Floating platform service life analysis method and system based on wind wave flow dynamic load

Abstract

The invention provides a floating platform service life analysis method and system based on wind wave flow dynamic loads, and the method comprises the steps: obtaining the monitoring data of a wind field, a wave field and a flow field of an ocean region where a to-be-analyzed floating platform is located in real time, and extracting an initial time-space sequence; extracting coupling features; according to the resolutions of different positions, grids with different fine degrees are generated respectively, and multi-resolution feature distribution is obtained; analyzing the time sequence of the feature distribution to obtain a load spectrum; calculating stress distribution of a plurality of key nodes of the platform by adopting finite element analysis to obtain stress state data of the platform; according to the stress state data, identifying to obtain fatigue cycle times of the key nodes; and in combination with a preset material fatigue curve, obtaining the residual life of the key node, and further obtaining the fatigue life of the floating platform to be analyzed. According to the method, the dynamic coupling load effect of wind-wave-flow is considered, the deviation between the service life analysis result and the actual service state is reduced, and the accuracy of the analysis result is improved.

Description

Technical Field

[0001] The present invention relates to the field of marine data processing, and in particular to a floating platform life analysis method and system based on wind, wave and current dynamic loads. Background Art

[0002] The field of marine engineering plays a key role in global energy development and resource utilization, and floating platforms are the core equipment for deep-sea energy development. Their design and operation and maintenance directly affect work safety and economic benefits. During the long-term service of floating platforms, they often face complex marine environments. The coupling of factors such as wind, waves, and currents places extremely high demands on the fatigue and life prediction of platform structures.

[0003] However, existing methods often rely on simplified models (such as wave field models that rely on a single factor) or low-resolution data analysis when dealing with wind-wave-current coupled loads. This makes it difficult to accurately capture the spatiotemporal characteristics of the dynamic interactions of two or more factors, namely wind, waves and currents. This leads to deviations between load predictions and actual service conditions, which in turn affects the accuracy of platform life analysis. Summary of the invention

[0004] The present invention provides a floating platform life analysis method and system based on wind, wave and current dynamic loads, so as to solve the technical problem of how to improve the accuracy of floating platform life analysis.

[0005] In order to solve the above technical problems, an embodiment of the present invention provides a floating platform life analysis method based on wind, wave and current dynamic loads, comprising: Acquire the monitoring data of wind field, wave field and flow field of the ocean area where the floating platform to be analyzed is located in real time, and extract the initial time-space sequence from the monitoring data; wherein the floating platform to be analyzed includes a plurality of key nodes; Extract coupling features from the initial spatiotemporal sequence; based on the coupling features, generate grids with different degrees of refinement according to the resolution of the monitoring data at different locations in the ocean area where the floating platform to be analyzed is located, and obtain a multi-resolution feature distribution according to the grid generation results; wherein the grid corresponding to the area with relatively large resolution has a finer degree than the grid corresponding to the area with relatively small resolution; Analyzing the time series of the multi-resolution characteristic distribution to obtain a load spectrum; Based on the load spectrum, finite element analysis is used to calculate the stress distribution of the plurality of key nodes to obtain stress state data of the floating platform to be analyzed; According to the stress state data, the number of fatigue cycles of the key nodes is identified; and in combination with the preset material fatigue curve, the remaining life of the key nodes is obtained, and then based on each of the remaining lives, the fatigue life of the floating platform to be analyzed is obtained.

[0006] As a preferred solution, the degree of refinement is set according to the unit length, the number of grids and the grid density; Based on the coupling feature, according to the resolution of the monitoring data at different locations in the ocean area where the floating platform to be analyzed is located, grids with different degrees of refinement are generated respectively, and multi-resolution feature distribution is obtained according to the grid generation result, including: Dividing the ocean area where the floating platform to be analyzed is located into a plurality of sub-areas; Build multiple computing queues, each of which corresponds to a sub-region; Determine the unit length, number of grids and grid density of each sub-area according to the resolution of the monitoring data of each sub-area; Controlling the plurality of calculation queues to generate target grids for each sub-region according to the unit length, the number of grids and the grid density of each sub-region; Based on the coupling features and the target grids of each sub-region, the multi-resolution feature distribution is obtained.

[0007] As a preferred solution, the controlling the plurality of calculation queues to generate target grids for each sub-region according to the unit length, the number of grids and the grid density of each sub-region respectively comprises: Controlling the plurality of calculation queues to respectively preliminarily generate mesh surface information according to the unit length, mesh number and mesh density of each of the sub-regions; Controlling the plurality of calculation queues to generate three-dimensional grids of each sub-area based on the grid surface information and obtain grid body information; According to the mesh body information, the vertices and body units of each three-dimensional mesh are obtained; and the vertices and body units of each three-dimensional mesh are numbered to obtain a unique string of each vertex and a unique string of each body unit; Controlling the multiple computing queues to perform synchronous communication so that the body units and unique character strings between adjacent sub-regions are projected onto each other to obtain projection information; The three-dimensional grid of each sub-region is optimized according to the projection information to obtain the target grid of each sub-region.

[0008] As a preferred solution, the step of extracting the initial spatiotemporal sequence from the monitoring data includes: Performing standardization processing on the monitoring data of the wind field, wave field and flow field to obtain standardized data; A preset convolutional neural network is used to extract wind field features, wave field features and flow field features from the standardized data to obtain the initial space-time sequence.

[0009] As a preferred solution, the step of extracting coupling features from the initial spatiotemporal sequence includes: extracting a preliminary feature set of wind field, wave field and flow field coupling from the initial space-time sequence; When the feature dimension of the preliminary feature set is greater than a preset dimension threshold, performing dimensionality reduction processing on the preliminary feature set to obtain dimensionality reduction data; The time series of the dimension reduction data is analyzed, and the correlation features of the dynamic characteristics of the interaction between the wind field, the wave field and the flow field are enhanced to obtain the coupling features.

[0010] As a preferred solution, the floating platform to be analyzed is connected to at least one anchor chain; the stress distribution of the plurality of key nodes is calculated by finite element analysis based on the load spectrum to obtain stress state data of the floating platform to be analyzed, including: Constructing a platform model of the floating platform to be analyzed; constructing anchor chain models of the anchor chains respectively, embedding all anchor chain models into the platform model, and obtaining a target model; Constructing external excitation based on the load spectrum; performing finite element analysis based on the target model and the external excitation in combination with preset boundary conditions to obtain stress distribution of each key node; According to the stress distribution of each key node, analyze the temporal and spatial variation trends of the stress of each key node; Based on the temporal variation trend and spatial variation trend of the stress of each of the key nodes, a dynamic stress distribution diagram of the floating platform to be analyzed is obtained, and the stress state data is obtained according to the dynamic stress distribution diagram.

[0011] As a preferred solution, the floating platform life analysis method further includes: Responding to an analysis instruction of a target node, wherein the analysis instruction includes a calculation accuracy requirement; According to the analysis instruction, extracting a data sequence of a position corresponding to the target node from the stress state data; And according to the calculation accuracy requirement, the stress analysis result of the target node is obtained based on the data sequence analysis.

[0012] As a preferred solution, the number of fatigue cycles of key nodes is identified based on stress state data; and the remaining life of key nodes is obtained in combination with preset material fatigue curves, and then the fatigue life of the floating platform to be analyzed is obtained based on each of the remaining lives, including: Dividing the floating platform to be analyzed into a plurality of sub-units, and obtaining the calculation accuracy of each of the sub-units; Extracting the stress data sequence of each of the key nodes from the stress state data according to the calculation accuracy of each of the sub-units; Eliminating the noise of the stress data sequence by a low-pass filter to obtain denoised data; and performing sliding average processing on the stress data to obtain smoothed data; According to the preset stress amplitude and stress mean, the smoothed data is counted to obtain the number of fatigue cycles of each key node; According to the fatigue cycle number and the material fatigue curve, combined with the preset single damage value, the cumulative damage of the key node is calculated using Miner linear cumulative damage theory; The preset initial life of the key node is obtained, and the accumulated damage is subtracted from the initial life to calculate the remaining life of the key node; based on each of the remaining lives, the fatigue life of the floating platform to be analyzed is obtained.

[0013] Correspondingly, the present invention also provides a floating platform life analysis system based on wind-wave-current dynamic loads, including a spatiotemporal sequence extraction module, a characteristic distribution generation module, a load spectrum analysis module, a stress acquisition module and a life analysis module; wherein, The spatiotemporal sequence extraction module is used to obtain in real time the monitoring data of the wind field, wave field and flow field of the ocean area where the floating platform to be analyzed is located, and extract the initial spatiotemporal sequence from the monitoring data; wherein the floating platform to be analyzed includes a plurality of key nodes; The feature distribution generation module is used to extract coupling features from the initial spatiotemporal sequence; based on the coupling features, grids with different degrees of refinement are generated according to the resolution of the monitoring data at different locations in the ocean area where the floating platform to be analyzed is located, and a multi-resolution feature distribution is obtained according to the grid generation results; wherein the grid corresponding to the area with relatively large resolution has a finer degree than the grid corresponding to the area with relatively small resolution; The load spectrum analysis module is used to analyze the time series of the multi-resolution feature distribution to obtain a load spectrum; The stress acquisition module is used to calculate the stress distribution of the plurality of key nodes by finite element analysis based on the load spectrum to obtain stress state data of the floating platform to be analyzed; The life analysis module is used to identify the number of fatigue cycles of key nodes according to stress state data; and to obtain the remaining life of key nodes in combination with preset material fatigue curves, and then to obtain the fatigue life of the floating platform to be analyzed based on each remaining life.

[0014] As a preferred solution, the degree of refinement is set according to the unit length, the number of grids and the grid density; The feature distribution generation module generates grids of different degrees of refinement based on the coupling feature and according to the resolution of the monitoring data at different locations in the ocean area where the floating platform to be analyzed is located, and obtains a multi-resolution feature distribution according to the grid generation result, including: The characteristic distribution generating module divides the ocean area where the floating platform to be analyzed is located into a plurality of sub-areas; Build multiple computing queues, each of which corresponds to a sub-region; Determine the unit length, number of grids and grid density of each sub-area according to the resolution of the monitoring data of each sub-area; Controlling the plurality of calculation queues to generate target grids for each sub-region according to the unit length, the number of grids and the grid density of each sub-region; Based on the coupling features and the target grids of each sub-region, the multi-resolution feature distribution is obtained.

[0015] As a preferred solution, the feature distribution generation module controls the multiple calculation queues to generate target grids for each sub-region according to the unit length, grid number and grid density of each sub-region, including: The feature distribution generation module controls the multiple calculation queues to respectively preliminarily generate grid surface information according to the unit length, grid number and grid density of each sub-region; Controlling the plurality of calculation queues to generate three-dimensional grids of each sub-area based on the grid surface information and obtain grid body information; According to the mesh body information, the vertices and body units of each three-dimensional mesh are obtained; and the vertices and body units of each three-dimensional mesh are numbered to obtain a unique string of each vertex and a unique string of each body unit; Controlling the multiple computing queues to perform synchronous communication so that the body units and unique character strings between adjacent sub-regions are projected onto each other to obtain projection information; The three-dimensional grid of each sub-region is optimized according to the projection information to obtain the target grid of each sub-region.

[0016] As a preferred solution, the spatiotemporal sequence extraction module extracts the initial spatiotemporal sequence from the monitoring data, including: The spatiotemporal sequence extraction module performs standardization processing on the monitoring data of the wind field, wave field and flow field to obtain standardized data; A preset convolutional neural network is used to extract wind field features, wave field features and flow field features from the standardized data to obtain the initial space-time sequence.

[0017] As a preferred solution, the feature distribution generation module extracts coupling features from the initial spatiotemporal sequence, including: The feature distribution generation module extracts a preliminary feature set of wind field, wave field and flow field coupling from the initial spatiotemporal sequence; When the feature dimension of the preliminary feature set is greater than a preset dimension threshold, performing dimensionality reduction processing on the preliminary feature set to obtain dimensionality reduction data; The time series of the dimension reduction data is analyzed, and the correlation features of the dynamic characteristics of the interaction between the wind field, the wave field and the flow field are enhanced to obtain the coupling features.

[0018] As a preferred solution, the floating platform to be analyzed is connected to at least one anchor chain; the stress acquisition module calculates the stress distribution of the multiple key nodes by finite element analysis based on the load spectrum to obtain stress state data of the floating platform to be analyzed, including: The stress acquisition module constructs a platform model of the floating platform to be analyzed; constructs anchor chain models of each anchor chain respectively, embeds all anchor chain models into the platform model, and obtains a target model; Constructing external excitation based on the load spectrum; performing finite element analysis based on the target model and the external excitation in combination with preset boundary conditions to obtain stress distribution of each key node; According to the stress distribution of each key node, analyze the temporal and spatial variation trends of the stress of each key node; Based on the temporal variation trend and spatial variation trend of the stress of each of the key nodes, a dynamic stress distribution diagram of the floating platform to be analyzed is obtained, and the stress state data is obtained according to the dynamic stress distribution diagram.

[0019] As a preferred solution, the floating platform life analysis system further includes a target node analysis module, and the target node analysis module is used to: Responding to an analysis instruction of a target node, wherein the analysis instruction includes a calculation accuracy requirement; According to the analysis instruction, extracting a data sequence of a position corresponding to the target node from the stress state data; And according to the calculation accuracy requirement, the stress analysis result of the target node is obtained based on the data sequence analysis.

[0020] As a preferred solution, the life analysis module identifies the number of fatigue cycles of key nodes according to the stress state data; and obtains the remaining life of the key nodes in combination with the preset material fatigue curve, and then obtains the fatigue life of the floating platform to be analyzed based on each of the remaining lives, including: The life analysis module divides the floating platform to be analyzed into a plurality of sub-units, and obtains the calculation accuracy of each of the sub-units; Extracting the stress data sequence of each of the key nodes from the stress state data according to the calculation accuracy of each of the sub-units; Eliminating the noise of the stress data sequence by a low-pass filter to obtain denoised data; and performing sliding average processing on the stress data to obtain smoothed data; According to the preset stress amplitude and stress mean, the smoothed data is counted to obtain the number of fatigue cycles of each key node; According to the fatigue cycle number and the material fatigue curve, combined with the preset single damage value, the cumulative damage of the key node is calculated using Miner linear cumulative damage theory; The preset initial life of the key node is obtained, and the accumulated damage is subtracted from the initial life to calculate the remaining life of the key node; based on each of the remaining lives, the fatigue life of the floating platform to be analyzed is obtained.

[0021] Compared with the prior art, the embodiments of the present invention have the following beneficial effects: The present invention obtains the monitoring data of the wind field, wave field and flow field in the ocean area where the floating platform to be analyzed is located, extracts the initial time-space sequence, and obtains the coupling characteristics, so as to determine the dynamic characteristics of the wind field, wave field and flow field under the interaction of multiple factors; further, according to the resolution of the monitoring data at different positions in the ocean area where the floating platform to be analyzed is located, grids with different degrees of fineness are generated respectively, and relatively fine grids are generated at positions with relatively large resolutions, and relatively sparse grids are generated at positions with relatively small resolutions, so as to obtain a multi-resolution characteristic distribution, so as to achieve targeted distribution of resolutions in different areas, and achieve dynamic adaptation of the ocean area where the floating platform to be analyzed is located to the wind-wave-current coupling load, thereby obtaining an accurate load spectrum, and on this basis, an effective balance between simulation accuracy and calculation cost is achieved; in addition, since the load spectrum is obtained based on the time series analysis of the multi-resolution characteristic distribution, the time evolution characteristics of the dynamic coupling loads of the wind field, wave field and flow field can be extracted, and when calculating the stress distribution of the key nodes of the floating platform to be analyzed, accurate stress distribution data can be obtained, thereby predicting The remaining life of the key nodes and the fatigue life of the floating platform are obtained. Compared with the single factor analysis of the prior art, the present invention takes into account the dynamic coupling load of wind, wave and current, reduces the deviation between the life analysis result of the floating platform and the actual service state, and improves the accuracy of the analysis result; finally, the present application calculates the stress distribution of multiple key nodes, obtains the stress state data of the floating platform to be analyzed as a whole, and uses the stress state data of the floating platform with integrity and smoothness to identify the fatigue cycle number of the key nodes and then obtain the remaining life. Compared with the method of directly using the stress distribution of the key nodes to predict the remaining life of the key nodes, it can avoid that the remaining life obtained by identifying each key node has too large deviation, resulting in distortion of the fatigue life data of the floating platform finally analyzed, that is, it is equivalent to using the stress state data of the floating platform to be analyzed as a whole to perform similar smoothing before identifying the remaining life of each key node, so as to control the remaining life of each key node within a relatively smaller range, thereby analyzing and obtaining a more accurate and stable fatigue life of the floating platform to be analyzed. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 : A flow chart of an embodiment of a method for analyzing the life of a floating platform based on wind, wave and current dynamic loads provided in the present invention.

[0023] Figure 2 : A schematic flow chart of another embodiment of a floating platform life analysis method based on wind, wave and current dynamic loads provided in the present application.

[0024] Figure 3 : A structural schematic diagram of an embodiment of a floating platform life analysis system based on wind, wave and current dynamic loads provided in the present invention application. DETAILED DESCRIPTION

[0025] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0026] Embodiment one: Please refer to Figure 1 , Figure 1 A floating platform life analysis method based on wind, wave and current dynamic loads provided in an embodiment of the present invention includes steps S101 to S105; wherein: Step S101, acquiring in real time monitoring data of the wind field, wave field and flow field in the ocean area where the floating platform to be analyzed is located, and extracting an initial spatiotemporal sequence from the monitoring data.

[0027] The floating platform to be analyzed includes a plurality of key nodes, and the key nodes can be pre-configured or selected.

[0028] In this step, the floating platform to be analyzed is located in an ocean area, and the floating platform to be analyzed may be connected to at least one anchor chain.

[0029] This embodiment can obtain the monitoring data of the wind field, wave field and flow field of the ocean area where the floating platform to be analyzed is located in real time through a preset sensor network.

[0030] Sensor networks include many different types of sensors. Therefore, for the monitoring data collected by the sensor network, a multi-source data fusion algorithm can be used to pre-process it and then extract the initial spatiotemporal sequence.

[0031] In a preferred embodiment, the extracting the initial spatiotemporal sequence from the monitoring data includes: Standardizing the monitoring data of the wind field, wave field and flow field so that the monitoring data of each field can be converted into a unified format to obtain standardized data; A preset convolutional neural network is used to extract wind field features, wave field features and flow field features from the standardized data to obtain the initial space-time sequence.

[0032] Exemplarily, the convolutional neural network can use a three-layer two-dimensional convolutional neural network architecture. The first layer uses 32 5×5 convolution kernels with ReLU activation function to extract local spatial features, the second layer captures multi-field coupling features through 64 3×3 convolution kernels, and the third layer uses 128 1×1 convolution kernels for feature compression. Each layer is followed by a 2×2 maximum pooling layer to reduce the dimension, thereby extracting wind field features, wave field features, and flow field features, and then obtaining the initial space-time sequence.

[0033] Step S102, extracting coupling features from the initial space-time sequence; based on the coupling features, generating grids with different degrees of refinement according to the resolution of the monitoring data at different locations in the ocean area where the floating platform to be analyzed is located, and obtaining a multi-resolution feature distribution according to the grid generation results; wherein the grid corresponding to the area with relatively large resolution has a finer degree than the grid corresponding to the area with relatively small resolution.

[0034] In a preferred embodiment, the degree of refinement is set according to parameters such as unit length, number of grids and grid density.

[0035] The step of extracting coupling features from the initial spatiotemporal sequence comprises: extracting a preliminary feature set of wind field, wave field and flow field coupling from the initial space-time sequence; When the feature dimension of the preliminary feature set is greater than a preset dimension threshold, the preliminary feature set is subjected to dimension reduction processing to obtain dimension-reduced data (when the feature dimension of the preliminary feature set is greater than the preset dimension threshold, no dimension reduction operation is required, and subsequent analysis is directly performed); The time series of the dimension reduction data is analyzed, and the associated features of the dynamic characteristics of the interaction between the wind field, the wave field and the flow field are enhanced (for example, the associated features are highlighted, or the data set is expanded as a whole), so as to obtain the coupling features.

[0036] The purpose of the operation of enhancing the associated features in this embodiment is to highlight the coupling effect of the wind field, wave field and flow field. For example, in some application examples, the coupling features of the present invention can be used for further analysis, such as performing clustering operations to obtain the classic mode of the coupling of the wind field, wave field and flow field.

[0037] For further information, please refer to Figure 2 ,exist Figure 1Based on the embodiment shown, Figure 2 A flow chart of step S101 of another embodiment of a floating platform life analysis method based on wind, wave and current dynamic loads is provided. Figure 1 The same steps will not be repeated here.

[0038] like Figure 2 As shown, based on the coupling feature, step S101 generates grids of different fineness according to the resolution of the monitoring data at different locations in the ocean area where the floating platform to be analyzed is located, and obtains a multi-resolution feature distribution according to the grid generation result, including steps S201 to S205, each of which is described in detail as follows: Step S201: dividing the ocean area where the floating platform to be analyzed is located into a plurality of sub-areas.

[0039] Step S202, construct multiple computing queues, each computing queue corresponds to a sub-area, and the tasks of each computing queue can be executed by different servers or different processors or different cores.

[0040] Step S203, according to the resolution of the monitoring data of each sub-region, the unit length, the number of grids and the grid density of each sub-region are determined, that is, the refinement degree of the grid corresponding to the generated sub-region is determined.

[0041] Step S204, controlling the plurality of calculation queues to generate a target grid for each sub-region according to the unit length, the number of grids and the grid density of each sub-region.

[0042] Step S205, obtaining the multi-resolution feature distribution based on the coupling feature and the target grid of each sub-region.

[0043] This implementation adaptively generates grids with different degrees of refinement according to the resolution of the monitoring data of each sub-area, so that for sub-areas with high resolution, relatively fine grids can be generated, and for sub-areas with low resolution, relatively sparse grids can be generated, thereby obtaining a multi-resolution characteristic distribution, and can achieve targeted distribution of resolutions in different areas, and achieve dynamic adaptation of the ocean area where the floating platform to be analyzed is located to the wind-wave-current coupled load (for example, high-resolution data can simulate fine loads, and low-resolution data does not need to generate loads with such high precision, but can save computing resources by reducing the degree of grid refinement), thereby obtaining an overall accurate load spectrum, and on this basis, achieving an effective balance between simulation accuracy and computing cost.

[0044] Furthermore, the controlling the plurality of calculation queues to generate a target grid for each sub-region according to the unit length, the number of grids and the grid density of each sub-region respectively comprises: Controlling the plurality of calculation queues to respectively preliminarily generate mesh surface information according to the unit length, mesh number and mesh density of each of the sub-regions; Controlling the plurality of calculation queues to generate three-dimensional grids of each sub-area based on the grid surface information and obtain grid body information; According to the mesh body information, the vertices and body units of each three-dimensional mesh are obtained; and the vertices and body units of each three-dimensional mesh are numbered to obtain a unique string of each vertex and a unique string of each body unit; Controlling the multiple computing queues to perform synchronous communication so that the body units and unique character strings between adjacent sub-regions are projected onto each other to obtain projection information; The three-dimensional grid of each sub-region is optimized according to the projection information to obtain the target grid of each sub-region.

[0045] This embodiment controls the synchronous communication between the multiple computing queues to project the body units and unique strings between adjacent sub-regions to each other to obtain projection information, and uses the projection information to optimize the three-dimensional mesh of each sub-region. Compared with the existing technical solution of directly merging the sub-regions, this avoids the problem of inconsistent interface between adjacent regions during merging, and no post-processing steps are required. In addition, the present invention application does not require a merging operation, but instead uses the projection information to optimize the three-dimensional mesh of each sub-region, and then in subsequent steps, according to the coupling features and the target mesh of each sub-region, the required multi-resolution feature distribution is obtained, which can avoid the large amount of CPU resources required for the merging operation, and at the same time has lower requirements on memory capacity (the existing technical solution requires a large memory to accommodate the entire mesh after merging).

[0046] Step S103: Analyze the time series of the multi-resolution characteristic distribution to obtain a load spectrum.

[0047] In a preferred embodiment, the time series of multi-resolution feature distribution is analyzed, and the distribution density in the time dimension can be calculated using methods such as kernel density estimation, thereby generating a load spectrum of the wind field, wave field and flow field coupling.

[0048] Furthermore, the frequency domain characteristics and time-frequency characteristics of the load spectrum can be analyzed, and the uncertainty of the load spectrum can be quantified to determine whether the generated load spectrum meets the requirements. If not, the load spectrum can be regenerated.

[0049] Step S104: Based on the load spectrum, finite element analysis is used to calculate the stress distribution of the plurality of key nodes to obtain stress state data of the floating platform to be analyzed.

[0050] In a preferred embodiment, based on the load spectrum, finite element analysis is used to calculate the stress distribution of the multiple key nodes, and stress state data of the floating platform to be analyzed is obtained, including: Construct a platform model of the floating platform to be analyzed; respectively construct chain models of each anchor chain, and embed all the chain models into the platform model to obtain a target model; Construct an external excitation based on the load spectrum; perform finite element analysis according to the target model and the external excitation in combination with preset boundary conditions to obtain the stress distribution of each key node; Analyze the time-varying trend and spatial-varying trend of the stress of each key node according to the stress distribution of each key node; Based on the time-varying trend and spatial-varying trend of the stress of each key node, obtain a dynamic stress distribution diagram of the floating platform to be analyzed, and obtain the stress state data according to the dynamic stress distribution diagram.

[0051] In this embodiment, finite element analysis tools such as Ansys can be used to construct the model. By constructing models for the floating platform to be analyzed and the anchor chain respectively, that is, the mutual independence of the floating platform to be analyzed and the anchor chain in the actual marine environment stress is considered when simulating the stress distribution, so it has higher accuracy compared with the overall construction of the model in stress simulation; further, by analyzing the time-varying trend and spatial-varying trend of the stress of each key node, the dynamic stress distribution diagram of the floating platform to be analyzed can combine the time-varying characteristics and spatial-varying characteristics of the stress of each key node, and reflect the stress characteristics of the platform in real time, with higher precision.

[0052] Step S105: Identify the fatigue cycle times of the key nodes according to the stress state data; and combine the preset material fatigue curve to obtain the remaining life of the key nodes, and then based on each remaining life, obtain the fatigue life of the floating platform to be analyzed.

[0053] It should be noted that in this application, the stress distribution of multiple key nodes is calculated to obtain the stress state data of the overall floating platform to be analyzed. By using the stress state data of the floating platform with integrity and smoothness, the fatigue cycle times of the key nodes are identified and then the remaining life is obtained. In this way, compared with the method of directly predicting the remaining life of the key nodes using the stress distribution of the key nodes, it can avoid the excessive deviation of the remaining life identified for each key node, resulting in the distortion of the fatigue life data of the finally analyzed floating platform. That is, it is equivalent to using the stress state data of the overall floating platform to be analyzed for similar smoothing processing before identifying the remaining life of each key node, controlling the remaining life of each key node within a relatively smaller interval, so as to analyze and obtain a more accurate and stable fatigue life of the floating platform to be analyzed.

[0054] In a preferred embodiment, as described above, since the present application calculates the stress distribution of multiple key nodes to obtain the stress state data of the floating platform to be analyzed as a whole, the fatigue cycle number of the key nodes is identified by analyzing the stress state data of the floating platform with integrity and smoothness, and then the remaining life is obtained. When the stress state data of the floating platform to be analyzed as a whole is obtained according to the stress distribution of multiple key nodes, it is not a simple splicing between nodes, and it has a certain smoothness. Therefore, for the target node of the non-key node, the floating platform life analysis method further includes: Responding to an analysis instruction of a target node, wherein the analysis instruction includes a calculation accuracy requirement; According to the analysis instruction, extracting a data sequence of a position corresponding to the target node from the stress state data; And according to the calculation accuracy requirement, the stress analysis result of the target node is obtained based on the data sequence analysis.

[0055] In a preferred embodiment, the method of identifying the number of fatigue cycles of key nodes according to the stress state data, and obtaining the remaining life of the key nodes in combination with a preset material fatigue curve, and then obtaining the fatigue life of the floating platform to be analyzed based on each of the remaining lives, includes: Dividing the floating platform to be analyzed into a plurality of sub-units, and obtaining the calculation accuracy of each of the sub-units; Extracting the stress data sequence of each of the key nodes from the stress state data according to the calculation accuracy of each of the sub-units; Eliminate high-frequency noise of the stress data sequence by a low-pass filter (such as a Butterworth low-pass filter, with a cutoff frequency of 5 Hz and an order of 4) to obtain denoised data; and perform sliding average processing on the stress data to obtain smoothed data; According to the preset stress amplitude (such as 0.1 MPa) and stress mean (such as 0.5 MPa), the smoothed data is counted to obtain the number of fatigue cycles of each key node (for example, valid cycles such as 128 cycles with an amplitude of 15.3 MPa / mean of -2.1 MPa and 256 cycles with an amplitude of 8.7 MPa / mean of 5.4 MPa are identified); According to the fatigue cycle number and the material fatigue curve, combined with the preset single damage value, the cumulative damage of the key node is calculated using Miner linear cumulative damage theory; The preset initial life of the key node is obtained, and the accumulated damage is subtracted from the initial life to calculate the remaining life of the key node; based on each of the remaining lives, the fatigue life of the floating platform to be analyzed is obtained.

[0056] Correspondingly, such as Figure 3As shown, the present invention also provides a floating platform life analysis system 300 based on wind-wave-current dynamic loads, including a spatiotemporal sequence extraction module 301, a characteristic distribution generation module 302, a load spectrum analysis module 303, a stress acquisition module 304 and a life analysis module 305; wherein, The spatiotemporal sequence extraction module 301 is used to obtain in real time the monitoring data of the wind field, wave field and flow field of the ocean area where the floating platform to be analyzed is located, and extract the initial spatiotemporal sequence from the monitoring data; wherein the floating platform to be analyzed includes a plurality of key nodes; The feature distribution generation module 302 is used to extract coupling features from the initial spatiotemporal sequence; based on the coupling features, grids with different degrees of refinement are generated according to the resolution of the monitoring data at different locations in the ocean area where the floating platform to be analyzed is located, and a multi-resolution feature distribution is obtained according to the grid generation results; wherein the grid corresponding to the area with relatively large resolution has a finer degree than the grid corresponding to the area with relatively small resolution; The load spectrum analysis module 303 is used to analyze the time series of the multi-resolution feature distribution to obtain a load spectrum; The stress acquisition module 304 is used to calculate the stress distribution of the plurality of key nodes by finite element analysis based on the load spectrum to obtain stress state data of the floating platform to be analyzed; The life analysis module 305 is used to identify the number of fatigue cycles of key nodes according to stress state data; and to obtain the remaining life of key nodes in combination with preset material fatigue curves, and then to obtain the fatigue life of the floating platform to be analyzed based on each remaining life.

[0057] As a preferred solution, the degree of refinement is set according to the unit length, the number of grids and the grid density; The feature distribution generation module 302 generates grids of different degrees of refinement based on the coupling feature and according to the resolution of the monitoring data at different locations in the ocean area where the floating platform to be analyzed is located, and obtains a multi-resolution feature distribution according to the grid generation result, including: The characteristic distribution generating module 302 divides the ocean area where the floating platform to be analyzed is located into a plurality of sub-areas; Build multiple computing queues, each of which corresponds to a sub-region; Determine the unit length, number of grids and grid density of each sub-area according to the resolution of the monitoring data of each sub-area; Controlling the plurality of calculation queues to generate target grids for each sub-region according to the unit length, the number of grids and the grid density of each sub-region; Based on the coupling features and the target grids of each sub-region, the multi-resolution feature distribution is obtained.

[0058] As a preferred solution, the feature distribution generation module 302 controls the multiple calculation queues to generate target grids for each sub-region according to the unit length, grid number and grid density of each sub-region, including: The feature distribution generation module 302 controls the plurality of calculation queues to respectively generate preliminary grid surface information according to the unit length, the number of grids and the grid density of each sub-region; Controlling the plurality of calculation queues to generate three-dimensional grids of each sub-area based on the grid surface information and obtain grid body information; According to the mesh body information, the vertices and body units of each three-dimensional mesh are obtained; and the vertices and body units of each three-dimensional mesh are numbered to obtain a unique string of each vertex and a unique string of each body unit; Controlling the multiple computing queues to perform synchronous communication so that the body units and unique character strings between adjacent sub-regions are projected onto each other to obtain projection information; The three-dimensional grid of each sub-region is optimized according to the projection information to obtain the target grid of each sub-region.

[0059] As a preferred solution, the spatiotemporal sequence extraction module 301 extracts the initial spatiotemporal sequence from the monitoring data, including: The spatiotemporal sequence extraction module 301 performs standardization processing on the monitoring data of the wind field, wave field and flow field to obtain standardized data; A preset convolutional neural network is used to extract wind field features, wave field features and flow field features from the standardized data to obtain the initial space-time sequence.

[0060] As a preferred solution, the feature distribution generating module 302 extracts coupling features from the initial spatiotemporal sequence, including: The feature distribution generation module 302 extracts a preliminary feature set of wind field, wave field and flow field coupling from the initial spatiotemporal sequence; When the feature dimension of the preliminary feature set is greater than a preset dimension threshold, performing dimensionality reduction processing on the preliminary feature set to obtain dimensionality reduction data; The time series of the dimension reduction data is analyzed, and the correlation features of the dynamic characteristics of the interaction between the wind field, the wave field and the flow field are enhanced to obtain the coupling features.

[0061] As a preferred solution, the floating platform to be analyzed is connected to at least one anchor chain; the stress acquisition module 304 calculates the stress distribution of the multiple key nodes using finite element analysis based on the load spectrum to obtain stress state data of the floating platform to be analyzed, including: The stress acquisition module 304 constructs a platform model of the floating platform to be analyzed; constructs anchor chain models of each anchor chain respectively, embeds all anchor chain models into the platform model, and obtains a target model; Constructing external excitation based on the load spectrum; performing finite element analysis based on the target model and the external excitation in combination with preset boundary conditions to obtain stress distribution of each key node; According to the stress distribution of each key node, analyze the temporal and spatial variation trends of the stress of each key node; Based on the temporal variation trend and spatial variation trend of the stress of each of the key nodes, a dynamic stress distribution diagram of the floating platform to be analyzed is obtained, and the stress state data is obtained according to the dynamic stress distribution diagram.

[0062] As a preferred solution, the floating platform life analysis system 300 further includes a target node analysis module, which is used to: Responding to an analysis instruction of a target node, wherein the analysis instruction includes a calculation accuracy requirement; According to the analysis instruction, extracting a data sequence of a position corresponding to the target node from the stress state data; And according to the calculation accuracy requirement, the stress analysis result of the target node is obtained based on the data sequence analysis.

[0063] As a preferred solution, the life analysis module 305 identifies the number of fatigue cycles of key nodes according to the stress state data; and obtains the remaining life of the key nodes in combination with the preset material fatigue curve, and then obtains the fatigue life of the floating platform to be analyzed based on each of the remaining lives, including: The life analysis module 305 divides the floating platform to be analyzed into a plurality of sub-units, and obtains the calculation accuracy of each of the sub-units; Extracting the stress data sequence of each of the key nodes from the stress state data according to the calculation accuracy of each of the sub-units; Eliminating the noise of the stress data sequence by a low-pass filter to obtain denoised data; and performing sliding average processing on the stress data to obtain smoothed data; According to the preset stress amplitude and stress mean, the smoothed data is counted to obtain the number of fatigue cycles of each key node; According to the fatigue cycle number and the material fatigue curve, combined with the preset single damage value, the cumulative damage of the key node is calculated using Miner linear cumulative damage theory; The preset initial life of the key node is obtained, and the accumulated damage is subtracted from the initial life to calculate the remaining life of the key node; based on each of the remaining lives, the fatigue life of the floating platform to be analyzed is obtained.

[0064] Compared with the prior art, the embodiments of the present invention have the following beneficial effects: The present invention obtains the monitoring data of the wind field, wave field and flow field in the ocean area where the floating platform to be analyzed is located, extracts the initial time-space sequence, and obtains the coupling characteristics, so as to determine the dynamic characteristics of the wind field, wave field and flow field under the interaction of multiple factors; further, according to the resolution of the monitoring data at different positions in the ocean area where the floating platform to be analyzed is located, grids with different degrees of fineness are generated respectively, and relatively fine grids are generated at positions with relatively large resolutions, and relatively sparse grids are generated at positions with relatively small resolutions, so as to obtain a multi-resolution characteristic distribution, so as to achieve targeted distribution of resolutions in different areas, and achieve dynamic adaptation of the ocean area where the floating platform to be analyzed is located to the wind-wave-current coupling load, thereby obtaining an accurate load spectrum, and on this basis, an effective balance between simulation accuracy and calculation cost is achieved; in addition, since the load spectrum is obtained based on the time series analysis of the multi-resolution characteristic distribution, the time evolution characteristics of the dynamic coupling loads of the wind field, wave field and flow field can be extracted, and when calculating the stress distribution of the key nodes of the floating platform to be analyzed, accurate stress distribution data can be obtained, thereby predicting The remaining life of the key nodes and the fatigue life of the floating platform are obtained. Compared with the single factor analysis of the prior art, the present invention takes into account the dynamic coupling load of wind, wave and current, reduces the deviation between the life analysis result of the floating platform and the actual service state, and improves the accuracy of the analysis result; finally, the present application calculates the stress distribution of multiple key nodes, obtains the stress state data of the floating platform to be analyzed as a whole, and uses the stress state data of the floating platform with integrity and smoothness to identify the fatigue cycle number of the key nodes and then obtain the remaining life. Compared with the method of directly using the stress distribution of the key nodes to predict the remaining life of the key nodes, it can avoid that the remaining life obtained by identifying each key node has too large deviation, resulting in distortion of the fatigue life data of the floating platform finally analyzed, that is, it is equivalent to using the stress state data of the floating platform to be analyzed as a whole to perform similar smoothing before identifying the remaining life of each key node, so as to control the remaining life of each key node within a relatively smaller range, thereby analyzing and obtaining a more accurate and stable fatigue life of the floating platform to be analyzed.

[0065] The specific embodiments described above further illustrate the purpose, technical solutions and beneficial effects of the present invention. It should be understood that the above description is only a specific embodiment of the present invention and is not intended to limit the scope of protection of the present invention. It is particularly pointed out that for those skilled in the art, any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the scope of protection of the present invention.

Claims

1. A floating platform life analysis method based on wind, wave and current dynamic loads, characterized in that: include: Acquire the monitoring data of wind field, wave field and flow field of the ocean area where the floating platform to be analyzed is located in real time, and extract the initial time-space sequence from the monitoring data; wherein the floating platform to be analyzed includes a plurality of key nodes; Extract coupling features from the initial spatiotemporal sequence; based on the coupling features, generate grids with different degrees of refinement according to the resolution of the monitoring data at different locations in the ocean area where the floating platform to be analyzed is located, and obtain a multi-resolution feature distribution according to the grid generation results; wherein the grid corresponding to the area with relatively large resolution has a finer degree than the grid corresponding to the area with relatively small resolution; Analyzing the time series of the multi-resolution characteristic distribution to obtain a load spectrum; Based on the load spectrum, finite element analysis is used to calculate the stress distribution of the plurality of key nodes to obtain stress state data of the floating platform to be analyzed; According to the stress state data, the number of fatigue cycles of the key nodes is identified; and in combination with the preset material fatigue curve, the remaining life of the key nodes is obtained, and then based on each of the remaining lives, the fatigue life of the floating platform to be analyzed is obtained.

2. A floating platform life analysis method based on wind, wave and current dynamic loads as claimed in claim 1, characterized in that: The degree of refinement is set according to the unit length, the number of grids and the grid density; Based on the coupling feature, according to the resolution of the monitoring data at different locations in the ocean area where the floating platform to be analyzed is located, grids with different degrees of refinement are generated respectively, and multi-resolution feature distribution is obtained according to the grid generation result, including: Dividing the ocean area where the floating platform to be analyzed is located into a plurality of sub-areas; Build multiple computing queues, each of which corresponds to a sub-region; Determine the unit length, number of grids and grid density of each sub-area according to the resolution of the monitoring data of each sub-area; Controlling the plurality of calculation queues to generate target grids for each sub-region according to the unit length, the number of grids and the grid density of each sub-region; Based on the coupling features and the target grids of each sub-region, the multi-resolution feature distribution is obtained.

3. A floating platform life analysis method based on wind, wave and current dynamic loads as claimed in claim 2, characterized in that: The controlling the plurality of calculation queues to generate target grids for each sub-region according to the unit length, the number of grids and the grid density of each sub-region respectively includes: Controlling the plurality of calculation queues to respectively preliminarily generate grid surface information according to the unit length, the number of grids and the grid density of each of the sub-regions; Controlling the plurality of calculation queues to generate three-dimensional grids of each sub-area based on the grid surface information and obtain grid body information; According to the mesh body information, the vertices and body units of each three-dimensional mesh are obtained; and the vertices and body units of each three-dimensional mesh are numbered to obtain a unique string of each vertex and a unique string of each body unit; Controlling the multiple computing queues to perform synchronous communication so that the body units and unique character strings between adjacent sub-regions are projected onto each other to obtain projection information; The three-dimensional grid of each sub-region is optimized according to the projection information to obtain the target grid of each sub-region.

4. A floating platform life analysis method based on wind, wave and current dynamic loads as claimed in claim 1, characterized in that: The step of extracting the initial spatiotemporal sequence from the monitoring data comprises: Performing standardization processing on the monitoring data of the wind field, wave field and flow field to obtain standardized data; A preset convolutional neural network is used to extract wind field features, wave field features and flow field features from the standardized data to obtain the initial space-time sequence.

5. The floating platform life analysis method based on wind, wave and current dynamic loads according to claim 1, characterized in that: The step of extracting coupling features from the initial spatiotemporal sequence comprises: extracting a preliminary feature set of wind field, wave field and flow field coupling from the initial space-time sequence; When the feature dimension of the preliminary feature set is greater than a preset dimension threshold, performing dimensionality reduction processing on the preliminary feature set to obtain dimensionality reduction data; The time series of the dimension reduction data is analyzed, and the correlation features of the dynamic characteristics of the interaction between the wind field, the wave field and the flow field are enhanced to obtain the coupling features.

6. A floating platform life analysis method based on wind, wave and current dynamic loads as claimed in claim 1, characterized in that: The floating platform to be analyzed is connected to at least one anchor chain; the stress distribution of the plurality of key nodes is calculated by finite element analysis based on the load spectrum to obtain stress state data of the floating platform to be analyzed, including: Constructing a platform model of the floating platform to be analyzed; constructing anchor chain models of the anchor chains respectively, embedding all anchor chain models into the platform model, and obtaining a target model; Constructing external excitation based on the load spectrum; performing finite element analysis based on the target model and the external excitation in combination with preset boundary conditions to obtain stress distribution of each key node; According to the stress distribution of each key node, analyze the temporal and spatial variation trends of the stress of each key node; Based on the temporal variation trend and spatial variation trend of the stress of each of the key nodes, a dynamic stress distribution diagram of the floating platform to be analyzed is obtained, and the stress state data is obtained according to the dynamic stress distribution diagram.

7. A floating platform life analysis method based on wind, wave and current dynamic loads as claimed in claim 1, characterized in that: The floating platform life analysis method further comprises: Responding to an analysis instruction of a target node, wherein the analysis instruction includes a calculation accuracy requirement; According to the analysis instruction, extracting a data sequence of a position corresponding to the target node from the stress state data; And according to the calculation accuracy requirement, the stress analysis result of the target node is obtained based on the data sequence analysis.

8. A floating platform life analysis system based on wind, wave and current dynamic loads, characterized in that: It includes time-space series extraction module, characteristic distribution generation module, load spectrum analysis module, stress acquisition module and life analysis module; among them, The spatiotemporal sequence extraction module is used to obtain in real time the monitoring data of the wind field, wave field and flow field of the ocean area where the floating platform to be analyzed is located, and extract the initial spatiotemporal sequence from the monitoring data; wherein the floating platform to be analyzed includes a plurality of key nodes; The feature distribution generation module is used to extract coupling features from the initial spatiotemporal sequence; based on the coupling features, grids with different degrees of refinement are generated according to the resolution of the monitoring data at different locations in the ocean area where the floating platform to be analyzed is located, and a multi-resolution feature distribution is obtained according to the grid generation results; wherein the grid corresponding to the area with relatively large resolution has a finer degree than the grid corresponding to the area with relatively small resolution; The load spectrum analysis module is used to analyze the time series of the multi-resolution feature distribution to obtain a load spectrum; The stress acquisition module is used to calculate the stress distribution of the plurality of key nodes by finite element analysis based on the load spectrum to obtain stress state data of the floating platform to be analyzed; The life analysis module is used to identify the number of fatigue cycles of key nodes according to stress state data; and to obtain the remaining life of key nodes in combination with preset material fatigue curves, and then to obtain the fatigue life of the floating platform to be analyzed based on each remaining life.

9. A floating platform life analysis system based on wind, wave and current dynamic loads as claimed in claim 8, characterized in that: The degree of refinement is set according to the unit length, the number of grids and the grid density; The feature distribution generation module generates grids of different degrees of refinement based on the coupling feature and according to the resolution of the monitoring data at different locations in the ocean area where the floating platform to be analyzed is located, and obtains a multi-resolution feature distribution according to the grid generation result, including: The characteristic distribution generating module divides the ocean area where the floating platform to be analyzed is located into a plurality of sub-areas; Build multiple computing queues, each of which corresponds to a sub-region; Determine the unit length, number of grids and grid density of each sub-area according to the resolution of the monitoring data of each sub-area; Controlling the plurality of calculation queues to generate target grids for each sub-region according to the unit length, the number of grids and the grid density of each sub-region; Based on the coupling features and the target grids of each sub-region, the multi-resolution feature distribution is obtained.

10. A floating platform life analysis system based on wind, wave and current dynamic loads as claimed in claim 9, characterized in that: The feature distribution generation module controls the multiple calculation queues to generate target grids for each sub-region according to the unit length, the number of grids and the grid density of each sub-region, including: The feature distribution generation module controls the multiple calculation queues to respectively preliminarily generate grid surface information according to the unit length, grid number and grid density of each sub-region; Controlling the plurality of calculation queues to generate three-dimensional grids of each sub-area based on the grid surface information and obtain grid body information; According to the mesh body information, the vertices and body units of each three-dimensional mesh are obtained; and the vertices and body units of each three-dimensional mesh are numbered to obtain a unique string of each vertex and a unique string of each body unit; Controlling the multiple computing queues to perform synchronous communication so that the body units and unique character strings between adjacent sub-regions are projected onto each other to obtain projection information; The three-dimensional grid of each sub-region is optimized according to the projection information to obtain the target grid of each sub-region.

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