A tension leg platform towing installation risk early warning method and device

Abstract

This invention relates to the field of marine engineering technology, and more particularly to a method and device for early warning of risks during the towing and installation of a tension leg platform. The invention acquires environmental data, platform motion data, structural response data, and operational status data during the towing and installation process of the tension leg platform; based on the environmental data, platform motion data, structural response data, and operational status data, it determines the motion parameters and mechanical parameters for the next moment; based on the mechanical parameters, it determines a comprehensive motion risk coefficient; wherein, the comprehensive motion risk coefficient is used to characterize the degree to which the overall motion attitude of the platform deviates from the safe range; based on the motion parameters, it determines a structural safety margin coefficient; the structural safety margin coefficient is used to characterize the overload risk of the tension leg tension force; based on the comprehensive motion risk coefficient and the structural safety margin coefficient, it provides early warning of risks during the towing and installation of the tension leg platform. Thus, this invention can accurately predict the risks of the tension leg platform during the towing and installation process.

Description

Technical Field

[0001] This invention relates to the field of marine engineering technology, and in particular to a method and device for early warning of risks associated with the towing and installation of tension leg platforms. Background Technology

[0002] Tension leg platforms are floating platforms widely used in deep-sea oil and gas development. Towing them to the target area and installing the tension legs is a technically complex and extremely risky offshore operation. This process is influenced by the coupling effects of dynamic marine environmental loads such as wind, waves, and currents, the dynamic response of the platform and tooling structures, and the timing of operations. Currently, the towing and installation of tension leg platforms mainly relies on engineers' experience and pre-established static operating procedures. The decision-making process suffers from the following significant problems: Poor risk predictability: There is a lack of effective real-time prediction and early warning methods for risks such as structural overload, mooring resonance, and excessive roll / pitch that may be caused by sudden environmental changes or improper matching of operating parameters. Existing early warning methods are mostly based on single-parameter threshold alarms, which cannot comprehensively assess the potential dangers caused by the coupling of multiple factors. Limited risk assessment dimensions: Current technology only monitors individual physical quantities (such as tension and tilt) independently, issuing an alarm when a certain indicator exceeds the limit. However, actual accidents are often triggered by multiple parameters simultaneously trending towards unfavorable states. Judging solely by a single indicator threshold is prone to missed or false alarms, failing to accurately reflect the overall risk situation.

[0003] With the development of artificial intelligence technology, especially the breakthroughs in deep learning in processing multidimensional time series data, a new technical path has been provided to solve the above problems.

[0004] Based on this, the present invention proposes a method and device for early warning of risks during the towing and installation of tension leg platforms to solve the problem of how to accurately predict the risks of tension leg platforms during the towing and installation process. Summary of the Invention

[0005] To address the problem of accurately predicting risks during the towing and installation of tension leg platforms, this invention provides a method and apparatus for predicting risks during the towing and installation of tension leg platforms.

[0006] In a first aspect, embodiments of the present invention provide a method for early warning of risks associated with the towing and installation of a tension leg platform, comprising: Acquire environmental data, platform motion data, structural response data, and operational status data during the towing and installation of the tension leg platform; Based on the environmental data, the platform motion data, the structural response data, and the operational status data, determine the motion parameters and mechanical parameters for the next moment; Based on the aforementioned mechanical parameters, a comprehensive motion risk coefficient is determined; wherein, the comprehensive motion risk coefficient is used to characterize the degree to which the overall motion posture of the platform deviates from the safe range; Based on the aforementioned motion parameters, a structural safety margin coefficient is determined; the structural safety margin coefficient is used to characterize the overload risk of the tension leg tension force. Based on the comprehensive motion risk coefficient and the structural safety margin coefficient, an early warning is issued for the towing installation risk of the tension leg platform.

[0007] Secondly, embodiments of the present invention provide an early warning device for the risk of towing and installation of a tension leg platform, comprising: The acquisition module is used to acquire environmental data, platform motion data, structural response data, and operational status data during the towing and installation of the tension leg platform. The first data processing module is used to determine the motion parameters and mechanical parameters at the next moment based on the environmental data, the platform motion data, the structural response data, and the operation status data. The second data processing module is used to determine the comprehensive motion risk coefficient based on the mechanical parameters; wherein the comprehensive motion risk coefficient is used to characterize the degree to which the overall motion posture of the platform deviates from the safe range; The third data processing module is used to determine the structural safety margin coefficient based on the motion parameters; the structural safety margin coefficient is used to characterize the overload risk of the tension leg tension force. The fourth data processing module is used to provide early warning of the risks associated with the towing and installation of the tension leg platform based on the comprehensive motion risk coefficient and the structural safety margin coefficient.

[0008] Thirdly, embodiments of the present invention also provide an electronic device, including a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, it implements the method described in any embodiment of the present invention.

[0009] Fourthly, embodiments of the present invention also provide a computer-readable storage medium having a computer program stored thereon, which, when executed in a computer, causes the computer to perform the methods described in any embodiment of the present invention.

[0010] This invention provides a method and apparatus for early warning of risks during the towing and installation of a tension leg platform. It acquires environmental data, platform motion data, structural response data, and operational status data during the towing and installation process. Based on these data, a pre-set deep learning prediction model is used for data mining and trend extrapolation to determine the platform's motion and mechanical parameters for the next moment, enabling proactive prediction of the operational status. Based on the obtained mechanical parameters, a comprehensive motion risk coefficient is calculated. This coefficient quantifies the degree to which the overall motion posture of the tension leg platform deviates from the safe operating range; a higher coefficient indicates a higher risk of platform instability, excessive roll / pitch, or drift. Simultaneously, based on the predicted motion parameters, a structural safety margin coefficient is calculated. This coefficient specifically characterizes the potential risks of tension leg tension overload, structural fatigue damage, and fracture failure; a lower coefficient indicates a greater overload risk and a smaller safety redundancy for the tension leg structure. Ultimately, by comprehensively considering the magnitude and changing trends of the comprehensive motion risk coefficient and the structural safety margin coefficient, multi-level early warning thresholds are set to provide real-time early warnings for various risks such as attitude instability and structural overload during the towing and installation of the tension leg platform, effectively preventing safety accidents. Thus, this invention can accurately provide early warnings of risks during the towing and installation of the tension leg platform. Attached Figure Description

[0011] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0012] Figure 1 A flowchart illustrating a risk warning method for towing installation of a tension leg platform according to one embodiment is shown; Figure 2 This is a hardware architecture diagram of an electronic device provided in an embodiment of the present invention; Figure 3 A structural diagram of a warning device for the risk of towing installation of a tension leg platform according to one embodiment is shown. Detailed Implementation

[0013] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are some embodiments of the present invention, but not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0014] Please refer to Figure 1 This invention provides a method for early warning of risks associated with the towing and installation of tension leg platforms, comprising: Step 100: Acquire environmental data, platform motion data, structural response data, and operational status data during the towing and installation of the tension leg platform; Step 102: Based on environmental data, platform motion data, structural response data, and operational status data, determine the motion parameters and mechanical parameters for the next moment; Step 104: Determine the comprehensive motion risk coefficient based on mechanical parameters; wherein, the comprehensive motion risk coefficient is used to characterize the degree to which the overall motion posture of the platform deviates from the safe range; Step 106: Determine the structural safety margin coefficient based on the motion parameters; the structural safety margin coefficient is used to characterize the overload risk of the tension leg tension force. Step 108: Based on the comprehensive motion risk coefficient and structural safety margin coefficient, provide early warning of the risks of towing and installing the tension leg platform.

[0015] In this embodiment, environmental data, platform motion data, structural response data, and operational status data are acquired during the towing and installation of the tension leg platform. Based on these data, a pre-defined deep learning prediction model is used for data mining and trend extrapolation to determine the platform's motion and mechanical parameters for the next moment, enabling proactive prediction of the operational status. Based on the obtained mechanical parameters, a comprehensive motion risk coefficient is calculated. This coefficient quantifies the degree to which the overall motion posture of the tension leg platform deviates from the safe operating range; a higher coefficient indicates a higher risk of platform instability, excessive roll / pitch, or drift. Simultaneously, based on the predicted motion parameters, a structural safety margin coefficient is calculated. This coefficient specifically characterizes the potential risks of tension leg tension overload, structural fatigue damage, and fracture failure; a lower coefficient indicates a greater risk of overload and a smaller safety redundancy for the tension leg structure. Ultimately, by comprehensively considering the magnitude and changing trends of the comprehensive motion risk coefficient and the structural safety margin coefficient, multi-level early warning thresholds are set to provide real-time early warnings for various risks such as attitude instability and structural overload during the towing and installation of the tension leg platform, effectively preventing safety accidents. Thus, this invention can accurately provide early warnings of risks during the towing and installation of the tension leg platform.

[0016] In one embodiment of the present invention, the motion parameters and mechanical parameters for the next moment are determined based on environmental data, platform motion data, structural response data, and operational state data, including: Based on environmental data, platform motion data, structural response data, and operational status data, the input state vector is determined. The input state vector is subjected to spatial feature extraction and temporal feature extraction respectively, resulting in spatial feature vector and temporal feature vector; Spatial feature vectors and temporal feature vectors are concatenated to obtain feature vectors that fuse spatiotemporal information; The feature vectors that integrate spatiotemporal information are input into a multi-task learning network to obtain the motion parameters and mechanical parameters at the next moment.

[0017] In this embodiment, based on environmental data, platform motion data, structural response data, and operational state data, the motion and mechanical parameters for the next moment are determined. Then, a dual-branch feature extraction structure is employed to extract spatial and temporal features from the input state vector. The spatial feature extraction branch uses a convolutional neural network to mine the spatial correlation characteristics between different types of data (such as environmental load and structural response), outputting a spatial feature vector that characterizes the coupling relationship between multiple data sources. The temporal feature extraction branch utilizes a long short-term memory network to capture the dynamic trends of various data over time, extracting a temporal feature vector that reflects the temporal evolution of the operation process. Subsequently, the extracted spatial and temporal feature vectors are dimensionally aligned and fused to form a fused spatiotemporal information feature vector that combines spatial coupling characteristics with temporal dynamic patterns. This effectively compensates for the limitations of a single feature dimension and improves the comprehensiveness and accuracy of feature representation. Finally, the feature vectors that integrate spatiotemporal information are input into a pre-set multi-task learning network. This network, through its structure design of shared feature layers and independent prediction branches, can simultaneously and accurately predict the motion parameters (roll angle, pitch angle, etc.) and mechanical parameters (tension leg tension, structural stress, etc.) of the next moment.

[0018] In one embodiment of the present invention, environmental data includes wind speed, significant wave height, surface current velocity, and ocean current direction; platform motion data includes roll angle, pitch angle, bow angle, sway displacement, yaw displacement, and heave displacement; structural response data includes equivalent stress at the top of the tension leg, equivalent stress of the connector, and tension leg tension force; operational status data includes current towing speed, tension leg displacement, and tension force inside the tension leg; mechanical parameters include predicted tension force and predicted nodal equivalent stress; and motion parameters include predicted roll angle and predicted pitch angle.

[0019] In this embodiment, environmental data includes wind speed, significant wave height, surface current velocity, and ocean current direction. Platform motion data consists of parameters characterizing changes in platform attitude and position, including three attitude angles: roll, pitch, and yaw, and three positional displacement parameters: sway, roll, and heave, reflecting the platform's motion state during towing and installation. Structural response data focuses on monitoring the stress and deformation of key platform structures, including the equivalent stress at the top of the tension leg, the equivalent stress of the connector, and the tension leg tension force, directly related to platform structural safety. Operational status data includes on-site operational parameters, including the current towing speed, tension leg displacement, and tension leg internal tension force, reflecting the impact of human operation on the operation process. Corresponding predicted mechanical parameters include predicted tension force and predicted nodal equivalent stress, while motion parameters include predicted roll and predicted pitch angles.

[0020] In one embodiment of the present invention, the comprehensive sports risk coefficient is determined by the following formula: In the formula, To assess the overall sports risk factor, To predict the roll angle, To predict the pitch angle, As the first weighting coefficient, This is the second weighting coefficient. The preset safety roll angle, This is the preset safety pitch angle.

[0021] In this embodiment, the calculation formula for the comprehensive motion risk coefficient abandons the traditional single-index threshold judgment mode. Instead, it uses a weighted sum of square roots to couple the deviations of the predicted roll and pitch angles from the corresponding safety thresholds, constructing a dimensionless structural safety margin coefficient to achieve a comprehensive quantitative assessment of two-dimensional attitude risk. By introducing weighting coefficients to adapt to the differences in working conditions at different stages of operation, and by directly using predicted data for calculation, it overcomes the limitations of post-event alarms, achieving proactive early warning and solving the shortcomings of traditional methods such as missed and false alarms and poor risk predictability.

[0022] In one embodiment of the present invention, the structural safety margin factor is determined by the following formula: In the formula, For structural safety margin coefficient, To predict tension, To predict nodal equivalent stress, For the preset safety tension, The preset nodal equivalent stress.

[0023] In this embodiment, the formula for calculating the structural safety margin coefficient abandons the traditional single-index independent alarm mode and adopts the maximum value method. It integrates the ratio of predicted tension force, predicted nodal equivalent stress, and corresponding safety limits to construct a dimensionless structural safety margin coefficient, while taking into account the stress risk of tension legs and critical nodes. Directly using predicted data for calculation, it achieves advanced early warning, solving the shortcomings of traditional methods that cannot simultaneously reflect the coupled risks of multiple mechanical parameters and have delayed early warnings, thus improving the comprehensiveness and timeliness of structural safety assessment.

[0024] In one embodiment of the present invention, an early warning is provided for the towing and installation risks of the tension leg platform based on a comprehensive motion risk coefficient and a structural safety margin coefficient, including: When the largest of the comprehensive motion risk coefficient and the structural safety margin coefficient is greater than the first preset threshold and less than the second preset threshold, a level one warning is issued. A level-two warning is issued when the largest of the comprehensive motion risk coefficient and the structural safety margin coefficient is greater than the second preset threshold and less than the third preset threshold. When the largest of the comprehensive motion risk coefficient and the structural safety margin coefficient exceeds the third preset threshold, a level three warning is issued.

[0025] In this embodiment, the maximum value of two coefficients is used as the basis for determining the risk level. A tiered early warning system is implemented by comparing the maximum value with preset thresholds: when the maximum value is greater than a first preset threshold but less than a second preset threshold, it is determined to be a low-risk state, triggering a Level 1 warning and prompting on-site personnel to pay attention to changes in operating conditions and strengthen monitoring; when the maximum value is greater than a second preset threshold but less than a third preset threshold, it is determined to be a medium-risk state, triggering a Level 2 warning and initiating targeted intervention measures, such as adjusting operating parameters or towing strategies; when the maximum value is greater than a third preset threshold, it is determined to be a high-risk state, triggering a Level 3 warning and requiring the immediate activation of emergency response plans to avoid structural overload or attitude instability risks. This tiered early warning mechanism can match corresponding response measures according to the severity of the risk, significantly improving the scientific nature and safety of operational decisions.

[0026] In one embodiment of the present invention, when a Level 1 warning is issued, a message indicating a minor risk is sent to the user terminal; When a Level 2 warning is issued, a message prompting the user to reduce speed will be sent to the user's device. When a Level 3 warning is issued, a prompt message to take emergency avoidance measures will be sent to the user's device.

[0027] In this embodiment, when a Level 1 warning is triggered, a minor risk alert message is pushed to the user terminal to remind on-site personnel to strengthen operational monitoring; when a Level 2 warning is triggered, an instruction message to reduce speed is sent to guide personnel to adjust towing parameters in a timely manner to reduce risk; when a Level 3 warning is triggered, an alert message to take emergency evasive measures is sent, requiring the immediate activation of emergency response procedures, thereby achieving precise linkage between warning signals and response actions and ensuring operational safety.

[0028] like Figure 2 , Figure 3 As shown, this embodiment of the invention provides an early warning device for the risk of towing and installation of a tension leg platform. The device embodiment can be implemented through software, hardware, or a combination of both. From a hardware perspective, such as... Figure 2 The diagram shown is a hardware architecture diagram of an electronic device for an early warning device of a tension leg platform towing installation risk provided in an embodiment of the present invention. (Except for...) Figure 2 In addition to the processor, memory, network interface, and non-volatile memory shown, the electronic device in the embodiment may also include other hardware, such as a forwarding chip responsible for processing packets. Taking software implementation as an example, such as... Figure 3 As shown, a device in a logical sense is formed by the CPU of the electronic device in which it is located reading the corresponding computer program from the non-volatile memory into the memory for execution.

[0029] like Figure 3 As shown, this embodiment provides an early warning device for the risk of towing and installing a tension leg platform, comprising: The acquisition module 300 is used to acquire environmental data, platform motion data, structural response data, and operational status data during the towing and installation of the tension leg platform. The first data processing module 302 is used to determine the motion parameters and mechanical parameters at the next moment based on the environmental data, the platform motion data, the structural response data and the operation status data. The second data processing module 304 is used to determine the comprehensive motion risk coefficient based on the mechanical parameters; wherein the comprehensive motion risk coefficient is used to characterize the degree to which the overall motion posture of the platform deviates from the safe range; The third data processing module 306 is used to determine the structural safety margin coefficient based on the motion parameters; the structural safety margin coefficient is used to characterize the overload risk of the tension leg tension force. The fourth data processing module 308 is used to provide early warning of the risks of towing and installing the tension leg platform based on the comprehensive motion risk coefficient and the structural safety margin coefficient.

[0030] In one embodiment of the present invention, the first data processing module 302 is configured to perform the following operations: Based on the environmental data, the platform motion data, the structural response data, and the operational state data, an input state vector is determined; The input state vector is subjected to spatial feature extraction and temporal feature extraction respectively to obtain spatial feature vector and temporal feature vector in sequence; The spatial feature vector and the temporal feature vector are concatenated to obtain a feature vector that integrates spatiotemporal information; The feature vector fused with spatiotemporal information is input into a multi-task learning network to obtain the motion parameters and mechanical parameters at the next moment.

[0031] In one embodiment of the present invention, the environmental data includes wind speed, significant wave height, surface current velocity, and ocean current direction; the platform motion data includes roll angle, pitch angle, bow angle, sway displacement, roll displacement, and heave displacement; the structural response data includes equivalent stress at the top of the tension leg, equivalent stress of the connector, and tension leg tension force; the operational status data includes current towing speed, tension leg displacement, and tension force inside the tension leg; the mechanical parameters include predicted tension force and predicted nodal equivalent stress; and the motion parameters include predicted roll angle and predicted pitch angle.

[0032] In one embodiment of the present invention, the structural safety margin factor is determined by the following formula: In the formula, The structural safety margin coefficient is... To predict the roll angle, To predict the pitch angle, As the first weighting coefficient, This is the second weighting coefficient. The preset safety roll angle, This is the preset safety pitch angle.

[0033] In one embodiment of the present invention, the structural safety margin factor is determined by the following formula: In the formula, The structural safety margin coefficient is... To predict tension, To predict nodal equivalent stress, For the preset safety tension, The preset nodal equivalent stress.

[0034] In one embodiment of the present invention, the fourth data processing module 308 is configured to perform the following operations: When the largest of the comprehensive motion risk coefficient and the structural safety margin coefficient is greater than a first preset threshold and less than a second preset threshold, a level one warning is issued; When the largest of the comprehensive motion risk coefficient and the structural safety margin coefficient is greater than the second preset threshold and less than the third preset threshold, a level two warning is issued. When the larger of the comprehensive motion risk coefficient and the structural safety margin coefficient is greater than the third preset threshold, a level three warning is issued.

[0035] In one embodiment of the present invention, the device further includes a fifth data processing module, which is configured to perform the following operations: When the Level 1 warning is issued, a message indicating a minor risk is sent to the user's device. When the Level 2 warning is issued, a message prompting the user to reduce speed is sent to the user's device. When the Level 3 warning is issued, a prompt message to take emergency avoidance measures is sent to the user's terminal.

[0036] It is understood that the structures illustrated in the embodiments of the present invention do not constitute a specific limitation on a warning device for the towing and installation risks of a tension leg platform. In other embodiments of the present invention, a warning device for the towing and installation risks of a tension leg platform may include more or fewer components than illustrated, or combine some components, or split some components, or have different component arrangements. The illustrated components may be implemented in hardware, software, or a combination of software and hardware.

[0037] The information interaction and execution process between the modules in the above-mentioned device are based on the same concept as the method embodiment of the present invention, and the specific details can be found in the description of the method embodiment of the present invention, and will not be repeated here.

[0038] This invention also provides an electronic device, including a memory and a processor. The memory stores a computer program, and when the processor executes the computer program, it implements a method for early warning of risks during the towing and installation of a tension leg platform according to any embodiment of this invention.

[0039] This invention also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, causes the processor to perform a method for early warning of risks during towing and installation of a tension leg platform according to any embodiment of this invention.

[0040] Specifically, a system or apparatus equipped with a storage medium may be provided, on which software program code implementing the functions of any of the embodiments described above is stored, and the computer (or CPU or Mpu) of the system or apparatus may read and execute the program code stored in the storage medium.

[0041] In this case, the program code read from the storage medium can itself implement the function of any of the above embodiments, and therefore the program code and the storage medium storing the program code constitute part of the present invention.

[0042] Examples of storage media used to provide program code include floppy disks, hard disks, magneto-optical disks, optical disks (such as Cd-ROM, Cd-R, Cd-Rw, DVD-ROM, DVD-Ram, DVD-Rw, DVD+Rw), magnetic tapes, non-volatile memory cards, and ROMs. Alternatively, program code can be downloaded from a server computer via a communication network.

[0043] Furthermore, it should be clear that not only can the program code read by the computer be executed, but also the operating system or other components operating on the computer can be instructed based on the program code to perform some or all of the actual operations, thereby realizing the function of any of the embodiments described above.

[0044] Furthermore, it is understood that the program code read from the storage medium is written to the memory set in the expansion board inserted into the computer or to the memory set in the expansion module connected to the computer. Then, based on the instructions of the program code, the CPU or other device installed on the expansion board or expansion module executes some and all of the actual operations, thereby realizing the function of any of the embodiments described above.

[0045] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus.

[0046] Those skilled in the art will understand that all or part of the steps of the above method embodiments can be implemented by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When the program is executed, it performs the steps of the above method embodiments. The aforementioned storage medium includes various media that can store program code, such as ROM, RAM, magnetic disk, or optical disk.

[0047] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for early warning of risks associated with the towing and installation of a tension leg platform, characterized in that, include: Acquire environmental data, platform motion data, structural response data, and operational status data during the towing and installation of the tension leg platform; Based on the environmental data, the platform motion data, the structural response data, and the operational status data, determine the motion parameters and mechanical parameters for the next moment; Based on the aforementioned mechanical parameters, a comprehensive motion risk coefficient is determined; wherein, the comprehensive motion risk coefficient is used to characterize the degree to which the overall motion posture of the platform deviates from the safe range; Based on the aforementioned motion parameters, a structural safety margin coefficient is determined; the structural safety margin coefficient is used to characterize the overload risk of the tension leg tension force. Based on the comprehensive motion risk coefficient and the structural safety margin coefficient, an early warning is issued for the towing installation risk of the tension leg platform.

2. The method according to claim 1, characterized in that, Based on the environmental data, the platform motion data, the structural response data, and the operational state data, the motion parameters and mechanical parameters for the next moment are determined, including: Based on the environmental data, the platform motion data, the structural response data, and the operational state data, an input state vector is determined; The input state vector is subjected to spatial feature extraction and temporal feature extraction respectively to obtain spatial feature vector and temporal feature vector in sequence; The spatial feature vector and the temporal feature vector are concatenated to obtain a feature vector that integrates spatiotemporal information; The feature vector fused with spatiotemporal information is input into a multi-task learning network to obtain the motion parameters and mechanical parameters at the next moment.

3. The method according to claim 1, characterized in that, The environmental data includes wind speed, significant wave height, surface current velocity, and ocean current direction; platform motion data includes roll angle, pitch angle, bow angle, sway displacement, yaw displacement, and heave displacement; structural response data includes equivalent stress at the top of the tension leg, equivalent stress of the connector, and tension leg tension; operational status data includes current towing speed, tension leg displacement, and tension leg internal tension; the mechanical parameters include predicted tension and predicted nodal equivalent stress; and the motion parameters include predicted roll angle and predicted pitch angle.

4. The method according to claim 3, characterized in that, The comprehensive sports risk coefficient is determined by the following formula: In the formula, The comprehensive sports risk coefficient, To predict the roll angle, To predict the pitch angle, As the first weighting coefficient, This is the second weighting coefficient. The preset safety roll angle, This is the preset safety pitch angle.

5. The method according to claim 3, characterized in that, The structural safety margin factor is determined by the following formula: In the formula, The structural safety margin coefficient is... To predict tension, To predict nodal equivalent stress, For the preset safety tension, The preset nodal equivalent stress.

6. The method according to claim 1, characterized in that, The method for providing early warning of risks associated with the towing and installation of the tension leg platform based on the comprehensive motion risk coefficient and the structural safety margin coefficient includes: When the largest of the comprehensive motion risk coefficient and the structural safety margin coefficient is greater than a first preset threshold and less than a second preset threshold, a level one warning is issued; When the largest of the comprehensive motion risk coefficient and the structural safety margin coefficient is greater than the second preset threshold and less than the third preset threshold, a level two warning is issued. When the larger of the comprehensive motion risk coefficient and the structural safety margin coefficient is greater than the third preset threshold, a level three warning is issued.

7. The method according to claim 6, characterized in that, Also includes: When the Level 1 warning is issued, a message indicating a minor risk is sent to the user's device. When the Level 2 warning is issued, a message prompting the user to reduce speed is sent to the user's device. When the Level 3 warning is issued, a prompt message to take emergency avoidance measures is sent to the user's terminal.

8. A warning device for the risk of towing and installing a tension leg platform, characterized in that, include: The acquisition module is used to acquire environmental data, platform motion data, structural response data, and operational status data during the towing and installation of the tension leg platform. The first data processing module is used to determine the motion parameters and mechanical parameters at the next moment based on the environmental data, the platform motion data, the structural response data, and the operation status data. The second data processing module is used to determine the comprehensive motion risk coefficient based on the mechanical parameters; wherein the comprehensive motion risk coefficient is used to characterize the degree to which the overall motion posture of the platform deviates from the safe range; The third data processing module is used to determine the structural safety margin coefficient based on the motion parameters; the structural safety margin coefficient is used to characterize the overload risk of the tension leg tension force. The fourth data processing module is used to provide early warning of the risks associated with the towing and installation of the tension leg platform based on the comprehensive motion risk coefficient and the structural safety margin coefficient.

9. An electronic device, characterized in that, It includes a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the method as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, It stores a computer program that, when executed in a computer, causes the computer to perform the method described in any one of claims 1-7.

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