Method, device and equipment for determining power generation efficiency of offshore power plant and medium

Abstract

The embodiment of the invention discloses a method, device and equipment for determining the power generation efficiency of an offshore power plant and a medium, and relates to the technical field of power generation. The method comprises the following steps: acquiring a current environment parameter at a current moment, and determining an influence metric value corresponding to the current environment parameter; inputting the current environment parameter and the influence metric value into a pre-trained power generation grade classification model to obtain a power generation grade at the current moment; inputting the current environment parameter and the influence metric value into a pre-trained power generation efficiency prediction model to obtain a power generation efficiency actual value at the current moment; and fusing the power generation level and the power generation efficiency actual value to obtain a power generation efficiency fusion value of the power generation field at the current moment. In the technical scheme of the embodiment of the invention, the total power generation efficiency of wind power generation and photovoltaic power generation can be more accurately determined, so that the offshore power plant can more effectively dispatch and distribute power resources, energy waste is reduced, and the efficiency and reliability of energy supply are improved.

Description

Technical Field

[0001] The embodiments of the present application relate to the field of power generation technology, and in particular to a method, device, equipment and medium for determining power generation efficiency of an offshore power plant. Background Art

[0002] As energy demand continues to grow, offshore renewable energy sources such as wind and solar are gaining more and more attention.

[0003] At present, traditional offshore power plants are usually fixed on the seabed and generate electricity by converting offshore wind energy into electrical energy, or converting offshore solar energy into electrical energy. However, traditional offshore power plants usually manage wind power generation and photovoltaic power generation separately, lack unified coordinated management, and are unable to accurately determine the total power generation efficiency of wind power generation and photovoltaic power generation, thereby reducing the efficiency and reliability of energy supply. Summary of the invention

[0004] The embodiments of the present application provide a method, device, equipment and medium for determining the power generation efficiency of an offshore power plant, which realize the function of determining the power generation efficiency of an offshore power plant to solve the problem in the prior art that the total power generation efficiency of wind power generation and photovoltaic power generation cannot be accurately determined.

[0005] In a first aspect, an embodiment of the present application provides a method for determining power generation efficiency of an offshore power plant, the method comprising:

[0006] Obtain the current environmental parameters at the current moment, and determine the impact measurement values ​​corresponding to the current environmental parameters;

[0007] Input the current environmental parameters and impact measurement values ​​into the pre-trained power generation level classification model to obtain the power generation level at the current moment;

[0008] Input the current environmental parameters and impact measurement values ​​into the pre-trained power generation efficiency prediction model to obtain the actual value of power generation efficiency at the current moment;

[0009] The power generation level and the actual value of power generation efficiency are fused to obtain the fused value of power generation efficiency of the power plant at the current moment.

[0010] In the embodiment of the present application, the current environmental parameters at the current moment can be obtained, and the impact measurement values ​​corresponding to the current environmental parameters can be determined, and then the current environmental parameters and the impact measurement values ​​are input into a pre-trained power generation grade classification model to obtain the power generation grade at the current moment, and the current environmental parameters and the impact measurement values ​​are input into a pre-trained power generation efficiency prediction model to obtain the actual value of the power generation efficiency at the current moment, and then the power generation grade and the actual value of the power generation efficiency are fused to obtain the fusion value of the power generation efficiency of the power plant at the current moment, thereby realizing the power generation efficiency determination function of the offshore power plant; the traditional separate management method cannot fully consider the mutual influence between the two power generation methods (i.e., wind power generation and photovoltaic power generation) and the comprehensive effect of environmental factors, and the current environmental parameters in the above technical solution include the influencing By determining the impact metrics corresponding to the current environmental parameters of wind power generation and photovoltaic power generation, the comprehensive impact of the current environmental parameters on power generation efficiency can be accurately determined, thereby realizing the coordinated management of wind power generation and photovoltaic power generation. After that, the pre-trained power generation grade classification model and power generation efficiency prediction model are used to determine the actual power generation grade and power generation efficiency at the current moment, and further integrate the power generation grade and the actual power generation efficiency value, the total power generation efficiency of the two power generation modes can be determined more accurately, which solves the problem that the total power generation efficiency of wind power generation and photovoltaic power generation cannot be accurately determined in the prior art, so that offshore power plants can more effectively dispatch and allocate power resources, and reduce energy waste, thereby improving the efficiency and reliability of energy supply.

[0011] In a second aspect, an embodiment of the present application provides a device for determining power generation efficiency of an offshore power plant, the device comprising:

[0012] The acquisition module is used to obtain the current environmental parameters at the current moment and determine the impact measurement value corresponding to the current environmental parameters;

[0013] The first prediction module is used to input the current environmental parameters and impact measurement values ​​into a pre-trained power generation level classification model to obtain the power generation level at the current moment;

[0014] The second prediction module is used to input the current environmental parameters and impact measurement values ​​into a pre-trained power generation efficiency prediction model to obtain the actual value of power generation efficiency at the current moment;

[0015] The fusion module is used to fuse the power generation level and the actual value of power generation efficiency to obtain the fusion value of power generation efficiency of the power plant at the current moment.

[0016] In a third aspect, an embodiment of the present application provides an electronic device, the electronic device comprising:

[0017] at least one processor; and a memory communicatively coupled to the at least one processor;

[0018] The memory stores a computer program that can be executed by at least one processor, and the computer program is executed by at least one processor so that the at least one processor can execute the method for determining the power generation efficiency of an offshore power plant of any embodiment of the present application.

[0019] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements a method for determining power generation efficiency of an offshore power plant as in any embodiment of the present application.

[0020] The description of the second, third and fourth aspects in this application can refer to the detailed description of the first aspect; and the beneficial effects described in the second, third and fourth aspects can refer to the beneficial effect analysis of the first aspect, which will not be repeated here.

[0021] In this application, the name of the above-mentioned offshore power plant power generation efficiency determination device does not limit the device or functional module itself. In actual implementation, these devices or functional modules may appear with other names. As long as the functions of each device or functional module are similar to those of this application, they belong to the scope of the claims of this application and their equivalent technologies.

[0022] These and other aspects of the present application will become more apparent from the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0024] Figure 1 It is a flow chart of a method for determining power generation efficiency of an offshore power plant provided in an embodiment of the present application;

[0025] Figure 2a is an example diagram of an offshore power plant provided by an embodiment of the present application;

[0026] Figure 2b is an example diagram of the turbine generator provided by the embodiment of the present application for wind intake and rotation;

[0027] Figure 3 is another flow chart of a method for determining power generation efficiency of an offshore power plant provided in an embodiment of the present application;

[0028] Figure 4 It is a structural schematic diagram of a device for determining power generation efficiency of an offshore power plant provided in an embodiment of the present application;

[0029] Figure 5 It is a structural schematic diagram of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0030] In order to make the purpose, technical solution and advantages of the embodiments of the present application clearer, 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 this application.

[0031] It should be noted that the terms "first", "second", "target", and "original" 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 interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than that illustrated or described herein. In addition, the terms "including", "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.

[0032] Figure 1 It is a flow chart of a method for determining the power generation efficiency of an offshore power plant provided in an embodiment of the present application. This embodiment can be applied to a scenario in which the total power generation efficiency of an offshore power plant is determined when the offshore power plant uses wind power and photovoltaic power generation. A method for determining the power generation efficiency of an offshore power plant provided in this embodiment can be executed by a device for determining the power generation efficiency of an offshore power plant provided in an embodiment of the present application, and the device can be implemented in software and / or hardware. In a specific embodiment, the device for determining the power generation efficiency of the offshore power plant can be integrated in an electronic device, for example, the electronic device can be a computer, etc. The execution subject of this method can be an electronic device.

[0033] Traditional offshore power plants are fixed on the seabed and have poor adaptability to harsh environments. Extreme weather conditions such as typhoons and huge waves can easily damage power generation equipment. In addition, offshore wind energy is not optimal, and the irregularity and depth of the seabed make construction difficult, which leads to low power generation efficiency. Therefore, in a specific embodiment, the offshore power plant in the embodiment of the present application can be a floating offshore power plant. The floating offshore power plant can be installed in an area far from the coastline, which can better adapt to harsh environments, reduce the interference of the marine environment on power generation equipment, and thus improve power generation efficiency. In the following description of the embodiment of the present application, the offshore power plant will be described as a floating offshore power plant.

[0034] The floating offshore power plant may include a floating board, a wind turbine, a photovoltaic panel and an energy storage device, wherein the wind turbine is used to convert offshore wind energy into electrical energy and store it in the energy storage device; the photovoltaic panel is used to convert offshore solar energy into electrical energy and store it in the energy storage device; the energy storage device is used to store electrical energy, and illustratively, the energy storage device may be a battery; the floating board is a combinable floating board, made of high-strength corrosion-resistant materials, such as polyethylene or polypropylene, to ensure its stability and durability in harsh environments, and illustratively, the floating board may have a size of 5 meters × 5 meters and a thickness of 0.5 meters, and may be capable of carrying multiple wind turbines and multiple photovoltaic panels.

[0035] For example, Figure 2a The figure shows an example diagram of an offshore power plant provided by an embodiment of the present application. Figure 2a The wind turbine and photovoltaic panels are installed on the surface of the floating board; the wind turbine is a turbine generator, which has no requirements on the direction of wind, has the characteristics of small blade rotation space, strong wind resistance and low starting wind speed, and is more typhoon-proof and anti-dumping than blade-type generators. For example, Figure 2b The figure is an example diagram of the turbine generator provided by the embodiment of the present application with respect to the wind intake rotation. Figure 2b The turbine generator in the invention adopts a spiral wind intake method, that is, the wind enters the generator in a spiral trajectory; and, Figure 2a The multiple turbine generators are arranged in a staggered manner, which can fully utilize wind energy and have high power generation efficiency.

[0036] See also Figure 1 The method for determining the power generation efficiency of an offshore power plant in this embodiment includes but is not limited to the following steps:

[0037] S110: Acquire current environmental parameters at the current moment, and determine impact metrics corresponding to the current environmental parameters.

[0038] Among them, the current environmental parameters are various natural conditions and meteorological data measured at the location of the offshore power plant at the current moment; optionally, the current environmental parameters may include multiple types of environmental parameters, that is, the current environmental parameters may include parameters such as wind speed, light intensity, temperature, humidity, wave height and weather data.

[0039] The impact metric value is calculated based on the current environmental parameters and is used to quantify the comprehensive impact of the current environmental parameters on the power generation efficiency.

[0040] Specifically, when it is necessary to determine the power generation efficiency of the offshore power plant at the current moment, the current environmental parameters at the current moment can be obtained. For example, monitoring equipment such as wind speed measuring equipment, light intensity sensors, temperature sensors, humidity sensors and wave measuring equipment can be installed in advance around the offshore power plant and at key locations, and then the installed monitoring equipment can be used to obtain the current environmental parameters at the current moment. For example, a wind speed measuring device (such as an anemometer or a wind speed sensor) can be used to detect the wind speed at the current moment; a light intensity sensor can be used to detect the light intensity at the current moment; a temperature sensor can be used to detect the temperature at the current moment; a humidity sensor can be used to detect the humidity at the current moment; a wave measuring device (such as a wave buoy or a wave sensor) can be used to detect the wave height at the current moment; weather data at the current moment can be obtained through a meteorological forecast system or a meteorological station, including weather type (such as sunny, cloudy and rainy days, etc.) and air pressure and other information; thereafter, the detected current environmental parameters are processed in a unified format to ensure data consistency and availability, and the current environmental parameters after the unified format processing are preprocessed, such as denoising, smoothing and feature selection and other preprocessing operations to improve the data quality of the current environmental parameters.

[0041] Afterwards, the impact measurement value corresponding to the current environmental parameter can be determined. For example, a pre-trained impact measurement model can be obtained, and then the current environmental parameter is input into the impact measurement model. After being processed by the impact measurement model, the comprehensive impact degree of the current environmental parameter on the power generation efficiency can be calculated, that is, the impact measurement value corresponding to the current environmental parameter. Among them, the impact measurement model is a pre-trained neural network model, which is used to determine the comprehensive impact degree of the environmental parameter on the power generation efficiency, that is, to determine the impact measurement value corresponding to the environmental parameter.

[0042] Optionally, the training process of the impact measurement model is as follows: historical environmental parameters and impact measurement value labels corresponding to the historical environmental parameters can be obtained, and then the historical environmental parameters and impact measurement value labels are input into the initial impact measurement model as training data sets, and the impact measurement value labels are used to guide the training output of the initial impact measurement model, and the initial impact measurement model is trained to obtain the impact measurement model. Among them, the historical environmental parameters are the environmental parameters before the current moment; the initial impact measurement model is the impact measurement model that has been constructed before the training process begins but has not yet been optimized by any training data.

[0043] S120, inputting the current environmental parameters and impact measurement values ​​into a pre-trained power generation level classification model to obtain the power generation level at the current moment.

[0044] The power generation level classification model is a pre-trained neural network model that can classify the power generation efficiency of the power plant into different levels according to the input environmental parameters and impact measurement values, and is used to quickly determine the power generation potential or performance level of wind turbines and photovoltaic panels under the current environmental parameters. Exemplarily, the power generation level classification model can be a support vector machine (SVM) model.

[0045] The power generation level is the output result of the power generation level classification model, which is used to indicate the power generation performance level that the power plant can achieve under the current environmental parameters; the power generation level can include high, medium and low.

[0046] Specifically, after obtaining the current environmental parameters and the impact measurement values ​​corresponding to the current environmental parameters, a pre-trained power generation level classification model can be obtained, and then the current environmental parameters and the impact measurement values ​​corresponding to the current environmental parameters can be input into the power generation level classification model. At this time, the power generation level classification model can process the current environmental parameters and the impact measurement values ​​corresponding to the current environmental parameters to predict the corresponding power generation level, and then determine the output value of the power generation level classification model as the power generation level at the current moment.

[0047] S130, inputting the current environmental parameters and impact measurement values ​​into a pre-trained power generation efficiency prediction model to obtain the actual value of power generation efficiency at the current moment.

[0048] The power generation efficiency prediction model is a pre-trained neural network model that predicts the specific power generation efficiency value of the power plant based on the input environmental parameters and impact measurement values, so as to more accurately predict the total power generation efficiency of wind turbines and photovoltaic panels under the current environment. Exemplarily, the power generation efficiency prediction model can be a long short-term memory network (Long Short-Term Memory, LSTM) model.

[0049] The actual value of power generation efficiency is the output result of the power generation efficiency prediction model, which is used to indicate the specific power generation efficiency value that the power plant can actually achieve under the current environmental parameters, so as to measure the power generation performance of the power plant.

[0050] Specifically, after obtaining the current environmental parameters and the impact measurement values ​​corresponding to the current environmental parameters, a pre-trained power generation efficiency prediction model can be obtained, and then the current environmental parameters and the impact measurement values ​​corresponding to the current environmental parameters can be input into the power generation efficiency prediction model. At this time, the power generation efficiency prediction model can process the current environmental parameters and the impact measurement values ​​corresponding to the current environmental parameters to predict the corresponding specific values ​​of the power generation efficiency, and then determine the output value of the power generation efficiency prediction model as the actual value of the power generation efficiency at the current moment.

[0051] S140: Fusing the power generation level and the actual value of power generation efficiency to obtain a fused value of power generation efficiency of the power plant at the current moment.

[0052] Among them, the fusion value of power generation efficiency is the result obtained by fusing the power generation level and the actual value of power generation efficiency. It aims to comprehensively consider the power generation level and the actual value of power generation efficiency to obtain a more comprehensive and accurate power generation efficiency value.

[0053] Specifically, after obtaining the actual values ​​of power generation level and power generation efficiency at the current moment, the power generation level and the actual values ​​of power generation efficiency can be fused. For example, the fuzzy sets that are pre-divided for the power generation level and the actual values ​​of power generation efficiency using fuzzy logic theory can be obtained, and then a membership function is established for each fuzzy set. At this time, the membership function describes the degree to which the input value belongs to a certain fuzzy set, and fuzzy rules are defined. At this time, the fuzzy rules describe the relationship between the input value (i.e., the actual values ​​of power generation level and power generation efficiency) and the output value (i.e., the fused value of power generation efficiency). Then, the actual values ​​of power generation level and power generation efficiency at the current moment are combined. The values ​​are respectively input into the corresponding membership functions to convert the power generation level and the actual value of power generation efficiency into fuzzy values ​​in the fuzzy set respectively. After that, the fuzzy value of the power generation level and the fuzzy value of the actual value of power generation efficiency are fuzzy inferenced based on fuzzy rules to obtain the fuzzy value of the output variable (i.e., the power generation efficiency fusion value). The fuzzy value of the output variable is defuzzified to obtain the power generation efficiency fusion value, i.e., the power generation efficiency fusion value of the power plant at the current moment. At this time, the power generation efficiency fusion value not only takes into account the classification information of the power generation level, but also contains the precise numerical information of the actual value of the power generation efficiency, thereby improving the accuracy of determining the power generation efficiency.

[0054] The technical solution of the embodiment of the present application can obtain the current environmental parameters at the current moment, and determine the impact measurement values ​​corresponding to the current environmental parameters, and then input the current environmental parameters and the impact measurement values ​​into a pre-trained power generation grade classification model to obtain the power generation grade at the current moment, and input the current environmental parameters and the impact measurement values ​​into a pre-trained power generation efficiency prediction model to obtain the actual value of the power generation efficiency at the current moment, and then fuse the power generation grade and the actual value of power generation efficiency to obtain the fusion value of the power generation efficiency of the power plant at the current moment, thereby realizing the power generation efficiency determination function of the offshore power plant; the traditional separate management method cannot fully consider the mutual influence between the two power generation methods (i.e., wind power generation and photovoltaic power generation) and the comprehensive effect of environmental factors, and the current environmental parameters in the above technical solution include By determining the various environmental parameters that affect wind power generation and photovoltaic power generation and the impact measurement values ​​corresponding to the current environmental parameters, the comprehensive impact of the current environmental parameters on power generation efficiency can be accurately determined, thereby realizing the coordinated management of wind power generation and photovoltaic power generation. After that, the pre-trained power generation grade classification model and power generation efficiency prediction model are used to determine the current power generation grade and actual power generation efficiency values, and further integrate the power generation grade and actual power generation efficiency values, the total power generation efficiency of the two power generation modes can be determined more accurately, which solves the problem that the total power generation efficiency of wind power generation and photovoltaic power generation cannot be accurately determined in the prior art, so that offshore power plants can more effectively dispatch and allocate power resources and reduce energy waste, thereby improving the efficiency and reliability of energy supply.

[0055] The following further describes a method for determining power generation efficiency of an offshore power plant provided by an embodiment of the present application. Figure 3 1 is another flow chart of a method for determining power generation efficiency of an offshore power plant provided in an embodiment of the present application. The embodiment of the present application is optimized based on the above embodiments.

[0056] See also Figure 3 The method of this embodiment includes but is not limited to the following steps:

[0057] S301, obtaining current environmental parameters at the current moment.

[0058] S302: Obtain the weight corresponding to each type of environmental parameter in the current environmental parameters, and the reference environmental parameters corresponding to each type of environmental parameter.

[0059] Among them, the weight is a relative measure of the degree of influence of different types of environmental parameters on power generation efficiency. Each type of environmental parameter has different weights due to its different importance to power generation efficiency. The larger the weight, the greater the influence of this type of environmental parameter on power generation efficiency.

[0060] The reference environmental parameters are used as benchmark values ​​for comparing or evaluating whether the current environmental parameters are in a normal or optimized state. By comparing the current environmental parameters with the reference environmental parameters, it can be evaluated whether the environmental parameters are conducive to improving power generation efficiency.

[0061] Specifically, after obtaining the current environmental parameters, the weights corresponding to each type of environmental parameters in the current environmental parameters can be obtained, that is, corresponding weights can be assigned to each type of environmental parameters in advance according to their impact on power generation efficiency. For example, when the environmental parameters have a greater impact on power generation efficiency, a larger weight is assigned to this type of environmental parameter; when the environmental parameters have a smaller impact on power generation efficiency, a smaller weight is assigned to this type of environmental parameter, so that the environmental parameters that have a greater impact on power generation efficiency receive more attention.

[0062] Afterwards, the reference environmental parameters corresponding to each type of environmental parameters may be obtained, that is, the reference environmental parameters corresponding to each type of environmental parameters may be determined in advance based on industry standards, national standards, historical data, and the like.

[0063] S303: Calculate the difference between each type of environmental parameter and the reference environmental parameter of the corresponding type to obtain the offset corresponding to each type of environmental parameter.

[0064] The offset is the difference between the current environmental parameters and the reference environmental parameters of the corresponding type, which reflects the degree of deviation of the current environmental parameters relative to the reference environmental parameters and helps to quantify the comprehensive impact of environmental parameters on power generation efficiency.

[0065] Exemplarily, it is known that the current environmental parameters include types such as wind speed, light intensity, temperature, humidity, wave height and weather data; when the current wind speed is a1 and the reference environmental parameter of the wind speed is b1, the offset corresponding to the wind speed is a1-b1; when the current light intensity is a2 and the reference environmental parameter of the light intensity is b2, the offset corresponding to the light intensity is a2-b2.

[0066] S304: Determine an impact metric value according to a weight corresponding to each type of environmental parameter and an offset corresponding to each type of environmental parameter.

[0067] Specifically, after obtaining the offset corresponding to each type of environmental parameter, the offset can be weighted according to the weight of each type of environmental parameter, that is, the product of the weight of each type of environmental parameter and the offset of the corresponding type is calculated, and then the sum of the products of each type of environmental parameter is calculated to obtain the impact measurement value corresponding to the current environmental parameter, which reflects the comprehensive impact of the current environmental parameter on the power generation efficiency; the calculation formula of the impact measurement value can be expressed as:

[0068]

[0069] Among them, Impactt represents the impact metric value corresponding to time t, N represents the number of types of environmental parameters, and α i represents the weight of the i-th environmental parameter, A ti A represents the value of the i-th environmental parameter collected at time t, i_ref The value of the reference environmental parameter of the i-th category environmental parameter.

[0070] S305: Input the current environmental parameters and impact measurement values ​​into a pre-trained power generation level classification model to obtain the power generation level at the current moment.

[0071] Optionally, the training process of the power generation grade classification model is as follows: obtain first training sample data, which may include historical environmental parameters, impact measurement values ​​corresponding to the historical environmental parameters, and power generation grade labels corresponding to the historical environmental parameters; input the first training sample data as a training data set into the initial power generation grade classification model, use the power generation grade label to guide the training output of the initial power generation grade classification model, train the initial power generation grade classification model, and obtain the power generation grade classification model.

[0072] The initial power generation grade classification model is a power generation grade classification model that has been constructed before the training process begins but has not yet been optimized by any training data.

[0073] Specifically, the historical environmental parameters and the impact metric values ​​corresponding to the historical environmental parameters can be input into the initial power generation grade classification model to obtain the power generation grade prediction result, and then the loss between the power generation grade prediction result and the power generation grade label is calculated based on the hinge loss function, and the hyperparameters of the initial power generation grade classification model are modified to minimize the loss between the power generation grade prediction result and the power generation grade label, so as to obtain the optimal initial power generation grade classification model, that is, the power generation grade classification model. By learning a large number of historical environmental parameters, impact metric values ​​and their corresponding power generation grade labels, the power generation grade classification model can find an optimal hyperplane to distinguish data points belonging to different power generation grades as much as possible, and the impact metric values ​​in the input values ​​can enhance the discrimination of the optimal hyperplane, thereby improving the classification accuracy of the power generation grade classification model, so that the power generation grade classification model can more accurately predict the power generation grade under different environmental conditions.

[0074] S306: Input the current environmental parameters and impact measurement values ​​into a pre-trained power generation efficiency prediction model to obtain the actual value of power generation efficiency at the current moment.

[0075] Optionally, the training process of the power generation efficiency prediction model is as follows: obtain second training sample data, the second training sample data includes historical environmental parameters, impact measurement values ​​corresponding to the historical environmental parameters, and power generation efficiency labels corresponding to the historical environmental parameters; input the second training sample data as a training data set into the initial power generation efficiency prediction model, use the power generation efficiency label to guide the training output of the initial power generation efficiency prediction model, train the initial power generation efficiency prediction model, and obtain the power generation efficiency prediction model.

[0076] The initial power generation efficiency prediction model is a power generation efficiency prediction model that has been constructed before the training process begins but has not yet been optimized by any training data.

[0077] Specifically, the historical environmental parameters and the impact metrics corresponding to the historical environmental parameters can be input into the initial power generation efficiency prediction model to obtain the power generation efficiency prediction value, and then the loss between the power generation efficiency prediction value and the power generation efficiency label is calculated based on the mean square error loss function, and the hyperparameters of the initial power generation efficiency prediction model are modified to minimize the loss between the power generation efficiency prediction value and the power generation efficiency label, so as to obtain the optimal initial power generation efficiency prediction model, that is, the power generation efficiency prediction model. By learning a large number of historical environmental parameters, impact metrics and their corresponding power generation efficiency labels, the power generation efficiency prediction model can accurately capture the association and regularity between environmental parameters and power generation efficiency, and the impact metrics in the input value can help the power generation efficiency prediction model capture the impact of different environmental parameters on power generation efficiency, thereby improving the prediction accuracy of the power generation efficiency prediction model, so that the power generation efficiency prediction model can more accurately predict the power generation efficiency under different environmental conditions.

[0078] S307: Obtain a first weight corresponding to the power generation grade classification model and a second weight corresponding to the power generation efficiency prediction model.

[0079] The first weight is the proportion or importance of the power generation level classification model in the process of determining the power generation efficiency fusion value. The second weight is the proportion or importance of the power generation efficiency prediction model in the process of determining the power generation efficiency fusion value.

[0080] Specifically, after obtaining the actual value of power generation efficiency, the weight pre-assigned to the power generation grade classification model can be obtained to obtain the first weight, and the weight pre-assigned to the power generation efficiency prediction model can be obtained to obtain the second weight. That is, after the training of the power generation grade classification model and the power generation efficiency prediction model is completed, different weights can be assigned to the power generation grade classification model and the power generation efficiency prediction model according to the accuracy of the power generation grade classification model and the power generation efficiency prediction model on the verification set, and the higher the accuracy of the model on the verification set, the greater the weight assigned to the model. For example, if the accuracy of the power generation grade classification model on the verification set is 85%, and the accuracy of the power generation efficiency prediction model is 90%, a larger weight can be assigned to the power generation efficiency prediction model, and a smaller weight can be assigned to the power generation grade classification model.

[0081] S308. Numerically encode the power generation level to obtain a power generation efficiency encoding value.

[0082] Among them, the power generation efficiency coding value is the value obtained after numerically encoding the power generation level; in the power generation level classification model, the power generation efficiency is usually divided into different levels, such as high, medium and low. In order to perform quantitative analysis and calculation, these power generation levels can be converted into corresponding numerical codes for subsequent fusion processing.

[0083] Specifically, after obtaining the first weight and the second weight, a pre-set numerical coding rule can be obtained, wherein the numerical coding rule can correspond each power generation level to a specific numerical value, for example, a high level is encoded as a higher numerical value, and a low level is encoded as a lower numerical value, and the specific numerical value of the code can be adjusted according to the actual situation, but should be kept consistent for subsequent calculations and analysis; then, according to the numerical coding rule, the power generation level at the current moment is numerically encoded, and the numerical value corresponding to the power generation level is determined as the power generation efficiency coding value.

[0084] Exemplarily, it is known that power generation levels include high, medium and low, and the numerical coding rule is: the numerical value corresponding to the high level is 1, the numerical value corresponding to the medium level is 2, the numerical value corresponding to the low level is 3, and the numerical value 1>the numerical value 2>the numerical value 3; at this time, when the power generation level at the current moment is medium, the power generation efficiency coding value at the current moment is 2.

[0085] S309: Based on the first weight and the second weight, weighted fusion is performed on the power generation efficiency coded value and the power generation efficiency actual value to obtain a power generation efficiency fusion value.

[0086] Specifically, after obtaining the coded value of the power generation efficiency, the product between the coded value of the power generation efficiency and the first weight (referred to as the first product) can be calculated, and the product between the actual value of the power generation efficiency and the second weight (referred to as the second product) can be calculated, and then the average value of the first product and the second product is calculated, and the average value is determined as the power generation efficiency fusion value.

[0087] Optionally, the output value of the power generation grade classification model may also include equipment status (such as normal and abnormal), that is, the first training sample data also includes an equipment status label. At this time, the training process of the power generation grade classification model can be expressed as: obtain the first training sample data, the first training sample data may include historical environmental parameters, impact measurement values ​​corresponding to the historical environmental parameters, power generation grade labels corresponding to the historical environmental parameters, and equipment status labels corresponding to the historical environmental parameters; input the first training sample data as a training data set into the initial power generation grade classification model, use the power generation grade label and the equipment status label to guide the training output of the initial power generation grade classification model, train the initial power generation grade classification model, and obtain the power generation grade classification model.

[0088] Optionally, the power generation efficiency prediction model may also include equipment performance indicators, that is, the second training sample data also includes equipment performance indicator labels. At this time, the training process of the power generation efficiency prediction model can be expressed as: obtain the second training sample data, the second training sample data includes historical environmental parameters, impact measurement values ​​corresponding to the historical environmental parameters, power generation efficiency labels corresponding to the historical environmental parameters, and equipment performance indicator labels corresponding to the historical environmental parameters; input the second training sample data as a training data set into the initial power generation efficiency prediction model, use the power generation efficiency label and the equipment performance indicator label to guide the training output of the initial power generation efficiency prediction model, train the initial power generation efficiency prediction model, and obtain the power generation efficiency prediction model.

[0089] S310: Input the power generation efficiency fusion value, equipment status and equipment performance index into a pre-trained fault prediction model to obtain the fault probability of the power plant.

[0090] Specifically, after obtaining the power generation efficiency fusion value, a pre-trained fault prediction model can be obtained, and then the power generation efficiency fusion value, equipment status and equipment performance indicators can be input into the fault prediction model. At this time, the fault prediction model can analyze the wind turbines and photovoltaic panels in the power plant according to the power generation efficiency fusion value, equipment status and equipment performance indicators at the current moment, predict the probability of failure of the wind turbines and photovoltaic panels at the current moment, and then determine the output value of the fault prediction model as the failure probability of the power plant.

[0091] Optionally, when determining the failure probability of a power plant, in addition to inputting the power generation efficiency fusion value, equipment status and equipment performance indicators into the fault prediction model, other relevant features (such as the operation history and maintenance records of wind turbines and photovoltaic panels, etc.) can also be input into the fault prediction model to assist the fault prediction model in predicting the failure probability of the power plant at the current moment.

[0092] Optionally, the training process of the fault prediction model is as follows: obtain third training sample data, the third training sample data includes historical power generation efficiency, historical equipment status, historical equipment performance indicators and fault probability labels; input the third training sample data as a training data set into the initial fault prediction model, guide the training output of the initial fault prediction model with the fault probability label, train the initial fault prediction model, and obtain the fault prediction model. The initial fault prediction model is a fault prediction model that has been constructed but has not been optimized by any training data before starting the training process. Exemplarily, the fault prediction model can be a logistic regression model.

[0093] S311. Determine whether the failure probability is greater than a preset failure threshold.

[0094] The preset fault threshold is a pre-set threshold used to determine whether the power generation equipment in the power plant is faulty. The user can adjust and set the preset fault threshold according to actual use requirements, and the embodiment of the present application does not specifically limit this. The power generation equipment may include a wind turbine and a photovoltaic panel.

[0095] Specifically, if the failure probability is greater than a preset failure threshold, S312 may be executed; if the failure probability is not greater than the preset failure threshold, S314 may be executed.

[0096] S312: When the failure probability is greater than a preset failure threshold, determine that the power generation equipment in the power plant is faulty.

[0097] Specifically, when the fault probability is greater than a preset fault threshold, it indicates that at least one of the wind turbine and the photovoltaic panel is in a fault state, and at this time, it can be determined that the power generation equipment of the power plant is faulty.

[0098] S313: Generate fault prompt information and display the fault prompt information.

[0099] Among them, the fault prompt information is used to prompt the user that the power generation equipment has a fault.

[0100] Specifically, after determining that the power generation equipment in the power plant has a fault, fault prompt information can be generated. The fault prompt information at this time may include key information such as the location, type, possible cause of the fault, and recommended maintenance measures of the faulty equipment. The fault prompt information is then displayed on the display screen of the electronic device, through an alarm light, text message or email, etc., to prompt the user to take timely response measures, such as immediately going to the fault site for inspection and repairing the faulty equipment.

[0101] S314: When the fault probability is not greater than a preset fault threshold, determine that the power generation equipment in the power plant is normal.

[0102] Specifically, when the fault probability is not greater than the preset fault threshold, it indicates that the wind turbines and photovoltaic panels are all in normal condition, and at this time it can be determined that the power generation equipment of the power plant is normal.

[0103] The technical solution of the embodiment of the present application can obtain the current environmental parameters at the current moment, and obtain the weight corresponding to each type of environmental parameters in the current environmental parameters, and the reference environmental parameters corresponding to each type of environmental parameters, and then calculate the difference between each type of environmental parameters and the reference environmental parameters of the corresponding type to obtain the offset corresponding to each type of environmental parameters, and determine the impact measurement value according to the weight corresponding to each type of environmental parameters and the offset corresponding to each type of environmental parameters, thereby realizing the function of determining the impact measurement value. By calculating the offset between each type of environmental parameters and the reference environmental parameters, the degree of influence of different environmental factors on the power generation efficiency can be quantified, and the weighted sum of the offsets can be further calculated, thereby improving the determination efficiency and accuracy of the impact measurement value, and providing data support for the subsequent determination of the power generation level and the actual value of the power generation efficiency.

[0104] After obtaining the power generation level at the current moment and the actual value of the power generation efficiency at the current moment, the first weight corresponding to the power generation level classification model and the second weight corresponding to the power generation efficiency prediction model can be obtained, so that the model with higher accuracy can get more attention, and then the power generation level is numerically encoded to obtain the power generation efficiency coding value, and based on the first weight and the second weight, the power generation efficiency coding value and the actual value of the power generation efficiency are weightedly fused to obtain the power generation efficiency fusion value. Compared with the single reliance on the actual value of the power generation efficiency, the power generation efficiency fusion value obtained by comprehensively considering the power generation level and the actual value of the power generation efficiency can more comprehensively reflect the actual power generation situation of the power plant, thereby improving the accuracy of determining the power generation efficiency fusion value.

[0105] Afterwards, the fusion value of power generation efficiency, equipment status and equipment performance indicators are input into the pre-trained fault prediction model to obtain the failure probability of the power plant, which can realize the failure prediction of the power plant and thus discover potential failure risks in advance; when the failure probability is greater than the preset failure threshold, the power generation equipment failure of the power plant is determined, and then fault prompt information is generated and displayed, which can prompt users to take timely countermeasures to reduce the downtime of power generation equipment, thereby reducing the risk of power supply interruption, thereby improving the reliability and stability of offshore power plants.

[0106] Figure 4 is a schematic diagram of a structure of a device for determining power generation efficiency of an offshore power plant provided in an embodiment of the present application, with reference to Figure 4 , the offshore power plant power generation efficiency determination device may include:

[0107] The acquisition module 410 is used to acquire the current environmental parameters at the current moment and determine the impact metric value corresponding to the current environmental parameters;

[0108] The first prediction module 420 is used to input the current environmental parameters and impact metric values ​​into a pre-trained power generation level classification model to obtain the power generation level at the current moment;

[0109] The second prediction module 430 is used to input the current environmental parameters and the impact metric value into a pre-trained power generation efficiency prediction model to obtain the actual value of the power generation efficiency at the current moment;

[0110] The fusion module 440 is used to fuse the power generation level and the actual value of power generation efficiency to obtain a fused value of power generation efficiency of the power plant at the current moment.

[0111] In one embodiment, the current environmental parameters include multiple types of environmental parameters, and the acquisition module 410 determines the impact measurement value corresponding to the current environmental parameters, including: obtaining the weight corresponding to each type of environmental parameters in the current environmental parameters, and the reference environmental parameters corresponding to each type of environmental parameters; calculating the difference between each type of environmental parameters and the reference environmental parameters of the corresponding type to obtain the offset corresponding to each type of environmental parameters; determining the impact measurement value according to the weight corresponding to each type of environmental parameters and the offset corresponding to each type of environmental parameters.

[0112] In one embodiment, the fusion module 440 is specifically used to: obtain a first weight corresponding to the power generation grade classification model and a second weight corresponding to the power generation efficiency prediction model; numerically encode the power generation grade to obtain a power generation efficiency coded value; and based on the first weight and the second weight, perform weighted fusion on the power generation efficiency coded value and the power generation efficiency actual value to obtain a power generation efficiency fused value.

[0113] In one embodiment, the training process of the power generation grade classification model in the first prediction module 420 is as follows: obtain first training sample data, the first training sample data includes historical environmental parameters, impact measurement values ​​corresponding to the historical environmental parameters, and power generation grade labels corresponding to the historical environmental parameters; input the first training sample data as a training data set into the initial power generation grade classification model, use the power generation grade label to guide the training output of the initial power generation grade classification model, train the initial power generation grade classification model, and obtain the power generation grade classification model.

[0114] In one embodiment, the training process of the power generation efficiency prediction model in the second prediction module 430 is as follows: obtain second training sample data, the second training sample data includes historical environmental parameters, impact measurement values ​​corresponding to the historical environmental parameters, and power generation efficiency labels corresponding to the historical environmental parameters; input the second training sample data into the initial power generation efficiency prediction model as a training data set, use the power generation efficiency label to guide the training output of the initial power generation efficiency prediction model, train the initial power generation efficiency prediction model, and obtain the power generation efficiency prediction model.

[0115] In one embodiment, the output value of the power generation level classification model also includes the equipment status, the power generation efficiency prediction model also includes the equipment performance index, and the power generation efficiency determination device of the offshore power plant also includes a fault prediction module. The fault prediction module is specifically used to: input the power generation efficiency fusion value, the equipment status and the equipment performance index into a pre-trained fault prediction model to obtain the failure probability of the power plant.

[0116] In one embodiment, the power generation efficiency determination device of the offshore power plant also includes a fault prompt module, which is specifically used to: determine the fault of the power generation equipment of the power plant when the fault probability is greater than a preset fault threshold; generate fault prompt information, and display the fault prompt information.

[0117] Those skilled in the art can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional modules is used as an example for illustration. In actual applications, the above-mentioned functions can be assigned to different functional modules as needed, that is, the internal structure of the device is divided into different functional modules to complete all or part of the functions described above. The specific working process of the functional modules described above can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here.

[0118] The device for determining the power generation efficiency of an offshore power plant provided in this embodiment can be applied to the method for determining the power generation efficiency of an offshore power plant provided in any of the above embodiments, and has corresponding functions and beneficial effects.

[0119] Figure 5 It is a structural schematic diagram of an electronic device provided in an embodiment of the present application. Figure 5A block diagram of an exemplary electronic device 11 suitable for implementing the embodiments of the present application is shown. Figure 5 The electronic device 11 shown is only an example and should not bring any limitation to the function and scope of use of the present embodiment.

[0120] like Figure 5 As shown, the electronic device 11 is in the form of a general purpose computing electronic device. The components of the electronic device 11 may include, but are not limited to: one or more processors or processing units 16, a system memory 28, and a bus 18 connecting different system components (including the system memory 28 and the processing unit 16).

[0121] Bus 18 represents one or more of several types of bus structures, including a memory bus or memory controller, a peripheral bus, an accelerated graphics port, a processor or a local bus using any of a variety of bus architectures. By way of example, these architectures include, but are not limited to, an Industry Standard Architecture (ISA) bus, a Micro Channel Architecture (MAC) bus, an Enhanced ISA bus, a Video Electronics Standards Association (VESA) local bus, and a Peripheral Component Interconnect (PCI) bus.

[0122] The electronic device 11 typically includes a variety of computer system readable media. These media can be any available media that can be accessed by the electronic device 11, including volatile and non-volatile media, removable and non-removable media.

[0123] The system memory 28 may include computer system readable media in the form of volatile memory, such as random access memory (RAM) 30 and / or cache memory 32. The electronic device 11 may further include other removable / non-removable, volatile / non-volatile computer system storage media. By way of example only, the storage system 34 may be used to read and write non-removable, non-volatile magnetic media ( Figure 5 not shown, usually called a "hard drive"). Although Figure 5 Not shown, a disk drive for reading and writing to a removable non-volatile disk (e.g., a "floppy disk"), and an optical drive for reading and writing to a removable non-volatile optical disk (e.g., a CD-ROM, a DVD-ROM, or other optical medium) may be provided. In these cases, each drive may be connected to the bus 18 via one or more data medium interfaces. The system memory 28 may include at least one program product having a set (e.g., at least one) of program modules that are configured to perform the functions of the various embodiments of the present application.

[0124] A program / utility 40 having a set (at least one) of program modules 42 may be stored, for example, in system memory 28, such program modules 42 including, but not limited to, an operating system, one or more application programs, other program modules, and program data, each of which or some combination may include an implementation of a network environment. Program modules 42 generally perform the functions and / or methods of the embodiments described herein.

[0125] The electronic device 11 may also communicate with one or more external devices 14 (e.g., keyboards, pointing devices, displays 24, etc.), may also communicate with one or more devices that enable a user to interact with the electronic device 11, and / or may communicate with any device that enables the electronic device 11 to communicate with one or more other computing devices (e.g., network cards, modems, etc.). Such communication may be performed through an input / output (I / O) interface 22. Furthermore, the electronic device 11 may also communicate with one or more networks (e.g., a local area network (LAN), a wide area network (WAN), and / or a public network, such as the Internet) through a network adapter 20.

[0126] like Figure 5 As shown, the network adapter 20 communicates with other modules of the electronic device 11 via the bus 18. It should be understood that although Figure 5 Not shown, other hardware and / or software modules may be used in conjunction with the electronic device 11, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems.

[0127] The processing unit 16 executes various functional applications and page displays by running the programs stored in the system memory 28, for example, implementing a method for determining power generation efficiency of an offshore power plant provided in any embodiment of the present application.

[0128] An embodiment of the present application provides a computer-readable storage medium having a computer program stored thereon. When the program is executed by a processor, it implements, for example, a method for determining power generation efficiency of an offshore power plant provided in any embodiment of the present application.

[0129] The computer storage medium of this embodiment can adopt any combination of one or more computer-readable media. Computer-readable media can be computer-readable signal media or computer-readable storage media. Computer-readable storage media can be, for example, but not limited to: electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices or devices, or any combination of the above. More specific examples (non-exhaustive list) of computer-readable storage media include: electrical connections with one or more wires, portable computer disks, hard disks, random access memories (RAM), read-only memories (ROM), erasable programmable read-only memories (EPROM or flash memory), optical fibers, portable compact disk read-only memories (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the above. In this document, computer-readable storage media can be any tangible medium containing or storing programs, which can be used by instruction execution systems, devices or devices or used in combination with them.

[0130] Computer-readable signal media may include data signals propagated in baseband or as part of a carrier wave, which carry computer-readable program code. Such propagated data signals may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. Computer-readable signal media may also be any computer-readable medium other than a computer-readable storage medium, which may send, propagate, or transmit a program for use by or in conjunction with an instruction execution system, apparatus, or device.

[0131] The program code embodied on the computer readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.

[0132] Computer program code for performing the operation of the present application can be written in one or more programming languages ​​or combinations thereof, including object-oriented programming languages, such as Java, Smalltalk, C++, and conventional procedural programming languages, such as "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as an independent software package, partially on the user's computer, partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer can be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or can be connected to an external computer (e.g., using an Internet service provider to connect through the Internet).

[0133] It should be understood by those skilled in the art 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, optionally, they can be implemented by a program code executable by a computer device, so that they can be stored in a storage device and executed by the computing device, 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.

[0134] In addition, the acquisition, storage, use, and processing of data in the technical solution of this application comply with the relevant provisions of national laws and regulations.

[0135] Note that the above are only preferred embodiments of the present application and the technical principles used. Those skilled in the art will understand that the present application is not limited to the specific embodiments herein, and that various obvious changes, readjustments and substitutions can be made by those skilled in the art without departing from the scope of protection of the present application. Therefore, although the present application is described in more detail through the above embodiments, the present application is not limited to the above embodiments, and may include more other equivalent embodiments without departing from the inventive concept of the present application, and the scope of the present application is determined by the scope of the appended claims.

Claims

1. A method for determining power generation efficiency of an offshore power plant, characterized in that: The method comprises: Obtaining current environmental parameters at the current moment, and determining impact metric values ​​corresponding to the current environmental parameters; Inputting the current environmental parameters and the impact metric value into a pre-trained power generation grade classification model to obtain the power generation grade at the current moment; Inputting the current environmental parameter and the impact metric value into a pre-trained power generation efficiency prediction model to obtain the actual value of the power generation efficiency at the current moment; The power generation level and the actual value of power generation efficiency are fused to obtain a fused value of power generation efficiency of the power plant at the current moment.

2. The method for determining power generation efficiency of an offshore power plant according to claim 1, characterized in that: The current environmental parameter includes multiple types of environmental parameters, and determining the impact metric value corresponding to the current environmental parameter includes: Obtaining the weight corresponding to each type of environmental parameter in the current environmental parameters, and the reference environmental parameters corresponding to each type of environmental parameter; Calculate the difference between each type of environmental parameter and the reference environmental parameter of the corresponding type to obtain the offset corresponding to each type of environmental parameter; The impact metric value is determined according to the weight corresponding to each type of environmental parameter and the offset corresponding to each type of environmental parameter.

3. The method for determining power generation efficiency of an offshore power plant according to claim 1, characterized in that: The step of fusing the power generation level and the actual value of power generation efficiency to obtain a fused value of power generation efficiency of the power plant at the current moment includes: Obtaining a first weight corresponding to the power generation grade classification model and a second weight corresponding to the power generation efficiency prediction model; Numerically encoding the power generation level to obtain a power generation efficiency encoding value; Based on the first weight and the second weight, the power generation efficiency coded value and the power generation efficiency actual value are weightedly fused to obtain the power generation efficiency fusion value.

4. The method for determining power generation efficiency of an offshore power plant according to claim 1, characterized in that: The training process of the power generation level classification model is as follows: Acquire first training sample data, wherein the first training sample data includes historical environmental parameters, impact metrics corresponding to the historical environmental parameters, and power generation level labels corresponding to the historical environmental parameters; The first training sample data is input as a training data set into an initial power generation grade classification model, the power generation grade label is used to guide the training output of the initial power generation grade classification model, the initial power generation grade classification model is trained, and the power generation grade classification model is obtained.

5. The method for determining power generation efficiency of an offshore power plant according to claim 1, characterized in that: The training process of the power generation efficiency prediction model is as follows: Acquire second training sample data, where the second training sample data includes historical environmental parameters, impact metrics corresponding to the historical environmental parameters, and power generation efficiency labels corresponding to the historical environmental parameters; The second training sample data is input as a training data set into an initial power generation efficiency prediction model, the power generation efficiency label is used to guide the training output of the initial power generation efficiency prediction model, the initial power generation efficiency prediction model is trained, and the power generation efficiency prediction model is obtained.

6. The method for determining power generation efficiency of an offshore power plant according to claim 1, characterized in that: The output value of the power generation level classification model also includes the equipment status, the power generation efficiency prediction model also includes the equipment performance index, and the method further includes: The power generation efficiency fusion value, the device state and the device performance index are input into a pre-trained fault prediction model to obtain the fault probability of the power plant.

7. The method for determining power generation efficiency of an offshore power plant according to claim 6, characterized in that: The method further comprises: When the fault probability is greater than a preset fault threshold, determining that the power generation equipment in the power plant is faulty; Generate fault prompt information and display the fault prompt information.

8. A device for determining power generation efficiency of an offshore power plant, characterized in that: The device comprises: An acquisition module, used to acquire the current environmental parameters at the current moment, and determine the impact metric value corresponding to the current environmental parameters; A first prediction module, used for inputting the current environmental parameters and the impact metric value into a pre-trained power generation level classification model to obtain the power generation level at the current moment; A second prediction module, used for inputting the current environmental parameters and the impact metric value into a pre-trained power generation efficiency prediction model to obtain the actual value of the power generation efficiency at the current moment; The fusion module is used to fuse the power generation level and the actual value of the power generation efficiency to obtain a fusion value of the power generation efficiency of the power plant at the current moment.

9. An electronic device, characterized in that: The electronic device comprises: at least one processor; and a memory communicatively connected to the at least one processor; wherein, The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the method for determining the power generation efficiency of an offshore power plant as described in any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the method for determining power generation efficiency of an offshore power plant as claimed in any one of claims 1 to 7 is implemented.

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