Method and system for identifying offshore wind power area

Abstract

The invention discloses a method and a system for identifying an offshore wind power area. The method comprises the following steps: acquiring satellite image information of a target ocean area and sound information of a plurality of ocean positions; based on a noise propagation analysis model and the sound information, determining a plurality of noise abnormal positions corresponding to the target ocean area; determining a plurality of wind power equipment positions corresponding to the target ocean area based on an image recognition algorithm and the satellite image information; and based on the noise abnormal position and the wind power equipment position, determining a wind power area in the target ocean area based on a probabilistic algorithm. Therefore, the offshore wind power area can be identified more efficiently and accurately, the cost and errors of manual identification are reduced, and a more accurate data basis is provided for subsequent environment evaluation work.

Description

Technical Field

[0001] The present invention relates to the field of data processing technology, and in particular to a method and system for identifying an offshore wind power area. Background Art

[0002] Offshore wind power uses stronger and more stable sea wind resources to promote the development of renewable energy, and is one of the important directions of the current mainstream energy transformation. However, since the establishment and operation of offshore wind farms and the changes in waterways along the way have adverse effects on the natural ecology, it is necessary to conduct effective management and environmental assessment of offshore wind farms to determine their adjustment strategies. When evaluating offshore wind farms, the existing technology generally uses manual identification and analysis based on building records to determine the offshore wind power areas in the ocean area. It does not further consider the use of multiple sensor information to accurately identify offshore wind power areas. Therefore, its recognition efficiency is low, the recognition cost is high, and the recognition accuracy is not ideal. It can be seen that the existing technology has defects that need to be solved urgently. Summary of the invention

[0003] The technical problem to be solved by the present invention is to provide a method and system for identifying offshore wind power areas, which can more efficiently and accurately identify offshore wind power areas, reduce the cost and errors of manual identification, and provide a more accurate data basis for subsequent environmental assessment work.

[0004] In order to solve the above technical problems, the first aspect of the present invention discloses a method for identifying an offshore wind power area, the method comprising: Obtain satellite image information of target ocean areas and sound information of multiple ocean locations; Based on the noise propagation analysis model and the sound information, determining multiple noise anomaly locations corresponding to the target ocean area; Based on the image recognition algorithm and the satellite image information, determine the locations of multiple wind power equipment corresponding to the target ocean area; Based on the abnormal noise location and the wind power equipment location, a wind power area in the target ocean area is determined based on a probability algorithm.

[0005] As an optional implementation, in the first aspect of the present invention, the determining of multiple noise anomaly locations corresponding to the target ocean area based on the noise propagation analysis model and the sound information includes: Based on a preset continuous sound change rule, grouping all the ocean locations according to the sound information corresponding to each of the ocean locations to obtain a plurality of continuous sound location groups; Inputting the sound information corresponding to all the ocean positions in each of the continuous sound position groups into a trained noise prediction neural network to obtain the noise prediction probability corresponding to each of the continuous sound position groups; the noise prediction neural network is trained by a training data set including a plurality of training sound groups and corresponding labels of whether they are wind power equipment noise; Screening out all the continuous sound position groups whose noise prediction probability is greater than a preset first probability threshold to obtain a plurality of high-risk position groups; According to the multiple high-risk location groups, multiple noise abnormality locations corresponding to the target ocean area are determined.

[0006] As an optional implementation, in the first aspect of the present invention, based on the preset continuous sound change rule, all the ocean locations are grouped according to the sound information corresponding to each ocean location to obtain a plurality of continuous sound location groups, including: For any two adjacent ocean locations, calculating the location distance between the two ocean locations; Calculating the difference in sound parameters between the sound information corresponding to the two ocean locations; Calculating the ratio between the sound parameter difference and the position distance to obtain the sound change parameters corresponding to the two ocean positions; Based on a dynamic programming algorithm, all the ocean positions are grouped and iteratively calculated based on the sound change parameters to obtain a plurality of continuous sound position groups.

[0007] As an optional implementation, in the first aspect of the present invention, the method is based on a dynamic programming algorithm, and all the ocean positions are grouped and iterated based on the sound change parameters to obtain a plurality of continuous sound position groups, including: Setting the objective function to maximize the total number of ocean locations in each continuous sound location group; The restriction conditions are set to include that the curvature of the lowest curvature line segment formed by connecting all the ocean positions included in each continuous sound position group is less than a preset curvature threshold, the sound change parameters of any two ocean positions in each continuous sound position group are greater than a first parameter threshold, and the sound propagation prediction probability corresponding to each continuous sound position group is greater than a preset second probability threshold; the sound propagation prediction probability is obtained by inputting a parameter sequence of the sound change parameters corresponding to all the ocean positions in the continuous sound position group arranged in position order into a trained sound propagation prediction neural network; the noise prediction neural network is obtained by including a plurality of training sound change parameter sequences and a corresponding training data set labeled as to whether the sound is continuously propagated; Based on a dynamic programming algorithm, according to the objective function and the constraint conditions, all the ocean positions are grouped and iteratively calculated until convergence, so as to obtain a plurality of continuous sound position groups.

[0008] As an optional implementation, in the first aspect of the present invention, determining a plurality of noise anomaly locations corresponding to the target ocean area according to the plurality of high-risk location groups includes: Connect all the ocean locations included in each high-risk location group with the lowest curvature to obtain a high-risk line segment corresponding to each high-risk location group; Calculate the intersections of all the high-risk line segments to obtain multiple intersection points; For each of the intersection points, calculating the number of line segments of the high-risk line segment where the intersection point is located; Calculate the average number of ocean positions corresponding to the high-risk position group corresponding to all the high-risk line segments where the intersection point is located, and obtain a position quantity parameter corresponding to the intersection point; Calculate the weighted average value of the number of line segments and the number of positions to obtain an abnormal parameter corresponding to the intersection; All the intersections whose abnormal parameters are greater than the second parameter threshold are screened out to obtain a plurality of noise abnormality positions.

[0009] As an optional implementation, in the first aspect of the present invention, the determining the locations of multiple wind power equipment corresponding to the target ocean area based on the image recognition algorithm and the satellite image information includes: Inputting the satellite image information into a trained wind power equipment area segmentation algorithm model to obtain a plurality of wind power equipment image parts corresponding to the satellite image information and corresponding image prediction probabilities; the wind power equipment area segmentation algorithm model is trained by a training data set including a plurality of training satellite image information and corresponding wind power equipment area annotations; The actual coordinate position corresponding to the position of each wind power equipment image part in the satellite image information is determined as the corresponding wind power equipment position.

[0010] As an optional implementation, in the first aspect of the present invention, the determining the wind power area in the target ocean area based on the abnormal noise location and the wind power equipment location based on a probability algorithm includes: For each wind power equipment location, determine a circular area with a preset radius value with the wind power equipment location as the center, and obtain an equipment range area corresponding to the wind power equipment location; Calculating the probability of a wind power area corresponding to the wind power equipment location according to the abnormal noise location being within the equipment range; The equipment range areas corresponding to the wind power equipment locations whose wind power area probabilities are greater than a third probability threshold are determined as wind power areas in the target ocean area.

[0011] As an optional implementation, in the first aspect of the present invention, the calculating the probability of a wind power area corresponding to the location of the wind power equipment according to the abnormal noise location being within the equipment range area includes: Counting the number of the abnormal noise locations within the equipment range to obtain the noise location parameters corresponding to the wind power equipment location; Calculate the average value of the abnormal parameters corresponding to all the abnormal noise positions within the equipment range to obtain the abnormal noise parameter corresponding to the wind power equipment position; The product of the image prediction probability corresponding to the wind power equipment image portion corresponding to the wind power equipment position, the noise position parameter and the noise anomaly parameter is calculated to obtain the wind power area probability corresponding to the wind power equipment position.

[0012] A second aspect of an embodiment of the present invention discloses a system for identifying an offshore wind power area, the system comprising: An acquisition module, used to acquire satellite image information of a target ocean area and sound information of multiple ocean locations; A first determination module is used to determine a plurality of noise anomaly locations corresponding to the target ocean area based on a noise propagation analysis model and the sound information; A second determination module is used to determine the locations of multiple wind power equipment corresponding to the target ocean area based on an image recognition algorithm and the satellite image information; The third determination module is used to determine the wind power area in the target ocean area based on the abnormal noise location and the wind power equipment location and based on a probability algorithm.

[0013] As an optional implementation, in the second aspect of the present invention, the first determination module determines the specific manner of the multiple noise anomaly locations corresponding to the target ocean area based on the noise propagation analysis model and the sound information, including: Based on a preset continuous sound change rule, grouping all the ocean locations according to the sound information corresponding to each of the ocean locations to obtain a plurality of continuous sound location groups; Inputting the sound information corresponding to all the ocean positions in each of the continuous sound position groups into a trained noise prediction neural network to obtain the noise prediction probability corresponding to each of the continuous sound position groups; the noise prediction neural network is trained by a training data set including a plurality of training sound groups and corresponding labels of whether they are wind power equipment noise; Screening out all the continuous sound position groups whose noise prediction probability is greater than a preset first probability threshold to obtain a plurality of high-risk position groups; According to the multiple high-risk location groups, multiple noise abnormality locations corresponding to the target ocean area are determined.

[0014] As an optional implementation, in the second aspect of the present invention, the first determination module groups all the ocean locations based on the preset continuous sound change rule according to the sound information corresponding to each ocean location to obtain a specific manner of multiple continuous sound location groups, including: For any two adjacent ocean locations, calculating the location distance between the two ocean locations; Calculating the difference in sound parameters between the sound information corresponding to the two ocean locations; Calculating the ratio between the sound parameter difference and the position distance to obtain the sound change parameters corresponding to the two ocean positions; Based on a dynamic programming algorithm, all the ocean positions are grouped and iteratively calculated based on the sound change parameters to obtain a plurality of continuous sound position groups.

[0015] As an optional implementation, in the second aspect of the present invention, the first determination module performs grouping and iterative calculations on all the ocean positions based on the sound change parameters based on a dynamic programming algorithm to obtain a specific method of multiple continuous sound position groups, including: Setting the objective function to maximize the total number of ocean locations in each continuous sound location group; The restriction conditions are set to include that the curvature of the lowest curvature line segment formed by connecting all the ocean positions included in each continuous sound position group is less than a preset curvature threshold, the sound change parameters of any two ocean positions in each continuous sound position group are greater than a first parameter threshold, and the sound propagation prediction probability corresponding to each continuous sound position group is greater than a preset second probability threshold; the sound propagation prediction probability is obtained by inputting a parameter sequence of the sound change parameters corresponding to all the ocean positions in the continuous sound position group arranged in position order into a trained sound propagation prediction neural network; the noise prediction neural network is obtained by including a plurality of training sound change parameter sequences and a corresponding training data set labeled as to whether the sound is continuously propagated; Based on a dynamic programming algorithm, according to the objective function and the constraint conditions, all the ocean positions are grouped and iteratively calculated until convergence, so as to obtain a plurality of continuous sound position groups.

[0016] As an optional implementation, in the second aspect of the present invention, the specific manner in which the first determination module determines the multiple noise abnormality locations corresponding to the target ocean area according to the multiple high-risk location groups includes: Connect all the ocean locations included in each high-risk location group with the lowest curvature to obtain a high-risk line segment corresponding to each high-risk location group; Calculate the intersections of all the high-risk line segments to obtain multiple intersection points; For each of the intersection points, calculating the number of line segments of the high-risk line segment where the intersection point is located; Calculate the average number of ocean positions corresponding to the high-risk position group corresponding to all the high-risk line segments where the intersection point is located, and obtain a position quantity parameter corresponding to the intersection point; Calculate the weighted average value of the number of line segments and the number of positions to obtain an abnormal parameter corresponding to the intersection; All the intersections whose abnormal parameters are greater than the second parameter threshold are screened out to obtain a plurality of noise abnormality positions.

[0017] As an optional implementation, in the second aspect of the present invention, the specific manner in which the second determination module determines the locations of the multiple wind power equipment corresponding to the target ocean area based on the image recognition algorithm and the satellite image information includes: Inputting the satellite image information into a trained wind power equipment area segmentation algorithm model to obtain a plurality of wind power equipment image parts corresponding to the satellite image information and corresponding image prediction probabilities; the wind power equipment area segmentation algorithm model is trained by a training data set including a plurality of training satellite image information and corresponding wind power equipment area annotations; The actual coordinate position corresponding to the position of each wind power equipment image part in the satellite image information is determined as the corresponding wind power equipment position.

[0018] As an optional implementation, in the second aspect of the present invention, the third determination module determines the specific manner of the wind power area in the target ocean area based on the abnormal noise location and the wind power equipment location based on a probability algorithm, including: For each wind power equipment location, determine a circular area with a preset radius value with the wind power equipment location as the center, and obtain an equipment range area corresponding to the wind power equipment location; Calculating the probability of a wind power area corresponding to the wind power equipment location according to the abnormal noise location being within the equipment range; The equipment range areas corresponding to the wind power equipment locations whose wind power area probabilities are greater than a third probability threshold are determined as wind power areas in the target ocean area.

[0019] As an optional implementation, in the second aspect of the present invention, the specific manner in which the third determination module calculates the probability of a wind power area corresponding to the wind power equipment location according to the abnormal noise location located within the equipment range area includes: Counting the number of the abnormal noise locations within the equipment range to obtain the noise location parameters corresponding to the wind power equipment location; Calculate the average value of the abnormal parameters corresponding to all the abnormal noise positions within the equipment range to obtain the abnormal noise parameter corresponding to the wind power equipment position; The product of the image prediction probability corresponding to the wind power equipment image portion corresponding to the wind power equipment position, the noise position parameter and the noise anomaly parameter is calculated to obtain the wind power area probability corresponding to the wind power equipment position.

[0020] A third aspect of the present invention discloses another offshore wind power area identification system, the system comprising: A memory storing executable program code; a processor coupled to the memory; The processor calls the executable program code stored in the memory to execute part or all of the steps in the offshore wind power area identification method disclosed in the first aspect of the present invention.

[0021] A fourth aspect of the present invention discloses a computer storage medium storing computer instructions, which, when called, are used to execute part or all of the steps in the offshore wind power area identification method disclosed in the first aspect of the present invention.

[0022] Compared with the prior art, the embodiments of the present invention have the following beneficial effects: The present invention can determine the location of noise anomalies based on sound information and noise propagation analysis models, determine the location of wind power equipment based on satellite image information and image recognition algorithms, and more accurately and comprehensively determine wind power areas in ocean areas based on probabilistic algorithms, thereby being able to more efficiently and accurately identify offshore wind power areas, reduce the cost and errors of manual identification, and provide a more accurate data basis for subsequent environmental assessment work. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0024] Figure 1 It is a flow chart of a method for identifying an offshore wind power area disclosed in an embodiment of the present invention.

[0025] Figure 2 It is a structural schematic diagram of an offshore wind power area identification system disclosed in an embodiment of the present invention.

[0026] Figure 3 It is a structural schematic diagram of another offshore wind power area identification system disclosed in an embodiment of the present invention. DETAILED DESCRIPTION

[0027] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, 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.

[0028] The terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish different objects, rather than to describe a specific order. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, device, product or equipment that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units that are not listed, or may optionally include other steps or units that are inherent to these processes, methods, products or equipment.

[0029] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present invention. The appearance of the phrase in various places in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0030] The present invention discloses a method and system for identifying offshore wind power areas, which can determine the location of abnormal noise based on sound information and noise propagation analysis models, determine the location of wind power equipment based on satellite image information and image recognition algorithms, and more accurately and comprehensively determine the wind power areas in the ocean area based on probability algorithms, thereby being able to more efficiently and accurately identify offshore wind power areas, reduce the cost and errors of manual identification, and provide a more accurate data basis for subsequent environmental assessment work. The following are detailed descriptions.

[0031] Embodiment 1 See also Figure 1 , Figure 1 1 is a flow chart of a method for identifying an offshore wind power area disclosed in an embodiment of the present invention. Figure 1 The offshore wind power area identification method described above can be applied to a data processing system / data processing device / data processing server (wherein the server includes a local processing server or a cloud processing server). Figure 1 As shown, the offshore wind power area identification method may include the following operations: 101. Obtain satellite image information of the target ocean area and sound information of multiple ocean locations.

[0032] 102. Based on the noise propagation analysis model and sound information, multiple noise anomaly locations corresponding to the target ocean area are determined. 103. Based on image recognition algorithms and satellite image information, determine the locations of multiple wind power equipment corresponding to the target ocean area. 104. Based on the location of the noise anomaly and the location of the wind power equipment, the wind power area in the target ocean area is determined based on a probability algorithm.

[0033] It can be seen that the above-mentioned embodiments of the invention can determine the location of noise anomalies based on sound information and noise propagation analysis models, and then determine the location of wind power equipment based on satellite image information and image recognition algorithms, and more accurately and comprehensively determine the wind power areas in the ocean area based on probabilistic algorithms, thereby being able to more efficiently and accurately identify offshore wind power areas, reduce the cost and errors of manual identification, and provide a more accurate data basis for subsequent environmental assessment work.

[0034] As an optional embodiment, in the above step, based on the noise propagation analysis model and the sound information, determining multiple noise anomaly locations corresponding to the target ocean area includes: Based on a preset continuous sound change rule, all ocean locations are grouped according to the sound information corresponding to each ocean location to obtain a plurality of continuous sound location groups; Input the sound information corresponding to all ocean positions in each continuous sound position group into a trained noise prediction neural network to obtain the noise prediction probability corresponding to each continuous sound position group; optionally, the noise prediction neural network is trained by a training data set including a plurality of training sound groups and corresponding labels of whether they are wind power equipment noise; Screening out all continuous sound position groups whose noise prediction probability is greater than a preset first probability threshold to obtain multiple high-risk position groups; Based on multiple high-risk location groups, multiple noise anomaly locations corresponding to the target ocean area are determined.

[0035] It can be seen that through the above optional embodiments, ocean locations can be grouped based on continuous sound change rules, and then the noise probability of each location group can be predicted based on a neural network to screen out high-risk location groups, so as to comprehensively and accurately determine the locations of noise anomalies, assist in more efficient and accurate identification of offshore wind power areas, reduce the cost and errors of manual identification, and provide a more accurate data basis for subsequent environmental assessment work.

[0036] As an optional embodiment, in the above steps, based on the preset continuous sound change rule, according to the sound information corresponding to each ocean position, all ocean positions are grouped to obtain multiple continuous sound position groups, including: For any two adjacent ocean locations, calculate the location distance between the two ocean locations; Calculating the difference in sound parameters between the sound information corresponding to the two ocean locations; Calculate the ratio between the difference of the sound parameters and the position distance to obtain the sound change parameters corresponding to the two ocean positions; Based on the dynamic programming algorithm, all ocean positions are grouped and iteratively calculated based on the sound change parameters to obtain multiple continuous sound position groups.

[0037] Optionally, the sound parameter difference may be the difference between sound decibel values, the difference between sound characteristic values, or the difference between sound change rates. Other characteristic values ​​that can effectively characterize the difference between sound information should also be considered to fall within the protection scope of the present invention.

[0038] It can be seen that through the above-mentioned optional embodiments, multiple continuous sound position groups can be obtained based on the calculation of sound change parameters between ocean positions and the iterative grouping of the dynamic programming algorithm, so as to comprehensively and accurately determine the abnormal noise locations in the subsequent period, assist in more efficient and accurate identification of offshore wind power areas, reduce the cost and errors of manual identification, and provide a more accurate data basis for subsequent environmental assessment work.

[0039] As an optional embodiment, in the above steps, based on the dynamic programming algorithm, all ocean positions are grouped and iteratively calculated based on the sound change parameters to obtain multiple continuous sound position groups, including: The objective function is set to maximize the total number of ocean locations in each continuous sound location group; The restriction conditions are set to include that the curvature of the lowest curvature line segment formed by connecting all ocean positions included in each continuous sound position group is less than a preset curvature threshold, the sound change parameter of any two ocean positions in each continuous sound position group is greater than a first parameter threshold, and the sound propagation prediction probability corresponding to each continuous sound position group is greater than a preset second probability threshold; optionally, the sound propagation prediction probability is obtained by inputting a parameter sequence of sound change parameters corresponding to all ocean positions in the continuous sound position group arranged in position order into a trained sound propagation prediction neural network; the noise prediction neural network is obtained by including a plurality of training sound change parameter sequences and a corresponding training data set labeled as to whether the sound is continuously propagated; Based on the dynamic programming algorithm, all ocean positions are grouped and iteratively calculated according to the objective function and constraints until convergence, and multiple continuous sound position groups are obtained.

[0040] Optionally, the dynamic programming algorithm may adopt a particle swarm evolution algorithm or a space search algorithm, or other dynamic programming algorithms capable of iterative calculation.

[0041] It can be seen that through the above-mentioned optional embodiments, multiple continuous sound position groups can be obtained based on the preset objective function and constraints and the iterative calculation grouping of the dynamic programming algorithm, so as to comprehensively and accurately determine the abnormal noise location in the subsequent process, assist in more efficient and accurate identification of offshore wind power areas, reduce the cost and errors of manual identification, and provide a more accurate data basis for subsequent environmental assessment work.

[0042] As an optional embodiment, in the above step, determining multiple noise abnormality locations corresponding to the target ocean area according to multiple high-risk location groups includes: Connect all ocean locations included in each high-risk location group with the lowest curvature to obtain a high-risk line segment corresponding to each high-risk location group; Calculate the intersections of all high-risk line segments to obtain multiple intersection points; For each intersection point, calculate the number of high-risk line segments where the intersection point is located; Calculate the average number of ocean positions corresponding to the high-risk position groups corresponding to all high-risk line segments where the intersection point is located, and obtain a position quantity parameter corresponding to the intersection point; Calculate the weighted sum average of the line segment quantity and position quantity parameters to obtain the abnormal parameter corresponding to the intersection point; All intersection points whose abnormal parameters are greater than the second parameter threshold are screened out to obtain multiple noise abnormality positions.

[0043] It can be seen that through the above-mentioned optional embodiments, the location of noise anomaly can be accurately determined based on the intersection calculation of the connecting line segments corresponding to multiple high-risk location groups, as well as the calculation and screening of multiple parameters corresponding to the intersection, thereby assisting in more efficient and accurate identification of offshore wind power areas, reducing the cost and errors of manual identification, and providing a more accurate data basis for subsequent environmental assessment work.

[0044] As an optional embodiment, in the above steps, determining the locations of multiple wind turbines corresponding to the target ocean area based on an image recognition algorithm and satellite image information includes: Inputting the satellite image information into a trained wind power equipment area segmentation algorithm model to obtain a plurality of wind power equipment image parts corresponding to the satellite image information and corresponding image prediction probabilities; optionally, the wind power equipment area segmentation algorithm model is trained by a training data set including a plurality of training satellite image information and corresponding wind power equipment area annotations; The actual coordinate position corresponding to the position of each wind power equipment image part in the satellite image information is determined as the corresponding wind power equipment position.

[0045] It can be seen that through the above-mentioned optional embodiments, it is possible to accurately determine the locations of multiple wind power equipment from satellite image information based on the trained wind power equipment area segmentation algorithm model, so as to facilitate the subsequent precise offshore wind power area, assist in more efficient and accurate identification of offshore wind power areas, reduce the cost and errors of manual identification, and provide a more accurate data basis for subsequent environmental assessment work.

[0046] As an optional embodiment, in the above steps, based on the abnormal noise location and the wind power equipment location, based on a probability algorithm, determining the wind power area in the target ocean area includes: For each wind power equipment location, determine a circular area with a preset radius value with the wind power equipment location as the center, and obtain an equipment range area corresponding to the wind power equipment location; According to the location of the abnormal noise within the equipment range, the probability of the wind power area corresponding to the location of the wind power equipment is calculated; The equipment range areas corresponding to the wind power equipment locations having all wind power area probabilities greater than the third probability threshold are determined as wind power areas in the target ocean area.

[0047] It can be seen that through the above-mentioned optional embodiments, the probability of each wind power equipment location belonging to a wind power area can be calculated based on the abnormal noise position within the range area corresponding to the location, and the wind power areas in the ocean area can be accurately screened out by combining noise factors and image factors, so as to achieve more efficient and accurate identification of offshore wind power areas, reduce the cost and errors of manual identification, and provide a more accurate data basis for subsequent environmental assessment work.

[0048] As an optional embodiment, in the above step, according to the abnormal noise position within the equipment range, calculating the probability of the wind power area corresponding to the wind power equipment position includes: Counting the number of abnormal noise locations within the equipment range to obtain noise location parameters corresponding to the wind power equipment location; Calculate the average value of abnormal parameters corresponding to all abnormal noise locations within the equipment range to obtain the abnormal noise parameter corresponding to the wind power equipment location; The image prediction probability corresponding to the wind power equipment image portion corresponding to the wind power equipment location and the product of the noise location parameter and the noise anomaly parameter are calculated to obtain the wind power area probability corresponding to the wind power equipment location.

[0049] It can be seen that through the above-mentioned optional embodiments, the probability of belonging to a wind power area can be accurately calculated based on the number of positions and abnormality levels corresponding to the noise abnormality positions in the range area corresponding to each wind power equipment position, as well as the image prediction probability of the wind power equipment position, so as to accurately screen out wind power areas in the ocean area based on this in the future, achieve more efficient and accurate identification of offshore wind power areas, reduce the cost and errors of manual identification, and provide a more accurate data basis for subsequent environmental assessment work.

[0050] Embodiment 2 See also Figure 2 , Figure 2 : is a schematic diagram of the structure of an offshore wind power area identification system disclosed in an embodiment of the present invention. Figure 2 The offshore wind power area identification system described can be applied to a data processing system / data processing device / data processing server (wherein the server includes a local processing server or a cloud processing server). Figure 2 As shown, the offshore wind power area identification system may include: The acquisition module 201 is used to acquire satellite image information of a target ocean area and sound information of multiple ocean locations.

[0051] The first determination module 202 is used to determine a plurality of noise anomaly locations corresponding to the target ocean area based on the noise propagation analysis model and the sound information. The second determination module 203 is used to determine the locations of multiple wind power equipment corresponding to the target ocean area based on an image recognition algorithm and satellite image information. The third determination module 204 is used to determine the wind power area in the target ocean area based on the abnormal noise location and the wind power equipment location and based on a probability algorithm.

[0052] It can be seen that the above-mentioned embodiments of the invention can determine the location of noise anomalies based on sound information and noise propagation analysis models, and then determine the location of wind power equipment based on satellite image information and image recognition algorithms, and more accurately and comprehensively determine the wind power areas in the ocean area based on probabilistic algorithms, thereby being able to more efficiently and accurately identify offshore wind power areas, reduce the cost and errors of manual identification, and provide a more accurate data basis for subsequent environmental assessment work.

[0053] As an optional embodiment, the first determination module determines a specific method of determining multiple noise anomaly locations corresponding to the target ocean area based on the noise propagation analysis model and the sound information, including: Based on a preset continuous sound change rule, all ocean locations are grouped according to the sound information corresponding to each ocean location to obtain a plurality of continuous sound location groups; Input the sound information corresponding to all ocean positions in each continuous sound position group into a trained noise prediction neural network to obtain the noise prediction probability corresponding to each continuous sound position group; optionally, the noise prediction neural network is trained by a training data set including a plurality of training sound groups and corresponding labels of whether they are wind power equipment noise; Screening out all continuous sound position groups whose noise prediction probability is greater than a preset first probability threshold to obtain multiple high-risk position groups; Based on multiple high-risk location groups, multiple noise anomaly locations corresponding to the target ocean area are determined.

[0054] It can be seen that through the above optional embodiments, ocean locations can be grouped based on continuous sound change rules, and then the noise probability of each location group can be predicted based on a neural network to screen out high-risk location groups, so as to comprehensively and accurately determine the locations of noise anomalies, assist in more efficient and accurate identification of offshore wind power areas, reduce the cost and errors of manual identification, and provide a more accurate data basis for subsequent environmental assessment work.

[0055] As an optional embodiment, the first determination module groups all ocean locations based on a preset continuous sound change rule according to the sound information corresponding to each ocean location to obtain a specific method of multiple continuous sound location groups, including: For any two adjacent ocean locations, calculate the location distance between the two ocean locations; Calculating the difference in sound parameters between the sound information corresponding to the two ocean locations; Calculate the ratio between the difference of the sound parameters and the position distance to obtain the sound change parameters corresponding to the two ocean positions; Based on the dynamic programming algorithm, all ocean positions are grouped and iteratively calculated based on the sound change parameters to obtain multiple continuous sound position groups.

[0056] It can be seen that through the above-mentioned optional embodiments, multiple continuous sound position groups can be obtained based on the calculation of sound change parameters between ocean positions and the iterative grouping of the dynamic programming algorithm, so as to comprehensively and accurately determine the abnormal noise locations in the subsequent period, assist in more efficient and accurate identification of offshore wind power areas, reduce the cost and errors of manual identification, and provide a more accurate data basis for subsequent environmental assessment work.

[0057] As an optional embodiment, the first determination module performs grouping and iterative calculations on all ocean positions based on the sound change parameters based on a dynamic programming algorithm to obtain a specific method of multiple continuous sound position groups, including: The objective function is set to maximize the total number of ocean locations in each continuous sound location group; The restriction conditions are set to include that the curvature of the lowest curvature line segment formed by connecting all ocean positions included in each continuous sound position group is less than a preset curvature threshold, the sound change parameter of any two ocean positions in each continuous sound position group is greater than a first parameter threshold, and the sound propagation prediction probability corresponding to each continuous sound position group is greater than a preset second probability threshold; optionally, the sound propagation prediction probability is obtained by inputting a parameter sequence of sound change parameters corresponding to all ocean positions in the continuous sound position group arranged in position order into a trained sound propagation prediction neural network; the noise prediction neural network is obtained by including a plurality of training sound change parameter sequences and a corresponding training data set labeled as to whether the sound is continuously propagated; Based on the dynamic programming algorithm, all ocean positions are grouped and iteratively calculated according to the objective function and constraints until convergence, and multiple continuous sound position groups are obtained.

[0058] It can be seen that through the above-mentioned optional embodiments, multiple continuous sound position groups can be obtained based on the preset objective function and constraints and the iterative calculation grouping of the dynamic programming algorithm, so as to comprehensively and accurately determine the abnormal noise location in the subsequent process, assist in more efficient and accurate identification of offshore wind power areas, reduce the cost and errors of manual identification, and provide a more accurate data basis for subsequent environmental assessment work.

[0059] As an optional embodiment, the specific manner in which the first determination module determines the multiple noise abnormality locations corresponding to the target ocean area according to the multiple high-risk location groups includes: Connect all ocean locations included in each high-risk location group with the lowest curvature to obtain a high-risk line segment corresponding to each high-risk location group; Calculate the intersections of all high-risk line segments to obtain multiple intersection points; For each intersection point, calculate the number of high-risk line segments where the intersection point is located; Calculate the average number of ocean positions corresponding to the high-risk position groups corresponding to all high-risk line segments where the intersection point is located, and obtain a position quantity parameter corresponding to the intersection point; Calculate the weighted sum average of the line segment quantity and position quantity parameters to obtain the abnormal parameter corresponding to the intersection point; All intersection points whose abnormal parameters are greater than the second parameter threshold are screened out to obtain multiple noise abnormality positions.

[0060] It can be seen that through the above-mentioned optional embodiments, the location of noise anomaly can be accurately determined based on the intersection calculation of the connecting line segments corresponding to multiple high-risk location groups, as well as the calculation and screening of multiple parameters corresponding to the intersection, thereby assisting in more efficient and accurate identification of offshore wind power areas, reducing the cost and errors of manual identification, and providing a more accurate data basis for subsequent environmental assessment work.

[0061] As an optional embodiment, the second determination module determines the specific manner of the locations of multiple wind power equipment corresponding to the target ocean area based on the image recognition algorithm and satellite image information, including: Inputting the satellite image information into a trained wind power equipment area segmentation algorithm model to obtain a plurality of wind power equipment image parts corresponding to the satellite image information and corresponding image prediction probabilities; optionally, the wind power equipment area segmentation algorithm model is trained by a training data set including a plurality of training satellite image information and corresponding wind power equipment area annotations; The actual coordinate position corresponding to the position of each wind power equipment image part in the satellite image information is determined as the corresponding wind power equipment position.

[0062] It can be seen that through the above-mentioned optional embodiments, it is possible to accurately determine the locations of multiple wind power equipment from satellite image information based on the trained wind power equipment area segmentation algorithm model, so as to facilitate the subsequent precise offshore wind power area, assist in more efficient and accurate identification of offshore wind power areas, reduce the cost and errors of manual identification, and provide a more accurate data basis for subsequent environmental assessment work.

[0063] As an optional embodiment, the third determination module determines the specific manner of the wind power area in the target ocean area based on the abnormal noise location and the wind power equipment location and the probability algorithm, including: For each wind power equipment location, determine a circular area with a preset radius value with the wind power equipment location as the center, and obtain an equipment range area corresponding to the wind power equipment location; According to the location of the abnormal noise within the equipment range, the probability of the wind power area corresponding to the location of the wind power equipment is calculated; The equipment range areas corresponding to the wind power equipment locations having all wind power area probabilities greater than the third probability threshold are determined as wind power areas in the target ocean area.

[0064] It can be seen that through the above-mentioned optional embodiments, the probability of each wind power equipment location belonging to a wind power area can be calculated based on the abnormal noise position within the range area corresponding to the location, and the wind power areas in the ocean area can be accurately screened out by combining noise factors and image factors, so as to achieve more efficient and accurate identification of offshore wind power areas, reduce the cost and errors of manual identification, and provide a more accurate data basis for subsequent environmental assessment work.

[0065] As an optional embodiment, the specific manner in which the third determination module calculates the probability of a wind power area corresponding to the wind power equipment location according to the abnormal noise location within the equipment range area includes: Counting the number of abnormal noise locations within the equipment range to obtain noise location parameters corresponding to the wind power equipment location; Calculate the average value of abnormal parameters corresponding to all abnormal noise locations within the equipment range to obtain the abnormal noise parameter corresponding to the wind power equipment location; The image prediction probability corresponding to the wind power equipment image portion corresponding to the wind power equipment location and the product of the noise location parameter and the noise anomaly parameter are calculated to obtain the wind power area probability corresponding to the wind power equipment location.

[0066] It can be seen that through the above-mentioned optional embodiments, the probability of belonging to a wind power area can be accurately calculated based on the number of positions and abnormality levels corresponding to the noise abnormality positions in the range area corresponding to each wind power equipment position, as well as the image prediction probability of the wind power equipment position, so as to accurately screen out wind power areas in the ocean area based on this in the future, achieve more efficient and accurate identification of offshore wind power areas, reduce the cost and errors of manual identification, and provide a more accurate data basis for subsequent environmental assessment work.

[0067] Embodiment 3 See also Figure 3 , Figure 3 This is another offshore wind power area identification system disclosed in an embodiment of the present invention. Figure 3 The offshore wind power area identification system described is applied to a data processing system / data processing device / data processing server (wherein the server includes a local processing server or a cloud processing server). Figure 3 As shown, the offshore wind power area identification system may include: A memory 301 storing executable program codes; a processor 302 coupled to the memory 301; The processor 302 calls the executable program code stored in the memory 301 to execute the steps of the offshore wind power area identification method described in the first embodiment.

[0068] Embodiment 4 An embodiment of the present invention discloses a computer-readable storage medium storing a computer program for electronic data exchange, wherein the computer program enables a computer to execute the steps of the offshore wind power area identification method described in the first embodiment.

[0069] Embodiment 5 An embodiment of the present invention discloses a computer program product, which includes a non-transitory computer-readable storage medium storing a computer program, and the computer program is operable to enable a computer to execute the steps of the offshore wind power area identification method described in the first embodiment.

[0070] The above describes specific embodiments of the present specification, and other embodiments are within the scope of the appended claims. In some cases, the actions or steps recorded in the claims can be performed in an order different from that in the embodiments and still achieve the desired results. In addition, the processes depicted in the accompanying drawings do not necessarily have to be performed in the specific order or sequential order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0071] The systems, devices, modules or units described in the above embodiments may be implemented by computer chips or entities, or by products with certain functions. A typical implementation device is a computer. Specifically, the computer may be, for example, a personal computer, a laptop computer, a cellular phone, a camera phone, a smart phone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or a combination of any of these devices.

[0072] For the convenience of description, the above device is described in various units according to their functions. Of course, when implementing this specification, the functions of each unit can be implemented in the same or multiple software and / or hardware.

[0073] Those skilled in the art will appreciate that the embodiments of this specification may be provided as methods, systems, or computer program products. Therefore, the embodiments of this specification may be in the form of complete hardware embodiments, complete software embodiments, or embodiments in combination with software and hardware. Moreover, the embodiments of this specification may be in the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.

[0074] This specification is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of this specification. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0075] These computer program instructions may also be stored in a computer readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture including an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0076] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0077] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0078] The memory may include non-permanent storage in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0079] Computer readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include temporary computer readable media (transitory media), such as modulated data signals and carrier waves.

[0080] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0081] This specification may be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform specific tasks or implement specific abstract data types. This specification may also be practiced in distributed computing environments where tasks are performed by remote processing devices connected through a communication network. In a distributed computing environment, program modules may be located in local and remote computer storage media, including storage devices.

[0082] Each embodiment in this specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the system embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0083] Finally, it should be noted that the offshore wind power area identification method and system disclosed in the embodiment of the present invention discloses only the preferred embodiments of the present invention, which are only used to illustrate the technical solution of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, ordinary technicians in this field should understand that it is still possible to modify the technical solutions recorded in the aforementioned embodiments, or to replace some of the technical features therein by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims

1. A method for identifying an offshore wind power area, characterized in that: The method comprises: Obtain satellite image information of target ocean areas and sound information of multiple ocean locations; Based on the noise propagation analysis model and the sound information, determining multiple noise anomaly locations corresponding to the target ocean area; Based on the image recognition algorithm and the satellite image information, determine the locations of multiple wind power equipment corresponding to the target ocean area; Based on the abnormal noise location and the wind power equipment location, a wind power area in the target ocean area is determined based on a probability algorithm.

2. The offshore wind power area identification method according to claim 1, characterized in that: The step of determining a plurality of noise anomaly locations corresponding to the target ocean area based on the noise propagation analysis model and the sound information includes: Based on a preset continuous sound change rule, grouping all the ocean locations according to the sound information corresponding to each of the ocean locations to obtain a plurality of continuous sound location groups; Inputting the sound information corresponding to all the ocean positions in each of the continuous sound position groups into a trained noise prediction neural network to obtain the noise prediction probability corresponding to each of the continuous sound position groups; the noise prediction neural network is trained by a training data set including a plurality of training sound groups and corresponding labels of whether they are wind power equipment noise; Screening out all the continuous sound position groups whose noise prediction probability is greater than a preset first probability threshold to obtain a plurality of high-risk position groups; According to the multiple high-risk location groups, multiple noise abnormality locations corresponding to the target ocean area are determined.

3. The offshore wind power area identification method according to claim 2, characterized in that: The method of grouping all the ocean locations based on the preset continuous sound change rule according to the sound information corresponding to each ocean location to obtain a plurality of continuous sound location groups includes: For any two adjacent ocean locations, calculating the location distance between the two ocean locations; Calculating the difference in sound parameters between the sound information corresponding to the two ocean locations; Calculating the ratio between the sound parameter difference and the position distance to obtain the sound change parameters corresponding to the two ocean positions; Based on a dynamic programming algorithm, all the ocean positions are grouped and iteratively calculated based on the sound change parameters to obtain a plurality of continuous sound position groups.

4. The offshore wind power area identification method according to claim 3, characterized in that: The method of performing grouping and iterative calculation on all the ocean positions based on the sound change parameters based on the dynamic programming algorithm to obtain a plurality of continuous sound position groups includes: Setting the objective function to maximize the total number of ocean locations in each continuous sound location group; The restriction conditions are set to include that the curvature of the lowest curvature line segment formed by connecting all the ocean positions included in each continuous sound position group is less than a preset curvature threshold, the sound change parameters of any two ocean positions in each continuous sound position group are greater than a first parameter threshold, and the sound propagation prediction probability corresponding to each continuous sound position group is greater than a preset second probability threshold; the sound propagation prediction probability is obtained by inputting a parameter sequence of the sound change parameters corresponding to all the ocean positions in the continuous sound position group arranged in position order into a trained sound propagation prediction neural network; the noise prediction neural network is obtained by including a plurality of training sound change parameter sequences and a corresponding training data set labeled as to whether the sound is continuously propagated; Based on a dynamic programming algorithm, according to the objective function and the constraint conditions, all the ocean positions are grouped and iteratively calculated until convergence, so as to obtain a plurality of continuous sound position groups.

5. The offshore wind power area identification method according to claim 2, characterized in that: Determining a plurality of abnormal noise locations corresponding to the target ocean area according to the plurality of high-risk location groups includes: Connect all the ocean locations included in each high-risk location group with the lowest curvature to obtain a high-risk line segment corresponding to each high-risk location group; Calculate the intersections of all the high-risk line segments to obtain multiple intersection points; For each of the intersection points, calculating the number of line segments of the high-risk line segment where the intersection point is located; Calculate the average number of ocean positions corresponding to the high-risk position group corresponding to all the high-risk line segments where the intersection point is located, and obtain a position quantity parameter corresponding to the intersection point; Calculate the weighted average value of the number of line segments and the number of positions to obtain an abnormal parameter corresponding to the intersection; All the intersections whose abnormal parameters are greater than the second parameter threshold are screened out to obtain a plurality of noise abnormality positions.

6. The offshore wind power area identification method according to claim 5, characterized in that: The determining of the locations of multiple wind power equipment corresponding to the target ocean area based on the image recognition algorithm and the satellite image information includes: Inputting the satellite image information into a trained wind power equipment area segmentation algorithm model to obtain a plurality of wind power equipment image parts corresponding to the satellite image information and corresponding image prediction probabilities; the wind power equipment area segmentation algorithm model is trained by a training data set including a plurality of training satellite image information and corresponding wind power equipment area annotations; The actual coordinate position corresponding to the position of each wind power equipment image part in the satellite image information is determined as the corresponding wind power equipment position.

7. The offshore wind power area identification method according to claim 6, characterized in that: The determining of the wind power area in the target ocean area based on the abnormal noise location and the wind power equipment location and based on a probability algorithm includes: For each wind power equipment location, determine a circular area with a preset radius value with the wind power equipment location as the center, and obtain an equipment range area corresponding to the wind power equipment location; Calculating the probability of a wind power area corresponding to the wind power equipment location according to the abnormal noise location being within the equipment range; The equipment range areas corresponding to the wind power equipment locations whose wind power area probabilities are greater than a third probability threshold are determined as wind power areas in the target ocean area.

8. The offshore wind power area identification method according to claim 7, characterized in that: The calculating the probability of a wind power area corresponding to the wind power equipment location according to the abnormal noise location being within the equipment range area includes: Counting the number of the abnormal noise locations within the equipment range to obtain the noise location parameters corresponding to the wind power equipment location; Calculate the average value of the abnormal parameters corresponding to all the abnormal noise positions within the equipment range to obtain the abnormal noise parameter corresponding to the wind power equipment position; The product of the image prediction probability corresponding to the wind power equipment image portion corresponding to the wind power equipment position, the noise position parameter and the noise anomaly parameter is calculated to obtain the wind power area probability corresponding to the wind power equipment position.

9. An offshore wind power area identification system, characterized in that: The system comprises: An acquisition module, used to acquire satellite image information of a target ocean area and sound information of multiple ocean locations; A first determination module is used to determine a plurality of noise anomaly locations corresponding to the target ocean area based on a noise propagation analysis model and the sound information; A second determination module is used to determine the locations of multiple wind power equipment corresponding to the target ocean area based on an image recognition algorithm and the satellite image information; The third determination module is used to determine the wind power area in the target ocean area based on the abnormal noise location and the wind power equipment location and based on a probability algorithm.

10. An offshore wind power area identification system, characterized in that: The system comprises: A memory storing executable program code; a processor coupled to the memory; The processor calls the executable program code stored in the memory to execute the offshore wind power area identification method according to any one of claims 1-8.

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