Operation and maintenance method and device of offshore wind plant, storage medium and electronic device

Abstract

The invention discloses an operation and maintenance method for an offshore wind plant, and the method comprises the steps: obtaining real-time operation data; determining degradation state parameters of a plurality of components of the wind power equipment based on the real-time operation data; respectively comparing the degradation state parameters of the plurality of components with a preset decision threshold, and determining real-time maintenance strategies respectively matched with the plurality of components; based on the determined real-time maintenance strategy, target spare parts matched with the real-time maintenance strategy are determined from a spare part library, the number of remaining spare parts in the spare part library is determined, and under the condition that the number of the remaining spare parts is smaller than a preset inventory threshold value, spare parts to be supplemented are determined; and according to the real-time maintenance strategy, the target spare parts perform maintenance operation on the multiple components of the wind power equipment, and replenishment processing is performed on the spare part library based on the spare parts to be replenished to obtain an operation and maintenance result. The problem that the offshore wind power plant is poor in long-term reliable operation and economical efficiency due to the lack of comprehensive consideration of maintenance strategies or inventory control in related technologies is solved.

Description

Technical Field

[0001] The present application relates to the field of computer technology, and more specifically, to an operation and maintenance device, a storage medium, and an electronic device for an offshore wind farm. Background Art

[0002] As an important part of renewable energy, offshore wind power has achieved rapid development in recent years, and its installed capacity and power generation efficiency have been continuously improved. However, the operation and maintenance management of offshore wind farms faces a series of unique challenges, especially in terms of maintenance strategy and inventory management. The traditional onshore wind farm maintenance model is often difficult to directly apply to the offshore environment, because offshore wind farms are usually located in the sea far away from the land, and the geographical isolation makes the operation and maintenance process complicated and costly.

[0003] Most existing offshore wind farm operation and maintenance management systems focus on a single maintenance strategy or inventory control, lacking an in-depth understanding and comprehensive consideration of the dynamic interaction between the two. Specifically, first, the maintenance strategy is not flexible enough to respond to changes in the health status of the equipment in a timely manner, which may lead to maintenance delays or unnecessary preventive maintenance, increasing operation and maintenance costs. Secondly, inventory management lacks accurate demand forecasts, which can easily lead to excessive or insufficient spare parts inventory, increasing inventory holding costs and the risk of spare parts shortages.

[0004] At present, how to achieve dynamic optimization of maintenance strategies and inventory levels to improve operation and maintenance efficiency, reduce costs, and ensure the long-term reliable operation and economy of offshore wind farms has become an urgent problem to be solved. Summary of the invention

[0005] The embodiments of the present application provide an operation and maintenance method for an offshore wind farm, so as to at least solve the problem that the lack of comprehensive consideration of maintenance strategy or inventory control leads to poor long-term reliable operation and economy of the offshore wind farm.

[0006] According to an embodiment of the present application, an operation and maintenance method for an offshore wind farm is provided, comprising: acquiring real-time operation data of wind power equipment in the offshore wind farm; the wind power equipment is composed of multiple components; based on the real-time operation data, determining degradation state parameters of multiple components of the wind power equipment; comparing the degradation state parameters of the multiple components with preset decision thresholds respectively, and determining real-time maintenance strategies that match the multiple components respectively; wherein the real-time maintenance strategy includes a maintenance type for real-time maintenance of the components; based on the determined real-time maintenance strategy, determining target spare parts matching the real-time maintenance strategy from a spare parts library, and determining the number of remaining spare parts in the spare parts library after removing the target spare parts, and determining spare parts to be replenished when the number of remaining spare parts is less than a preset inventory threshold; performing maintenance operations on multiple components of the wind power equipment according to the real-time maintenance strategy and the target spare parts, and replenishing the spare parts library based on the spare parts to be replenished, to obtain operation and maintenance results.

[0007] In an exemplary embodiment, the degradation state parameters of the multiple components of the wind power equipment are determined based on the real-time operation data, including: for any one of the multiple components, the degradation state parameters are determined by the following steps: determining the operating time of the component; generating a random number that obeys the Weibull distribution based on the real-time operation data, and determining the life of the component according to the random number; and determining the quotient of the operating time and the life as the degradation state parameter of the component.

[0008] In an exemplary embodiment, the method for determining the preset decision threshold and the preset inventory threshold includes: determining an initial decision threshold based on a decision threshold range, and determining the total maintenance cost of the offshore wind farm based on the initial decision threshold; determining an initial inventory threshold based on an inventory threshold range, and determining the total inventory cost of the offshore wind farm based on the initial inventory threshold; determining the loss cost incurred by performing maintenance on the offshore wind farm; determining the total operation and maintenance cost based on the loss cost, the total inventory cost, the total maintenance cost and the life of the offshore wind farm; with the goal of minimizing the total operation and maintenance cost, updating the initial inventory threshold within the initial inventory threshold range, and updating the initial decision threshold within the decision threshold range, to obtain the preset inventory threshold and the preset decision threshold.

[0009] In an exemplary embodiment, determining the total maintenance cost of the offshore wind farm based on the initial decision threshold includes: acquiring historical operating data of wind power equipment in the offshore wind farm; the wind power equipment is composed of multiple components; based on the historical operating data, determining historical degradation state parameters of multiple components of the wind power equipment; comparing the historical degradation state parameters with the initial decision threshold, and determining historical maintenance strategies that match the multiple components respectively; wherein the historical maintenance strategy includes the maintenance type for the components to be maintained; based on the historical maintenance strategy, determining the total maintenance cost for maintaining the wind power equipment.

[0010] In an exemplary embodiment, determining the total maintenance cost for maintaining the wind power equipment based on the historical maintenance strategy includes: determining the total unit maintenance cost, total vessel cost, total technical expense cost, and total mobilization cost for maintaining the wind power equipment based on the historical maintenance strategy; and determining the total maintenance cost based on the sum of the total unit maintenance cost, total vessel cost, total technical expense cost, and total mobilization cost.

[0011] In an exemplary embodiment, determining the total unit maintenance cost, total vessel cost, total technical expense cost, and total mobilization cost for maintaining the wind power equipment based on the historical maintenance strategy includes:

[0012] Obtain the unit cost of maintaining the components to be maintained, the number of different maintenance types, maintenance costs, the number of technicians, the technician cost, and the mobilization cost included in the historical maintenance strategy; determine the maintenance type for the components to be maintained based on the historical maintenance strategy; determine the total unit maintenance cost according to the maintenance type and the unit cost of maintaining the components to be maintained; determine the total vessel cost according to the maintenance type, the number of different maintenance types, and the maintenance costs; determine the total technical cost cost according to the maintenance type, the number of different maintenance types, the number of technicians, and the technician cost; determine the total mobilization cost according to the mobilization cost.

[0013] In an exemplary embodiment, determining the total inventory cost of the offshore wind farm based on the initial inventory threshold includes: acquiring historical operating data of the wind power equipment, and determining a historical maintenance strategy based on the historical operating data; determining the number of spare parts matching the historical maintenance strategy; determining the number of spare parts to be replenished when the number of spare parts matching the historical maintenance strategy is greater than the number of spare parts in the spare parts library; performing an initial replenishment of spare parts on the spare parts library based on the number of spare parts to be replenished, updating the spare parts library, and determining the emergency cost generated by the spare parts replenishment process; comparing the spare parts library after the initial update with the initial inventory threshold, updating the updated spare parts library again, and determining the total ordering cost and the total holding cost based on the spare parts library after the second update; determining the total inventory cost based on the sum of the emergency cost, the total ordering cost and the total holding cost.

[0014] According to another embodiment of the embodiment of the present application, an operation and maintenance device for an offshore wind farm is also provided, comprising: an acquisition module, used to acquire real-time operation data of wind power equipment in the offshore wind farm; the wind power equipment is composed of multiple components; a determination module, used to determine the degradation state parameters of multiple components of the wind power equipment based on the real-time operation data; a comparison module, used to compare the degradation state parameters of the multiple components with preset decision thresholds respectively, and determine the real-time maintenance strategies that match the multiple components respectively; wherein the real-time maintenance strategy includes a maintenance type for real-time maintenance of the components; the determination module is also used to determine the target spare parts matching the real-time maintenance strategy from the spare parts library based on the determined real-time maintenance strategy, and determine the number of remaining spare parts in the spare parts library after removing the target spare parts, and determine the spare parts to be replenished when the number of remaining spare parts is less than the preset inventory threshold; a processing module, used to perform maintenance operations on the multiple components of the wind power equipment according to the real-time maintenance strategy and the target spare parts, and replenish the spare parts library based on the spare parts to be replenished to obtain operation and maintenance results.

[0015] According to another aspect of the embodiments of the present application, a computer-readable storage medium is provided, in which a computer program is stored, wherein the computer program is configured to execute the above method when running.

[0016] According to another aspect of the embodiments of the present application, an electronic device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the method through the computer program.

[0017] According to another aspect of the embodiments of the present application, a computer program product is provided, including a computer program, and when the computer program is executed by a processor, the steps in any of the above method embodiments are implemented.

[0018] In an embodiment of the present application, real-time operation data of a wind power device in an offshore wind farm is obtained; the wind power device is composed of multiple components; based on the real-time operation data, the degradation state parameters of the multiple components of the wind power device are determined; the degradation state parameters of the multiple components are compared with preset decision thresholds respectively, and real-time maintenance strategies that match the multiple components are determined; wherein the real-time maintenance strategy includes a maintenance type for real-time maintenance of the components; based on the determined real-time maintenance strategy, a target spare part matching the real-time maintenance strategy is determined from a spare parts library, and the number of remaining spare parts in the spare parts library is determined, and when the number of remaining spare parts is less than a preset inventory threshold, the spare parts to be replenished are determined; maintenance operations are performed on the multiple components of the wind power device according to the real-time maintenance strategy and the target spare parts, and the spare parts library is replenished based on the spare parts to be replenished to obtain operation and maintenance results. By integrating real-time operation data. Dynamically evaluate the degradation state of each component, generate accurate degradation state parameters, and compare the degradation state parameters of each component with the preset decision threshold to determine the real-time maintenance strategy. This real-time maintenance strategy based on the health status of components avoids unnecessary preventive maintenance and emergency repairs after failures, achieving the best balance between cost and efficiency and based on component health assessment, intelligent decision-making algorithms and precise inventory management. Furthermore, according to the determined real-time maintenance strategy, the required spare parts types and quantities are predicted, and the spare parts inventory can be effectively managed. By setting an inventory threshold, replenishment is triggered when the number of remaining spare parts is lower than the threshold, avoiding the unsalable costs caused by excessive inventory and maintenance delays caused by insufficient inventory, and ensuring the timely supply of spare parts. By comprehensively considering maintenance strategies and inventory, the operation and maintenance efficiency is improved, the cost is reduced, and the long-term reliable operation and economy of offshore wind farms are ensured. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0020] Figure 1 It is a hardware structure block diagram of a computer terminal of an offshore wind farm operation and maintenance method according to an embodiment of the present application;

[0021] Figure 2 is a flow chart of an operation and maintenance method of an offshore wind farm according to an embodiment of the present application;

[0022] Figure 3is a flowchart of an operation and maintenance method for an offshore wind farm according to an embodiment of the present application;

[0023] Figure 4 It is a structural block diagram of an operation and maintenance device for an offshore wind farm according to an embodiment of the present application. DETAILED DESCRIPTION

[0024] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present application.

[0025] It should be noted that the terms "first", "second", etc. in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product, or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products, or devices; "plurality" means two or more.

[0026] The method embodiments provided in the embodiments of the present application can be executed in a computer terminal or similar computing device or cloud platform or independent physical server or software platform, wherein the above software platform is run through one or more servers. Taking running on a computer terminal as an example, Figure 1 1 is a hardware structure diagram of an offshore wind farm operation and maintenance method according to an embodiment of the present application. Figure 1 As shown, the computer terminal may include one or more ( Figure 1 Only one is shown in the figure) processor 102 and memory 104 for storing data. In an exemplary embodiment, the computer terminal may also include a transmission device 106 for communication functions and an input and output device 108, wherein the processor 102 may include but is not limited to a processing device such as a microprocessor MCU or a programmable logic device FPGA. A person skilled in the art can understand that Figure 1 The structure shown is only for illustration and does not limit the structure of the above-mentioned computer terminal. Figure 1More or fewer components as shown, or with Figure 1 Equivalent functions or comparisons shown Figure 1 A different configuration with more features is shown.

[0027] The memory 104 can be used to store computer programs, for example, software programs and modules of application software, such as computer programs corresponding to the methods in the embodiments of the present application. The processor 102 executes various functional applications and data processing by running the computer programs stored in the memory 104, that is, the above-mentioned methods are implemented. The memory 104 may include a high-speed random access memory, and may also include a non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some examples, the memory 104 may further include a memory remotely arranged relative to the processor 102, and these remote memories may be connected to the computer terminal via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0028] The transmission device 106 is used to receive or send data via a network. The specific example of the above network may include a wireless network provided by a communication provider of a computer terminal. In one example, the transmission device 106 includes a network adapter (Network Interface Controller, referred to as NIC), which can be connected to other network devices through a base station so as to communicate with the Internet. In one example, the transmission device 106 can be a radio frequency (RF) module, which is used to communicate with the Internet wirelessly.

[0029] In this embodiment, an operation and maintenance method of an offshore wind farm is provided, which is applied to the above-mentioned computer terminal. Figure 2 : is a flow chart of an operation and maintenance method of an offshore wind farm according to an embodiment of the present application, the process includes the following steps:

[0030] Step S202, obtaining real-time operation data of wind power equipment in an offshore wind farm; the wind power equipment is composed of multiple components;

[0031] The operation data may include power output data, speed and torque data, temperature data, vibration data, electrical parameters, maintenance operation status, etc. of each component in the wind power equipment. After obtaining the real-time operation data, the real-time operation data may be cleaned and standardized.

[0032] In some embodiments, the wind power equipment may be a wind turbine. Assuming that the offshore wind farm includes K wind turbines, each wind turbine is composed of I series components, then the acquired real-time operating data may refer to the real-time operating data of the I series components.

[0033] Step S204, determining degradation state parameters of multiple components of the wind power equipment based on the real-time operation data;

[0034] In an exemplary embodiment, determining the degradation state parameters of the multiple components of the wind power equipment based on the real-time operation data includes: for any one of the multiple components, the degradation state parameters are determined by the following steps: determining the operating time of the component; generating a random number that obeys the Weibull distribution based on the real-time operation data, and determining the life of the component according to the random number; and determining the quotient of the operating time and the life as the degradation state parameter of the component.

[0035] It is understandable that for wind turbines in offshore wind farms, inspections can be performed at fixed intervals. The design life of the wind farm is Z, and the total number of inspections during the life of the wind farm is L. Assume that l is the number of inspections since the offshore wind farm began operation, and use the degradation state parameter represents the degradation state of the i-th (i=1,2,…,I) component on the k-th (k=1,2,…,K) turbine at the l-th (l=1,2,…,L) inspection. The life of component i in turbine k is subject to the scale parameter σ ik and shape parameter ∈ ik Weibull distribution. Random numbers are randomly generated by real-time data sampling Weibull distribution, and the life of components is generated based on the random numbers. For example: At the lth inspection, the life of component i in turbine k is predicted to be The elapsed time since the component started running, that is, the running time, is expressed as The wind turbine is inspected and the remaining life is predicted, and finally the degradation state parameters are calculated as follows:

[0036]

[0037] In the above embodiment, the Weibull distribution not only provides a scientific basis for the offshore wind farm operation and maintenance strategy based on component life prediction, but also supports the formulation of dynamic maintenance strategies, which helps to balance maintenance costs and equipment operating efficiency and improve the operation and maintenance decision-making level of offshore wind farms.

[0038] Step S206, comparing the degradation state parameters of the multiple components with preset decision thresholds respectively, and determining real-time maintenance strategies that match the multiple components respectively; wherein the real-time maintenance strategy includes a maintenance type for real-time maintenance of the components;

[0039] It should be noted that the maintenance type refers to the way in which components are maintained, which may include fault replacement, preventive replacement and basic repair of components.

[0040] In some embodiments, the preset decision thresholds include a preventive replacement threshold Ω, a major maintenance threshold Θ, and a specific threshold ζ. When the proportion of aging components (i.e., components that have failed) in the offshore wind farm exceeds the specific threshold ζ, a maintenance cycle will be initiated. The classification of component conditions and the corresponding maintenance strategies are as follows: (1) When When it reaches 100%, it means that the component has failed because it has reached the end of its service life. In this case, the component needs to be replaced, and after the replacement, the component status will be restored to perfect condition. (2) When When the component is considered a managed component, it needs to be replaced preventively. (3) When , it means that the component is a defective component, and the defective component needs incomplete maintenance to improve the status. (4) When , it means the components are younger and no expensive repairs are needed, only basic repairs are required to maintain the current condition.

[0041] Step S208, based on the determined real-time maintenance strategy, determine the target spare parts matching the real-time maintenance strategy from the spare parts library, and determine the number of spare parts remaining after removing the target spare parts from the spare parts library, and determine the spare parts to be replenished when the number of remaining spare parts is less than a preset inventory threshold.

[0042] It is understood that according to the real-time maintenance strategy, the spare parts to be replenished are determined to ensure the timely supply of spare parts and the optimization of inventory levels. The maintenance demand will be transmitted to the spare parts warehouse, which may include component warehouses and sub-component warehouses. By comparing the spare parts required in the real-time maintenance strategy with the current inventory level, if the number of spare parts is sufficient, the target spare parts are determined and transferred to nearby ports. If the quantity is insufficient, all available spare parts in the current warehouse will be transported out, and an emergency order will be placed to replenish the remaining spare parts. Subsequently, the number of remaining spare parts remaining in the spare parts warehouse will be compared with the preset inventory threshold to decide whether to order new spare parts.

[0043] Step S210 , performing maintenance operations on multiple components of the wind power equipment according to the real-time maintenance strategy and the target spare parts, and replenishing the spare parts warehouse based on the spare parts to be replenished, to obtain an operation and maintenance result.

[0044] Specifically, the computer terminal can perform maintenance operations according to the generated maintenance decisions, replenish the spare parts warehouse based on the spare parts to be replenished, and record the operation and maintenance results and changes in the status of wind power equipment as a basis for subsequent data analysis and model optimization.

[0045] In the above steps S202-210, real-time operation data of wind power equipment in an offshore wind farm is obtained; the wind power equipment is composed of multiple components; based on the real-time operation data, degradation state parameters of multiple components of the wind power equipment are determined; the degradation state parameters of the multiple components are compared with preset decision thresholds respectively, and real-time maintenance strategies that match the multiple components are determined; wherein the real-time maintenance strategy includes maintenance types for real-time maintenance of the components; based on the determined real-time maintenance strategy, target spare parts matching the real-time maintenance strategy are determined from the spare parts library, and the number of remaining spare parts in the spare parts library is determined, and when the number of remaining spare parts is less than the preset inventory threshold, spare parts to be replenished are determined; maintenance operations are performed on multiple components of the wind power equipment according to the real-time maintenance strategy and the target spare parts, and the spare parts library is replenished based on the spare parts to be replenished to obtain operation and maintenance results. By integrating real-time operation data. Dynamically evaluate the degradation state of each component, generate accurate degradation state parameters, and compare the degradation state parameters of each component with the preset decision threshold to determine the real-time maintenance strategy. This real-time maintenance strategy based on the health status of components avoids unnecessary preventive maintenance and emergency repairs after failures, achieving the best balance between cost and efficiency and based on component health assessment, intelligent decision-making algorithms and precise inventory management. Furthermore, according to the determined real-time maintenance strategy, the required spare parts types and quantities are predicted, and the spare parts inventory can be effectively managed. By setting an inventory threshold, replenishment is triggered when the number of remaining spare parts is lower than the threshold, avoiding the unsalable costs caused by excessive inventory and maintenance delays caused by insufficient inventory, and ensuring the timely supply of spare parts. By comprehensively considering maintenance strategies and inventory, the operation and maintenance efficiency is improved, the cost is reduced, and the long-term reliable operation and economy of offshore wind farms are ensured.

[0046] In an exemplary embodiment, the method for determining the preset decision threshold and the preset inventory threshold includes: determining an initial decision threshold based on a decision threshold range, and determining the total maintenance cost of the offshore wind farm based on the initial decision threshold; determining an initial inventory threshold based on an inventory threshold range, and determining the total inventory cost of the offshore wind farm based on the initial inventory threshold; determining the loss cost incurred by performing maintenance on the offshore wind farm; determining the total operation and maintenance cost based on the loss cost, the total inventory cost, the total maintenance cost and the life of the offshore wind farm; with the goal of minimizing the total operation and maintenance cost, updating the initial inventory threshold within the initial inventory threshold range, and updating the initial decision threshold within the decision threshold range, to obtain the preset inventory threshold and the preset decision threshold.

[0047] In some embodiments, the preset decision thresholds include a preventive replacement threshold Ω, a major maintenance threshold Θ, and a specific threshold ζ. The preset inventory thresholds are respectively a preset component inventory threshold set for a component and a preset subcomponent inventory threshold set for a subcomponent. C ,S C , where s C Indicates the minimum component threshold, S C Indicates the maximum value of the component threshold. For preset subcomponent inventory thresholds, including s L ,S L , where s L represents the minimum threshold of the subcomponent, S L Indicates the maximum value of the subcomponent threshold.

[0048] The decision threshold range and inventory threshold range define the range of the initial decision threshold and initial inventory threshold, as follows:

[0049] 0%<Θ<Ω<100%

[0050] 0%<ζ≤100%

[0051]

[0052] 0≤s L L

[0053] 0≤s C C

[0054]

[0055] in, represents a real number, Represents an integer.

[0056] In some embodiments, the initial decision threshold and the initial inventory threshold are optimized by a genetic algorithm (GA) to minimize the operation and maintenance costs, as shown in the following formula:

[0057]

[0058] in, Refers to the total operation and maintenance cost, C I Refers to the total inventory cost, C M Refers to the total maintenance cost, C P Refers to the loss cost, Z refers to the life of the offshore wind farm, and the total operation and maintenance cost is minimized by updating the initial decision threshold within the decision threshold range and the initial inventory threshold within the inventory threshold range. The decision threshold and inventory threshold when the operation and maintenance cost is lowest are determined as the preset decision threshold and preset inventory threshold.​​

[0059] In an exemplary embodiment, determining the total maintenance cost of the offshore wind farm based on the initial decision threshold includes: acquiring historical operation data of wind power equipment in the offshore wind farm; the wind power equipment is composed of multiple components; based on the historical operation data, determining historical degradation state parameters of multiple components of the wind power equipment; comparing the historical degradation state parameters with the initial decision threshold, and determining historical maintenance strategies that match the multiple components respectively; wherein the historical maintenance strategy includes the maintenance type for the components to be maintained; based on the historical maintenance strategy, determining the total maintenance cost for maintaining the wind power equipment.

[0060] In an exemplary embodiment, determining the total maintenance cost for maintaining the wind power equipment based on the historical maintenance strategy includes: determining the total unit maintenance cost, total vessel cost, total technical expense cost, and total mobilization cost for maintaining the wind power equipment based on the historical maintenance strategy; and determining the total maintenance cost based on the sum of the total unit maintenance cost, total vessel cost, total technical expense cost, and total mobilization cost.

[0061] It can be understood that the total maintenance cost C is obtained by adding the total unit maintenance cost, total ship cost, total technical cost, and total mobilization cost as shown in the following formula: M :

[0062] C M =C R +C V +C T +C L

[0063] Among them, C R Refers to the total unit maintenance cost, C V Refers to the total ship cost, C T Refers to the total technical cost, C L Refers to the total mobilization cost.

[0064] In an exemplary embodiment, the total unit maintenance cost, total vessel cost, total technical expense cost, and total mobilization cost for maintaining the wind power equipment based on the historical maintenance strategy include: obtaining the unit cost of maintaining the components to be maintained included in the historical maintenance strategy, the number of different maintenance types, maintenance costs, the number of technicians, the technician cost, and the mobilization cost; determining the maintenance type for the components to be maintained based on the historical maintenance strategy; determining the total unit maintenance cost according to the maintenance type and the unit cost of maintaining the components to be maintained; determining the total vessel cost according to the maintenance type, the number of different maintenance types, and the maintenance cost; determining the total technical expense cost according to the maintenance type, the number of different maintenance types, the number of technicians, and the technician cost; and determining the total mobilization cost according to the mobilization cost.

[0065] In determining the total unit repair cost, total vessel cost, total technical expense cost, and total mobilization cost, the lead time for heavy lift vessels (HLVs), field support vessels (FLVs), and crew transfer vessels (CTVs) during maintenance cycle n (n = 1, 2, …, N) is taken into account. and They follow the Weibull distribution respectively:

[0066]

[0067] Among them, when the wind power equipment is a turbine, the total unit maintenance cost C R for:

[0068]

[0069] In maintenance cycle n, the binary variable are the decisions to perform failure replacement, preventive replacement, and basic maintenance on component i on turbine k, respectively. Binary variables Indicates whether component i on turbine k needs to be replaced by subcomponent j during maintenance cycle n. i If a maintenance decision is made, the variable value is 1, otherwise it is 0. i , δ ji They are the costs of breakdown replacement, preventive replacement, basic repair and incomplete maintenance.

[0070] Over the life cycle of the offshore wind farm Z, the total vessel cost C V The calculation is as follows:

[0071]

[0072] is the number of different maintenance types; q H ,q C ,q F This is the corresponding daily fee.

[0073] Total technical cost C T :

[0074]

[0075] h H ,h C ,h F is the number of technicians on the three types of ships, and ρ is the cost of each technician.

[0076] Total mobilization cost C L :

[0077]

[0078] is the mobilization cost of operation and maintenance cycle n.

[0079] In an exemplary embodiment, determining the total inventory cost of the offshore wind farm based on the initial inventory threshold includes: acquiring historical operating data of the wind power equipment, and determining a historical maintenance strategy based on the historical operating data; determining the number of spare parts matching the historical maintenance strategy; determining the number of spare parts to be replenished when the number of spare parts matching the historical maintenance strategy is greater than the number of spare parts in the spare parts library; performing an initial replenishment of spare parts on the spare parts library based on the number of spare parts to be replenished, updating the spare parts library, and determining the emergency cost generated by the spare parts replenishment process; comparing the spare parts library after the initial update with the initial inventory threshold, updating the updated spare parts library again, and determining the total ordering cost and the total holding cost based on the spare parts library after the second update; determining the total inventory cost according to the sum of the emergency cost, the total ordering cost and the total holding cost.

[0080] Specifically, a multi-level inventory network consisting of component warehouses and sub-component warehouses is established. The inventory policy (s, S) is that when the inventory level drops to the reorder point s, an order is placed to restore the inventory level to the order limit S.

[0081] In the component warehouse and subcomponent warehouse, the inventory policies used are (s C ,S C ) and (s L ,S L ). Therefore, the decision vector β of the inventory model is:

[0082] β=(s C ,SC ,s L ,S L )

[0083] At the beginning of maintenance period n, the number of component-level units is S C , the number of sub-component level units is S L Assume that the required number of component-level units i is Required sub-assembly level unit j i The number of

[0084] When the number of spare parts matching the historical maintenance strategy is greater than the number of spare parts in the spare parts warehouse, the number of spare parts to be replenished is determined, that is, at the beginning of maintenance cycle n, it is checked whether the spare parts inventory level in the warehouse is sufficient to support the maintenance needs. At this time, the number of component-level and sub-component-level units is and If the current inventory level is sufficient to support the maintenance implementation, a standing order will be placed to deliver the required spare parts to a port close to the offshore wind farm. The lead time for standing orders for module-level and sub-module-level units is and

[0085] Binary variables and Indicates whether a rush order has been placed for component-level units and sub-component-level units. The delivery time for rush orders for component-level and sub-component-level units is and The total emergency cost is:

[0086]

[0087] Among them, E c is the emergency cost rate.

[0088] After the spare parts are delivered to the offshore wind farm, the stock level at the component warehouse is updated to:

[0089]

[0090] The stock level at the subcomponent warehouse is updated to:

[0091]

[0092] After that, the spare parts inventory after the initial update is compared with the initial inventory threshold, that is, the inventory level is compared with the minimum inventory limit s C and L In the component repository, if Then you need to restore the number of units to S C , and the binary variable is equal to 1. Similarly, if the inventory level in the subcomponent warehouse falls below its minimum limit, a corresponding action is required, and the binary variable is equal to 1.

[0093] Binary variables and Indicates whether it is necessary to place an order to replenish the unit in the component warehouse and sub-component warehouse respectively:

[0094]

[0095] After replenishing the units to the warehouse, the stock level in the component warehouse is updated to:

[0096]

[0097] The stock level of the subcomponent warehouse is updated to:

[0098]

[0099] Total ordering cost:

[0100]

[0101] is the cost of the complementary component level unit, is the cost of replenishing the sub-assembly level unit.

[0102] Assume t n represents the time of maintenance cycle n. After the units required for maintenance are delivered to the maintenance location and the units in the warehouse are replenished, the quantities of spare parts in the component warehouse and the sub-component warehouse are and The total cost of ownership is:

[0103]

[0104] Ξ is the holding cost rate. Finally, add up to get the total inventory cost:

[0105] C I =C O +C H +C E

[0106]

[0107] In some embodiments, the production loss during the downtime caused by turbine failure and maintenance implementation is:

[0108]

[0109] where r is the expected production loss cost per turbine per day, is the failure time of turbine k before maintenance period n.

[0110] In order to better understand the process of the above-mentioned offshore wind farm operation and maintenance method, the above-mentioned offshore wind farm operation and maintenance method is described below in combination with optional embodiments, but it is not used to limit the technical solution of the embodiments of the present application.

[0111] Offshore wind power operation and maintenance is crucial to ensure the reliability and economy of wind farms. Since wind farms are usually located in sea areas far away from land, the operation and maintenance process is complex and costly. Most existing operation and maintenance management systems focus on single maintenance or inventory control, and lack comprehensive consideration of the relationship between the two. First, the maintenance strategy is not flexible enough and cannot respond to changes in the health status of equipment in a timely manner, which may lead to maintenance delays or unnecessary preventive maintenance, and increase operation and maintenance costs. Secondly, inventory management lacks accurate demand forecasts, which can easily lead to excessive or insufficient spare parts inventory, increase inventory holding costs and spare parts shortage risks. In addition, the existing system is insufficient in data utilization and fails to fully utilize equipment operation data and fault data for predictive maintenance and inventory optimization.

[0112] Therefore, this application proposes an operation and maintenance method for an offshore wind farm, which can optimize maintenance strategies and inventory levels, improve operation and maintenance efficiency, and reduce costs. Figure 3 As shown, it is a flowchart of the operation and maintenance method of the offshore wind farm of the present application, which is divided into an offline stage and an online stage. In the offline stage, it includes: data collection and preprocessing. Collect historical operation data, fault records and maintenance records of wind power equipment, and clean and standardize the data; health status assessment. Evaluate the health status of wind power equipment and generate a health status score; inventory demand forecast. Based on the health status score of the equipment, predict future maintenance needs and determine the required spare parts inventory; optimization model construction. Combine the health status assessment and inventory demand forecast results to build a multi-objective optimization model to determine the optimal maintenance strategy and inventory level. Model storage, store the parameters and structure of the optimization model in the system for calling in the online stage.

[0113] In the online stage, real-time monitoring and data update: Through sensors and monitoring systems, the operation data and health status information of wind power equipment are collected in real time and updated to the system.

[0114] Health status update and maintenance decision, using real-time updated data, the remaining service life of key components is predicted. The health status of all components in the wind farm is comprehensively considered to determine the decision variable set α, which is used to determine whether to start the maintenance cycle. If the triggering conditions are not met, no maintenance is required. Otherwise, potential maintenance opportunities are identified and service vessels are mobilized. At the same time, according to the component condition, the appropriate maintenance operation for each component is decided, thereby determining the maintenance needs for various spare parts.

[0115] Inventory management. Based on the maintenance decision, the inventory management strategy is updated to ensure timely supply of spare parts and optimization of inventory levels. Maintenance requirements are passed to the component and subassembly warehouses, where they are compared with the current inventory levels. If the number of spare parts is sufficient, the required spare parts are transferred to a nearby port. If the number is insufficient, all spare parts currently available in the warehouse are shipped out and an emergency order is placed to replenish the remaining spare parts. Subsequently, the number of spare parts remaining in the warehouse is compared with the decision variable set β to decide whether to order new parts.

[0116] Operation and maintenance execution and feedback: perform maintenance operations according to the generated maintenance decisions, and record maintenance results and changes in equipment status as the basis for subsequent data analysis and model optimization.

[0117] Through the description of the above implementation methods, those skilled in the art can clearly understand that the method according to the above embodiment can be implemented by means of software plus a necessary general hardware platform, and of course by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present application, or the part that contributes to the prior art, can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk), and includes a number of instructions for a terminal device (which can be a mobile phone, computer, server, or network device, etc.) to execute the methods of each embodiment of the present application.

[0118] The present application also provides a structural block diagram of an operation and maintenance device for an offshore wind farm. Figure 4 is a structural block diagram of an operation and maintenance device for an offshore wind farm according to an embodiment of the present application; Figure 4 As shown, including:

[0119] The acquisition module 42 is used to acquire real-time operation data of wind power equipment in an offshore wind farm; the wind power equipment is composed of multiple components;

[0120] A determination module 44, configured to determine degradation state parameters of a plurality of components of the wind power equipment based on the real-time operation data;

[0121] A comparison module 46 is used to compare the degradation state parameters of the multiple components with preset decision thresholds respectively, and determine real-time maintenance strategies that match the multiple components respectively; wherein the real-time maintenance strategy includes a maintenance type for real-time maintenance of the components;

[0122] The determination module 44 is further configured to determine, based on the determined real-time maintenance strategy, a target spare part matching the real-time maintenance strategy from the spare parts library, and determine the number of spare parts remaining after removing the target spare part from the spare parts library, and determine the spare parts to be replenished when the number of spare parts remaining is less than a preset inventory threshold;

[0123] The processing module 46 is used to perform maintenance operations on multiple components of the wind power equipment according to the real-time maintenance strategy and the target spare parts, and to replenish the spare parts warehouse based on the spare parts to be replenished, so as to obtain operation and maintenance results.

[0124] In an exemplary embodiment, the determination module 44 is also used to determine the degradation state parameter for any one of the multiple components by adopting the following steps: determining the operating time of the component; generating a random number that obeys the Weibull distribution based on the real-time operating data, and determining the life of the component according to the random number; and determining the quotient of the operating time and the life as the degradation state parameter of the component.

[0125] In an exemplary embodiment, the determination module 44 is also used to determine an initial decision threshold based on a decision threshold range, and determine the total maintenance cost of the offshore wind farm based on the initial decision threshold; determine an initial inventory threshold based on an inventory threshold range, and determine the total inventory cost of the offshore wind farm based on the initial inventory threshold; determine the loss cost incurred by maintenance treatment for the offshore wind farm; determine the total operation and maintenance cost based on the loss cost, the total inventory cost, the total maintenance cost and the life of the offshore wind farm; with the goal of minimizing the total operation and maintenance cost, update the initial inventory threshold within the initial inventory threshold range, and update the initial decision threshold within the decision threshold range to obtain the preset inventory threshold and the preset decision threshold.

[0126] In an exemplary embodiment, the determination module 44 is also used to obtain historical operation data of the wind power equipment in the offshore wind farm; the wind power equipment is composed of multiple components; based on the historical operation data, the historical degradation state parameters of the multiple components of the wind power equipment are determined; the historical degradation state parameters are compared with the initial decision threshold to determine the historical maintenance strategies that match the multiple components respectively; wherein the historical maintenance strategies include the maintenance types for the components to be maintained; based on the historical maintenance strategies, the total maintenance cost for maintaining the wind power equipment is determined.

[0127] In an exemplary embodiment, the determination module 44 is also used to determine the total unit maintenance cost, total vessel cost, total technical expense cost, and total mobilization cost for maintaining the wind power equipment based on the historical maintenance strategy; and determine the total maintenance cost based on the sum of the total unit maintenance cost, total vessel cost, total technical expense cost, and total mobilization cost.

[0128] In an exemplary embodiment, the determination module 44 is also used to obtain the unit cost of maintaining the components to be maintained included in the historical maintenance strategy, the number of different maintenance types, the maintenance cost, the number of technicians, the technician cost, and the mobilization cost; based on the historical maintenance strategy, determine the maintenance type for the components to be maintained; determine the total unit maintenance cost according to the maintenance type and the unit cost of maintaining the components to be maintained; determine the total vessel cost according to the maintenance type, the number of different maintenance types, and the maintenance cost; determine the total technical cost cost according to the maintenance type, the number of different maintenance types, the number of technicians, and the technician cost; determine the total mobilization cost according to the mobilization cost.

[0129] In an exemplary embodiment, the determination module 44 is also used to obtain the historical operation data of the wind power equipment, and determine the historical maintenance strategy based on the historical operation data; determine the number of spare parts matching the historical maintenance strategy; when the number of spare parts matching the historical maintenance strategy is greater than the number of spare parts in the spare parts warehouse, determine the number of spare parts to be replenished; based on the number of spare parts to be replenished, perform an initial spare parts replenishment process on the spare parts warehouse, update the spare parts warehouse, and determine the emergency cost generated by the spare parts replenishment process; based on the comparison between the spare parts warehouse after the initial update and the initial inventory threshold, update the updated spare parts warehouse again, and determine the total ordering cost and the total holding cost based on the spare parts warehouse after the second update; determine the total inventory cost according to the sum of the emergency cost, the total ordering cost and the total holding cost.

[0130] Through the above device, the real-time operation data of the wind power equipment in the offshore wind farm is obtained; the wind power equipment is composed of multiple components; based on the real-time operation data, the degradation state parameters of the multiple components of the wind power equipment are determined; the degradation state parameters of the multiple components are compared with the preset decision thresholds respectively, and the real-time maintenance strategies that match the multiple components are determined; wherein the real-time maintenance strategy includes the maintenance type for the real-time maintenance of the components; based on the determined real-time maintenance strategy, the target spare parts matching the real-time maintenance strategy are determined from the spare parts library, and the number of remaining spare parts in the spare parts library is determined, and when the number of remaining spare parts is less than the preset inventory threshold, the spare parts to be replenished are determined; maintenance operations are performed on the multiple components of the wind power equipment according to the real-time maintenance strategy and the target spare parts, and the spare parts library is replenished based on the spare parts to be replenished to obtain the operation and maintenance results. By integrating the real-time operation data. Dynamically evaluate the degradation state of each component, generate accurate degradation state parameters, and compare the degradation state parameters of each component with the preset decision threshold to determine the real-time maintenance strategy. This real-time maintenance strategy based on the health status of components avoids unnecessary preventive maintenance and emergency repairs after failures, achieving the best balance between cost and efficiency and based on component health assessment, intelligent decision-making algorithms and precise inventory management. Furthermore, according to the determined real-time maintenance strategy, the required spare parts types and quantities are predicted, and the spare parts inventory can be effectively managed. By setting an inventory threshold, replenishment is triggered when the number of remaining spare parts is lower than the threshold, avoiding the unsalable costs caused by excessive inventory and maintenance delays caused by insufficient inventory, and ensuring the timely supply of spare parts. By comprehensively considering maintenance strategies and inventory, the operation and maintenance efficiency is improved, the cost is reduced, and the long-term reliable operation and economy of offshore wind farms are ensured.

[0131] An embodiment of the present application further provides a storage medium, which includes a stored program, wherein the program executes any of the above methods when it is run.

[0132] Optionally, in this embodiment, the storage medium may be configured to store program codes for executing the following steps:

[0133] S1, obtaining real-time operating data of wind power equipment in an offshore wind farm; the wind power equipment is composed of multiple components;

[0134] S2, determining degradation state parameters of multiple components of the wind power equipment based on the real-time operation data;

[0135] S3, comparing the degradation state parameters of the multiple components with preset decision thresholds respectively, and determining real-time maintenance strategies that match the multiple components respectively; wherein the real-time maintenance strategy includes a maintenance type for real-time maintenance of the components;

[0136] S4, based on the determined real-time maintenance strategy, determining a target spare part matching the real-time maintenance strategy from the spare parts library, and determining the number of spare parts remaining after removing the target spare part from the spare parts library, and determining spare parts to be replenished when the number of spare parts remaining is less than a preset inventory threshold;

[0137] S5, performing maintenance operations on multiple components of the wind power equipment according to the real-time maintenance strategy and the target spare parts, and replenishing the spare parts warehouse based on the spare parts to be replenished, to obtain operation and maintenance results.

[0138] An embodiment of the present application further provides an electronic device, including a memory and a processor, wherein a computer program is stored in the memory, and the processor is configured to run the computer program to execute the steps in any one of the above method embodiments.

[0139] Optionally, the electronic device may further include a transmission device and an input / output device, wherein the transmission device is connected to the processor, and the input / output device is connected to the processor.

[0140] Optionally, in this embodiment, the processor may be configured to perform the following steps through a computer program:

[0141] S1, obtaining real-time operating data of wind power equipment in an offshore wind farm; the wind power equipment is composed of multiple components;

[0142] S2, determining degradation state parameters of a plurality of components of the wind power equipment based on the real-time operation data;

[0143] S3, comparing the degradation state parameters of the multiple components with preset decision thresholds respectively, and determining real-time maintenance strategies that match the multiple components respectively; wherein the real-time maintenance strategy includes a maintenance type for real-time maintenance of the components;

[0144] S4, based on the determined real-time maintenance strategy, determining a target spare part matching the real-time maintenance strategy from the spare parts library, and determining the number of spare parts remaining after removing the target spare part from the spare parts library, and determining spare parts to be replenished when the number of spare parts remaining is less than a preset inventory threshold;

[0145] S5, performing maintenance operations on multiple components of the wind power equipment according to the real-time maintenance strategy and the target spare parts, and replenishing the spare parts warehouse based on the spare parts to be replenished, to obtain operation and maintenance results.

[0146] Optionally, in this embodiment, the above-mentioned storage medium may include but is not limited to: a USB flash drive, a read-only memory (ROM), a random access memory (RAM), a mobile hard disk, a magnetic disk or an optical disk, and other media that can store program codes.

[0147] An embodiment of the present application further provides a computer program product, which includes a computer program. When the computer program is executed by a processor, the steps in any one of the above method embodiments are implemented.

[0148] An embodiment of the present application further provides another computer program product, including a non-volatile computer-readable storage medium, wherein the non-volatile computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps in any of the above method embodiments are implemented.

[0149] An embodiment of the present application also provides a computer program, which includes computer instructions stored in a computer-readable storage medium; a processor of a computer device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, so that the computer device performs the steps in any one of the above method embodiments.

[0150] Optionally, the specific examples in this embodiment may refer to the examples described in the above embodiments and optional implementation modes, and this embodiment will not be described in detail here.

[0151] Obviously, those skilled in the art should understand that the above modules or steps of the present application can be implemented by a general computing device, they can be concentrated on a single computing device, or distributed on a network composed of multiple computing devices, and optionally, they can be implemented by a program code executable by a computing device, so that they can be stored in a storage device and executed by the computing device, and in some cases, the steps shown or described can be executed in a different order from that herein, or they can be made into individual integrated circuit modules, or multiple modules or steps therein can be made into a single integrated circuit module for implementation. Thus, the present application is not limited to any specific combination of hardware and software.

[0152] The above description is only the preferred embodiment of the present application and is not intended to limit the present application. For those skilled in the art, the present application may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the principles of the present application shall be included in the protection scope of the present application.

Claims

1. An operation and maintenance method for an offshore wind farm, characterized in that: include: Obtain real-time operating data of wind power equipment in offshore wind farms; The wind power equipment is composed of multiple components; Determining degradation state parameters of a plurality of components of the wind power equipment based on the real-time operation data; Comparing the degradation state parameters of the multiple components with preset decision thresholds respectively, and determining real-time maintenance strategies that match the multiple components respectively; wherein the real-time maintenance strategy includes a maintenance type for real-time maintenance of the components; Based on the determined real-time maintenance strategy, determining a target spare part matching the real-time maintenance strategy from a spare parts library, and determining the number of spare parts remaining after removing the target spare part from the spare parts library, and determining spare parts to be replenished when the number of spare parts remaining is less than a preset inventory threshold; Maintenance operations are performed on multiple components of the wind power equipment according to the real-time maintenance strategy and the target spare parts, and replenishment processing is performed on the spare parts warehouse based on the spare parts to be replenished, so as to obtain operation and maintenance results.

2. The method according to claim 1, characterized in that The step of determining degradation state parameters of a plurality of components of the wind power equipment based on the real-time operation data comprises: For any one of the multiple components, the degradation state parameter is determined by adopting the following steps: determining a runtime of the component; generating a random number obeying a Weibull distribution based on the real-time operation data, and determining the life of the component according to the random number; A quotient of the operating time and the lifetime is determined as a degradation state parameter of the component.

3. The method according to claim 1, characterized in that The method for determining the preset decision threshold and the preset inventory threshold includes: Determining an initial decision threshold based on the decision threshold range, and determining a total maintenance cost of the offshore wind farm based on the initial decision threshold; Determining an initial inventory threshold based on the inventory threshold range, and determining a total inventory cost of the offshore wind farm based on the initial inventory threshold; Determining the loss cost incurred by carrying out maintenance treatment on the offshore wind farm; Determining a total operation and maintenance cost based on the loss cost, the total inventory cost, the total maintenance cost, and the life of the offshore wind farm; Taking minimizing the total operation and maintenance cost as the goal, the initial inventory threshold is updated within the initial inventory threshold range, and the initial decision threshold is updated within the decision threshold range to obtain the preset inventory threshold and the preset decision threshold.

4. The method according to claim 3, characterized in that: The determining, based on the initial decision threshold, the total maintenance cost of the offshore wind farm comprises: Acquiring historical operation data of wind power equipment in the offshore wind farm; the wind power equipment is composed of multiple components; Determining historical degradation state parameters of a plurality of components of the wind power equipment based on the historical operation data; Comparing the historical degradation state parameter with the initial decision threshold, determining historical maintenance strategies that match the multiple components respectively; wherein the historical maintenance strategy includes a maintenance type for the component to be maintained; Based on the historical maintenance strategy, a total maintenance cost for maintaining the wind power equipment is determined.

5. The method according to claim 4, characterized in that The determining of the total maintenance cost for maintaining the wind power equipment based on the historical maintenance strategy includes: Based on the historical maintenance strategy, determining a total unit maintenance cost, a total vessel cost, a total technical expense cost, and a total mobilization cost for maintaining the wind power equipment; The total maintenance cost is determined based on the sum of the total unit repair cost, the total vessel cost, the total technical expense cost, and the total mobilization cost.

6. The method according to claim 5, characterized in that The determining of the total unit maintenance cost, total vessel cost, total technical cost, and total mobilization cost for maintaining the wind power equipment based on the historical maintenance strategy includes: Obtaining the unit cost of maintaining the components to be maintained included in the historical maintenance strategy, the number of different maintenance types, the maintenance fee, the number of technicians, the technician cost, and the mobilization cost; Based on the historical maintenance strategy, determining a maintenance type for the component to be maintained; Determining the total unit maintenance cost according to the maintenance type and the unit cost of maintaining the component to be maintained; determining the total vessel cost according to the maintenance types, the number of different maintenance types, and the maintenance costs; Determine the total technical expense cost according to the maintenance type, the number of different maintenance types, the number of technicians and the technician cost; According to the mobilization cost, the total mobilization cost is determined.

7. The method according to claim 3, characterized in that The determining, based on the initial inventory threshold, the total inventory cost of the offshore wind farm comprises: Acquiring historical operation data of the wind power equipment, and determining a historical maintenance strategy based on the historical operation data; determining a quantity of spare parts matching the historical maintenance strategy; When the number of spare parts matching the historical maintenance strategy is greater than the number of spare parts in the spare parts library, determining the number of spare parts to be replenished; Based on the number of spare parts to be replenished, perform initial replenishment processing on the spare parts library, update the spare parts library, and determine the emergency cost generated by the spare parts replenishment processing; Based on the comparison between the initially updated spare parts library and the initial inventory threshold, the updated spare parts library is updated again, and based on the updated spare parts library, the total ordering cost and the total holding cost are determined; The total inventory cost is determined based on the sum of the rush cost, the total ordering cost, and the total holding cost.

8. An operation and maintenance device for an offshore wind farm, characterized in that: include: An acquisition module is used to acquire real-time operation data of wind power equipment in an offshore wind farm; The wind power equipment is composed of multiple components; A determination module, configured to determine degradation state parameters of a plurality of components of the wind power equipment based on the real-time operation data; A comparison module, used to compare the degradation state parameters of the multiple components with preset decision thresholds respectively, and determine real-time maintenance strategies that match the multiple components respectively; wherein the real-time maintenance strategy includes a maintenance type for real-time maintenance of the components; The determination module is further configured to determine, based on the determined real-time maintenance strategy, a target spare part matching the real-time maintenance strategy from the spare parts library, and determine the number of spare parts remaining after removing the target spare part from the spare parts library, and determine the spare parts to be replenished when the number of spare parts remaining is less than a preset inventory threshold; A processing module is used to perform maintenance operations on multiple components of the wind power equipment according to the real-time maintenance strategy and the target spare parts, and to replenish the spare parts warehouse based on the spare parts to be replenished to obtain operation and maintenance results.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium includes a stored program, wherein the program executes the method described in any one of claims 1 to 7 when executed.

10. An electronic device comprising a memory and a processor, characterized in that: A computer program is stored in the memory, and the processor is configured to execute the method according to any one of claims 1 to 7 through the computer program.

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