A path optimization method and system for intelligent unmanned boat

Abstract

The present invention relates to the field of intelligent unmanned boats, and in particular to a path optimization method and system for intelligent unmanned boats, which are used to solve the problem that the existing intelligent unmanned boats are unable to perform abnormality detection and real-time path optimization, resulting in the inability of the intelligent unmanned boats to perform efficient, accurate and safe operations in a complex and changeable marine environment; the system realizes abnormality detection of the unmanned boat, effectively reduces the risk of failure of the unmanned boat during the execution of the mission, ensures the continuous and stable operation of the unmanned boat, improves the safety and reliability of the mission execution, and can also dynamically adjust the navigation path, actively avoid danger, and automatically plan a safe and efficient driving route, thereby improving the operating safety and reliability of the unmanned boat, gradually improving its prediction accuracy of future road conditions and path adaptability, and further enhancing the operation capability of the intelligent unmanned boat in a complex and changeable environment.

Description

Technical Field

[0001] The present invention relates to the field of intelligent unmanned boats, and in particular to a path optimization method and system for intelligent unmanned boats. Background Art

[0002] With the continuous development of science and technology, intelligent unmanned boats have been widely used in the fields of marine scientific research, maritime search and rescue, maritime patrol and law enforcement, military applications, etc. However, during the long-term autonomous navigation process, intelligent unmanned boats may encounter various faults, which seriously affect their mission execution capabilities and safety. Moreover, traditional intelligent unmanned boats mainly rely on manually preset routes or use simple automated navigation algorithms. They lack real-time path optimization functions and are difficult to respond to emergencies, resulting in reduced operating efficiency of intelligent unmanned boats and even safety hazards, which will damage the safety and mission completion of intelligent unmanned boats.

[0003] How to improve the existing intelligent unmanned boats' inability to perform abnormality detection and real-time path optimization, which results in the inability of intelligent unmanned boats to perform efficient, accurate and safe operations in complex and changeable marine environments, is the key to the present invention. Therefore, it is of great significance to develop a path optimization method and system for intelligent unmanned boats. Summary of the invention

[0004] In order to overcome the above-mentioned technical problems, the purpose of the present invention is to provide a path optimization method and system for an intelligent unmanned boat, which solves the problem that the existing intelligent unmanned boats are unable to perform abnormality detection and real-time path optimization, resulting in the inability of intelligent unmanned boats to perform efficient, accurate and safe operations in complex and changeable marine environments.

[0005] The purpose of the present invention can be achieved through the following technical solutions:

[0006] A path optimization system for an intelligent unmanned boat, comprising:

[0007] A path monitoring module is used to control the selected unmanned boat to travel according to the primary driving path, obtain the path monitoring parameters of the selected unmanned boat, and send the path monitoring parameters to the data processing module; wherein the path monitoring parameters include shaking information HD, environmental information HJ and obstacle information ZA;

[0008] A data processing module, used for obtaining a path monitoring coefficient LJ according to the path monitoring parameter, and sending the path monitoring coefficient LJ to the determination analysis module;

[0009] The specific process of the data processing module obtaining the path monitoring coefficient LJ is as follows:

[0010] The sway information HD, the environmental information HJ and the obstacle information ZA are normalized, and the values ​​of the sway information HD, the environmental information HJ and the obstacle information ZA are quantified according to a preset path monitoring model to obtain a path monitoring coefficient LJ;

[0011] Among them, the path monitoring model is as follows:

[0012]

[0013] Where:

[0014] η is the preset error adjustment factor, and η=1.029;

[0015] Both π and e are mathematical constants;

[0016] λ1, λ2 and λ3 are respectively the preset weight factors corresponding to the set shaking information HD, environmental information HJ and obstacle information ZA, λ1, λ2 and λ3 satisfy λ3>λ1>λ2>2.125, and λ1=3.03, λ2=2.55, λ3=3.71;

[0017] A determination and analysis module is used to identify a path danger area according to a path monitoring coefficient LJ, obtain a path optimization area according to the path danger area, and send the path optimization area to a path optimization module;

[0018] The path optimization module is used to replace the path danger area with the path optimization area to achieve path optimization.

[0019] As a further solution of the present invention: the specific process of the path monitoring module obtaining the path monitoring parameters is as follows:

[0020] The path destination is used as the preset end point and the current position is used as the preset starting point. A primary driving path is formed on the sea surface according to the preset starting point and the preset end point. A plurality of rectangular areas connected in sequence are drawn in sequence with the primary driving path as the center line, and they are marked as path monitoring areas. A plurality of rectangular areas of the same area are drawn on both sides of the path monitoring area, and they are marked as path candidate areas.

[0021] Control the selected unmanned boat to travel according to the primary driving path, obtain the maximum vertical swing displacement and the maximum horizontal swing angle of the selected unmanned boat during the driving process, and mark them as displacement value WY and angle value JD respectively, quantify the displacement value WY and angle value JD, extract the values ​​of the displacement value WY and angle value JD, and substitute them into the formula for calculation. According to the formula Obtain shaking information HD; wherein e is a mathematical constant, h1 and h2 are preset proportional coefficients corresponding to the set displacement value WY and angle value JD, respectively, h1 and h2 satisfy h1+h2=1, 0

[0022] Obtain the wind speed and the height of the highest point of the sea surface in the path monitoring area at a preset distance in front of the selected unmanned boat, and mark them as the wind speed value FS and the height value GD respectively, draw a diamond figure with the values ​​of the wind speed value FS and the height value GD as the lengths of the two diagonal lines of the diamond figure, obtain the area of ​​the diamond figure, and mark it as environmental information HJ;

[0023] Obtain the total volume of obstacles on the sea surface and the distance between the top of the obstacles below the sea surface and the sea surface in the path monitoring area at a preset distance in front of the selected unmanned boat, and mark them as obstacle value ZT and obstacle distance value ZJ respectively. Quantify the obstacle value ZT and obstacle distance value ZJ, extract the values ​​of the obstacle value ZT and obstacle distance value ZJ, and substitute them into the formula for calculation. Obtain obstacle information ZA; wherein e is a mathematical constant, z1 and z2 are preset proportional coefficients corresponding to the set obstacle value ZT and obstacle distance value ZJ, respectively, z1 and z2 satisfy z1+z2=1, 0<z1<z2<1, and z1=0.41 and z2=0.59;

[0024] The shaking information HD, environmental information HJ and obstacle information ZA are sent to the data processing module.

[0025] As a further solution of the present invention: the specific process of the determination and analysis module obtaining the path optimization area is as follows:

[0026] The path monitoring coefficient LJ is compared with the preset path monitoring threshold LJy, and the comparison results are as follows:

[0027] If the path monitoring coefficient LJ ≥ the path monitoring threshold LJy, the path monitoring area corresponding to the path monitoring coefficient LJ is marked as a path danger area, and the path candidate area corresponding to the path danger area is marked as a path pre-optimization area;

[0028] Obtain the path monitoring coefficient LJ of the path pre-optimization area, the path distance between the path pre-optimization area and the preset end point, and respectively pre-optimize the monitoring coefficient and the pre-optimization distance to obtain the product of the pre-optimization monitoring coefficient and the pre-optimization distance, and mark it as the pre-optimization coefficient YH;

[0029] All path pre-optimization areas are sorted in ascending order according to the pre-optimization coefficient YH, and the path pre-optimization area at the first position is marked as the path optimization area;

[0030] ​Send the path optimization area to the path optimization module.

[0031] As a further solution of the present invention: the unmanned boat detection module is also used to obtain the unmanned boat detection parameters of the detection object i, and send the unmanned boat detection parameters to the data processing module; wherein the unmanned boat detection parameters include damage information SS, rotation speed information ZS and reaction information FY.

[0032] As a further solution of the present invention: the specific process of the unmanned boat detection module obtaining the unmanned boat detection parameters is as follows:

[0033] All intelligent unmanned boats are marked as detection objects i in sequence, where i=1, ..., n, where n is a positive integer, i is the number of any detection object, and n is the total number of detection objects;

[0034] Get the number of pits, protrusions and cracks on the outer surface of the boat body of the detection object i at the current moment, and mark them as the concave value AS, the convex value TS and the crack value LS respectively; get the pit area, protrusion area and crack area on the outer surface of the boat body of the detection object i at the current moment, and mark them as the concave value AM, the convex value TM and the crack value LM respectively; get the product of the concave value AS and the concave value AM, and mark them as the pit value AK; get the product of the convex value TS and the convex value TM, and mark them as the convex value TQ; get the product of the crack value LS and the crack value LM The product is marked as the crack value LW, and the pit value AK, the protrusion value TQ and the crack value LW are quantized, and the values ​​of the pit value AK, the protrusion value TQ and the crack value LW are multiplied by their corresponding preset proportional coefficients respectively to obtain the sum of the three, and the damage information SS is obtained; wherein, the preset proportional coefficients corresponding to the pit value AK, the protrusion value TQ and the crack value LW are s1, s2 and s3 respectively, and s1, s2 and s3 satisfy s1+s2+s3=1, 0<s1<s2<s3<1, and s1=0.21, s2=0.34, s3=0.45;

[0035] Obtain the real-time speed and preset speed of the engine during the most recent operation of the detection object i, obtain the difference between the two, and mark it as the speed difference value SC, obtain the average value of all speed difference values ​​SC within a unit time, and mark it as the speed difference mean value SCj, obtain the maximum speed difference value SC within a unit time, and mark it as the speed difference maximum value SCz, quantize the speed difference mean value SCj and the speed difference maximum value SCz, multiply the values ​​of the speed difference mean value SCj and the speed difference maximum value SCz by their corresponding preset proportional coefficients, obtain the sum of the two, and mark it as the speed information ZS; wherein the preset proportional coefficients corresponding to the speed difference mean value SCj and the speed difference maximum value SCz are a1 and a2 respectively, and a1 and a2 satisfy a1+a2=1, 0<a2<a1<1, and a1=0.63, a2=0.37;

[0036] Obtain the time when the user sends the operation instruction during the most recent operation of the detection object i, the time when the detection object i receives the operation instruction, and the time when the detection object i operates according to the operation instruction, and mark them as the sending time value, the receiving time value, and the operating time value respectively, obtain the time difference between the sending time value and the receiving time value, and mark it as the receiving reaction value JF, obtain the time difference between the receiving time value and the operating time value, and mark it as the operating reaction value CF, quantize the receiving reaction value JF and the operating reaction value CF, multiply the values ​​of the receiving reaction value JF and the operating reaction value CF by their corresponding preset proportional coefficients respectively, obtain the sum of the two, and mark it as the reaction value, obtain the maximum reaction value within the unit time, and mark it as the reaction information FY; wherein, the preset proportional coefficients corresponding to the receiving reaction value JF and the operating reaction value CF are f1 and f2 respectively, and f1 and f2 satisfy f1+f2=1, 0<f2<f1<1, and f1=0.55, f2=0.45;

[0037] The damage information SS, the rotation speed information ZS and the reaction information FY are sent to the data processing module.

[0038] As a further solution of the present invention: the data processing module is also used to obtain the unmanned boat detection coefficient TJi according to the unmanned boat detection parameters, and send the unmanned boat detection coefficient TJi to the determination analysis module.

[0039] As a further solution of the present invention: the specific process of the data processing module obtaining the unmanned boat detection coefficient TJi is as follows:

[0040] The damage information SS, the rotation speed information ZS and the reaction information FY are normalized, and the values ​​of the damage information SS, the rotation speed information ZS and the reaction information FY are quantified according to a preset unmanned boat detection model to obtain the unmanned boat detection coefficient TJi;

[0041] Among them, the unmanned boat detection model is as follows:

[0042]

[0043] Where:

[0044] δ is the preset error adjustment factor, which is δ=0.921;

[0045] Both e and π are mathematical constants;

[0046] κ1, κ2 and κ3 are preset weight factors corresponding to the set damage information SS, speed information ZS and reaction information FY respectively, κ1, κ2 and κ3 satisfy κ1>κ2>κ3>1.518, and κ1=2.96, κ2=2.49, κ3=1.83 are taken;

[0047] The unmanned boat detection coefficient TJi is sent to the judgment analysis module.

[0048] As a further solution of the present invention: the path optimization system for the intelligent unmanned boat also includes:

[0049] The judgment and analysis module is used to obtain the selected unmanned boat according to the unmanned boat detection coefficient TJi, and send the selected unmanned boat to the path monitoring module.

[0050] As a further solution of the present invention: the specific process of the determination and analysis module obtaining the selected unmanned boat is as follows:

[0051] All detection objects i are sorted in ascending order according to the unmanned boat detection coefficient TJi, and the detection object i at the first position is marked as the selected unmanned boat, and the selected unmanned boat is sent to the path monitoring module.

[0052] As a further solution of the present invention: a path optimization method for an intelligent unmanned boat comprises the following steps:

[0053] Step 1: The unmanned boat detection module obtains the unmanned boat detection parameters of the detection object i, wherein the unmanned boat detection parameters include damage information SS, rotation speed information ZS and reaction information FY, and sends the unmanned boat detection parameters to the data processing module;

[0054] Step 2: The data processing module obtains the unmanned boat detection coefficient TJi according to the unmanned boat detection parameters, and sends the unmanned boat detection coefficient TJi to the judgment analysis module;

[0055] Step 3: The determination and analysis module obtains the selected unmanned boat according to the unmanned boat detection coefficient TJi, and sends the selected unmanned boat to the path monitoring module;

[0056] Step 4: The path monitoring module controls the selected unmanned boat to travel according to the primary driving path, and obtains the path monitoring parameters of the selected unmanned boat, wherein the path monitoring parameters include shaking information HD, environmental information HJ and obstacle information ZA, and sends the path monitoring parameters to the data processing module;

[0057] Step 5: The data processing module obtains the path monitoring coefficient LJ according to the path monitoring parameter, and sends the path monitoring coefficient LJ to the determination analysis module;

[0058] Step 6: The determination and analysis module identifies the path danger area according to the path monitoring coefficient LJ, obtains the path optimization area according to the path danger area, and sends the path optimization area to the path optimization module;

[0059] Step 7: The path optimization module uses the path optimization area to replace the path danger area to achieve path optimization.

[0060] Beneficial effects of the present invention:

[0061] A path optimization method and system for an intelligent unmanned boat of the present invention first collects abnormal data of the intelligent unmanned boat to obtain unmanned boat detection parameters, then uses data analysis technology to obtain an unmanned boat detection coefficient based on the unmanned boat detection parameters to comprehensively measure the abnormality of the intelligent unmanned boat, and the larger the unmanned boat detection coefficient is, the higher the abnormality is. Then, the intelligent unmanned boat with the lowest abnormality is selected as the selected unmanned boat for offshore operations, then collects danger data on the driving path of the selected unmanned boat to obtain path monitoring parameters, then uses data analysis technology to obtain a path monitoring coefficient based on the path monitoring parameters to comprehensively measure the danger level of the driving path, and the larger the path monitoring coefficient is, the higher the danger level is. Finally, a comprehensive analysis is performed on the path candidate area to obtain a pre-optimization coefficient, and the smaller the pre-optimization coefficient is, the higher the comprehensive excellence is. Finally, a path optimization area is obtained according to the pre-optimization coefficient, and path optimization is implemented according to the pre-optimization coefficient.

[0062] The path optimization method and system for intelligent unmanned boats of the present invention realize abnormal detection of unmanned boats, effectively reduce the risk of failure of unmanned boats during mission execution, ensure the continuous and stable operation of unmanned boats, improve the safety and reliability of mission execution, and can dynamically adjust the navigation path, actively avoid danger, and automatically plan a safe and efficient driving route, thereby improving the operational safety and reliability of unmanned boats, gradually improving the prediction accuracy of future road conditions and path adaptability, and further enhancing the operation capability of intelligent unmanned boats in complex and changeable environments. In summary, the proposed method and system effectively enhance the autonomy and intelligence of intelligent unmanned boats, and provide strong support and technical guarantee for the wide application of unmanned boats in the fields of marine scientific research, maritime search and rescue, maritime patrol and law enforcement, military applications, etc. BRIEF DESCRIPTION OF THE DRAWINGS

[0063] The present invention will be further described below in conjunction with the accompanying drawings.

[0064] Figure 1 It is a principle block diagram of a path optimization system for an intelligent unmanned boat in the present invention;

[0065] Figure 2 It is a flow chart of a path optimization method for an intelligent unmanned boat in the present invention. DETAILED DESCRIPTION

[0066] The following will be combined with the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0067] Embodiment 1:

[0068] See also Figure 1 As shown, this embodiment is a path optimization system for an intelligent unmanned boat, comprising the following modules: an unmanned boat detection module, a data processing module, a determination and analysis module, a path monitoring module, and a path optimization module;

[0069] The unmanned boat detection module is used to obtain the unmanned boat detection parameters of the detection object i and send the unmanned boat detection parameters to the data processing module; wherein the unmanned boat detection parameters include damage information SS, rotation speed information ZS and reaction information FY;

[0070] The data processing module is used to obtain the unmanned boat detection coefficient TJi according to the unmanned boat detection parameters, and send the unmanned boat detection coefficient TJi to the determination analysis module; it is also used to obtain the path monitoring coefficient LJ according to the path monitoring parameters, and send the path monitoring coefficient LJ to the determination analysis module;

[0071] The determination and analysis module is used to obtain the selected unmanned boat according to the unmanned boat detection coefficient TJi, and send the selected unmanned boat to the path monitoring module; it is also used to identify the path danger area according to the path monitoring coefficient LJ, and obtain the path optimization area according to the path danger area, and send the path optimization area to the path optimization module;

[0072] The path monitoring module is used to control the selected unmanned boat to travel according to the primary driving path, obtain the path monitoring parameters of the selected unmanned boat, and send the path monitoring parameters to the data processing module; wherein the path monitoring parameters include shaking information HD, environmental information HJ and obstacle information ZA;

[0073] The path optimization module is used to replace the path danger area with the path optimization area to achieve path optimization.

[0074] Embodiment 2:

[0075] See also Figure 2 As shown, this embodiment is a path optimization method for an intelligent unmanned boat, comprising the following steps:

[0076] Step 1: The unmanned boat detection module obtains the unmanned boat detection parameters of the detection object i, wherein the unmanned boat detection parameters include damage information SS, rotation speed information ZS and reaction information FY, and sends the unmanned boat detection parameters to the data processing module;

[0077] Step 2: The data processing module obtains the unmanned boat detection coefficient TJi according to the unmanned boat detection parameters, and sends the unmanned boat detection coefficient TJi to the judgment analysis module;

[0078] Step 3: The determination and analysis module obtains the selected unmanned boat according to the unmanned boat detection coefficient TJi, and sends the selected unmanned boat to the path monitoring module;

[0079] Step 4: The path monitoring module controls the selected unmanned boat to travel according to the primary driving path, and obtains the path monitoring parameters of the selected unmanned boat, wherein the path monitoring parameters include shaking information HD, environmental information HJ and obstacle information ZA, and sends the path monitoring parameters to the data processing module;

[0080] Step 5: The data processing module obtains the path monitoring coefficient LJ according to the path monitoring parameter, and sends the path monitoring coefficient LJ to the determination analysis module;

[0081] Step 6: The determination and analysis module identifies the path danger area according to the path monitoring coefficient LJ, obtains the path optimization area according to the path danger area, and sends the path optimization area to the path optimization module;

[0082] Step 7: The path optimization module uses the path optimization area to replace the path danger area to achieve path optimization.

[0083] Embodiment 3:

[0084] Based on any of the above embodiments, embodiment 3 of the present invention is an unmanned boat detection module. The function of the unmanned boat detection module is to obtain unmanned boat detection parameters, wherein the unmanned boat detection parameters include damage information SS, speed information ZS and reaction information FY. The specific process is as follows:

[0085] The unmanned boat detection module marks all intelligent unmanned boats as detection objects i in sequence, where i=1, ..., n, where n is a positive integer, i is the number of any detection object, and n is the total number of detection objects;

[0086] The unmanned boat detection module obtains the number of pits, protrusions and cracks on the outer surface of the boat body of the detection object i at the current moment, and marks them as the concave value AS, the convex value TS and the crack value LS respectively, obtains the pit area, protrusion area and crack area on the outer surface of the boat body of the detection object i at the current moment, and marks them as the concave value AM, the convex value TM and the crack value LM respectively, obtains the product of the concave value AS and the concave value AM, and marks it as the pit value AK, obtains the product of the convex value TS and the convex value TM, and marks it as the convex value TQ, obtains the crack value LS and the crack value L The product of the two is obtained by multiplying the pit value AK, the protrusion value TQ and the crack value LW, and marking it as the crack value LW, quantizing the pit value AK, the protrusion value TQ and the crack value LW, and multiplying the values ​​of the pit value AK, the protrusion value TQ and the crack value LW by their corresponding preset proportional coefficients, and obtaining the sum of the three to obtain the damage information SS; wherein, the preset proportional coefficients corresponding to the pit value AK, the protrusion value TQ and the crack value LW are s1, s2 and s3 respectively, and s1, s2 and s3 satisfy s1+s2+s3=1, 0<s1<s2<s3<1, and s1=0.21, s2=0.34, s3=0.45;

[0087] The unmanned boat detection module obtains the real-time speed and preset speed of the engine of the detection object i during the most recent operation, obtains the difference between the two, and marks it as the speed difference value SC, obtains the average value of all speed difference values ​​SC in a unit time, and marks it as the speed difference mean value SCj, obtains the maximum speed difference value SC in a unit time, and marks it as the speed difference maximum value SCz, quantizes the speed difference mean value SCj and the speed difference maximum value SCz, multiplies the values ​​of the speed difference mean value SCj and the speed difference maximum value SCz by their corresponding preset proportional coefficients, obtains the sum of the two, and marks it as the speed information ZS; wherein, the preset proportional coefficients corresponding to the speed difference mean value SCj and the speed difference maximum value SCz are a1 and a2 respectively, and a1 and a2 satisfy a1+a2=1, 0<a2<a1<1, and a1=0.63, a2=0.37;

[0088] The unmanned boat detection module obtains the time when the user sends the operation instruction during the most recent operation of the detection object i, the time when the detection object i receives the operation instruction, and the time when the detection object i operates according to the operation instruction, and marks them as the sending time value, the receiving time value, and the operating time value, respectively, obtains the time difference between the sending time value and the receiving time value, and marks it as the receiving reaction value JF, obtains the time difference between the receiving time value and the operating time value, and marks it as the operating reaction value CF, quantizes the receiving reaction value JF and the operating reaction value CF, multiplies the values ​​of the receiving reaction value JF and the operating reaction value CF by their corresponding preset proportional coefficients, obtains the sum of the two, and marks it as the reaction value, obtains the maximum reaction value within a unit time, and marks it as the reaction information FY; wherein, the preset proportional coefficients corresponding to the receiving reaction value JF and the operating reaction value CF are f1 and f2, respectively, and f1 and f2 satisfy f1+f2=1, 0<f2<f1<1, and f1=0.55 and f2=0.45;

[0089] The unmanned boat detection module sends the damage information SS, rotation speed information ZS and reaction information FY to the data processing module.

[0090] Embodiment 4:

[0091] Based on any of the above embodiments, Embodiment 4 of the present invention is a data processing module, and the data processing module has two functions:

[0092] One of the functions is to obtain the unmanned boat detection coefficient TJi. The specific process is as follows:

[0093] The data processing module normalizes the damage information SS, the speed information ZS and the reaction information FY, and quantifies the values ​​of the damage information SS, the speed information ZS and the reaction information FY according to the preset unmanned boat detection model to obtain the unmanned boat detection coefficient TJi;

[0094] Among them, the unmanned boat detection model is as follows:

[0095]

[0096] Where:

[0097] δ is the preset error adjustment factor, which is δ=0.921;

[0098] Both e and π are mathematical constants;

[0099] κ1, κ2 and κ3 are preset weight factors corresponding to the set damage information SS, speed information ZS and reaction information FY respectively, κ1, κ2 and κ3 satisfy κ1>κ2>κ3>1.518, and κ1=2.96, κ2=2.49, κ3=1.83 are taken;

[0100] The data processing module sends the unmanned boat detection coefficient TJi to the judgment analysis module;

[0101] The second function is to obtain the path monitoring coefficient LJ. The specific process is as follows:

[0102] The data processing module normalizes the sway information HD, the environmental information HJ and the obstacle information ZA, and quantifies the values ​​of the sway information HD, the environmental information HJ and the obstacle information ZA according to a preset path monitoring model to obtain a path monitoring coefficient LJ;

[0103] Among them, the path monitoring model is as follows:

[0104]

[0105] Where:

[0106] η is the preset error adjustment factor, and η=1.029;

[0107] Both π and e are mathematical constants;

[0108] λ1, λ2 and λ3 are respectively the preset weight factors corresponding to the set shaking information HD, environmental information HJ and obstacle information ZA, λ1, λ2 and λ3 satisfy λ3>λ1>λ2>2.125, and λ1=3.03, λ2=2.55, λ3=3.71;

[0109] The data processing module sends the path monitoring coefficient LJ to the decision analysis module.

[0110] Embodiment 5:

[0111] Based on any of the above embodiments, Embodiment 5 of the present invention is a determination and analysis module, which has two functions:

[0112] One of the functions is to obtain the selection of unmanned boats. The specific process is as follows:

[0113] The judgment and analysis module sorts all the detection objects i according to the unmanned boat detection coefficient TJi from small to large, marks the detection object i at the first position as the selected unmanned boat, and sends the selected unmanned boat to the path monitoring module;

[0114] The second function is to obtain the path optimization area. The specific process is as follows:

[0115] The judgment analysis module compares the path monitoring coefficient LJ with the preset path monitoring threshold LJy. The comparison results are as follows:

[0116] If the path monitoring coefficient LJ ≥ the path monitoring threshold LJy, the path monitoring area corresponding to the path monitoring coefficient LJ is marked as a path danger area, and the path candidate area corresponding to the path danger area is marked as a path pre-optimization area;

[0117] The determination and analysis module obtains the path monitoring coefficient LJ of the path pre-optimization area, the path distance between the path pre-optimization area and the preset end point, and respectively pre-optimizes the monitoring coefficient and the pre-optimization distance to obtain the product of the pre-optimization monitoring coefficient and the pre-optimization distance, and marks it as the pre-optimization coefficient YH;

[0118] The determination and analysis module sorts all path pre-optimization areas in ascending order according to the pre-optimization coefficient YH, and marks the path pre-optimization area at the first position as the path optimization area;

[0119] The decision analysis module sends the path optimization area to the path optimization module.

[0120] Embodiment 6:

[0121] Based on any of the above embodiments, Embodiment 6 of the present invention is a path monitoring module. The function of the path monitoring module is to obtain path monitoring parameters, wherein the path monitoring parameters include shaking information HD, environmental information HJ and obstacle information ZA. The specific process is as follows:

[0122] The path monitoring module takes the path destination as the preset end point and the current position as the preset starting point, forms a primary driving path on the sea surface according to the preset starting point and the preset end point, draws a number of rectangular areas connected in sequence with the primary driving path as the center line, and marks them as path monitoring areas, draws a number of rectangular areas of the same area on both sides of the path monitoring area, and marks them as path candidate areas;

[0123] The path monitoring module controls the selected unmanned boat to travel according to the primary driving path, obtains the maximum vertical swing displacement and the maximum horizontal swing angle of the selected unmanned boat during the driving process, and marks them as displacement value WY and angle value JD respectively, quantifies the displacement value WY and angle value JD, extracts the values ​​of the displacement value WY and angle value JD, and substitutes them into the formula for calculation. Obtain shaking information HD; wherein e is a mathematical constant, h1 and h2 are preset proportional coefficients corresponding to the set displacement value WY and angle value JD, respectively, h1 and h2 satisfy h1+h2=1, 0

[0124] ​The path monitoring module obtains the wind speed and the height of the highest point of the sea surface in the path monitoring area at a preset distance in front of the selected unmanned boat, and marks them as the wind speed value FS and the height value GD respectively, draws a diamond figure with the values ​​of the wind speed value FS and the height value GD as the lengths of the two diagonal lines of the diamond figure, obtains the area of ​​the diamond figure, and marks it as environmental information HJ;

[0125] The path monitoring module obtains the total volume of obstacles on the sea surface and the distance between the top of the obstacles below the sea surface and the sea surface in the path monitoring area at a preset distance in front of the selected unmanned boat, and marks them as obstacle value ZT and obstacle distance value ZJ respectively, quantifies the obstacle value ZT and obstacle distance value ZJ, extracts the values ​​of the obstacle value ZT and obstacle distance value ZJ, and substitutes them into the formula for calculation. Obtain obstacle information ZA; wherein e is a mathematical constant, z1 and z2 are preset proportional coefficients corresponding to the set obstacle value ZT and obstacle distance value ZJ, respectively, z1 and z2 satisfy z1+z2=1, 0<z1<z2<1, and z1=0.41 and z2=0.59;

[0126] The path monitoring module sends the shaking information HD, the environmental information HJ and the obstacle information ZA to the data processing module.

[0127] Embodiment 7:

[0128] Based on any of the above embodiments, Embodiment 7 of the present invention is a path optimization module. The function of the path optimization module is to perform path optimization. The specific process is as follows:

[0129] The path optimization module replaces the path danger area in the primary driving path with the path optimization area to form an optimized driving path, and controls the selected unmanned boat to travel according to the optimized driving path.

[0130] Based on the above embodiments 1-7, the working principle of the present invention is as follows:

[0131] The present invention provides a path optimization method and system for an intelligent unmanned boat, wherein an unmanned boat detection module is used to obtain unmanned boat detection parameters of a detection object, wherein the unmanned boat detection parameters include damage information, rotation speed information, and reaction information, an unmanned boat detection coefficient is obtained according to the unmanned boat detection parameters by a data processing module, a selected unmanned boat is obtained according to the unmanned boat detection coefficient by a determination analysis module, a selected unmanned boat is controlled to travel according to a primary driving path by a path monitoring module, and path monitoring parameters of the selected unmanned boat are obtained, wherein the path monitoring parameters include shaking information, environmental information, and obstacle information, a path monitoring coefficient is obtained according to the path monitoring parameters by a data processing module, a path danger zone is identified according to the path monitoring coefficient by a determination analysis module, a path optimization zone is obtained according to the path danger zone, and a path optimization module replaces the path danger zone with the path optimization zone to achieve path optimization; the system first collects abnormal data of the intelligent unmanned boat to obtain the unmanned boat detection parameters, and then uses data analysis technology to obtain the unmanned boat detection coefficient according to the unmanned boat detection parameters to comprehensively measure the abnormality of the intelligent unmanned boat, and The larger the unmanned boat detection coefficient is, the higher the degree of abnormality is. Then the intelligent unmanned boat with the lowest degree of abnormality is selected as the selected unmanned boat for offshore operations. Then the dangerous data of the driving path of the selected unmanned boat is collected to obtain the path monitoring parameters. Then the path monitoring coefficient obtained according to the path monitoring parameters using data analysis technology can comprehensively measure the degree of danger of the driving path. The larger the path monitoring coefficient is, the higher the degree of danger is. Finally, a comprehensive analysis is conducted on the path candidate area to obtain the pre-optimization coefficient. The smaller the pre-optimization coefficient is, the higher the comprehensive excellence is. Finally, the path optimization area is obtained according to the pre-optimization coefficient, and the path optimization is realized. The system realizes abnormal detection of unmanned boats, effectively reduces the risk of failure of unmanned boats during mission execution, ensures the continuous and stable operation of unmanned boats, improves the safety and reliability of mission execution, and can dynamically adjust the navigation path, actively avoid danger, and automatically plan a safe and efficient driving route, which improves the operating safety and reliability of the unmanned boat, gradually improves its prediction accuracy of future road conditions and path adaptability, and further enhances the operation capability of intelligent unmanned boats in complex and changing environments. In summary, the proposed method and system effectively enhance the autonomy and intelligence of intelligent unmanned boats, and provide strong support and technical guarantee for the wide application of unmanned boats in the fields of marine scientific research, maritime search and rescue, maritime patrol and law enforcement, and military applications.

[0132] It should be further explained that the above formulas are obtained by collecting a large amount of data and performing software simulation, and a formula close to the actual value is selected. The coefficients in the formula are set by technical personnel in this field according to actual conditions.

[0133] In the description of this specification, the description with reference to the terms "one embodiment", "example", "specific example", etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner.

[0134] The above contents are merely examples and explanations of the present invention. Those skilled in the art may make various modifications or additions to the specific embodiments described or replace them in a similar manner. As long as they do not deviate from the invention or exceed the scope defined in this application, they shall all fall within the protection scope of the present invention.

Claims

1. A path optimization system for an intelligent unmanned boat, characterized in that: include: A path monitoring module is used to control the selected unmanned boat to travel according to the primary driving path, obtain the path monitoring parameters of the selected unmanned boat, and send the path monitoring parameters to the data processing module; wherein the path monitoring parameters include shaking information HD, environmental information HJ and obstacle information ZA; A data processing module, used for obtaining a path monitoring coefficient LJ according to the path monitoring parameter, and sending the path monitoring coefficient LJ to the determination analysis module; The specific process of the data processing module obtaining the path monitoring coefficient LJ is as follows: The values ​​of the shaking information HD, the environmental information HJ and the obstacle information ZA are quantified according to the preset path monitoring model to obtain the path monitoring coefficient LJ; Among them, the path monitoring model is as follows: Where: η is the preset error adjustment factor; Both π and e are mathematical constants; λ1, λ2 and λ3 are preset weight factors corresponding to the set shaking information HD, environmental information HJ and obstacle information ZA respectively; A determination and analysis module is used to identify a path danger area according to a path monitoring coefficient LJ, obtain a path optimization area according to the path danger area, and send the path optimization area to a path optimization module; The path optimization module is used to replace the path danger area with the path optimization area to achieve path optimization.

2. A path optimization system for an intelligent unmanned boat according to claim 1, characterized in that: The specific process of the path monitoring module obtaining the path monitoring parameters is as follows: The path destination is used as the preset end point and the current position is used as the preset starting point. A primary driving path is formed on the sea surface according to the preset starting point and the preset end point. A plurality of rectangular areas connected in sequence are drawn in sequence with the primary driving path as the center line, and they are marked as path monitoring areas. A plurality of rectangular areas of the same area are drawn on both sides of the path monitoring area, and they are marked as path candidate areas. Control the selected unmanned boat to travel according to the primary driving path, obtain the maximum vertical swing displacement and the maximum horizontal swing angle of the selected unmanned boat during the driving process, and mark them as displacement value WY and angle value JD respectively, quantize the displacement value WY and the angle value JD to obtain shaking information HD; Obtain the wind speed and the height of the highest point of the sea surface in the path monitoring area at a preset distance in front of the selected unmanned boat, and mark them as the wind speed value FS and the height value GD respectively, draw a diamond figure with the values ​​of the wind speed value FS and the height value GD as the lengths of the two diagonal lines of the diamond figure, obtain the area of ​​the diamond figure, and mark it as environmental information HJ; Obtain the total volume of obstacles on the sea surface and the distance between the top of the obstacles below the sea surface and the sea surface in the path monitoring area at a preset distance in front of the selected unmanned boat, and mark them as obstacle value ZT and obstacle distance value ZJ respectively, and quantify the obstacle value ZT and obstacle distance value ZJ to obtain obstacle information ZA; The shaking information HD, environmental information HJ and obstacle information ZA are sent to the data processing module.

3. The path optimization system for an intelligent unmanned boat according to claim 1, characterized in that: The specific process of the determination and analysis module obtaining the path optimization area is as follows: The path monitoring coefficient LJ is compared with the preset path monitoring threshold LJy, and the comparison results are as follows: If the path monitoring coefficient LJ ≥ the path monitoring threshold LJy, the path monitoring area corresponding to the path monitoring coefficient LJ is marked as a path danger area, and the path candidate area corresponding to the path danger area is marked as a path pre-optimization area; Obtain the path monitoring coefficient LJ of the path pre-optimization area, the path distance between the path pre-optimization area and the preset end point, and respectively pre-optimize the monitoring coefficient and the pre-optimization distance to obtain the product of the pre-optimization monitoring coefficient and the pre-optimization distance, and mark it as the pre-optimization coefficient YH; All path pre-optimization areas are sorted in ascending order according to the pre-optimization coefficient YH, and the path pre-optimization area at the first position is marked as the path optimization area; Send the path optimization area to the path optimization module.

4. The path optimization system for an intelligent unmanned boat according to claim 1, characterized in that: Also includes: The unmanned boat detection module is used to obtain the unmanned boat detection parameters of the detection object i and send the unmanned boat detection parameters to the data processing module; wherein the unmanned boat detection parameters include damage information SS, rotation speed information ZS and reaction information FY.

5. A path optimization system for an intelligent unmanned boat according to claim 4, characterized in that: The specific process of the unmanned boat detection module obtaining the unmanned boat detection parameters is as follows: All intelligent unmanned boats are marked as detection objects i in sequence, where i=1, ..., n, where n is a positive integer, i is the number of any detection object, and n is the total number of detection objects; Obtain the number of pits, protrusions and cracks on the outer surface of the boat body of the detection object i at the current moment, and mark them as the concave value AS, the convex value TS and the crack value LS respectively; obtain the pit area, protrusion area and crack area on the outer surface of the boat body of the detection object i at the current moment, and mark them as the concave value AM, the convex value TM and the crack value LM respectively; obtain the product of the concave value AS and the concave value AM, and mark them as the pit value AK; obtain the product of the convex value TS and the convex value TM, and mark them as the convex value TQ; obtain the product of the crack value LS and the crack value LM, and mark them as the crack value LW; quantize the pit value AK, the convex value TQ and the crack value LW to obtain the damage information SS; Obtain the real-time speed and preset speed of the engine during the most recent operation of the detection object i, obtain the difference between the two, and mark it as the speed difference value SC, obtain the average value of all speed difference values ​​SC within a unit time, and mark it as the speed difference mean value SCj, obtain the maximum speed difference value SC within a unit time, and mark it as the speed difference maximum value SCz, quantize the speed difference mean value SCj and the speed difference maximum value SCz, multiply the values ​​of the speed difference mean value SCj and the speed difference maximum value SCz by their corresponding preset proportional coefficients, obtain the sum of the two, and mark it as the speed information ZS; Obtain the time when the user sends the operation instruction during the most recent operation of the detection object i, the time when the detection object i receives the operation instruction, and the time when the detection object i operates according to the operation instruction, and mark them as the sending time value, the receiving time value, and the operating time value respectively, obtain the time difference between the sending time value and the receiving time value, and mark it as the receiving reaction value JF, obtain the time difference between the receiving time value and the operating time value, and mark it as the operating reaction value CF, quantize the receiving reaction value JF and the operating reaction value CF, and obtain the reaction information FY; The damage information SS, the rotation speed information ZS and the reaction information FY are sent to the data processing module.

6. The path optimization system for an intelligent unmanned boat according to claim 1, characterized in that: The data processing module is also used to obtain the unmanned boat detection coefficient TJi according to the unmanned boat detection parameters, and send the unmanned boat detection coefficient TJi to the determination analysis module.

7. A path optimization system for an intelligent unmanned boat according to claim 6, characterized in that: The specific process of the data processing module obtaining the unmanned boat detection coefficient TJi is as follows: The values ​​of the damage information SS, the rotation speed information ZS and the reaction information FY are quantified according to the preset unmanned boat detection model to obtain the unmanned boat detection coefficient TJi; Among them, the unmanned boat detection model is as follows: Where: δ is the preset error adjustment factor; Both e and π are mathematical constants; κ1, κ2 and κ3 are preset weight factors corresponding to the set damage information SS, rotation speed information ZS and reaction information FY respectively; The unmanned boat detection coefficient TJi is sent to the judgment analysis module.

8. The path optimization system for an intelligent unmanned boat according to claim 1, characterized in that: Also includes: The judgment and analysis module is used to obtain the selected unmanned boat according to the unmanned boat detection coefficient TJi, and send the selected unmanned boat to the path monitoring module.

9. A path optimization system for an intelligent unmanned boat according to claim 8, characterized in that: The specific process of the determination and analysis module obtaining the selected unmanned boat is as follows: All detection objects i are sorted in ascending order according to the unmanned boat detection coefficient TJi, and the detection object i at the first position is marked as the selected unmanned boat, and the selected unmanned boat is sent to the path monitoring module.

10. A path optimization method for an intelligent unmanned boat, characterized in that: The following steps are involved: Step 1: The unmanned boat detection module obtains the unmanned boat detection parameters of the detection object i, wherein the unmanned boat detection parameters include damage information SS, rotation speed information ZS and reaction information FY, and sends the unmanned boat detection parameters to the data processing module; Step 2: The data processing module obtains the unmanned boat detection coefficient TJi according to the unmanned boat detection parameters, and sends the unmanned boat detection coefficient TJi to the judgment analysis module; Step 3: The determination and analysis module obtains the selected unmanned boat according to the unmanned boat detection coefficient TJi, and sends the selected unmanned boat to the path monitoring module; Step 4: The path monitoring module controls the selected unmanned boat to travel according to the primary driving path, and obtains the path monitoring parameters of the selected unmanned boat, wherein the path monitoring parameters include shaking information HD, environmental information HJ and obstacle information ZA, and sends the path monitoring parameters to the data processing module; Step 5: The data processing module obtains the path monitoring coefficient LJ according to the path monitoring parameter, and sends the path monitoring coefficient LJ to the determination analysis module; The specific process of the data processing module obtaining the path monitoring coefficient LJ is as follows: The values ​​of the shaking information HD, the environmental information HJ and the obstacle information ZA are quantified according to the preset path monitoring model to obtain the path monitoring coefficient LJ; Among them, the path monitoring model is as follows: Where: η is the preset error adjustment factor; Both π and e are mathematical constants; λ1, λ2 and λ3 are preset weight factors corresponding to the set shaking information HD, environmental information HJ and obstacle information ZA respectively; Step 6: The determination and analysis module identifies the path danger area according to the path monitoring coefficient LJ, obtains the path optimization area according to the path danger area, and sends the path optimization area to the path optimization module; Step 7: The path optimization module uses the path optimization area to replace the path danger area to achieve path optimization.

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