Operation and maintenance method, device and equipment for offshore wind power, medium and program product

Abstract

The invention relates to the technical field of wind power generation, and discloses an operation and maintenance method, device and equipment for offshore wind power, a medium and a program product. The method comprises the following steps: acquiring current operation data of an offshore wind plant, inputting the current operation data into a pre-trained anomaly prediction model, and acquiring an anomaly prediction result output by the anomaly prediction model; obtaining abnormal wind power equipment and a corresponding abnormal type according to the abnormal prediction result, and obtaining target inspection equipment according to the abnormal wind power equipment and the corresponding abnormal type; and obtaining an inspection route corresponding to the target inspection equipment, controlling the target inspection equipment to inspect the abnormal wind power equipment based on the inspection route, obtaining an inspection result, and visually displaying the inspection result. According to the scheme of the embodiment, when the abnormal wind power equipment is detected, the corresponding inspection equipment is arranged for automatic inspection, so that the operation and maintenance efficiency of offshore wind power can be improved, the operation and maintenance cost can be reduced, and the safety of operation and maintenance operation can be ensured.

Description

Technical Field

[0001] The present invention relates to the technical field of wind power generation, and particularly to an operation and maintenance method, device, equipment, medium and program product for offshore wind power. Background Art

[0002] As a key area for the development of renewable energy, offshore wind power is gradually entering a new development stage. Offshore wind farms are characterized by wide distribution and harsh environments, posing huge challenges to the operation and maintenance of wind farms.

[0003] Currently, the existing operation and maintenance methods for offshore wind power usually rely on manually operated maintenance vessels to regularly patrol and maintain inside the wind farm. However, this method has low work efficiency, high operation and maintenance costs, and it is difficult to ensure the safety of operations. Summary of the Invention

[0004] The present invention provides an operation and maintenance method, device, equipment, medium and program product for offshore wind power, which can improve the operation and maintenance efficiency of offshore wind power, reduce operation and maintenance costs, and ensure the safety of operation and maintenance work.

[0005] According to one aspect of the present invention, there is provided an operation and maintenance method for offshore wind power, including:

[0006] Obtaining the current operation data of an offshore wind farm, inputting the current operation data into a pre-trained anomaly prediction model, and obtaining the anomaly prediction result output by the anomaly prediction model;

[0007] According to the anomaly prediction result, obtaining the abnormal wind power equipment and the corresponding abnormal type, and according to the abnormal wind power equipment and the corresponding abnormal type, obtaining the target inspection equipment;

[0008] Obtaining the inspection route corresponding to the target inspection equipment, and based on the inspection route, controlling the target inspection equipment to inspect the abnormal wind power equipment, obtaining the inspection result, and visually displaying the inspection result.

[0009] According to another aspect of the present invention, there is provided an operation and maintenance device for offshore wind power, including:

[0010] An anomaly prediction module, configured to obtain the current operation data of an offshore wind farm, input the current operation data into a pre-trained anomaly prediction model, and obtain the anomaly prediction result output by the anomaly prediction model;

[0011] An inspection equipment acquisition module, configured to obtain the abnormal wind power equipment and the corresponding abnormal type according to the anomaly prediction result, and obtain the target inspection equipment according to the abnormal wind power equipment and the corresponding abnormal type;

[0012] The equipment inspection module is used to obtain the inspection route corresponding to the target inspection equipment, and based on the inspection route, control the target inspection equipment to inspect the abnormal wind power equipment, obtain the inspection results, and visually display the inspection results.

[0013] According to another aspect of the present invention, there is provided an electronic device, which includes:

[0014] At least one processor; and

[0015] A memory communicatively connected to the at least one processor; wherein,

[0016] The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the operation and maintenance method of the offshore wind power according to any embodiment of the present invention.

[0017] According to another aspect of the present invention, there is provided a computer-readable storage medium storing a computer program, and the computer program is used to implement the operation and maintenance method of the offshore wind power according to any embodiment of the present invention when executed by a processor.

[0018] According to another aspect of the present invention, there is provided a computer program product including a computer program, and the computer program implements the operation and maintenance method of the offshore wind power according to any embodiment of the present invention when executed by a processor.

[0019] The technical solution of the embodiment of the present invention is to obtain the current operation data of the offshore wind farm, input the current operation data into a pre-trained anomaly prediction model, and obtain the anomaly prediction result output by the anomaly prediction model; according to the anomaly prediction result, obtain the abnormal wind power equipment and the corresponding anomaly type, and according to the abnormal wind power equipment and the corresponding anomaly type, obtain the target inspection equipment; obtain the inspection route corresponding to the target inspection equipment, and based on the inspection route, control the target inspection equipment to inspect the abnormal wind power equipment, obtain the inspection results, and visually display the inspection results; by arranging the corresponding inspection equipment to automatically inspect the abnormal wind power equipment according to the inspection route when detecting the abnormal wind power equipment, the operation and maintenance efficiency of the offshore wind power can be improved, the operation and maintenance cost can be reduced, and the safety of the operation and maintenance work can be ensured.

[0020] It should be understood that the content described in this part is not intended to identify the key or important features of the embodiments of the present invention, nor is it used to limit the scope of the present invention. Other features of the present invention will become easily understood through the following description. Description of the Drawings

[0021] To more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for the description of the embodiments. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0022] Figure 1 It is a flowchart of an operation and maintenance method for offshore wind power according to Embodiment 1 of the present invention;

[0023] Figure 2 It is a flowchart of an operation and maintenance method for offshore wind power according to Embodiment 2 of the present invention;

[0024] Figure 3 It is a schematic structural diagram of an operation and maintenance device for offshore wind power according to Embodiment 3 of the present invention;

[0025] Figure 4 It is a schematic structural diagram of an electronic device for implementing the operation and maintenance method of offshore wind power in the embodiments of the present invention. Detailed implementation manners

[0026] In order to enable those skilled in the art to better understand the solutions of the present invention, the following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0027] It should be noted that the terms "first", "second", "target", etc. in the specification and claims of the present invention and the above drawings are used to distinguish similar objects, and do not necessarily need to describe a specific order or sequence. It should be understood that such data can be interchanged under appropriate circumstances so that the embodiments of the present invention described here can be implemented in an order other than those illustrated or described here. In addition, the terms "comprising" and "having" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product or device including a series of steps or units does not necessarily have to be limited to those clearly listed steps or units, but may include other steps or units not clearly listed or inherent to these processes, methods, products or devices.

[0028] The operation and maintenance system of an offshore wind farm can consist of unmanned devices, an offshore substation, and an onshore centralized control center. As a remote monitoring point, the onshore centralized control center is equipped with a relay device, and the offshore substation is equipped with two relay devices (directional antennas), one facing the shore direction and the other facing the direction of the offshore wind turbines. Each unmanned device is equipped with a mobile radio. The onshore centralized control center sends remote control instructions through an operation console. The instructions are transmitted to the relay device of the offshore substation through the relay device of the centralized control center, and then transmitted to the mobile radio of the unmanned device by the relay device of the offshore substation, realizing the movement and operation control of the unmanned device.

[0029] Embodiment 1

[0030] Figure 1 FIG. 7 is a flowchart of an operation and maintenance method for offshore wind power provided in Embodiment 1 of the present invention. This embodiment is applicable to the situation of operating and maintaining an offshore wind farm. This method can be executed by an operation and maintenance device for offshore wind power. The operation and maintenance device for offshore wind power can be implemented in the form of hardware and / or software. Typically, the operation and maintenance device for offshore wind power can be configured in an electronic device, such as a computer device or a server, etc. As Figure 1 shown, the method includes:

[0031] S110. Obtain the current operation data of the offshore wind farm, input the current operation data into a pre-trained anomaly prediction model, and obtain the anomaly prediction result output by the anomaly prediction model.

[0032] Among them, the operation data includes wind turbine data and environmental data. The wind turbine data includes at least one of the operating state, device log, vibration data, temperature data, pressure data, and power output data. The environmental data includes at least one of wind speed, air temperature, humidity, sea current, and wave height. In this embodiment, different types of sensors and cameras can be pre-deployed on each offshore wind turbine for detecting wind turbine data. At the same time, meteorological monitoring equipment can be deployed for detecting the real-time environmental data of the wind farm. Or, the operation data of the offshore wind farm can be obtained by regularly performing automatic inspections by unmanned devices. This embodiment establishes a unified data interface standard and communication protocol, supports the seamless access of multi-source device data, supports the automatic adaptation of multiple communication protocols and data formats, eliminates the information island phenomenon, and realizes data sharing and collaboration across devices and systems. Secondly, the scattered device data is centrally processed to form a system state view from a global perspective, providing comprehensive support for operation and maintenance decisions.

[0033] Optionally, after obtaining the operation data of the offshore wind farm, data processing techniques can be used to preprocess the operation data, for example, removing noise, unifying formats, etc., to obtain preprocessed operation data. By preprocessing the monitoring data, data from different sources can be made compatible.

[0034] Among them, the anomaly prediction model can be established based on methods such as deep learning and isolation forest, and obtained through supervised learning or unsupervised learning. The anomaly prediction model can identify abnormal patterns during the operation of wind power equipment and timely detect potential fault patterns. For example, abnormal vibration patterns, temperature changes, abnormal fluctuations in power, etc. can all be used as indicators for anomaly detection. When using supervised learning, an initial anomaly prediction model can be established based on algorithms such as support vector machines, random supervised forests, decision trees, and neural networks, and the initial anomaly prediction model can be supervised-trained based on pre-labeled historical equipment fault data to obtain a trained anomaly prediction model. When using unsupervised learning, an initial anomaly prediction model can be established based on algorithms such as K-means clustering and principal component analysis, and the initial anomaly prediction model can be unsupervised-trained based on unlabeled historical equipment fault data to obtain a trained anomaly prediction model. By using unsupervised learning for model training, accurate early warning of sudden unknown faults can be achieved.

[0035] In this embodiment, first, the current operation data can be feature-extracted to obtain initial features. For example, vibration frequency, temperature fluctuation, etc.; then, the initial features can be screened through feature selection or dimensionality reduction techniques (such as principal component analysis, etc.) to obtain the key features that have the greatest impact on the performance of wind power equipment; finally, the anomaly prediction model can perform anomaly prediction based on the feature values of the key features to obtain an anomaly prediction result. The anomaly prediction result can include the identification of wind power equipment with abnormal risks, the type of anomaly, the confidence level corresponding to each type of anomaly, etc.

[0036] The types of wind power equipment can include offshore wind turbines, submarine cables, booster station equipment, etc. The corresponding abnormal types of offshore wind turbines can include blade anomalies, tower anomalies, foundation anomalies, etc. The corresponding abnormal types of submarine cables can include cable breaks, aging of the protective layer, etc.

[0037] It should be noted that the offshore environment is complex and changeable, and the equipment status may change over time. In this embodiment, the current operation data can be manually labeled, and an online learning algorithm can be used to optimize and train the anomaly prediction model according to the labeled current operation data, so that the model can be adjusted and self-optimized in real time to adapt to new data changes.

[0038] In this embodiment, by integrating big data analysis and machine learning algorithms, advanced analysis functions such as fault prediction and performance optimization of wind power equipment are realized, and the intelligent level of operation and maintenance is improved.

[0039] S120. According to the anomaly prediction result, obtain the abnormal wind power equipment and the corresponding abnormal type, and according to the abnormal wind power equipment and the corresponding abnormal type, obtain the target inspection equipment.

[0040] Specifically, for each abnormal wind power device, the abnormal type with the highest confidence can be selected to obtain the abnormal wind power device and the corresponding abnormal type. Then, when the number of each type of inspection device is one, the inspection device that matches the type of the current abnormal wind power device and the abnormal type can be obtained by looking up the mapping relationship preset among the wind power device type, the abnormal type, and the inspection device, and used as the target inspection device.

[0041] Among them, the inspection devices include unmanned aerial vehicles, unmanned boats, and / or underwater robots. In this embodiment, when an abnormality is detected in an offshore wind farm, unmanned inspection devices can be dispatched to conduct on-site inspections of the abnormal location to locate the specific fault location.

[0042] In another case, when the number of each type of inspection device is multiple, for each type of inspection device, the inspection range of a single device can be obtained in advance, and the device deployment location and deployment quantity can be determined according to the overall range of the offshore wind farm, the inspection range of each device, and the position information of the offshore wind turbines. The inspection devices are based on the offshore wind turbines. Thus, it can be ensured that the overall inspection range of each type of inspection device can cover the entire offshore wind farm.

[0043] Optionally, when determining the deployment location and deployment quantity of the inspection devices, first, the offshore wind turbines can be regarded as discrete points, and the inspection range of the inspection devices can be regarded as the coverage radius, which is transformed into a set covering problem. The goal is to select the fewest offshore wind turbines as device bases to ensure that all offshore wind turbines are within the radius coverage range of at least one device base. Then, calculate the distance matrix, establish the two-dimensional coordinates between all offshore wind turbines, and calculate the coverage neighborhood (the set of wind turbines with a distance ≤ coverage radius) of each offshore wind turbine. After that, use the greedy algorithm to solve, iteratively select the device base that covers the most uncovered offshore wind turbines until all are covered. Finally, use integer programming to optimize the calculation result of the greedy algorithm, establish the objective function (minimize the deployment quantity) and the constraint condition (all wind turbines are covered), and obtain the optimal result, that is, the optimal deployment location and deployment quantity of the inspection devices.

[0044] In this embodiment, when there are multiple of each type of inspection device, after determining the abnormal wind power device and the corresponding abnormal type, first, the target inspection device type that matches can be obtained according to the mapping relationship preset among the wind power device type, the abnormal type, and the inspection device type. Then, according to the position information of the abnormal wind power device and the position information of each inspection device belonging to the target inspection device type, the inspection device with the minimum distance from the abnormal wind power device can be found as the target inspection device.

[0045] Optionally, obtaining a target inspection device according to the abnormal wind power device and the corresponding abnormal type may include:

[0046] If it is detected that the abnormal wind power device is an offshore wind turbine and the abnormal type is blade abnormality and / or tower abnormality, the unmanned aerial vehicle is determined as the target inspection device;

[0047] If it is detected that the abnormal wind power device is an offshore wind turbine and the abnormal type is foundation abnormality, the unmanned ship is determined as the target inspection device;

[0048] If it is detected that the abnormal wind power device is a submarine cable, the underwater robot is determined as the target inspection device.

[0049] In this embodiment, according to the applicable scenarios and task scenario requirements of different unmanned devices, the device collaborative work strategy can be optimized. Typically, the unmanned ship and the unmanned aerial vehicle are responsible for the abnormal inspection of offshore wind turbines, and the underwater robot is responsible for the abnormal inspection of submarine cables. Further, for the abnormalities at higher positions (such as blades, towers, etc.) of offshore wind turbines, the unmanned aerial vehicle is responsible for inspection, while for the abnormalities at lower positions (such as foundations, etc.), the unmanned ship is responsible for inspection.

[0050] The advantage of the above settings is that accurate scheduling of inspection devices can be achieved, ensuring that each device can play its maximum role, and the operation and maintenance efficiency can be improved.

[0051] S130. Obtain the inspection route corresponding to the target inspection device, and based on the inspection route, control the target inspection device to inspect the abnormal wind power device, obtain the inspection result, and visually display the inspection result.

[0052] In this embodiment, for each inspection device, a corresponding inspection route can be pre-configured, and the inspection route covers all wind power devices within the inspection range of the inspection device. Thus, after obtaining the target inspection device, according to the configuration information, the inspection route corresponding to the target inspection device can be obtained, and the inspection route can be sent to the target inspection device. Then, the target inspection device can move to the vicinity of the abnormal wind power device according to the inspection route and automatically take pictures of the abnormal wind power device according to the specified rules to obtain the on-site image or video of the abnormal wind power device as the inspection result, and the inspection result can be sent to the onshore centralized control center. The onshore centralized control center can visually display the inspection result, operation data, abnormal prediction result, etc. in the form of charts, dashboards, etc. on the screen. The operation and maintenance personnel can view the real-time status, alarm information, historical records, etc. of the wind power device at any time through a smart phone, tablet or desktop console, and remotely manage the wind power device.

[0053] It is understandable that the operation and maintenance personnel can take over the inspection equipment at any time to ensure the safety of the inspection equipment. In a specific example, an unmanned ship can be dispatched to approach the abnormal wind power equipment and shout through the on-board loudspeaker to drive it away. At the same time, a drone can be sent to record the abnormality by video, which can improve the ability of the operation and maintenance personnel to judge risks. The underwater robot can replace divers to collect underwater risks, can achieve the positioning of underwater risks and the recording of high-definition videos, and can collect information such as temperature, depth, and obstacles at the target location through various sensors. By dispatching drones, unmanned ships, and underwater robots to take close-up photos of the abnormal wind power equipment when an abnormality of the offshore wind power is detected, the operation and maintenance efficiency can be improved, the operation and maintenance costs can be reduced, the risks can be processed quickly, problems can be discovered and handled immediately, there is no need for a large operation and maintenance ship to specifically check the problems, and there is no need for personnel to go underwater to check for faults, which can ensure the safety of personnel.

[0054] In this embodiment, by adopting the design concept of cloud-edge collaboration, the organic combination of local rapid response and in-depth cloud analysis is realized, and the overall efficiency of the system is improved. Secondly, through the intelligent operation and maintenance platform, the operation and maintenance efficiency of offshore wind power is significantly improved, the failure rate of wind power equipment is reduced, and the losses caused by operation and maintenance costs and equipment failures are reduced.

[0055] Optionally, obtaining the inspection route corresponding to the target inspection equipment may include:

[0056] Obtaining the first position information corresponding to the abnormal wind power equipment and the second position information corresponding to the target inspection equipment, and planning the inspection route corresponding to the target inspection equipment according to the first position information and the second position information.

[0057] In another case, a path planning algorithm based on search or an algorithm combining geometry and topological maps can be used to plan the optimal (for example, the shortest distance, the shortest time-consuming, etc.) inspection route in real time according to the position information corresponding to the abnormal wind power equipment and the target inspection equipment, and the position information corresponding to each wind power equipment. The inspection route starts from the second position information and ends at the first position information. The second position information may be the position information of the equipment base where the target inspection equipment is located.

[0058] It should be noted that when the abnormal wind power equipment is a submarine cable, the first position information corresponding to the abnormal wind power equipment can be composed of a series of discrete coordinate points. At this time, when planning the inspection route, the two end points of the discrete coordinate points can be determined first, and the end point on the side closer to the second position information can be selected as the first end point, and the other end point can be used as the second end point; then, starting from the second position information and ending at the first end point, the first route can be planned, and the line of the submarine cable can be used as the second route; finally, the first route and the second route are combined to form the inspection route.

[0059] The advantage of the above setting is that it can make the inspection route adapt to the changing task scenarios and improve the inspection efficiency.

[0060] The technical solution of the embodiment of the present invention is to obtain the current operation data of the offshore wind farm, input the current operation data into the pre-trained abnormal prediction model, and obtain the abnormal prediction result output by the abnormal prediction model; according to the abnormal prediction result, obtain the abnormal wind power equipment and the corresponding abnormal type, and according to the abnormal wind power equipment and the corresponding abnormal type, obtain the target inspection equipment; obtain the inspection route corresponding to the target inspection equipment, and based on the inspection route, control the target inspection equipment to inspect the abnormal wind power equipment, obtain the inspection result, and visually display the inspection result; by arranging the corresponding inspection equipment to automatically inspect the abnormal wind power equipment according to the inspection route when detecting the abnormal wind power equipment, the operation and maintenance efficiency of the offshore wind power can be improved, the operation and maintenance cost can be reduced, and the safety of the operation and maintenance work can be ensured.

[0061] Embodiment Two

[0062] Figure 2 The flowchart of a method for operating and maintaining offshore wind power provided by the second embodiment of the present invention. This embodiment further refines the above technical solution, and the technical solution in this embodiment can be combined with one or more of the above embodiments. As Figure 2 shown, the method includes:

[0063] S210. Obtain the current operation data of the offshore wind farm.

[0064] S220. Obtain the historical operation data of the offshore wind farm, and perform regression analysis on the historical operation data to obtain key parameters.

[0065] In this embodiment, after collecting the operation data of the offshore wind farm, it can be stored in a specified database. Thus, the historical operation data of different periods can be read from the specified database. Then, a regression analysis method can be used to analyze the historical operation data to obtain the key factors affecting the equipment performance as key parameters. For example, they can be wind speed, wave height, etc.

[0066] S230. Obtain the current parameter value corresponding to the key parameter according to the current operation data, and obtain the initial operation and maintenance strategy according to the current parameter value corresponding to the key parameter.

[0067] Specifically, the current parameter value corresponding to the key parameter can be extracted from the current operation data, and based on the current parameter value corresponding to the key parameter, the mapping relationship between the preset key parameter, parameter value and operation and maintenance strategy is searched to obtain the matching operation and maintenance strategy as the initial operation and maintenance strategy. The operation and maintenance strategy can include whether the blade rotates, the blade position, the blade rotation speed, etc.

[0068] S240. Optimize the initial operation and maintenance strategy through a multi-objective optimization algorithm to obtain the target operation and maintenance strategy, and perform scheduling control on each offshore wind turbine of the offshore wind farm according to the target operation and maintenance strategy.

[0069] In this embodiment, a multi-objective optimization algorithm, such as a genetic algorithm, a particle swarm optimization algorithm, etc., can be used to comprehensively consider multiple optimization objectives, such as maximizing power generation, minimizing the failure rate, and minimizing the maintenance cost, to optimize the initial operation and maintenance strategy to obtain the target operation and maintenance strategy. Then, the operating states of each offshore wind turbine can be adjusted according to the target operation and maintenance strategy.

[0070] Optionally, in this embodiment, a machine learning model can also be used to predict the remaining life of the wind power equipment according to the historical data and failure modes of the wind power equipment to help the operation and maintenance personnel determine the best maintenance time, so as to avoid sudden failures and reduce the operation and maintenance cost.

[0071] The technical solution of the embodiment of the present invention obtains the historical operation data of the offshore wind farm, performs regression analysis on the historical operation data to obtain the key parameters; obtains the current parameter value corresponding to the key parameter according to the current operation data, and obtains the initial operation and maintenance strategy according to the current parameter value corresponding to the key parameter; optimizes the initial operation and maintenance strategy through a multi-objective optimization algorithm to obtain the target operation and maintenance strategy, and performs scheduling control on each offshore wind turbine of the offshore wind farm according to the target operation and maintenance strategy; by dynamically adjusting the operation and maintenance strategy of the offshore wind turbine according to the parameter value corresponding to the key parameter, the offshore wind turbine can operate in the best state, and the operation and maintenance efficiency of the offshore wind power can be further improved.

[0072] Embodiment III

[0073] Figure 3 It is a schematic structural diagram of an operation and maintenance device for offshore wind power provided by Embodiment III of the present invention. As Figure 3 shown, the device includes: an anomaly prediction module 310, an inspection device acquisition module 320, and a device inspection module 330; wherein,

[0074] Anomaly prediction module 310, configured to obtain the current operation data of the offshore wind farm, input the current operation data into a pre-trained anomaly prediction model, and obtain the anomaly prediction result output by the anomaly prediction model;

[0075] Inspection equipment acquisition module 320, configured to obtain the abnormal wind power equipment and the corresponding abnormal type according to the anomaly prediction result, and obtain the target inspection equipment according to the abnormal wind power equipment and the corresponding abnormal type;

[0076] Equipment inspection module 330, configured to obtain the inspection route corresponding to the target inspection equipment, and based on the inspection route, control the target inspection equipment to inspect the abnormal wind power equipment, obtain the inspection result, and visually display the inspection result.

[0077] The technical solution of the embodiment of the present invention obtains the current operation data of the offshore wind farm, inputs the current operation data into a pre-trained anomaly prediction model, and obtains the anomaly prediction result output by the anomaly prediction model; according to the anomaly prediction result, obtains the abnormal wind power equipment and the corresponding abnormal type, and obtains the target inspection equipment according to the abnormal wind power equipment and the corresponding abnormal type; obtains the inspection route corresponding to the target inspection equipment, and based on the inspection route, controls the target inspection equipment to inspect the abnormal wind power equipment, obtains the inspection result, and visually displays the inspection result; by arranging the corresponding inspection equipment to automatically inspect the abnormal wind power equipment according to the inspection route when detecting the abnormal wind power equipment, the operation and maintenance efficiency of the offshore wind power can be improved, the operation and maintenance cost can be reduced, and the safety of the operation and maintenance operation can be ensured.

[0078] Optionally, the inspection equipment includes an unmanned aerial vehicle, an unmanned ship, and / or an underwater robot.

[0079] Optionally, the inspection equipment acquisition module 320 is specifically configured to, if it is detected that the abnormal wind power equipment is an offshore wind turbine and the abnormal type is blade abnormality and / or tower abnormality, determine the unmanned aerial vehicle as the target inspection equipment;

[0080] If it is detected that the abnormal wind power equipment is an offshore wind turbine and the abnormal type is foundation abnormality, determine the unmanned ship as the target inspection equipment;

[0081] If it is detected that the abnormal wind power equipment is a submarine cable, determine the underwater robot as the target inspection equipment.

[0082] Optionally, the equipment inspection module 330 is specifically configured to obtain the first position information corresponding to the abnormal wind power equipment and the second position information corresponding to the target inspection equipment, and plan the inspection route corresponding to the target inspection equipment according to the first position information and the second position information.

[0083] Optionally, the operation and maintenance device for offshore wind power further includes:

[0084] A key parameter analysis module, configured to obtain historical operation data of an offshore wind farm, and perform regression analysis on the historical operation data to obtain key parameters;

[0085] An initial operation and maintenance strategy acquisition module, configured to obtain current parameter values corresponding to the key parameters according to the current operation data, and obtain an initial operation and maintenance strategy according to the current parameter values corresponding to the key parameters;

[0086] A target operation and maintenance strategy acquisition module, configured to optimize the initial operation and maintenance strategy through a multi-objective optimization algorithm to obtain a target operation and maintenance strategy, and perform scheduling control on each offshore wind turbine of the offshore wind farm according to the target operation and maintenance strategy.

[0087] Optionally, the operation data includes wind turbine data and environmental data. The wind turbine data includes at least one of an operating state, equipment logs, vibration data, temperature data, pressure data, and power output data. The environmental data includes at least one of wind speed, air temperature, humidity, ocean current, and wave height.

[0088] The operation and maintenance device for offshore wind power provided by the embodiments of the present invention can execute the operation and maintenance method for offshore wind power provided by any embodiment of the present invention, and has corresponding functional modules and beneficial effects for executing the method.

[0089] Embodiment 4

[0090] Figure 4 FIG. shows a schematic structural diagram of an electronic device 40 that can be used to implement the embodiments of the present invention. The electronic device 40 is intended to represent various forms of digital computers, such as, a laptop computer, a desktop computer, a workbench, a personal digital assistant, a server, a blade server, a mainframe computer, and other suitable computers. The electronic device 40 can also represent various forms of mobile devices, such as, a personal digital processor, a cellular phone, a smart phone, a wearable device (such as a helmet, glasses, a watch, etc.) and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present invention described herein and / or claimed.

[0091] As Figure 4As shown, the electronic device 40 includes at least one processor 41 and a memory communicatively connected to the at least one processor 41, such as a Read-Only Memory (ROM) 42, a Random Access Memory (RAM) 43, etc. Among them, the memory stores a computer program executable by the at least one processor. The processor 41 can execute various appropriate actions and processes according to the computer program stored in the read-only memory 42 or the computer program loaded from the storage unit 48 into the random access memory 43. In the RAM 43, various programs and data required for the operation of the electronic device 40 can also be stored. The processor 41, the ROM 42, and the RAM 43 are connected to each other through a bus 44. An Input / Output (I / O) interface 45 is also connected to the bus 44.

[0092] Multiple components in the electronic device 40 are connected to the I / O interface 45, including: an input unit 46, such as a keyboard, a mouse, etc.; an output unit 47, such as various types of displays, speakers, etc.; a storage unit 48, such as a disk, an optical disc, etc.; and a communication unit 49, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 49 allows the electronic device 40 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0093] The processor 41 can be various general and / or special processing components with processing and computing capabilities. Some examples of the processor 41 include, but are not limited to, a central processing unit, a graphics processing unit, various dedicated artificial intelligence computing chips, various processors running machine learning model algorithms, a digital signal processor, and any appropriate processor, controller, microcontroller, etc. The processor 41 executes the various methods and processes described above, such as the operation and maintenance method of offshore wind power.

[0094] In some embodiments, the operation and maintenance method of offshore wind power can be implemented as a computer program, which is tangibly contained in a computer-readable storage medium, such as the storage unit 48. In some embodiments, part or all of the computer program can be loaded and / or installed onto the electronic device 40 via the ROM 42 and / or the communication unit 49. When the computer program is loaded into the RAM 43 and executed by the processor 41, one or more steps of the operation and maintenance method of offshore wind power described above can be executed. Alternatively, in other embodiments, the processor 41 can be configured to execute the operation and maintenance method of offshore wind power in any other appropriate way (for example, by means of firmware).

[0095] The various embodiments of the systems and techniques described above in this specification can be implemented in digital electronic circuitry, integrated circuit systems, field programmable gate arrays, application specific integrated circuits, application specific standard products, systems on a chip, programmable logic devices loaded, computer hardware, firmware, software, and / or combinations thereof. These various embodiments can include: being implemented in one or more computer programs that are executable and / or interpretable on a programmable system including at least one programmable processor, which can be a special-purpose or general-purpose programmable processor that receives data and instructions from, and transmits data and instructions to, a storage system, at least one input device, and at least one output device.

[0096] The computer programs for implementing the methods of the present invention can be written in any combination of one or more programming languages. These computer programs can be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the computer programs, when executed by the processor, cause the functions / operations specified in the flowchart and / or block diagram to be implemented. The computer programs can be executed entirely on the machine, partly on the machine, as a stand-alone software package partly on the machine and partly on a remote machine or entirely on the remote machine or server.

[0097] In the context of the present invention, a computer-readable storage medium can be a tangible medium that can contain or store a computer program for use by or in connection with an instruction execution system, apparatus, or device. The computer-readable storage medium can include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. Alternatively, the computer-readable storage medium can be a machine-readable signal medium. More specific examples of the machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a random access memory, a read-only memory, an erasable programmable read-only memory, a fiber optic, a portable compact disc read-only memory, an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0098] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device 40 having: a display device (e.g., a cathode ray tube or a liquid crystal display) for displaying information to the user; and a keyboard and a pointing device (e.g., a mouse or a trackball) by which the user can provide input to the electronic device 40. Other kinds of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0099] The systems and techniques described herein can be implemented in a computing system including backend components (e.g., as a data server), or a computing system including middleware components (e.g., an application server), or a computing system including frontend components (e.g., a user computer having a graphical user interface or a web browser through which the user can interact with an implementation of the systems and techniques described herein), or a computing system including any combination of such backend components, middleware components, or frontend components. The components of the system can be interconnected to each other by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local area networks, wide area networks, blockchain networks, and the Internet.

[0100] The computing system can include a client and a server. The client and the server are generally remote from each other and typically interact through a communication network. The client-server relationship is created by computer programs running on respective computers and having a client-server relationship with each other. The server can be a cloud server.

[0101] This embodiment may further include a computer program product including a computer program which, when executed by a processor, implements the operation and maintenance method for offshore wind power provided in any embodiment of the present invention.

[0102] It should be understood that various forms of the processes shown above can be used, with steps reordered, added, or deleted. For example, the steps recited in the present invention can be executed in parallel, sequentially, or in a different order, as long as the desired results of the technical solution of the present invention can be achieved, and no limitation is made herein.

[0103] The above specific embodiments do not constitute a limitation on the protection scope of the present invention. Those skilled in the art should understand that various modifications, combinations, sub - combinations and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims

1. An offshore wind power operation and maintenance method, characterized in that: include: Acquire current operating data of the offshore wind farm, input the current operating data into a pre-trained abnormality prediction model, and acquire an abnormality prediction result output by the abnormality prediction model; According to the abnormal prediction result, abnormal wind power equipment and corresponding abnormal type are obtained, and according to the abnormal wind power equipment and corresponding abnormal type, target inspection equipment is obtained; The inspection route corresponding to the target inspection device is obtained, and based on the inspection route, the target inspection device is controlled to inspect the abnormal wind power equipment, the inspection result is obtained, and the inspection result is visually displayed.

2. The method according to claim 1, characterized in that Inspection equipment includes drones, unmanned vessels and / or underwater robots.

3. The method according to claim 2, characterized in that According to the abnormal wind power equipment and the corresponding abnormal type, the target inspection equipment is obtained, including: If it is detected that the abnormal wind power equipment is an offshore wind turbine, and the abnormality type is a blade abnormality and / or a tower abnormality, the drone is determined as a target inspection equipment; If it is detected that the abnormal wind power equipment is an offshore wind turbine, and the abnormality type is a base abnormality, the unmanned boat is determined as a target inspection equipment; If it is detected that the abnormal wind power equipment is a submarine cable, the underwater robot is determined as the target inspection equipment.

4. The method according to claim 1, characterized in that: Obtaining the inspection route corresponding to the target inspection device includes: The first position information corresponding to the abnormal wind power equipment and the second position information corresponding to the target inspection equipment are obtained, and the inspection route corresponding to the target inspection equipment is planned based on the first position information and the second position information.

5. The method according to claim 1, characterized in that Also includes: Acquire historical operation data of the offshore wind farm, and perform regression analysis on the historical operation data to obtain key parameters; According to the current operation data, obtain the current parameter value corresponding to the key parameter, and according to the current parameter value corresponding to the key parameter, obtain the initial operation and maintenance strategy; The initial operation and maintenance strategy is optimized by a multi-objective optimization algorithm to obtain a target operation and maintenance strategy, and the offshore wind turbines of the offshore wind farm are dispatched and controlled according to the target operation and maintenance strategy.

6. The method according to claim 1 or 5, characterized in that: The operating data includes wind turbine data and environmental data. The wind turbine data includes at least one of operating status, equipment log, vibration data, temperature data, pressure data and power output data. The environmental data includes at least one of wind speed, temperature, humidity, ocean current and wave height.

7. An offshore wind power operation and maintenance device, characterized in that: include: An abnormality prediction module is used to obtain current operating data of the offshore wind farm, input the current operating data into a pre-trained abnormality prediction model, and obtain an abnormality prediction result output by the abnormality prediction model; An inspection equipment acquisition module is used to acquire abnormal wind power equipment and corresponding abnormal types according to the abnormal prediction result, and acquire target inspection equipment according to the abnormal wind power equipment and corresponding abnormal types; The equipment inspection module is used to obtain the inspection route corresponding to the target inspection equipment, and based on the inspection route, control the target inspection equipment to inspect the abnormal wind power equipment, obtain the inspection results, and visualize the inspection results.

8. An electronic device, characterized in that: The electronic device comprises: at least one processor, and a memory communicatively connected to the at least one processor; wherein, The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the offshore wind power operation and maintenance method according to any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and the computer program is used to enable a processor to implement the offshore wind power operation and maintenance method according to any one of claims 1 to 6 when executed.

10. A computer program product, characterized in that It comprises a computer program, which, when executed by a processor, implements the offshore wind power operation and maintenance method according to any one of claims 1 to 6.

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