Inspection system, method and equipment for wind power plant, storage medium and program product

Abstract

The invention relates to the technical field of wind power plant inspection, and discloses an inspection system, method and device of a wind power plant, a storage medium and a program product, the inspection system of the wind power plant comprises an application layer, an interface layer, a container cloud, an edge hardware layer, a network communication layer and an intelligent sensing layer; the application layer is used for sending the inspection task to the container cloud; the container cloud is used for disassembling the inspection task into a plurality of subtasks; the edge hardware layer is used for determining a target flight path of the unmanned aerial vehicle according to the real-time meteorological data, the terrain obstacle information and the unmanned aerial vehicle inspection subtasks, and transmitting the target flight path and the multiple subtasks to the intelligent sensing layer; the intelligent sensing layer is used for controlling the unmanned aerial vehicle to inspect according to the target flight path and the unmanned aerial vehicle inspection subtask; and the intelligent sensing layer is also used for controlling the multi-source sensing equipment to carry out monitoring according to the multi-source monitoring sub-task in the plurality of sub-tasks. By automatically controlling the operation of the intelligent sensing layer, the inspection efficiency is improved.

Description

Technical Field

[0001] This invention relates to the field of wind farm inspection technology, specifically to wind farm inspection systems, methods, equipment, storage media, and program products. Background Technology

[0002] With the increasing demand for clean energy, wind power, as an important form of renewable energy, has been widely adopted. As a crucial component of modern wind power systems, the operational efficiency and safety of smart wind farms are paramount; therefore, regular inspections of smart wind farms are necessary.

[0003] Traditional wind farm inspections rely primarily on manual operation, which suffers from low efficiency, high costs, and slow response times. With the development of emerging Internet of Things (IoT) technologies, unmanned inspection systems are gradually becoming a key means to improve the operation and maintenance management of wind farms.

[0004] In related technologies, the automated inspection method for wind farms involves using drones or inspection robots along fixed routes. However, this method can only obtain data at fixed intervals and locations, resulting in low efficiency and inaccurate inspection results. Summary of the Invention

[0005] In view of this, the present invention provides a wind farm inspection system, method, equipment, storage medium and program product to solve the problems of low inspection efficiency and inaccurate inspection results caused by automated inspection methods for wind farms in related technologies.

[0006] In a first aspect, the present invention provides a wind farm inspection system, comprising: an application layer, an interface layer, a container cloud, an edge hardware layer, a network communication layer, and an intelligent sensing layer. The application layer is used to acquire inspection tasks input by the user and send the inspection tasks to the container cloud via the interface layer. The container cloud is used to decompose the inspection tasks into multiple sub-tasks and send these sub-tasks to the edge hardware layer. The edge hardware layer is used to determine the target flight path of the drone based on real-time meteorological data, terrain obstacle information, and the drone inspection sub-task among the multiple sub-tasks, and send the target flight path and the multiple sub-tasks to the intelligent sensing layer via the network communication layer. The intelligent sensing layer is used to control the drone to perform inspections based on the target flight path and the drone inspection sub-task, and send the inspection data obtained through the inspection to the edge hardware layer via the network communication layer. The intelligent sensing layer is also used to control multi-source sensing devices to perform monitoring based on the multi-source monitoring sub-task among the multiple sub-tasks, and send the monitoring data obtained through the monitoring to the edge hardware layer via the network communication layer.

[0007] The wind farm inspection system of this invention includes an application layer, an interface layer, a container cloud, an edge hardware layer, a network communication layer, and an intelligent sensing layer. The application layer acquires inspection tasks input by the user and sends these tasks to the container cloud via the interface layer. A standardized task input and transmission process ensures accurate and efficient input and distribution of inspection tasks. The container cloud breaks down the inspection task into multiple sub-tasks and sends these sub-tasks to the edge hardware layer. This task decomposition using the container cloud allows for finer task granularity based on the actual needs of wind farm inspections, making the division of complex inspection tasks more rational and improving the targeting and flexibility of inspection task execution. The edge hardware layer determines the target flight path of the drone based on real-time meteorological data, terrain obstacle information, and the drone inspection sub-task among the multiple sub-tasks. It then sends the target flight path and multiple sub-tasks to the intelligent sensing layer via the network communication layer. This layer plans the drone path by combining meteorological, terrain, and other actual environmental factors, making drone inspections more suitable for the complex scenarios of wind farms. The intelligent sensing layer controls the drone to perform inspections based on the target flight path and the drone inspection sub-task, and sends the inspection data to the edge hardware layer via the network communication layer. This invention precisely controls drone operations, ensuring the accurate execution of inspection tasks and timely data transmission, enabling the edge hardware layer and other components to quickly acquire on-site information and effectively control the drone inspection process. The intelligent sensing layer also controls multi-source sensing devices to perform monitoring based on the multi-source monitoring sub-task, and sends the monitoring data to the edge hardware layer via the network communication layer. In addition to drone inspections, this invention also uses multi-source sensing devices for monitoring, increasing the inspection dimensions and enabling comprehensive monitoring of wind farm equipment and the environment. Compared with related technologies, this invention achieves automated wind farm inspections, improving the flexibility, efficiency, and accuracy of automated inspections, thus ensuring the stable operation of the wind farm.

[0008] In one optional implementation, the edge hardware layer includes a data synchronization module, an anomaly detection module, and a health scoring module. The data synchronization module is used to determine the synchronization period based on the data synchronization success rate, so as to synchronize inspection data and monitoring data to the container cloud based on the synchronization period. The anomaly detection module is used to evaluate the abnormal data of each measurement point based on the standard deviation of the data of multiple measurement points corresponding to the inspection data and monitoring data, so as to perform anomaly detection. The health scoring module is used to perform health scoring on wind farm equipment based on multiple evaluation indicators and inspection data and monitoring data.

[0009] The data synchronization module of this invention determines the synchronization cycle based on the data synchronization success rate. It synchronizes inspection and monitoring data to the container cloud based on this cycle, intelligently adjusting the next synchronization cycle according to the success rate of the most recent synchronization. This improves the accuracy of the synchronization cycle, reduces waste of network bandwidth and computing resources, and enhances inspection efficiency. Synchronizing inspection and monitoring data to the container cloud based on the synchronization cycle ensures that all modules access the latest and unified data version, avoiding misjudgments caused by data delays or conflicts. This invention saves network bandwidth and computing resources by dynamically adjusting the synchronization cycle. The anomaly detection module of this invention evaluates abnormal data at each measurement point based on the standard deviation of multiple measurement points corresponding to the inspection and monitoring data. This enables rapid and accurate detection of abnormal fluctuations in the data, providing timely warnings of potential problems. This helps maintenance personnel take proactive measures to avoid faults or safety incidents, ensuring the stable operation of the wind farm. The health scoring module of this invention performs a health score on wind farm equipment based on multiple evaluation indicators and inspection and monitoring data. This predicts potential future problems and allows for advance maintenance planning, improving the reliability and lifespan of wind farm equipment and reducing the risk of sudden failures.

[0010] In one optional implementation, the container cloud includes a task allocation optimization module; the task allocation optimization module is used to allocate corresponding execution devices to multiple sub-tasks with the optimization objectives of minimizing inspection time and energy consumption.

[0011] The task allocation optimization module of this invention is used to allocate corresponding execution devices to multiple sub-tasks with the goal of minimizing inspection time and energy consumption, so as to ensure that all inspection tasks are completed in the shortest time and with the lowest energy consumption, significantly improving inspection efficiency and resource utilization, and reducing inspection costs.

[0012] In one optional implementation, the application layer includes an inspection report generation module; the inspection report generation module is used to generate inspection reports based on inspection data, monitoring data, abnormal data, and health scores for users to query.

[0013] The inspection report generation module of this invention integrates and presents multi-dimensional information such as inspection data, monitoring data, abnormal data, and health scores in a centralized and standardized manner by generating an inspection report, providing users with a comprehensive reference for the complete inspection report.

[0014] In one optional implementation, the edge hardware layer includes a periodic fault detection module; the periodic fault detection module is used to perform fault detection on the drone, multi-source sensing devices, application applications in the application layer, and multiple algorithms in the edge hardware layer according to a preset detection cycle.

[0015] The periodic fault detection module of this invention is used to perform fault detection on drones, multi-source sensing devices, application applications in the application layer, and multiple algorithms in the edge hardware layer according to a preset detection cycle. It can promptly detect potential fault hazards, perform maintenance in advance, avoid the impact of faults on the execution of inspection tasks, and ensure the continuous and stable operation of wind farm inspection work.

[0016] In one optional implementation, the wind farm inspection system further includes: a remote operation and maintenance module; the remote operation and maintenance module is used to execute remote operation commands input by the user through a preset encrypted channel.

[0017] The remote operation and maintenance module of this invention, with the help of a preset encrypted channel, enables users to send operation commands from a remote location, quickly respond to equipment failures, system anomalies and other situations, significantly shorten the operation and maintenance response time and improve the efficiency of problem solving.

[0018] Secondly, the present invention provides a method for inspecting a wind farm, comprising: acquiring an inspection task input by a user and breaking down the inspection task into multiple sub-tasks; determining the target flight path of a drone based on real-time meteorological data, terrain obstacle information, and the drone inspection sub-task among the multiple sub-tasks; controlling the drone to perform inspection based on the target flight path and the drone inspection sub-task; and controlling a multi-source sensing device to perform monitoring based on the multi-source monitoring sub-task among the multiple sub-tasks.

[0019] Thirdly, the present invention provides a computer device, comprising: a memory and a processor, wherein the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the computer instructions to perform the wind farm inspection method of the second aspect described above.

[0020] Fourthly, the present invention provides a computer-readable storage medium storing computer instructions for causing a computer to execute the wind farm inspection method described in the second aspect above.

[0021] Fifthly, the present invention provides a computer program product, including computer instructions for causing a computer to execute the wind farm inspection method described in the second aspect above. Attached Figure Description

[0022] To more clearly illustrate the technical solutions in the specific embodiments or related technologies of the present invention, the drawings used in the description of the specific embodiments or related technologies will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0023] Figure 1This is a schematic diagram of the structure of a wind farm inspection system according to an embodiment of the present invention.

[0024] Figure 2 This is a schematic diagram of another inspection system for a wind farm according to an embodiment of the present invention.

[0025] Figure 3 This is a flowchart illustrating a wind farm inspection method according to an embodiment of the present invention.

[0026] Figure 4 This is a schematic diagram of the hardware structure of a computer device according to an embodiment of the present invention. Detailed Implementation

[0027] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0028] With the ever-increasing demand for clean energy, wind power, as an important form of renewable energy, has been widely adopted. As a crucial component of modern wind power systems, the operational efficiency and safety of smart wind farms are paramount. Traditional wind farm inspections primarily rely on manual operation, resulting in low efficiency, high costs, and slow response times. Currently, with the development of emerging Internet of Things (IoT) technologies, unmanned inspection systems are gradually becoming a key means to improve the operation and maintenance management of wind farms.

[0029] In related technologies, the automated inspection method for wind farms involves using drones or inspection robots along fixed routes. While these technologies have achieved automated inspection of wind farms to some extent, they still have some limitations. First, the data synchronization mechanism is not flexible enough; fixed-period data synchronization often leads to unnecessary resource waste or an inability to adjust in time when synchronization fails. Second, the anomaly detection methods are relatively simple, making it difficult to quickly and accurately identify potential problems, which affects the early warning capability of automated inspections. In addition, the related technologies lack intelligent scheduling in task allocation and fail to fully consider the complex factors in actual scenarios, resulting in inefficient inspection path planning.

[0030] This invention provides a wind farm inspection system that improves inspection efficiency by automatically controlling the operation of the intelligent sensing layer.

[0031] According to an embodiment of the present invention, an inspection system for a wind farm is provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0032] This embodiment provides a wind farm inspection system, which includes computer equipment for configuring the wind farm inspection system. Figure 1 This is a flowchart of a wind farm inspection system according to an embodiment of the present invention, such as... Figure 1 As shown, the system includes: application layer 101, interface layer 102, container cloud 103, edge hardware layer 104, network communication layer 105, and intelligent sensing layer 106.

[0033] Application layer 101 is used to obtain the inspection task input by the user and send the inspection task to container cloud 103 through interface layer 102.

[0034] The application layer 101 includes a station-side task management subsystem, through which users can issue inspection tasks. Users can set the inspection scope (such as wind turbine blades, gearboxes, towers, etc.), priority (urgent, routine), and inspection mode (drone inspection, multi-source sensing device inspection, and both) for inspection tasks.

[0035] In some optional implementations, interface layer 102 defines a variety of standardized interfaces to ensure cloud-edge collaboration, with interface response time controlled within 500 milliseconds. For example, the standardized interface could be a RESTful API (REpresentational State Transfer Application Programming Interface).

[0036] In some optional implementations, the application layer 101 includes an inspection report generation module; the inspection report generation module is used to generate inspection reports based on inspection data, monitoring data, abnormal data and health scores for users to query.

[0037] The inspection report generation module is also used to respond to user query commands by sending the inspection report to the user terminal through interface layer 102, and to store the inspection report in the database. For example, user query commands include: viewing real-time inspection screen, querying historical reports, querying alarm information, triggering secondary re-inspection, and approving maintenance work orders.

[0038] The inspection report generation module of this invention integrates and presents multi-dimensional information such as inspection data, monitoring data, abnormal data, and health scores in a centralized and standardized manner by generating an inspection report, providing users with a comprehensive reference for the complete inspection report.

[0039] Container cloud 103 is used to break down inspection tasks into multiple sub-tasks and send the multiple sub-tasks to the edge hardware layer 104.

[0040] Among them, Container Cloud 103 is used to decompose the inspection task into multiple sub-tasks according to a preset strategy. The preset strategy is a task decomposition strategy set in advance based on resource load and geographical location. For example, if the inspection task is "inspecting 12 wind turbines in area C of the wind farm", the preset strategy can be to decompose by geographical location: divide area C into "C1 (6 turbines on the east side)" and "C2 (6 turbines on the west side)" according to the direction. At the same time, the preset strategy also includes allocation according to resource load: detect edge nodes. If the CPU (Central Processing Unit) load of node M1 is 40% (low) and the CPU load of node M2 ​​is 55% (medium), then "inspection of area C1" is assigned to node M1 and "inspection of area C2" is assigned to node M2 ​​(matching the node load capacity), thus obtaining two sub-tasks.

[0041] In some alternative implementations, the container cloud 103 is built on Kubernetes (an open-source container orchestration platform), and the servers of the edge hardware layer 104 can serve as worker nodes of the Kubernetes cluster. The container cloud 103 is used to schedule multiple subtasks to the server cluster of the edge hardware layer 104 via Kubernetes.

[0042] In some optional implementations, the container cloud 103 includes a task allocation optimization module; the task allocation optimization module is used to allocate corresponding execution devices to multiple sub-tasks with the optimization goals of minimizing inspection time and energy consumption.

[0043] The task allocation optimization module utilizes a linear programming model to minimize the total inspection time and resource consumption. For example, let X... ij C represents the task assignment variable that moves the character from position i to position j. ij If the cost (time or energy) is the corresponding cost, then the objective function is to minimize the total cost of all tasks. The specific formula for the objective function is as follows:

[0044]

[0045] Where F is the objective function, min is the minimum value, and C ij X is the cost of moving from position i to position j. ijLet n represent the task assignment variable (execution device) for moving from position i to position j, n be the total number of positions i, m be the total number of positions j, and the objective function satisfy the constraints.

[0046] In this embodiment of the invention, the task allocation optimization module is used to allocate corresponding execution devices to multiple sub-tasks with the goal of minimizing inspection time and energy consumption, so as to ensure that all inspection tasks are completed in the shortest time and with the lowest energy consumption, thereby significantly improving inspection efficiency and resource utilization and reducing inspection costs.

[0047] The edge hardware layer 104 is used to determine the target flight path of the UAV based on real-time meteorological data, terrain obstacle information and the UAV inspection sub-task among multiple sub-tasks, and send the target flight path and multiple sub-tasks to the intelligent perception layer 106 through the network communication layer 105.

[0048] The edge hardware layer 104 includes edge computing node servers, video intelligent application servers, and other auxiliary equipment, with a processing capacity of at least trillions of operations per second, used for local data processing and intelligent analysis algorithm execution.

[0049] In some optional implementations, the edge hardware layer 104 invokes a preset flight path planning algorithm to generate a target flight path based on real-time weather data, terrain obstacle information, and the UAV inspection sub-task among multiple sub-tasks. The target flight path is the optimal flight path. For example, the preset flight path planning algorithm may be the Dijkstra shortest path algorithm.

[0050] In this embodiment of the invention, the edge hardware layer 104 uses Dijkstra's shortest path algorithm to optimize the flight path of the UAV, reduce energy consumption and flight time, and generate the optimal flight path by taking into account the actual terrain, obstacle distribution and weather conditions, so as to ensure the safety and efficiency of UAV inspection.

[0051] In some optional implementations, for routine inspection tasks, users set inspection plans (such as daily full-site inspections and weekly inspections of key equipment) through the station-side task management subsystem of application layer 101. The task instructions are transmitted to the edge hardware layer 104 through interface layer 102 to generate target flight paths. For emergency re-inspection instructions, when the anomaly detection module detects a potential fault (such as excessive gearbox vibration), a re-inspection request is automatically generated, the instruction priority is increased, and the UAV is required to go to the designated location immediately.

[0052] In some optional implementations, the edge hardware layer 104 includes a data synchronization module, an anomaly detection module, and a health scoring module; the data synchronization module is used to determine the synchronization period based on the data synchronization success rate, so as to synchronize inspection data and monitoring data to the container cloud based on the synchronization period; the anomaly detection module is used to evaluate the abnormal data of each measurement point based on the standard deviation of the data of multiple measurement points corresponding to the inspection data and monitoring data, so as to perform anomaly detection; the health scoring module is used to perform health scoring on wind farm equipment based on multiple evaluation indicators and inspection data and monitoring data.

[0053] The data synchronization module employs a dynamic adjustment algorithm to optimize the synchronization period between different sources. For any two data sources A and B, if the initial synchronization period is T0, the next synchronization period T is dynamically adjusted based on the success rate P of the most recent synchronization. next The adjustment formula is:

[0054]

[0055] Among them, T next For the next synchronization period T next T0 is the initial synchronization period, α is the proportional coefficient for reducing the synchronization period, usually taken as 0.1, β is the proportional coefficient for increasing the synchronization period, usually taken as 0.2, and P is the success rate of the most recent synchronization, which is usually obtained by the ratio of the total number of synchronization attempts to the number of successful synchronizations in the most recent time.

[0056] For example, when P is 0.95 and T0 is 5, according to the adjustment formula T next =5*(1+0.2*0.05)=5.05 minutes. When P is 0.8 and T0 is 5, according to the adjustment formula T... next =5*(1-0.2*0.2)=4.5 minutes.

[0057] In this embodiment of the invention, the data synchronization module ensures that unnecessary synchronization frequency is reduced when the success rate is high, while the synchronization frequency is appropriately increased when the success rate is low to improve the reliability of data synchronization.

[0058] In some optional implementations, the anomaly detection module uses a statistical process control (SPC) method to identify anomalous data points. By calculating the standard deviation σ of each measurement point over a period of time, it then assesses whether new data points fall within the range of [μ-3σ, μ+3σ]. Data points outside this range are marked as potential anomalies. The SPC method can quickly and accurately detect abnormal fluctuations in the data and provide timely warnings of potential problems. When anomaly data is detected, alarm levels are classified according to the severity of the anomaly (such as "warning," "critical," and "urgent"), and pushed to the mobile terminals, PCs, or audible and visual alarm devices of maintenance personnel through the application layer 101. At the same time, a work order is recorded and generated. The characteristics of this anomaly (such as vibration spectrum and temperature gradient) are incorporated into the training set of the statistical process control (SPC) model to optimize future detection thresholds. If similar anomalies occur frequently, the synchronization cycle, health score threshold, or task scheduling parameters are adjusted.

[0059] In some optional implementations, the health scoring module is used to normalize multiple assessment indicators to the interval [0, 1], and then perform a weighted summation to obtain the health score. For example, the formula for determining the health score is:

[0060]

[0061] Where H is the health score, K is the total number of assessment indicators, and W... k Let x be the weight of the k-th evaluation indicator. k Let x be the value of the k-th evaluation indicator obtained from inspection data and monitoring data. k,min x is the historical minimum value of the k-th evaluation indicator. k,max This represents the historical maximum value of the k-th evaluation indicator.

[0062] In some alternative implementations, a health scoring module is used to generate maintenance recommendations when the health score is less than a preset health score threshold.

[0063] The data synchronization module of this invention determines the synchronization cycle based on the data synchronization success rate. It synchronizes inspection and monitoring data to the container cloud based on this cycle, intelligently adjusting the next synchronization cycle according to the success rate of the most recent synchronization. This improves the accuracy of the synchronization cycle, reduces waste of network bandwidth and computing resources, and enhances inspection efficiency. Synchronizing inspection and monitoring data to the container cloud based on the synchronization cycle ensures that all modules access the latest and unified data version, avoiding misjudgments caused by data delays or conflicts. This invention saves network bandwidth and computing resources by dynamically adjusting the synchronization cycle. The anomaly detection module of this invention evaluates abnormal data at each measurement point based on the standard deviation of multiple measurement points corresponding to the inspection and monitoring data. This enables rapid and accurate detection of abnormal fluctuations in the data, providing timely warnings of potential problems. This helps maintenance personnel take proactive measures to avoid faults or safety incidents, ensuring the stable operation of the wind farm. The health scoring module of this invention scores the wind farm equipment based on multiple evaluation indicators and inspection and monitoring data. This predicts potential future problems and allows for advance maintenance planning, improving the reliability and lifespan of wind farm equipment and reducing the risk of sudden failures.

[0064] In some optional implementations, the edge hardware layer 104 includes a periodic fault detection module; the periodic fault detection module is used to perform fault detection on the drone, multi-source sensing devices, application applications of the application layer, and multiple algorithms of the edge hardware layer according to a preset detection cycle.

[0065] The periodic fault detection module is used to perform operations including but not limited to equipment self-testing, software updates, and security patch installation through an automated periodic inspection mechanism. This ensures the operational stability and security of the wind farm's inspection system, with an inspection cycle of no more than one week. It also supports automated report generation, automatically generating a detailed inspection report after each inspection for maintenance personnel to review.

[0066] The periodic fault detection module of this invention is used to perform fault detection on drones, multi-source sensing devices, application applications in the application layer, and multiple algorithms in the edge hardware layer 104 according to a preset detection cycle. It can promptly detect potential fault hazards, perform maintenance in advance, avoid the impact of faults on the execution of inspection tasks, and ensure the continuous and stable operation of wind farm inspection work.

[0067] The intelligent perception layer 106 is used to control the UAV to perform inspections based on the target flight path and the UAV inspection sub-task, and to send the inspection data obtained from the inspections to the edge hardware layer 104 through the network communication layer 105.

[0068] The intelligent sensing layer 106 is also used to control the multi-source sensing device to perform monitoring according to the multi-source monitoring sub-task among multiple sub-tasks, and to send the monitoring data obtained by monitoring to the edge hardware layer 104 through the network communication layer 105.

[0069] The intelligent sensing layer 106 includes multi-source sensing devices such as high-definition cameras, temperature and humidity sensors, and vibration sensors deployed indoors and outdoors in the wind farm area and booster station, which are used to monitor environmental parameters, equipment status and safety conditions in real time. The intelligent sensing layer 106 also includes multiple drones for inspecting the wind farm.

[0070] For example, the drone flies along the target flight path, its onboard high-definition camera captures images of the wind turbine surface, temperature and humidity sensors collect environmental data, and multi-source sensing devices monitor parameters such as equipment vibration amplitude, temperature, and current in real time.

[0071] In some optional implementations, the network communication layer 105 uses a redundantly designed fiber optic ring network or wireless communication network to transmit inspection and monitoring data, ensuring a data transmission rate of no less than 1Gbps (gigabits per second) and a latency of no more than 10ms (milliseconds). Specifically, the topology of the network communication layer 105 is a ring fiber optic network, where each node (such as a wind turbine or substation) is connected in series via optical fibers to form a closed loop. The dual-ring structure of the network communication layer 105 allows data to be transmitted simultaneously in both clockwise and counterclockwise directions. If a fiber optic segment breaks or a node fails, the data automatically switches to the other direction for transmission, ensuring uninterrupted communication. The ring network and wireless communication of the network communication layer 105 work together through wired and wireless networks to prevent data loss due to a single line failure. The self-healing function of the network communication layer 105 supports a fast ring network protection protocol, and the fault switching time can be controlled within 50ms.

[0072] In some optional implementations, the wind farm inspection system further includes: a remote operation and maintenance module; the remote operation and maintenance module is used to execute remote operation commands input by the user through a preset encrypted channel.

[0073] The remote operation and maintenance module supports remote operation and maintenance management, but prohibits the use of specific remote access tools. It only allows remote operation through authorized secure channels to ensure system security and controllability. It supports SSH (Secure Shell) and TLS (Transport Layer Security) encrypted communication, and remote maintenance records must be archived and retained for at least one year.

[0074] The remote operation and maintenance module of this invention uses a preset encrypted channel to enable users to send operation commands from different locations, quickly respond to equipment failures, system anomalies, and other situations, significantly shorten operation and maintenance response time, and improve problem-solving efficiency.

[0075] The wind farm inspection system provided in this embodiment includes an application layer 101, an interface layer 102, a container cloud 103, an edge hardware layer 104, a network communication layer 105, and an intelligent sensing layer 106. The application layer 101 acquires inspection tasks input by the user and sends these tasks to the container cloud 103 via the interface layer 102. Through a standardized task input and transmission process, it ensures accurate and efficient input and distribution of inspection tasks. The container cloud 103 breaks down the inspection task into multiple sub-tasks and sends these sub-tasks to the edge hardware layer 104. By using the container cloud 103 for task decomposition, the granularity of tasks can be refined according to the actual needs of wind farm inspections, making the division of complex inspection tasks more reasonable and improving the targeting and flexibility of inspection task execution. The edge hardware layer 104 is used to determine the target flight path of the drone based on real-time meteorological data, terrain obstacle information, and the drone inspection sub-task among multiple sub-tasks. It then sends the target flight path and multiple sub-tasks to the intelligent perception layer 106 via the network communication layer 105. This allows for the planning of the drone path in conjunction with actual environmental factors such as weather and terrain, making the drone inspection more suitable for the complex scenarios of wind farms. The intelligent perception layer 106 is used to control the drone to perform inspections based on the target flight path and the drone inspection sub-tasks. It then sends the inspection data obtained to the edge hardware layer 104 via the network communication layer 105. This embodiment of the invention precisely controls the drone operation, ensuring the accurate execution of the inspection task and timely data transmission. This enables the edge hardware layer 104 and other components to quickly acquire on-site information, achieving effective control over the drone inspection process. The intelligent sensing layer 106 is also used to control multi-source sensing devices to perform monitoring according to the multi-source monitoring sub-task among multiple sub-tasks, and to send the monitoring data obtained through the network communication layer 105 to the edge hardware layer 104. In addition to UAV inspection, this embodiment of the invention also improves the inspection dimension by performing monitoring, enabling comprehensive monitoring of the equipment and environment of the wind farm. Compared with related technologies, this embodiment of the invention realizes automated inspection of wind farms, improves the flexibility of automated inspection of wind farms, improves the efficiency of the inspection process, and enhances the accuracy of inspection results, thereby ensuring the stable operation of wind farms.

[0076] This embodiment provides a wind farm inspection system, which includes computer equipment for configuring the wind farm inspection system. Figure 2 This is a flowchart of another inspection system for a wind farm according to an embodiment of the present invention, such as... Figure 2 As shown, the system includes:

[0077] Application layer 201 is used to receive inspection tasks initiated by users through the station-side task management subsystem, and to transmit the task inspection request to container cloud 203 through the standardized protocol of interface layer 202.

[0078] Container Cloud 203 is used to break down inspection tasks into multiple sub-tasks according to preset strategies (such as resource load and geographical location) and schedule them to the server cluster of the edge hardware layer 204 via Kubernetes.

[0079] The edge hardware layer 204 is used to call the Dijkstra algorithm, combine real-time meteorological data (wind speed, rainfall) and terrain obstacle information to generate the optimal flight path, and the command is sent to the intelligent perception layer 206 via the network communication layer 205.

[0080] The intelligent sensing layer 206 controls the drone to fly along the planned path, and the onboard high-definition camera captures images of the wind turbine surface, while the temperature and humidity sensor collects environmental data. It is also used to control and monitor parameters such as vibration amplitude, temperature, and current of the equipment in real time, and uploads the raw data to the edge hardware layer 204 through the network communication layer 205.

[0081] This embodiment provides a method for inspecting a wind farm, which can be used with computer equipment. Figure 3 This is a flowchart of a wind farm inspection method according to an embodiment of the present invention, such as... Figure 3 As shown, the process includes the following steps:

[0082] Step S301: Obtain the inspection task input by the user and break it down into multiple sub-tasks.

[0083] Step S302: Determine the target flight path of the UAV based on real-time meteorological data, terrain obstacle information, and the UAV inspection sub-task among multiple sub-tasks.

[0084] Step S303: Control the drone to perform inspection according to the target flight path and the drone inspection sub-task.

[0085] Step S304: Control the multi-source sensing device to perform monitoring according to the multi-source monitoring sub-task among the multiple sub-tasks.

[0086] In some alternative implementations, the wind farm inspection method further includes: determining a synchronization period based on the data synchronization success rate, so as to synchronize inspection data and monitoring data to the container cloud based on the synchronization period.

[0087] In some alternative implementations, the wind farm inspection method further includes: evaluating abnormal data at each measurement point based on the standard deviation of data from multiple measurement points corresponding to inspection data and monitoring data, in order to detect anomalies.

[0088] In some alternative implementations, the health of wind farm equipment is scored based on multiple evaluation metrics, including inspection and monitoring data.

[0089] In some alternative implementations, the wind farm inspection method also includes: assigning corresponding execution equipment to multiple sub-tasks with the optimization objectives of minimizing inspection time and energy consumption.

[0090] In some alternative implementations, the wind farm inspection method also includes generating an inspection report based on inspection data, monitoring data, abnormal data, and health scores for users to query.

[0091] In some alternative implementations, the wind farm inspection method also includes: performing fault detection on drones, multi-source sensing devices, application layer applications, and multiple algorithms at the edge hardware layer according to a preset detection cycle.

[0092] In some alternative implementations, the wind farm inspection method may further include: executing remote operation commands input by the user through a preset encrypted channel.

[0093] This invention also provides a computer device for performing the above-described actions. Figure 3 The inspection method for wind farms is shown.

[0094] Please see Figure 4 , Figure 4 This is a schematic diagram of the structure of a computer device provided in an optional embodiment of the present invention, such as... Figure 4 As shown, the computer device includes one or more processors 10, memory 20, and interfaces for connecting the components, including high-speed interfaces and low-speed interfaces. The components communicate with each other via different buses and can be mounted on a common motherboard or otherwise installed as needed. The processors can process instructions executed within the computer device, including instructions stored in or on memory to display graphical information of a GUI on external input / output devices (such as display devices coupled to the interfaces). In some alternative implementations, multiple processors and / or multiple buses can be used with multiple memories and multiple memory modules, if desired. Similarly, multiple computer devices can be connected, each providing some of the necessary operations (e.g., as a server array, a group of blade servers, or a multiprocessor system). Figure 4 Take a processor 10 as an example.

[0095] Processor 10 may be a central processing unit, a network processor, or a combination thereof. Processor 10 may further include a hardware chip. The hardware chip may be an application-specific integrated circuit (ASIC), a programmable logic device (PLD), or a combination thereof. The programmable logic device may be a complex programmable logic device (CAMP), a field-programmable gate array (FPGA), a general-purpose array logic (GDA), or any combination thereof.

[0096] The memory 20 stores instructions executable by at least one processor 10 to cause the at least one processor 10 to perform the method shown in the above embodiments.

[0097] The memory 20 may include a program storage area and a data storage area. The program storage area may store the operating system and applications required for at least one function; the data storage area may store data created based on the use of the computer device. Furthermore, the memory 20 may include high-speed random access memory and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some alternative embodiments, the memory 20 may optionally include memory remotely located relative to the processor 10, and these remote memories may be connected to the computer device via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

[0098] The memory 20 may include volatile memory, such as random access memory; the memory may also include non-volatile memory, such as flash memory, hard disk or solid-state drive; the memory 20 may also include a combination of the above types of memory.

[0099] The computer device also includes a communication interface 30 for communicating with other devices or communication networks.

[0100] This invention also provides a computer-readable storage medium. The methods described above according to embodiments of the invention can be implemented in hardware or firmware, or implemented as computer code that can be recorded on a storage medium, or implemented as computer code downloaded via a network and originally stored on a remote storage medium or a non-transitory machine-readable storage medium and then stored on a local storage medium. Thus, the methods described herein can be processed by software stored on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. The storage medium can be a magnetic disk, optical disk, read-only memory, random access memory, flash memory, hard disk, or solid-state drive, etc.; further, the storage medium can also include combinations of the above types of memory. It is understood that computers, processors, microprocessor controllers, or programmable hardware include storage components capable of storing or receiving software or computer code, which, when accessed and executed by the computer, processor, or hardware, implements the methods shown in the above embodiments.

[0101] A portion of this invention can be applied as a computer program product, such as computer program instructions, which, when executed by a computer, can invoke or provide the methods and / or technical solutions according to the invention through the operation of the computer. Those skilled in the art will understand that the forms in which computer program instructions exist in a computer-readable medium include, but are not limited to, source files, executable files, installation package files, etc. Correspondingly, the ways in which computer program instructions are executed by a computer include, but are not limited to: the computer directly executing the instructions, or the computer compiling the instructions and then executing the corresponding compiled program, or the computer reading and executing the instructions, or the computer reading and installing the instructions and then executing the corresponding installed program. Here, the computer-readable medium can be any available computer-readable storage medium or communication medium accessible to a computer.

[0102] Although embodiments of the invention have been described in conjunction with the accompanying drawings, those skilled in the art can make various modifications and variations without departing from the spirit and scope of the invention, and such modifications and variations all fall within the scope defined by the appended claims.

Claims

1. A wind farm inspection system, characterized in that, The system includes: an application layer, an interface layer, a container cloud, an edge hardware layer, a network communication layer, and an intelligent sensing layer. The application layer is used to obtain the inspection task input by the user and send the inspection task to the container cloud through the interface layer; The container cloud is used to break down the inspection task into multiple sub-tasks and send the multiple sub-tasks to the edge hardware layer; The edge hardware layer is used to determine the target flight path of the UAV based on real-time meteorological data, terrain obstacle information and the UAV inspection sub-task among the multiple sub-tasks, and send the target flight path and the multiple sub-tasks to the intelligent perception layer through the network communication layer. The intelligent perception layer is used to control the UAV to perform inspections according to the target flight path and the UAV inspection sub-task, and to send the inspection data obtained from the inspections to the edge hardware layer through the network communication layer. The intelligent sensing layer is also used to control the multi-source sensing device to perform monitoring according to the multi-source monitoring sub-task among the multiple sub-tasks, and to send the monitoring data obtained from the monitoring to the edge hardware layer through the network communication layer.

2. The system according to claim 1, characterized in that, The edge hardware layer includes a data synchronization module, an anomaly detection module, and a health scoring module; The data synchronization module is used to determine the synchronization period based on the data synchronization success rate, so as to synchronize the inspection data and the monitoring data to the container cloud based on the synchronization period; The anomaly detection module is used to evaluate the abnormal data of each measurement point based on the standard deviation of the data of multiple measurement points corresponding to the inspection data and the monitoring data, so as to perform anomaly detection; The health scoring module is used to score the health of wind farm equipment based on multiple evaluation indicators, the inspection data, and the monitoring data.

3. The system according to claim 1 or 2, characterized in that, The container cloud includes a task allocation optimization module; The task allocation optimization module is used to allocate corresponding execution devices to multiple sub-tasks with the optimization goals of minimizing inspection time and energy consumption.

4. The system according to claim 2, characterized in that, The application layer includes an inspection report generation module; The inspection report generation module is used to generate an inspection report based on the inspection data, monitoring data, abnormal data, and health score, for users to query.

5. The system according to claim 1 or 2, characterized in that, The edge hardware layer includes a periodic fault detection module; The periodic fault detection module is used to perform fault detection on the drone, multi-source sensing device, application application of the application layer, and multiple algorithms of the edge hardware layer according to a preset detection cycle.

6. The system according to claim 1 or 2, characterized in that, The system also includes: a remote operation and maintenance module; The remote operation and maintenance module is used to execute remote operation commands input by the user through a preset encrypted channel.

7. A method for inspecting a wind farm, characterized in that, The method includes: Obtain the inspection task input by the user and break it down into multiple sub-tasks; Based on real-time meteorological data, terrain obstacle information, and the UAV inspection sub-task among the various sub-tasks, determine the target flight path of the UAV; The drone is controlled to perform inspections according to the target flight path and the drone inspection sub-task. Control the multi-source sensing device to perform monitoring according to the multi-source monitoring sub-task among the multiple sub-tasks.

8. A computer device, characterized in that, include: The system includes a memory and a processor, which are interconnected. The memory stores computer instructions, and the processor executes the computer instructions to perform the wind farm inspection method of claim 7.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions for causing the computer to execute the wind farm inspection method of claim 7.

10. A computer program product, characterized in that, It includes computer instructions for causing a computer to execute the wind farm inspection method of claim 7.

Images