Marine photovoltaic pile peripheral ice warning method and device, storage medium and electronic equipment
By collecting nighttime infrared images of floating ice using drones and performing image sequence analysis, the problem of relying on human judgment for early warning of floating ice around marine photovoltaic piles has been solved. This has enabled automated and accurate early warning of floating ice, reducing labor costs and improving identification accuracy.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- NORTHWEST ENGINEERING CORPORATION LIMITED
- Filing Date
- 2025-07-01
- Publication Date
- 2026-07-21
AI Technical Summary
In offshore photovoltaic projects, early warning of floating ice around the piles mainly relies on human judgment, which results in high manpower costs. Furthermore, existing technologies are affected by daytime light fluctuations, leading to low accuracy in identification.
UAVs are used to collect nighttime infrared images of floating ice. Image sequence analysis is used to obtain the location and size characteristics of floating ice fragments. Convolutional neural networks are used to extract features, and machine learning is combined to perform trend analysis and automatically execute floating ice warning operations.
It has automated the early warning of floating ice, reduced labor costs, improved the accuracy of identification and early warning, and can accurately track the changing trend of floating ice.
Smart Images
Figure CN120689816B_ABST
Abstract
Description
Technical Field
[0001] This disclosure relates to the field of engineering safety technology, and more specifically, to a method for early warning of floating ice around marine photovoltaic piles, a device for early warning of floating ice around marine photovoltaic piles, a computer-readable storage medium, and an electronic device. Background Technology
[0002] In the field of offshore photovoltaics, pile-based photovoltaics refers to photovoltaic modules supported by pipe piles erected on the sea surface, thereby utilizing the vast space of the sea for solar power generation. This form is currently the mainstream offshore photovoltaic structure, and it can at least solve the problem of limited land resources.
[0003] In offshore photovoltaic (PV) applications, the low sea surface temperature can lead to the formation of floating ice, especially in areas with strong currents and winds. This can cause the floating ice around the PV piles to break and fragment, forming small chunks or pieces. The presence of floating ice can impact the normal operation and power generation efficiency of the PV system.
[0004] Currently, early warning and monitoring of floating ice mainly rely on human judgment, which is labor-intensive.
[0005] It should be noted that the information disclosed in the background section above is only used to enhance the understanding of the background of this disclosure, and therefore may include information that does not constitute prior art known to those skilled in the art. Summary of the Invention
[0006] The purpose of this disclosure is to provide a method, device, computer-readable storage medium, and electronic device for early warning of floating ice around marine photovoltaic piles, thereby overcoming, to some extent, the problem of relying on human judgment for early warning of floating ice around piles in marine photovoltaic scenarios, which consumes a lot of manpower.
[0007] According to a first aspect of this disclosure, a method for early warning of floating ice around marine photovoltaic piles is provided, comprising: acquiring infrared floating ice images of the area around a pipe pile in a marine photovoltaic area at a predetermined frequency during the night to obtain an infrared floating ice image sequence; determining the trend characteristic value of the floating ice fragments around the pipe pile changing over time based on the infrared floating ice image sequence; determining the floating ice fragment identification result around the pipe pile based on the trend characteristic value; and performing a floating ice early warning operation based on the floating ice fragment identification result.
[0008] Optionally, acquiring infrared images of floating ice around the pipe piles in the offshore photovoltaic area at night, collected at a predetermined frequency, includes: sending image acquisition control commands to the UAV at a predetermined frequency, the image acquisition control commands instructing the UAV to acquire infrared images of floating ice around the pipe piles in the offshore photovoltaic area at night; and acquiring the infrared images of floating ice collected by the UAV.
[0009] Optionally, determining the trend characteristic values of the floating ice fragments around the pipe pile changing over time based on the infrared floating ice image sequence includes: identifying floating ice in each infrared floating ice image in the infrared floating ice image sequence to determine the floating ice fragment characteristics of each infrared floating ice image; determining the floating ice feature change sequence corresponding to the infrared floating ice image sequence based on the floating ice fragment characteristics of each infrared floating ice image; and determining the trend characteristic values of the floating ice fragments around the pipe pile changing over time based on the floating ice feature change sequence.
[0010] Optionally, the characteristics of ice floe fragments include location characteristics and size characteristics. In this case, determining the trend characteristic values of ice floe fragments around the pipe pile over time based on the ice floe characteristic change sequence includes: converting a first ice floe characteristic change sequence determined based on the location characteristics of ice floe fragments into a first vector representation; converting a second ice floe characteristic change sequence determined based on the size characteristics of ice floe fragments into a second vector representation; and fusing the first and second vector representations to determine the trend characteristic values of ice floe fragments around the pipe pile over time.
[0011] Optionally, the ice floe fragment identification results include ice floe fragment grade and trend fluctuation entropy. In this case, determining the ice floe fragment identification results around the pipe pile based on trend feature values includes: performing a classification operation on the trend feature values to determine the ice floe fragment grade; and determining the trend fluctuation entropy based on the trend feature values.
[0012] Optionally, determining the trend fluctuation entropy based on the trend characteristic value includes: determining the trend entropy and fluctuation entropy separately based on the trend characteristic value; and performing a weighted summation of the trend entropy and fluctuation entropy to obtain the trend fluctuation entropy.
[0013] Optionally, performing a floating ice warning operation based on the floating ice fragment identification result includes: if the floating ice fragment identification result indicates the existence of a corresponding warning object, determining the identifier of the warning device held by the warning object; and sending warning information to the warning device based on the identifier of the warning device.
[0014] According to a second aspect of this disclosure, a marine photovoltaic (PV) pile perimeter ice warning device is provided, comprising: an image acquisition module for acquiring infrared ice images of the perimeter of a pipe pile within a marine PV area at night, collected at a predetermined frequency, to obtain an infrared ice image sequence; a trend feature determination module for determining, based on the infrared ice image sequence, trend feature values of ice fragments around the pipe pile changing over time; an ice result determination module for determining, based on the trend feature values, an ice fragment identification result around the pipe pile; and an ice warning module for performing an ice warning operation based on the ice fragment identification result.
[0015] Optionally, the image acquisition module is configured to send image acquisition control commands to the UAV at a predetermined frequency. The image acquisition control commands instruct the UAV to acquire infrared images of floating ice around the pipe piles in the offshore photovoltaic area at night; and acquire the infrared floating ice images acquired by the UAV.
[0016] Optionally, the trend feature determination module is configured to identify ice in each infrared ice image in the infrared ice image sequence to determine the ice fragment features of each infrared ice image; determine the ice feature change sequence corresponding to the infrared ice image sequence based on the ice fragment features of each infrared ice image; and determine the trend feature value of the ice fragments around the pipe pile changing over time based on the ice feature change sequence.
[0017] Optionally, the ice floe fragment features include ice floe fragment location features and ice floe fragment size features. In this case, the trend feature determination module is configured to convert a first ice floe feature change sequence determined based on the ice floe fragment location features into a first vector representation; convert a second ice floe feature change sequence determined based on the ice floe fragment size features into a second vector representation; and fuse the first and second vector representations to determine the trend feature value of the ice floe fragments around the pipe pile changing over time.
[0018] Optionally, the ice floe fragment identification results include ice floe fragment grade and trend fluctuation entropy. In this case, the ice floe result determination module is configured to perform a classification operation on the trend feature values to determine the ice floe fragment grade; and to determine the trend fluctuation entropy based on the trend feature values.
[0019] Optionally, the floating ice result determination module is configured to determine the trend entropy and fluctuation entropy based on the trend characteristic values respectively; and to perform a weighted summation of the trend entropy and fluctuation entropy to obtain the trend fluctuation entropy.
[0020] Optionally, the ice floe warning module is configured to, when the ice floe fragment identification result indicates the existence of a corresponding warning object, determine the identifier of the warning device held by the warning object; and send warning information to the warning device according to the identifier of the warning device.
[0021] According to a third aspect of this disclosure, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements any of the above-described methods for early warning of floating ice around marine photovoltaic piles.
[0022] According to a fourth aspect of this disclosure, an electronic device is provided, comprising: a processor; and a memory for storing executable instructions of the processor; the processor being configured to implement any of the above-described methods for early warning of floating ice around marine photovoltaic piles by executing the executable instructions.
[0023] In some embodiments of the present disclosure, the technical solutions provided include, on the one hand, the entire ice floe warning process is automatically executed by computer, greatly reducing labor costs. On the other hand, the present disclosure utilizes nighttime infrared images for analysis. Compared to acquiring color images during the day, the present disclosure's solution is not affected by daytime light fluctuations in image acquisition, improving the accuracy of ice floe identification and thus enhancing the accuracy of the ice floe warning. Furthermore, the present disclosure analyzes image sequences rather than recognizing single images, thereby accurately determining the changing trends of ice floes and further contributing to improved warning accuracy.
[0024] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and are not intended to limit this disclosure. Attached Figure Description
[0025] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this disclosure and, together with the description, serve to explain the principles of this disclosure. It is obvious that the drawings described below are merely some embodiments of this disclosure, and those skilled in the art can obtain other drawings based on these drawings without any inventive effort.
[0026] Figure 1 A schematic diagram of a marine photovoltaic pile perimeter ice warning system according to an exemplary embodiment of the present disclosure is shown.
[0027] Figure 2 A flowchart illustrating an exemplary embodiment of the floating ice warning method around marine photovoltaic piles is shown.
[0028] Figure 3 A flowchart illustrating the process of determining trend feature values according to an embodiment of the present disclosure is shown.
[0029] Figure 4 A block diagram of an exemplary embodiment of a marine photovoltaic pile surrounding ice warning device is shown schematically.
[0030] Figure 5 A block diagram of an electronic device according to an exemplary embodiment of the present disclosure is shown schematically. Detailed Implementation
[0031] Example embodiments will now be described more fully with reference to the accompanying drawings. However, example embodiments can be implemented in many forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided to make this disclosure more comprehensive and complete, and to fully convey the concept of the example embodiments to those skilled in the art. The described features, structures, or characteristics can be combined in any suitable manner in one or more embodiments. In the following description, numerous specific details are provided to give a full understanding of embodiments of this disclosure. However, those skilled in the art will recognize that the technical solutions of this disclosure can be practiced with one or more of the specific details omitted, or other methods, components, apparatus, steps, etc., can be employed. In other instances, well-known technical solutions are not shown or described in detail to avoid obscuring various aspects of this disclosure.
[0032] Furthermore, the accompanying drawings are merely illustrative of this disclosure and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and therefore repeated descriptions of them will be omitted. Some block diagrams shown in the drawings are functional entities and do not necessarily correspond to physically or logically independent entities. These functional entities may be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor devices and / or microcontroller devices.
[0033] The flowcharts shown in the accompanying drawings are merely illustrative and do not necessarily include all steps. For example, some steps may be broken down, while others may be combined or partially combined; therefore, the actual execution order may change depending on the specific circumstances. Furthermore, all terms such as "first," "second," etc., used below are for distinction purposes only and should not be construed as limiting the scope of this disclosure.
[0034] Figure 1 A schematic diagram of a marine photovoltaic pile perimeter ice warning system according to an exemplary embodiment of the present disclosure is shown. (Reference) Figure 1 The floating ice warning system around marine photovoltaic piles according to 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 via wired or wireless means.
[0035] The drone is equipped with an infrared sensor, enabling it to capture infrared images of the floating ice around the pipe piles in the offshore photovoltaic area at night. This disclosure does not limit the number or configuration of the drones.
[0036] The warning device is a device that performs ice floe warning operations, which may include one or more of the following: light warning, audio warning, SMS warning, email warning, and telephone warning. The warning device itself may be a monitoring device, mobile device, personal computer, smart wearable device, etc., and this disclosure does not impose any restrictions on its type.
[0037] The processing device is the equipment that executes the main process of the offshore photovoltaic pile perimeter ice warning method according to the embodiments of this disclosure. Specifically, firstly, the processing device can acquire infrared images of the perimeter of the pipe piles in the offshore photovoltaic area at night, collected by a UAV at a predetermined frequency, to obtain an infrared ice image sequence. Next, the processing device can determine the trend characteristic value of the ice fragments around the pipe piles changing over time based on the infrared ice image sequence. Subsequently, the processing device can determine the ice fragment identification result around the pipe piles based on the trend characteristic value. Then, the processing device can control the warning device to perform ice warning operations based on the ice fragment identification result.
[0038] This disclosure does not limit the type of processing device, such as any one of a server, mobile device, monitoring device, or personal computer.
[0039] Figure 2 A flowchart illustrating an exemplary embodiment of the floating ice warning method for marine photovoltaic piles is shown. (Reference) Figure 2 The method for early warning of floating ice around marine photovoltaic piles may include the following steps:
[0040] S22. Acquire infrared images of floating ice around the piles in the offshore photovoltaic area at night, collected at a predetermined frequency, to obtain an infrared floating ice image sequence.
[0041] According to some embodiments of this disclosure, a drone can be configured to acquire infrared images of floating ice around pipe piles in an offshore photovoltaic area at night at a predetermined frequency. The drone can then transmit these images to a processing device.
[0042] According to other embodiments of this disclosure, the processing device can send image acquisition control commands to the drone at a predetermined frequency. These commands instruct the drone to acquire infrared images of floating ice around the piles in the offshore photovoltaic area at night. In other words, in these embodiments, the drone performs flight photography only in response to commands sent by the processing device. In this case, the drone can send the captured images to the processing device.
[0043] This disclosure does not limit the value of the predetermined frequency mentioned above. For example, the predetermined frequency can be every 1 hour, 2 hours, 3 hours, etc.
[0044] Furthermore, the infrared ice images in the infrared ice image sequence described in this disclosure may be images taken at one night or multiple nights. This disclosure does not limit the number of infrared ice images included in the infrared ice image sequence.
[0045] S24. Determine the trend characteristics of ice fragments around the pipe pile over time based on the infrared ice image sequence.
[0046] First, the processing device can identify ice floes in each infrared ice floe image in the infrared ice floe image sequence to determine the ice floe fragment features of each image. These features include ice floe fragment location and size characteristics. Specifically, a convolutional neural network can be used to extract the ice floe fragment features from the infrared ice floe images; this disclosure does not limit the network structure or training process of such a convolutional neural network.
[0047] Next, the processing device can determine a sequence of changes in ice features corresponding to the sequence of infrared ice images based on the ice fragment features of each ice image. It should be understood that for ice fragment location features, a first sequence of changes in ice features corresponding to the location is generated; for ice fragment size features, a second sequence of changes in ice features corresponding to the size is generated. Here, the size mentioned in this embodiment includes the area of the ice fragment on the sea surface and / or the thickness of the ice fragment.
[0048] Subsequently, the processing equipment can determine the trend characteristic values of the floating ice fragments around the pipe pile changing over time based on the floating ice characteristic change sequence.
[0049] Specifically, on the one hand, the processing device can convert a first sequence of floating ice feature changes determined based on the location characteristics of floating ice fragments into a first vector representation; on the other hand, the processing device can convert a second sequence of floating ice feature changes determined based on the size characteristics of floating ice fragments into a second vector representation. Then, the first and second vector representations are fused to determine the trend characteristic value of the floating ice fragments around the pipe pile changing over time. For example, the first and second vector representations can be concatenated to determine a trend characteristic value with a fixed vector length. Alternatively, the corresponding trend characteristic value can be mapped based on the first and second vector representations; this disclosure does not limit the mapping process.
[0050] Figure 3 A flowchart illustrating the process of determining trend feature values according to an embodiment of the present disclosure is shown.
[0051] In step S302, the processing device can determine the location features and size features of ice fragments in each infrared ice image.
[0052] In step S304, the processing device can determine the first floating ice feature change sequence based on the location characteristics of the floating ice fragments.
[0053] In step S306, the processing device can convert the first ice floe feature change sequence into a first vector representation.
[0054] In step S308, the processing device can determine the second ice feature change sequence based on the size characteristics of the ice fragments.
[0055] In step S310, the processing device can convert the second ice floe feature change sequence into a second vector representation.
[0056] In step S312, the processing device can determine the trend feature value described in the embodiments of this disclosure based on the first vector representation and the second vector representation.
[0057] S26. Determine the identification results of floating ice fragments around the pipe pile based on trend feature values.
[0058] In an exemplary embodiment of this disclosure, the ice floe fragment identification result includes ice floe fragment grade and trend fluctuation entropy. It should be understood that the trend fluctuation entropy in this embodiment of the disclosure indicates the degree of uncertainty or complexity of ice floe fragment changes over time.
[0059] On the one hand, the processing device can classify the trend feature values determined in step S24 to determine the level of ice floe fragments. Specifically, a machine learning-based classification model can be used to implement the above classification operation, and this disclosure does not limit the model architecture and training process of the classification model.
[0060] On the other hand, the processing equipment can determine the trend fluctuation entropy based on the trend characteristic values. Specifically, the trend fluctuation entropy can be obtained by calculating the probability distribution of the trend characteristic values.
[0061] According to some embodiments of this disclosure, trend fluctuation entropy may include trend entropy and fluctuation entropy. In this case, the processing device can determine the trend entropy and fluctuation entropy separately based on the trend characteristic value, and then perform a weighted summation of the trend entropy and fluctuation entropy to obtain the trend fluctuation entropy.
[0062] S28. Execute ice floe warning operation based on the ice floe fragment identification results.
[0063] When the ice floe fragment identification results indicate the existence of a corresponding warning target, the processing equipment 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. The warning target can be security personnel, equipment maintenance personnel, etc., at the offshore photovoltaic field.
[0064] If the ice debris identification result indicates that there is no corresponding warning object, it means that the current situation of ice debris is not sufficient to warrant a warning, and no action will be taken.
[0065] It should be noted that although the steps of the method in this disclosure are described in a specific order in the accompanying drawings, this does not require or imply that the steps must be performed in that specific order, or that all the steps shown must be performed to achieve the desired result. Additional or alternative steps may be omitted, multiple steps may be combined into one step, and / or a step may be broken down into multiple steps.
[0066] Furthermore, this example embodiment also provides a device for early warning of floating ice around marine photovoltaic piles.
[0067] Figure 4 A block diagram schematically illustrates an exemplary embodiment of a marine photovoltaic pile perimeter ice warning device according to this disclosure. (Reference) Figure 4 The floating ice warning device 4 around marine photovoltaic piles according to an exemplary embodiment of the present disclosure may include an image acquisition module 41, a trend feature determination module 43, a floating ice result determination module 45, and a floating ice warning module 47.
[0068] Specifically, the image acquisition module 41 can be used to acquire infrared images of floating ice around the pipe piles in the offshore photovoltaic area at night, collected at a predetermined frequency, to obtain an infrared floating ice image sequence; the trend feature determination module 43 can be used to determine the trend feature value of the floating ice fragments around the pipe piles changing over time based on the infrared floating ice image sequence; the floating ice result determination module 45 can be used to determine the floating ice fragment identification result around the pipe piles based on the trend feature value; and the floating ice warning module 47 can be used to perform a floating ice warning operation based on the floating ice fragment identification result.
[0069] According to an exemplary embodiment of the present disclosure, the image acquisition module 41 can be configured to send image acquisition control commands to the UAV at a predetermined frequency. The image acquisition control commands instruct the UAV to acquire infrared images of floating ice around the piles in the offshore photovoltaic area at night; and acquire the infrared floating ice images acquired by the UAV.
[0070] According to an exemplary embodiment of the present disclosure, the trend feature determination module 43 may be configured to identify ice in each infrared ice image in the infrared ice image sequence to determine the ice fragment features of each infrared ice image; determine the ice feature change sequence corresponding to the infrared ice image sequence based on the ice fragment features of each infrared ice image; and determine the trend feature value of the ice fragments around the pipe pile changing over time based on the ice feature change sequence.
[0071] According to an exemplary embodiment of this disclosure, the characteristics of ice floe fragments include ice floe fragment location characteristics and ice floe fragment size characteristics. In this case, the trend feature determination module 43 can be configured to convert a first ice floe feature change sequence determined based on the ice floe fragment location characteristics into a first vector representation; convert a second ice floe feature change sequence determined based on the ice floe fragment size characteristics into a second vector representation; and fuse the first vector representation and the second vector representation to determine the trend feature value of the ice floe fragments around the pile changing over time.
[0072] According to an exemplary embodiment of this disclosure, the ice floe fragment identification result includes an ice floe fragment grade and a trend fluctuation entropy. In this case, the ice floe result determination module 45 can be configured to perform a classification operation on the trend feature value to determine the ice floe fragment grade; and determine the trend fluctuation entropy based on the trend feature value.
[0073] According to an exemplary embodiment of this disclosure, the floating ice result determination module 45 can be configured to determine the trend entropy and fluctuation entropy respectively based on the trend feature value; and to perform a weighted summation of the trend entropy and fluctuation entropy to obtain the trend fluctuation entropy.
[0074] According to an exemplary embodiment of this disclosure, the ice floe warning module 47 can be configured to determine the identifier of the warning device held by the warning device when the ice floe fragment identification result indicates the existence of a corresponding warning object; and send warning information to the warning device according to the identifier of the warning device.
[0075] Since the functional modules of the marine photovoltaic pile surrounding ice early warning device in this embodiment are the same as those in the above-described method embodiments, they will not be described again here.
[0076] In exemplary embodiments of this disclosure, a computer-readable storage medium is also provided, on which a program product capable of implementing the methods described above is stored. In some possible implementations, various aspects of this disclosure may also be implemented as a program product including program code that, when the program product is run on a terminal device, causes the terminal device to perform the steps of the various exemplary embodiments of this disclosure described in the "Exemplary Methods" section above.
[0077] The program product for implementing the above-described method according to embodiments of the present disclosure may employ a portable compact disc read-only memory (CD-ROM) and include program code, and may 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, the readable storage medium may be any tangible medium containing or storing a program that may be used by or in conjunction with an instruction execution system, apparatus, or device.
[0078] The program product may take the form of any combination of one or more readable media. A readable medium may be a readable signal medium or a readable storage medium. A readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of readable storage media (a non-exhaustive list) include: an electrical connection having one or more wires, a portable disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), an optical disk, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof.
[0079] Computer-readable signal media may include data signals propagated in baseband or as part of a carrier wave, carrying readable program code. Such propagated data signals may take various 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, capable of sending, propagating, or transmitting programs for use by or in conjunction with an instruction execution system, apparatus, or device.
[0080] The program code contained on the readable medium may be transmitted using any suitable medium, including but not limited to wireless, wired, optical fiber, RF, etc., or any suitable combination thereof.
[0081] Program code for performing the operations of this disclosure can be written in any combination of one or more programming languages, including object-oriented programming languages such as Java and C++, and conventional procedural programming languages such as C or similar languages. The program code can execute entirely on the user's computing device, partially on the user's computing 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 remote computing devices, the remote computing device can 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 it can be connected to an external computing device (e.g., via the Internet using an Internet service provider).
[0082] In an exemplary embodiment of this disclosure, an electronic device capable of implementing the above-described method is also provided. The processing device can be configured as the electronic device described below.
[0083] Those skilled in the art will understand that various aspects of this disclosure can be implemented as a system, method, or program product. Therefore, various aspects of this disclosure can be specifically implemented in the following forms: a completely hardware implementation, a completely software implementation (including firmware, microcode, etc.), or a combination of hardware and software aspects, collectively referred to herein as a "circuit," "module," or "system."
[0084] The following reference Figure 5 To describe an electronic device 500 according to such an embodiment of the present disclosure. Figure 5 The electronic device 500 shown is merely an example and should not impose any limitation on the functionality and scope of use of the embodiments disclosed herein.
[0085] like Figure 5 As shown, the electronic device 500 is manifested in the form of a general-purpose computing device. The components of the electronic device 500 may include, but are not limited to: at least one processing unit 510, at least one storage unit 520, a bus 530 connecting different system components (including storage unit 520 and processing unit 510), and a display unit 540.
[0086] 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 of this specification according to various exemplary embodiments of this disclosure. For example, the processing unit 510 can perform various steps of the offshore photovoltaic pile perimeter ice warning method according to embodiments of this disclosure.
[0087] Storage unit 520 may include a readable medium in the form of a volatile storage unit, such as random access memory (RAM) 5201 and / or cache memory 5202, and may further include a read-only memory (ROM) 5203.
[0088] Storage unit 520 may also include a program / utility 5204 having a set (at least one) program module 5205, such program module 5205 including but not limited to: operating system, one or more application programs, other program modules and program data, each or some combination of these examples may include an implementation of a network environment.
[0089] Bus 530 can represent one or more of several types of bus structures, including a memory cell bus or memory cell controller, a peripheral bus, a graphics acceleration port, a processing unit, or a local bus using any of the various bus structures.
[0090] Electronic device 500 can also communicate with one or more external devices 600 (e.g., keyboard, pointing device, Bluetooth device, etc.), and with one or more devices that enable a user to interact with electronic device 500, and / or with any device that enables electronic device 500 to communicate with one or more other computing devices (e.g., router, modem, etc.). This communication can be performed via input / output (I / O) interface 550. Furthermore, electronic device 500 can also communicate with one or more networks (e.g., local area network (LAN), wide area network (WAN), and / or public networks, such as the Internet) via network adapter 560. As shown, network adapter 560 communicates with other modules of electronic device 500 via bus 530. It should be understood that, although not shown in the figures, other hardware and / or software modules can be used in conjunction with 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.
[0091] From the above description of the embodiments, those skilled in the art will readily understand that the exemplary embodiments described herein can be implemented by software or by combining software with necessary hardware. Therefore, the technical solutions according to the embodiments of this 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, external hard drive, etc.) or on a network, including several instructions to cause a computing device (such as a personal computer, server, terminal device, or network device, etc.) to execute the methods according to the embodiments of this disclosure.
[0092] Furthermore, the above figures are merely illustrative of the processes included in the method according to exemplary embodiments of this disclosure and are not intended to be limiting. It is readily understood that the processes shown in the above figures do not indicate or limit the temporal order of these processes. Additionally, it is readily understood that these processes may be executed synchronously or asynchronously, for example, in multiple modules.
[0093] It should be noted that although several modules or units for the device used to perform actions have been mentioned in the detailed description above, this division is not mandatory. In fact, according to embodiments of this disclosure, the features and functions of two or more modules or units described above can be embodied in one module or unit. Conversely, the features and functions of one module or unit described above can be further divided and embodied by multiple modules or units.
[0094] Other embodiments of this disclosure will readily occur to those skilled in the art upon consideration of the specification and practice of the disclosure herein. This application is intended to cover any variations, uses, or adaptations of this disclosure that follow the general principles of this disclosure and include common knowledge or customary techniques in the art not disclosed herein. The specification and examples are to be considered exemplary only, and the true scope and spirit of this disclosure are indicated by the claims.
[0095] It should be understood that this disclosure is not limited to the precise structures described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of this disclosure is limited only by the appended claims.
Claims
1. A method for early warning of floating ice around marine photovoltaic piles, characterized in that, include: Infrared images of floating ice around the piles in the offshore photovoltaic area at night, collected at a predetermined frequency, are obtained to obtain an infrared floating ice image sequence. Each infrared ice floe image in the infrared ice floe image sequence is subjected to ice floe identification to determine the ice floe fragment features of each infrared ice floe image; the ice floe fragment features include ice floe fragment location features and ice floe fragment size features; an ice floe feature change sequence corresponding to the infrared ice floe image sequence is determined based on the ice floe fragment features of each infrared ice floe image; the first ice floe feature change sequence determined based on the ice floe fragment location features is converted into a first vector representation; the second ice floe feature change sequence determined based on the ice floe fragment size features is converted into a second vector representation; the first vector representation and the second vector representation are concatenated to determine the trend feature value of the ice floe fragments around the pile of the pipe pile with a fixed vector length changing over time; The identification result of floating ice fragments around the pipe pile is determined based on the trend feature value; The ice floe fragment identification result includes ice floe fragment level and trend fluctuation entropy; wherein, determining the ice floe fragment identification result around the pipe pile based on the trend feature value includes: using a machine learning-based classification model to classify the trend feature value to determine the ice floe fragment level; determining the trend entropy and fluctuation entropy according to the trend feature value; and performing a weighted summation of the trend entropy and fluctuation entropy to obtain the trend fluctuation entropy; Execute ice blizzard warning operations based on the ice blizzard fragment identification results.
2. The method for early warning of floating ice around marine photovoltaic piles according to claim 1, characterized in that, Acquiring infrared images of the periphery of pipe piles within a marine photovoltaic area at night, collected at a predetermined frequency, includes: The image acquisition control command is sent to the UAV at a predetermined frequency. The image acquisition control command instructs the UAV to acquire infrared images of floating ice around the piles in the offshore photovoltaic area at night. The infrared images of the floating ice captured by the drone are obtained.
3. The method for early warning of floating ice around marine photovoltaic piles according to claim 1, characterized in that, The ice floe early warning operation based on the ice floe fragment identification results includes: If the ice floe fragment identification result indicates the existence of a corresponding warning target, the identifier of the warning device held by the warning target is determined; wherein, the warning target is a security personnel or equipment maintenance personnel of the offshore photovoltaic field. Warning information is sent to the warning device based on its identifier.
4. A floating ice early warning device around a marine photovoltaic pile, characterized in that, include: The image acquisition module is used to acquire infrared images of floating ice around the piles in the offshore photovoltaic area at night, collected at a predetermined frequency, so as to obtain an infrared floating ice image sequence. A trend feature determination module is used to identify ice floes in each infrared ice floe image in the infrared ice floe image sequence to determine the ice floe fragment features of each infrared ice floe image; the ice floe fragment features include ice floe fragment location features and ice floe fragment size features; a sequence of ice floe feature changes corresponding to the infrared ice floe image sequence is determined based on the ice floe fragment features of each infrared ice floe image; a first sequence of ice floe feature changes determined based on ice floe fragment location features is converted into a first vector representation; a second sequence of ice floe feature changes determined based on ice floe fragment size features is converted into a second vector representation; the first vector representation and the second vector representation are concatenated to determine the trend feature value of the ice floe fragments around the pile of the pipe pile with a fixed vector length changing over time; The floating ice result determination module is used to determine the floating ice fragment identification result around the pipe pile based on the trend feature value; The ice floe fragment identification result includes ice floe fragment level and trend fluctuation entropy; wherein, the ice floe result determination module is configured to use a machine learning-based classification model to classify the trend feature value to determine the ice floe fragment level; determine the trend entropy and fluctuation entropy according to the trend feature value respectively; and perform a weighted summation of the trend entropy and fluctuation entropy to obtain the trend fluctuation entropy; The ice floe warning module is used to perform ice floe warning operations based on the ice floe fragment identification results.
5. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the method for early warning of floating ice around marine photovoltaic piles as described in any one of claims 1 to 3.
6. An electronic device, characterized in that, include: processor; as well as Memory for storing the executable instructions of the processor; The processor is configured to implement the offshore photovoltaic pile perimeter ice warning method according to any one of claims 1 to 3 by executing the executable instructions.