Control system and method of offshore wind power anti-scouring device based on digital twinborn technology

Abstract

The invention relates to the technical field of offshore wind power protection, and particularly discloses a control system and method of an offshore wind power anti-scouring device based on a digital twinning technology, and the system comprises a data sampling subsystem, an intelligent analysis and calculation module, a digital twinning combined drive module, and a database storage module. A data preprocessing module in the data sampling subsystem is used for obtaining preprocessed data and transmitting the preprocessed data to an intelligent analysis and calculation module, and the intelligent analysis and calculation module is used for carrying out optimization solution on influence factors of the offshore wind power anti-scouring device to obtain optimization result data; data acquisition, intelligent analysis, digital twinning and data storage technologies are comprehensively utilized, the defects of a traditional offshore wind power anti-scouring device in the aspects of control, maintenance, performance evaluation and the like are overcome, the overall performance and reliability of the offshore wind power foundation anti-scouring device are improved, the maintenance cost and risk are reduced, and the service life of the offshore wind power foundation anti-scouring device is prolonged. And stable operation of the offshore wind power industry is ensured.

Description

Technical Field

[0001] The present invention relates to the technical field of offshore wind power protection, and in particular to a control system and method for an offshore wind power scour prevention device based on digital twin technology. Background Art

[0002] An offshore wind power foundation is usually a structure that supports a wind turbine. It has to bear the huge weight of the wind turbine and stands in the marine environment. In the marine environment, there are factors such as water flow and waves, which will scour the seabed. When the water flow (especially the water flow caused by tides, ocean currents, and waves) flows through the offshore wind power foundation, the dynamic force of the water flow will gradually carry away the seabed sediment around the foundation, forming a scour pit. As the scour pit gradually deepens and expands, it will pose a threat to the stability of the offshore wind power foundation. In severe cases, it may cause the foundation to tilt or even collapse, thus endangering the safe operation of the entire offshore wind power generation system. The offshore wind power foundation scour prevention device is a device designed to prevent or slow down this scour phenomenon.

[0003] However, traditional offshore wind power foundation scour prevention devices face many technical bottlenecks, which seriously restrict the further improvement of their performance and the sustainable development of the industry. Traditional offshore wind power foundation scour prevention devices lack refined consideration for control in design and use. The operation modes of most devices are relatively fixed and cannot be dynamically adjusted according to the real-time changes in the marine environment (such as instantaneous changes in water flow velocity, water flow characteristics under different seasons and different climate conditions, and marine storms). For active protection devices, such as jet systems or mechanical protection devices, their control usually relies on simple sensors and preset programs, and these programs cannot accurately consider complex marine environmental factors, resulting in being unable to cope when precise control of the device working state is required. In addition, under different sea areas and different seabed conditions, the operating parameters of the device need to be finely adjusted, but the existing technology lacks sufficient feedback and control mechanisms, making it difficult to achieve precise control of the scour prevention device, thus affecting its overall protection efficiency.

[0004] Therefore, there is an urgent need for a control system and method for an offshore wind power scour prevention device based on digital twin technology to solve the above problems. Summary of the Invention

[0005] The object of the present invention is to provide a control system for an offshore wind power scour prevention device based on digital twin technology, including: a data acquisition subsystem, an intelligent analysis and calculation module, a digital twin joint drive module, and a database storage module;

[0006] The data acquisition subsystem includes multiple sensors, a data transmission module, and a data preprocessing module. Among them, the multiple sensors are used to collect multi-source data of the offshore wind power anti-erosion device. The data transmission module is connected to the multiple sensors and is used to transmit the collected data to a computer via Ethernet. The data preprocessing module is connected to the data transmission module and is used to obtain preprocessed data and transmit the preprocessed data to the intelligent analysis and calculation module;

[0007] The intelligent analysis and calculation module is connected to the data preprocessing module and is used to receive the preprocessed data and optimize and solve the influencing factors of the offshore wind power anti-erosion device to obtain optimized result data;

[0008] The digital twin joint driving module is connected to the intelligent analysis and calculation module and the database storage module. The digital twin joint driving module is used to fuse the optimized result data and the historical motion data of the offshore wind power anti-erosion device system, and map the offshore wind power anti-erosion device to the virtual space as a high-precision twin model in combination with graphical rendering technology. At the same time, each result is inserted into the historical data to correct and optimize the twin model.

[0009] Furthermore, the multiple sensors in the data acquisition subsystem are respectively arranged at the traveling mechanism, the collection component, the sleeve structure, and the motor. Among them, the sensors of the traveling mechanism are used to detect the energy consumption and force conditions of different positions during the movement of the collection component, providing a data basis for dynamic analysis;

[0010] The sensors of the collection component include sensors arranged on the lifting mechanism and the rotating mechanism. The sensors of the lifting mechanism are used to monitor the lifting height controlled by hydraulic pressure and the change of the included angle between the internal scene and the horizontal to determine the working state of the collection component. The sensors of the rotating mechanism are used to identify the ocean current direction and feedback it to the device;

[0011] The sensors of the sleeve structure are used to monitor the penetration of the sleeve in the soil section and the adjustment effect of the reverse arc sleeve on the water flow direction and velocity;

[0012] The sensors of the motor are used to observe the power and energy consumption power of the motor under different working conditions and at different times. The multiple sensors are used to identify the content of quicksand and gravel in the shovel, the working condition information in different postures of the manipulator, the flow velocity and resistance of the submarine fluid, and transmit the above data to the data transmission module.

[0013] Further, the energy analysis and calculation module is used to receive the preprocessed data, and optimize and solve the influencing factors of multidisciplinary data such as the mechanical structure, motion control, and hydraulic flow rate of the offshore wind power anti-scouring device by combining multidisciplinary collaborative optimization, flow field analysis, motor dynamics analysis, finite element analysis, and regression prediction based on long short-term memory neural network, so as to obtain the optimization result. When performing multidisciplinary collaborative optimization, the specific steps are as follows:

[0014] According to the motion characteristics of the offshore wind power anti-scouring device in a complex marine environment, considering the stability of the mechanical structure, the accuracy of motion control, the rationality of hydraulic flow rate, and the reliability of the entire system, a digital model is constructed;

[0015] Use algorithms such as improved support vector machine long short-term memory neural network to optimize and solve the model to obtain optimized data values;

[0016] Obtain the best configuration data of the offshore wind power anti-scouring device system according to the optimized data values.

[0017] Further, let the best configuration data be X, the comprehensive value of mechanical structure-related variables be M, the comprehensive value of motion control-related variables be C, the comprehensive value of hydraulic flow rate-related variables be F, the comprehensive value of system reliability-related variables be R, and their respective weights be w m 、w c 、w f 、w r , (and w m +w c +w f +w r =1), then, the calculation formula for the best configuration data X is:

[0018]

[0019] In actual calculations, use the improved algorithm to continuously adjust each variable and weight to obtain the result that makes X reach the optimum.

[0020] Further, when constructing the twin model, the digital twin joint drive system uses 3D mapping software to draw a 3D model, and combines the optimal configuration data of the offshore wind power anti-erosion device system to map the operating state of the automated anti-erosion system into the 3D digital model in real time. The movement mode and trajectory of each component in the model are controlled by a preset program, and the twin model is driven by key data collected by different types of sensors to achieve real-time monitoring of motion simulation and performance evaluation. At the same time, a performance evaluation and state prediction module is set in the digital twin joint drive system. The module uses a neural network deep learning model built by TensorFlow to evaluate and predict the operating state of the offshore wind power anti-erosion device, obtains the structural parameters under the minimum energy consumption of the system, and conducts fault diagnosis by combining the collection of operating state data and the establishment of a learning library. The diagnostic data and operating data are classified and archived, and provide a basis for the correction and optimization of the prediction model of the twin body.

[0021] Further, the database storage module is connected to the digital twin joint drive module. The database storage module is used to store the optimization results and twin model data. The database storage module classifies and stores the results of the optimized calculation and the twin results according to the time series and data type, providing data correction support for the subsequent digital model state prediction and real-time monitoring.

[0022] The present invention also discloses a control method for an offshore wind power anti-erosion device based on digital twin technology, including the following steps:

[0023] Collect multi-source data of the offshore wind power anti-erosion device through multiple sensors in the data acquisition subsystem;

[0024] Transmit the collected data to a computer via Ethernet through the data transmission module;

[0025] Process the transmitted data through the data preprocessing module to obtain preprocessed data;

[0026] According to the preprocessed data received by the intelligent analysis and calculation module, optimize and solve the influencing factors of the offshore wind power anti-erosion device to obtain optimized result data;

[0027] Fuse the optimized result data with the historical motion data of the offshore wind power anti-erosion device system;

[0028] Map the offshore wind power anti-erosion device into a virtual space through the digital twin joint drive module combined with graphical rendering technology to form a high-precision twin model;

[0029] Insert the results of each time into the historical data through the digital twin joint drive module to correct and optimize the twin model.

[0030] The present application also provides a computer device, including a memory and a processor. The memory stores a computer program, and when the processor executes the computer program, the steps of the above method are implemented.

[0031] The present application also provides a computer-readable storage medium, on which a computer program is stored. It is characterized in that when the computer program is executed by a processor, the steps of the above method are implemented.

[0032] The beneficial effects of the present application are as follows:

[0033] The present invention comprehensively utilizes advanced data acquisition, intelligent analysis, digital twin, and data storage technologies, overcomes the deficiencies of traditional offshore wind power anti-scour devices in aspects such as control, maintenance, and performance evaluation, provides a more reliable, efficient, and intelligent solution for the sustainable development of the offshore wind power industry, improves the overall performance and reliability of offshore wind power foundation anti-scour devices, reduces maintenance costs and risks, and at the same time improves the adaptability to complex marine environments. Through the comprehensive processing and analysis of multi-source data, the system can achieve precise control, fault diagnosis, performance evaluation, and status prediction of the device, better protect the offshore wind power foundation, and ensure the stable operation of the offshore wind power industry. Brief Description of the Drawings

[0034] Figure 1 It is a schematic flowchart of the method proposed in an embodiment of the present application.

[0035] The realization of the purpose of the present application, functional features, and advantages will be further described in conjunction with the embodiments and with reference to the drawings. Detailed Embodiments

[0036] It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0037] The present application provides a control system for an offshore wind power anti-scour device based on digital twin technology, including: a data acquisition subsystem, an intelligent analysis and calculation module, a digital twin joint drive module, and a database storage module;

[0038] The data acquisition subsystem includes multiple sensors, a data transmission module, and a data preprocessing module. Among them, the multiple sensors are used to collect multi-source data of the offshore wind power anti-scour device. The data transmission module is connected to the multiple sensors, and the data transmission module is used to transmit the collected data to a computer through Ethernet. The data preprocessing module is connected to the data transmission module, and the data preprocessing module is used to obtain preprocessed data and transmit the preprocessed data to the intelligent analysis and calculation module;

[0039] The intelligent analysis and calculation module is connected to the data preprocessing module. The intelligent analysis and calculation module is used to receive the preprocessed data and optimize and solve the influencing factors of the offshore wind power anti-scouring device to obtain optimized result data;

[0040] The digital twin joint drive module is connected to the intelligent analysis and calculation module and the database storage module. The digital twin joint drive module is used to fuse the optimized result data and the historical motion data of the offshore wind power anti-scouring device system, and map the offshore wind power anti-scouring device to the virtual space as a high-precision twin model in combination with the graphical rendering technology. At the same time, the results of each time are inserted into the historical data to correct and optimize the twin model.

[0041] The present invention comprehensively utilizes advanced data acquisition, intelligent analysis, digital twin and data storage technologies, overcomes the deficiencies of traditional offshore wind power anti-scouring devices in aspects such as control, maintenance and performance evaluation, provides a more reliable, efficient and intelligent solution for the sustainable development of the offshore wind power industry, improves the overall performance and reliability of the offshore wind power foundation anti-scouring device, reduces the maintenance cost and risk, and at the same time improves the adaptability to complex marine environments. Through the comprehensive processing and analysis of multi-source data, the system can achieve precise control, fault diagnosis, performance evaluation and status prediction of the device, better protect the offshore wind power foundation, and ensure the stable operation of the offshore wind power industry.

[0042] Through multiple sensors in the data acquisition subsystem of the present invention, multi-source data of the offshore wind power anti-scouring device can be comprehensively collected, and rich and diverse information can be obtained, including but not limited to the structural state of the device, surrounding environment information (such as water flow velocity, water flow direction, marine organism attachment situation, seabed condition, etc.) and the operation data of each component of the device (such as the operation parameters of mechanical components, the performance indicators of electrical equipment, etc.).

[0043] Using Ethernet for data transmission can ensure the speed and stability of data transmission. Compared with traditional data transmission methods, Ethernet can carry a large amount of data information and has a long transmission distance. It can quickly and accurately transmit the data collected by sensors to a computer for subsequent processing, providing a data basis for subsequent analysis and control. For example, in a large offshore wind farm, multiple offshore wind power foundation anti-scouring devices can centrally transmit their respective sensor data to the control center through Ethernet, avoiding data loss and delay problems that may occur in traditional data transmission methods, and ensuring the timeliness and integrity of data.

[0044] After receiving the preprocessed data, the intelligent analysis and calculation module optimizes and solves the influencing factors of the offshore wind power anti-erosion device. It can comprehensively consider multiple complex influencing factors and, through advanced algorithms and models, such as multidisciplinary collaborative optimization, flow field analysis, motor dynamics analysis, finite element analysis, and regression prediction based on long short-term memory neural networks, deeply explore the internal relationships between data to achieve more accurate analysis and optimization. For example, for the changes in water flow velocity and direction in different ocean environments, this module can analyze historical data and real-time collected data, combine with flow field analysis, predict the impact force of water flow on the anti-erosion device, and find the best coping strategy through optimization and solution, and adjust the working state of the device, such as adjusting the angle of the anti-erosion plate, the spraying parameters of the spraying system, etc., to achieve better anti-erosion effect.

[0045] The digital twin joint drive module fuses the optimized result data and the historical motion data of the offshore wind power anti-erosion device system, and maps the device to the virtual space as a high-precision twin model, which brings many benefits to the system. First of all, by constructing the twin model, virtual simulation and emulation of the device can be realized, and the operation status and performance of the device can be intuitively displayed. In practical applications, operators can observe the operation of the anti-erosion device in the virtual environment without directly entering the complex and dangerous ocean environment, reducing the operation risk. For example, when it is necessary to evaluate the performance of the anti-erosion device, various simulation tests can be carried out in the virtual environment to discover potential problems in advance.

[0046] Secondly, inserting each result into the historical data to correct and optimize the twin model enables the model to be continuously updated, improving the accuracy and reliability of the model. Over time, the system can continuously learn and adapt to new data, thus more accurately reflecting the performance and status of the actual device. For example, during the long-term operation, with the seasonal changes of the ocean environment and the wear of the equipment, the twin model will continuously adjust according to the new optimization results and historical data to better reflect the actual state of the current device, providing a more accurate basis for maintenance and optimization.

[0047] The database storage module can store the optimized results and twin model data, facilitating subsequent data query and analysis. By storing a large amount of data, it can provide data support for the long-term operation and optimization of the system.

[0048] For example, when maintenance and upgrade of the offshore wind power anti-erosion device in a certain area are required, historical operation data and optimization data of the device can be retrieved from the database to analyze the past performance of the device, identify possible fault points and performance bottlenecks, and provide a reference basis for the upgrade plan. At the same time, the stored data can also be used for big data analysis of the anti-erosion devices of the entire wind farm to identify common problems and optimization spaces, providing data support for the development of the entire industry.

[0049] Furthermore, multiple sensors in the data sampling subsystem are respectively arranged at the traveling mechanism, the collection component, the sleeve structure, and the motor. Among them, the sensors of the traveling mechanism are used to detect the energy consumption and force conditions of different positions during the movement of the collection component, providing a data basis for dynamic analysis.

[0050] It should be noted that the offshore wind power anti-erosion device is an existing technology, which includes a traveling mechanism, a collection component, a sleeve structure, and a motor.

[0051] The sensors of the collection component include sensors arranged on the lifting mechanism and the rotating mechanism. The sensors of the lifting mechanism are used to monitor the lifting height controlled by hydraulic pressure and the change in the angle between the internal scene and the horizontal to determine the working state of the collection component. The sensors of the rotating mechanism are used to identify the ocean current direction and feedback it to the device.

[0052] The sensors of the sleeve structure are used to monitor the entry condition of the sleeve in the soil section and the adjustment effect of the reverse arc sleeve on the water flow direction and velocity.

[0053] The sensors of the motor are used to observe the power and energy consumption power of the motor under different working conditions and at different times. Multiple sensors are used to identify the content of quicksand and gravel in the shovel, the working condition information of the manipulator in different postures, the seabed fluid velocity and resistance, and transmit the above data to the data transmission module.

[0054] The sensors are respectively arranged at multiple positions such as the traveling mechanism, the collection component, the sleeve structure, and the motor. This multi-position setting can comprehensively obtain the working state data of different components of the entire offshore wind power anti-erosion device, avoiding blind spots in data collection. For example, the sensors of the traveling mechanism can detect the energy consumption and force conditions during the movement of the collection component, which is very important for understanding the power consumption and force distribution during the movement of the device, and helps to optimize the design and operation parameters of the traveling mechanism.

[0055] Sensors at different positions monitor different parameters. For example, sensors of the collection component can monitor the lifting height controlled by hydraulics, the change in the angle between the internal scene and the horizontal, ocean current direction, etc.; sensors of the sleeve structure can monitor the soil penetration situation, the adjustment effect of water flow direction and velocity; sensors of the motor can monitor the power and energy consumption power of the motor. Such multi-parameter monitoring can comprehensively reflect the working status of each component of the device, providing a rich data basis for the overall performance evaluation and fault diagnosis of the device.

[0056] Sensors of the traveling mechanism provide a data basis for dynamic analysis, which enables researchers to deeply analyze the dynamic characteristics of the device during walking, such as force, energy consumption, etc., based on the collected data, and then optimize the walking performance of the device. For example, by analyzing the energy consumption data of the traveling mechanism collected over a long time, the working states or time periods with high energy consumption can be found, so as to adjust the operation strategy of the device and reduce energy consumption.

[0057] Sensors of the collection component can monitor the lifting height and angle change controlled by hydraulics, which is crucial for determining the working status of the collection component. Through these data, it can be timely found whether the collection component is in a normal working state and whether there are problems such as hydraulic faults or position deviations. For example, when the lifting height controlled by hydraulics is abnormal, it may mean that there is a leakage or fault in the hydraulic system, and problems can be warned and investigated in time through sensor data.

[0058] Through comprehensive and targeted data collection, the operating parameters of the device can be optimized according to the actual operating data. For example, according to the data of the adjustment effect of water flow direction and velocity monitored by the sleeve structure sensors, the structure or working parameters of the sleeve can be adjusted to improve its adjustment effect on water flow and enhance the anti-scouring ability. The data collected by multiple sensors can be used for fault diagnosis and predictive maintenance. For example, when the power and energy consumption power data monitored by the motor sensors show abnormal fluctuations, it may indicate that there are potential faults in the motor. Through the analysis of these data, maintenance can be carried out in advance to avoid the device from stopping due to motor faults and improve the reliability and stability of the device.

[0059] Furthermore, the intelligent analysis and calculation module is used to receive the preprocessed data, and combine multi-disciplinary collaborative optimization, flow field analysis, motor dynamics analysis, finite element analysis and regression prediction based on long short-term memory neural network to optimize and solve the influencing factors of multi-disciplinary data such as the mechanical structure, motion control, and hydraulic flow rate of the offshore wind power anti-scouring device, and obtain the optimization results. When performing multi-disciplinary collaborative optimization, the specific steps are as follows:

[0060] According to the motion characteristics of the offshore wind power anti-erosion device in a complex marine environment, considering the stability of the mechanical structure, the accuracy of motion control, the rationality of hydraulic flow, and the reliability of the entire system, a digital model is constructed;

[0061] The model is optimized and solved using algorithms such as the improved support vector machine long short-term memory neural network to obtain optimized data values;

[0062] Based on the optimized data values, the optimal configuration data of the offshore wind power anti-erosion device system is obtained.

[0063] In the present invention, the intelligent analysis and calculation module combines methods such as multi-disciplinary collaborative optimization, flow field analysis, motor dynamics analysis, finite element analysis, and regression prediction based on long short-term memory neural network to optimize and solve the influencing factors of multi-disciplinary data such as the mechanical structure, motion control, and hydraulic flow of the offshore wind power anti-erosion device. This multi-disciplinary comprehensive approach can comprehensively consider various influencing factors during the actual operation of the device, avoiding the limitations brought by single-disciplinary analysis. For example, when considering the stability of the mechanical structure, not only from the perspective of mechanical design, but also combined with flow field analysis to consider the impact of water flow on the mechanical structure, and motor dynamics analysis to consider the driving impact of the motor on the mechanical structure, etc., so that the optimization results are more accurate and reliable.

[0064] Through multi-disciplinary collaborative optimization, the overall performance of the offshore wind power anti-erosion device can be comprehensively improved. For example, when optimizing the hydraulic flow, combined with motor dynamics analysis and finite element analysis, the hydraulic system can ensure the strength and stability of the hydraulic structure while meeting the motor driving ability, thereby improving the working efficiency and reliability of the device.

[0065] The model is optimized and solved using algorithms such as the improved support vector machine long short-term memory neural network. This neural network algorithm has powerful data analysis and prediction capabilities and can handle complex non-linear relationships. For example, when processing the operation data of the offshore wind power anti-erosion device in a complex marine environment, the long short-term memory neural network can effectively capture the time series characteristics in the data and accurately predict the future operation state of the device, providing more valuable data for optimization and solution. Advanced algorithms can improve the accuracy and efficiency of optimization and solution. Compared with traditional optimization algorithms, algorithms such as the improved support vector machine long short-term memory neural network can converge to the optimal solution faster and can better adapt to different working conditions and environmental changes. For example, when optimizing the motion control of the device, using these algorithms can more accurately calculate the optimal motion control parameters, enabling the device to operate more precisely in a complex marine environment and improving the anti-erosion effect.

[0066] Further, let the optimal configuration data be X, the comprehensive value of mechanical structure-related variables be M, the comprehensive value of motion control-related variables be C, the comprehensive value of hydraulic flow-related variables be F, the comprehensive value of system reliability-related variables be R, and their respective weights be w m 、w c 、w f 、w r ,(and w m +w c +w f +w r = 1), then the calculation formula for the optimal configuration data X is:

[0067]

[0068] In actual calculations, the improved algorithm is used to continuously adjust each variable and weight to obtain the result that optimizes X.

[0069] Through the above formula, the comprehensive value of mechanical structure-related variables (M), the comprehensive value of motion control-related variables (C), the comprehensive value of hydraulic flow-related variables (F), and the comprehensive value of system reliability-related variables (R) are comprehensively considered. This way of comprehensively considering multiple factors can comprehensively evaluate the performance of the offshore wind power anti-scouring device and avoid the one-sidedness of optimization caused by only focusing on a single factor. For example, in actual applications, the stability of the mechanical structure, the accuracy of motion control, the rationality of the hydraulic system, and the reliability of the entire system are all important factors affecting the device performance. Considering these factors comprehensively can make the optimization result more in line with actual needs.

[0070] The improved algorithm is used to continuously adjust each variable and weight to obtain the result that optimizes X. This dynamic optimization method based on the improved algorithm can adapt to the operation requirements of the device under different working conditions and environments. As the marine environment changes (such as water flow velocity, wave height, etc.), the operation parameters of the device need to be continuously adjusted. Through the improved algorithm, the variables and weights can be dynamically adjusted in real time according to the new environmental data and device operation data, so that the device always maintains the optimal operation state.

[0071] The dynamic optimization method makes the device highly adaptable. Whether it is in the initial design stage of the device or in the maintenance and upgrade stage during the long-term operation process, the configuration of the device can be continuously optimized through this method to improve the performance and reliability of the device. For example, during the long-term operation of the device, due to factors such as component wear and marine organism attachment, the device performance will decline. Through the dynamic optimization method, the operation parameters of the device can be adjusted in time to extend the service life of the device and reduce the maintenance cost.

[0072] Furthermore, when constructing the twin model, the digital twin joint drive system uses 3D mapping software to draw a 3D model, and combines the optimal configuration data of the offshore wind power anti-erosion device system to map the operating state of the automated anti-erosion system into the 3D digital model in real time. The movement modes and trajectories of each component in the model are controlled by a preset program. The twin model is driven by key data collected by different types of sensors to achieve real-time monitoring of motion simulation and performance evaluation. At the same time, a performance evaluation and state prediction module is set in the digital twin joint drive system. The module uses a neural network deep learning model built by TensorFlow to evaluate and predict the operating state of the offshore wind power anti-erosion device, obtains the structural parameters under the minimum energy consumption of the system, and conducts fault diagnosis by combining the collection of operating state data and the establishment of a learning library. The diagnostic data and operating data are classified and archived, providing a basis for the correction and optimization of the prediction model of the twin body.

[0073] The digital twin joint drive system uses 3D mapping software to draw a 3D model and maps the operating state of the offshore wind power anti-erosion device system into the 3D digital model in real time. This visual way enables operators to directly observe the operating state of the device without being on-site. For example, through the 3D model, managers can clearly see the working conditions of each component of the anti-erosion device in the control room, such as the movement trajectory of the robotic arm and the erosion situation of the water flow, facilitating the timely discovery of abnormalities.

[0074] By combining the data collected by sensors in the 3D model to drive the model movement, real-time monitoring of the device movement simulation and performance evaluation are achieved. This helps to timely discover potential performance problems. For example, when the force data of a certain key part collected by the sensor shows abnormalities, it can be immediately reflected in the 3D model, and operators can evaluate whether the performance of the device is affected and take timely measures accordingly.

[0075] Furthermore, the database storage module is connected to the digital twin joint drive module. The database storage module is used to store the optimization results and twin model data. The database storage module classifies and stores the optimized calculation results and twin results according to time series and data types, providing data correction support for the subsequent digital model state prediction and real-time monitoring.

[0076] As Figure 1 shown, the present invention also discloses a control method for an offshore wind power anti-erosion device based on digital twin technology, including the following steps:

[0077] S1, collecting multi-source data of the offshore wind power anti-erosion device through multiple sensors in the data acquisition subsystem;

[0078] S2. Transmit the collected data to the computer via Ethernet through the data transmission module;

[0079] S3. Process the transmitted data through the data preprocessing module to obtain preprocessed data;

[0080] S4. According to the preprocessed data received by the intelligent analysis and calculation module, optimize and solve the influencing factors of the offshore wind power anti-erosion device to obtain optimized result data;

[0081] S5. Perform data fusion on the optimized result data and the historical motion data of the offshore wind power anti-erosion device system;

[0082] S6. Map the offshore wind power anti-erosion device to the virtual space through the digital twin joint drive module combined with graphical rendering technology to form a high-precision twin model;

[0083] S7. Insert the result of each time into the historical data through the digital twin joint drive module to correct and optimize the twin model.

[0084] This application also provides a computer device, including a memory and a processor. The memory stores a computer program, and when the processor executes the computer program, the steps of the above method are implemented.

[0085] This application also provides a computer-readable storage medium, on which a computer program is stored. It is characterized in that when the computer program is executed by a processor, the steps of the above method are implemented.

[0086] Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be completed by instructing relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above methods. Among them, any reference to a memory, storage, value library, or other medium provided in this application and used in the embodiments can include non-volatile and / or volatile memories. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (SSRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and Rambus dynamic RAM (RDRAM), etc.

[0087] It should be noted that in this text, the term "including", "comprising", or any other variant thereof is intended to cover non-exclusively, so that a process, device, article, or method including a series of elements not only includes those elements, but also includes other elements not explicitly listed, or further includes elements inherent to such a process, device, article, or method. Without further limitation, an element defined by the statement "including one..." does not exclude the existence of another identical element in the process, device, article, or method including that element.

[0088] The above are only the preferred embodiments of the present invention, and do not limit the patent scope of the present invention. Any equivalent results or equivalent process transformations made by using the content of the specification and drawings of the present invention, or directly or indirectly applied in other related technical fields, are equally included in the patent protection scope of the present invention.

Claims

1. A control system for an offshore wind power anti-scour device based on digital twin technology, characterized in that: include: Data sampling subsystem, intelligent analysis and calculation module, digital twin joint drive module and database storage module; The data sampling subsystem includes a plurality of sensors, a data transmission module and a data preprocessing module, wherein the plurality of sensors are used to collect multi-source data of the offshore wind power anti-scour device, the data transmission module is connected to the plurality of sensors, the data transmission module is used to transmit the collected data to a computer via Ethernet, the data preprocessing module is connected to the data transmission module, the data preprocessing module is used to obtain preprocessed data, and transmit the preprocessed data to the intelligent analysis and calculation module; The intelligent analysis and calculation module is connected to the data preprocessing module, and the intelligent analysis and calculation module is used to receive the preprocessing data, and optimize and solve the influencing factors of the offshore wind power anti-scour device to obtain optimization result data; The digital twin joint drive module is connected to the intelligent analysis and calculation module and the database storage module. The digital twin joint drive module is used to fuse the optimization result data with the historical motion data of the offshore wind power anti-scour device system, and combine the graphical rendering technology to map the offshore wind power anti-scour device to the virtual space as a high-precision twin model, and at the same time insert each result into the historical data to correct and optimize the twin model.

2. The control system of an offshore wind power anti-scour device based on digital twin technology according to claim 1 is characterized in that: The multiple sensors in the data sampling subsystem are respectively arranged at the walking mechanism, the collection component, the sleeve structure and the motor, wherein the sensors of the walking mechanism are used to detect the energy consumption and force conditions of the power at different positions when the collection component moves, so as to provide a data basis for dynamic analysis; The sensors of the collection assembly include sensors arranged on the lifting mechanism and the rotating mechanism. The sensors of the lifting mechanism are used to monitor the lifting height of the hydraulic control and the change of the angle between the internal surface and the horizontal to determine the working state of the collection assembly. The sensors of the rotating mechanism are used to identify the direction of the ocean current and feed back to the device. The sensor of the sleeve structure is used to monitor the burying condition of the sleeve in the burying section and the adjustment effect of the anti-arc sleeve on the water flow direction and flow rate; The motor's sensors are used to observe the motor's power and energy consumption under different working conditions and times. Multiple sensors are used to identify the content of quicksand and gravel in the shovel, the working condition information of the robotic arm under different postures, the flow rate and resistance of the seabed fluid, and transmit the above data to the data transmission module.

3. The control system of an offshore wind power anti-scour device based on digital twin technology according to claim 1 is characterized in that: The energy analysis and calculation module is used to receive the pre-processed data, and optimize and solve the multidisciplinary data influencing factors such as the mechanical structure, motion control, hydraulic flow, etc. of the offshore wind power anti-scour device by combining multidisciplinary collaborative optimization, flow field analysis, motor dynamics analysis, finite element analysis and regression prediction based on long short-term memory neural network to obtain the optimization results. When performing multidisciplinary collaborative optimization, the specific steps are as follows: According to the movement characteristics of the offshore wind power anti-scour device in a complex marine environment, the digital model is constructed by considering the stability of the mechanical structure, the accuracy of motion control, the rationality of the hydraulic flow and the reliability of the entire system; Use improved support vector machine long short memory neural network and other algorithms to optimize and solve the model and obtain optimized data values; The optimal configuration data of the offshore wind power anti-scour device system is obtained according to the optimized data value.

4. The control system of an offshore wind power anti-scour device based on digital twin technology according to claim 1 is characterized in that: Assume that the optimal configuration data is X, the comprehensive value of mechanical structure related variables is M, the comprehensive value of motion control related variables is C, the comprehensive value of hydraulic flow related variables is F, the comprehensive value of system reliability related variables is R, and their respective weights are w m 、w c 、w f 、w r , (and w m +w c +w f +w r =1), then the calculation formula for the optimal configuration data X is: In actual calculations, the improved algorithm is used to continuously adjust the variables and weights to obtain the optimal result for X.

5. The control system of an offshore wind power anti-scour device based on digital twin technology according to claim 4 is characterized in that: When constructing the twin model, the digital twin joint drive system uses three-dimensional mapping software to draw a three-dimensional model, and combines the optimal configuration data of the offshore wind power anti-scour device system to map the operating status of the automatic anti-scour system to the three-dimensional digital model in real time. The movement mode and trajectory of each component in the model are controlled by a preset program, and the twin model movement is driven according to the key data collected by different types of sensors to achieve real-time monitoring and performance evaluation of motion simulation. At the same time, a performance evaluation and state prediction module is provided in the digital twin joint drive system. The module uses the neural network deep learning model constructed by Tensorflow to evaluate and predict the operating status of the offshore wind power anti-scour device, obtains the structural parameters of the system under the minimum energy consumption, and performs fault diagnosis by combining the collection of operating status data with the establishment of a learning library. The diagnostic data and operating data are classified and archived, and a basis is provided for the correction and optimization of the twin prediction model.

6. The control system of an offshore wind power anti-scour device based on digital twin technology according to claim 5 is characterized in that: The database storage module is connected to the digital twin joint drive module, and the database storage module is used to store the optimization results and twin model data. The database storage module classifies and stores the results of the optimization calculation and the twin results according to time series and data type, providing data correction support for subsequent digital model status prediction and real-time monitoring.

7. A control method for an offshore wind power anti-scour device based on digital twin technology, characterized in that: The following steps are involved: Collect multi-source data of offshore wind power anti-scour devices through multiple sensors in the data sampling subsystem; The collected data is transmitted to the computer via Ethernet through the data transmission module; Process the transmitted data through a data preprocessing module to obtain preprocessed data; According to the pre-processed data received by the intelligent analysis and calculation module, the influencing factors of the offshore wind power anti-scour device are optimized and solved to obtain the optimization result data; Fusing the optimization result data with the historical motion data of the offshore wind power anti-scour device system; Through the digital twin joint drive module combined with graphical rendering technology, the offshore wind power anti-scour device is mapped into the virtual space to form a high-precision twin model; Through the digital twin joint drive module, each result is inserted into the historical data to correct and optimize the twin model.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of a control method for an offshore wind power anti-scour device based on digital twin technology as described in claim 7 are implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by the processor, the steps of a control method of an offshore wind power anti-scour device based on digital twin technology as described in claim 7 are implemented.

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