Path planning and optimization system for offshore wind power minesweeping operations

Abstract

The present invention relates to the field of path planning technology, specifically to a path planning and optimization system for offshore wind power minesweeping operations, the system comprising a magnetic anomaly data processing module, a target identification and classification module, a path planning optimization module and a path simulation verification module. The present invention realizes accurate magnetic field data processing and spatial correlation analysis by detecting the magnetic field strength of the operation area in real time and eliminating the interference of environmental noise, identifies abnormal points by gradient analysis method and extracts magnetic dipole features and compares physical properties, improves the accuracy of target identification, optimizes the classification process, ensures a more accurate distinction effect, and in terms of path planning optimization, dynamically adjusts the path planning by analyzing the spatial characteristics of metal thunder and lightning devices and combining real-time environmental data, such as water flow velocity and wind speed, to achieve dual optimization of cost and efficiency, and effectively reduces the operation risk and improves the efficiency of minesweeping operations by a method of comprehensive consideration of data analysis and environmental factors.

Description

Technical Field

[0001] The present invention relates to the technical field of path planning, and in particular to a path planning and optimization system for offshore wind power minesweeping operations. Background Art

[0002] The field of path planning technology is mainly used to determine the best path from the starting point to the end point. The technology is widely used in logistics, self-driving cars, drone navigation, robotics, and motion control in complex industrial environments. The key challenges of path planning include path optimization, obstacle avoidance, dynamic environment adaptation, and real-time path updates. The core goal is to improve efficiency, reduce costs, and optimize the path under specific constraints.

[0003] Among them, the purpose of the path planning and optimization system for offshore wind power minesweeping operations is to ensure that potential marine minefields or other obstacles can be safely and efficiently cleared during offshore wind power maintenance and expansion. By using a geographic information system, the system can analyze marine environmental data, plan the best route to avoid obstacles, and optimize the navigation path of the minesweeper, thereby reducing operation time, reducing risks, and improving the operation and maintenance efficiency of wind farms. This technology is crucial to ensuring the safety of offshore wind farm construction and maintenance, and helps promote the sustainable development of renewable energy.

[0004] When faced with a dynamic and complex marine environment, the path planning technology of traditional optimization systems is difficult to respond to environmental changes in real time, and it is difficult to accurately identify and classify potential minefields or other obstacles on the seabed. For example, the traditional system lacks comprehensive consideration of real-time environmental variables, such as the failure to fully integrate information such as wind speed and water flow speed, which will lead to inefficient path planning in actual operation, high costs, and prone to safety hazards. Insufficient information will affect the construction and maintenance of wind farms, extend operation time, increase economic costs and potential safety risks, and thus restrict the sustainable development of the renewable energy sector. Summary of the invention

[0005] The purpose of the present invention is to solve the shortcomings of the prior art and to propose a path planning and optimization system for offshore wind power minesweeping operations.

[0006] In order to achieve the above-mentioned purpose, the present invention adopts the following technical solution: a path planning and optimization system for offshore wind power minesweeping operations, the system comprising:

[0007] The magnetic anomaly data processing module is based on marine magnetometer equipment, which detects the magnetic field strength value of the operating area in real time, screens the magnetic field data of each measuring point, eliminates the influence of environmental noise, matches the magnetic field strength with the geographical location, constructs a continuous magnetic field distribution map, and obtains the magnetic field distribution information;

[0008] The target recognition and classification module is based on the magnetic field distribution information, by comparing the magnetic field intensity gradients of adjacent data points, marking potential abnormal areas, extracting signal features from the marked abnormal points, and distinguishing between metal explosive devices and abandoned metal objects by comparing with a preset target physical property database to obtain target distribution classification results;

[0009] The path planning optimization module analyzes the spatial distribution characteristics of metal mine explosive devices based on the target distribution classification results, integrates the geographical location data of each target point, compares and analyzes the boundaries of the wind power area, analyzes the shortest distance and the optimal access sequence, constructs a basic minesweeping path, and selects the target path according to the real-time environmental data, including water flow velocity and wind speed, to obtain an optimized path sequence;

[0010] Based on the optimized path sequence, the path simulation verification module loads the spatial data of each path node and the target point in the simulation environment, performs coverage analysis, verifies whether the path of each node covers the target area, adjusts the path nodes for the uncovered areas, and obtains the path coverage verification result.

[0011] The present invention is improved in that the step of eliminating the influence of environmental noise is:

[0012] Based on the marine magnetometer equipment, the magnetic field strength value of the operating area is detected in real time, the basic magnetic field strength data of each measuring point is collected, and the basic magnetic field data record is obtained;

[0013] Based on the basic magnetic field data record, signal processing is performed on the basic magnetic field data, and low-frequency and high-frequency environmental noises are removed by using a bandpass filter to obtain filtered magnetic field data;

[0014] Based on the filtered magnetic field data, using the formula:

[0015] ;

[0016] Calculate the calibrated magnetic field strength data to obtain optimized magnetic field data;

[0017] in, Represents the basic magnetic field strength data, Represents the average noise value of the ambient noise Indicates the calibrated magnetic field strength data.

[0018] The present invention is improved in that the step of acquiring the magnetic field distribution information is:

[0019] Based on the optimized magnetic field data, the magnetic field intensity information is combined with the geographical location data of the corresponding measurement point using geographic information system technology to obtain magnetic field data matching the geographical location;

[0020] Based on the magnetic field data matched with the geographical location, spatial correlation analysis is performed on the magnetic field data matched with the geographical location to detect the location points where the magnetic field strength is missing, and the magnetic field data of the adjacent location points are used through the formula:

[0021] ;

[0022] Calculate the magnetic field strength value of the interpolation point;

[0023] in, represents the magnetic field strength value at the interpolation point, Indicates The magnetic field strength at each location, Indicates The spatial distance between the position and the interpolation point, is the smoothing parameter, Indicates the total number of neighboring location points;

[0024] Based on the magnetic field intensity values ​​of the interpolation points and in combination with the magnetic field data matched with the geographical location, a spatially continuous magnetic field distribution map is constructed to obtain magnetic field distribution information.

[0025] The present invention is improved in that the step of marking the potential abnormal area is:

[0026] Based on the magnetic field distribution information, the difference in magnetic field intensity between each data point and adjacent points is analyzed, and the formula is:

[0027] ;

[0028] Calculate the magnetic field gradient value between adjacent data points;

[0029] in, Representative The magnetic field strength of each data point, Representative The magnetic field strength of each data point, Represents the physical distance between two data points, is the magnetic field gradient value;

[0030] Based on the magnetic field gradient value between the adjacent data points, it is compared with a preset gradient threshold, and the data points whose magnetic field gradient value is greater than the preset gradient threshold are screened, and potential abnormal areas are marked to obtain abnormal area identification results.

[0031] The present invention is improved in that the step of obtaining the target distribution classification result is:

[0032] Based on the abnormal area identification result, a magnetic dipole analysis is performed on each marked abnormal point, the intensity and spatial distribution characteristics of the magnetic dipole are extracted, and the magnetic dipole characteristic data of the abnormal point is obtained;

[0033] Based on the magnetic dipole characteristic data of the abnormal point, it is compared with the preset target physical property database, through the formula:

[0034] ;

[0035] Calculate feature matching scores;

[0036] in, It represents the first The eigenvalues ​​of the magnetic dipoles, Represents the first eigenvalues, For the The weight coefficient of the feature, For the The standard deviation of the feature, is the feature matching score, Indicates the total number of features;

[0037] Based on the feature matching score, the item type with the maximum score is selected as the matching type according to the size of the score, and each abnormal point is distinguished as a metal explosive device and an abandoned metal object, so as to obtain a target distribution classification result.

[0038] The present invention is improved in that the steps of constructing the basic minesweeping path are:

[0039] Based on the target distribution classification result, the location of each metal explosive device is extracted, and the corresponding geographical location data is integrated to obtain the spatial distribution information of the metal explosive device;

[0040] Based on the spatial distribution information of the metal lightning device, compared with the boundary data of the wind power area, the formula is:

[0041] ;

[0042] Calculate the total distance of the shortest path;

[0043] in, is the total distance of the shortest path, For the The geographical coordinates of the nodes, represents the total number of nodes in the path, , for The geographical coordinates of the nodes;

[0044] Based on the total distance of the shortest path, the corresponding node access sequence is extracted, and the key turning points and necessary nodes are marked to obtain the basic minesweeping path construction information.

[0045] The present invention is improved in that the step of obtaining the optimized path sequence is:

[0046] Based on the basic minesweeping path construction information, real-time environmental data of the target sea area, including water flow velocity and wind speed, are collected to obtain environmental impact data;

[0047] Based on the environmental impact data, through the formula:

[0048] ;

[0049] Calculate path cost;

[0050] in, represents the total number of nodes in the path, is the path cost, For the The wind speed of each node, For the The angle between the wind speed at each node and the path direction, For the The water flow velocity at each node, For the The angle between the water flow at each node and the path direction, For the best driving speed, For slave nodes arrive Length of the path segment;

[0051] Based on the path cost, the path with the lowest cost is extracted, compared with the basic minesweeping path construction information, and the target path is selected according to the current minesweeping demand to obtain the optimized path sequence.

[0052] The present invention is improved in that the steps of obtaining the path coverage verification result are:

[0053] Based on the optimized path sequence, the optimized path sequence is imported into a simulation environment, and spatial mapping data of the path and the target point are obtained by combining the spatial data of each node and the target point associated with the path;

[0054] Based on the spatial mapping data of the path and the target point, a coverage analysis is performed to verify whether each path node covers the predetermined target area, identify and record the uncovered area, and obtain a coverage analysis result;

[0055] Based on the coverage analysis results, according to the identified uncovered areas, the positions and order of the path nodes are adjusted to optimize the path, cover each target point, and obtain a path coverage verification result.

[0056] Compared with the prior art, the advantages and positive effects of the present invention are:

[0057] In the present invention, by real-time detection of the magnetic field strength in the operating area and elimination of interference from environmental noise, accurate magnetic field data processing and spatial correlation analysis are achieved, making the construction of the magnetic field distribution map more continuous and precise. The gradient analysis method is used to identify abnormal points and perform magnetic dipole feature extraction and physical property comparison, thereby improving the accuracy of target recognition, optimizing the classification process, and ensuring a more accurate distinction effect. In terms of path planning optimization, the spatial characteristics of the metal mine blasting device are analyzed and combined with real-time environmental data, such as water flow velocity and wind speed, to dynamically adjust the path planning, thereby achieving dual optimization of cost and efficiency. The method of comprehensive consideration of data analysis and environmental factors effectively reduces the operation risk and improves the efficiency of mine sweeping operations. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] Figure 1 is a system flow chart of the present invention;

[0059] Figure 2 This is a flow chart of the present invention for eliminating the influence of environmental noise;

[0060] Figure 3 A flow chart of obtaining magnetic field distribution information of the present invention;

[0061] Figure 4 Flowchart for marking potential abnormal areas for the present invention;

[0062] Figure 5 A flow chart for obtaining target distribution classification results for the present invention;

[0063] Figure 6 A flow chart for constructing a basic minesweeping path for the present invention;

[0064] Figure 7 A flow chart for obtaining an optimized path sequence for the present invention;

[0065] Figure 8 A flow chart of obtaining path coverage verification results of the present invention. DETAILED DESCRIPTION

[0066] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0067] In the description of the present invention, it should be understood that the terms "length", "width", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", etc., indicating positions or positional relationships, are based on the positions 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 orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present invention. In addition, in the description of the present invention, the meaning of "multiple" is two or more, unless otherwise clearly and specifically defined.

[0068] See also Figure 1 The present invention provides a technical solution: a path planning and optimization system for offshore wind power minesweeping operations, the system comprising:

[0069] The magnetic anomaly data processing module is based on marine magnetometer equipment, which detects the magnetic field strength value of the operating area in real time, screens the magnetic field data of each measuring point, eliminates the influence of environmental noise, performs spatial correlation analysis on the magnetic field data, matches the magnetic field strength with the geographical location, reconstructs the spatial distribution of the matched data, constructs a continuous magnetic field distribution map, and obtains the magnetic field distribution information;

[0070] The target recognition and classification module uses the gradient analysis method to detect abnormal points in the magnetic field data based on the magnetic field distribution information. By comparing the magnetic field intensity gradients of adjacent data points, potential abnormal areas are marked, and signal features are extracted from the marked abnormal points. The magnetic dipole strength and distribution characteristics of each abnormal point are extracted, and the abnormal points are classified by comparing with the preset target physical property database to distinguish between metal explosive devices and abandoned metal objects, and obtain the target distribution classification results.

[0071] The path planning optimization module analyzes the spatial distribution characteristics of metal mine explosive devices based on the target distribution classification results, integrates the geographical location data of each target point, compares and analyzes the boundaries of the wind power area, analyzes the shortest distance and the optimal access sequence, constructs the basic minesweeping path, and analyzes the path cost based on real-time environmental data, including water flow velocity and wind speed, selects the target path, and obtains the optimized path sequence;

[0072] The path simulation verification module is based on the optimized path sequence. It loads the spatial data of each path node and target point in the simulation environment, performs coverage analysis, verifies whether the path of each node covers the target area, adjusts the path nodes for the uncovered areas, optimizes the target points to be effectively covered, and obtains the path coverage verification results.

[0073] The magnetic field distribution information includes magnetic field strength information and geographic coordinate information. The target distribution classification results include metal explosive device identification information, abandoned metal object identification information and magnetic dipole characteristics. The optimized path sequence includes path cost evaluation information, safety level evaluation information and environmental adaptability adjustment information. The path coverage verification results include coverage integrity analysis, node adjustment records and target coverage status.

[0074] See also Figure 2 , the steps to remove the influence of environmental noise are:

[0075] Based on the marine magnetometer equipment, the magnetic field strength value of the operating area is detected in real time, the basic magnetic field strength data of each measuring point is collected, and the basic magnetic field data record is obtained;

[0076] Based on marine magnetometer equipment, high-precision magnetometers are used to determine the magnetic characteristics of metals or geological structures. The magnetometer can capture the magnetic field data of each measuring point. The positioning accuracy and measurement frequency of the magnetometer are key parameters and must be precisely adjusted to adapt to different depths and ocean conditions. In actual operation, the equipment is usually set on the hull and scans on a predetermined route. The selection of sampling points is based on a pre-set grid pattern to ensure coverage of specific areas. The sampling frequency and the operating speed of the equipment will be adjusted according to the complexity of the ocean fluidity and the seabed geology to ensure high-quality data. The initial processing of the data includes basic cleaning and correction to eliminate the magnetic field interference of the equipment itself and other potential sources of error.

[0077] Based on the basic magnetic field data record, the basic magnetic field data is signal processed, and low-frequency and high-frequency environmental noises are removed by using a bandpass filter to obtain filtered magnetic field data;

[0078] The collected basic magnetic field data needs to be processed to filter out noise. The bandpass filter technology is mainly used in the process. The bandpass filter allows signals in a specific frequency range to pass through while blocking other frequency signals. This is crucial to improving data quality. In actual operation, it is necessary to determine the low-frequency and high-frequency cutoff thresholds for filtering. The thresholds are set according to the characteristics of the magnetic field data and the noise data. For example, if the target magnetic field signal is mainly concentrated between 0.1Hz and 10Hz, the low-frequency and high-frequency thresholds may be set to 0.05Hz and 15Hz respectively. The setting of the threshold is based on field data analysis and pre-conducted frequency response tests. In this way, low-frequency noise caused by natural environmental factors such as waves and wind speed changes and high-frequency noise that may be caused by improper operation of electronic equipment can be effectively removed, thereby ensuring the accuracy and reliability of the data.

[0079] Based on the filtered magnetic field data, using the formula:

[0080] ;

[0081] Calculate the calibrated magnetic field strength data to obtain optimized magnetic field data;

[0082] in, Represents the basic magnetic field strength data, Represents the average noise value of the ambient noise, Indicates the calibrated magnetic field strength data;

[0083] formula:

[0084] ;

[0085] The benefit of the formula is that, through the application of the formula, the pure magnetic field signal can be accurately extracted from the original magnetic field data containing noise. represents the magnetic field data after bandpass filtering, is the average value of the ambient noise measured without the influence of magnetic field source, and Indicates the calibrated magnetic field strength data.

[0086] Detailed explanation of the formula and the process of formula calculation and derivation:

[0087] Assume that the raw magnetic field data collected from the ocean magnetometer After being processed by a bandpass filter, The data example is (Unit: nT, nanotesla). Under the same measurement conditions, the measurement data of environmental noise for (Unit: nT). Find the average nT.

[0088] Calculation process:

[0089] beg The average value of:

[0090] ;

[0091] calculate :

[0092] when nT: ;

[0093] when nT: ;

[0094] when nT: ;

[0095] when nT: ;

[0096] The calculation results show that through noise elimination technology, purer magnetic field signals can be effectively extracted from the original data.

[0097] See also Figure 3 , the steps to obtain the magnetic field distribution information are:

[0098] Based on the optimized magnetic field data, the geographic information system technology is used to combine the magnetic field intensity information with the geographical location data of the corresponding measurement points to obtain the magnetic field data matching the geographical location;

[0099] GIS technology matches the magnetic field strength data of each measurement point with its geographic location information based on a coordinate matching algorithm. The key operations in the process include the selection and conversion of coordinate systems, data synchronization, and error correction. In actual implementation, magnetic field data is usually provided in longitude and latitude format, and the GIS needs to select WGS84 or other geographic coordinate systems according to project requirements. Coordinate conversion involves mathematical conversion formulas and geophysical models. The model takes into account the influence of the irregular shape of the earth and local geographic features to ensure accurate data correspondence. The spatial database in the GIS stores and manages this data to provide support for subsequent analysis and visualization. Through professional GIS software, the data will be visualized to provide users with an intuitive magnetic field distribution map that matches the geographic location.

[0100] Based on the magnetic field data matched with the geographical location, the spatial correlation analysis is performed on the magnetic field data matched with the geographical location to detect the location points where the magnetic field strength is missing. The magnetic field data of the neighboring location points is used through the formula:

[0101] ;

[0102] Calculate the magnetic field strength value of the interpolation point;

[0103] in, represents the magnetic field strength value at the interpolation point, Indicates The magnetic field strength at each location, Indicates The spatial distance between the position and the interpolation point, is the smoothing parameter, Indicates the total number of neighboring location points;

[0104] formula:

[0105] ;

[0106] The benefit of the formula is that it optimizes the magnetic field data through spatial interpolation methods, enhances the continuity and integrity of the data, and can effectively estimate reasonable magnetic field intensity values ​​for geographical locations with missing data. represents the magnetic field strength value at the interpolation point, Indicates The magnetic field strength at a nearby location, It is The spatial distance from the location point to the interpolation point, is a smoothing parameter that controls the strength of the distance effect. is the total number of neighboring location points.

[0107] Detailed explanation of the formula and the process of formula calculation and derivation:

[0108] For each geographical location with missing data, select neighboring points within a certain range and calculate the magnetic field strength of each neighboring point. and distance For measurement, there are 4 adjacent points with magnetic field strengths of nT, the distances are m, smoothing parameter Set to 10 meters, for each ,calculate , the specific calculation is as follows:

[0109] ;

[0110] The results show that through the above calculation method, a more reasonable magnetic field intensity value can be obtained after smoothing, which helps to fill data gaps, ensure the spatial continuity and integrity of magnetic field data, and provide a basis for further geographic magnetic field analysis.

[0111] Based on the magnetic field intensity value of the interpolation point and the magnetic field data matched with the geographical location, a spatially continuous magnetic field distribution map is constructed to obtain the magnetic field distribution information;

[0112] Geographic Information System technology uses spatial analysis tools to grid the magnetic field intensity data, and then applies a variety of visualization techniques such as color gradients, contour lines or heat maps to convert the data into easy-to-understand graphical representations. Operations include setting layer properties, adjusting visual effects, and optimizing user interaction. In practical applications, graphics are often used to display the geographical distribution characteristics of the magnetic field to help researchers identify abnormal areas. The key to the steps is to choose the appropriate rendering method and adjust the graphic parameters to achieve the best visual effects and data accuracy.

[0113] See also Figure 4 , the steps to mark potential abnormal areas are:

[0114] Based on the magnetic field distribution information, the difference in magnetic field strength between each data point and its neighboring points is analyzed, using the formula:

[0115] ;

[0116] Calculate the magnetic field gradient value between adjacent data points;

[0117] in, Representative The magnetic field strength of each data point, Representative The magnetic field strength of each data point, Represents the physical distance between two data points, is the magnetic field gradient value;

[0118] formula:

[0119] ;

[0120] The benefit of the formula is that by calculating the magnetic field gradient between adjacent data points, the exact location of underwater structure changes can be effectively identified. and Respectively represent and The magnetic field strength of each data point, is the physical distance between these two points, is the calculated magnetic field gradient value.

[0121] Detailed explanation of the formula and the process of formula calculation and derivation:

[0122] Assume that in an actual measurement scenario, there are continuous data points. nT and nT, and the distance between these two points m, the gradient is calculated as follows:

[0123] ;

[0124] The results show that this method can quantify the rate of change of magnetic field intensity between each point, thereby accurately marking potential geological or environmental anomalies.

[0125] Based on the magnetic field gradient value between adjacent data points, the data points whose magnetic field gradient value is greater than the preset gradient threshold are compared, and the potential abnormal areas are marked to obtain the abnormal area identification result;

[0126] By comparing with the preset gradient threshold, data points with magnetic field gradient values ​​greater than the preset gradient threshold are screened. The magnetic field gradient threshold needs to be set. The threshold is usually set based on historical data analysis. All calculated gradient values ​​are automatically scanned, and potential abnormal areas are marked. Using geographic information systems and data visualization tools, the marked points are highlighted on the map and provided to researchers and decision makers for analysis.

[0127] See also Figure 5 , the steps to obtain the target distribution classification results are:

[0128] Based on the abnormal area identification results, magnetic dipole analysis is performed on each marked abnormal point to extract the intensity and spatial distribution characteristics of the magnetic dipole and obtain the magnetic dipole characteristic data of the abnormal point;

[0129] Based on the abnormal area identification results, the data of the abnormal points are obtained through a high-precision magnetometer, and the magnetic dipole characteristics of each abnormal point are extracted, including intensity and spatial distribution characteristics. In the process, key technologies include the establishment of a magnetic dipole model and parameter estimation. The parameters are usually determined by physical properties such as magnetism, conductivity and geological structure. The magnetic dipole characteristic data of each abnormal point is obtained through a comprehensive geographic information system and geophysical measurement data, including spatial interpolation and modeling of magnetic field data, and the use of statistical methods to estimate dipole parameters. The analysis process also needs to consider the noise and uncertainty of the data, and use filtering and data smoothing techniques to improve data quality and analysis accuracy.

[0130] Based on the magnetic dipole characteristic data of the abnormal point, it is compared with the preset target physical property database, through the formula:

[0131] ;

[0132] Calculate feature matching scores;

[0133] in, It represents the first The eigenvalues ​​of the magnetic dipoles, Represents the first eigenvalues, For the The weight coefficient of the feature, For the The standard deviation of the feature, is the feature matching score, Indicates the total number of features;

[0134] formula:

[0135] ;

[0136] The benefit of the formula is that it can quantify the degree of match between the magnetic dipole features and the physical properties recorded in the target database, thereby effectively identifying and classifying underwater objects. represents the magnetic dipole extracted from the outlier point eigenvalues, is the first eigenvalues, is the weight coefficient of the feature, which affects the contribution of each feature in the total score. is the standard deviation, which is used to normalize the scale of feature differences to reduce the impact of dimension and range, is the calculated feature matching score.

[0137] Detailed explanation of the formula and the process of formula calculation: There are 3 features to match, corresponding to The value is [150, 200, 250], The value is [145, 205, 245], and the weight are [0.3, 0.4, 0.3] respectively, and the standard deviation is [5, 5, 5], the calculation process is as follows:

[0138] ;

[0139] It can provide a quantitative score for the similarity between the magnetic dipole characteristics of each anomaly point and the characteristics of known objects, providing a basis for identification and classification.

[0140] Based on the feature matching score, the item type with the maximum score is selected as the matching type according to the size of the score, and each abnormal point is distinguished as a metal explosive device and an abandoned metal object, and the target distribution classification result is obtained;

[0141] The computing platform calculates the feature matching score of each anomaly point, and then compares the score with the preset threshold to determine the type of object each anomaly point is most likely to belong to. This usually involves comparing and selecting multiple possibilities, such as metal explosive devices or abandoned metal objects. In this way, the types of underwater objects can be effectively distinguished, thereby enabling more accurate assessment and management of the underwater environment. In addition, this analysis result can also provide decision support for geological exploration, environmental monitoring or military applications. The classification process relies on data analysis technology and decision-making algorithms to ensure the accuracy and reliability of the results.

[0142] See also Figure 6 , the steps to construct a basic minesweeper path are:

[0143] Based on the target distribution classification results, the location of each metal explosive device is extracted, and the corresponding geographical location data is integrated to obtain the spatial distribution information of the metal explosive device;

[0144] The integration process of geographic location data includes collecting the precise coordinates of each marked metal explosive device, which is usually completed by integrating with existing map data in the geographic information system. The point data is converted into a visual spatial distribution map using geographic information system software, which helps to analyze and understand the spatial layout of the explosive devices. It includes data quality control and coordinate accuracy correction to ensure that the extracted location information is accurate. The acquisition of spatial distribution information is achieved through systematic archiving and analysis of geographic coordinates, including basic data input and editing, and complex spatial data processing and analysis techniques.

[0145] Based on the spatial distribution information of metal explosive devices and compared with the boundary data of wind power areas, the formula is:

[0146] ;

[0147] Calculate the total distance of the shortest path;

[0148] in, is the total distance of the shortest path, For the The geographical coordinates of the nodes, represents the total number of nodes in the path, , for The geographical coordinates of the nodes;

[0149] formula:

[0150] ;

[0151] The benefit of the formula is that by calculating the shortest distance of all possible paths, the most economical path can be effectively planned, reducing the consumption of resources and time. and Indicates The geographical coordinates of the nodes, and and For the The geographical coordinates of the nodes, is the total number of nodes in the path.

[0152] Detailed explanation of the formula and the process of formula calculation: Assume that the coordinates of the three nodes are (1, 2), (3, 5), and (6, 7), then the path calculation process is as follows:

[0153] ;

[0154] ;

[0155] ;

[0156] The results show that obtaining the total length of the shortest path connecting the points is helpful for planning an effective path to ensure that all key areas are covered without duplication, providing a scientific path planning basis for subsequent mine clearance or monitoring activities.

[0157] Based on the total distance of the shortest path, the corresponding node access order is extracted, and the key turning points and must-go nodes are marked to obtain the basic minesweeping path construction information;

[0158] Using the path calculation results, geographic information systems and path planning software can automatically extract the optimal order for visiting nodes. The software can also identify and mark key turning points and must-go nodes in the path. Nodes are usually key in path decisions. For example, turning points may involve road intersections, terrain obstacles or other important landmarks. Must-go nodes are points that must be passed in the path, such as important monitoring areas or specific geographical structures. The analysis also includes fine-tuning the path to avoid potentially dangerous areas or unstable terrain. The minesweeping or monitoring path generated by the process not only takes into account efficiency, but also safety and feasibility to ensure the successful execution of the operation.

[0159] See also Figure 7 , the steps to obtain the optimized path sequence are:

[0160] Based on the basic minesweeping path construction information, collect real-time environmental data of the target sea area, including water flow speed and wind speed, and obtain environmental impact data;

[0161] The collection of environmental data includes real-time monitoring of water flow and wind speed, which is usually completed through various sensor networks installed in the sea area. The sensors can transmit data to the central monitoring platform in real time. The collected data needs to undergo preliminary processing, including data cleaning and calibration, to ensure its accuracy and reliability. After data processing, the environmental impact data obtained will provide key input for path optimization. The data is not only used to understand the current environmental conditions, but also to predict environmental change trends in the short term, thereby providing a basis for path planning of minesweeping vessels.

[0162] Based on environmental impact data, through the formula:

[0163] ;

[0164] Calculate path cost;

[0165] in, represents the total number of nodes in the path, is the path cost, For the The wind speed of each node, For the The angle between the wind speed at each node and the path direction, For the The water flow velocity at each node, For the The angle between the water flow at each node and the path direction, For the best driving speed, For slave nodes arrive Length of the path segment;

[0166] formula:

[0167] ;

[0168] The benefit of the formula is that it can comprehensively consider the impact of wind speed and water flow speed on the path cost, and optimize the path to reduce energy consumption and time. and Respectively represent the nodes wind speed and water flow speed, and are the angles between wind speed and water flow and the path direction, is the optimal speed of the ship, is the distance between two consecutive nodes, detailed explanation of the formula and the derivation process of the formula calculation: Assuming that there is a path consisting of two nodes, the distance between the nodes is 100 meters, the wind speed is 2 m / s, the water flow speed is 1 m / s, the angle between the wind and water flow directions and the path direction is 45 degrees, and the optimal driving speed is 5 m / s, then the path cost is calculated as follows:

[0169] ;

[0170] The results show that the additional energy consumption and time extension caused by adverse environmental conditions can be significantly reduced by optimizing the path.

[0171] Based on the path cost, the path with the lowest cost is extracted, compared with the basic minesweeping path construction information, and the target path is selected according to the current minesweeping requirements to obtain the optimized path sequence;

[0172] By analyzing all possible path variables, the selected path will be compared with the existing basic minesweeping path to ensure that the selected path not only has the lowest cost but also meets the current minesweeping needs. In addition, the final selection of the path also needs to take into account safety and feasibility of execution, including checking whether the path passes through possible dangerous areas or sensitive ecological environments, outputting the optimized path sequence, and providing specific navigation information for minesweeping operations to ensure efficient and safe execution of the task.

[0173] See also Figure 8 , the steps to obtain the path coverage verification results are:

[0174] Based on the optimized path sequence, the optimized path sequence is imported into the simulation environment, and the spatial mapping data of the path and the target point are obtained by combining the spatial data of each node and the target point associated with the path;

[0175] The import of path sequence needs to be completed through geographic information system software. The software can process and parse path data and visualize it. Combined with the spatial data of each node and target point associated with the path, the operator needs to ensure the consistency of all data coordinates to ensure accurate mapping of spatial data. In addition, the generation of spatial mapping data of paths and target points usually involves spatial analysis functions, such as buffer analysis, intersection analysis, etc., to ensure that the relationship between each path node and target point is accurately identified and recorded. The generated mapping data is used for subsequent coverage analysis and path optimization to ensure the effectiveness of path design and the accuracy of execution.

[0176] Based on the spatial mapping data of the path and target points, coverage analysis is performed to verify whether each path node covers the predetermined target area, identify and record the uncovered areas, and obtain coverage analysis results;

[0177] Using spatial analysis tools such as the coverage analysis tool in ArcGIS, it is possible to verify whether each path node covers the predetermined target area. This involves comparing the geographic information of the target area with the geographic information of the path node to identify covered and uncovered areas. All uncovered areas will be recorded and marked for subsequent path adjustments. In addition, the coverage analysis results not only provide a basis for path optimization, but also provide support for the feasibility assessment of the final operation plan to ensure that all key target areas are effectively covered.

[0178] Based on the coverage analysis results, the position and order of the path nodes are adjusted according to the identified uncovered areas, the path is optimized, each target point is covered, and the path coverage verification result is obtained.

[0179] Based on the identified uncovered areas, path optimization software is used to reorder and adjust the positions of path nodes, which involves genetic algorithms, ant colony optimization or other heuristic algorithms to find the optimal solution. Each adjustment requires a re-analysis of the coverage range to ensure that each adjustment moves closer to the goal of comprehensive coverage. The final optimized path not only covers all target points, but also takes into account cost, time and safety to ensure the actual feasibility and efficiency of the path. The optimized path is verified through simulation again to confirm its coverage completeness and execution effectiveness. The obtained path coverage verification results provide a solid foundation for subsequent actual operations.

[0180] The above are only preferred embodiments of the present invention and are not intended to limit the present invention in other forms. Any technician familiar with the profession may use the technical contents disclosed above to change or modify them into equivalent embodiments with equivalent changes and apply them to other fields. However, any simple modification, equivalent change and modification made to the above embodiments based on the technical essence of the present invention without departing from the technical solution of the present invention still falls within the protection scope of the technical solution of the present invention.

Claims

1. The path planning and optimization system for offshore wind power minesweeping operations is characterized by: The system comprises: The magnetic anomaly data processing module is based on marine magnetometer equipment, which detects the magnetic field strength value of the operating area in real time, screens the magnetic field data of each measuring point, eliminates the influence of environmental noise, matches the magnetic field strength with the geographical location, constructs a continuous magnetic field distribution map, and obtains the magnetic field distribution information; The target recognition and classification module is based on the magnetic field distribution information, by comparing the magnetic field intensity gradients of adjacent data points, marking potential abnormal areas, extracting signal features from the marked abnormal points, and distinguishing between metal explosive devices and abandoned metal objects by comparing with a preset target physical property database to obtain target distribution classification results; The path planning optimization module analyzes the spatial distribution characteristics of metal mine explosive devices based on the target distribution classification results, integrates the geographical location data of each target point, compares and analyzes the boundaries of the wind power area, analyzes the shortest distance and the optimal access sequence, constructs a basic minesweeping path, and selects the target path according to the real-time environmental data, including water flow velocity and wind speed, to obtain an optimized path sequence; Based on the optimized path sequence, the path simulation verification module loads the spatial data of each path node and the target point in the simulation environment, performs coverage analysis, verifies whether the path of each node covers the target area, adjusts the path nodes for the uncovered areas, and obtains the path coverage verification result.

2. The path planning and optimization system for offshore wind power minesweeping operations according to claim 1 is characterized in that: The steps of eliminating the influence of environmental noise are as follows: Based on the marine magnetometer equipment, the magnetic field strength value of the operating area is detected in real time, the basic magnetic field strength data of each measuring point is collected, and the basic magnetic field data record is obtained; Based on the basic magnetic field data record, signal processing is performed on the basic magnetic field data, and low-frequency and high-frequency environmental noises are removed by using a bandpass filter to obtain filtered magnetic field data; Based on the filtered magnetic field data, using the formula: ； Calculate the calibrated magnetic field strength data to obtain optimized magnetic field data; in, Represents the basic magnetic field strength data, Represents the average noise value of the ambient noise, Indicates the calibrated magnetic field strength data.

3. The path planning and optimization system for offshore wind power minesweeping operations according to claim 2 is characterized in that: The steps for obtaining the magnetic field distribution information are: Based on the optimized magnetic field data, the magnetic field intensity information is combined with the geographical location data of the corresponding measurement point using geographic information system technology to obtain magnetic field data matching the geographical location; Based on the magnetic field data matched with the geographical location, spatial correlation analysis is performed on the magnetic field data matched with the geographical location to detect the location points where the magnetic field strength is missing, and the magnetic field data of the adjacent location points are used through the formula: ； Calculate the magnetic field strength value of the interpolation point; in, represents the magnetic field strength value at the interpolation point, Indicates The magnetic field strength at each location, Indicates The spatial distance between the position and the interpolation point, is the smoothing parameter, Indicates the total number of neighboring location points; Based on the magnetic field intensity values ​​of the interpolation points and in combination with the magnetic field data matched with the geographical location, a spatially continuous magnetic field distribution map is constructed to obtain magnetic field distribution information.

4. The path planning and optimization system for offshore wind power minesweeping operations according to claim 1 is characterized in that: The steps of marking potential abnormal areas are: Based on the magnetic field distribution information, the difference in magnetic field intensity between each data point and adjacent points is analyzed, and the formula is: ； Calculate the magnetic field gradient value between adjacent data points; in, Representative The magnetic field strength of each data point, Representative The magnetic field strength of each data point, Represents the physical distance between two data points, is the magnetic field gradient value; Based on the magnetic field gradient value between the adjacent data points, it is compared with a preset gradient threshold, and the data points whose magnetic field gradient value is greater than the preset gradient threshold are screened, and potential abnormal areas are marked to obtain abnormal area identification results.

5. The path planning and optimization system for offshore wind power minesweeping operations according to claim 4 is characterized in that: The steps for obtaining the target distribution classification result are: Based on the abnormal area identification result, a magnetic dipole analysis is performed on each marked abnormal point, the intensity and spatial distribution characteristics of the magnetic dipole are extracted, and the magnetic dipole characteristic data of the abnormal point is obtained; Based on the magnetic dipole characteristic data of the abnormal point, it is compared with the preset target physical property database, through the formula: ； Calculate feature matching scores; in, It represents the first The eigenvalues ​​of the magnetic dipoles, Represents the first eigenvalues, For the The weight coefficient of the feature, For the The standard deviation of the feature, is the feature matching score, Indicates the total number of features; Based on the feature matching score, the item type with the maximum score is selected as the matching type according to the size of the score, and each abnormal point is distinguished as a metal explosive device and an abandoned metal object, so as to obtain a target distribution classification result.

6. The path planning and optimization system for offshore wind power minesweeping operations according to claim 1 is characterized in that: The steps of constructing the basic minesweeping path are: Based on the target distribution classification result, the location of each metal explosive device is extracted, and the corresponding geographical location data is integrated to obtain the spatial distribution information of the metal explosive device; Based on the spatial distribution information of the metal lightning device, compared with the boundary data of the wind power area, the formula is: ； Calculate the total distance of the shortest path; in, is the total distance of the shortest path, For the The geographical coordinates of the nodes, represents the total number of nodes in the path, , for The geographical coordinates of the nodes; Based on the total distance of the shortest path, the corresponding node access sequence is extracted, and the key turning points and necessary nodes are marked to obtain the basic minesweeping path construction information.

7. The path planning and optimization system for offshore wind power minesweeping operations according to claim 6 is characterized in that: The steps for obtaining the optimized path sequence are: Based on the basic minesweeping path construction information, real-time environmental data of the target sea area, including water flow velocity and wind speed, are collected to obtain environmental impact data; Based on the environmental impact data, through the formula: ； Calculate path cost; in, represents the total number of nodes in the path, is the path cost, For the The wind speed of each node, For the The angle between the wind speed at each node and the path direction, For the The water flow velocity at each node, For the The angle between the water flow at each node and the path direction, For the best driving speed, For slave nodes arrive Length of the path segment; Based on the path cost, the path with the lowest cost is extracted, compared with the basic minesweeping path construction information, and the target path is selected according to the current minesweeping demand to obtain the optimized path sequence.

8. The path planning and optimization system for offshore wind power minesweeping operations according to claim 1 is characterized in that: The steps for obtaining the path coverage verification result are: Based on the optimized path sequence, the optimized path sequence is imported into a simulation environment, and spatial mapping data of the path and the target point are obtained by combining the spatial data of each node and the target point associated with the path; Based on the spatial mapping data of the path and the target point, a coverage analysis is performed to verify whether each path node covers the predetermined target area, identify and record the uncovered area, and obtain a coverage analysis result; Based on the coverage analysis results, according to the identified uncovered areas, the positions and order of the path nodes are adjusted to optimize the path, cover each target point, and obtain a path coverage verification result.

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