Dispelling notification method, device and equipment for wind power plant and storage medium

Abstract

The embodiment of the invention provides a expelling notification method and device for a wind power plant, equipment and a storage medium, and relates to the technical field of wind power plant safety management. The method comprises the following steps: collecting video image data of a to-be-notified area; performing identification processing on the video image data by adopting an improved target detection algorithm model so as to identify all ships in the to-be-notified area; classifying all the ships to obtain at least one non-operation and maintenance ship in the to-be-notified area; position information of a non-operation and maintenance ship is acquired, and a expelling notification position is determined according to the position information; generating a sailing route according to the expelling notification position, and controlling the unmanned ship to travel to the expelling notification position along the sailing route; and generating a expelling notification instruction, and controlling the unmanned ship to broadcast the expelling notification instruction at the expelling notification position. According to the method provided by the embodiment of the invention, the problem of low safety of the wind power plant caused by the fact that the staff on the non-operation and maintenance ship cannot be notified in time is solved.

Description

Technical Field

[0001] The present application relates to the technical field of wind farm safety management, and in particular to a wind farm eviction notification method, device, equipment and storage medium. Background Art

[0002] Wind farms are large facilities that convert wind power into electricity and transmit the generated electricity to the power grid. During the operation and maintenance of offshore wind power, especially during the fishing season, there are many non-operation and maintenance ships passing by the wind farm. Sometimes, for convenience, non-operation and maintenance ships will berth at the wind turbines, or directly anchor at the wind farm, or even cast nets to catch fish at the wind farm, which will undoubtedly increase the probability of damage to the wind turbine structure and submarine cables.

[0003] At present, in order to drive away non-operation and maintenance ships passing by wind farms, high-frequency notifications of the Automatic Identification System (AIS) system or text message reminders are usually used to drive away non-operation and maintenance ships that have entered the wind farms.

[0004] However, the ship-driving method of wind farms in the prior art may not be able to promptly notify the staff on non-operation and maintenance ships, resulting in low safety of the wind farms. Summary of the invention

[0005] The embodiments of the present application provide a wind farm eviction notification method, device, equipment and storage medium, which are used to solve the problem that the personnel on non-operation and maintenance vessels cannot be notified in time, resulting in low safety of the wind farm.

[0006] In a first aspect, an embodiment of the present application provides a method for notifying a wind farm of its departure, comprising:

[0007] Collecting video image data of the area to be notified;

[0008] An improved target detection algorithm model is used to process the video image data to identify all ships in the area to be notified;

[0009] All ships are classified to obtain at least one non-operational ship in the area to be notified;

[0010] Obtain the location information of the non-operation and maintenance ship, and determine the location of the expulsion notice based on the location information;

[0011] Generate a navigation route based on the location of the drive-away notification, and control the unmanned boat to travel along the navigation route to the location of the drive-away notification;

[0012] Generate a drive-away notification command and control the unmanned boat to broadcast the drive-away notification command at the drive-away notification location.

[0013] In the above wind farm eviction notification method, optionally, the improved target detection algorithm model includes a starting module and a fast spatial pyramid pooling module;

[0014] The initiation module is used to receive data input into the improved target detection algorithm model and perform preliminary feature extraction;

[0015] The fast spatial pyramid pooling module is used to extract multi-scale feature information through pooling operations at multiple different scales.

[0016] In the above wind farm eviction notification method, optionally, the improved target detection algorithm model further includes a small target detection layer;

[0017] The small target detection layer is used to detect ships smaller than a preset size in the waiting notification area.

[0018] In the above wind farm departure notification method, optionally, an improved target detection algorithm model is used to perform recognition processing on the video image data to identify all ships in the area to be notified, including:

[0019] According to the number of frames of the video image, the video image is split and processed to obtain a plurality of images to be analyzed;

[0020] The image to be analyzed is input into the improved target detection algorithm model, so as to determine all ships in the area to be notified through the output of the improved target detection algorithm model.

[0021] The above wind farm eviction notification method may optionally include collecting video image data of the area to be notified, including:

[0022] The video image data of the area to be notified is collected through a first camera, wherein the first camera is arranged at a preset position of the wind farm.

[0023] As for the above wind farm eviction notification method, optionally, a sound amplification device is provided on the unmanned boat;

[0024] Accordingly, the unmanned boat is controlled to broadcast the drive-away notification instruction at the drive-away notification location, including:

[0025] Control the unmanned boat and broadcast the departure notification instruction at the departure notification location through the loudspeaker.

[0026] In the above wind farm eviction notification method, optionally, a second camera is also provided on the unmanned boat;

[0027] Accordingly, after generating the drive-away notification instruction and controlling the unmanned boat to broadcast the drive-away notification instruction at the drive-away notification location, the method further includes:

[0028] The video information of non-operation and maintenance ships is collected through the second camera.

[0029] In the above wind farm departure notification method, optionally, all ships are classified to obtain at least one non-operation and maintenance ship in the area to be notified, including:

[0030] Obtaining the pre-stored location information of the operation and maintenance vessel;

[0031] According to the position information of the maintenance ship, all ships are classified to obtain at least one non-maintenance ship in the area to be notified.

[0032] In a second aspect, an embodiment of the present application provides a wind farm departure notification device, including:

[0033] A collection module, used for collecting video image data of the area to be notified;

[0034] An identification module is used to use an improved target detection algorithm model to perform identification processing on the video image data to identify all ships in the area to be notified;

[0035] A classification module, used for classifying all ships to obtain at least one non-operational ship in the area to be notified;

[0036] A determination module is used to obtain the location information of the non-operation and maintenance ship and determine the location of the expulsion notification according to the location information;

[0037] The first control module is used to generate a navigation route according to the driving-away notification position, and control the unmanned boat to travel along the navigation route to the driving-away notification position;

[0038] The second control module is used to generate a drive-away notification instruction and control the unmanned boat to broadcast the drive-away notification instruction at the drive-away notification location.

[0039] In a third aspect, an embodiment of the present application provides a wind farm departure notification device, including:

[0040] at least one processor; and a memory communicatively coupled to the at least one processor;

[0041] The memory stores instructions that can be executed by at least one processor, and the instructions are executed by at least one processor so that the at least one processor can execute the wind farm departure notification method of any one of the first aspects.

[0042] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, in which computer-executable instructions are stored. When the computer-executable instructions are executed by a processor, they are used to implement the wind farm eviction notification method of any one of the first aspects.

[0043] In a fifth aspect, an embodiment of the present application provides a computer program product, including a computer program. When the computer program is executed by a processor, the wind farm eviction notification method according to any one of the first aspects above is implemented.

[0044] The embodiments of the present application provide a method, device, equipment and storage medium for notifying a wind farm of its departure. The embodiments of the present application can use an improved target detection algorithm model, combined with real-time detected video image data, to accurately identify all ships in the area to be notified, and determine non-maintenance ships through classification processing. Based on the location information of non-maintenance ships, the unmanned boat is controlled to directly travel to the vicinity of the non-maintenance ships to broadcast departure notification instructions. This avoids the situation where the departure notification methods such as the AIS system high-frequency notification or SMS reminder fail due to the staff turning off the AIS system or shielding SMS messages. It can accurately broadcast the departure notification instructions at close range, and promptly notify the staff on the non-maintenance ships, thereby improving the safety of the wind farm. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following is a brief introduction to the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0046] Figure 1 A schematic diagram of an application data processing system architecture provided in an embodiment of the present application;

[0047] Figure 2 A wind farm eviction notification method process diagram provided in an embodiment of the present application Figure 1 ;

[0048] Figure 3 A wind farm eviction notification method process diagram provided in an embodiment of the present application Figure 2 ;

[0049] Figure 4 A schematic diagram of the structure of a wind farm departure notification device provided in an embodiment of the present application;

[0050] Figure 5 A schematic diagram of the structure of a wind farm departure notification device provided in an embodiment of the present application.

[0051] Reference numerals:

[0052] 101-data acquisition device; 102-processing device; 103-display device;

[0053] 401-acquisition module; 402-identification module; 403-classification module; 404-determination module; 405-first control module; 406-second control module;

[0054] 501 - processor; 502 - memory; 503 - communication component; 504 - bus. DETAILED DESCRIPTION

[0055] Exemplary embodiments will be described in detail herein, examples of which are shown in the accompanying drawings. When the following description refers to the drawings, the same numbers in different drawings represent the same or similar elements unless otherwise indicated. The implementations described in the following exemplary embodiments do not represent all implementations consistent with the present application. Instead, they are merely examples of devices and methods consistent with some aspects of the present application as detailed in the appended claims.

[0056] In the embodiments of the present application, words such as "first" and "second" are used to distinguish between identical or similar items with substantially the same functions and effects. Those skilled in the art will understand that words such as "first" and "second" do not limit the quantity and execution order, and words such as "first" and "second" do not necessarily limit the difference. It should be noted that in the embodiments of the present application, words such as "exemplary" or "for example" are used to indicate examples, illustrations or explanations. Any embodiment or design described as "exemplary" or "for example" in the present application should not be interpreted as being more preferred or more advantageous than other embodiments or design. Specifically, the use of words such as "exemplary" or "for example" is intended to present related concepts in a specific way. In the embodiments of the present application, "at least one" refers to one or more, and "more" refers to two or more.

[0057] It should be noted that the "at..." in the embodiment of the present application can be the instant when a certain situation occurs, or can be a period of time after a certain situation occurs, and the embodiment of the present application does not specifically limit this. In addition, the wind farm eviction notification method provided in the embodiment of the present application is only an example, and the wind farm eviction notification method can also include more or less content.

[0058] In order to ensure the safety of offshore wind power structures and submarine cables, avoid collisions with offshore wind power structures by non-maintenance ships, and damage to submarine cables caused by ships anchoring in offshore wind farms, the AIS system is currently often used to issue high-frequency notifications to drive away non-maintenance ships passing by wind farms, or to cooperate with the three major telecommunications operators, China Telecom, China Mobile and China Unicom, to send text messages to remind staff on passing ships to drive away passing non-maintenance ships from the wind farm as soon as possible to avoid damage to wind turbine structures and submarine cables.

[0059] However, non-operational vessels turn off the AIS system during fishing, causing the AIS system's high-frequency notification to fail. At the same time, the crew blocks reminder text messages during work, making the above method ineffective.

[0060] In summary, the existing method of driving away ships from wind farms may not be able to promptly notify the staff on non-operation and maintenance ships, resulting in low safety of the wind farm.

[0061] In response to the above problems, in the process of studying the failure of ship drive-away notification, it was found that after identifying non-maintenance ships in the video image data through the target detection algorithm model, the unmanned boat was controlled to travel near the non-maintenance ship and broadcast the drive-away notification, which can avoid the failure of the ship drive-away notification due to the staff turning off the AIS system or blocking text messages. At the same time, the target detection algorithm model can be used to identify ships in the video image data for accurate identification.

[0062] Optionally, when identifying non-operation ships in video image data through the target detection algorithm model, the Stem module and the Spatial Pyramid Pooling-Fast (SPFF) module can be introduced into the backbone network of the existing target detection algorithm model to reduce redundant calculations and expand the receptive field, thereby improving the efficiency and accuracy of detection. With the support of the channel attention mechanism, the model can more accurately extract key image features and improve the overall recognition effect.

[0063] Based on this, the embodiments of the present application provide a wind farm eviction notification method, device, equipment and storage medium, which can be used in the field of wind farm safety management technology, aiming to solve the above technical problems of the prior art.

[0064] Figure 1 The present invention provides an application data processing system architecture diagram, wherein the application data processing system is a computer device. Figure 1 In the above architecture, the above architecture includes at least one of a data acquisition device 101, a processing device 102 and a display device 103.

[0065] It is understandable that the structure illustrated in the embodiment of the present application does not constitute a specific limitation on the architecture of the wind farm eviction notification system. In other feasible implementations of the present application, the above architecture may include more or fewer components than shown in the figure, or combine certain components, or split certain components, or arrange the components differently, which can be determined according to the actual application scenario and is not limited here. Figure 1 The components shown may be hardware, software, or a combination of software and hardware.

[0066] In a specific implementation process, the data acquisition device 101 may include an input / output interface and may also include a communication interface. The data acquisition device 101 may be connected to the processing device via the input / output interface or the communication interface.

[0067] The processing device 102 can identify the non-maintenance ship in the video image data through the target detection algorithm model, and then control the unmanned boat to travel to the vicinity of the non-maintenance ship to broadcast a notice of departure.

[0068] The display device 103 may also be a touch display screen or a screen of a terminal device, which is used to receive user instructions while displaying the above content to achieve interaction with the user.

[0069] It should be understood that the above-mentioned processing device can be implemented by a processor reading instructions in a memory and executing the instructions, or it can be implemented by a chip circuit.

[0070] In addition, the network architecture and business scenarios described in the embodiments of the present application are intended to more clearly illustrate the technical solutions of the embodiments of the present application, and do not constitute a limitation on the technical solutions provided in the embodiments of the present application. A person of ordinary skill in the art can appreciate that with the evolution of the network architecture and the emergence of new business scenarios, the technical solutions provided in the embodiments of the present application are also applicable to similar technical problems.

[0071] Figure 2 A wind farm eviction notification method process diagram provided in an embodiment of the present application Figure 1 .

[0072] like Figure 2 As shown, the method includes:

[0073] S201, collecting video image data of the area to be notified.

[0074] Specifically, the area to be notified is the offshore wind farm area. Video image data of the area to be notified can be collected by deploying cameras, including:

[0075] The video image data of the area to be notified is collected through a first camera, wherein the first camera is arranged at a preset position of the wind farm.

[0076] Through the above-mentioned first camera, real-time monitoring of the area to be notified can be achieved, and all ships in the area to be notified can be accurately identified, providing an accurate basis for the identification, monitoring and notification of non-operational ships. Non-operational ships can also be discovered in a timely manner, improving the safety of offshore wind farms.

[0077] Specifically, high-resolution, all-weather HD first cameras are deployed at key locations, i.e. preset locations, in each wind turbine and submarine cable distribution area. These cameras have infrared night vision capabilities, ensuring that offshore targets can be clearly captured at night or in low light conditions.

[0078] The cameras are arranged to cover the entire wind farm area, including all wind turbine bases, connected submarine cable areas and surrounding sea areas, ensuring that there are no blind spots in monitoring.

[0079] The collected video image data is transmitted to the data processing center via wireless network or optical fiber to ensure real-time and stability. At the same time, the video image data can be stored in the data processing center for subsequent calls.

[0080] S202: Using an improved target detection algorithm model, the video image data is processed to identify all ships in the area to be notified.

[0081] Among them, the target detection algorithm model is used to identify and locate specific target objects in images or videos. You Only Look Once v7 (YOLOv7) can be selected as the target detection algorithm model, which is improved on the basis of YOLOv7; all ships include operation and maintenance ships and non-operation and maintenance ships.

[0082] Among them, S202 specifically includes:

[0083] Step 202.1: Split the video image according to the number of frames of the video image to obtain a plurality of images to be analyzed.

[0084] Step 202.2: input the image to be analyzed into the improved target detection algorithm model, so as to determine all ships in the area to be notified through the output of the improved target detection algorithm model.

[0085] By splitting the video images and using the improved target detection algorithm model to analyze the images to be analyzed, all ships in the area to be notified can be accurately identified.

[0086] Among them, the improved target detection algorithm model includes: Stem module, SPFF module and small target detection layer.

[0087] The Stem module is used to receive data input into the improved target detection algorithm model and perform preliminary feature extraction; the SPFF module is used to extract multi-scale feature information through pooling operations at multiple different scales; the small target detection layer is used to detect ships smaller than a preset size in the area to be notified.

[0088] By introducing the Stem module, SPFF module and small target detection layer, redundant calculations are reduced and the receptive field is expanded, the efficiency and accuracy of detection are improved, and the detection precision and response speed are improved.

[0089] Specifically, YOLOv7 is used as the basis of the target detection algorithm model, and the model is further optimized to adapt to the complexity of the marine environment.

[0090] First, through the feature pyramid network (FPN) structure in the feature enhancement structure (Neck) of YOLOv7, the image features to be analyzed are extracted step by step from the bottom to the top, and multiple levels of features are obtained to ensure that targets of different scales can be effectively identified, which is convenient for detecting small ships in the distance. Through the path aggregation network (PAN), high-level features and low-level features are fused to obtain fused features, shortening the feature transmission path, so that low-level detail features can more quickly affect the final prediction and improve detection accuracy.

[0091] Secondly, the Stem module is introduced to replace the original Focus module of YOLOv7 with the Stem module for preliminary feature extraction. The Stem module improves the generalization ability of the network and significantly reduces the computational complexity by reducing unnecessary calculations without affecting the performance of the model. At the same time, the Squeeze-and-Excitation (SE) module is added to the YOLOv7 backbone network as a channel attention mechanism, which enhances the model's ability to focus on important image features by adaptively redistributing the weights of different channels.

[0092] Thirdly, the SPPF structure is introduced to extract multi-scale feature information through pooling operations of multiple scales, integrate information from different receptive fields, expand the perception range of the network, and significantly improve the ability to extract global information when processing large scene images.

[0093] Finally, a small target detection layer is added to deal with small targets. In the waiting area, ships smaller than the preset size are detected. By directly predicting on the high-resolution layer of the feature map, the detection accuracy of small ships at a long distance is improved, and the loss of small targets due to reduced resolution is avoided. In the monitoring of the sea area of ​​wind farms, small ships are often difficult to be identified by traditional detection models. By adding a small target detection layer, the system effectively improves the detection accuracy of small ships at a long distance, and accurately improves the detection accuracy of small targets in complex marine environments.

[0094] S203: Classify all ships to obtain at least one non-operation ship in the area to be notified.

[0095] Specifically, all ships can be classified according to the identification of operation and maintenance ships, such as classification labels, location information, etc.

[0096] Exemplarily, S203 specifically includes:

[0097] Step 203.1, obtaining the pre-stored location information of the operation and maintenance vessel;

[0098] Specifically, the location information of the operation and maintenance ship is pre-stored, and the location of the operation and maintenance ship can be located in real time.

[0099] Step 203.2: Classify all ships according to the location information of the maintenance ship to obtain at least one non-maintenance ship in the area to be notified.

[0100] Through the real-time location information of the maintenance ship, the maintenance ship can be accurately distinguished from the non-maintenance ship to facilitate the subsequent removal operation.

[0101] Specifically, according to the position information of the maintenance ship, the maintenance ship among all the ships is identified, and then the non-maintenance ship is identified, and the classification process is completed to obtain at least one non-maintenance ship in the area to be notified, and the position of the non-maintenance ship is determined.

[0102] S204: Acquire the location information of the non-operation and maintenance vessel, and determine the location of the expulsion notification according to the location information.

[0103] Specifically, the drive-away notification position may be a position at a distance from the non-maintenance ship that is less than a first preset distance, or a position in the positive direction of the non-maintenance ship at a distance from the non-maintenance ship that is a second preset distance, or other positions that can notify the non-maintenance ship.

[0104] When a non-operation and maintenance ship is identified in the wind farm, the drive-away notification position is determined and an early warning signal is sent to the unmanned boat, the early warning signal including the drive-away notification position.

[0105] Furthermore, it is possible to first determine whether the non-maintenance ship is likely to damage the wind farm equipment, such as colliding with wind turbines, breaking submarine cables, etc., and then determine whether to notify immediately, not notify temporarily and keep observing, etc. For example, it is possible to determine whether the non-maintenance ship is likely to damage the wind farm equipment based on the location of the non-maintenance ship and the location of the wind farm equipment.

[0106] S205: Generate a navigation route according to the drive-away notification location, and control the unmanned boat to travel along the navigation route to the drive-away notification location.

[0107] Specifically, after receiving the warning signal, the unmanned boat will quickly navigate to the corresponding location through the automatic driving system according to the driving away notification location in the warning signal. During the navigation of the unmanned boat, the dynamics of the non-maintenance ship will be continuously monitored, and the driving route of the unmanned boat will be adjusted to ensure that the unmanned boat can accurately approach the non-maintenance ship.

[0108] S206: Generate a drive-away notification instruction, and control the unmanned boat to broadcast the drive-away notification instruction at the drive-away notification location.

[0109] The eviction notification instruction includes specific broadcast content.

[0110] Wherein, the unmanned boat is provided with a sound amplification device; accordingly, the unmanned boat is controlled to broadcast the expulsion notification instruction at the expulsion notification location, including:

[0111] Control the unmanned boat and broadcast the departure notification instruction at the departure notification location through the loudspeaker.

[0112] Use the loudspeaker to issue clear instructions for non-operation and maintenance vessels to leave the wind farm area immediately. Increase the volume to ensure that the crew can clearly hear the information even in bad weather conditions.

[0113] Based on the above embodiment, a second camera is further provided on the unmanned boat;

[0114] Accordingly, after generating the drive-away notification instruction and controlling the unmanned boat to broadcast the drive-away notification instruction at the drive-away notification location, the method further includes:

[0115] The video information of non-operation and maintenance ships is collected through the second camera.

[0116] The second camera collects and stores video information, providing a basis for determining accident responsibility.

[0117] Specifically, during the expulsion process, the camera of the unmanned boat will record the video of the non-operation and maintenance ship, capturing the detailed characteristic information of the ship, such as the ship's hull number, ship name, crew activities, etc. This information will be transmitted back to the data processing center in real time, and stored in the local storage device of the unmanned boat for backup at the same time, as evidence for future tracing. If an accident occurs in a wind farm where a ship collides with a wind turbine or damages a submarine cable, the video data recorded by the unmanned boat can be retrieved, combined with the analysis results of image recognition, to track the ship at fault, providing a basis for determining the responsibility for the accident.

[0118] In one possible example, Figure 3 A wind farm eviction notification method process diagram provided in an embodiment of the present application Figure 2 ,like Figure 3 As shown, the expulsion notification method includes: monitoring system detection, image recognition and analysis, sending warning signals, unmanned boat response, expulsion warning, information recording and data storage, and system and recognition model optimization.

[0119] Monitoring system detection captures offshore wind farm targets through real-time monitoring and target detection.

[0120] The image recognition and analysis module has preset hardware configuration and algorithm structure. The image recognition and analysis module is deployed in the data processing center and is equipped with a high-performance graphics processor to support real-time deep learning computing needs.

[0121] The ship's position information is sent through the hardware communication module to send an early warning signal.

[0122] The unmanned boat response is achieved through the unmanned boat navigation system and power system. The unmanned boat is equipped with a navigation system, amplification device, power system, camera and communication equipment. Specifically, the unmanned boat is equipped with a high-precision global positioning system and an automatic driving module to ensure that it can quickly and accurately navigate to the vicinity of the target ship after receiving the warning signal; it is equipped with a 360-degree panoramic camera and an efficient data transmission module to transmit video data to the control center in real time and record detailed information of the target ship; the power system adopts a dual-engine design to provide sufficient power to maintain stability in a strong current environment and has the ability to cruise for a long time. The operation of the unmanned boat is carried out by the remote control center, and it can also perform tasks independently according to the preset route and target, and automatically return to the base station for charging or mooring after completing the task.

[0123] Eviction warning is achieved through on-site broadcast of eviction message notification.

[0124] Record vessel information and store it in the database to complete information recording and data storage.

[0125] The system and identification model optimization are completed according to the historical data in the database, and then the monitoring system is monitored.

[0126] In summary, the method of the embodiment of the present application, by identifying non-maintenance ships in the video image data, and controlling the unmanned boat to broadcast the expulsion notification instruction to the non-maintenance ship, avoids the failure of the AIS system high-frequency notification or SMS reminder and other expulsion notification methods due to the staff turning off the AIS system or shielding SMS, and solves the problem that the staff on the non-maintenance ship cannot be notified in time, resulting in low safety of the wind farm. By combining image recognition technology with the unmanned boat system, the automation and precise expulsion of non-maintenance ships in offshore wind farms is realized. By focusing on the combination of low power consumption and high efficiency in hardware selection, the lightweight transformation of the YOLOv7 model significantly reduces the occupation of computing resources, allowing the system to still operate efficiently in low power consumption mode, adapting to long-term stable operation in complex offshore environments, and reducing resource consumption. While improving the overall operating efficiency and detection accuracy. By introducing the Stem module and SPPF structure in the backbone network, reducing redundant calculations and expanding the receptive field, the efficiency and accuracy of detection are improved. With the support of the channel attention mechanism, the model can more accurately extract key image features and improve the overall recognition effect. In the monitoring of wind farm sea areas, small ships are often difficult to be identified by traditional detection models. By adding a small target detection layer, the detection accuracy of small ships at long distances is effectively improved. Experiments show that in complex marine environments, the detection accuracy of small targets has increased by more than 15%. With the support of optimized algorithms and hardware, the overall response time of the system is controlled within 2 seconds. From target detection to the dispatch of unmanned boats, the entire process can be completed within 1 minute, ensuring timeliness.

[0127] The embodiment of the present application can divide the functional modules of the electronic device or the main control device according to the above method example. For example, each functional module can be divided according to each function, or two or more functions can be integrated into one processing unit. The above integrated unit can be implemented in the form of hardware or in the form of software functional modules. It should be noted that the division of modules in the embodiment of the present application is schematic and is only a logical function division. There may be other division methods in actual implementation.

[0128] Figure 4 This is a schematic diagram of the structure of a wind farm departure notification device provided in an embodiment of the present application. Figure 4 As shown, the wind farm departure notification device includes: a collection module 401 , an identification module 402 , a classification module 403 , a determination module 404 , a first control module 405 and a second control module 406 .

[0129] The acquisition module 401 is used to acquire video image data of the area to be notified;

[0130] The recognition module 402 is used to use an improved target detection algorithm model to perform recognition processing on the video image data to identify all ships in the area to be notified;

[0131] The classification module 403 is used to classify all ships to obtain at least one non-operational ship in the area to be notified;

[0132] A determination module 404 is used to obtain the location information of the non-operation and maintenance ship and determine the location of the expulsion notification according to the location information;

[0133] The first control module 405 is used to generate a navigation route according to the driving-away notification position, and control the unmanned boat to travel along the navigation route to the driving-away notification position;

[0134] The second control module 406 is used to generate a drive-away notification instruction and control the unmanned boat to broadcast the drive-away notification instruction at the drive-away notification location.

[0135] Optionally, the improved target detection algorithm model includes a starting module and a fast spatial pyramid pooling module; the starting module is used to receive data input into the improved target detection algorithm model and perform preliminary feature extraction; the fast spatial pyramid pooling module is used to extract multi-scale feature information through pooling operations of multiple different scales.

[0136] Optionally, the improved target detection algorithm model also includes a small target detection layer; the small target detection layer is used to detect ships smaller than a preset size in the area to be notified.

[0137] Optionally, the identification module 402 is specifically used for:

[0138] The video image is split according to the number of frames of the video image to obtain a plurality of images to be analyzed; the images to be analyzed are input into the improved target detection algorithm model to determine all ships in the area to be notified through the output of the improved target detection algorithm model.

[0139] Optionally, the acquisition module 401 is specifically used for:

[0140] The video image data of the area to be notified is collected through a first camera, wherein the first camera is arranged at a preset position of the wind farm.

[0141] Optionally, the unmanned boat is provided with a sound amplification device, and the second control module 406 is specifically used for:

[0142] Control the unmanned boat and broadcast the departure notification instruction at the departure notification location through the loudspeaker.

[0143] Optionally, the unmanned boat is further provided with a second camera and a second control module 406, which are specifically used for:

[0144] The video information of non-operation and maintenance ships is collected through the second camera.

[0145] Optionally, the classification module 403 is specifically used for:

[0146] The pre-stored location information of the maintenance ship is obtained; and all ships are classified according to the location information of the maintenance ship to obtain at least one non-maintenance ship in the area to be notified.

[0147] The present embodiment provides a wind farm departure notification device, which can execute the wind farm departure notification method of the above embodiment. The implementation principle and technical effect are similar, and will not be described in detail in the present embodiment.

[0148] In the specific implementation of the aforementioned wind farm departure notification device, each module may be implemented as a processor, and the processor may execute computer-executable instructions stored in the memory, so that the processor executes the aforementioned wind farm departure notification method.

[0149] Figure 5 This is a schematic diagram of the structure of a wind farm departure notification device provided in an embodiment of the present application. Figure 5 As shown, the device includes: at least one processor 501 and a memory 502. The device also includes a communication component 503. The processor 501, the memory 502 and the communication component 503 are connected via a bus 504.

[0150] In a specific implementation process, at least one processor 501 executes the computer-executable instructions stored in the memory 502, so that the at least one processor 501 executes a wind farm eviction notification method executed by the device side as described above.

[0151] The specific implementation process of the processor 501 can be found in the above method embodiment, and its implementation principle and technical effect are similar, so this embodiment will not be repeated here.

[0152] In the above embodiments, it should be understood that the processor may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), etc. The general-purpose processor may be a microprocessor or the processor may be any conventional processor, etc. The steps of the method disclosed in the invention may be directly embodied as being executed by a hardware processor, or may be executed by a combination of hardware and software modules in the processor.

[0153] The memory may include a high-speed random-access memory (RAM) and may also include a non-volatile memory (NVM), such as at least one disk memory.

[0154] The bus may be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. The bus may be divided into an address bus, a data bus, a control bus, etc. For ease of representation, the bus in the drawings of the embodiments of the present application is not limited to only one bus or one type of bus.

[0155] The above-mentioned functions implemented by the device are introduced to the scheme provided by the embodiment of the present application. It is understandable that, in order to implement the above-mentioned functions, the device includes a hardware structure and / or software module corresponding to the execution of each function. In combination with the units and algorithm steps of each example described in the embodiment disclosed in the embodiment of the present application, the embodiment of the present application can be implemented in the form of hardware or a combination of hardware and computer software. Whether a certain function is executed in the form of hardware or computer software driving hardware depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of the technical solution of the embodiment of the present application.

[0156] The embodiment of the present application further provides a computer-readable storage medium, in which computer-executable instructions are stored. When a processor executes the computer-executable instructions, the computer-readable storage medium is used to implement the above wind farm eviction notification method.

[0157] The above-mentioned readable storage medium can be any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk or optical disk. The readable storage medium can be any available medium that can be accessed by a general or special-purpose computer.

[0158] An exemplary readable storage medium is coupled to a processor so that the processor can read information from the readable storage medium and write information to the readable storage medium. Of course, the readable storage medium can also be a component of the processor. The processor and the readable storage medium can be located in a dedicated integrated circuit. Of course, the processor and the readable storage medium can also exist as discrete components in an electronic device or a main control device.

[0159] An embodiment of the present application also provides a computer program product, which includes: a computer program, which is stored in a readable storage medium, and at least one processor of an electronic device can read the computer program from the readable storage medium, and at least one processor executes the computer program so that the electronic device executes the solution provided by any of the above embodiments.

[0160] Those skilled in the art can understand that all or part of the steps of implementing the above-mentioned method embodiments can be completed by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When the program is executed, the steps of the above-mentioned method embodiments are executed; and the aforementioned storage medium includes: ROM, RAM, disk or optical disk and other media that can store program codes.

[0161] So far, the technical solution of the present application has been described in conjunction with the preferred embodiments shown in the accompanying drawings. However, it is easy for those skilled in the art to understand that the protection scope of the present application is obviously not limited to these specific embodiments, and the above embodiments are only used to illustrate the technical solution of the present application rather than to limit it. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein by equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present application.

Claims

1. A method for notifying a wind farm of its departure, characterized in that: include: Collecting video image data of the area to be notified; Using an improved target detection algorithm model, the video image data is processed for identification to identify all ships in the area to be notified; Classifying all the ships to obtain at least one non-operation ship in the area to be notified; Acquiring the location information of the non-operation and maintenance ship, and determining the expulsion notification location according to the location information; Generate a navigation route according to the drive-away notification location, and control the unmanned boat to travel along the navigation route to the drive-away notification location; Generate a drive-away notification instruction, and control the unmanned boat to broadcast the drive-away notification instruction at the drive-away notification location.

2. The method according to claim 1, characterized in that The improved target detection algorithm model includes a starting module and a fast spatial pyramid pooling module; The starting module is used to receive data input into the improved target detection algorithm model and perform preliminary feature extraction; The fast spatial pyramid pooling module is used to extract multi-scale feature information through pooling operations of multiple different scales.

3. The method according to claim 2, characterized in that The improved target detection algorithm model also includes a small target detection layer; The small target detection layer is used to detect ships smaller than a preset size in the to-be-notified area.

4. The method according to claim 1, characterized in that: The improved target detection algorithm model is used to perform recognition processing on the video image data to identify all ships in the area to be notified, including: According to the number of frames of the video image, the video image is split to obtain a plurality of images to be analyzed; The image to be analyzed is input into an improved target detection algorithm model, so as to determine all ships in the area to be notified through the output of the improved target detection algorithm model.

5. The method according to any one of claims 1 to 4, characterized in that: The collecting of video image data of the area to be notified includes: The video image data of the area to be notified is collected by a first camera, wherein the first camera is arranged at a preset position of the wind farm.

6. The method according to any one of claims 1 to 4, characterized in that: The unmanned boat is provided with a sound amplification device; Accordingly, the controlling the unmanned boat to broadcast the drive-away notification instruction at the drive-away notification location includes: The unmanned boat is controlled to broadcast the drive-away notification instruction at the drive-away notification location through the loudspeaker.

7. The method according to claim 6, characterized in that The unmanned boat is also provided with a second camera; Accordingly, after generating the drive-away notification instruction and controlling the unmanned boat to broadcast the drive-away notification instruction at the drive-away notification location, the method further includes: The video information of the non-operation and maintenance ship is collected through the second camera.

8. The method according to any one of claims 1 to 4, characterized in that: The classifying of all the ships to obtain at least one non-operation ship in the area to be notified includes: Obtaining the pre-stored location information of the operation and maintenance vessel; According to the position information of the maintenance ship, all the ships are classified to obtain at least one non-maintenance ship in the area to be notified.

9. A wind farm eviction notification device, characterized in that: include: A collection module, used for collecting video image data of the area to be notified; An identification module, used to use an improved target detection algorithm model to perform identification processing on the video image data to identify all ships in the area to be notified; A classification module, used for classifying all the ships to obtain at least one non-operational ship in the area to be notified; A determination module, used to obtain the position information of the non-operation and maintenance ship, and determine the expulsion notification position according to the position information; A first control module is used to generate a navigation route according to the drive-away notification location, and control the unmanned boat to travel along the navigation route to the drive-away notification location; The second control module is used to generate a drive-away notification instruction and control the unmanned boat to broadcast the drive-away notification instruction at the drive-away notification location.

10. A wind farm eviction notification device, characterized in that: include: at least one processor; and a memory communicatively coupled to the at least one processor; The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the wind farm eviction notification method according to any one of claims 1 to 8.

11. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer-executable instructions, and when the computer-executable instructions are executed by a processor, they are used to implement the wind farm eviction notification method according to any one of claims 1 to 8.

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