Offshore wind power plant communication relay and edge calculation method

Abstract

The invention discloses an offshore wind power plant communication relay and edge calculation method, which comprises a plurality of aerostats deployed in an offshore wind power plant, each aerostat is provided with an edge calculation module, and the method comprises the following steps: sending preset configuration parameters to the plurality of aerostats, the preset configuration parameters comprising flight paths, flight heights and target task areas of the aerostats; and when the aerostat flies to the target task area according to the preset configuration parameters, a communication link establishment instruction is sent to the aerostat residing in the target task area, and communication links of the offshore wind turbine generator, the aerostat, the shore-based control center and the booster station control center are established. And the offshore wind turbine generator uploads the real-time operation data to the aerostat, and the aerostat outputs a processing result based on the real-time operation data and sends the processing result to the shore-based control center or the booster station control center. The timeliness and accuracy of operation monitoring and fault early warning are improved, the centralized monitoring capability and operation and maintenance efficiency of the offshore wind power plant are effectively improved, and good adaptability and expansibility are achieved.

Description

Technical Field

[0001] This invention relates to the field of offshore wind farm data processing technology, and in particular to a method for offshore wind farm communication relay and edge computing. Background Technology

[0002] As the installed capacity of offshore wind power continues to expand, the number and distribution range of wind turbine units are also increasing. However, since offshore wind farms are usually built in near-shore or deep-sea areas far from shore, the traditional communication methods for offshore wind farms mostly rely on submarine optical cables or a small number of fixed relay stations.

[0003] However, these communication methods suffer from high construction costs and long construction periods. Furthermore, when wind turbines are widely distributed and cover a large area, communication blind spots and insufficient coverage are likely to occur. On the other hand, offshore wind turbines generate a large amount of operational data (power output, vibration information, tilt information, temperature information, etc.) during operation. Based on existing communication methods, this operational data is typically transmitted back to shore-based or cloud-based systems for processing, which can easily lead to bandwidth constraints, large data transmission delays, and difficulty in meeting the real-time data processing requirements.

[0004] In summary, existing communication and data processing methods negatively impact centralized monitoring and operation and maintenance of offshore wind farms. Therefore, there is an urgent need for a new technology that can be flexibly applied, enhance communication coverage, and enable rapid data processing and intelligent analysis. Summary of the Invention

[0005] To address the technical problems of existing communication and data processing methods in offshore wind farms, which are prone to communication blind spots and insufficient coverage, and which make it difficult to meet the real-time requirements of data processing, the present invention aims to provide a communication relay and edge computing method for offshore wind farms.

[0006] The objective of this invention is achieved through the following technical solution:

[0007] This invention provides a communication relay and edge computing method for offshore wind farms, comprising multiple aerobatic units deployed in an offshore wind farm, each aerobatic unit having an edge computing module, and including the following steps:

[0008] Preset configuration parameters are sent to multiple airships, including the airship's flight trajectory, flight altitude, and target mission area;

[0009] When the airship flies to the target mission area according to the preset configuration parameters, a communication link establishment command is sent to the airship stationed in the target mission area to establish a communication link between the offshore wind turbine, the airship, the shore-based control center and the booster station control center.

[0010] The offshore wind turbine uploads real-time operating data to the airship, and the airship outputs processing results based on the real-time operating data;

[0011] The airship sends the processing results to the shore-based control center or the booster station control center.

[0012] In some preferred embodiments, the airship outputs processing results based on real-time operational data, specifically including:

[0013] The real-time running data is preprocessed, and preprocessed data is output.

[0014] The preprocessed data is input into a preset anomaly detection model, which outputs an anomaly warning signal.

[0015] In some preferred embodiments, the airship outputs processing results based on real-time operational data, specifically including:

[0016] The real-time running data is preprocessed, and preprocessed data is output.

[0017] Obtain historical operating data of offshore wind turbines;

[0018] The historical operating data and the preprocessed data are input into the health assessment model, which outputs predicted failure risk information.

[0019] In some preferred embodiments, wireless communication links are established between the plurality of airships, forming a wireless distributed multi-hop network based on the Ad-hoc network protocol;

[0020] Each of the aforementioned airships can be used as a data relay, transmitting the real-time operational data back to the shore-based control center or the booster station control center via a satellite link.

[0021] In some preferred embodiments, the airship includes a high-altitude balloon and a payload compartment, the payload compartment being connected to and located below the high-altitude balloon;

[0022] The payload compartment contains a circuit board, which is equipped with an STM32 chip, a LoRa communication module, a BeiDou communication module, a Wi-Fi module, a Bluetooth module, and an edge computing module.

[0023] The STM32 chip is connected to the LoRa communication module, Beidou communication module, Wi-Fi module, Bluetooth module and edge computing module respectively;

[0024] The edge computing module uses an AI processing device called Ascend Atlas 200 DK.

[0025] In some preferred embodiments, the airship is configured to perform a three-dimensional potential field trajectory planning method based on a path search algorithm, the three-dimensional potential field trajectory planning method comprising the following steps:

[0026] A three-dimensional grid map of an offshore wind farm is constructed, and the spatial distribution information of wind turbine towers, substation control centers, wind field vector information, and height information are integrated into the cost function of the three-dimensional grid map.

[0027] On the three-dimensional raster map, candidate paths are searched based on a path search algorithm;

[0028] During the search process, the dominant wind layer is identified and utilized based on the cost function to generate a globally optimal reference path that guides the airship from the starting point to the target point.

[0029] The globally optimal reference path is output and used for the flight control of the airship.

[0030] In some preferred embodiments, the mathematical expression of the cost function of the three-dimensional raster map is f(n) = g(n) + h(n), where g(n) is the actual cost from the starting point to the current node n; and h(n) is the estimated cost from the current node n to the target point, wherein the estimated cost is the Manhattan distance or Euclidean distance.

[0031] Compared with the prior art, the present invention has at least the following beneficial effects:

[0032] This invention provides a communication relay and edge computing method for offshore wind farms, comprising multiple aerobatic units deployed in the offshore wind farm. Each aerobatic unit has an edge computing module. The method includes the following steps: sending preset configuration parameters to the multiple aerobatic units, the preset configuration parameters including the aerobatic unit's flight trajectory, flight altitude, and target mission area. When the aerobatic unit flies to the target mission area according to the preset configuration parameters, a communication link establishment command is sent to the aerobatic units stationed in the target mission area to establish a communication link between the offshore wind turbine, the aerobatic unit, the shore-based control center, and the booster station control center. The offshore wind turbine uploads real-time operating data to the aerobatic unit, and the aerobatic unit outputs processing results based on the real-time operating data. The aerobatic unit sends the processing results to the shore-based control center or the booster station control center.

[0033] This invention deploys multiple airships equipped with edge computing modules within the offshore wind farm area. Based on preset flight parameters, it establishes flexible communication links and processes wind turbine operating data locally in real time, transmitting the results back to the shore-based or booster station control center. This method significantly improves communication coverage and flexibility, reduces construction costs and bandwidth pressure, shortens data transmission and processing latency, and enhances the timeliness and accuracy of operation monitoring and fault early warning. Therefore, it effectively improves the centralized monitoring capabilities and operation and maintenance efficiency of offshore wind farms, exhibiting good adaptability and scalability. Attached Figure Description

[0034] Figure 1 This is a schematic diagram of a communication link for a communication relay and edge computing method for offshore wind farms according to the present invention;

[0035] Figure 2 This is a schematic diagram of the structure of the airship of the present invention;

[0036] Figure 3 This is a functional architecture diagram of the circuit board of the airship of the present invention;

[0037] Figure 4 This is a flowchart illustrating a communication relay and edge computing method for offshore wind farms according to the present invention.

[0038] Figure 5 This is a flowchart of a wind farm communication relay and edge technology method according to the present invention;

[0039] In the picture:

[0040] 1-High-altitude balloon, 2-Altitude control system, 3-Payload compartment. Detailed Implementation

[0041] To facilitate understanding of the present invention, the technical solutions and advantages of the invention will be further described in detail below with reference to the accompanying drawings and embodiments. Any mechanisms or methods not elaborated in this invention can be referred to in the prior art. The specific structures and features of the present invention are illustrated below by way of example and should not be construed as limiting the present invention in any way. Furthermore, any of the technical features mentioned below (including implicit or disclosed features), as well as any technical features directly shown or implied in the figures, can be arbitrarily combined or deleted among these technical features to form more other embodiments that may not be directly or indirectly mentioned in this invention. The accompanying drawings show preferred embodiments of the present invention. However, the present invention can be implemented in many different forms and is not limited to the embodiments described herein. Rather, these embodiments are provided to provide a more thorough and complete understanding of the disclosure of the present invention.

[0042] The technical solution provided in this application can be applied to various offshore wind farms, such as nearshore fixed-foundation wind farms and deep-sea floating wind farms. The technical solution provided in this application can also be applied to other marine clean energy sources, such as offshore photovoltaic systems. This application does not limit the application in this regard.

[0043] Figure 1 This is a schematic diagram illustrating an application scenario provided by an embodiment of this application. For example... Figure 1 As shown, this technical solution deploys multiple controllable and plannable aerostats as relay connections above the offshore wind farm for data communication and real-time on-site data processing. The communication link in the application scenario includes the offshore wind turbine, the aerostat, the shore-based control center, and the booster station control center. The offshore wind turbine communicates with the shore-based control center or the booster station control center through one or more aerostats to complete the data relay communication and data processing for the offshore wind farm described in this application.

[0044] In this embodiment, the offshore wind turbine is the basic power generation unit of the offshore wind farm, typically consisting of a wind turbine, tower, foundation, and its associated electrical system. Each wind turbine needs to upload operating parameters, power data, status information, and alarm information in real time, and receive dispatch instructions from the control center.

[0045] For example, offshore wind turbines are equipped with SCADA systems, including sensors, data acquisition units, communication modules, monitoring terminals, and software platforms. Sensors deployed on the turbine can collect real-time operating parameters such as wind speed, engine speed, power generation, gearbox temperature, and vibration status. The SCADA system collects multi-dimensional data on the electrical, mechanical, and environmental operating status of the turbine, obtaining real-time operating data. This collected real-time operating data is uploaded to the airship via the turbine's communication module, and then relayed to the booster station control center or shore-based control center.

[0046] In this embodiment, the aerostat is an airborne, long-term stationary device whose hovering altitude can be adjusted according to weather conditions and communication coverage. The aerostat features controllable and plannable trajectories, enabling it to hover, move, and adjust formation within a predetermined airspace. As an aerial relay node between offshore wind turbines and shore-based / booster station control centers, the aerostat ensures the stability and redundancy of the communication link.

[0047] In this application, wireless communication links are established between the multiple airships, forming a wireless distributed multi-hop network based on the Ad-hoc network protocol. Each airship can be used as a data relay, transmitting real-time operational data back to the shore-based control center or the booster station control center via satellite links.

[0048] Understandably, each aerostat acts as both a network node and a data relay node, capable of transmitting data it collects or receives to adjacent aerostats, achieving multi-hop transmission. Through multi-hop communication, wind turbines far from shore-based control centers or booster stations can communicate reliably. Furthermore, when an aerostat fails or communication is interrupted, the network can automatically reroute, ensuring continuous data transmission. Reduced latency and power consumption: Nodes can dynamically select the optimal path based on network topology and link quality, improving transmission efficiency.

[0049] In some specific implementations, such as Figure 2 As shown, the aerostat includes a high-altitude balloon and a payload compartment, with the payload compartment connected to and located below the high-altitude balloon. Specifically, as... Figure 3 As shown, a circuit board is installed inside the payload compartment of the airship. The circuit board houses an STM32 chip, a LoRa communication module, a BeiDou communication module, a Wi-Fi module, a Bluetooth module, and an edge computing module. The STM32 chip is connected to the LoRa communication module, the BeiDou communication module, the Wi-Fi module, the Bluetooth module, and the edge computing module, respectively.

[0050] In some implementations, the STM32 chip serves as the main control unit, used for data processing and coordinated control between various modules. It features low power consumption and high performance, meeting the real-time control requirements of the airship. The LoRa communication module enables long-distance, low-power data transmission, suitable for link communication between the wind turbine's real-time operating data and the airship. It is used for the acquisition and uploading of sensor data from the wind turbine, achieving stable coverage through low-speed, wide-area IoT communication. The BeiDou communication module is used by the airship to transmit received real-time operating data back to the shore-based or booster station control center via satellite, ensuring reliable data delivery. The Wi-Fi and Bluetooth modules are used for short-range, high-speed data interaction, supporting maintenance personnel to debug the airship and download data at close range. When stationed in the airship area, short-term Wi-Fi or Bluetooth access can be provided to meet the emergency data access needs of maintenance personnel or temporary equipment.

[0051] In some implementations, the edge computing module uses the Ascend Atlas 200 DK AI processing device. The edge computing module is responsible for data processing and analysis near the data source, reducing the load on the central server and improving response speed. The Ascend Atlas 200 DK possesses high-performance computing capabilities, making it suitable for performing complex data processing and AI tasks.

[0052] In some possible implementations, the airship's external structure combines the characteristics of a stratospheric airship and a high-altitude balloon. The airship consists of a main and auxiliary gasbags and a pod fixed beneath it. It is mainly divided into a floating platform, an altitude control system, an energy system, a propulsion system, and a payload bay. A high-altitude balloon includes a main gasbag and an auxiliary gasbag located inside the main gasbag.

[0053] For example, the altitude control system primarily uses the auxiliary airbag blower to draw in / exhaust air, altering the mass of the stratospheric airship and thus controlling its altitude. The energy system employs solar cells and energy storage batteries to ensure long-duration day and night flight endurance. The propulsion system provides some wind resistance through two propellers fixed to the pod, enabling regional dwell control in windy environments. The payload bay can be equipped with a flywheel and anti-twist mechanism to ensure stable ground-oriented attitude of the pod, allowing the airship to always receive communication from the ground station.

[0054] For example, the altitude control system is mainly achieved through auxiliary airbags and their associated blowers. The blowers can draw in or expel air according to control commands, thereby changing the overall buoyancy and mass of the airship. By adjusting the inflation and deflation of the auxiliary airbags, the altitude of the airship in the stratosphere can be precisely controlled, ensuring that the airship maintains a stable operating position under different wind fields and meteorological conditions.

[0055] For example, the energy system combines solar cells and energy storage batteries to provide the airship with long-term endurance. During the day, solar panels convert sunlight into electricity and charge the batteries, while also directly powering the various electronic systems of the airship; at night, the energy storage batteries release electrical energy to ensure the airship's continuous flight and stable operation under day-night conditions.

[0056] For example, the propulsion system is mounted on the payload bay and includes two fixed propellers. The propellers provide thrust and wind resistance, enabling the airship to maintain its position or make minor maneuvers even in the presence of wind disturbances, thus achieving area-based stationary control. This system is particularly suitable for the complex wind conditions of offshore wind farms and can effectively prevent the airship from deviating from its designated airspace.

[0057] For example, the payload compartment can be equipped with a flywheel and an anti-twist mechanism. The flywheel provides angular momentum stability, giving the airship stronger anti-disturbance capabilities in attitude control; the anti-twist mechanism is used to counteract the torsion caused by wind and airflow during the suspension of the payload compartment, preventing directional deviation of the signal antenna or communication module. With the above devices, the payload compartment can maintain ground-oriented attitude stability, thereby ensuring that the airship can always receive communication signals from the ground station in any state.

[0058] In this embodiment, the shore-based control center is generally located at the onshore dispatch base where the wind farm is located, and undertakes centralized monitoring and dispatch functions. Its main functions include: receiving operating data from offshore wind turbines, performing power prediction and dispatch for the wind farm, issuing control commands, and connecting with the power grid dispatch center to achieve unified management of large-scale wind farms.

[0059] In this embodiment, the booster station control center is typically deployed in an offshore booster station, primarily for power collection and voltage boosting within the wind farm. In addition to monitoring electrical equipment, the booster station control center also needs to collect and process data from surrounding turbine units in real time to enable rapid response in near-shore environments. This center can maintain data exchange with the shore-based control center via an airship, thus forming a multi-level control and communication system.

[0060] In this embodiment, the communication link is a data channel connecting the offshore wind turbine, the aerostat, the shore-based control center, and the booster station control center. This link can employ multi-hop transmission, star topology, or mesh topology depending on the scenario. The aerostat acts as a bridge and amplifier in the link, effectively solving the communication blind spots and signal attenuation problems caused by the long distances and complex sea conditions of offshore wind farms.

[0061] In this embodiment, data relay communication refers to the forwarding and transmission of information via an airship. In this invention, data generated by offshore wind turbines can first be transmitted to the airship, and then relayed by the airship to the shore-based control center or booster station control center. This relay method features low latency, dynamic adjustment, and redundancy protection, ensuring the real-time performance and reliability of the data.

[0062] In this embodiment, data processing includes real-time on-site processing at the airship side and centralized processing at the control center side. The airship can preprocess the unit's operating data, such as through edge computing, anomaly detection, and compression encoding, to reduce the burden on the backhaul link; while the shore-based control center and the booster station control center are responsible for big data analysis, power optimization, fault diagnosis, and operation and maintenance decisions, thereby improving overall operating efficiency.

[0063] Figure 4 A flowchart illustrating a communication relay and edge computing method for offshore wind farms provided in this application embodiment is provided. This method is based on the above. Figure 1 The communication link implementation is shown. For example... Figure 4 As shown in the embodiment of this application, a communication relay and edge computing method for offshore wind farms is provided. The method includes:

[0064] Step S110: Send preset configuration parameters to multiple airships, the preset configuration parameters including the airship's flight trajectory, flight altitude and target mission area.

[0065] In this application, this step enables the initial deployment of the airship and provides the foundation for subsequent data communication and edge computing. For example, the flight trajectory is used to plan the airship's movement path and hovering points over the wind farm. The flight altitude is used to ensure stable communication links with offshore wind turbines and other airships. The target mission area is used to indicate the offshore wind farm area where the airship needs to be stationed and serviced.

[0066] Step S120: When the airship flies to the target mission area according to the preset configuration parameters, a communication link establishment command is sent to the airship stationed in the target mission area to establish a communication link between the offshore wind turbine, the airship, the shore-based control center and the booster station control center.

[0067] In this application, a distributed multi-hop network is formed between airships using a wireless ad-hoc network protocol, enabling data relay and self-organization between airships. The process of establishing communication links supports dynamic adjustment; airships can optimize link paths based on wind farm meteorological conditions and network topology, improving link reliability and redundancy. Through the multi-hop network, offshore wind turbines far from shore bases can also achieve reliable communication, ensuring continuous data transmission.

[0068] For a detailed description of the communication link, please refer to the above content.

[0069] Step S130: The offshore wind turbine uploads real-time operating data to the airship, and the airship outputs processing results based on the real-time operating data.

[0070] In this application, the airship performs edge computing processing based on the real-time operating data, specifically including the following two aspects:

[0071] (1) Anomaly Detection: The edge computing module built into the airship performs real-time monitoring and feature analysis on the unit's operating data. For example, abnormal data points or trends can be identified through model comparison and threshold determination, such as abnormally high wind turbine vibration values, excessively high gearbox temperatures, and excessive power output fluctuations. The anomaly detection results can generate preliminary alarms locally and be quickly transmitted back to the booster station control center or shore-based control center via communication links to achieve early warning and rapid response.

[0072] (2) Health assessment: Based on anomaly detection, the edge computing module further conducts a health assessment of the wind turbine's operating status. For example, the assessment methods include: trend prediction based on historical operating data, life estimation based on AI algorithms, and health scoring based on key performance indicators (KPIs). The assessment results can generate a unit health report, which includes predictions of the wind turbine's remaining service life, wear trends of key components, and potential failure risk levels.

[0073] In one specific implementation, the airship outputs processing results based on real-time operational data, specifically including:

[0074] Step S131: Preprocess the real-time running data and output the preprocessed data.

[0075] In practice, the airship preprocesses real-time operational data, including data cleaning, formatting, feature extraction, and compression encoding, thereby removing abnormal noise values, standardizing data formats, and reducing transmission load. The output preprocessed data is better suited for subsequent anomaly detection and health assessment models.

[0076] Step S132: Input the preprocessed data into the preset anomaly detection model and output an anomaly warning signal.

[0077] In some specific implementations, the pre-set anomaly detection models include power monitoring, vibration analysis, and temperature monitoring.

[0078] For example, power monitoring can use a pre-set wind speed-power curve lookup table for rapid consistency comparison, avoiding complex calculations. The aerostat can quickly compare the real-time collected wind speed with the unit's power; if the deviation exceeds a preset threshold, it is determined that there is an anomaly in power generation efficiency. This method does not require complex physical modeling or real-time numerical calculations, and can achieve rapid consistency detection at the edge.

[0079] For example, vibration analysis can focus on key characteristic frequencies (1P, 3P) and extract a limited frequency band amplitude using an optimized lightweight FFT algorithm for threshold judgment. Vibration monitoring focuses on the key characteristic frequency range of the unit: 1P frequency: the low-frequency excitation generated by the wind turbine rotor per revolution; 3P frequency: the frequency component caused by the periodic aerodynamic forces of the blades. The airship uses an optimized lightweight FFT (Fast Fourier Transform) algorithm to extract only the amplitude features within a limited frequency band and compares them with a preset threshold. When the amplitude exceeds the threshold, it is determined that the unit may have an imbalance or structural fault.

[0080] For example, temperature monitoring employs a combination of fixed threshold detection and short-term moving average temperature rise rate detection. This method combines fixed threshold detection with short-term moving average temperature rise rate detection: the fixed threshold directly identifies severe overheating conditions; the moving average temperature rise rate captures potential early overheating trends, making it suitable for monitoring the health status of critical components such as gearboxes and generator windings.

[0081] In some preferred implementations, all judgment modules are fused using state machine logic, ultimately outputting tiered early warning signals. The detection results from each monitoring module are input to the airship's state machine logic unit, achieving comprehensive judgment based on multi-source information through logic fusion. When a single module triggers an anomaly, the state machine outputs a primary early warning; when multiple modules trigger anomalies simultaneously or exhibit overlapping trends, the state machine outputs a higher-level early warning signal. The final result is transmitted back to the shore-based control center or the booster station control center in the form of tiered early warning signals, enabling maintenance personnel to make rapid decisions.

[0082] Step S133: Obtain historical operating data of offshore wind turbines.

[0083] In practice, the airship acquires historical operating data of the corresponding wind turbine from local storage or via a communication link from the shore-based control center. This historical data includes long-term monitoring records of the turbine under different meteorological conditions, operating conditions, and maintenance cycles. This data provides a reference baseline and trend information for health assessment.

[0084] Step S134: Input the historical operating data and the preprocessed data into the health assessment model and output the predicted fault risk information.

[0085] In this embodiment, the historical operational data and real-time preprocessed data are input into the health assessment model, which outputs predicted failure risk information. The health assessment model can be built based on deep learning or time series analysis algorithms, such as LSTM (Long Short-Term Memory) networks or Transformer models. The prediction results include the remaining useful life (RUL) estimate of key components, the potential failure risk level, and maintenance priority recommendations. The health assessment results can serve as an important basis for wind farm maintenance planning and scheduling optimization.

[0086] In some possible implementations, health indicators are constructed for key components (such as spindles and gearboxes) based on real-time and historical operating data. The evaluation process employs an operating status zoning strategy, calling preset thresholds for judgment based on different speed-power ranges, and combining short-term trend analysis (such as moving averages) to identify performance degradation.

[0087] For example, health indicators include, but are not limited to: spindle torque fluctuation amplitude, gearbox vibration energy characteristics, temperature rise rate and absolute temperature level, power deviation and energy efficiency indicators. To avoid misjudgment under different operating conditions, this invention adopts an operating state zoning strategy. Specifically, the aerostat divides the operating state into several intervals based on the unit's real-time speed and power. For example: low wind speed operating interval, medium wind speed rated power interval, and high wind speed power-limited interval. Within different intervals, the aerostat calls the corresponding preset thresholds or diagnostic rules to achieve targeted judgment. Based on the threshold judgment, the aerostat further performs short-term trend analysis, such as moving average or exponential smoothing calculation. By modeling the short-term evolution trend of health indicators, early signs of performance degradation can be identified. For example, gearbox vibration energy rises continuously and slowly, although not exceeding the threshold, it already shows a potential abnormal trend.

[0088] Specifically, the airship integrates the results of operational status zoning and trend analysis to output a health status level. Examples include: Normal (no abnormalities); Caution (slight degradation trend); Warning (exceeding threshold or significant trend); and Critical (persistent high-risk state). This health status level is transmitted back to the shore-based control center or the booster station control center via wireless communication, allowing maintenance personnel to intervene proactively.

[0089] Step S140: The airship sends the processing results to the shore-based control center or the booster station control center.

[0090] In this embodiment, the airship transmits the processed results to the shore-based control center or the booster station control center via satellite link or other reliable communication links. This ensures that offshore wind turbine data can be transmitted back to the ground control center in real time and securely. It is understood that the shore-based control center and the booster station control center centrally monitor, schedule, and make operational and maintenance decisions for the wind turbines. Combining edge computing processing results enables rapid response, intelligent operation and maintenance, and remote fault diagnosis.

[0091] In one specific implementation, the airship is configured to execute a three-dimensional potential field trajectory planning method based on a path search algorithm, the three-dimensional potential field trajectory planning method comprising the following steps:

[0092] Step S210: Construct a three-dimensional grid map of the offshore wind farm, and integrate the spatial distribution information of the wind turbine towers, the substation control center, the wind field vector information, and the height information into the cost function of the three-dimensional grid map.

[0093] In practice, a three-dimensional grid map covering offshore wind farms is constructed. This map not only includes spatial distribution information of facilities such as wind turbine towers and booster station control centers, but also integrates real-time or predicted wind field vector information, airship flight altitude information, and other elements.

[0094] During the construction process, a cost function is established for each grid cell, and the cost function takes into account the following factors:

[0095] 1. Safe distance from obstacles (to avoid collisions with facilities such as towers and substations);

[0096] 2. Wind speed and direction distribution (used to assess flight energy consumption and stability);

[0097] 3. Altitude restrictions and meteorological conditions (such as no-fly zones such as inversion layers and turbulent layers).

[0098] For example, the path search algorithm is described as follows: the optimal path to the target point is found by iteratively exploring the surrounding nodes with the minimum estimated total cost, starting from the starting point in the graph grid.

[0099] In one specific implementation, the mathematical expression of the cost function of the three-dimensional grid map is f(n) = g(n) + h(n), where g(n) is the actual cost from the starting point to the current node n; h(n) is the estimated cost from the current node n to the target point, and the estimated cost adopts Manhattan distance or Euclidean distance.

[0100] Understandably, the algorithm maintains an open list (a priority queue, sorted by f(n)) and a closed list. It starts at the starting point, adds it to the open list, and then repeatedly checks the node with the smallest f(n) from the open list: if it's the target node, the path is found; otherwise, it adds its neighbors to the open list and updates their g(n) and f(n) values, until a path is found or the open list is empty.

[0101] Step S220: On the three-dimensional raster map, candidate paths are searched based on a path search algorithm.

[0102] In practice, on a 3D grid map, the airship searches for candidate paths based on path search algorithms (such as the A-star algorithm or the Rapidly-exploring Random Tree Star algorithm). This process can quickly generate multiple feasible paths while taking into account spatial obstacles and wind field distribution.

[0103] Step S230: During the search process, the dominant wind layer is identified and utilized based on the cost function to generate a globally optimal reference path that guides the airship from the starting point to the target point.

[0104] In practice, during the path search process, the airship dynamically evaluates candidate paths based on a cost function. Specifically, the algorithm identifies dominant wind layers (such as downwind areas and low-energy airflow channels) and prioritizes guiding the airship along these layers, thereby reducing energy consumption and improving endurance. Simultaneously, the system avoids highly turbulent or headwind areas to enhance path safety and stability. Finally, the search process outputs a globally optimal reference path from the starting point to the target point.

[0105] Step S240: Output the global optimal reference path and use it for the flight control of the airship.

[0106] In practice, the generated global optimal reference path is transmitted as input to the airship flight control system. Combined with the altitude control system and propulsion system, it drives the airship to perform flight missions according to the planned path, ensuring that the airship can achieve safe, stable and energy-efficient trajectory control in windy environments.

[0107] The above embodiments are merely preferred embodiments of the present invention and should not be construed as limiting the scope of protection of the present invention. For those skilled in the art, it will be understood that various changes, modifications, substitutions and variations can be made to these embodiments without departing from the principles and spirit of the present invention. The scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A communication relay and edge computing method for offshore wind farms, characterized in that, This includes multiple aerostats deployed in offshore wind farms, each aerostat having an edge computing module, and includes the following steps: Preset configuration parameters are sent to multiple airships, including the airship's flight trajectory, flight altitude, and target mission area; When the airship flies to the target mission area according to the preset configuration parameters, a communication link establishment command is sent to the airship stationed in the target mission area to establish a communication link between the offshore wind turbine, the airship, the shore-based control center and the booster station control center. The offshore wind turbine uploads real-time operating data to the airship, and the airship outputs processing results based on the real-time operating data; The airship sends the processing results to the shore-based control center or the booster station control center.

2. The method for communication relay and edge computing in offshore wind farms as described in claim 1, characterized in that, The airship outputs processing results based on real-time operational data, specifically including: The real-time running data is preprocessed, and preprocessed data is output. The preprocessed data is input into a preset anomaly detection model, which outputs an anomaly warning signal.

3. The method for communication relay and edge computing in offshore wind farms as described in claim 2, characterized in that, The airship outputs processing results based on real-time operational data, specifically including: The real-time running data is preprocessed, and preprocessed data is output. Obtain historical operating data of offshore wind turbines; The historical operating data and the preprocessed data are input into the health assessment model, which outputs predicted failure risk information.

4. The method for communication relay and edge computing in offshore wind farms as described in claim 1, characterized in that: The multiple airships establish wireless communication links and form a wireless distributed multi-hop network based on the Ad-hoc network protocol. Each of the aforementioned airships can be used as a data relay, transmitting the real-time operational data back to the shore-based control center or the booster station control center via a satellite link.

5. The method for communication relay and edge computing in offshore wind farms as described in claim 1, characterized in that: The airship includes a high-altitude balloon and a payload compartment, the payload compartment being connected to the high-altitude balloon and located below the high-altitude balloon; The payload compartment contains a circuit board, which is equipped with an STM32 chip, a LoRa communication module, a BeiDou communication module, a Wi-Fi module, a Bluetooth module, and an edge computing module. The STM32 chip is connected to the LoRa communication module, Beidou communication module, Wi-Fi module, Bluetooth module and edge computing module respectively; The edge computing module uses an AI processing device called Ascend Atlas 200 DK.

6. The method for communication relay and edge computing in offshore wind farms as described in claim 1, characterized in that: The airship is configured to execute a three-dimensional potential field trajectory planning method based on a path search algorithm, the three-dimensional potential field trajectory planning method comprising the following steps: A three-dimensional grid map of an offshore wind farm is constructed, and the spatial distribution information of wind turbine towers, substation control centers, wind field vector information, and height information are integrated into the cost function of the three-dimensional grid map. On the three-dimensional raster map, candidate paths are searched based on a path search algorithm; During the search process, the dominant wind layer is identified and utilized based on the cost function to generate a globally optimal reference path that guides the airship from the starting point to the target point. The globally optimal reference path is output and used for the flight control of the airship.

7. The method for communication relay and edge computing in offshore wind farms as described in claim 6, characterized in that: The mathematical expression for the cost function of the three-dimensional grid map is f(n) = g(n) + h(n), where g(n) is the actual cost from the starting point to the current node n. h(n) is the estimated cost from the current node n to the target point, using Manhattan distance or Euclidean distance.

Images