Method and device for determining fatigue loss of offshore photovoltaic pile foundation stand column and medium

Abstract

The invention relates to a method and device for determining the fatigue loss of an offshore photovoltaic pile foundation stand column and a medium, and relates to the technical field of fatigue analysis. The method comprises the steps that pile foundation load dynamic data of the pile foundation stand column are determined according to historical stand column state data; determining a target residual life of the three-dimensional digital twin model under an extreme service condition based on the pile foundation load dynamic data; based on the target residual life, the column material parameters of the pile foundation column, the pile foundation load dynamic data and historical environment variable data corresponding to the historical column state data, training a to-be-trained neural network model to obtain a fatigue life prediction model; and inputting real-time model state data of the three-dimensional digital twinborn model into the fatigue life prediction model to obtain real-time residual life probability distribution of the three-dimensional digital twinborn model, and determining real-time fatigue loss of the pile foundation stand column according to the real-time residual life probability distribution. The accuracy of the fatigue loss analysis result is improved.

Description

Technical Field

[0001] Embodiments of the present disclosure relate to the technical field of fatigue analysis. Specifically, the present disclosure relates to a method for determining fatigue loss of a pile foundation column of an offshore photovoltaic power station, a device for determining fatigue loss of a pile foundation column of an offshore photovoltaic power station, and a computer-readable storage medium. Background Art

[0002] In the existing solutions, there is no mention of how to analyze the real-time fatigue damage of pile foundation columns.

[0003] It should be noted that the information disclosed in the background art above is only used to enhance the understanding of the background of the present disclosure. Therefore, it may include information that does not constitute the prior art known to those of ordinary skill in the art. Summary of the Invention

[0004] An object of the present disclosure is to provide a method for determining fatigue loss of a pile foundation column of an offshore photovoltaic power station, a device for determining fatigue loss of a pile foundation column of an offshore photovoltaic power station, and a computer-readable storage medium, so as to at least overcome to some extent the problem that the real-time fatigue damage of the pile foundation column cannot be analyzed due to the limitations and defects of the related art.

[0005] According to one aspect of the present disclosure, a method for determining the fatigue loss of a pile foundation column of an offshore photovoltaic system is provided, including: obtaining historical column state data of the pile foundation column in an offshore photovoltaic scenario, and determining the dynamic pile foundation load data of the pile foundation column according to the historical column state data; wherein, the dynamic pile foundation load data is obtained through the following method: obtaining a buoy, an anemometer and a displacement sensor corresponding to the pile foundation column in the offshore photovoltaic scenario, and obtaining historical wave buoy data monitored based on the buoy, historical wind speed and direction data monitored based on the anemometer, and historical pile foundation strain data monitored based on the displacement sensor; determining the historical ocean energy spectrum data of the pile foundation column according to the historical wave buoy data, and determining the historical three-dimensional turbulent wind field data of the pile foundation column according to the historical wind speed and direction data; determining the historical fluid-structure interaction data of the pile foundation column according to the historical pile foundation strain data, and constructing the dynamic pile foundation load data of the pile foundation column according to the historical ocean energy spectrum data, historical three-dimensional turbulent wind field data and historical fluid-structure interaction data; constructing a three-dimensional digital twin model corresponding to the pile foundation column, and determining the target remaining life of the three-dimensional digital twin model under extreme service conditions based on the dynamic pile foundation load data; training a neural network model to be trained based on the target remaining life, the column material parameters of the pile foundation column, the dynamic pile foundation load data and the historical environmental variable data corresponding to the historical column state data to obtain a fatigue life prediction model; inputting the real-time model state data of the three-dimensional digital twin model into the fatigue life prediction model to obtain the real-time remaining life probability distribution of the three-dimensional digital twin model, and determining the real-time fatigue loss of the pile foundation column according to the real-time remaining life probability distribution.

[0006] In an exemplary embodiment of the present disclosure, determining the historical ocean energy spectrum data of the pile foundation column according to the historical wave buoy data includes: determining a wave spectrum function to determine a spectrogram based on the wave spectrum function, and determining the up-crossing mean period of the wave based on the spectrogram and the spectral peak mean period ; wherein, the up-crossing mean period and the spectral peak mean period satisfy the following relationship: ; wherein, is a spectral coefficient; according to the up-crossing mean period and the spectral peak mean period and the relationship between the up-crossing mean period and the spectral peak mean period , determining the mean period ; wherein, the specific value of the mean period is: ; based on the up-crossing mean period , the spectral peak mean period , average period , and the height of the wave to determine the historical ocean energy spectrum data of the pile foundation column.

[0007] In an exemplary embodiment of the present disclosure, constructing a three-dimensional digital twin model corresponding to the pile foundation column includes: obtaining the original geometric model of the pile foundation column in the offshore photovoltaic scenario, and configuring first component performance parameters for the original geometric model; wherein, the first component performance parameters include at least one of the first elastic modulus, the first Poisson's ratio, the first tensile strength, the first compressive strength, and the first horizontal bearing capacity of the first component material used to prepare the pile foundation column; applying a first simulated service load under the simulated service condition to the original geometric model; wherein, the simulated service condition is obtained by simulating the actual service condition of the pile foundation column, and the first simulated service load includes at least one of the first weather scenario load, the first pressure load, and the first support force load borne by the pile foundation column during service; generating a three-dimensional digital twin model corresponding to the pile foundation column according to the original geometric model, the first component performance parameters, and the first simulated service load.

[0008] In an exemplary embodiment of the present disclosure, determining the target remaining life of the three-dimensional digital twin model under extreme service conditions based on the pile foundation load dynamic data includes: determining the fatigue damage coefficient of the pile foundation column under the combined action of waves - tides - sea winds according to the pile foundation load dynamic data and the historical environmental variable data corresponding to the historical column state data; determining the dangerous stress area of the three-dimensional digital twin model, and determining the original remaining life of the dangerous stress area under extreme service conditions according to the fatigue damage coefficient; determining the cumulative damage of the dangerous stress area and the crack propagation path caused by the cumulative damage, and correcting the original remaining life based on the crack propagation path to obtain the target remaining life of the three-dimensional digital twin model under extreme service conditions.

[0009] In an exemplary embodiment of the present disclosure, training a neural network model to be trained based on the target remaining life, the column material parameters of the pile foundation column, the pile foundation load dynamic data, and the historical environmental variable data corresponding to the historical column state data to obtain a fatigue life prediction model includes: constructing a first original feature map according to the column material parameters of the pile foundation column, the pile foundation load dynamic data, and the historical environmental variable data corresponding to the historical column state data; inputting the first original feature into the neural network model to be trained to obtain the fatigue life prediction result of the pile foundation column; constructing a loss function according to the fatigue life prediction result and the target remaining life, and training the neural network model to be trained based on the loss function to obtain the fatigue life prediction model.

[0010] In an exemplary embodiment of the present disclosure, constructing a first original feature map according to the column material parameters of the pile foundation column, the dynamic data of the pile foundation load, and the historical environmental variable data corresponding to the historical column state data includes: taking the pile foundation column in the offshore photovoltaic scenario as the first vertex and the connection relationship between the pile foundation columns as the first connection edge; determining the first edge weight of the first connection edge according to the positional relationship between the pile foundation columns, and taking the column material parameters of the pile foundation column, the dynamic data of the pile foundation load, and the historical environmental variable data corresponding to the historical column state data as the second vertex, the third vertex, and the fourth vertex; determining the second connection edge, the third connection edge, and the fourth connection edge according to the relationship between the pile foundation column and the column material parameters, the dynamic data of the pile foundation load, and the historical environmental variable data; constructing a first original feature map based on the first vertex, the second vertex, the third vertex, the fourth vertex, the first connection edge, the second connection edge, the third connection edge, the fourth connection edge, and the first edge weight.

[0011] In an exemplary embodiment of the present disclosure, the fatigue life prediction model includes a first graph convolutional layer, a second graph convolutional layer,..., an Nth graph convolutional layer, and a classification layer; wherein, inputting the real-time model state data of the three-dimensional digital twin model into the fatigue life prediction model to obtain the real-time remaining life probability distribution of the three-dimensional digital twin model includes: constructing a second original feature map according to the real-time model state data of the three-dimensional digital twin model and the column material parameters of the three-dimensional digital twin model, and updating the node features of the second original feature map based on the first graph convolutional layer to obtain a first graph convolutional processing result; updating the node features of the first graph convolutional processing result based on the second graph convolutional layer to obtain a second graph convolutional processing result, and sequentially repeating the determination process of the second graph convolutional processing result to obtain a third graph convolutional processing result,..., an Nth graph convolutional processing result; classifying the Nth graph convolutional processing result based on the classification layer to obtain the real-time remaining life probability distribution of the three-dimensional digital twin model.

[0012] According to one aspect of the present disclosure, there is provided a device for determining the fatigue loss of a pile foundation column of an offshore photovoltaic power station, including: a pile foundation load dynamic data determination module, configured to obtain historical column state data of the pile foundation column in an offshore photovoltaic power station scenario, and determine the pile foundation load dynamic data of the pile foundation column according to the historical column state data; a target remaining life determination module, configured to construct a three-dimensional digital twin model corresponding to the pile foundation column, and determine the target remaining life of the three-dimensional digital twin model under extreme service conditions based on the pile foundation load dynamic data; a fatigue life prediction model determination module, configured to train a neural network model to be trained based on the target remaining life, the column material parameters of the pile foundation column, the pile foundation load dynamic data, and the historical environmental variable data corresponding to the historical column state data to obtain a fatigue life prediction model; and a real-time fatigue loss determination module, configured to input the real-time model state data of the three-dimensional digital twin model into the fatigue life prediction model to obtain the real-time remaining life probability distribution of the three-dimensional digital twin model, and determine the real-time fatigue loss of the pile foundation column according to the real-time remaining life probability distribution.

[0013] According to one aspect of the present disclosure, there is provided a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, it implements the method for determining the fatigue loss of the pile foundation column of the offshore photovoltaic power station described in any one of the above.

[0014] According to one aspect of the present disclosure, there is provided an electronic device, including: a processor; and a memory, configured to store executable instructions of the processor; wherein, the processor is configured to execute the method for determining the fatigue loss of the pile foundation column of the offshore photovoltaic power station described in any one of the above by executing the executable instructions.

[0015] A method for determining the fatigue loss of the pile foundation columns of an offshore photovoltaic power station, on the one hand, obtains the historical column state data of the pile foundation columns in the offshore photovoltaic scenario, and determines the dynamic pile foundation load data of the pile foundation columns according to the historical column state data; then constructs a three-dimensional digital twin model corresponding to the pile foundation columns, and determines the target remaining life of the three-dimensional digital twin model under extreme service conditions based on the dynamic pile foundation load data; furthermore, trains a neural network model to be trained based on the target remaining life, the column material parameters of the pile foundation columns, the dynamic pile foundation load data, and the historical environmental variable data corresponding to the historical column state data to obtain a fatigue life prediction model; finally, inputs the real-time model state data of the three-dimensional digital twin model into the fatigue life prediction model to obtain the real-time remaining life probability distribution of the three-dimensional digital twin model, and determines the real-time fatigue loss of the pile foundation columns according to the real-time remaining life probability distribution, realizing the real-time analysis of the fatigue loss of the pile foundation columns and solving the problem in the prior art that the fatigue loss of the pile foundation columns cannot be analyzed in real time; on the other hand, since the real-time model state data of the three-dimensional digital twin model can be input into the fatigue life prediction model to obtain the real-time remaining life probability distribution of the three-dimensional digital twin model, the accuracy of the obtained real-time remaining life probability distribution is improved; on the further hand, since the fatigue loss of the pile foundation columns can be analyzed in real time, the dangerous columns can be repaired in time based on the analysis results, thereby avoiding safety problems caused by the damage of the pile foundation columns.

[0016] It should be understood that the above general description and the following detailed description are only exemplary and explanatory, and cannot limit the present disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The accompanying drawings herein are incorporated into the specification and form a part of the specification, showing embodiments consistent with the present disclosure, and together with the specification are used to explain the principles of the present disclosure. Obviously, the drawings in the following description are only some embodiments of the present disclosure, and those of ordinary skill in the art can obtain other drawings without creative efforts based on these drawings.

[0018] Figure 1 Schematically shows a flowchart of a method for determining the fatigue loss of the pile foundation columns of an offshore photovoltaic power station according to an exemplary embodiment of the present disclosure.

[0019] Figure 2 Schematically shows a structural example diagram of a fatigue life prediction model obtained according to an exemplary embodiment of the present disclosure.

[0020] Figure 3 Schematically shows a structural example diagram of a content generation large model used to generate a three-dimensional digital twin model according to an exemplary embodiment of the present disclosure.

[0021] Figure 4 Schematic diagram showing an example structure of a mixture-of-experts model layer in a content generation large model according to an exemplary embodiment of the present disclosure.

[0022] Figure 5 Schematic diagram showing an example scenario of a first original feature map obtained according to an exemplary embodiment of the present disclosure.

[0023] Figure 6 Schematic diagram showing an example scenario of a prediction process of a fatigue life prediction result according to an exemplary embodiment of the present disclosure.

[0024] Figure 7 Schematic diagram showing an example structure of a device for determining fatigue loss of a pile foundation column of an offshore photovoltaic according to an exemplary embodiment of the present disclosure.

[0025] Figure 8 Schematic diagram showing an electronic device for implementing a method for determining fatigue loss of a pile foundation column of an offshore photovoltaic according to an exemplary embodiment of the present disclosure. Detailed implementation manners

[0026] Now, example embodiments will be described more fully with reference to the accompanying drawings. However, the example embodiments can be implemented in various forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided so that this disclosure will be more thorough and complete, and will fully convey the concept of the example embodiments to those skilled in the art. The described features, structures, or characteristics can be combined in any suitable manner in one or more embodiments. In the following description, numerous specific details are provided to give a thorough understanding of the embodiments of the present disclosure. However, those skilled in the art will realize that the technical solutions of the present disclosure can be practiced without one or more of the specific details, or other methods, components, devices, steps, etc. can be adopted. In other cases, well-known technical solutions are not shown or described in detail to avoid obscuring various aspects of the present disclosure.

[0027] In addition, the accompanying drawings are only schematic illustrations of the present disclosure and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and thus their repeated description will be omitted. Some of the block diagrams shown in the drawings are functional entities and do not necessarily correspond to physically or logically independent entities. These functional entities can be implemented in software form, or in one or more hardware modules or integrated circuits, or in different networks and / or processor devices and / or microcontroller devices.

[0028] In this exemplary embodiment, a method for determining the fatigue loss of the pile foundation columns of an offshore photovoltaic system is first provided. This method can run on terminal devices, servers, server clusters, cloud servers, etc. Of course, those skilled in the art can also run the method of the present disclosure on other platforms according to requirements, and no special limitation is made in this exemplary embodiment. Specifically, referring to Figure 1 As shown, the method for determining the fatigue loss of the pile foundation columns of the offshore photovoltaic system may include the following steps: Step S110. Obtain the historical column state data of the pile foundation columns in the offshore photovoltaic scenario, and determine the pile foundation load dynamic data of the pile foundation columns according to the historical column state data; Step S120. Construct a three-dimensional digital twin model corresponding to the pile foundation columns, and determine the target remaining life of the three-dimensional digital twin model under extreme service conditions based on the pile foundation load dynamic data; Step S130. Based on the target remaining life, the column material parameters of the pile foundation columns, the pile foundation load dynamic data, and the historical environmental variable data corresponding to the historical column state data, train the neural network model to be trained to obtain a fatigue life prediction model; Step S140. Input the real-time model state data of the three-dimensional digital twin model into the fatigue life prediction model to obtain the real-time remaining life probability distribution of the three-dimensional digital twin model, and determine the real-time fatigue loss of the pile foundation columns according to the real-time remaining life probability distribution.

[0029] In the method for determining the fatigue loss of the pile foundation columns of the offshore photovoltaic described above, on the one hand, by obtaining the historical column state data of the pile foundation columns in the offshore photovoltaic scenario, and determining the dynamic pile foundation load data of the pile foundation columns according to the historical column state data; then constructing a three-dimensional digital twin model corresponding to the pile foundation columns, and determining the target remaining life of the three-dimensional digital twin model under extreme service conditions based on the dynamic pile foundation load data; furthermore, based on the target remaining life, the column material parameters of the pile foundation columns, the dynamic pile foundation load data, and the historical environmental variable data corresponding to the historical column state data, training the neural network model to be trained to obtain a fatigue life prediction model; finally, inputting the real-time model state data of the three-dimensional digital twin model into the fatigue life prediction model to obtain the real-time remaining life probability distribution of the three-dimensional digital twin model, and determining the real-time fatigue loss of the pile foundation columns according to the real-time remaining life probability distribution, realizing the real-time analysis of the fatigue loss of the pile foundation columns, and solving the problem that the fatigue loss of the pile foundation columns cannot be analyzed in real time in the prior art; on the other hand, since the real-time model state data of the three-dimensional digital twin model can be input into the fatigue life prediction model to obtain the real-time remaining life probability distribution of the three-dimensional digital twin model, the accuracy of the obtained real-time remaining life probability distribution is improved; on the other hand, since the fatigue loss of the pile foundation columns can be analyzed in real time, the dangerous columns can be repaired in time based on the analysis results, thereby avoiding safety problems caused by the damage of the pile foundation columns.

[0030] Hereinafter, the method for determining the fatigue loss of the pile foundation columns of the offshore photovoltaic described in the exemplary embodiments of the present disclosure will be further explained and described with reference to the accompanying drawings.

[0031] First, the application scenario of the exemplary embodiments of the present disclosure will be explained and described. Specifically, the method for determining the fatigue loss of the pile foundation columns of the offshore photovoltaic described in the exemplary embodiments of the present disclosure can be used to analyze the real-time fatigue loss of the pile foundation columns in the offshore photovoltaic scenario, and when it is determined that the real-time fatigue loss value of any pile foundation column reaches the critical threshold, corresponding measures (such as replacement or repair, etc.) are taken in time, thereby avoiding safety problems and economic losses caused by the damage of the pile foundation columns.

[0032] Secondly, the neural network model to be trained involved in the exemplary embodiments of the present disclosure will be explained and described. Specifically, as shown in Figure 2 The fatigue life prediction model may include a first input layer 201, a plurality of graph convolutional layers (such as a first graph convolutional layer, a second graph convolutional layer,..., an Nth graph convolutional layer) 202, a classification layer 203, and a first output layer 204; among them, the functions of each model layer in the fatigue life prediction process will be described in detail later, and no further elaboration will be made here.

[0033] Hereinafter, Figure 1 the method for determining the fatigue loss of the pile foundation columns of the offshore photovoltaic will shown in

[0034] In step S110, historical column state data of the pile foundation columns in the offshore photovoltaic scenario is obtained, and the dynamic pile foundation load data of the pile foundation columns is determined according to the historical column state data.

[0035] Specifically, the historical column state data of the pile foundation columns recorded here may include, but is not limited to, historical wave buoy data, historical wind speed and direction data, and historical pile foundation strain data. In the actual application process, it can be selected according to actual needs, and this example does not make special restrictions on this; moreover, in the actual offshore photovoltaic scenario, each pile foundation column will be equipped with a corresponding buoy, anemometer, and displacement sensor. Then, based on the equipped buoy, anemometer, and displacement sensor, historical wave buoy data, historical wind speed and direction data, and historical pile foundation strain data at different times are collected; if it is necessary to obtain the historical column state data of the pile foundation columns in the offshore photovoltaic scenario, the buoy, anemometer, and displacement sensor corresponding to the pile foundation columns can be determined according to the mapping relationship between the pile foundation columns and the buoy, anemometer, and displacement sensor, and the historical wave buoy data monitored based on the buoy, the historical wind speed and direction data monitored based on the anemometer, and the historical pile foundation strain data monitored based on the displacement sensor are obtained, and the above-mentioned historical column state data can be obtained.

[0036] Secondly, the dynamic pile foundation load data of the pile foundation columns is determined according to the historical column state data; specifically, it can be achieved in the following way: the historical ocean energy spectrum data of the pile foundation columns is determined according to the historical wave buoy data, and the historical three-dimensional turbulent wind field data of the pile foundation columns is determined according to the historical wind speed and direction data; the historical fluid-structure interaction data of the pile foundation columns is determined according to the historical pile foundation strain data, and the dynamic pile foundation load data of the pile foundation columns is constructed according to the historical ocean energy spectrum data, historical three-dimensional turbulent wind field data, and historical fluid-structure interaction data.

[0037] In an exemplary embodiment, the historical ocean energy spectrum data of the pile foundation columns is determined according to the historical wave buoy data, and it can be achieved in the following way: First, the calculation formula of the wave spectrum is determined; specifically, the calculation formula of the wave spectrum recorded here can be shown as the following formula (1): ; Formula (1) Wherein, is the spectrum function, is the actual undulation frequency of the wave (the specific value can be determined by historical wave buoy data), is the height of the wave (the specific value can be determined by historical wave buoy data), is the reference undulation frequency of the wave, 、 and are spectral coefficients; further, the specific value of ; formula (2) where, when is the case, ; when is the case, .

[0038] Meanwhile, the specific value of ; formula (3) that is, it is necessary to ensure that and can satisfy formula (3). Further, the value of

[0039] can be between 1 and 10, and the standard value is 3.3. and the spectral peak average period ; meanwhile, the up-crossing average period and the spectral peak average period satisfy the following relationship: ; formula (4) After obtaining the relationship between the up-crossing average period and the spectral peak average period and the up-crossing average period and the spectral peak average period , the average period can be obtained; where, the specific value of the average period is: .

[0040] Finally, based on the up-crossing average period , the spectral peak average period , the average period , and the height of the wave, the historical ocean energy spectrum data can be obtained.

[0041] In an exemplary embodiment, the historical three-dimensional turbulent wind field data of the pile foundation column can be determined based on the historical wind speed and direction data by the following method: First, a theoretical power density spectrum formula for the longitudinal wind speed of the pile foundation column model is constructed. The power density spectrum formula can be shown as the following formula (5): ; Formula (5) Wherein, is the wind blowing frequency (the specific value can be determined by the historical wind speed and direction data), is the power density, is the standard deviation of the actual wind speed (the specific value can be determined by the historical wind speed and direction data), is the turbulence scale parameter, which can be determined according to the height of the pile foundation column above the sea level; is the wind speed at the pile foundation column (the specific value can be determined by the historical wind speed and direction data). Based on the obtained power density, wind blowing frequency, standard deviation of the actual wind speed, turbulence scale parameter, and the wind speed at the pile foundation column, the historical three-dimensional turbulent wind field data can be obtained.

[0042] In an exemplary embodiment, the historical fluid-structure interaction data of the pile foundation column can be determined based on the historical pile foundation strain data by the following method: First, a solid field corresponding to the pile foundation column and a fluid field corresponding to the seawater are constructed. Second, the pressure of the flow field is obtained by solving in the fluid field according to the historical pile foundation strain data. Then, the deformation of the pile foundation column is obtained by solving the solid field in the structural module (steady state or transient state) according to the historical pile foundation strain data and the solid field. Further, the historical fluid-structure interaction data can be obtained based on the obtained pressure of the flow field and the deformation of the pile foundation column.

[0043] In step S120, a three-dimensional digital twin model corresponding to the pile foundation column is constructed, and the target remaining life of the three-dimensional digital twin model under extreme service conditions is determined based on the dynamic pile foundation load data.

[0044] In this exemplary embodiment, first, a three-dimensional digital twin model corresponding to the pile foundation column is constructed; specifically, it can be achieved in the following manner: obtaining the original geometric model of the pile foundation column in the offshore photovoltaic scenario, and configuring the first component performance parameters for the original geometric model; wherein, the first component performance parameters include at least one of the first elastic modulus, the first Poisson's ratio, the first tensile strength, the first compressive strength, and the first horizontal bearing capacity of the first component material used to prepare the pile foundation column; applying the first simulated service load under the simulated service condition to the original geometric model; wherein, the simulated service condition is obtained by simulating the actual service condition of the pile foundation column, and the first simulated service load includes at least one of the first weather scenario load, the first pressure load, and the first support force load borne by the pile foundation column during service; generating a three-dimensional digital twin model corresponding to the pile foundation column according to the original geometric model, the first component performance parameters, and the first simulated service load. It should be supplemented here that in generating the three-dimensional digital twin model corresponding to the pile foundation column, it can be achieved by generating a large model based on the corresponding content, or by using three-dimensional simulation software (such as ANSYS Workbench), and this example does not make special restrictions on this.

[0045] In one exemplary embodiment, if the three-dimensional digital twin model is determined by generating a large model through content, it can be achieved in the following manner: First, referring to Figure 3 As shown, the content generation large model recorded here may include a second input layer 301, an embedding mapping layer 302, an encoding layer 303, a mixture of experts model layer 304, and a second output layer 305; further, when generating the three-dimensional digital twin model, the original geometric model, the first component performance parameters, and the first simulated service load can be input into the content generation large model, and then the three-dimensional digital twin model can be obtained.

[0046] In an exemplary embodiment, the original geometric model, the first component performance parameters, and the first simulated service load are input into the content generation large model to obtain a three-dimensional digital twin model, which can be achieved in the following manner: generating the first basic information to be predicted according to the original geometric model, the first component performance parameters, and the first simulated service load, and generating the first context information to be predicted according to the preset first model prompt parameters; performing an embedding mapping process on the first basic information to be predicted based on the embedding mapping layer to obtain the first model feature, and performing an embedding mapping process on the first context information to be predicted based on the embedding mapping layer to obtain the first context flag sequence; performing an encoding process on the first model feature and the first context flag sequence based on the encoding layer to obtain the first context overall representation, and performing model generation on the first context flag sequence and the first context overall representation based on the mixture of experts model layer to obtain the three-dimensional digital twin model. Among them, the preset first model prompt parameters recorded here can be, for example: Your task is xxx, and you need to generate a xxx according to the input data; the generated model needs to be displayed in the form of xxx; the generated model can include x,... In the actual application process, the corresponding first model prompt parameters can be set according to actual needs, and this example does not make special restrictions on this. Further, the embedding mapping layer recorded here can include an Embedding embedding mapping layer and a Bert embedding mapping layer. In the actual application process, the Embedding embedding mapping layer can be used to perform an embedding mapping process on the first basic information to be predicted to obtain the first model feature, and the Bert embedding mapping layer can be used to perform an embedding mapping process on the first context information to be predicted to obtain the first context flag sequence; the encoding layer recorded here can be a bidirectional multi-layer Transformer; the mixture of experts model layer recorded here can include a first gating network model and multiple first expert neural network models. For a specific example diagram, reference can be made to Figure 4 as shown.

[0047] In an exemplary embodiment, a three-dimensional digital twin model can be generated based on a first context flag sequence and a first overall context representation through a hybrid expert model layer in the following manner: Based on a first gating network model, determine a first model weight for the first expert neural network model to perform the model generation task in the model fitting degree dimension and a second model weight for the model generation task in the model adaptation degree dimension according to the first context flag sequence; determine a first target neural network model required for the model generation task in the model fitting degree dimension from multiple first expert neural network models according to the first model weight, and determine a second target neural network model required for the model generation task in the model adaptation degree dimension from multiple first expert neural network models according to the second model weight; input the first context flag sequence and the first overall context representation into the first target neural network model and the second target neural network model respectively to obtain a first model prediction result in the model fitting degree dimension and a second model prediction result in the model adaptation degree dimension, and determine the three-dimensional digital twin model according to the first model prediction result and the second model prediction result. Among them, the model fitting degree dimension described here refers to the degree of fit between the generated three-dimensional digital twin model and the pile foundation column; the model adaptation dimension described here refers to the degree of adaptation between the generated three-dimensional digital twin model and the first component performance parameters and the first simulated service load; further, after obtaining the first model prediction result and the second model prediction result, the three-dimensional digital twin model can be obtained by performing weighted summation on the first model prediction result and the second model prediction result; the weight value used in the weighted summation process can be determined according to actual needs, and this example does not make special restrictions on this.

[0048] Secondly, determine the target remaining life of the 3D digital twin model under extreme service conditions based on the dynamic data of pile foundation loads; specifically, it can be achieved through the following methods: First, determine the fatigue damage coefficient of the pile foundation columns under the combined action of waves - tides - sea winds according to the dynamic data of pile foundation loads and the historical environmental variable data corresponding to the historical column state data; Secondly, determine the dangerous stress area of the 3D digital twin model, and determine the original remaining life of the dangerous stress area under extreme service conditions according to the fatigue damage coefficient; Finally, determine the cumulative damage of the dangerous stress area and the crack propagation path caused by the cumulative damage, and correct the original remaining life based on the crack propagation path to obtain the target remaining life of the 3D digital twin model under extreme service conditions. That is, in the actual application process, first, the spatio - temporal correlation between the dynamic data of pile foundation loads and the historical environmental variable data needs to be established; then, based on the spatio - temporal correlation, determine the fatigue damage coefficient of the pile foundation columns under the combined action of waves - tides - sea winds (i.e., the amplification effect of the combined action of waves - tides - sea winds on the pile foundation columns); Further, calculate the stress distribution of the 3D digital twin model, and determine the dangerous stress area according to the stress distribution; among them, the specific calculation process of the stress distribution can be realized based on 3D simulation software (such as ANSYS Workbench), and this example does not make special restrictions on this; at the same time, after obtaining the dangerous stress area, the original remaining life of the dangerous stress area under extreme service conditions can be determined based on the 3D simulation software according to the fatigue damage coefficient; Furthermore, in order to further improve the accuracy of the obtained life, the cumulative damage of the dangerous stress area can also be determined based on the improved Miner criterion, combined with the damage memory effect (such as considering the load sequence and overload strengthening / weakening), then introduce the phase - field model of material micro - crack propagation, realize the macro - micro damage coupling through finite - element discretization to determine the crack propagation path caused by the cumulative damage, and finally correct the original remaining life based on the crack propagation path to obtain the target remaining life.

[0049] In step S130, based on the target remaining life, the column material parameters of the pile foundation column, the dynamic data of pile foundation loads, and the historical environmental variable data corresponding to the historical column state data, train the neural network model to be trained to obtain a fatigue life prediction model.

[0050] Specifically, the specific determination process of the fatigue life prediction model can be achieved through the following steps: constructing a first original feature map based on the column material parameters of the pile foundation column, the dynamic data of the pile foundation load, and the historical environmental variable data corresponding to the historical column state data; inputting the first original feature into a neural network model to be trained to obtain the fatigue life prediction result of the pile foundation column; constructing a loss function based on the fatigue life prediction result and the target remaining life, and training the neural network model to be trained based on the loss function to obtain the fatigue life prediction model.

[0051] In an exemplary embodiment, constructing a first original feature map based on the column material parameters of the pile foundation column, the dynamic data of the pile foundation load, and the historical environmental variable data corresponding to the historical column state data includes: using the pile foundation column in the offshore photovoltaic scenario as the first vertex, and using the connection relationship between the pile foundation columns as the first connection edge; determining the first edge weight of the first connection edge according to the positional relationship between the pile foundation columns, and using the column material parameters of the pile foundation column, the dynamic data of the pile foundation load, and the historical environmental variable data corresponding to the historical column state data as the second vertex, the third vertex, and the fourth vertex; determining the second connection edge, the third connection edge, and the fourth connection edge according to the relationship between the pile foundation column and the column material parameters, the dynamic data of the pile foundation load, and the historical environmental variable data; constructing the first original feature map based on the first vertex, the second vertex, the third vertex, the fourth vertex, and the first connection edge, the second connection edge, the third connection edge, the fourth connection edge, and the first edge weight. Among them, the positional relationship described here refers to the distance between the positions of two pile foundation columns, which can be determined according to the position coordinates of each pile foundation column; if the distance exceeds a certain distance, it is determined that there is no connection relationship; otherwise, it is determined that there is a connection relationship between the two pile foundation columns; specifically, the first edge weight is inversely proportional to the distance; that is, the closer the distance, the greater the first edge weight; otherwise, the first edge weight is smaller; the second edge weight, the third edge weight, and the fourth edge weight of the second connection edge, the third connection edge, and the fourth connection edge can be set to 1, or other values, and this example does not make special restrictions on this; at the same time, the obtained first original feature map can be referred to Figure 5 as shown.

[0052] In an example embodiment, inputting the first original feature into a neural network model to be trained to obtain the fatigue life prediction result of the pile foundation column can be achieved through the following steps: assuming that there are N1 nodes in the first original feature map , M1 edges ; at the same time, represents the node in the Nth step vector (vector dimension is 128); on this premise, in the graph convolutional neural network, the first connection edge in the first original feature map can be updated through the following formulas (1) and (2): ; Formula (1) ; Formula (2) Among them, is the Leaky ReLU activation function; represents the node 's set of neighbor nodes, represents and 's inner product; is the parameter of the graph neural network; at the same time, the attention weight represents the strength of the connection between nodes i and k; in this exemplary embodiment, first, the parameters of the graph neural network and the initial vectors of each node are randomly initialized; second, the graph convolutional neural network is iterated through the first original feature map. Assuming the maximum value of N is 5 (that is, including 5 graph convolutional neural networks), that is, the graph neural network obtains the final vector representation of each node (that is, the pile foundation column) after 5 iterations (that is, the result of the fifth graph convolution processing); finally, after obtaining the result of the fifth graph convolution processing, the result of the fifth graph convolution processing can be input into the classification layer to obtain the fatigue life prediction result; among them, for the scenario graph of the specific prediction process of the fatigue life prediction result, reference can be made to Figure 6 as shown.

[0053] Furthermore, after obtaining the fatigue life prediction result, a loss function can be constructed according to the fatigue life prediction result and the target remaining life, and the neural network model to be trained can be trained based on the loss function to obtain a fatigue life prediction model; among them, the loss function recorded here can be a mean square error loss function or a cross-entropy loss function, and this example does not make special restrictions on this. Further, after obtaining the fatigue life prediction model, the model parameters of the fatigue life prediction model can also be dynamically updated in combination with the Bayesian optimization method to make the fatigue life prediction model better adapt to environmental changes (such as seasonal wave characteristics), so as to achieve the purpose of improving the accuracy of the obtained life prediction result.

[0054] In step S140, the real-time model state data of the three-dimensional digital twin model is input into the fatigue life prediction model to obtain the real-time remaining life probability distribution of the three-dimensional digital twin model, and the real-time fatigue loss of the pile foundation column is determined according to the real-time remaining life probability distribution.

[0055] In this exemplary embodiment, first, determine the real-time remaining life probability distribution of the 3D digital twin model; specifically, it can be achieved through the following method: construct a second original feature map based on the real-time model state data of the 3D digital twin model and the column material parameters of the 3D digital twin model, and update the node features of the second original feature map based on the first graph convolutional layer to obtain a first graph convolutional processing result; update the node features of the first graph convolutional processing result based on the second graph convolutional layer to obtain a second graph convolutional processing result, and sequentially repeat the determination process of the second graph convolutional processing result to obtain a third graph convolutional processing result, …, an Nth graph convolutional processing result; perform classification processing on the Nth graph convolutional processing result based on the classification layer to obtain the real-time remaining life probability distribution of the 3D digital twin model. Among them, the specific construction process of the second original feature map is similar to the specific construction process of the first original feature map, and no further elaboration will be made here; at the same time, the real-time model state data recorded here can be determined according to the real-time wave buoy data, real-time wind speed and direction data, and real-time pile foundation strain data of the pile foundation column. The column material parameters recorded here are the relevant parameters of the materials used for the pile foundation column (such as elastic modulus, Poisson's ratio, tensile strength, compressive strength, and horizontal bearing capacity), etc. Of course, other parameters can also be included, and this example does not make special restrictions on this.

[0056] Further, after obtaining the real-time remaining life probability distribution, the real-time fatigue loss of the pile foundation column can be determined according to the real-time remaining life probability distribution; that is, the real-time damage value can be determined according to the real-time remaining life probability distribution. If the real-time damage value is greater than the critical threshold (such as 0.8), an alarm message can be sent so that the operation and maintenance personnel can perform maintenance or use other methods to maintain the pile foundation column according to this alarm message. This example does not make special restrictions on this.

[0057] So far, the method for determining the fatigue loss of the pile foundation column of the offshore photovoltaic in the disclosed exemplary embodiment has been fully implemented. Based on the foregoing content, it can be known that the method for determining the fatigue loss of the pile foundation column of the offshore photovoltaic in the disclosed exemplary embodiment can achieve preventive maintenance and reduce the cost of unplanned downtime; at the same time, it can also predict the damage evolution under extreme loads such as typhoons and storm surges and take reinforcement measures in advance; further, the method for determining the fatigue loss of the pile foundation column of the offshore photovoltaic in the disclosed exemplary embodiment breaks through the linear assumption and single physical field limitation of traditional fatigue analysis, and realizes the accurate prediction of the fatigue damage of the pile foundation column in the offshore photovoltaic scenario through interdisciplinary integration, providing an innovative method for the reliability guarantee of new marine energy infrastructure.

[0058] The following are embodiments of the disclosed device, which can be used to implement the method embodiments of the present disclosure. For details not disclosed in the device embodiments of the present disclosure, please refer to the method embodiments of the present disclosure.

[0059] The exemplary embodiments of the present disclosure also provide a device for determining the fatigue loss of a pile foundation column of an offshore photovoltaic. Specifically, as shown in Figure 7 the device for determining the fatigue loss of a pile foundation column of an offshore photovoltaic may include a pile foundation load dynamic data determination module 710, a target remaining life determination module 720, a fatigue life prediction model determination module 730, and a real-time fatigue loss determination module 740.

[0060] The pile foundation load dynamic data determination module 710 can be used to obtain the historical column state data of the pile foundation column in the offshore photovoltaic scenario, and determine the pile foundation load dynamic data of the pile foundation column according to the historical column state data; wherein, the pile foundation load dynamic data is obtained in the following manner: obtaining a buoy, an anemometer and a displacement sensor corresponding to the pile foundation column in the offshore photovoltaic scenario, and obtaining the historical wave buoy data monitored based on the buoy, the historical wind speed and direction data monitored based on the anemometer, and the historical pile foundation strain data monitored based on the displacement sensor; determining the historical ocean energy spectrum data of the pile foundation column according to the historical wave buoy data, and determining the historical three-dimensional turbulent wind field data of the pile foundation column according to the historical wind speed and direction data; determining the historical fluid-structure interaction data of the pile foundation column according to the historical pile foundation strain data, and constructing the pile foundation load dynamic data of the pile foundation column according to the historical ocean energy spectrum data, historical three-dimensional turbulent wind field data and historical fluid-structure interaction data; the target remaining life determination module 720 can be used to construct a three-dimensional digital twin model corresponding to the pile foundation column, and determine the target remaining life of the three-dimensional digital twin model under extreme service conditions based on the pile foundation load dynamic data; the fatigue life prediction model determination module 730 can be used to train a neural network model to be trained based on the target remaining life, the column material parameters of the pile foundation column, the pile foundation load dynamic data, and the historical environmental variable data corresponding to the historical column state data to obtain a fatigue life prediction model; the real-time fatigue loss determination module 740 can be used to input the real-time model state data of the three-dimensional digital twin model into the fatigue life prediction model to obtain the real-time remaining life probability distribution of the three-dimensional digital twin model, and determine the real-time fatigue loss of the pile foundation column according to the real-time remaining life probability distribution.

[0061] In an exemplary embodiment of the present disclosure, a wave spectrum function is determined to determine a spectrogram based on the wave spectrum function and historical wave buoy data, and determine the up-crossing average period of the wave based on the spectrogram and the spectral peak average period ; wherein, the upper-crossing average period and the spectral peak average period satisfy the following relationship: ; wherein, is the spectral coefficient; according to the upper-crossing average period and the spectral peak average period and the relationship between the upper-crossing average period and the spectral peak average period , determine the average period ; wherein, the specific value of the average period is: ; based on the upper-crossing average period , the spectral peak average period , the average period , and the height of the wave, determine the historical ocean energy spectrum data of the pile foundation column.

[0062] In an exemplary embodiment of the present disclosure, constructing a three-dimensional digital twin model corresponding to the pile foundation column includes: obtaining the original geometric model of the pile foundation column in the offshore photovoltaic scenario, and configuring first component performance parameters for the original geometric model; wherein, the first component performance parameters include at least one of the first elastic modulus, the first Poisson's ratio, the first tensile strength, the first compressive strength, and the first horizontal bearing capacity of the first component material used to prepare the pile foundation column; applying a first simulated service load under the simulated service condition to the original geometric model; wherein, the simulated service condition is obtained by simulating the actual service condition of the pile foundation column, and the first simulated service load includes at least one of the first weather scenario load, the first pressure load, and the first support force load borne by the pile foundation column during service; generating a three-dimensional digital twin model corresponding to the pile foundation column according to the original geometric model, the first component performance parameters, and the first simulated service load.

[0063] In an exemplary embodiment of the present disclosure, determining the target remaining life of the three-dimensional digital twin model under extreme service conditions based on the pile foundation load dynamic data includes: determining the fatigue damage coefficient of the pile foundation column under the combined action of waves - tides - sea winds according to the pile foundation load dynamic data and the historical environmental variable data corresponding to the historical column state data; determining the dangerous stress area of the three-dimensional digital twin model, and determining the original remaining life of the dangerous stress area under extreme service conditions according to the fatigue damage coefficient; determining the cumulative damage of the dangerous stress area and the crack propagation path caused by the cumulative damage, and correcting the original remaining life based on the crack propagation path to obtain the target remaining life of the three-dimensional digital twin model under extreme service conditions.

[0064] In an exemplary embodiment of the present disclosure, based on the target remaining life, the column material parameters of the pile foundation column, the dynamic data of the pile foundation load, and the historical environmental variable data corresponding to the historical column state data, a neural network model to be trained is trained to obtain a fatigue life prediction model, including: constructing a first original feature map according to the column material parameters of the pile foundation column, the dynamic data of the pile foundation load, and the historical environmental variable data corresponding to the historical column state data; inputting the first original feature into the neural network model to be trained to obtain the fatigue life prediction result of the pile foundation column; constructing a loss function according to the fatigue life prediction result and the target remaining life, and training the neural network model to be trained based on the loss function to obtain the fatigue life prediction model.

[0065] In an exemplary embodiment of the present disclosure, constructing a first original feature map according to the column material parameters of the pile foundation column, the dynamic data of the pile foundation load, and the historical environmental variable data corresponding to the historical column state data includes: using the pile foundation column in the offshore photovoltaic scenario as the first vertex, and using the connection relationship between the pile foundation columns as the first connection edge; determining the first edge weight of the first connection edge according to the positional relationship between the pile foundation columns, and using the column material parameters of the pile foundation column, the dynamic data of the pile foundation load, and the historical environmental variable data corresponding to the historical column state data as the second vertex, the third vertex, and the fourth vertex; determining the second connection edge, the third connection edge, and the fourth connection edge according to the relationship between the pile foundation column and the column material parameters, the dynamic data of the pile foundation load, and the historical environmental variable data; constructing a first original feature map based on the first vertex, the second vertex, the third vertex, the fourth vertex, the first connection edge, the second connection edge, the third connection edge, the fourth connection edge, and the first edge weight.

[0066] In an exemplary embodiment of the present disclosure, the fatigue life prediction model includes a first graph convolutional layer, a second graph convolutional layer,..., an Nth graph convolutional layer, and a classification layer; wherein, inputting the real-time model state data of the three-dimensional digital twin model into the fatigue life prediction model to obtain the real-time remaining life probability distribution of the three-dimensional digital twin model includes: constructing a second original feature map according to the real-time model state data of the three-dimensional digital twin model and the column material parameters of the three-dimensional digital twin model, and updating the node features of the second original feature map based on the first graph convolutional layer to obtain the first graph convolutional processing result; updating the node features of the first graph convolutional processing result based on the second graph convolutional layer to obtain the second graph convolutional processing result, and sequentially repeating the determination process of the second graph convolutional processing result to obtain the third graph convolutional processing result,..., the Nth graph convolutional processing result; classifying the Nth graph convolutional processing result based on the classification layer to obtain the real-time remaining life probability distribution of the three-dimensional digital twin model.

[0067] The specific details of each module in the above device for determining the fatigue loss of the pile foundation columns of the offshore photovoltaic power generation have been described in detail in the corresponding method for determining the fatigue loss of the pile foundation columns of the offshore photovoltaic power generation, and thus will not be elaborated herein.

[0068] It should be noted that although several modules or units of the device for action execution are mentioned in the above detailed description, such a division is not mandatory. In fact, according to the embodiments of the present disclosure, the features and functions of the two or more modules or units described above can be embodied in one module or unit. Conversely, the features and functions of one module or unit described above can be further divided and embodied by multiple modules or units. In addition, although the steps of the methods in the present disclosure are described in a specific order in the drawings, this does not require or imply that the steps must be executed in that specific order, or that all the steps shown must be executed to achieve the desired result. Additionally or alternatively, some steps may be omitted, multiple steps may be combined into one step for execution, and / or one step may be decomposed into multiple steps for execution, etc.

[0069] In an exemplary embodiment of the present disclosure, an electronic device capable of implementing the above method is also provided. Those skilled in the art can understand that various aspects of the present disclosure can be implemented as a system, method, or program product. Therefore, various aspects of the present disclosure can be specifically implemented in the following forms, namely: a complete hardware implementation, a complete software implementation (including firmware, microcode, etc.), or an implementation combining hardware and software aspects, which can be collectively referred to as a circuit, module, or system here.

[0070] Reference is made below to Figure 8 describe the electronic device 800 according to this embodiment of the present disclosure. Figure 8 The electronic device 800 shown is merely an example and should not impose any limitation on the functions and usage scope of the embodiments of the present disclosure.

[0071] As Figure 8 shown, the electronic device 800 is presented in the form of a general-purpose computing device. The components of the electronic device 800 may include, but are not limited to: the above at least one processing unit 810, the above at least one storage unit 820, a bus 830 connecting different system components (including the storage unit 820 and the processing unit 810), and a display unit 840.

[0072] Among them, the storage unit stores program codes, which can be executed by the processing unit 810, so that the processing unit 810 executes the steps according to various exemplary embodiments of the present disclosure described in the above "Exemplary Method" section of this specification. For example, the processing unit 810 can execute steps such as Figure 1 the step S110 shown in: obtaining historical column state data of a pile foundation column in an offshore photovoltaic scenario, and determining the pile foundation load dynamic data of the pile foundation column according to the historical column state data; step S120: constructing a three-dimensional digital twin model corresponding to the pile foundation column, and determining the target remaining life of the three-dimensional digital twin model under extreme service conditions based on the pile foundation load dynamic data; step S130: training a neural network model to be trained based on the target remaining life, the column material parameters of the pile foundation column, the pile foundation load dynamic data, and the historical environmental variable data corresponding to the historical column state data to obtain a fatigue life prediction model; step S140: inputting the real-time model state data of the three-dimensional digital twin model into the fatigue life prediction model to obtain the real-time remaining life probability distribution of the three-dimensional digital twin model, and determining the real-time fatigue loss of the pile foundation column according to the real-time remaining life probability distribution. The storage unit 820 may include a readable medium in the form of a volatile storage unit, such as a random access storage unit (RAM) 8201 and / or a cache storage unit 8202, and may further include a read-only storage unit (ROM) 8203.

[0073] The storage unit 820 may further include a program / utilities 8204 having a set (at least one) of program modules 8205. Such program modules 8205 include, but are not limited to: an operating system, one or more application programs, other program modules, and program data. Each or some combination of these examples may include the implementation of a network environment.

[0074] The bus 830 may represent one or more of several types of bus structures, including a memory bus or memory controller, a peripheral bus, a graphics acceleration port, a processing unit, or a local bus using any of the various bus structures.

[0075] The electronic device 800 can also communicate with one or more external devices 900 (such as a keyboard, a pointing device, a Bluetooth device, etc.), and can also communicate with one or more devices that enable a user to interact with the electronic device 800, and / or communicate with any device that enables the electronic device 800 to communicate with one or more other computing devices (such as a router, a modem, etc.). Such communication can be carried out through the input / output (I / O) interface 850. Moreover, the electronic device 800 can also communicate with one or more networks (such as a local area network (LAN), a wide area network (WAN), and / or a public network, such as the Internet) through the network adapter 860. As shown in the figure, the network adapter 860 communicates with other modules of the electronic device 800 through the bus 830. It should be understood that, although not shown in the figure, other hardware and / or software modules can be used in combination with the electronic device 800, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems, etc.

[0076] Through the description of the above embodiments, those skilled in the art can easily understand that the exemplary embodiments described herein can be implemented by software, or can be implemented by the way of software combined with necessary hardware. Therefore, the technical solutions according to the embodiments of the present disclosure can be embodied in the form of a software product, and the software product can be stored in a non-volatile storage medium (which can be a CD-ROM, a USB flash drive, a mobile hard disk, etc.) or on a network, including several instructions to enable a computing device (which can be a personal computer, a server, a terminal device, or a network device, etc.) to execute the method according to the embodiments of the present disclosure.

[0077] In an exemplary embodiment of the present disclosure, there is also provided a computer-readable storage medium, on which a program product capable of implementing the above method of the present specification is stored. In some possible implementation manners, various aspects of the present disclosure can also be implemented in the form of a program product, which includes program code. When the program product runs on a terminal device, the program code is used to enable the terminal device to execute the steps according to various exemplary embodiments of the present disclosure described in the above "Exemplary Method" section of the present specification. The program product for implementing the above method according to the embodiments of the present disclosure can adopt a portable compact disc read-only memory (CD-ROM) and include program code, and can run on a terminal device, such as a personal computer. However, the program product of the present disclosure is not limited thereto. In this document, the readable storage medium can be any tangible medium that contains or stores a program, and the program can be used by or combined with an instruction execution system, apparatus, or device.

[0078] The program product may employ any combination of one or more readable media. The readable media may be a readable signal medium or a readable storage medium. A readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples of the readable storage medium (a non-exhaustive list) include: an electrical connection having one or more wires, a portable disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0079] A computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, in which the readable program code is carried. Such a propagated data signal may take many forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the foregoing. The readable signal medium may also be any readable medium other than a readable storage medium, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device.

[0080] The program code contained on the readable medium may be transmitted with any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.

[0081] The program code for performing the operations of the present disclosure may be written in any combination of one or more programming languages, including object-oriented programming languages such as Java, C++, etc., and also including conventional procedural programming languages such as the "C" language or similar programming languages. The program code may be executed entirely on the user's computing device, partially on the user's device, executed as a stand-alone software package, partially on the user's computing device and partially on a remote computing device, or entirely on a remote computing device or server. In the case of a remote computing device, the remote computing device may be connected to the user's computing device through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computing device (e.g., by using an Internet service provider to connect through the Internet).

[0082] In addition, the above-mentioned drawings are only schematic illustrations of the processes included in the method according to the exemplary embodiments of the present disclosure, rather than for limiting purposes. It is easy to understand that the processes shown in the above-mentioned drawings do not indicate or limit the chronological order of these processes. Additionally, it is also easy to understand that these processes may be executed, for example, synchronously or asynchronously in multiple modules.

[0083] Other embodiments of the present disclosure will be readily apparent to those skilled in the art upon consideration of the specification and practice of the invention herein. This application is intended to cover any variations, uses, or adaptations of the present disclosure that follow the general principles of the present disclosure and include known common general knowledge or conventional technical means in the technical field not invented by the present disclosure. The specification and examples are only to be considered as exemplary, and the true scope and spirit of the present disclosure are pointed out by the claims.

Claims

1. A method for determining the fatigue loss of a pile foundation column for offshore photovoltaic power generation, characterized in that, Including: Obtaining historical column state data of a pile foundation column in an offshore photovoltaic scenario, and determining dynamic pile foundation load data of the pile foundation column according to the historical column state data; wherein, the dynamic pile foundation load data is obtained through the following method: obtaining a buoy, an anemometer and a displacement sensor corresponding to the pile foundation column in the offshore photovoltaic scenario, and obtaining historical wave buoy data monitored based on the buoy, historical wind speed and direction data monitored based on the anemometer, and historical pile foundation strain data monitored based on the displacement sensor; determining historical ocean energy spectrum data of the pile foundation column according to the historical wave buoy data, and determining historical three-dimensional turbulent wind field data of the pile foundation column according to the historical wind speed and direction data; determining historical fluid-structure coupling data of the pile foundation column according to the historical pile foundation strain data, and constructing dynamic pile foundation load data of the pile foundation column according to the historical ocean energy spectrum data, historical three-dimensional turbulent wind field data and historical fluid-structure coupling data; Constructing a three-dimensional digital twin model corresponding to the pile foundation column, and determining the target remaining life of the three-dimensional digital twin model under extreme service conditions based on the dynamic pile foundation load data; Training a neural network model to be trained based on the target remaining life, column material parameters of the pile foundation column, dynamic pile foundation load data, and historical environmental variable data corresponding to the historical column state data to obtain a fatigue life prediction model; Inputting the real-time model state data of the three-dimensional digital twin model into the fatigue life prediction model to obtain the real-time remaining life probability distribution of the three-dimensional digital twin model, and determining the real-time fatigue loss of the pile foundation column according to the real-time remaining life probability distribution; 2. The method for determining the fatigue loss of the pile foundation column of the offshore photovoltaic according to claim 1, wherein Determining historical ocean energy spectrum data of the pile foundation column according to the historical wave buoy data, including: Determine the wave spectrum function, determine the spectrogram based on the wave spectrum function and historical wave buoy data, and determine the up-crossing average period of the waves based on the spectrogram and the spectral peak average period ; among them, the up-crossing average period and the spectral peak average period satisfy the following relationship: ; wherein, is a spectral coefficient; According to the upward crossing average period and the spectral peak average period and the upward crossing average period and the spectral peak average period to determine the average period ; wherein, the specific value of the average period is: ; Based on the up-crossing average period , the spectral peak average period , the average period , and the height of the wave, determine the historical ocean energy spectrum data of the pile foundation column.

3. The method for determining the fatigue loss of the pile foundation columns of the offshore photovoltaic according to claim 1, characterized in that, Constructing a three-dimensional digital twin model corresponding to the pile foundation column, including: Obtaining an original geometric model of a pile foundation column in an offshore photovoltaic scenario, and configuring first component performance parameters for the original geometric model; wherein, the first component performance parameters include at least one of the first elastic modulus, the first Poisson's ratio, the first tensile strength, the first compressive strength, and the first horizontal bearing capacity of the first component material used to prepare the pile foundation column; Applying a first simulated service load under a simulated service condition to the original geometric model; wherein, the simulated service condition is obtained by simulating the real service condition of the pile foundation column, and the first simulated service load includes at least one of the first weather scenario load, the first pressure load, and the first support force load borne by the pile foundation column during service; Generating a three-dimensional digital twin model corresponding to the pile foundation column according to the original geometric model, the first component performance parameters, and the first simulated service load; 4. The method for determining the fatigue loss of the pile foundation column of the offshore photovoltaic according to claim 1, characterized in that Determining the target remaining life of the three-dimensional digital twin model under extreme service conditions based on the dynamic pile foundation load data, including: Determine the fatigue damage coefficient of the pile foundation column under the combined action of waves, tides, and sea breezes based on the dynamic data of the pile foundation load and the historical environmental variable data corresponding to the historical column state data; Determine the dangerous stress area of the three-dimensional digital twin model, and determine the original remaining life of the dangerous stress area under extreme service conditions based on the fatigue damage coefficient; Determine the cumulative damage of the dangerous stress area and the crack propagation path caused by the cumulative damage, and correct the original remaining life based on the crack propagation path to obtain the target remaining life of the three-dimensional digital twin model under extreme service conditions.

5. The method for determining the fatigue loss of the pile foundation columns of the offshore photovoltaic power generation according to claim 1, wherein, Train the neural network model to be trained to obtain a fatigue life prediction model based on the target remaining life, the column material parameters of the pile foundation column, the dynamic data of the pile foundation load, and the historical environmental variable data corresponding to the historical column state data, including: Construct a first original feature map according to the column material parameters of the pile foundation column, the dynamic data of the pile foundation load, and the historical environmental variable data corresponding to the historical column state data; Input the first original feature into the neural network model to be trained to obtain the fatigue life prediction result of the pile foundation column; Construct a loss function according to the fatigue life prediction result and the target remaining life, and train the neural network model to be trained based on the loss function to obtain a fatigue life prediction model.

6. The method for determining the fatigue loss of the pile foundation columns of the offshore photovoltaic according to claim 5, characterized in that, Construct a first original feature map according to the column material parameters of the pile foundation column, the dynamic data of the pile foundation load, and the historical environmental variable data corresponding to the historical column state data, including: Use the pile foundation column in the offshore photovoltaic scenario as the first vertex, and use the connection relationship between the pile foundation columns as the first connection edge; Determine the first edge weight of the first connection edge according to the positional relationship between the pile foundation columns, and use the column material parameters of the pile foundation column, the dynamic data of the pile foundation load, and the historical environmental variable data corresponding to the historical column state data as the second vertex, the third vertex, and the fourth vertex; Determine the second connection edge, the third connection edge, and the fourth connection edge according to the relationship between the pile foundation column and the column material parameters, the dynamic data of the pile foundation load, and the historical environmental variable data; Construct a first original feature map based on the first vertex, the second vertex, the third vertex, the fourth vertex, and the first connection edge, the second connection edge, the third connection edge, the fourth connection edge, and the first edge weight.

7. The method for determining the fatigue loss of the pile foundation columns of the offshore photovoltaic power generation according to claim 1, wherein The fatigue life prediction model includes a first graph convolutional layer, a second graph convolutional layer,..., an Nth graph convolutional layer, and a classification layer; Among them, input the real-time model state data of the three-dimensional digital twin model into the fatigue life prediction model to obtain the real-time remaining life probability distribution of the three-dimensional digital twin model, including: Construct a second original feature map according to the real-time model state data of the three-dimensional digital twin model and the column material parameters of the three-dimensional digital twin model, and update the node features of the second original feature map based on the first graph convolutional layer to obtain the first graph convolutional processing result; Based on the second graph convolutional layer, update the node features of the first graph convolutional processing result to obtain the second graph convolutional processing result, and sequentially repeat the determination process of the second graph convolutional processing result to obtain the third graph convolutional processing result, …, the Nth graph convolutional processing result; Based on the classification layer, perform classification processing on the Nth graph convolutional processing result to obtain the real-time remaining life probability distribution of the 3D digital twin model.

8. An apparatus for determining the fatigue loss of a pile foundation column for offshore photovoltaic power, characterized in that, Comprising: A pile foundation load dynamic data determination module, configured to obtain the historical column state data of the pile foundation column in the offshore photovoltaic scenario, and determine the pile foundation load dynamic data of the pile foundation column according to the historical column state data; wherein, the pile foundation load dynamic data is obtained in the following manner: obtain the buoy, wind speed and direction sensor, and displacement sensor corresponding to the pile foundation column in the offshore photovoltaic scenario, and obtain the historical wave buoy data monitored based on the buoy, the historical wind speed and direction data monitored based on the wind speed and direction sensor, and the historical pile foundation strain data monitored based on the displacement sensor; determine the historical ocean energy spectrum data of the pile foundation column according to the historical wave buoy data, and determine the historical three-dimensional turbulent wind field data of the pile foundation column according to the historical wind speed and direction data; determine the historical fluid-structure interaction data of the pile foundation column according to the historical pile foundation strain data, and construct the pile foundation load dynamic data of the pile foundation column according to the historical ocean energy spectrum data, historical three-dimensional turbulent wind field data, and historical fluid-structure interaction data; A target remaining life determination module, configured to construct a 3D digital twin model corresponding to the pile foundation column, and determine the target remaining life of the 3D digital twin model under extreme service conditions based on the pile foundation load dynamic data; A fatigue life prediction model determination module, configured to train a neural network model to be trained based on the target remaining life, the column material parameters of the pile foundation column, the pile foundation load dynamic data, and the historical environmental variable data corresponding to the historical column state data to obtain a fatigue life prediction model; A real-time fatigue loss determination module, configured to input the real-time model state data of the 3D digital twin model into the fatigue life prediction model to obtain the real-time remaining life probability distribution of the 3D digital twin model, and determine the real-time fatigue loss of the pile foundation column according to the real-time remaining life probability distribution.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the method for determining the fatigue loss of the pile foundation column of the offshore photovoltaic as recited in any one of claims 1-7.

10. An electronic device, characterized in that, Comprising: A processor; And A memory, configured to store executable instructions of the processor; Wherein, the processor is configured to execute the method for determining the fatigue loss of the pile foundation column of the offshore photovoltaic as recited in any one of claims 1-7 by executing the executable instructions.

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