Early warning method and device for floating ice around offshore photovoltaic pile, storage medium and electronic equipment

Abstract

The invention provides an early warning method for floating ice around an offshore photovoltaic pile, an early warning device for floating ice around the offshore photovoltaic pile, a computer readable storage medium and electronic equipment, and relates to the technical field of engineering safety. The method comprises the following steps: acquiring a floating ice image set around a tubular pile in an offshore photovoltaic area; determining floating ice fragment characteristics around the pipe pile according to the floating ice image set, and clustering floating ice images in the floating ice image set according to the floating ice fragment characteristics to obtain a plurality of floating ice image clusters; the edge similarity of floating ice around the pipe pile is determined according to the floating ice image clusters, and the edge recognition coefficient of floating ice fragments around the pipe pile is determined according to the floating ice fragment features and the edge similarity; determining an identification result of the floating ice fragments around the pipe pile in combination with the edge identification coefficient of the floating ice fragments; and executing floating ice early warning operation according to the floating ice fragment identification result. According to the invention, the labor cost of floating ice early warning judgment can be reduced.

Description

Technical Field

[0001] The present disclosure relates to the field of engineering safety technology, and in particular to a method for early warning of floating ice around offshore photovoltaic piles, an early warning device for early warning of floating ice around offshore photovoltaic piles, a computer-readable storage medium, and an electronic device. Background Art

[0002] In the offshore photovoltaic sector, pile-based photovoltaics (PWP) utilizes pipe piles on the sea surface to support photovoltaic modules, thereby utilizing the vast expanse of the ocean surface for solar power generation. This type of structure is currently the mainstream offshore photovoltaic structure, at least in part addressing the issue of limited land resources.

[0003] In offshore photovoltaic scenarios, low sea surface temperatures may lead to the formation of floating ice. This is especially true in areas with strong currents and winds. The floating ice around the pipe piles can break and split, forming small blocks or fragments of floating ice. The presence of floating ice can affect the normal operation and power generation efficiency of the photovoltaic system.

[0004] At present, early warning and monitoring of floating ice mainly rely on human judgment, which consumes manpower.

[0005] It should be noted that the information disclosed in the above background technology section is only used to enhance the understanding of the background of the present disclosure, and therefore may include information that does not constitute prior art known to ordinary technicians in the field. Summary of the Invention

[0006] The purpose of the present disclosure is to provide a method for warning of floating ice around offshore photovoltaic piles, a device for warning of floating ice around offshore photovoltaic piles, a computer-readable storage medium and an electronic device, thereby at least to a certain extent overcoming the problem that warning of floating ice around piles in offshore photovoltaic scenarios relies on human judgment and consumes manpower.

[0007] According to a first aspect of the present disclosure, a method for early warning of floating ice around offshore photovoltaic piles is provided, comprising: acquiring an image set of floating ice around pipe piles in an offshore photovoltaic area; determining, based on the image set of floating ice, characteristics of floating ice fragments around the pipe piles, and clustering the floating ice images in the image set of floating ice according to the characteristics of floating ice fragments to obtain a plurality of floating ice image clusters; determining, based on each of the floating ice image clusters, edge similarity of floating ice around the pipe piles, and determining, based on the characteristics of floating ice fragments and the edge similarity, edge recognition coefficients of floating ice fragments around the pipe piles; determining a floating ice fragment recognition result around the pipe piles in combination with the edge recognition coefficients of the floating ice fragments; and performing an ice warning operation based on the floating ice fragment recognition result.

[0008] Optionally, clustering the ice floe images in the ice floe image set according to the ice floe fragment features to obtain a plurality of ice floe image clusters, including: determining a plurality of initialization clustering centers according to the ice floe fragment features; determining the similarity of each ice floe image in the ice floe image set; and similarity clustering each ice floe image in the ice floe image set according to the plurality of initialization clustering centers and the similarity of each ice floe image to obtain a plurality of ice floe image clusters.

[0009] Optionally, determining the edge similarity of ice floes around the pipe pile based on each ice floe image cluster includes: extracting line features of each ice floe image in the ice floe image cluster to determine a line feature vector of each ice floe image; and determining the edge similarity of ice floes around the pipe pile based on the line feature vector.

[0010] Optionally, determining the edge recognition coefficient of the floating ice fragments around the pipe pile based on the floating ice fragment characteristics and edge similarity includes: performing linear fitting on all edge similarities to obtain an edge similarity fitting curve; determining the edge gradient of the floating ice fragments around the pipe pile based on the edge similarity fitting curve; and determining the edge recognition coefficient of the floating ice fragments around the pipe pile based on the edge gradient and the floating ice fragment characteristics.

[0011] Optionally, determining the result of identifying the floating ice fragments around the pipe pile in combination with the edge recognition coefficient of the floating ice fragments includes: determining a trend characteristic value of the floating ice fragments around the pipe pile changing over time; and determining the result of identifying the floating ice fragments around the pipe pile according to the trend characteristic value and the edge recognition coefficient.

[0012] Optionally, the floating ice fragment characteristics include floating ice fragment position characteristics and floating ice fragment size characteristics; wherein, determining the trend characteristic value of the floating ice fragments around the pipe pile changing over time includes: determining a first floating ice characteristic change sequence based on the floating ice fragment position characteristics, and converting the first floating ice characteristic change sequence into a first vector representation; determining a second floating ice characteristic change sequence based on the floating ice fragment size characteristics, and converting the second floating ice characteristic change sequence into a second vector representation; and fusing the first vector representation and the second vector representation to determine the trend characteristic value of the floating ice fragments around the pipe pile changing over time.

[0013] Optionally, the floating ice debris identification result includes the floating ice debris level and trend fluctuation entropy; wherein, determining the floating ice debris identification result around the pipe pile based on the trend characteristic value and the edge recognition coefficient includes: determining the trend fluctuation entropy based on the trend characteristic value; determining a feature data matrix using the trend characteristic value and the edge recognition coefficient, and performing a classification operation based on the feature data matrix to determine the floating ice debris level.

[0014] According to a second aspect of the present disclosure, a floating ice warning device around offshore photovoltaic piles is provided, comprising: an image acquisition module for acquiring a floating ice image set around pipe piles in an offshore photovoltaic area; an image clustering module for determining, based on the floating ice image set, features of floating ice debris around the pipe piles, and clustering the floating ice images in the floating ice image set based on the features of the floating ice debris to obtain a plurality of floating ice image clusters; an edge recognition module for determining, based on each floating ice image cluster, edge similarity of floating ice around the pipe piles, and edge recognition coefficients of floating ice debris around the pipe piles based on the features of the floating ice debris and the edge similarity; a floating ice recognition module for determining a floating ice debris recognition result around the pipe piles in combination with the edge recognition coefficients of the floating ice debris; and a floating ice warning module for executing a floating ice warning operation based on the floating ice debris recognition result.

[0015] Optionally, the image clustering module is configured to determine multiple initialization cluster centers based on the characteristics of the ice floe fragments; determine the similarity of each ice floe image in the ice floe image set; and perform similarity clustering on each ice floe image in the ice floe image set based on the multiple initialization cluster centers and the similarity of each ice floe image to obtain multiple ice floe image clusters.

[0016] Optionally, the edge recognition module is configured to extract line features of each ice floe image in the ice floe image cluster, determine a line feature vector of each ice floe image; and determine edge similarity of ice floes around the pipe pile based on the line feature vector.

[0017] Optionally, the edge recognition module is configured to perform linear fitting on all edge similarities to obtain an edge similarity fitting curve; determine the edge gradient of the floating ice fragments around the pipe pile based on the edge similarity fitting curve; and determine the edge recognition coefficient of the floating ice fragments around the pipe pile based on the edge gradient and the characteristics of the floating ice fragments.

[0018] Optionally, the ice floe identification module is configured to determine a trend characteristic value of ice floes around the pipe pile that changes over time; and determine an identification result of ice floes around the pipe pile according to the trend characteristic value and an edge identification coefficient.

[0019] Optionally, the ice floe fragment characteristics include ice floe fragment location characteristics and ice floe fragment size characteristics. In this case, the ice floe identification module is configured to determine a first ice floe feature change sequence based on the ice floe fragment location characteristics and convert the first ice floe feature change sequence into a first vector representation; determine a second ice floe feature change sequence based on the ice floe fragment size characteristics and convert the second ice floe feature change sequence into a second vector representation; and fuse the first and second vector representations to determine a trend characteristic value of ice floe fragments around the pipe pile over time.

[0020] Optionally, the ice floe identification result includes an ice floe grade and a trend fluctuation entropy. In this case, the ice floe identification module is configured to determine the trend fluctuation entropy based on the trend eigenvalue; determine a feature data matrix using the trend eigenvalue and the edge recognition coefficient; and perform a classification operation based on the feature data matrix to determine the ice floe grade.

[0021] According to a third aspect of the present disclosure, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, any of the above-mentioned floating ice warning methods around offshore photovoltaic piles is implemented.

[0022] According to a fourth aspect of the present disclosure, an electronic device is provided, comprising: a processor; and a memory for storing executable instructions of the processor; the processor is configured to implement any of the above-mentioned floating ice warning methods around offshore photovoltaic piles by executing the executable instructions.

[0023] In the technical solutions provided by some embodiments of the present disclosure, on the one hand, the entire ice floe warning process is automatically executed by a computer, significantly reducing labor costs. On the other hand, the present disclosure combines image clustering, edge analysis, and other technical means to identify ice floes with high accuracy, helping to improve the accuracy of ice floe warnings.

[0024] It is to be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] The accompanying drawings are incorporated into and constitute a part of the specification, illustrate embodiments consistent with the present disclosure, and together with the specification, are used to explain the principles of the present disclosure. Obviously, the drawings described below are only some embodiments of the present disclosure, and those skilled in the art can derive other drawings based on these drawings without inventive effort.

[0026] Figure 1 A schematic structural diagram of a floating ice warning system around offshore photovoltaic piles according to an exemplary embodiment of the present disclosure is shown.

[0027] Figure 2 The flowchart of the method for early warning of floating ice around offshore photovoltaic piles according to an exemplary embodiment of the present disclosure is schematically shown.

[0028] Figure 3 The flowchart of the process of determining the trend characteristic value according to the embodiment of the present disclosure is schematically shown.

[0029] Figure 4 The block diagram of the floating ice warning device around offshore photovoltaic piles according to an exemplary embodiment of the present disclosure is schematically shown.

[0030] Figure 5 A block diagram schematically illustrates an electronic device according to an exemplary embodiment of the present disclosure. DETAILED DESCRIPTION

[0031] Example embodiments will now be described more fully with reference to the accompanying drawings. However, example embodiments can be implemented in a variety of forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided so that the present disclosure will be more comprehensive and complete and will fully convey the concepts of the example embodiments to those skilled in the art. The described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments. In the following description, many specific details are provided to provide a full understanding of the embodiments of the present disclosure. However, those skilled in the art will appreciate that the technical solutions of the present disclosure may be practiced while omitting one or more of the specific details, or that other methods, components, devices, steps, etc. may be employed. In other cases, well-known technical solutions are not shown or described in detail to avoid obscuring various aspects of the present disclosure.

[0032] In addition, the accompanying drawings are merely schematic illustrations of the present disclosure and are not necessarily drawn to scale. Identical reference numerals in the figures denote identical or similar parts, and thus repetitive descriptions thereof will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities that do not necessarily correspond to physically or logically separate entities. These functional entities may be implemented in software, in one or more hardware modules or integrated circuits, or in different networks and / or processor devices and / or microcontroller devices.

[0033] The flowcharts shown in the accompanying drawings are for illustrative purposes only and do not necessarily include all steps. For example, some steps may be decomposed, while others may be combined or partially combined. Therefore, the actual execution order may vary depending on the actual situation. In addition, all terms such as "first" and "second" below are used for the purpose of distinction only and should not be construed as limitations of this disclosure.

[0034] Figure 1 FIG2 shows a schematic structural diagram of an offshore photovoltaic pile surrounding ice warning system according to an exemplary embodiment of the present disclosure. Figure 1 The floating ice warning system around offshore photovoltaic piles of the embodiment of the present disclosure may include a processing device, a drone and a warning device, wherein the drone and the warning device can be connected to the processing device for data via wired or wireless means.

[0035] The drone is equipped with a camera that can capture images of floating ice around pipe piles in an offshore photovoltaic area. This disclosure does not limit the number or configuration of drones. Furthermore, the captured floating ice images can be RGB images and / or infrared images.

[0036] Warning devices are devices that perform floating ice warning operations. These warning operations can include one or more of light warnings, audio warnings, text message warnings, email warnings, and phone warnings. The warning devices themselves can be monitoring devices, mobile devices, personal computers, smart wearable devices, and other types, and this disclosure does not limit these.

[0037] The processing device is a device that executes the main process of the floating ice warning method around offshore photovoltaic piles according to the embodiment of the present disclosure. Specifically, the processing device can obtain a floating ice image set around pipe piles in an offshore photovoltaic area; determine the floating ice fragment characteristics around the pipe piles based on the floating ice image set, and cluster the floating ice images in the floating ice image set based on the floating ice fragment characteristics to obtain multiple floating ice image clusters; determine the edge similarity of the floating ice around the pipe piles based on each floating ice image cluster; determine the edge recognition coefficient of the floating ice fragments around the pipe piles based on the floating ice fragment characteristics and edge similarity; determine the floating ice fragment recognition result around the pipe piles based on the floating ice fragment edge recognition coefficient; and perform the floating ice warning operation based on the floating ice fragment recognition result.

[0038] The embodiments of the present disclosure do not limit the type of the processing device. For example, the processing device may be any one of a server, a mobile device, a monitoring device, and a personal computer.

[0039] Figure 2 The flowchart of the method for early warning of floating ice around offshore photovoltaic piles according to an exemplary embodiment of the present disclosure is schematically shown. Figure 2 The method for early warning of floating ice around offshore photovoltaic piles may include the following steps: S20. Obtain a set of floating ice images around pipe piles in an offshore photovoltaic area.

[0040] According to some embodiments of the present disclosure, a drone can be directly programmed to automatically collect ice floe images around pipe piles in an offshore photovoltaic area at a predetermined frequency. The drone can send the ice floe image sets to a processing device.

[0041] According to other embodiments of the present disclosure, a processing device may send image acquisition control instructions to a drone at a predetermined frequency, instructing the drone to collect images of floating ice around pipe piles in an offshore photovoltaic area. In other words, in these embodiments, the drone only performs flight photography operations in response to instructions sent by the processing device. In this case, the drone may transmit the captured images to the processing device.

[0042] For example, a processing device can acquire ambient temperature data for an offshore photovoltaic area. If the ambient temperature data indicates that the current ambient temperature is below a threshold, the processing device can send image acquisition control instructions to a drone at a predetermined frequency. This approach can be used to monitor floating ice only in low-temperature conditions, enhancing the overall system's intelligence.

[0043] The present disclosure does not limit the value of the predetermined frequency. For example, the predetermined frequency may be every 1 hour, 2 hours, 3 hours, 6 hours, 12 hours, etc.

[0044] On the one hand, the present disclosure does not limit the number of ice floe images included in the ice floe image set. On the other hand, the collected ice floe images can be images of a pipe pile or images of all pipe piles in an offshore area, and the present disclosure does not limit this.

[0045] S22. Determine features of floating ice debris around the pipe pile based on the floating ice image set, and cluster the floating ice images in the floating ice image set based on the features of the floating ice debris to obtain multiple floating ice image clusters.

[0046] According to some embodiments of the present disclosure, ice floe fragment features may include ice floe fragment location features and ice floe fragment size features. Specifically, ice floe images may be used as input, and a convolutional neural network may be employed to extract ice floe fragment features from each ice floe image in an ice floe image set. The present disclosure does not restrict the network structure or training process of this convolutional neural network.

[0047] After the ice floe fragment features are obtained, the ice floe images in the ice floe image set may be clustered according to the ice floe fragment features.

[0048] First, the processing device may determine multiple initial cluster centers based on the characteristics of the ice floes. Next, the processing device may determine the similarity of each ice floe image in the ice floe image set. This disclosure does not limit the specific algorithm for determining image similarity. Subsequently, the processing device may perform similarity clustering on each ice floe image in the ice floe image set based on the similarity between the multiple initial cluster centers and the ice floe images, thereby obtaining multiple ice floe image clusters.

[0049] S24. Determine edge similarity of ice floes around the pipe pile based on each ice floe image cluster, and determine edge recognition coefficients of ice floes around the pipe pile based on ice floe fragment features and edge similarity.

[0050] According to some embodiments of the present disclosure, a processing device can use a line detection algorithm to extract line features from each ice floe image in a cluster of ice floe images, determine a line feature vector for each ice floe image, and determine edge similarity of ice floes around a pipe pile based on the line feature vectors. Specifically, vector similarity of the line feature vectors can be calculated, and the result of this vector similarity can be used as edge similarity.

[0051] Next, the processing device may perform linear fitting on all edge similarities to obtain an edge similarity fitting curve.

[0052] Subsequently, the processing device can determine the edge gradient of the floating ice fragments around the pipe pile based on the edge similarity fitting curve, and determine the edge recognition coefficient of the floating ice fragments around the pipe pile based on the edge gradient and the floating ice fragment characteristics. Specifically, the edge gradient and the floating ice fragment characteristics can be weightedly combined to obtain the edge recognition coefficient.

[0053] S26. Determine the ice floe fragment identification result around the pipe pile based on the ice floe fragment edge identification coefficient.

[0054] First, the processing device may determine a trend characteristic value of the floating ice debris around the pipe pile over time. Figure 3 The flowchart of the process of determining the trend characteristic value according to the embodiment of the present disclosure is schematically shown.

[0055] In step S302 , the processing device determines the ice floe fragment position characteristics and ice floe fragment size characteristics of each ice floe image.

[0056] In step S304, the processing device may determine a first ice floe characteristic change sequence according to the position characteristics of the ice floe fragments.

[0057] In step S306 , the processing device may convert the first ice floe characteristic change sequence into a first vector representation.

[0058] In step S308, the processing device may determine a second ice floe characteristic change sequence according to the size characteristics of the ice floe fragments.

[0059] In step S310 , the processing device may convert the second ice floe characteristic change sequence into a second vector representation.

[0060] In step S312 , the processing device may fuse the first vector representation and the second vector representation to determine a trend characteristic value of the ice debris around the tubular pile that changes over time.

[0061] For example, the first vector representation and the second vector representation are concatenated to determine a trend feature value with a fixed vector length. For another example, the first vector representation and the second vector representation are used to map corresponding trend feature values. This disclosure does not limit the mapping process.

[0062] Next, the processing device may determine an identification result of floating ice fragments around the pipe pile according to the trend characteristic value and the edge identification coefficient.

[0063] Specifically, the ice floe fragment identification result may include the ice floe fragment level and the trend fluctuation entropy. It should be understood that the trend fluctuation entropy of the embodiment of the present disclosure indicates the uncertainty or complexity of the ice floe fragment changes over time.

[0064] On the one hand, the processing device can determine the trend fluctuation entropy based on the trend characteristic value. Specifically, the trend fluctuation entropy can be obtained by calculating the probability distribution of the trend characteristic value.

[0065] According to some embodiments of the present disclosure, the trend fluctuation entropy may include trend entropy and fluctuation entropy. In this case, the processing device may determine the trend entropy and fluctuation entropy respectively according to the trend characteristic value, and then perform a weighted summation of the trend entropy and fluctuation entropy to obtain the trend fluctuation entropy.

[0066] Alternatively, the processing device can use the trend eigenvalues ​​and edge recognition coefficients to determine a feature data matrix, and then perform a classification operation based on this feature data matrix to determine the ice debris level. Specifically, this classification operation can be implemented using a machine learning-based classification model. This disclosure does not limit the model architecture, training process, or number of classification categories of this classification model.

[0067] Furthermore, in some other embodiments of the present disclosure, the ice debris identification results may include only the ice debris grade, which indicates the severity of ice contamination around the pipe piles. In this case, the processing device may determine the ice debris grade based solely on the edge recognition coefficient of the ice debris. For example, a mapping relationship between the two may be pre-determined. Once the edge recognition coefficient is determined, the corresponding ice debris grade can be obtained based on this mapping relationship.

[0068] S28. Execute ice floe warning operations based on the ice floe debris identification results.

[0069] If the ice debris identification results indicate the presence of a corresponding warning target, the processing device can determine the identifier of the warning device held by the warning target and then send a warning message to the warning device based on the identifier of the warning device. The warning target can include security personnel and equipment maintenance personnel at the offshore photovoltaic site.

[0070] When the ice floe identification result indicates that there is no corresponding warning object, it means that the current ice floe situation is not sufficient to reach the warning level and no processing is performed.

[0071] It should be noted that although the steps of the method of the present disclosure are described in a specific order in the drawings, this does not require or imply that the steps must be performed in this specific order, or that all steps must be performed to achieve the desired results. Additionally or alternatively, some steps may be omitted, multiple steps may be combined into one step, and / or one step may be decomposed into multiple steps.

[0072] Furthermore, this exemplary embodiment also provides an early warning device for floating ice around offshore photovoltaic piles.

[0073] Figure 4 The block diagram of the floating ice warning device around offshore photovoltaic piles according to an exemplary embodiment of the present disclosure is schematically shown. Figure 4 According to an exemplary embodiment of the present disclosure, the floating ice warning device 4 around offshore photovoltaic piles may include an image acquisition module 41 , an image clustering module 43 , an edge recognition module 45 , a floating ice recognition module 47 and a floating ice warning module 49 .

[0074] Specifically, the image acquisition module 41 can be used to obtain a set of floating ice images around the pipe piles in the offshore photovoltaic area; the image clustering module 43 can be used to determine the floating ice fragment characteristics around the pipe piles based on the floating ice image set, and cluster the floating ice images in the floating ice image set according to the floating ice fragment characteristics to obtain multiple floating ice image clusters; the edge recognition module 45 can be used to determine the edge similarity of the floating ice around the pipe piles based on each floating ice image cluster, and determine the edge recognition coefficient of the floating ice fragments around the pipe piles based on the floating ice fragment characteristics and edge similarity; the floating ice recognition module 47 can be used to determine the floating ice fragment recognition result around the pipe pile in combination with the edge recognition coefficient of the floating ice fragments; the floating ice warning module 49 can be used to perform a floating ice warning operation based on the floating ice fragment recognition result.

[0075] Optionally, the image clustering module 43 can be configured to determine multiple initialization cluster centers based on the characteristics of the ice floe fragments; determine the similarity of each ice floe image in the ice floe image set; and perform similarity clustering on each ice floe image in the ice floe image set based on the multiple initialization cluster centers and the similarity of each ice floe image to obtain multiple ice floe image clusters.

[0076] Optionally, the edge recognition module 45 may be configured to extract line features of each ice floe image in the ice floe image cluster, determine a line feature vector of each ice floe image, and determine edge similarity of ice floes around the pipe pile based on the line feature vector.

[0077] Optionally, the edge recognition module 45 can be configured to perform linear fitting on all edge similarities to obtain an edge similarity fitting curve; determine the edge gradient of the floating ice fragments around the pipe pile based on the edge similarity fitting curve; and determine the edge recognition coefficient of the floating ice fragments around the pipe pile based on the edge gradient and the characteristics of the floating ice fragments.

[0078] Optionally, the ice floe identification module 47 may be configured to determine a trend characteristic value of ice floes around the pile over time; and determine an identification result of the ice floes around the pile according to the trend characteristic value and an edge identification coefficient.

[0079] Optionally, the ice floe fragment characteristics include ice floe fragment position characteristics and ice floe fragment size characteristics. In this case, ice floe identification module 47 can be configured to determine a first ice floe feature change sequence based on the ice floe fragment position characteristics, convert the first ice floe feature change sequence into a first vector representation; determine a second ice floe feature change sequence based on the ice floe fragment size characteristics, convert the second ice floe feature change sequence into a second vector representation; and fuse the first vector representation and the second vector representation to determine a trend characteristic value of the ice floe fragments around the pipe pile over time.

[0080] Optionally, the ice floe identification results include the ice floe debris level and trend fluctuation entropy. In this case, ice floe identification module 47 can be configured to determine the trend fluctuation entropy based on the trend characteristic value; determine a feature data matrix using the trend characteristic value and the edge recognition coefficient; and perform a classification operation based on the feature data matrix to determine the ice floe debris level. Since the functional modules of the ice floe warning device around offshore photovoltaic piles in this embodiment are the same as those in the aforementioned method embodiment, they will not be further described here.

[0081] In exemplary embodiments of the present disclosure, a computer-readable storage medium is also provided, on which is stored a program product capable of implementing the aforementioned methods of this specification. In some possible implementations, various aspects of the present disclosure may also be implemented in the form of a program product comprising program code. When the program product is executed on a terminal device, the program code is configured to cause the terminal device to execute the steps described in the "Exemplary Methods" section of this specification according to various exemplary embodiments of the present disclosure.

[0082] The program product for implementing the above-mentioned method according to an embodiment of the present disclosure may be a portable compact disc read-only memory (CD-ROM) and include program code, and may be run on a terminal device, such as a personal computer. However, the program product of the present disclosure is not limited thereto. In this document, a readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.

[0083] The program product may utilize any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. The readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or component, or any combination thereof. More specific examples (a non-exhaustive list) of readable storage media include: an electrical connection having one or more wires, a portable disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical disk, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof.

[0084] A computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, which carries readable program code. Such propagated data signals may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A readable signal medium may also be any readable medium other than a readable storage medium that can transmit, propagate, or transfer a program for use by or in conjunction with an instruction execution system, apparatus, or device.

[0085] The program code embodied on the readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.

[0086] Program code for performing the operations of the present disclosure may be written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Java, C++, and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user's computing device, partially on the user's device, as a standalone software package, partially on the user's computing device and partially on a remote computing device, or entirely on a remote computing device or server. In cases involving a remote computing device, the remote computing device may be connected to the user's computing device via any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computing device (e.g., via the Internet using an Internet service provider).

[0087] In an exemplary embodiment of the present disclosure, an electronic device capable of implementing the above method is also provided. The above processing device can be configured in the form of the following electronic device.

[0088] Those skilled in the art will appreciate that various aspects of the present disclosure may be implemented as systems, methods, or program products. Therefore, various aspects of the present disclosure may be implemented in the following forms: entirely in hardware, entirely in software (including firmware, microcode, etc.), or in a combination of hardware and software, collectively referred to herein as "circuits," "modules," or "systems."

[0089] Refer to the following Figure 5 hereinafter, an electronic device 500 according to this embodiment of the present disclosure is described. Figure 5 The electronic device 500 shown is merely an example and should not limit the functions and scope of use of the embodiments of the present disclosure.

[0090] like Figure 5 As shown, electronic device 500 is implemented as a general-purpose computing device. Components of electronic device 500 may include, but are not limited to, the aforementioned at least one processing unit 510, the aforementioned at least one storage unit 520, a bus 530 connecting various system components (including storage unit 520 and processing unit 510), and a display unit 540.

[0091] The storage unit stores program code that can be executed by the processing unit 510, causing the processing unit 510 to perform the steps described in the "Exemplary Methods" section above according to various exemplary embodiments of the present disclosure. For example, the processing unit 510 can perform each step of the method for early warning of floating ice around offshore photovoltaic piles according to an embodiment of the present disclosure.

[0092] The storage unit 520 may include a readable medium in the form of a volatile storage unit, such as a random access memory unit (RAM) 5201 and / or a cache memory unit 5202 , and may further include a read-only memory unit (ROM) 5203 .

[0093] The storage unit 520 may also include a program / utility 5204 having a set (at least one) of program modules 5205, such program modules 5205 including but not limited to: an operating system, one or more application programs, other program modules, and program data, each of which or some combination may include an implementation of a network environment.

[0094] Bus 530 may represent one or more of several types of bus structures, including a memory bus or memory controller, a peripheral bus, an accelerated graphics port, a processing unit, or a local bus using any of a variety of bus architectures.

[0095] The electronic device 500 can also communicate with one or more external devices 600 (e.g., a keyboard, pointing device, Bluetooth device, etc.), one or more devices that enable a user to interact with the electronic device 500, and / or any device that enables the electronic device 500 to communicate with one or more other computing devices (e.g., a router, modem, etc.). This communication can occur via an input / output (I / O) interface 550. Furthermore, the electronic device 500 can communicate with one or more networks (e.g., a local area network (LAN), a wide area network (WAN), and / or a public network such as the Internet) via a network adapter 560. As shown, the network adapter 560 communicates with other modules of the electronic device 500 via a bus 530. It should be understood that, although not shown in the figure, other hardware and / or software modules can be used in conjunction with the electronic device 500, including but not limited to microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems.

[0096] Through the description of the above embodiments, it will be readily understood by those skilled in the art that the example embodiments described herein can be implemented via software or via a combination of software and necessary hardware. Therefore, the technical solutions according to the embodiments of the present disclosure can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (such as a CD-ROM, USB flash drive, or mobile hard drive) or on a network and includes several instructions for enabling a computing device (such as a personal computer, server, terminal device, or network device) to execute the methods according to the embodiments of the present disclosure.

[0097] Furthermore, the figures above are merely illustrative of the processes included in the methods according to exemplary embodiments of the present disclosure and are not intended to be limiting. It is readily understood that the processes illustrated in the figures above do not indicate or limit the temporal order of these processes. Furthermore, it is readily understood that these processes may be executed synchronously or asynchronously, for example, in multiple modules.

[0098] It should be noted that although several modules or units of the device for action execution are mentioned in the detailed description above, this division is not mandatory. In fact, according to the embodiments of the present disclosure, the features and functions of two or more modules or units described above can be concretized in one module or unit. Conversely, the features and functions of one module or unit described above can be further divided into multiple modules or units to be concretized.

[0099] Other embodiments of the present disclosure will readily occur to those skilled in the art after considering the specification and practicing what is disclosed herein. This application is intended to cover any variations, uses, or adaptations of the present disclosure that follow the general principles of the present disclosure and include common knowledge or customary techniques in the art not disclosed herein. The specification and examples are to be considered as exemplary only, with the true scope and spirit of the present disclosure being indicated by the claims.

[0100] It should be understood that the present disclosure is not limited to the exact structures that have been described above and shown in the drawings, and that various modifications and changes can be made without departing from the scope thereof. The scope of the present disclosure is limited only by the appended claims.

Claims

1. A method for early warning of floating ice around offshore photovoltaic piles, characterized in that: include: Acquire a set of floating ice images around pipe piles in an offshore photovoltaic area; determining features of floating ice debris around the pipe pile according to the floating ice image set, and clustering the floating ice images in the floating ice image set according to the floating ice debris features to obtain a plurality of floating ice image clusters; determining edge similarities of ice floes around the pipe pile according to the ice floe image clusters, and determining edge recognition coefficients of ice floes around the pipe pile according to the ice floe fragment features and the edge similarities; Determining the ice floe fragment recognition result around the pipe pile in combination with the ice floe fragment edge recognition coefficient; An ice floe warning operation is performed according to the ice floe fragment identification result.

2. The method for early warning of floating ice around offshore photovoltaic piles according to claim 1, characterized in that: Clustering the ice floe images in the ice floe image set according to the ice floe fragment features to obtain a plurality of ice floe image clusters, including: determining a plurality of initialization cluster centers according to the characteristics of the floating ice debris; Determining the similarity of each ice floe image in the ice floe image set; The ice floe images in the ice floe image set are similarly clustered according to the similarities between the multiple initialized cluster centers and the ice floe images, so as to obtain multiple ice floe image clusters.

3. The method for early warning of floating ice around offshore photovoltaic piles according to claim 1, characterized in that: Determining the edge similarity of ice floes around the pipe pile according to each of the ice floe image clusters includes: extracting line features of each ice floe image in the ice floe image cluster, and determining a line feature vector of each ice floe image; The edge similarity of the floating ice around the pipe pile is determined according to the line feature vector.

4. The method for early warning of floating ice around offshore photovoltaic piles according to claim 1 or 3, characterized in that: Determining the edge recognition coefficient of the ice floes around the pipe pile according to the ice floe characteristics and the edge similarity includes: Perform linear fitting on all edge similarities to obtain an edge similarity fitting curve; determining the edge gradient of the floating ice fragments around the pipe pile according to the edge similarity fitting curve; An edge recognition coefficient of the floating ice fragments around the pipe pile is determined according to the edge gradient and the floating ice fragment characteristics.

5. The method for early warning of floating ice around offshore photovoltaic piles according to claim 1, characterized in that: Determining the floating ice debris recognition result around the pipe pile in combination with the edge recognition coefficient of the floating ice debris includes: Determine the trend characteristic value of the floating ice debris around the pile over time; The identification result of floating ice fragments around the pipe pile is determined according to the trend characteristic value and the edge identification coefficient.

6. The method for early warning of floating ice around offshore photovoltaic piles according to claim 5, characterized in that: The floating ice debris characteristics include floating ice debris location characteristics and floating ice debris size characteristics; wherein, determining the trend characteristic value of the floating ice debris around the pipe pile over time includes: determining a first ice floe characteristic change sequence based on the position characteristics of the ice floe fragments, and converting the first ice floe characteristic change sequence into a first vector representation; determining a second ice floe characteristic change sequence based on the size characteristics of the ice floe fragments, and converting the second ice floe characteristic change sequence into a second vector representation; The first vector representation and the second vector representation are fused to determine a trend characteristic value of the ice debris around the tubular pile changing over time.

7. The method for early warning of floating ice around offshore photovoltaic piles according to claim 5, characterized in that: The ice floe debris identification result includes the ice floe debris level and the trend fluctuation entropy; wherein, determining the ice floe debris identification result around the pipe pile based on the trend characteristic value and the edge identification coefficient includes: determining the trend fluctuation entropy according to the trend characteristic value; A characteristic data matrix is ​​determined using the trend characteristic value and the edge recognition coefficient, and a classification operation is performed based on the characteristic data matrix to determine the ice floe debris grade.

8. An ice warning device around offshore photovoltaic piles, characterized in that: include: An image acquisition module is used to acquire an image set of floating ice around the pipe piles in the offshore photovoltaic area; an image clustering module, configured to determine features of ice floes around the pipe pile based on the ice floe image set, and cluster the ice floes images in the ice floe image set based on the features of the ice floes to obtain a plurality of ice floe image clusters; an edge recognition module, configured to determine edge similarities of ice floes around the pipe pile according to the ice floe image clusters, and determine edge recognition coefficients of ice floes around the pipe pile according to the ice floe fragment features and the edge similarities; An ice floe identification module, configured to determine an identification result of ice floes around the pipe pile in combination with an edge identification coefficient of the ice floes; The floating ice warning module is used to perform a floating ice warning operation according to the floating ice fragment identification result.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method for early warning of floating ice around offshore photovoltaic piles according to any one of claims 1 to 7 is implemented.

10. An electronic device, characterized in that: include: processor; as well as a memory for storing executable instructions of the processor; The processor is configured to implement the method for early warning of floating ice around offshore photovoltaic piles according to any one of claims 1 to 7 by executing the executable instructions.

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