Ocean monitoring buoy system based on Internet of Things technology

Abstract

The present invention discloses a buoy system for ocean monitoring based on the Internet of Things technology, including a communication buoy unit, a land receiving system and a wind and wave avoidance mechanism, wherein the land receiving system is wirelessly connected to the communication buoy unit, and the communication buoy unit is provided with a plurality of wind and wave monitoring and early warning units, and the wind and wave monitoring and early warning units include an image acquisition module, an image processing module, a wind and wave intensity monitoring module and an early warning judgment module. The present invention belongs to the field of ocean monitoring technology, and specifically refers to a buoy system for ocean monitoring based on the Internet of Things technology. In view of the technical problem of damage to ocean buoys due to extreme weather, the present solution obtains the comprehensive wind and wave intensity value around the communication buoy unit in real time, and avoids the wind and waves in time, thereby solving the technical problem of damage to ocean buoys due to extreme weather. At the same time, the present solution resists network attacks from within the ocean buoy system, and improves the credibility of data collected by the ocean buoy.

Description

Technical Field

[0001] The present invention belongs to the technical field of ocean monitoring, and specifically refers to an ocean monitoring buoy system based on Internet of Things technology. Background Art

[0002] In order to obtain the hydrological, water quality and meteorological data of the ocean, it is usually necessary to establish an ocean buoy system to monitor the ocean conditions. When establishing an ocean buoy system, the existing technology usually places ocean buoys on the sea surface of the monitored sea area. However, in extreme weather conditions, strong winds and huge waves in the ocean will inevitably damage the ocean buoys, causing economic losses.

[0003] In addition, with the rapid development of Internet of Things technology, ocean buoy systems often face various network attacks. Attackers add ocean buoys mixed with malicious attacks to the ocean buoy system and attack the ocean buoy system from the inside. Existing technologies usually find it difficult to quickly identify ocean buoys mixed with malicious attacks, thereby reducing the credibility of data collected by ocean buoys, which is not conducive to monitoring the situation in the ocean. Summary of the invention

[0004] In view of the above situation, in order to overcome the defects of the prior art, the present invention provides a buoy system for ocean monitoring based on the Internet of Things technology. In view of the technical problem of damage to ocean buoys due to extreme weather, this solution obtains the comprehensive wind and wave intensity value around the communication buoy unit in real time through a wind and wave monitoring and early warning unit. When it is monitored that the wind and waves of the current intensity will damage the communication buoy unit, the wind and wave avoidance structure promptly controls the communication buoy unit to avoid the wind and waves, thereby solving the technical problem of damage to ocean buoys due to extreme weather; at the same time, in order to resist network attacks from within the ocean buoy system, this solution calculates the overall risk of each communication buoy unit through an overall risk determination algorithm, thereby quickly determining whether this communication buoy is an ocean buoy mixed with malicious attacks, thereby improving the security of the ocean buoy system and the credibility of the data collected by the ocean buoy, which is helpful for users to monitor the situation of the ocean through the ocean buoy system.

[0005] The technical solution adopted by the present invention is as follows: The buoy system for ocean monitoring based on the Internet of Things technology provided by the present invention includes a communication buoy unit, a land receiving system and a wind and wave avoidance mechanism. The land receiving system is wirelessly connected to the communication buoy unit. The communication buoy unit is provided with a plurality of wind and wave monitoring and early warning units. The wind and wave monitoring and early warning units include an image acquisition module, an image processing module, a wind and wave intensity monitoring module and an early warning judgment module. The image acquisition module acquires video image data around the communication buoy unit and sends the video image data to the land receiving system. The image processing module selects two adjacent frames of images in the video image data as a reference image and an offset image, performs grayscale processing on the reference image and the offset image, and obtains a reference grayscale image and an offset grayscale image. The wind wave intensity monitoring module calculates the horizontal wind wave baseline intensity and the vertical wind wave baseline intensity of the benchmark gray image and the horizontal wind wave offset intensity and the vertical wind wave offset intensity of the offset gray image, processes the horizontal wind wave baseline intensity and the vertical wind wave baseline intensity of the benchmark gray image and the horizontal wind wave offset intensity and the vertical wind wave offset intensity of the offset gray image, obtains the horizontal wind wave offset curve and the vertical wind wave offset curve, obtains the horizontal wind wave intensity value and the vertical wind wave intensity value through the horizontal wind wave offset curve and the vertical wind wave offset curve, and obtains the comprehensive wind wave intensity value through the horizontal wind wave intensity value and the vertical wind wave intensity value. The calculation formula of the horizontal wind wave baseline intensity of the benchmark gray image is:

[0006]

[0007]

[0008] HWS r (i) = HW r (i)-HW r ,

[0009] Where (i, j) is the horizontal and vertical coordinates of the pixel, M is the number of columns of pixels, N is the number of rows of pixels, HS(i, j) is the gray value of the pixel with coordinates (i, j) in the reference gray image, and HW r (i) is the grayscale value of the i-th row of the reference grayscale image, HW r is the average row grayscale value of the reference grayscale image, HWS r (i) is the horizontal wind wave benchmark intensity of the benchmark grayscale image;

[0010] The calculation formula of the vertical wind wave reference intensity of the reference grayscale image is:

[0011]

[0012]

[0013] VWS r (j) = VW r (j)-VW r ,

[0014] Among them, VW r (j) is the grayscale value of the jth column of the reference grayscale image, VW r is the average column grayscale value of the reference grayscale image, VWS r (j) is the vertical wind and wave reference intensity of the reference grayscale image;

[0015] The calculation formula of the horizontal wind wave offset intensity of the offset grayscale image is:

[0016]

[0017]

[0018] HWS o (i) = HW o (i)-HW o ,

[0019] Among them, HO(i,j) is the gray value of the pixel with coordinates (i,j) in the offset gray image, and HW o (i) is the grayscale value of the i-th row of the offset grayscale image, HW o is the average row grayscale value of the offset grayscale image, HWS o (i) is the horizontal wind wave offset intensity of the offset grayscale image;

[0020] The calculation formula for the vertical wind wave offset intensity of the offset grayscale image is:

[0021]

[0022]

[0023] VWS o (j) = VW o (j)-VW o ,

[0024] Among them, VW o (j) represents the grayscale value of the jth row of the offset grayscale image, VW o Represents the average column grayscale value of the offset grayscale image, VWS o (j) represents the vertical wind wave offset intensity of the offset grayscale image;

[0025] The calculation formula of the horizontal wind wave offset curve is:

[0026]

[0027] Wherein, LMC(P) is the value corresponding to the ordinate of the horizontal wind wave offset curve, SHL is the search range of the horizontal wind wave intensity, the value of SHL is manually input by the user, and the value range of P is [1,2SHL+1];

[0028] The calculation formula of the horizontal wind wave intensity value is:

[0029] S P =SHL+1-P min ,

[0030] Among them, S P is the horizontal wind wave intensity value, P min The value of is the value of P when LMC(P) takes the minimum value;

[0031] The calculation formula of the vertical wind wave offset curve is:

[0032]

[0033] Wherein, MMC(Q) is the value corresponding to the ordinate of the vertical wind wave offset curve, SHV is the search range of vertical wind wave intensity, the value of SHV is manually input by the user, and the value range of Q is [1, 2SHV+1];

[0034] The calculation formula of the vertical wind wave intensity value is:

[0035] S Q =SHV+1-Q min ,

[0036] Among them, S Q is the vertical wind wave intensity value, Q min The value of is the value of Q when MMC(Q) takes the minimum value;

[0037] The calculation formula of the comprehensive wind and wave intensity value is:

[0038]

[0039] Among them, S PQ is the comprehensive wind and wave intensity value;

[0040] The early warning module is pre-set with a wind and wave intensity early warning value EW, and the early warning module compares the wind and wave intensity early warning value EW with the comprehensive wind and wave intensity value S PQ For comparison, if S PQ ≥EW, it means that the current strength of wind and waves will damage the communication buoy unit. PQIf <EW, it means that the wind and waves at the current intensity will not damage the communication buoy unit.

[0041] Furthermore, the land receiving system includes a risk determination unit and a real-time display and monitoring unit. The risk determination unit includes a direct risk determination module, a relative risk determination module, an overall risk determination module, and a risk determination module. The risk determination unit selects a communication buoy unit as the reference buoy unit a from multiple communication buoy units, and sets the communication buoy units within the communication radius of the reference buoy unit a as the indirect buoy units of the reference buoy unit a. The communication time DT and the risk critical value DTV are set. The risk determination unit selects a communication buoy unit as the to-be-detected buoy unit b from multiple indirect buoy units of the reference buoy unit a, and obtains the number of successful communications CS between the reference buoy unit a and the to-be-detected buoy unit b within the communication time DT ab And the number of unsuccessful communications CUS ab , and the direct risk determination module uses the direct risk determination algorithm to obtain the direct risk of the reference buoy unit a to the to-be-detected buoy unit b. The formula of the direct risk determination algorithm is as follows:

[0042]

[0043] Among them, RDD(a, b) represents the direct risk of the reference buoy unit a to the to-be-detected buoy unit b.

[0044] Furthermore, the relative risk determination module sets the other communication buoy units within the common communication radius of the reference buoy unit a and the to-be-detected buoy unit b as the common indirect buoy units, calculates the direct risk of the reference buoy unit a and each common indirect buoy unit, and compares the direct risk of the reference buoy unit a and each common indirect buoy unit with the risk critical value DTV. If the direct risk of a common indirect buoy unit is greater than or equal to the risk critical value DTV, then this common indirect buoy unit is considered a normal buoy unit, and a set BST of normal buoy units is generated. The relative risk determination module obtains the relative risk of the reference buoy unit a to the to-be-detected buoy unit b through the relative risk determination algorithm. The formula of the relative risk determination algorithm is as follows:

[0045]

[0046] Among them, RDR(a, b) represents the relative risk of the reference buoy unit a to the buoy unit b to be detected, e represents the normal buoy unit in the normal buoy unit set BST, RDD(a, e) represents the direct risk of the reference buoy unit a to the normal buoy unit e, RDD(e, b) represents the direct risk of the normal buoy unit e to the buoy unit b to be detected, and |BST| represents the number of normal buoy units in the normal buoy unit set BST.

[0047] Further, the overall risk determination module processes the direct risk of the reference buoy unit a to the buoy unit b to be detected and the relative risk of the reference buoy unit a to the buoy unit b to be detected by using an overall risk determination algorithm, and obtains the overall risk of the reference buoy unit a to the buoy unit b to be detected. The formula of the overall risk determination algorithm is as follows:

[0048] RDW(a, b) = αRDD(a, b) + (1 - α)RDR(a, b),

[0049] Among them, RDW(a, b) represents the overall risk of the reference buoy unit a to the buoy unit b to be detected, α represents the overall risk weight, and the value of α is set to 0.5.

[0050] Preferably, the risk determination module is provided with a risk judgment value TGV. The risk determination module obtains the determination result of the buoy unit b to be detected by comparing the risk judgment value TGV with the overall risk RDW(a, b) of the reference buoy unit a to the buoy unit b to be detected. If RDW(a, b) < TGV, the buoy unit b to be detected is a communication buoy unit mixed with malicious attacks. If RDW(a, b) ≥ TGV, the buoy unit b to be detected is a normal communication buoy unit.

[0051] Preferably, the real-time display monitoring unit receives the video image data from the image acquisition module and displays the video image data in real time.

[0052] Preferably, the communication buoy unit is arranged on the sea surface, and the wind and wave avoidance mechanism is arranged below the communication buoy unit, the wind and wave avoidance mechanism comprises a counterweight block, a winding box and a traction chain, the counterweight block is arranged on the seabed, and the winding box is arranged on the counterweight block, a sealed chamber one is arranged in the winding box, a winding motor is arranged in the sealed chamber one, a sealed chamber two is arranged in the winding box, a winding controller is arranged in the sealed chamber two, a battery is arranged in the sealed chamber two, a winding chamber is arranged on the winding box, a winding roller is rotatably arranged in the winding chamber, and the winding roller is coaxially fixed with the output end of the winding motor, one end of the traction chain is wound around the winding roller, and the other end of the traction chain is arranged at the bottom of the communication buoy unit, the winding controller is wirelessly connected to the communication buoy unit, and the battery is electrically connected to the winding motor and the winding controller.

[0053] The beneficial effects achieved by the present invention using the above scheme are as follows:

[0054] (1) In order to resist cyber attacks from within the ocean buoy system, this solution calculates the overall risk of each communication buoy unit through an overall risk determination algorithm, thereby quickly determining whether the communication buoy is an ocean buoy mixed with malicious attacks, thereby improving the security of the ocean buoy system and the credibility of the data collected by the ocean buoy, and helping users to monitor the situation of the ocean through the ocean buoy system.

[0055] (2) To avoid damage to ocean buoys due to extreme weather, this solution uses a wind and wave monitoring and early warning unit to obtain the comprehensive wind and wave intensity value around the communication buoy unit in real time. When it is detected that the wind and wave of the current intensity will damage the communication buoy unit, the wind and wave avoidance structure promptly controls the communication buoy unit to avoid the wind and waves, thereby solving the technical problem of damage to ocean buoys due to extreme weather. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] Figure 1 It is a structural block diagram of the communication buoy unit;

[0057] Figure 2 The structural block diagram of the risk determination unit;

[0058] Figure 3 This is a block diagram of the land receiving system;

[0059] Figure 4 This is the pin diagram of the winding controller;

[0060] Figure 5 The use status diagram of the communication buoy unit and the wind and wave avoidance structure;

[0061] Figure 6 This is a schematic diagram of the internal structure of the wind and wave shelter structure;

[0062] Figure 7 Schematic diagram of communication between the reference buoy unit a and the buoy unit b to be detected.

[0063] Among them, 1000, communication buoy unit, 1100, wind and wave monitoring and early warning unit, 1001, image acquisition module, 1002, image processing module, 1003, wind and wave intensity monitoring module, 1004, early warning judgment module, 2000, land receiving system, 2100, danger determination unit, 2101, direct danger determination module, 2102, relative danger determination module, 2103, overall danger determination module, 2104, danger judgment module, 2200, real-time display monitoring unit, 3000, wind and wave avoidance mechanism, 3100, counterweight, 3200, winding box, 3201, sealed chamber one, 3202, sealed chamber two, 3203, winding chamber, 3204, winding motor, 3205, winding controller, 3206, battery, 3207, winding roller, 3300, traction chain.

[0064] The accompanying drawings are used to provide further understanding of the present invention and constitute a part of the specification. They are used to explain the present invention together with the embodiments of the present invention and do not constitute a limitation of the present invention. DETAILED DESCRIPTION

[0065] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments; based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0066] In the description of the present invention, it should be understood that terms such as “upper”, “lower”, “front”, “back”, “left”, “right”, “top”, “bottom”, “inside” and “outside” indicating directions or positional relationships are based on the directions or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific direction, be constructed and operated in a specific direction, and therefore should not be understood as limiting the present invention.

[0067] See also Figure 1 , Figure 2 , Figure 3 , Figure 5 and Figure 7The buoy system for ocean monitoring based on the Internet of Things technology provided by the present invention includes a communication buoy unit 1000, a land receiving system 2000 and a wind and wave avoidance mechanism 3000. The land receiving system 2000 is wirelessly connected to the communication buoy unit 1000. The communication buoy unit 1000 is provided with a plurality of land receiving systems 2000. The land receiving system 2000 includes a risk determination unit 2100 and a real-time display monitoring unit 2200. The risk determination unit 2100 includes a direct risk determination module 2101, a relative risk determination module 2102, an overall risk determination module 2103 and a risk determination module 2104, the risk determination unit 2100 selects a communication buoy unit 1000 from the plurality of communication buoy units 1000 as a reference buoy unit a, and sets the communication buoy units 1000 within the communication radius of the reference buoy unit a as indirect buoy units of the reference buoy unit a, sets the communication time DT and the risk threshold DTV, the risk determination unit 2100 selects a communication buoy unit 1000 from the plurality of indirect buoy units of the reference buoy unit a as a buoy unit to be detected, and obtains the number of successful communications CS between the reference buoy unit a and the buoy unit to be detected within the communication time DT ab Number of unsuccessful communications CUS ab The direct risk determination module 2101 adopts a direct risk determination algorithm to obtain the direct risk of the reference buoy unit a to the buoy unit b to be detected. The formula of the direct risk determination algorithm is as follows:

[0068]

[0069] Among them, RDD(a,b) represents the direct risk of the reference buoy unit a to the buoy unit b to be detected;

[0070] The relative risk determination module 2102 sets other communication buoy units within the common communication radius of the reference buoy unit a and the buoy unit to be detected b as common indirect buoy units, calculates the direct risk of the reference buoy unit a and each common indirect buoy unit, compares the direct risk of the reference buoy unit a and each common indirect buoy unit with the danger threshold value DTV, if the direct risk of the common indirect buoy unit is greater than or equal to the danger threshold value DTV, then the common indirect buoy unit is considered to be a normal buoy unit, and generates a normal buoy unit set BST, the relative risk determination module 2102 calculates the relative risk of the reference buoy unit a to the buoy unit to be detected b through a relative risk determination algorithm, the formula of the relative risk determination algorithm is as follows:

[0071]

[0072] Among them, RDR(a, b) represents the relative risk of the reference buoy unit a to the buoy unit b to be detected, e represents the normal buoy unit in the normal buoy unit set BST, RDD(a, e) represents the direct risk of the reference buoy unit a to the normal buoy unit e, RDD(e, b) represents the direct risk of the normal buoy unit e to the buoy unit b to be detected, and |BST| represents the number of normal buoy units in the normal buoy unit set BST;

[0073] The overall risk determination module 2103 processes the direct risk of the reference buoy unit a to the buoy unit b to be detected and the relative risk of the reference buoy unit a to the buoy unit b to be detected by using an overall risk determination algorithm, and obtains the overall risk of the reference buoy unit a to the buoy unit b to be detected. The formula of the overall risk determination algorithm is as follows:

[0074] RDW(a, b) = αRDD(a, b) + (1 - α)RDR(a, b),

[0075] Among them, RDW(a, b) represents the overall risk of the reference buoy unit a to the buoy unit b to be detected, α represents the overall risk weight, and the value of α is set to 0.5;

[0076] The risk determination module 2104 is set with a risk judgment value TGV. The risk determination module 2104 obtains the determination result of the buoy unit b to be detected by comparing the risk judgment value TGV with the overall risk RDW(a, b) of the reference buoy unit a to the buoy unit b to be detected. If RDW(a, b) < TGV, the buoy unit b to be detected is a communication buoy unit mixed with malicious attacks. If RDW(a, b) ≥ TGV, the buoy unit b to be detected is a normal communication buoy unit.

[0077] Refer to Figure 1 and Figure 5The communication buoy unit 1000 is provided with a wind and wave monitoring and early warning unit 1100, and the wind and wave monitoring and early warning unit 1100 includes an image acquisition module 1001, an image processing module 1002, a wind and wave intensity monitoring module 1003 and an early warning judgment module 1004. The image acquisition module 1001 acquires video image data around the communication buoy unit 1000 and sends the video image data to the land receiving system 2000. The image processing module 1002 selects two adjacent frames of images in the video image data as a reference image and an offset image, performs grayscale processing on the reference image and the offset image to obtain a reference grayscale image and an offset grayscale image, and respectively counts the grayscale value of each pixel point of the reference grayscale image and the offset grayscale image. The wind and wave intensity monitoring module 1003 calculates the horizontal wind and wave reference intensity and the vertical wind and wave reference intensity of the reference grayscale image and the horizontal wind and wave offset intensity and the vertical wind and wave offset intensity of the offset grayscale image, processes the horizontal wind and wave reference intensity and the vertical wind and wave reference intensity of the reference grayscale image and the horizontal wind and wave offset intensity and the vertical wind and wave offset intensity of the offset grayscale image to obtain a horizontal wind and wave offset curve and a vertical wind and wave offset curve, obtains a horizontal wind and wave intensity value and a vertical wind and wave intensity value through the horizontal wind and wave offset curve and the vertical wind and wave offset curve, obtains a comprehensive wind and wave intensity value through the horizontal wind and wave intensity value and the vertical wind and wave intensity value, and the calculation formula of the horizontal wind and wave reference intensity of the reference grayscale image is:

[0078]

[0079]

[0080] HWS r (i) = HW r (i)-HW r ,

[0081] Where (i, j) is the horizontal and vertical coordinates of the pixel, M is the number of columns of pixels, N is the number of rows of pixels, HS(i, j) is the grayscale value of the pixel with coordinates (i, j) in the reference grayscale image, and HW r (i) is the grayscale value of the i-th row of the reference grayscale image, HW r is the average row grayscale value of the reference grayscale image, HWS r (i) is the horizontal wind wave benchmark intensity of the benchmark grayscale image;

[0082] The calculation formula of the vertical wind wave reference intensity of the reference grayscale image is:

[0083]

[0084]

[0085] VWS r (j) = VW r (j)-VW r ,

[0086] Among them, VW r (j) is the grayscale value of the jth column of the reference grayscale image, VW r is the average column grayscale value of the reference grayscale image, VWS r (j) is the vertical wind and wave reference intensity of the reference grayscale image;

[0087] The calculation formula of the horizontal wind wave offset intensity of the offset grayscale image is:

[0088]

[0089]

[0090] HWS o (i) = HW o (i)-HW o ,

[0091] Among them, HO(i,j) is the gray value of the pixel with coordinates (i,j) in the offset gray image, and HW o (i) is the grayscale value of the i-th row of the offset grayscale image, HW o is the average row grayscale value of the offset grayscale image, HWS o (i) is the horizontal wind wave offset intensity of the offset grayscale image;

[0092] The calculation formula for the vertical wind wave offset intensity of the offset grayscale image is:

[0093]

[0094]

[0095] VWS o (j) = VW o (j)-VW o ,

[0096] Among them, VW o (j) represents the grayscale value of the jth row of the offset grayscale image, VW o Represents the average column grayscale value of the offset grayscale image, VWS o (j) represents the vertical wind wave offset intensity of the offset grayscale image;

[0097] The calculation formula of the horizontal wind wave offset curve is:

[0098]

[0099] Among them, the value corresponding to the ordinate of the horizontal wind-wave offset curve is LMC(P), the search range of the horizontal wind-wave intensity is SHL, the value of SHL is manually input by the user, and the value range of P is [1, 2SHL + 1];

[0100] The calculation formula for the horizontal wind-wave intensity value is:

[0101] S P = SHL + 1 - P min ,

[0102] Among them, S P is the horizontal wind-wave intensity value, and the value of P min is the value of P when LMC(P) takes the minimum value;

[0103] The calculation formula for the vertical wind-wave offset curve is:

[0104]

[0105] Among them, the value corresponding to the ordinate of the vertical wind-wave offset curve is MMC(Q), the search range of the vertical wind-wave intensity is SHV, the value of SHV is manually input by the user, and the value range of Q is [1, 2SHV + 1];

[0106] The calculation formula for the vertical wind-wave intensity value is:

[0107] S Q = SHV + 1 - Q min ,

[0108] Among them, S Q is the vertical wind-wave intensity value, and the value of Q min is the value of Q when MMC(Q) takes the minimum value;

[0109] The calculation formula for the comprehensive wind-wave intensity value is:

[0110]

[0111] Among them, S PQ is the comprehensive wind-wave intensity value;

[0112] A wind-wave intensity warning value EW is preset in the warning judgment module 1004. The warning judgment module 1004 compares the wind-wave intensity warning value EW with the comprehensive wind-wave intensity value S PQ . If S PQ ≥ EW, it means that the wind-wave of the current intensity will damage the communication buoy unit 1000. If S PQ < EW, it means that the wind-wave of the current intensity will not damage the communication buoy unit 1000.

[0113] See Figure 1 and Figure 3 The real-time display monitoring unit 2200 receives the video image data from the image acquisition module 1001 and displays the video image data in real time.

[0114] See also Figure 4 , Figure 5 and Figure 6 The communication buoy unit 1000 is arranged on the sea surface, the wind and wave avoidance mechanism 3000 is arranged below the communication buoy unit 1000, the wind and wave avoidance mechanism 3000 includes a counterweight block 3100, a winding box 3200 and a traction chain 3300, the counterweight block 3100 is arranged on the seabed, the winding box 3200 is arranged on the counterweight block 3100, a sealed chamber 1 3201 is arranged in the winding box 3200, a winding motor 3204 is arranged in the sealed chamber 1 3201, a sealed chamber 2 3202 is arranged in the winding box 3200, and a winding controller 3200 is arranged in the sealed chamber 2 3202. 05. A battery 3206 is provided in the sealed chamber 3202. A winding chamber 3203 is provided on the winding box 3200. A winding roller 3207 is rotatably provided in the winding chamber 3203. The winding roller 3207 is coaxially fixed to the output end of the winding motor 3204. One end of the traction chain 3300 is wound around the winding roller 3207. The other end of the traction chain 3300 is provided at the bottom of the communication buoy unit 1000. The winding controller 3205 is wirelessly connected to the communication buoy unit 1000. The battery 3206 is electrically connected to the winding motor 3204 and the winding controller 3205.

[0115] Example 1, see Figure 1 , Figure 3 , Figure 4 , Figure 5 and Figure 6 When facing extreme weather, the wind and wave avoidance structure promptly controls the communication buoy unit to avoid wind and waves.

[0116] In order to prevent the communication buoy unit 1000 from being damaged by strong winds and huge waves, the image acquisition module 1001 in this solution can acquire the video image data around the communication buoy unit 1000 and send the video image data to the land receiving system 2000. The image processing module 1002 selects two adjacent frames of images in the video image data as the reference image and the offset image, performs grayscale processing on the reference image and the offset image to obtain the reference grayscale image and the offset grayscale image, and respectively counts the grayscale value of each pixel point of the reference grayscale image and the grayscale value of each pixel point of the offset grayscale image. The wind and wave intensity monitoring module 1003 calculates the reference grayscale image. The horizontal wind wave benchmark intensity and vertical wind wave benchmark intensity of the image and the horizontal wind wave offset intensity and vertical wind wave offset intensity of the offset grayscale image are processed to obtain a horizontal wind wave offset curve and a vertical wind wave offset curve, and the horizontal wind wave intensity value and the vertical wind wave intensity value are obtained through the horizontal wind wave offset curve and the vertical wind wave offset curve, and the comprehensive wind wave intensity value is obtained through the horizontal wind wave intensity value and the vertical wind wave intensity value. The calculation formula of the horizontal wind wave benchmark intensity of the reference grayscale image is:

[0117]

[0118]

[0119] HWS r (i) = HW r (i)-HW r ,

[0120] Where (i, j) is the horizontal and vertical coordinates of the pixel, M is the number of columns of pixels, N is the number of rows of pixels, HS(i, j) is the gray value of the pixel with coordinates (i, j) in the reference gray image, and HW r (i) is the grayscale value of the i-th row of the reference grayscale image, HW r is the average row grayscale value of the reference grayscale image, HWS r (i) is the horizontal wind wave benchmark intensity of the benchmark grayscale image;

[0121] The calculation formula of the vertical wind wave reference intensity of the reference grayscale image is:

[0122]

[0123]

[0124] VWS r (j) = VW r (j)-VW r ,

[0125] Among them, VW r (j) is the grayscale value of the jth column of the reference grayscale image, VW r is the average column grayscale value of the reference grayscale image, VWS r (j) is the vertical wind and wave reference intensity of the reference grayscale image;

[0126] The calculation formula for the horizontal wind wave offset intensity of the offset grayscale image is:

[0127]

[0128]

[0129] HWS o (i) = HW o (i)-HW o ,

[0130] Among them, HO(i,j) is the gray value of the pixel with coordinates (i,j) in the offset gray image, and HW o (i) is the grayscale value of the i-th row of the offset grayscale image, HW o is the average row grayscale value of the offset grayscale image, HWS o (i) is the horizontal wind wave offset intensity of the offset grayscale image;

[0131] The calculation formula for the vertical wind wave offset intensity of the offset grayscale image is:

[0132]

[0133]

[0134] VWS o (j) = VW o (j)-VW o ,

[0135] Among them, VW o (j) represents the grayscale value of the jth row of the offset grayscale image, VW o Represents the average column grayscale value of the offset grayscale image, VWS o (j) represents the vertical wind wave offset intensity of the offset grayscale image;

[0136] The calculation formula of the horizontal wind wave offset curve is:

[0137]

[0138] Among them, the value corresponding to the ordinate of the horizontal wind and wave offset curve is LMC(P), SHL is the search range of the horizontal wind and wave intensity, the value of SHL is manually input by the user, and the value range of P is [1, 2SHL + 1];

[0139] The calculation formula for the horizontal wind and wave intensity value is:

[0140] S P = SHL + 1 - P min ,

[0141] Among them, S P is the horizontal wind and wave intensity value, and the value of P min is the value of P when LMC(P) takes the minimum value;

[0142] The calculation formula for the vertical wind and wave offset curve is:

[0143]

[0144] Among them, the value corresponding to the ordinate of the vertical wind and wave offset curve is MMC(Q), SHV is the search range of the vertical wind and wave intensity, the value of SHV is manually input by the user, and the value range of Q is [1, 2SHV + 1];

[0145] The calculation formula for the vertical wind and wave intensity value is:

[0146] S Q = SHV + 1 - Q min ,

[0147] Among them, S Q is the vertical wind and wave intensity value, and the value of Q min is the value of Q when MMC(Q) takes the minimum value;

[0148] The calculation formula for the comprehensive wind and wave intensity value is:

[0149]

[0150] Among them, S PQ is the comprehensive wind and wave intensity value;

[0151] In the early warning judgment module 1004, a wind and wave intensity early warning value EW is preset. The early warning judgment module 1004 compares the wind and wave intensity early warning value EW with the comprehensive wind and wave intensity value S PQ for comparison. If S PQ < EW, it means that the current wind and wave intensity will not damage the communication buoy unit 1000. If S PQ≥EW, it means that the wind and waves of the current intensity will damage the communication buoy unit 1000. When it is monitored that the wind and waves of the current intensity will damage the communication buoy unit 1000, the communication buoy unit 1000 sends a control signal to the winding controller 3205. The winding controller 3205 receives the control signal from the communication buoy unit 1000 and starts the winding motor 3204. The winding motor 3204 drives the winding roller 3207 to rotate, so that the traction chain 3300 is wound on the winding roller 3207, so that the communication buoy unit 1000 is pulled to the seabed, thereby avoiding the wind and waves, and solving the technical problem of damage to ocean buoys due to extreme weather.

[0152] Example 2, see Figure 1 , Figure 2 , Figure 3 and Figure 7 This embodiment is based on the above embodiment. This solution calculates the overall risk of each communication buoy unit through an overall risk determination algorithm, so as to quickly determine whether this communication buoy is an ocean buoy mixed with malicious attacks.

[0153] In order to resist the network attack from inside the ocean buoy system, the danger determination unit 2100 in this solution selects a communication buoy unit 1000 from multiple communication buoy units 1000 as the reference buoy unit a, and sets the communication buoy units 1000 within the communication radius of the reference buoy unit a as indirect buoy units of the reference buoy unit a, sets the communication time DT and the danger threshold DTV, and the danger determination unit 2100 selects a communication buoy unit 1000 from multiple indirect buoy units of the reference buoy unit a as the buoy unit b to be detected, and obtains the number of successful communications CS between the reference buoy unit a and the buoy unit b to be detected within the communication time DT. ab Number of unsuccessful communications CUS ab The direct risk determination module 2101 uses a direct risk determination algorithm to obtain the direct risk of the reference buoy unit a to the buoy unit b to be detected. The formula of the direct risk determination algorithm is as follows:

[0154]

[0155] Among them, RDD(a,b) represents the direct risk of the reference buoy unit a to the buoy unit b to be detected;

[0156] The relative risk determination module 2102 sets other communication buoy units within the common communication radius of the reference buoy unit a and the buoy unit b to be detected as common indirect buoy units, calculates the direct risk of the reference buoy unit a and each common indirect buoy unit, compares the direct risk of the reference buoy unit a and each common indirect buoy unit with the risk threshold DTV. If the direct risk of a common indirect buoy unit is greater than or equal to the risk threshold DTV, this common indirect buoy unit is considered a normal buoy unit, and a set BST of normal buoy units is generated. The relative risk determination module 2102 calculates the relative risk of the reference buoy unit a to the buoy unit b to be detected through the relative risk determination algorithm. The formula of the relative risk determination algorithm is as follows:

[0157]

[0158] Among them, RDR(a, b) represents the relative risk of the reference buoy unit a to the buoy unit b to be detected, e represents the normal buoy unit in the set BST of normal buoy units, RDD(a, e) represents the direct risk of the reference buoy unit a to the normal buoy unit e, RDD(e, b) represents the direct risk of the normal buoy unit e to the buoy unit b to be detected, and |BST| represents the number of normal buoy units in the set BST of normal buoy units;

[0159] The overall risk determination module 2103 processes the direct risk of the reference buoy unit a to the buoy unit b to be detected and the relative risk of the reference buoy unit a to the buoy unit b to be detected using the overall risk determination algorithm to obtain the overall risk of the reference buoy unit a to the buoy unit b to be detected. The formula of the overall risk determination algorithm is as follows:

[0160] RDW(a, b) = αRDD(a, b) + (1 - α)RDR(a, b),

[0161] Among them, RDW(a, b) represents the overall risk of the reference buoy unit a to the buoy unit b to be detected, α represents the overall risk weight, and the value of α is set to 0.5;

[0162] The risk determination module 2104 is set with a risk judgment value TGV. The risk determination module 2104 obtains the determination result of the buoy unit b to be detected by comparing the risk judgment value TGV with the overall risk RDW(a, b) of the reference buoy unit a to the buoy unit b to be detected. If RDW(a, b) < TGV, the buoy unit b to be detected is a communication buoy unit mixed with malicious attacks. If RDW(a, b) ≥ TGV, the buoy unit b to be detected is a normal communication buoy unit;

[0163] By performing the above operations, this solution can quickly identify ocean buoys that are mixed with malicious attacks, thereby resisting network attacks from within the ocean buoy system, improving the security of the ocean buoy system and the credibility of the data collected by the ocean buoy, and helping users to monitor the situation of the ocean through the ocean buoy system.

[0164] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0165] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

[0166] The present invention and its embodiments are described above, and such description is not restrictive. The drawings show only one embodiment of the present invention, and the actual structure is not limited thereto. In short, if ordinary technicians in the field are inspired by it, without departing from the purpose of the invention, they can design a structure and embodiment similar to the technical solution without creativity, which should belong to the protection scope of the present invention.

Claims

1. The ocean monitoring buoy system based on Internet of Things technology is characterized by: The invention comprises a communication buoy unit (1000), a land receiving system (2000) and a wind and wave avoiding mechanism (3000); the land receiving system (2000) is wirelessly connected to the communication buoy unit (1000); a plurality of the communication buoy units (1000) are provided; the land receiving system (2000) comprises a risk determination unit (2100) and a real-time display monitoring unit (2200); the risk determination unit (2100) comprises a direct risk determination module (2101), a relative risk determination module (2102), an overall risk determination module (2103) and a risk determination module (2104) The risk determination unit (2100) selects a communication buoy unit (1000) from the plurality of communication buoy units (1000) as a reference buoy unit a, sets the communication buoy units (1000) within the communication radius of the reference buoy unit a as indirect buoy units of the reference buoy unit a, sets a communication time DT and a risk threshold value DTV, and the risk determination unit (2100) selects a communication buoy unit (1000) from the plurality of indirect buoy units of the reference buoy unit a as a buoy unit to be detected b, and obtains the number of successful communications between the reference buoy unit a and the buoy unit to be detected b within the communication time DT. Number of unsuccessful communications The direct risk determination module (2101) uses a direct risk determination algorithm to obtain the direct risk of the reference buoy unit a to the buoy unit b to be detected. The formula of the direct risk determination algorithm is as follows: ， in, Indicates the direct risk degree of the reference buoy unit a to the buoy unit b to be detected; The relative risk determination module (2102) sets other communication buoy units within the common communication radius of the reference buoy unit a and the buoy unit to be detected b as common indirect buoy units, calculates the direct risk of the reference buoy unit a and each common indirect buoy unit, compares the direct risk of the reference buoy unit a and each common indirect buoy unit with the risk threshold value DTV, and if the direct risk of the common indirect buoy unit is greater than or equal to the risk threshold value DTV, the common indirect buoy unit is considered to be a normal buoy unit, and a normal buoy unit set is generated. The relative risk determination module (2102) calculates the relative risk of the reference buoy unit a to the buoy unit b to be detected by using a relative risk determination algorithm. The formula of the relative risk determination algorithm is as follows: ， in, represents the relative risk of the reference buoy unit a to the buoy unit b to be detected, e represents the normal buoy unit in the normal buoy unit set BST, Indicates the direct risk of the reference buoy unit a to the normal buoy unit e, Indicates the direct risk of the normal buoy unit e to the buoy unit b to be detected, Indicates the number of normal buoy units in the normal buoy unit set BST; The overall risk determination module (2103) uses an overall risk determination algorithm to process the direct risk of the reference buoy unit a to the buoy unit b to be detected and the relative risk of the reference buoy unit a to the buoy unit b to be detected, and obtains the overall risk of the reference buoy unit a to the buoy unit b to be detected. The formula of the overall risk determination algorithm is as follows: ， in, It represents the overall risk of the reference buoy unit a to the buoy unit b to be detected. represents the overall risk weight, The value of is set to 0.5; The risk level determination module (2104) is provided with a risk level determination value TGV. The risk level determination module (2104) determines the overall risk level of the buoy unit b to be detected by comparing the risk level determination value TGV with the reference buoy unit a. , get the judgment result of the buoy unit b to be detected, if , then the buoy unit to be detected b is a communication buoy unit mixed with malicious attacks. If , then the buoy unit b to be detected is a normal communication buoy unit; The communication buoy unit (1000) is provided with a wind and wave monitoring and early warning unit (1100), and the wind and wave monitoring and early warning unit (1100) comprises an image acquisition module (1001), an image processing module (1002), a wind and wave intensity monitoring module (1003) and an early warning judgment module (1004), wherein the image acquisition module (1001) acquires video image data around the communication buoy unit (1000) and sends the video image data to a land receiving system (2000), and the image processing module (1002) selects two adjacent frames of images in the video image data as a reference image and an offset image, performs grayscale processing on the reference image and the offset image to obtain a reference grayscale image and an offset grayscale image, and respectively counts the grayscale value of each pixel point of the reference grayscale image and the grayscale value of each pixel point of the offset grayscale image; The wind and wave intensity monitoring module (1003) calculates the horizontal wind and wave baseline intensity and the vertical wind and wave baseline intensity of the reference grayscale image and the horizontal wind and wave offset intensity and the vertical wind and wave offset intensity of the offset grayscale image, processes the horizontal wind and wave baseline intensity and the vertical wind and wave baseline intensity of the reference grayscale image and the horizontal wind and wave offset intensity and the vertical wind and wave offset intensity of the offset grayscale image to obtain a horizontal wind and wave offset curve and a vertical wind and wave offset curve, obtains a horizontal wind and wave intensity value and a vertical wind and wave intensity value through the horizontal wind and wave offset curve and obtains a comprehensive wind and wave intensity value through the horizontal wind and wave intensity value and the vertical wind and wave intensity value, and the calculation formula of the horizontal wind and wave baseline intensity of the reference grayscale image is: ， ， ， in, are the horizontal and vertical coordinates of the pixel point, M is the number of columns of the pixel point, and N is the number of rows of the pixel point. The coordinates in the reference grayscale image are The gray value of the pixel, is the grayscale value of the i-th row of the reference grayscale image, is the average row grayscale value of the reference grayscale image, is the horizontal wind and wave benchmark intensity of the benchmark grayscale image; The calculation formula of the vertical wind wave reference intensity of the reference grayscale image is: ， ， ， in, is the column grayscale value of the jth column of the reference grayscale image, is the average column grayscale value of the reference grayscale image, is the vertical wind and wave benchmark intensity of the benchmark grayscale image; The calculation formula of the horizontal wind wave offset intensity of the offset grayscale image is: ， ， ， in, The coordinates in the offset grayscale image are The gray value of the pixel, is the grayscale value of the i-th row of the offset grayscale image, is the average row grayscale value of the offset grayscale image, is the horizontal wind wave offset intensity of the offset grayscale image; The calculation formula for the vertical wind wave offset intensity of the offset grayscale image is: ， ， ， in, represents the row grayscale value of the jth row of the offset grayscale image, represents the average column grayscale value of the offset grayscale image, Indicates the vertical wind and wave offset intensity of the offset grayscale image; The calculation formula of the horizontal wind wave offset curve is: ， in, is the value corresponding to the ordinate of the horizontal wind wave offset curve, SHL is the search range of horizontal wind wave intensity, the value of SHL is manually input by the user, and the value range of P is ; The calculation formula of the horizontal wind wave intensity value is: ， in, is the horizontal wind wave intensity value, The value of The value of P when it takes the minimum value; The calculation formula of the vertical wind wave offset curve is: ， in, is the value corresponding to the ordinate of the vertical wind wave offset curve, SHV is the search range of vertical wind wave intensity, the value of SHV is manually input by the user, and the value range of Q is ; The calculation formula of the vertical wind wave intensity value is: ， in, is the vertical wind wave intensity value, The value of The value of Q when it takes the minimum value; The calculation formula of the comprehensive wind and wave intensity value is: ， in, is the comprehensive wind and wave intensity value; The warning judgment module (1004) is pre-set with a wind and wave intensity warning value The warning judgment module (1004) sets the wind and wave intensity warning value and comprehensive wind and wave intensity value For comparison, if , it means that the current strength of wind and waves will damage the communication buoy unit (1000). , it means that the current strength of wind and waves will not damage the communication buoy unit (1000); The real-time display monitoring unit (2200) receives the video image data from the image acquisition module (1001), and displays the video image data in real time; The communication buoy unit (1000) is arranged on the sea surface, the wind and wave avoidance mechanism (3000) is arranged below the communication buoy unit (1000), the wind and wave avoidance mechanism (3000) comprises a counterweight block (3100), a reel box (3200) and a towing chain (3300), the counterweight block (3100) is arranged on the seabed, the reel box (3200) is arranged on the counterweight block (3100), a sealed chamber 1 (3201) is arranged in the reel box (3200), a reel motor (3204) is arranged in the sealed chamber 1 (3201), a sealed chamber 2 (3202) is arranged in the reel box (3200), and a reel controller (3204) is arranged in the sealed chamber 2 (3202). 5), a battery (3206) is provided in the sealed chamber 2 (3202), a winding chamber (3203) is provided on the winding box (3200), a winding roller (3207) is rotatably provided in the winding chamber (3203), the winding roller (3207) is coaxially fixedly connected to the output end of the winding motor (3204), one end of the traction chain (3300) is wound around the winding roller (3207), the other end of the traction chain (3300) is provided at the bottom of the communication buoy unit (1000), the winding controller (3205) is wirelessly connected to the communication buoy unit (1000), and the battery (3206) is electrically connected to the winding motor (3204) and the winding controller (3205).

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