battery power percentage parameter obtained by the data acquisition instrument.

[0060] Furthermore, the wave correction monitoring model adopts a classifier based on the Fisher criterion, and the calculation formula is as follows:

[0061] F(S)=KQ T T

[0062] T is the first wave correction feature, F(S) is the current effective wave height output, Q T is the normal vector perpendicular to the hyperplane, and K is the correction coefficient.

[0063] Furthermore, the correction coefficient K is calculated as follows:

[0064]

[0065] Where: Representing a function equation The period value, i and j represent different observation buoys, s and m represent different Beidou navigation satellites, is the multi-difference measurement value at time t, h represents the satellite frequency, c represents the average velocity of the ocean waves, represents the integer ambiguity after the second difference, represents the atmospheric ionospheric residual, represents the tropospheric delay residual.

[0066] In this embodiment, due to the distance between the two buoys, a huge error will occur if the distance is too far. In this embodiment, the value of K is generally between 0.02 and 0.1, and the time t is calibrated and synchronized by the satellite terminal.

[0067] A small self-powered wave observation intelligent monitoring buoy system is also provided, the system includes a target buoy, a second buoy, a first satellite terminal, and a second satellite terminal, and is characterized in that: the target buoy includes a wave data acquisition module, a wave correction monitoring model generation module, a wave correction monitoring model processing module, and a data storage and early warning module,

[0068] The wave data acquisition module is connected to the first satellite terminal or the second satellite terminal, and is used to acquire the geographic coordinate parameters of the first target buoy or the second target buoy sent by the first satellite terminal or the second satellite terminal, and is also used to acquire the first wave sensor parameters or the second wave sensor parameters and the first battery power percentage parameter or the second battery power percentage parameter, and is also used to acquire the corresponding historical effective wave height and send it to the wave correction monitoring model generation module or the wave correction monitoring model processing module;

[0069] The wave correction monitoring model generation module is connected to the wave data acquisition module, obtains and combines the geographic coordinate parameters of the first target buoy, the first wave sensor parameters and the first battery power percentage parameters, and the corresponding historical effective wave height to obtain the first wave correction feature, uses the first wave correction feature and the corresponding historical effective wave height to train a wave correction monitoring model, and sends the wave correction monitoring model to the wave correction monitoring model processing module;

[0070] The wave correction monitoring model processing module receives the wave correction monitoring model transmitted by the wave correction monitoring model generating module, receives the geographic coordinate parameters of the second target buoy, the second wave sensor parameters and the second battery power percentage parameters transmitted by the wave data acquiring module to obtain the second wave correction feature, and inputs the second wave correction feature into the wave correction monitoring model to generate the current effective wave height;

[0071] The data storage and warning module is used to store the current effective wave height and turn on the navigation light and control the power switch to warn of danger;

[0072] Further, the geographic coordinate parameters of the first target buoy or the geographic coordinate parameters of the second target buoy are obtained by Beidou terminal positioning, and the geographic coordinate parameters of the first target buoy or the geographic coordinate parameters of the second target buoy are set in three dimensions with the original placement position as the origin;

[0073] The first wave sensor parameter or the second wave sensor parameter is obtained through a sensor fixed at the center of the float, and the first wave sensor parameter or the second wave sensor parameter includes the wave height, wavelength, and wave speed of the waves; the first battery power percentage parameter or the second battery power percentage parameter is obtained through an internal data acquisition instrument of the buoy.

[0074] Furthermore, the wave correction monitoring model is constructed using a machine learning algorithm, a classifier or a SVM model based at least on the Fisher criterion, and is trained by using the geographic coordinate parameters of the first target buoy, the first wave sensor parameters, the first battery power percentage parameters to obtain the first wave correction characteristics and the historical effective wave height.

[0075] Furthermore, the wave correction monitoring model adopts a classifier based on the Fisher criterion, and the calculation formula is as follows:

[0076] F(S)=KQ T T

[0077] T is the first wave correction feature, F(S) is the current effective wave height output, Q T is the normal vector perpendicular to the hyperplane, and K is the correction coefficient.

[0078] Furthermore, the correction coefficient K is calculated as follows:

[0079]

[0080]

[0081] Where: Representing a function equation The period value, i and j represent different observation buoys, s and m represent different Beidou navigation satellites, is the multi-difference measurement value at time t, h represents the satellite frequency, c represents the average velocity of the ocean waves, represents the integer ambiguity after the second difference, represents the atmospheric ionospheric residual, represents the tropospheric delay residual.

[0082] Beneficial effects of the present invention:

[0083] Using different buoys and different satellites, multi-directional full-state calibration is carried out, which improves the consistency of effective wave height observation of wave buoys to a certain extent, improves the application effect of buoy observation data, uses high-precision positioning system to correct errors during the calibration process, and takes into account that the buoy will move repeatedly under the action of waves at a certain reference position, which improves the measurement accuracy. In addition, the power supply is effectively controlled to achieve long-term wave detection and ensure navigation safety.

[0084] Of course, it can be understood that each embodiment of the present invention can achieve one of the effects alone, and a combination of multiple embodiments of the present invention can achieve all of the above effects, but it is not required that every embodiment of the present invention achieve all of the above advantages and effects, because each embodiment of the present invention can constitute a separate technical solution and make one or more contributions to the prior art.

[0085] The present invention does not particularly specify the structure of some modules, which shall be subject to the contents recorded in the prior art. The prior art mentioned in the above background technology section and the specific embodiment section of the present invention can be used as part of the present invention to understand the meaning of some technical features or parameters. The scope of protection of the present invention shall be subject to the contents actually recorded in the claims.

Claims

1. A small self-powered wave observation intelligent monitoring buoy method, characterized in that: The method comprises: Obtain geographic coordinate parameters of the first target buoy, first wave sensor parameters, and first battery power percentage parameters, and obtain corresponding historical effective wave heights; Constructing a wave correction monitoring model according to the geographic coordinate parameters of the first target buoy, the first wave sensor parameters, the first battery power percentage parameters, and the historical effective wave height; The wave correction monitoring model receives the geographic coordinate parameters of the second target buoy, the second wave sensor parameters, and the second battery power percentage parameters to generate the current effective wave height; According to the current effective wave height, turning on the navigation light and controlling the power switch to warn of danger; The geographic coordinate parameters of the first target buoy or the geographic coordinate parameters of the second target buoy are obtained by Beidou terminal positioning, and the geographic coordinate parameters of the first target buoy or the geographic coordinate parameters of the second target buoy are set in three dimensions with the original placement position as the origin; The first wave sensor parameter or the second wave sensor parameter is obtained by a sensor fixed at the center of the floating body, and the first wave sensor parameter or the second wave sensor parameter includes the wave height, wavelength, and wave speed of the sea wave; The first battery power percentage parameter or the second battery power percentage parameter is obtained by a data acquisition instrument inside the buoy; The wave correction monitoring model is constructed by using a machine learning algorithm, a classifier or a SVM model based at least on the Fisher criterion, and the wave correction monitoring model is trained by using the geographic coordinate parameters of the first target buoy, the first wave sensor parameters, the first battery power percentage parameters, and the first wave correction characteristics obtained by combining them, and the historical effective wave height; The wave correction monitoring model adopts a classifier based on the Fisher criterion, and the calculation formula is as follows: F(S)=KQ T T T is the first wave correction feature, F(S) is the current effective wave height output, Q T is the normal vector perpendicular to the hyperplane, and K is the correction coefficient.

2. A small self-powered wave observation intelligent monitoring buoy method as claimed in claim 1, characterized in that: The calculation method of the correction coefficient K is as follows: Where: Representing a function equation The period value, i and j represent different observation buoys, s and m represent different Beidou navigation satellites, is the multi-difference measurement value at time t, h represents the satellite frequency, c represents the average velocity of the ocean waves, represents the integer ambiguity after the second difference, represents the atmospheric ionospheric residual, represents the tropospheric delay residual.

3. A small self-powered wave observation intelligent monitoring buoy system, the system comprising a target buoy, a second buoy, a first satellite terminal, and a second satellite terminal, characterized in that: The target buoy includes a wave data acquisition module, a wave correction monitoring model generation module, a wave correction monitoring model processing module and a data storage and early warning module. The wave data acquisition module is connected to the first satellite terminal or the second satellite terminal, and is used to acquire the geographic coordinate parameters of the first target buoy or the second target buoy sent by the first satellite terminal or the second satellite terminal, and is also used to acquire the first wave sensor parameters or the second wave sensor parameters and the first battery power percentage parameter or the second battery power percentage parameter, and is also used to acquire the corresponding historical effective wave height and send it to the wave correction monitoring model generation module or the wave correction monitoring model processing module; The wave correction monitoring model generation module is connected to the wave data acquisition module, obtains and combines the geographic coordinate parameters of the first target buoy, the first wave sensor parameters and the first battery power percentage parameters, and the corresponding historical effective wave height to obtain the first wave correction feature, uses the first wave correction feature and the corresponding historical effective wave height to train a wave correction monitoring model, and sends the wave correction monitoring model to the wave correction monitoring model processing module; The wave correction monitoring model processing module receives the wave correction monitoring model transmitted by the wave correction monitoring model generating module, receives the geographic coordinate parameters of the second target buoy, the second wave sensor parameters and the second battery power percentage parameters transmitted by the wave data acquiring module to obtain the second wave correction feature, and inputs the second wave correction feature into the wave correction monitoring model to generate the current effective wave height; The data storage and warning module is used to store the current effective wave height and turn on the navigation light and control the power switch to warn of danger; The geographic coordinate parameters of the first target buoy or the geographic coordinate parameters of the second target buoy are obtained by Beidou terminal positioning, and the geographic coordinate parameters of the first target buoy or the geographic coordinate parameters of the second target buoy are set in three dimensions with the original placement position as the origin; The first wave sensor parameter or the second wave sensor parameter is obtained by a sensor fixed at the center of the floating body, and the first wave sensor parameter or the second wave sensor parameter includes the wave height, wavelength, and wave speed of the sea wave; the first battery power percentage parameter or the second battery power percentage parameter is obtained by a data acquisition instrument inside the buoy; The wave correction monitoring model is constructed by using a machine learning algorithm, a classifier or a SVM model based at least on the Fisher criterion, and the wave correction monitoring model is trained by using the geographic coordinate parameters of the first target buoy, the first wave sensor parameters, the first battery power percentage parameters, and the first wave correction characteristics obtained by combining them, and the historical effective wave height; The wave correction monitoring model adopts a classifier based on the Fisher criterion, and the calculation formula is as follows: F(S)=KQ T T T is the first wave correction feature, F(S) is the current effective wave height output, Q T is the normal vector perpendicular to the hyperplane, and K is the correction coefficient.

4. A small self-powered wave observation intelligent monitoring buoy system as claimed in claim 3, characterized in that: The calculation method of the correction coefficient K is as follows: Where: Representing a function equation The period value, i and j represent different observation buoys, s and m represent different Beidou navigation satellites, is the multi-difference measurement value at time t, h represents the satellite frequency, c represents the average velocity of the ocean waves, represents the integer ambiguity after the second difference, represents the atmospheric ionospheric residual, represents the tropospheric delay residual.

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