Online Prediction Method and System for Fatigue Life of Jacket-Type Platform Based on Digital Twin

Abstract

The present invention provides an online prediction method and system for the fatigue life of a jacket-type platform based on digital twin, belonging to the technical field of ocean engineering equipment. The present invention collects historical stress test data, material property data, stress data, and environmental data of the jacket-type platform material; constructs an ideal fatigue life prediction model, obtains strain stress from the stress data, inputs the strain stress into the ideal fatigue life prediction model to obtain the ideal fatigue life, corrects the ideal fatigue life using the environmental data to obtain the corrected fatigue life, analyzes the material property data and stress data of the jacket-type platform material to obtain fatigue damage, and performs secondary correction on the corrected fatigue life using the fatigue damage to obtain the actual fatigue life; constructs a digital twin platform to realize high-precision real-time mapping between the physical model and the digital model of the jacket-type platform.

Description

Technical Field

[0001] The present invention relates to the technical field of offshore engineering equipment, and particularly to an online prediction method and system for the fatigue life of a jacket-type platform based on digital twin. Background Art

[0002] In the field of offshore oil and gas exploitation, jacket-type platforms are widely used due to their stable structure and strong adaptability. However, such platforms are in the marine environment for a long time and are affected by dynamic environmental factors such as waves, tides, and wind loads. Their structures are subjected to complex stress effects. The stress fluctuations and corrosion in this environment will cause the material to gradually generate fatigue damage, thereby reducing the structural strength and service life of the platform. Therefore, accurately predicting the fatigue life of jacket-type platforms is of great significance for ensuring the safety of offshore operations and extending the service life of the platforms.

[0003] Traditional fatigue life prediction methods mainly rely on linear regression of historical data, statistical models, or accelerated fatigue tests in laboratories. Although these methods can provide estimated values of fatigue life to a certain extent, they generally have problems such as poor real-time performance, inability to dynamically reflect the state of the structure in the actual complex environment, and insufficient consideration of environmental impacts. These deficiencies make it difficult to timely discover and handle potential structural problems during platform operation, posing significant safety hazards. Therefore, there is an urgent need for a fatigue life prediction method that can comprehensively consider the actual working environment, material properties, and real-time stress state to improve the safety and operation efficiency of the platform.

[0004] Digital twin technology has been widely applied in various industrial fields in recent years. By closely integrating virtual and real, it can reflect the state and behavior of physical systems in real time. Applying digital twin technology to the fatigue life prediction of jacket-type platforms can achieve precise monitoring and prediction of the actual operating state of the platforms, providing a new way to timely discover and respond to structural fatigue damage.

[0005] In the prior art, Application No. CN116992733A discloses an online prediction method for the fatigue life of a jacket structure based on digital twin, which includes: establishing a three-dimensional model of the jacket; performing dynamic simulation analysis on the three-dimensional model of the jacket, importing a time history data of wind and wave loads as the input of environmental loads, and setting the boundary conditions to obtain a finite element model of the jacket; using finite element software to analyze the dynamic response and fatigue characteristics of the jacket structure and establishing a dynamic response data set and a fatigue life data set; using model reduction technology to construct a dynamic reduced-order model of the jacket; using the long short-term memory neural network algorithm to construct a fatigue life prediction model based on time series; and establishing a digital twin monitoring system for the fatigue life of the jacket structure. This prior art can establish a digital twin model for the real jacket-type platform structure, realize real-time online accurate prediction of the fatigue life of the jacket structure, and can effectively improve the timeliness and accuracy of fatigue life prediction. However, there are still defects in the prior art. This prior art only considers the stress and strain data during the wind and wave process, while ignoring the impact of the marine environment on the fatigue life of the jacket-type platform, which will lead to an incomplete assessment of the fatigue life; secondly, the fatigue life of the material will be reduced under the action of stress. The prior art only takes a set of data for life prediction while ignoring the impact of the previous stress on the fatigue life of the material, which will make the prediction results lack accuracy and reliability.

[0006] The above information disclosed in the background art section is only used to enhance the understanding of the background of the present disclosure, and 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

[0007] The purpose of the present invention is to provide an online prediction method and system for the fatigue life of a jacket-type platform based on digital twin to solve the problems raised in the above background art.

[0008] To achieve the above purpose, the present invention provides the following technical solutions:

[0009] An online prediction method for the fatigue life of a jacket-type platform based on digital twin, the specific steps include:

[0010] Step 1: Collect historical stress test data of the jacket-type platform material, material property data of the jacket-type platform, stress data and environmental data; the historical stress test data includes the test stress magnitude and the test fatigue life; the material property data of the jacket-type platform includes the fatigue limit, toughness and coating corrosion resistance; the stress data includes the stress magnitude time series data, and the environmental data includes the environmental temperature of the sea area where the platform is located, the seawater pH value, dissolved oxygen content and salinity;

[0011] Step 2: Compose the historical stress magnitudes into a dataset, construct an ideal fatigue life prediction model with the test fatigue life as the label, and use the dataset to train and optimize the ideal fatigue life prediction model to obtain the trained ideal fatigue life prediction model;

[0012] Step 3: Extract features from the stress data to obtain strain stress, input the strain stress into the optimized ideal fatigue life prediction model to obtain the ideal fatigue life, perform mathematical analysis on the environmental data to generate an environmental correction factor, and use the environmental correction factor to correct the ideal fatigue life to generate the corrected fatigue life;

[0013] Step 4: Conduct damage analysis on the stress data to calculate the cumulative stress damage, obtain the material fatigue resistance based on the material property data of the jacket platform, and generate fatigue damage based on the cumulative stress damage and the material fatigue resistance; perform secondary correction on the corrected fatigue life through the fatigue damage to obtain the actual fatigue life;

[0014] Step 5: Construct a digital twin platform, transmit the stress data and environmental data of the jacket platform to the digital twin platform of the jacket platform to construct a digital model of the jacket platform, and achieve high-precision real-time mapping between the physical model and the digital model of the jacket platform.

[0015] Further, the specific logic for extracting the strain stress is as follows: Calculate the average stress value between the maximum stress value and the adjacent minimum stress value of each segment, and perform a weighted mean operation on the average stress value to obtain the strain stress; The specific formula for calculating the average stress value is:

[0016]

[0017] where is the average stress value, T is the total time of the time series data, and F(t) is the stress time series data;

[0018] The specific logic for obtaining the strain stress is:

[0019]

[0020] where Ff is the strain stress, t i is the time interval of the time period corresponding to the i-th average stress value, is the i-th average stress value, and n is the number of average stress values in the stress time series data.

[0021] Further, the specific logic for generating the corrected fatigue life is as follows: Input the strain stress into the optimized ideal fatigue life prediction model to obtain the ideal fatigue life, perform mathematical analysis on the environmental data to generate an environmental correction factor, and use the environmental correction factor to correct the ideal fatigue life to generate the corrected fatigue life; the specific formula for generating the environmental correction factor is:

[0022] A = W(PH - 7) 2 tan -1 C O *C Y

[0023] where A is the environmental correction factor, W is the temperature, PH is the seawater pH value, C o is the dissolved oxygen content, and C Y is the salinity;

[0024] The specific formula for generating the corrected fatigue life is:

[0025]

[0026] where TD is the corrected fatigue life and Td is the ideal fatigue life.

[0027] Further, the specific logic for generating the stress damage is as follows: Perform threshold processing on the stress data through the fatigue limit, transform the stress data into effective stress data, and generate the stress damage from the effective stress data; the specific logic for generating the effective stress data is:

[0028]

[0029] where F i ′ is the effective stress data, F i is the stress data, and F O is the fatigue limit;

[0030] The specific formula for generating the stress damage is:

[0031]

[0032] where Hu is the stress damage, F′ i (t) is the effective stress time series data, and L is the length of the time series data.

[0033] Further, the specific logic for generating the fatigue damage is as follows: Obtain the material fatigue resistance based on the material property data of the jacket platform, and generate the fatigue damage based on the cumulative stress damage and the material fatigue resistance; the specific logic for generating the material fatigue resistance is:

[0034] K = F O *R*e Hi

[0035] Among them, K is the fatigue resistance of the material, F O is the fatigue limit, R is the toughness, and Hi is the corrosion resistance of the coating;

[0036] The specific formula for generating fatigue damage is:

[0037]

[0038] Among them, Hp is the fatigue damage and Hu is the stress damage.

[0039] Furthermore, the specific logic for generating the actual fatigue life is:

[0040] The maximum fatigue amount of the jacket platform is calculated by correcting the fatigue life and the real-time stress value. The remaining damage amount is obtained by subtracting the fatigue damage from the maximum fatigue amount, and the actual fatigue life is obtained by dividing the remaining damage amount by the real-time stress. The specific formula for calculating the maximum fatigue amount is:

[0041] Mo = TD * F1

[0042] Among them, Mo is the maximum fatigue amount, TD is the corrected fatigue life, and F1 is the real-time stress;

[0043] The specific formula for generating the actual fatigue life is:

[0044] Ms = Mo - Hp

[0045] Among them, Ms is the actual fatigue amount and Hp is the fatigue damage;

[0046] The specific formula for generating the actual fatigue life is:

[0047]

[0048] Among them, Tn is the actual fatigue life.

[0049] The present invention further provides an online prediction system for the fatigue life of a jacket platform based on digital twin. The system is used to implement the online prediction method for the fatigue life of a jacket platform based on digital twin, and specifically includes:

[0050] A data acquisition module, which is used to collect historical stress test data of the jacket platform material, material property data, stress data, and environmental data of the jacket platform; the historical stress test data includes the magnitude of the test stress and the test fatigue life; the material property data of the jacket platform includes the fatigue limit, toughness, and corrosion resistance of the coating; the stress data includes the time series data of the stress magnitude, and the environmental data includes the environmental temperature of the sea area where the platform is located, the pH value of seawater, the dissolved oxygen content, and the salinity;

[0051] The modeling optimization module is used to form a dataset with historical stress magnitudes, construct an ideal fatigue life prediction model with the test fatigue life as the label, and use the dataset to train and optimize the ideal fatigue life prediction model to obtain the trained ideal fatigue life prediction model;

[0052] The preliminary correction module is used to extract features from the stress data to obtain strain stress, input the strain stress into the optimized ideal fatigue life prediction model to obtain the ideal fatigue life, perform mathematical analysis on the environmental data to generate an environmental correction factor, and use the environmental correction factor to correct the ideal fatigue life to generate the corrected fatigue life;

[0053] The damage calculation module is used to perform damage analysis and calculation on the stress data to accumulate stress damage, obtain the material fatigue resistance according to the material property data of the jacket platform, and generate fatigue damage based on the accumulated stress damage and the material fatigue resistance; the actual fatigue life is obtained by performing secondary correction on the corrected fatigue life through the fatigue damage;

[0054] The digital twin module is used to construct a digital twin platform, transmit the stress data and environmental data of the jacket platform to the digital twin platform of the jacket platform to construct a digital model of the jacket platform, and realize the high-precision real-time mapping between the physical model and the digital model of the jacket platform.

[0055] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0056] The present invention generates an environmental correction factor through environmental data and uses this correction factor to correct the predicted ideal fatigue life, fully considering the influence of environmental factors on the fatigue life, making the fatigue life prediction in the marine environment more accurate. In addition, the present invention performs damage analysis on all previous stress data, calculates the accumulated stress damage reflecting the material damage of the jacket platform, and performs secondary correction on the predicted fatigue life based on this to improve the reliability of the prediction results. Through the dual correction mechanism, the present invention can more comprehensively consider the influence of actual working conditions on the fatigue life of the jacket platform, thereby significantly improving the accuracy and reliability of the fatigue life prediction. Description of the Drawings

[0057] Figure 1 It is a schematic diagram of the overall method flow of the present invention.

[0058] Figure 2 It is a schematic diagram of the overall system structure of the present invention. Detailed Embodiments

[0059] To make the objectives, technical solutions, and advantages of the present invention clearer and more understandable, the following further details the present invention in conjunction with specific embodiments.

[0060] It should be noted that, unless otherwise defined, the technical terms or scientific terms used in the present invention shall have the ordinary meanings understood by those with ordinary skills in the field to which the present invention pertains. The "first", "second" and similar terms used in the present invention do not denote any order, quantity or importance, but are only used to distinguish different components. The terms such as "comprising" or "including" mean that the elements or objects appearing before this word cover the elements or objects listed after this word and their equivalents, without excluding other elements or objects. The terms such as "connected" or "linked" are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. The terms such as "upper", "lower", "left" and "right" are only used to represent relative position relationships. When the absolute position of the object being described changes, the relative position relationship may also change accordingly.

[0061] Embodiment:

[0062] Please refer to Figure 1 , the present invention provides a technical solution:

[0063] An online prediction method for the fatigue life of a jacket-type platform based on digital twin, the specific steps include:

[0064] Step 1: Collect historical stress test data of the jacket-type platform material, material property data, stress data and environmental data of the jacket-type platform; the historical stress test data includes the magnitude of the test stress and the test fatigue life; the material property data of the jacket-type platform includes fatigue limit, toughness and coating corrosion resistance; the stress data includes stress magnitude time series data, and the environmental data includes the environmental temperature of the sea area where the platform is located, seawater pH value, dissolved oxygen content and salinity;

[0065] The intersection position of the diagonal braces is the stress concentration position of the jacket. The stress damage condition at this position directly determines the fatigue life of the entire jacket-type platform. The stress test data is obtained by conducting fatigue life tests on the materials at the intersection position of the diagonal braces of the jacket-type platform. The materials are subjected to fatigue tests using a fatigue testing machine in the laboratory, and the number of cycles at different stress levels is measured until the materials fracture. The S-N curve is used to determine the fatigue limit of the materials. The toughness of the materials is measured through Charpy impact tests; the corrosion resistance of the coating is measured through salt spray tests. The specific logic is as follows: Salt spray data in the ocean is collected and used to configure simulated salt spray. The metal coating of the jacket-type platform materials is placed in the configured simulated salt spray environment, and the time required for the metal coating to be completely corroded is recorded. Dividing the recorded time by the thickness of the metal coating gives the corrosion resistance of the coating. These material data can reflect the ability of the materials at the intersection position of the diagonal braces to resist stress damage and corrosion. The stress data and environmental data at the intersection position of the diagonal braces of the jacket-type platform are measured by stress sensors, temperature sensors, pH meters, dissolved oxygen sensors, and salinity meters placed on the jacket-type platform; the stress data can provide an important basis for the estimation of stress damage, and the environmental data can provide an important basis for evaluating the corrosion ability of the environment on the materials at the intersection position of the diagonal braces and further evaluating the estimation of stress damage suffered by the materials at the intersection position of the diagonal braces.

[0066] Step 2: Construct a dataset with historical stress magnitudes, build an ideal fatigue life prediction model with the test fatigue life as the label, and use the dataset to train and optimize the ideal fatigue life prediction model to obtain the trained ideal fatigue life prediction model;

[0067] A feedforward neural network is adopted, with the test fatigue life as the label, and the dataset is used to train and optimize the ideal fatigue life prediction model; it should be noted that training and optimizing the ideal fatigue life prediction model with the test fatigue life as the label can adopt existing technologies, specifically including: an input layer, a hidden layer, an output layer, and an activation function, and the root mean square error loss function is adopted; the input data is calculated through the network once to obtain the output result, the loss function is calculated based on the predicted value and the true value, the gradient of the loss function with respect to each weight and bias is calculated through the chain rule, and the gradient descent algorithm is used to update the weights and biases of the network to minimize the loss function. The feedforward neural network has a strong non-linear mapping ability. Fatigue life prediction usually involves complex material properties and various working conditions, and non-linear relationships are difficult to describe with traditional linear models. The neural network can capture complex non-linear relationships through multiple hidden layers, thus predicting the fatigue life more accurately.

[0068] Step 3: Extract features from the stress data to obtain strain stress, input the strain stress into the optimized ideal fatigue life prediction model to obtain the ideal fatigue life, conduct a mathematical analysis on temperature, seawater pH value, dissolved oxygen content, and salinity to generate an environmental correction factor, and use the environmental correction factor to correct the ideal fatigue life to generate the corrected fatigue life;

[0069] The specific logic for extracting the strain stress is as follows: Calculate the average stress value between the maximum stress value and the adjacent minimum stress value for each segment, and perform a weighted average operation on the average stress value to obtain the strain stress; The specific formula for calculating the average stress value is:

[0070]

[0071] where, is the average stress value, T is the total time of the time series data, and F(t) is the stress time series data;

[0072] The specific logic for obtaining the strain stress is:

[0073]

[0074] where, Ff is the strain stress, t i is the time interval of the time period corresponding to the i-th average stress value, is the i-th average stress value, and n is the number of average stress values in the stress time series data. The strain stress Ff comprehensively reflects the comprehensive situation of the stress data. The larger its value, the greater the comprehensive stress on the jacket-type platform. Its generation can provide an important basis for predicting the life of the jacket-type platform.

[0075] The specific logic for generating the corrected fatigue life is as follows: Input the strain stress into the optimized ideal fatigue life prediction model to obtain the ideal fatigue life, conduct a mathematical analysis on temperature, seawater pH value, dissolved oxygen content, and salinity to generate an environmental correction factor, and use the environmental correction factor to correct the ideal fatigue life to generate the corrected fatigue life; The specific formula for generating the environmental correction factor is:

[0076] A = W(PH - 7) 2 tan -1 C O *C Y

[0077] where, A is the environmental correction factor, W is the temperature, PH is the seawater pH value, C O is the dissolved oxygen content, C Yis the salinity; the environmental correction factor A reflects the comprehensive influence of the marine environment on the fatigue life of the jacket platform. The larger its value, the greater the negative impact of the marine environment on the fatigue life of the jacket. The generation of this environmental factor can provide an important basis for the accurate assessment of the fatigue life, temperature W and dissolved oxygen C O The larger they are, the more serious the biocorrosion of the jacket platform by microorganisms in the marine environment; pH value and salinity C Y The larger the value, the more intense the chemical corrosion of the jacket platform by the marine environment.

[0078] The specific formula for generating the corrected fatigue life is:

[0079]

[0080] Among them, TD is the corrected fatigue life, and TD is the ideal fatigue life. The ideal fatigue life Td reflects the fatigue life of the jacket platform material under ideal conditions. The larger its value, the longer the fatigue life of the jacket platform; the corrected fatigue life TD reflects the fatigue life of the jacket platform under the marine environment. The larger its value, the longer the fatigue life of the jacket platform material. The generation of the corrected fatigue life TD can provide an important basis for accurately predicting the fatigue life of the jacket platform

[0081] Step 4: Perform damage analysis on the stress data to calculate the cumulative stress damage, obtain the material fatigue resistance according to the material property data of the jacket platform, and generate fatigue damage based on the cumulative stress damage and the material fatigue resistance; perform secondary correction on the corrected fatigue life through the fatigue damage to obtain the actual fatigue life;

[0082] The specific logic for generating stress damage is: perform threshold processing on the stress data through the fatigue limit, transform the stress data into effective stress data, and generate stress damage from the effective stress data; the specific logic for generating effective stress data is:

[0083]

[0084] Among them, F i ′ is the effective stress data, F i is the stress data, F O is the fatigue limit; when the stress received does not reach the fatigue limit, the fatigue life of the material is infinite, and threshold processing of the stress data can obtain the effective stress data that can directly affect the fatigue life.

[0085] The specific formula for generating stress damage is:

[0086]

[0087] Among them, Hu is the stress damage, F′ i(t) is the time series data of effective stress, and L is the length of the time series data. The stress damage Hu reflects the damage of the jacket platform caused by the stress since the installation of the jacket platform. The larger the value, the more damage the jacket platform suffers from the stress.

[0088] The specific logic for generating fatigue damage is as follows: The fatigue resistance of the material is obtained based on the material property data of the jacket platform, and the fatigue damage is generated according to the cumulative stress damage and the fatigue resistance of the material. The specific logic for generating the fatigue resistance of the material is as follows:

[0089] K = F O *R*e Hi

[0090] where K is the fatigue resistance of the material, F O is the fatigue limit, R is the toughness, and Hi is the coating corrosion resistance. The fatigue resistance K of the material reflects the ability of the jacket platform to resist external stress and marine erosion. The larger the value, the stronger the ability of the jacket platform to resist external stress and marine erosion;

[0091] The specific formula for generating fatigue damage is:

[0092]

[0093] where Hp is the fatigue damage and Hu is the stress damage. The fatigue damage Hp reflects the fatigue damage situation of the jacket platform. The larger the value, the more fatigue damage the jacket platform suffers.

[0094] The maximum fatigue amount of the jacket platform is calculated by correcting the fatigue life and the real-time stress value. The remaining damage amount is obtained by subtracting the fatigue damage from the maximum fatigue amount, and the actual fatigue life is obtained by dividing the remaining damage amount by the real-time stress. The specific formula for calculating the maximum fatigue amount is:

[0095] Mo = TD * F1

[0096] where Mo is the maximum fatigue amount, TD is the corrected fatigue life, and F1 is the real-time stress. The maximum fatigue amount Mo reflects the stress amount that the jacket platform can withstand. The larger the value, the more stress the jacket platform can withstand;

[0097] The specific formula for generating the actual fatigue life is:

[0098] Ms = Mo - Hp

[0099] where Ms is the actual fatigue amount and Hp is the fatigue damage. The actual fatigue amount Ms reflects the stress amount that the jacket platform can still withstand;

[0100] The specific formula for generating the actual fatigue life is:

[0101]

[0102] Among them, Tn is the actual fatigue life. The actual fatigue life Tn reflects the durability and reliability of the jacket-type platform under actual working conditions. The larger its value, the higher the durability and reliability.

[0103] Step 5: Build a digital twin platform, transmit the stress data and environmental data of the jacket-type platform to the digital twin platform of the jacket-type platform to build a digital model of the jacket-type platform, and realize the high-precision real-time mapping between the physical model and the digital model of the jacket-type platform.

[0104] Please refer to Figure 2 , the present invention further provides an online fatigue life prediction system for a jacket-type platform based on digital twin. The system is used to implement the online fatigue life prediction method for a jacket-type platform based on digital twin, and specifically includes:

[0105] A data acquisition module, which is used to collect historical stress test data of the jacket-type platform material, material property data of the jacket-type platform, stress data and environmental data; the historical stress test data includes the magnitude of the test stress and the test fatigue life; the material property data of the jacket-type platform material includes fatigue limit, toughness and coating corrosion resistance; the stress data includes stress magnitude time series data, and the environmental data includes the environmental temperature of the sea area where the platform is located, the pH value of seawater, dissolved oxygen content and salinity;

[0106] A modeling and optimization module, which is used to form a data set with the historical stress magnitudes, build an ideal fatigue life prediction model with the test fatigue life as the label, and use the data set to train and optimize the ideal fatigue life prediction model to obtain the trained ideal fatigue life prediction model;

[0107] A preliminary correction module, which is used to extract the strain stress by performing feature extraction on the stress data, input the strain stress into the optimized ideal fatigue life prediction model to obtain the ideal fatigue life, perform mathematical analysis on the environmental data to generate an environmental correction factor, and use the environmental correction factor to correct the ideal fatigue life to generate a corrected fatigue life;

[0108] A damage calculation module, which is used to perform damage analysis on the stress data to calculate the cumulative stress damage, obtain the material fatigue resistance according to the material property data of the jacket-type platform material, and generate fatigue damage according to the cumulative stress damage and the material fatigue resistance; perform secondary correction on the corrected fatigue life through the fatigue damage to obtain the actual fatigue life;

[0109] A digital twin module is used to build a digital twin platform, transmit the stress data and environmental data of the jacket platform to the digital twin platform of the jacket platform, construct a digital model of the jacket platform, and achieve high-precision real-time mapping between the physical model and the digital model of the jacket platform.

[0110] All the above formulas are dimensionless and take their numerical values for calculation. The formulas are obtained by collecting a large amount of data for software simulation to get a formula closest to the actual situation. The preset parameters in the formulas are set by those skilled in the art according to the actual situation.

[0111] The above embodiments can be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. Those skilled in the art can realize that the units and algorithm steps of each example described in combination with the embodiments disclosed herein can be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are executed by hardware or software methods depends on the specific application and design constraints of the technical solution.

[0112] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units. They can be located in one place or distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0113] The above is only the specific implementation manner of this application, but the protection scope of this application is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed in this application, and all should be covered by the protection scope of this application.

Claims

1. An online prediction method for the fatigue life of a jacket-type platform based on digital twin, characterized in that, The specific steps include: Step 1: Collect historical stress test data of jacket platform materials, material property data, stress data, and environmental data of the jacket platform; the historical stress test data includes the magnitude of the test stress and the test fatigue life; the material property data of the jacket platform includes the fatigue limit, toughness, and coating corrosion resistance; the stress data includes stress magnitude time series data, and the environmental data includes the environmental temperature of the sea area where the platform is located, the seawater pH value, dissolved oxygen content, and salinity; Step 2: Construct a dataset with historical stress magnitudes, build an ideal fatigue life prediction model with the test fatigue life as the label, and use the dataset to train and optimize the ideal fatigue life prediction model to obtain the trained ideal fatigue life prediction model; Step 3: Extract features from the stress data to obtain strain stress, input the strain stress into the optimized ideal fatigue life prediction model to obtain the ideal fatigue life, perform mathematical analysis on the environmental data to generate an environmental correction factor, and use the environmental correction factor to correct the ideal fatigue life to generate the corrected fatigue life; Step 4: Conduct damage analysis on the stress data to calculate the cumulative stress damage, obtain the material fatigue resistance according to the material property data of the jacket platform, and generate fatigue damage based on the cumulative stress damage and the material fatigue resistance; perform secondary correction on the corrected fatigue life through the fatigue damage to obtain the actual fatigue life; Step 5: Construct a digital twin platform, transmit the stress data and environmental data of the jacket platform to the digital twin platform of the jacket platform to build a digital model of the jacket platform, and achieve high-precision real-time mapping between the physical model and the digital model of the jacket platform.

2. The online prediction method for the fatigue life of a jacket-type platform based on digital twin according to claim 1, wherein: The specific logic for extracting strain stress is: Calculate the average stress value between the maximum stress value and the adjacent minimum stress value for each segment, and perform weighted mean operation on the average stress value to obtain the strain stress; the specific formula for calculating the average stress value is: Among them, is the mean stress value, T is the total time of the time series data, and F(t) is the stress time series data; The specific logic for obtaining the strain stress is: Among them, Ff is the strain stress, t i is the time interval of the time period corresponding to the i-th mean stress value, is the i-th mean stress value, and n is the number of mean stress values in the stress time series data.

3. The online prediction method for the fatigue life of a jacket-type platform based on digital twin according to claim 1, wherein: The specific logic for generating the corrected fatigue life is: Input the strain stress into the optimized ideal fatigue life prediction model to obtain the ideal fatigue life, perform mathematical analysis on the environmental data to generate an environmental correction factor, and use the environmental correction factor to correct the ideal fatigue life to generate the corrected fatigue life; the specific formula for generating the environmental correction factor is: A = W(PH - 7) 2 tan -1 C O *C Y Among them, A is the environmental correction factor, W is the temperature, PH is the seawater PH value, and C o is the dissolved oxygen content, and C Y is the salinity; The specific formula for generating the corrected fatigue life is: Where, TD is the corrected fatigue life, and Td is the ideal fatigue life.

4. A method for online prediction of the fatigue life of a jacket-type platform based on digital twin according to claim 1, characterized in that: The specific logic for generating stress damage is: Perform threshold processing on the stress data through the fatigue limit to transform the stress data into effective stress data, and generate stress damage from the effective stress data; the specific logic for generating the effective stress data is: Among them, F i ′ is the effective stress data, and F i is the stress data, and F o is the fatigue limit; The specific formula for generating stress damage is: Among them, Hu is the stress damage, and F′ i (t) is the effective stress time series data, and L is the length of the time series data.

5. The online prediction method for the fatigue life of a jacket-type platform based on digital twin according to claim 1, wherein: The specific logic for generating fatigue damage is: Obtain the material fatigue resistance according to the material property data of the jacket platform, and generate fatigue damage based on the cumulative stress damage and the material fatigue resistance; the specific logic for generating the material fatigue resistance is: K = F O *R*e Hi where K is the material fatigue resistance, F O is the fatigue limit, R is the toughness, and Hi is the coating corrosion resistance; The specific formula for generating fatigue damage is: Where, Hp is the fatigue damage, and Hu is the stress damage.

6. The online prediction method for the fatigue life of a jacket-type platform based on digital twin according to claim 1, wherein: The specific logic for generating the actual fatigue life is as follows: The maximum fatigue amount of the jacket platform is calculated by correcting the fatigue life and the real-time stress value. The remaining damage amount is obtained by subtracting the fatigue damage from the maximum fatigue amount, and the actual fatigue life is obtained by dividing the remaining damage amount by the real-time stress. The specific formula for calculating the maximum fatigue amount is: Mo = TD * F1 where Mo is the maximum fatigue amount, TD is the corrected fatigue life, and F1 is the real-time stress; The specific formula for generating the actual fatigue life is: Ms = Mo - Hp where Ms is the actual fatigue amount and Hp is the fatigue damage; The specific formula for generating the actual fatigue life is: where Tn is the actual fatigue life.

7. An online prediction system for the fatigue life of a jacket-type platform based on digital twin, characterized in that: The system is used to implement the online prediction method for the fatigue life of the jacket platform based on digital twin described in any one of claims 1-6, and specifically includes: A data acquisition module, which is used to collect historical stress test data of the jacket platform material, material property data of the jacket platform, stress data, and environmental data; the historical stress test data includes the magnitude of the test stress and the test fatigue life; the material property data of the jacket platform includes the fatigue limit, toughness, and coating corrosion resistance; the stress data includes the stress magnitude time series data, and the environmental data includes the environmental temperature of the sea area where the platform is located, the seawater pH value, dissolved oxygen content, and salinity; A modeling and optimization module, which is used to form a data set with the historical stress magnitudes, construct an ideal fatigue life prediction model with the test fatigue life as the label, and use the data set to train and optimize the ideal fatigue life prediction model to obtain the trained ideal fatigue life prediction model; A preliminary correction module, which is used to extract features from the stress data to obtain strain stress, input the strain stress into the optimized ideal fatigue life prediction model to obtain the ideal fatigue life, perform mathematical analysis on the environmental data to generate an environmental correction factor, and use the environmental correction factor to correct the ideal fatigue life to generate a corrected fatigue life; A damage calculation module, which is used to: perform damage analysis on the stress data to calculate the cumulative stress damage, obtain the material fatigue resistance according to the material property data of the jacket platform, and generate fatigue damage according to the cumulative stress damage and the material fatigue resistance; perform secondary correction on the corrected fatigue life through the fatigue damage to obtain the actual fatigue life; A digital twin module, which is used to construct a digital twin platform, transmit the stress data and environmental data of the jacket platform to the digital twin platform of the jacket platform to construct a digital model of the jacket platform, and realize the high-precision real-time mapping between the physical model and the digital model of the jacket platform.

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