Overall construction method of deep-water jacket

Abstract

The present application relates to the technical field of deep-water jacket construction and provides a general construction method for deep-water jackets. The method includes: linking a remote sensing database to obtain environmental data of the construction sea area; collecting jacket structure and lifting equipment data, and constructing multi-dimensional fusion data in combination with the environmental data; constructing a digital twin platform based on the multi-dimensional fusion data; inputting the multi-dimensional fusion data into a path generation model to generate an initial lifting path; finely dividing the path into layers to obtain a set of layered paths, including the stages of hoisting and lifting, horizontal transfer, positioning adjustment, and fixed installation; optimizing the path on the digital twin platform based on the construction constraint space to generate an optimized set of layered paths; splicing the optimized paths, inputting them into the digital twin platform, and performing construction display to solve the technical problems of inaccurate lifting path planning caused by the complexity of the marine environment and the performance fluctuations of lifting equipment during the construction of deep-water jackets, and the difficulty in balancing multiple objectives such as safety, efficiency, and energy consumption.

Description

Technical Field

[0001] This application relates to the field of deep - sea engineering technology, specifically to the field of deep - water jacket construction technology, and particularly to the overall construction method of deep - water jackets. Background Art

[0002] In the field of deep - sea engineering, the construction of deep - water jackets is an extremely challenging and complex task. This process not only requires extremely high technical precision and safety, but also has to face numerous uncertainties and complexities brought about by the marine environment. The complexity and uncertainty of the marine environment are important factors that cannot be ignored during the construction of deep - water jackets. Deep - sea areas are often accompanied by extreme climatic conditions, powerful ocean currents, complex seabed topography, and unpredictable storms and waves. These environmental factors not only increase the construction difficulty, but also directly affect the safety and efficiency of lifting operations. For example, strong winds and large waves may cause the instability of lifting equipment, increasing the risk of operation errors; while the complex and variable seabed topography requires that the lifting path must be accurate to avoid collisions with seabed obstacles. The performance fluctuations of lifting equipment under different working conditions are also important factors affecting the planning of lifting paths. The lifting operation of deep - water jackets usually requires the use of heavy equipment such as large floating cranes and lifting slings. These devices are affected by various factors when working in the deep - sea environment, such as seawater pressure, temperature, salinity, etc. These factors may cause fluctuations in the performance of the equipment, thereby affecting the accuracy and stability of the lifting operation. In addition, the lifting requirements under different working conditions are also different. Changes in factors such as water depth, weight, and size may all affect the performance of the lifting equipment. Summary of the Invention

[0003] This application provides an overall construction method for deep - water jackets, aiming to solve the technical problems of inaccurate lifting path planning and difficulty in balancing multiple objectives such as safety, efficiency, and energy consumption during the construction of deep - water jackets due to the complexity of the marine environment and the performance fluctuations of lifting equipment.

[0004] In view of the above problems, this application provides an overall construction method for deep - water jackets.

[0005] The present application provides a general construction method for a deep-water jacket, and the method includes: connecting to a remote sensing database to obtain environmental data of a target construction sea area; collecting structural data of the deep-water jacket and performance data of lifting equipment, and constructing multi-dimensional fusion data in combination with the environmental data; based on the multi-dimensional fusion data, constructing a digital twin platform for the target construction sea area, where the digital twin platform is used to simulate the construction process of the deep-water jacket in real time; inputting the multi-dimensional fusion data into a path generation model to generate a plurality of initial lifting paths, where the path generation model is constructed based on a deep neural network; performing path refinement layering on the plurality of initial lifting paths to obtain a plurality of layered path sets, and the layered path sets include a lifting stage, a horizontal transfer stage, a positioning adjustment stage, and a fixed installation stage; based on a construction constraint space, performing multi-objective path optimization on the lifting stage, the horizontal transfer stage, the positioning adjustment stage, and the fixed installation stage in the digital twin platform to generate an optimized layered path set; performing layered path splicing based on the optimized layered path set to obtain an optimized lifting path, inputting the optimized lifting path into the digital twin platform, and performing the construction display of the deep-water jacket.

[0006] One or more technical solutions provided in the present application have at least the following technical effects or advantages:

[0007] For the above general construction method for a deep-water jacket, the method obtains detailed environmental data of the target construction sea area by connecting to a remote sensing database. At the same time, it collects the structural parameters of the deep-water jacket and the performance data of the lifting equipment, combines these data with the environmental data, and constructs a multi-dimensional fusion data set. Subsequently, a digital twin platform is constructed using this multi-dimensional fusion data. This platform can simulate the entire construction process of the deep-water jacket in real time. Then, using a path generation model based on a deep neural network, a plurality of initial lifting paths are generated from the multi-dimensional fusion data. These paths are preliminary construction plans. The initial paths are refined and layered to obtain four stages: lifting, horizontal transfer, positioning adjustment, and fixed installation. Each stage has its specific requirements. Then, in the digital twin platform, the paths of each stage are multi-objectively optimized according to the construction constraint space. The optimization objectives are to balance multiple aspects such as safety, efficiency, and energy consumption, ensuring that the lifting process is both safe and efficient. Through optimization, a series of optimized layered path sets are obtained. Finally, these optimized layered paths are spliced together to form a complete optimized lifting path, and this lifting path is input into the digital twin platform to perform the construction display of the deep-water jacket. In this way, the optimization effect of the entire lifting process can be seen in a virtual environment, providing strong guidance and support for actual construction. The entire process not only improves the construction efficiency but also reduces the construction risk.

[0008] The above description is only an overview of the technical solution of the present application. In order to better understand the technical means of the present application, it can be implemented according to the content of the specification. Moreover, in order to make the above and other purposes, features, and advantages of the present application more obvious and understandable, the following specifically illustrates the specific implementation manners of the present application. Brief Description of the Drawings

[0009] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0010] Figure 1 It is a schematic flow chart of the overall construction method of a deep-water jacket in one embodiment;

[0011] Figure 2 It is a schematic flow chart of constructing the construction constraint space in the overall construction method of a deep-water jacket in one embodiment. Detailed Description of the Embodiments

[0012] By providing an overall construction method for a deep-water jacket, the embodiments of the present application solve the technical problems of inaccurate lifting path planning and difficulty in balancing multiple objectives such as safety, efficiency, and energy consumption during the construction of a deep-water jacket due to the complexity of the marine environment and the performance fluctuations of lifting equipment.

[0013] The following will clearly and completely describe the technical solutions in the embodiments of the present application with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments of the present application, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present application.

[0014] It should be noted that the terms "include" and "have" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or server that includes a series of steps or units does not necessarily have to be limited to those clearly listed steps or units, but may include other steps or modules that are not clearly listed or are inherent to these processes, methods, products, or equipment.

[0015] Embodiment, as Figure 1 As shown, the present application provides an overall construction method for a deep-water jacket, and the method includes:

[0016] Link to the remote sensing database to obtain the environmental data of the target construction sea area.

[0017] In the embodiments of the present application, in order to obtain accurate environmental information of the target construction sea area, the system terminal is linked to the remote sensing database, and various environmental information of the target construction sea area is extracted from the remote sensing database, including sea current velocity, water temperature, salinity, tide, wave height, wind direction and wind speed, seabed topography, and possible obstacles, etc. These data can not only help the system terminal understand the complexity and uncertainty of the construction sea area, but also provide a basis for subsequent construction planning to ensure the safety and efficiency of the construction process.

[0018] Collect deepwater jacket structure data and lifting equipment performance data, and construct multi-dimensional fusion data in combination with the environmental data.

[0019] In one embodiment, in the construction preparation stage of the deepwater jacket, the system terminal not only needs to understand the environmental conditions of the construction sea area, but also needs to understand the structural characteristics of the jacket itself and the performance parameters of the lifting equipment. This includes collecting detailed structural data such as the size, material, weight distribution, and connection nodes of the jacket, as well as performance data such as the load-bearing capacity, working radius, and speed control of the lifting equipment. After the collection of these structural data and performance data is completed, the system terminal combines the collected deepwater jacket structure data and lifting equipment performance data with the environmental data obtained from the remote sensing database, and performs multi-dimensional fusion processing to integrate data from different sources and different dimensions together to form a comprehensive and three-dimensional multi-dimensional fusion data. This multi-dimensional fusion data provides data support for subsequent construction planning and helps to obtain a more reasonable, safe and efficient lifting path.

[0020] Based on the multi-dimensional fusion data, construct a digital twin platform for the target construction sea area, and the digital twin platform is used to simulate the construction process of the deepwater jacket in real time.

[0021] In one embodiment, after obtaining the multi-dimensional fusion data, the system terminal inputs the multi-dimensional fusion data into the modeling tool to construct a virtual construction sea area environment model, a virtual jacket model and a virtual lifting equipment model. The virtual construction sea area environment model includes the three-dimensional topography and hydrological characteristics of the construction sea area. At the same time, environmental change factors such as tides and wind speeds are parameterized so that actual construction conditions can be dynamically simulated. This step is to accurately reflect the actual conditions of the construction environment. The virtual jacket model includes the geometric shape of the jacket, the connection method of each component, and the material properties. At the same time, the physical properties of the jacket, such as stiffness and mass distribution, are parameterized to ensure that the actual mechanical properties can be accurately reflected in subsequent simulations. The virtual lifting equipment model includes parameters such as the arm length, lifting capacity, and rotation angle range of the lifting equipment. At the same time, the dynamic characteristics of the equipment, such as the behavior under different loads and operating speeds, are parameterized so as to accurately simulate the operating performance of the equipment in the simulation. Subsequently, the constructed virtual construction sea area environment model, virtual jacket model, and virtual lifting equipment model are packaged and integrated to build a digital twin platform. This digital twin platform can dynamically present the construction process in a virtual environment and make adjustments based on real-time data. After that, the real-time data interface of the digital twin platform is configured to receive data and adjustment parameters from the site, dynamically update the model, and evaluate the adjustment parameters to ensure the synchronization of the virtual environment and real conditions during the construction process, as well as the accuracy of the simulation.

[0022] The multi-dimensional fusion data is input into a path generation model to generate multiple initial lifting paths, wherein the path generation model is constructed based on a deep neural network.

[0023] In one embodiment, in the construction planning of deepwater jackets, the system terminal uses multi-dimensional fusion data as input and transmits it to a path generation model built based on a deep neural network. The design goal of this path generation model is to mine potential lifting paths from complex data. Since the deep neural network has strong learning and nonlinear processing capabilities, it can analyze the inherent connections in the multi-dimensional fusion data, generate multiple initial lifting paths that may meet the requirements, provide the system terminal with a variety of lifting path options, and reduce the uncertainty of the lifting path.

[0024] Furthermore, the present application provides a method for constructing the path generation model, including:

[0025] Historical environmental data, historical deepwater jacket structure data, and historical lifting equipment performance data are obtained to construct historical multi-dimensional fusion data, and training data is constructed by combining historical lifting path data. Based on the training data, a path generation model is constructed through supervised learning and deep neural networks.

[0026] Preferably, in order to construct a model for generating a lifting path, the system terminal collects a large amount of information from multiple historical data sources, including historical environmental data, historical deepwater jacket structure data, and historical lifting equipment performance data, and integrates these data into a multi-dimensional data set, namely historical multi-dimensional fusion data. Subsequently, combined with the simultaneously obtained historical lifting path data, these historically planned paths are used as labels and corresponding to each group of data in the historical multi-dimensional fusion data to construct training data. After that, a path generation model is constructed using supervised learning and a deep neural network. Supervised learning allows the model to learn from known training data, that is, to adjust its own parameters by analyzing the historical multi-dimensional fusion data and the corresponding historical lifting path data to minimize the prediction error. Taking the long short-term memory network (LSTM) as an example, the system terminal determines the network structure according to the data characteristics and business requirements, including the input layer, LSTM layer, fully connected layer, output layer, etc. Then, the training data is input into the LSTM model, and the input training data is processed step by step to generate a hidden state sequence. The output layer of the LSTM model generates the time-step outputs of multiple paths according to the hidden state sequence. These outputs together form a path sequence, and each sequence corresponds to a complete lifting path. Then, the loss value between the path sequence and the historical lifting path data is calculated through the mean square error. The gradient of the loss function with respect to each parameter of the LSTM model is calculated through the backpropagation algorithm. The LSTM layer performs backpropagation through each time step in the time series to adjust the prediction accuracy of the entire path sequence. The optimization algorithm Adam is used to update the internal weights and biases of the LSTM model according to the calculated gradients, gradually reducing the value of the loss function and improving the generation accuracy of the path sequence. After the end of each training cycle, the performance of the LSTM model is evaluated using the validation data. The evaluation criteria include the generation accuracy and overall error of multiple path sequences, and according to the validation results, hyperparameters such as the learning rate, number of LSTM layers, and number of hidden units are adjusted to improve the accuracy of the generated path sequence. When the generation accuracy and overall error tend to be stable, the system terminal stops training and uses the test data to evaluate whether the generation accuracy and overall error meet the requirements of the corresponding thresholds to ensure the generalization ability and practicality of the LSTM model on unseen data. When the test passes, the system terminal outputs the current LSTM model as the path generation model. This path generation model can provide a scientific planning scheme for the lifting operation of deepwater jackets based on complex environmental, structural, and equipment conditions.

[0027] Perform path refinement layering on the multiple initial lifting paths to obtain multiple layered path sets, and the layered path sets include a lifting stage, a horizontal transfer stage, a positioning adjustment stage, and a fixed installation stage.

[0028] In one embodiment, after obtaining multiple initial lifting paths, in order to improve the accuracy and efficiency of the lifting operation, the system terminal performs refined hierarchical processing on these initial lifting paths. This process subdivides each initial lifting path into multiple key stages, and each stage corresponds to a specific link in the lifting operation, namely the lifting stage, the horizontal transfer stage, the positioning and adjustment stage, and the fixed installation stage. Among them, the lifting stage is the starting stage of the lifting operation, focusing on how to smoothly lift the deepwater jacket from the initial position, involving parameters such as lifting capacity, lifting speed, lifting height, and rope strength to ensure that the jacket can safely leave the ground or water surface. The horizontal transfer stage is the process of horizontally transferring the jacket to the designated installation position after it has been lifted to a certain height, involving transfer path, transfer speed, transfer angle, moving direction, etc., to minimize transfer time and cost. The positioning and adjustment stage is the precise positioning and adjustment performed when the jacket approaches the installation position, involving fine-tuning length, safety margin, etc., to eliminate minor errors. The fixed installation stage is the fixed installation performed after the jacket has been precisely positioned, involving connection strength, levelness, verticality, tightening torque, etc., to ensure installation quality and safety. By performing refined hierarchical processing on these initial lifting paths, multiple hierarchical path sets can be obtained, and each set contains the detailed steps and precautions throughout the process from lifting to fixed installation. These hierarchical path sets not only help improve the accuracy and efficiency of the lifting operation but also help reduce operation risks and potential safety hazards.

[0029] Furthermore, the present application provides a method for performing path refinement and layering on the multiple initial lifting paths to obtain multiple hierarchical path sets, including:

[0030] Performing stage-by-stage analysis on the multiple initial lifting paths, extracting path features and operation nodes, and generating multiple path stage identifiers; performing eigenvector decomposition on the initial lifting paths based on the path stage identifiers to generate multiple lifting stages, multiple horizontal transfer stages, multiple positioning and adjustment stages, and multiple fixed installation stages; performing multi-dimensional integration on the multiple lifting stages, the multiple horizontal transfer stages, the multiple positioning and adjustment stages, and the multiple fixed installation stages to generate the multiple hierarchical path sets.

[0031] Preferably, the system terminal divides each initial lifting path into several stages according to the operation process. Each stage corresponds to a key operation node in the lifting operation. The goal of this step is to divide the continuous path data into multiple independent operation stages for further analysis and feature extraction. Then, time annotation is performed on the path data of each stage to clarify the position and sequence of each stage in the entire lifting path, facilitating path feature extraction and operation node identification in subsequent steps. Subsequently, for each segmented operation stage, specific operation steps are identified. For example, in the lifting stage, the operation steps include starting to lift, reaching a certain height, stopping lifting, etc. Then, the key parameters of each operation step are extracted. These parameters include but are not limited to the motion state of the equipment (such as lifting speed, rotation angle), load conditions (such as weight distribution), environmental impacts (such as wind speed changes), etc., to construct multiple operation feature sets. After that, according to the extracted operation parameters, the key operation nodes within each stage are determined. These nodes are important conversion points in the operation process, such as the node from lifting to transferring. Then, based on these key operation nodes and features, multiple path stage identifiers are generated. These identifiers are used to describe the start and end of each operation stage and the main operation features of that stage. Then, the system terminal extracts the feature parameters of the corresponding stage from multiple operation feature sets according to the determined multiple path stage identifiers, and uses these feature parameters to match multiple initial lifting paths, and intercepts the lifting path segments corresponding to each stage. Then, the intercepted paths are filled into a multi-dimensional vector structure set based on the number of parameters in chronological order to construct the feature vectors of each stage. After obtaining the feature vectors of each stage, the system terminal performs multi-dimensional integration on the divided feature vectors, that is, integrates the feature vectors belonging to the same initial lifting path to form a complete hierarchical path set. In this way, the system terminal obtains multiple hierarchical path sets, and each set contains all the key stages and path information from lifting to fixed installation. In this way, the complexity of the lifting path can be better understood and analyzed, providing more accurate and efficient guidance for subsequent lifting operations.

[0032] Based on the construction constraint space, multi-objective path optimization is performed on the lifting stage, the horizontal transfer stage, the positioning and adjustment stage, and the fixed installation stage in the digital twin platform to generate an optimized hierarchical path set.

[0033] In one embodiment, the system terminal screens the lifting stage, horizontal transfer stage, positioning and adjustment stage, and fixed installation stage according to the pre-constructed construction constraint space, eliminates the same-group stages that do not meet the construction constraint space, and then inputs the screened lifting stage, horizontal transfer stage, positioning and adjustment stage, and fixed installation stage into the digital twin platform for multiple simulations and optimizations in combination with the pre-constructed multi-dimensional performance adjustment function. This process comprehensively considers multiple objectives, such as safety performance, equipment load, time consumption, etc., and adjusts the parameters corresponding to each stage continuously to achieve the optimal comprehensive effect. Finally, the hierarchical paths that meet the adjustment coefficient threshold are sorted into an optimized hierarchical path set. This optimized hierarchical path set can provide scientific guidance for actual construction and improve construction efficiency and quality.

[0034] Further, as Figure 2 shown, the present application provides for constructing the construction constraint space, including:

[0035] Performing time series segmentation on the historical multi-dimensional fusion data to form a multi-stage historical data cluster; performing multi-dimensional analysis on the multi-stage historical data cluster, extracting and defining the operation boundary parameters of each stage to form a stage boundary feature set; based on the stage boundary feature set, constructing a construction constraint space, the construction constraint space including multiple stage sub-spaces.

[0036] Preferably, the system terminal analyzes the obtained historical multi-dimensional fusion data, obtains the timestamps of each data in the historical multi-dimensional fusion data, and divides the historical multi-dimensional fusion data according to these timestamps, extracts the data of each stage in the historical multi-dimensional fusion data, and constructs a multi-stage historical data cluster. Each cluster represents a construction stage. Subsequently, multi-dimensional feature extraction is performed on the historical data cluster of each stage, including the operating parameters of the equipment, the changes in environmental conditions, the dynamic response of the jacket structure, etc. Through the statistical analysis of the features of each stage, the key parameters affecting the operation safety and stability of each stage are determined. These parameters reflect the operation boundaries between stages, such as load limits, speed thresholds, stress upper limits, etc. Then, the key boundary parameter sets of each stage are grouped together to form a stage boundary feature set. This feature set defines the operation limits and safety boundaries of each stage. After that, based on the obtained stage boundary feature set, the construction process is divided into multiple stage sub-spaces. Each sub-space corresponds to a specific stage, such as lifting and hoisting, horizontal transfer, etc., and its operation limits are defined by the boundary parameters of this stage. Within each sub-space, the system terminal sets corresponding operation constraint conditions according to the stage boundary feature set. For example, the sub-space of the lifting and hoisting stage includes the upper limit of the crane's load capacity, the lifting speed range, the operation angle limit, etc. Then, the stage sub-spaces are integrated to form an overall construction constraint space. This constraint space ensures that the operation of each stage in the construction process is carried out within a safe and effective range, improving the controllability and success rate of the construction.

[0037] Furthermore, the present application provides multi-objective path optimization for the lifting and hoisting stage, the horizontal transfer stage, the positioning and adjustment stage, and the fixed installation stage on the digital twin platform, including:

[0038] Based on the construction constraint space, perform constraint pre-screening on the lifting and hoisting stage, the horizontal transfer stage, the positioning and adjustment stage, and the fixed installation stage of the multiple hierarchical path sets to generate a pre-screened path set; input the pre-screened path set into the digital twin platform for virtual working condition simulation to obtain a multi-dimensional response data set; perform multi-dimensional parameter deconstruction and performance mapping on the multi-dimensional response data set to obtain multi-stage performance indicators; perform multi-objective path optimization on the multiple hierarchical path sets based on the multi-dimensional performance adjustment function and the multi-stage performance indicators to generate an optimized hierarchical path set.

[0039] Optionally, the system terminal loads the operation constraint conditions of each stage from the construction constraint space. These constraint conditions include safety, equipment load, operation speed, environmental conditions, etc. For each stage in multiple hierarchical path sets, the hierarchical paths are matched with the constraint conditions. Specifically, the system terminal checks whether the lifting hierarchical path meets the conditions such as load capacity, lifting height, speed limit, etc. during the lifting stage, checks the balance, equipment bearing capacity, and transfer time, etc. during the horizontal transfer process of the transfer hierarchical path, checks the requirements such as fine-tuning length and safety margin during the positioning and adjustment stage of the positioning hierarchical path, checks the operation stability, equipment coordination, and installation accuracy, etc. during the fixed installation process of the installation hierarchical path, and adds the hierarchical paths that do not meet the conditions to the abnormal hierarchical paths. Subsequently, the abnormal hierarchical paths are traversed and grouped and matched with the hierarchical paths in multiple hierarchical path sets, the corresponding hierarchical path sets in multiple hierarchical path sets are excluded, and the hierarchical path sets that meet all constraint conditions are retained to generate a preliminary screening path set. This path set contains the feasible paths for the operations of each stage. After that, the system terminal inputs the generated preliminary screening path set into the digital twin platform for virtual simulation. During the simulation process, the key parameters of the digital twin platform are configured according to the hierarchical path sets in the preliminary screening path set, including equipment status, environmental conditions, etc., to truly reproduce the construction process of each path. After the parameter configuration is completed, the digital twin platform gradually executes the four stages of lifting, horizontal transfer, positioning and adjustment, and fixed installation, and records the performance of each path during the simulation process. After the simulation is completed, the digital twin platform will generate a multi-dimensional response data set, which includes the response parameters of each stage during the path execution process, including actual load, maximum load, number of risk events, path length, power consumption, start time, end time, etc. Then, the system terminal deconstructs the data in the multi-dimensional response data set, decomposes the complex response data into an operable parameter set, and maps it to the performance indicators of each stage according to the deconstructed parameters to form a complete multi-stage performance indicator set. This multi-stage performance indicator set includes safety indicators, equipment load, efficiency indicators, energy consumption, and time consumption. Taking the lifting stage as an example, the system terminal obtains the safety indicator by calculating the ratio of the number of risk events in the lifting stage to the total number of risk events in the four stages. Among them, the risk events include equipment overload, emergency stop, path deviation, etc. The equipment load is obtained by calculating the ratio of the average actual load of the equipment in this stage to the maximum load of the equipment. The efficiency indicator is obtained by calculating the ratio of the path length in this stage to the time consumption. The energy consumption is obtained by calculating the product of the power consumption in this stage and the time consumption. The time consumption is obtained by calculating the difference between the end time and the start time of this stage. After obtaining the multi-stage performance indicator set, the system terminal applies the multi-dimensional performance adjustment function to evaluate and adjust each stage.The multi-dimensional performance adjustment function is used to balance various optimization objectives, ensure the coordination among different objectives, calculate the adjustment coefficients to iteratively optimize the parameters of each stage, and gradually adjust the operation parameters of each stage to achieve the optimum under multiple objectives. After multiple rounds of optimization, the system terminal generates an optimized hierarchical path set. This path set performs well in terms of performance indicators at each stage and can meet various requirements in actual construction.

[0040] Furthermore, the present application provides a multi-dimensional performance adjustment function, including:

[0041] The multi-dimensional performance adjustment function is specifically as follows:

[0042] Optionally, the multi-dimensional performance adjustment function generates adjustment coefficients by integrating the key indicators of each operation stage to optimize the performance of the corresponding stage. This function takes into account multiple factors such as safety, equipment load, efficiency, energy consumption, and time consumption, and adjusts the influence of these factors on the final path optimization through the weight factors of the adjustment coefficients. The multi-dimensional performance adjustment function is specifically as follows: where C i represents the adjustment coefficient of stage i, which is used to adjust the operation parameters of a certain stage in path optimization. S i represents the safety index of stage i, which reflects the safety level of the operation in a certain stage. L i represents the equipment load of stage i, indicating the load condition that the equipment bears in a certain stage. E i represents the efficiency index of stage i, measuring the efficiency of the operation in this stage. N i represents the energy consumption of stage i, indicating the energy consumption of the equipment in a certain stage. T i represents the time consumption of stage i, which is the time required to complete a certain stage. α and β represent the weight factors of the adjustment coefficients, which are used to adjust the influence intensity of different indicators when generating the adjustment coefficients, ensuring that when considering all indicators comprehensively, the influence of certain factors can be appropriately highlighted or weakened. The selection and adjustment of these two factors depend on specific optimization objectives and application scenarios. Through this multi-dimensional performance adjustment function, different factors in each stage can be balanced, so that when optimizing the path, it can ensure both efficient operation and safety, reduce energy consumption and equipment load.

[0043] Furthermore, the present application provides multi-objective path optimization for the multiple hierarchical path sets based on the multi-dimensional performance adjustment function and the multi-stage performance indicators, generating an optimized hierarchical path set, including:

[0044] Analyze the multi-stage performance indicators to obtain multiple performance indicators for the lifting stage, multiple performance indicators for the horizontal transfer stage, multiple performance indicators for the positioning and adjustment stage, and multiple performance indicators for the fixed installation stage; input the multiple performance indicators for the lifting stage, multiple performance indicators for the horizontal transfer stage, multiple performance indicators for the positioning and adjustment stage, and multiple performance indicators for the fixed installation stage into the multi-dimensional performance adjustment function to generate multiple adjustment coefficients for the lifting stage, multiple adjustment coefficients for the horizontal transfer stage, multiple adjustment coefficients for the positioning and adjustment stage, and multiple adjustment coefficients for the fixed installation stage; based on the multiple adjustment coefficients for the lifting stage, the multiple adjustment coefficients for the horizontal transfer stage, the multiple adjustment coefficients for the positioning and adjustment stage, and the multiple adjustment coefficients for the fixed installation stage, adjust the parameters of the multiple hierarchical path sets to obtain multiple adjusted hierarchical path sets; perform path convergence judgment based on the adjustment coefficient threshold to obtain the optimized hierarchical path sets.

[0045] Optionally, after obtaining the multi-stage performance indicators, the system terminal analyzes the multi-stage performance indicators, extracts the performance indicators of each stage such as lifting, horizontal transfer, positioning adjustment, and fixed installation respectively, and forms multiple performance indicators for the lifting stage, multiple performance indicators for the horizontal transfer stage, multiple performance indicators for the positioning and adjustment stage, and multiple performance indicators for the fixed installation stage. The performance indicators of these stages all include safety indicators, equipment load, efficiency indicators, energy consumption, and time consumption. Subsequently, the parsed performance indicators of multiple stages are respectively input into the multi-dimensional performance adjustment function. This multi-dimensional performance adjustment function generates corresponding adjustment coefficients according to the specific conditions of each stage, comprehensively considering factors such as safety, equipment load, efficiency, energy consumption, and time consumption, including multiple adjustment coefficients for the lifting stage, multiple adjustment coefficients for the horizontal transfer stage, multiple adjustment coefficients for the positioning and adjustment stage, and multiple adjustment coefficients for the fixed installation stage. Then, the generated multiple adjustment coefficients are applied to the hierarchical path set to adjust each parameter in the path set, that is, multiply the adjustment coefficient by the parameter of the corresponding hierarchical path. After parameter adjustment, multiple new hierarchical path sets are formed, namely multiple adjusted hierarchical path sets, to ensure that their performance in each stage is optimized. Then, the system terminal obtains the preset adjustment coefficient threshold as the standard for path convergence judgment, and this adjustment coefficient threshold is set based on historical experience and expert advice. If the adjustment coefficients of the four stages of a hierarchical path set are all less than the adjustment coefficient threshold, it is considered that the hierarchical path set has converged. At this time, the system terminal outputs this hierarchical path set as the optimized hierarchical path set. These optimized hierarchical path sets have optimized parameters in each stage and meet the set performance requirements, and will be used in subsequent actual construction to ensure the efficiency and safety of the construction.

[0046] Furthermore, the present application provides a method for performing path convergence judgment based on the adjustment coefficient threshold to obtain the optimized hierarchical path sets, including

[0047] Homologously match the adjustment coefficients of the multiple lifting stages, the adjustment coefficients of the multiple horizontal transfer stages, the adjustment coefficients of the multiple positioning adjustment stages, and the adjustment coefficients of the multiple fixed installation stages to obtain multiple combinations of stage adjustment coefficients; perform a numerical judgment on the multiple combinations of stage adjustment coefficients and the adjustment coefficient threshold; for the first stage adjustment coefficient in the combination of stage adjustment coefficients that is less than the adjustment coefficient threshold, perform a parameter freezing operation, retain and solidify the adjustment hierarchical path of the first stage adjustment coefficient to generate a frozen hierarchical path, and for the second stage adjustment coefficient that is greater than or equal to the adjustment coefficient threshold, perform a dynamic optimization adjustment of the parameter to obtain a dynamic hierarchical path; perform multi-objective path optimization based on the dynamic hierarchical path until all the adjustment hierarchical paths are frozen hierarchical paths to obtain an optimized hierarchical path set.

[0048] Optionally, the system terminal classifies and summarizes the adjustment coefficients of different construction stages (lifting, horizontal transfer, positioning adjustment, fixed installation). The adjustment coefficient of each stage represents the optimization requirements and adjustment degree of that stage. Then, match the adjustment coefficients of each construction stage in the same hierarchical path set to form a complete combination of stage adjustment coefficients. These combinations represent the optimization states of the hierarchical path set at different stages. Subsequently, compare the adjustment coefficients in each combination of stage adjustment coefficients with the adjustment coefficient threshold. This threshold is used to determine whether it is still necessary to optimize the parameters of a certain stage. For the first stage adjustment coefficient that is less than the adjustment coefficient threshold, perform a parameter freezing operation, which means that the path parameters of this stage have reached the optimization requirements and no further adjustment is needed, and apply these frozen stage adjustment coefficients to the corresponding stage, retain and solidify these parameters to generate a frozen hierarchical path. The relevant parameters of this path will not be modified in the subsequent optimization process. For the second stage adjustment coefficient that is greater than or equal to the adjustment coefficient threshold, it means that there is still room for optimization in this stage. The system terminal performs a dynamic optimization adjustment on these unfrozen adjustment coefficients, that is, multiply the stage adjustment coefficient by the parameters of the corresponding construction stage to form a dynamic hierarchical path, and combine it with the frozen hierarchical path as the input of the adjustment hierarchical path set into the digital twin platform for simulation, and then repeat the above optimization process for the dynamic hierarchical path in the adjustment hierarchical path set, gradually adjusting the parameters of each stage to ensure an optimal balance among the safety, efficiency, load, energy consumption, and time consumption of the path. For the remaining hierarchical path sets, the system terminal performs the same operation until all the adjustment hierarchical paths in a certain adjustment hierarchical path set are frozen hierarchical paths, and output this adjustment hierarchical path set as the optimized hierarchical path set to ensure the safety and efficiency of the construction process.

[0049] Perform hierarchical path splicing based on the optimized hierarchical path set to obtain an optimized lifting path, input the optimized lifting path into the digital twin platform, and perform the construction display of the deepwater jacket.

[0050] In one embodiment, after obtaining the optimized hierarchical path set, the system terminal performs sequential verification on this optimized hierarchical path set to ensure that the phase order in the optimized hierarchical path set is correct. For example, the lifting phase should be before the horizontal transfer phase, and the fixed installation phase should be after all other phases. Subsequently, according to the phase order, the optimized hierarchical paths in the optimized hierarchical path set are spliced to form a complete optimized lifting path. During the splicing process, attention is paid to the connection points of the paths in each phase to ensure the continuity and smooth transition of the paths. If the path connection between two phases is not smooth or there are mutations, smoothing processing is performed, that is, the connection points are averaged to ensure the coherence of the path and the stability of the operation. After that, the generated optimized lifting path is input into the digital twin platform for simulating the real construction process, and the simulation processes of the lifting phase, horizontal transfer phase, positioning adjustment phase, and fixed installation phase are demonstrated visually and data-wise throughout the process, providing reference and guidance for the actual construction.

[0051] In summary, the embodiments of the present application have at least the following technical effects:

[0052] The embodiments of the present application obtain the environmental data of the construction sea area by linking the remote sensing database, and at the same time collect the structural data of the deep-water jacket and the performance data of the lifting equipment. These data are integrated into multi-dimensional fusion data, and a digital twin platform is constructed based on this data for real-time simulating the construction process of the jacket. Subsequently, an initial lifting path is generated by a path generation model based on a deep neural network, and through refined hierarchical path division, a hierarchical path set of multiple operation phases is formed. After that, on the digital twin platform, based on the construction constraint space, multi-objective path optimization is performed on the path sets of each operation phase to generate an optimized hierarchical path set. On this basis, a complete optimized lifting path is obtained through hierarchical path splicing. Finally, the optimized path is input into the digital twin platform for construction display to ensure the executability and optimization effect of the path. These technical effects together solve the technical problems of inaccurate lifting path planning caused by the complexity of the marine environment and the performance fluctuations of lifting equipment during the construction of deep-water jackets, and it is difficult to balance multiple objectives such as safety, efficiency, and energy consumption, achieving the effect of improving the efficiency and safety of deep-sea jacket lifting operations through optimized lifting path planning, ensuring the safe construction of deep-water jackets and the smooth progress of the overall project.

[0053] It should be noted that the above sequence of the embodiments of the present application is only for description and does not represent the superiority or inferiority of the embodiments. And the above describes specific embodiments of this specification. The processes depicted in the drawings do not necessarily require the specific order and continuous order shown to achieve the desired results. In some embodiments, multi-tasking and parallel processing are also possible or may be advantageous.

[0054] The above are only the preferred embodiments of the present application, and are not intended to limit the present application. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present application shall be included within the protection scope of the present application.

[0055] This specification and the drawings are merely exemplary descriptions of the present application and are considered to cover any and all modifications, variations, combinations, or equivalents within the scope of the present application. Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the present application and its equivalent technologies, the present application is intended to include these changes and modifications.

Claims

1. A method for constructing a deepwater jacket, characterized in that: include: Link to remote sensing database to obtain environmental data of target construction sea areas; Collect deepwater jacket structure data and lifting equipment performance data, and build multi-dimensional fusion data in combination with the environmental data; Based on the multi-dimensional fusion data, a digital twin platform of the target construction sea area is constructed, and the digital twin platform is used to simulate the construction process of the deepwater jacket in real time; Inputting the multi-dimensional fusion data into a path generation model to generate a plurality of initial lifting paths, wherein the path generation model is constructed based on a deep neural network; The multiple initial hoisting paths are finely layered to obtain multiple layered path sets, wherein the layered path sets include a lifting stage, a horizontal transfer stage, a positioning adjustment stage, and a fixed installation stage; Based on the construction constraint space, multi-objective path optimization is performed on the digital twin platform for the lifting stage, the horizontal transfer stage, the positioning adjustment stage, and the fixed installation stage to generate an optimized hierarchical path set; Performing layered path splicing based on the optimized layered path set to obtain an optimized lifting path, inputting the optimized lifting path into the digital twin platform, and executing the construction display of the deepwater jacket; Among them, multi-objective path optimization includes: Based on the construction constraint space, the lifting and hoisting stage, the horizontal transfer stage, the positioning adjustment stage, and the fixed installation stage of the multiple hierarchical path sets are initially screened to generate an initially screened path set; the initially screened path set is input into the digital twin platform for virtual working condition simulation to obtain a multi-dimensional response data set; multi-dimensional parameter deconstruction and performance mapping are performed on the multi-dimensional response data set to obtain multi-stage performance indicators; multi-objective path optimization is performed on the multiple hierarchical path sets based on the multi-dimensional performance adjustment function and the multi-stage performance indicators to generate an optimized hierarchical path set.

2. The method for constructing a deepwater jacket as claimed in claim 1, characterized in that: Constructing the construction constraint space includes: Perform time series segmentation on historical multi-dimensional fusion data to form multi-stage historical data clusters; Performing multi-dimensional analysis on the multi-stage historical data clusters, extracting and defining the operation boundary parameters of each stage, and forming a stage boundary feature set; Based on the stage boundary feature set, a construction constraint space is constructed, where the construction constraint space includes a plurality of stage subspaces.

3. The method for constructing a deepwater jacket as claimed in claim 1, characterized in that: The multi-dimensional performance adjustment functions are as follows: ； in, Characterizes the adjustment coefficient of stage i; Characterize the safety indicators of stage i; Characterize the equipment load in phase i; Characterize the efficiency index of stage i; Characterize the energy consumption of stage i; Characterize the time consumption of stage i; and The weight factor that represents the adjustment coefficient is used to adjust the influence of each part.

4. The method for constructing a deepwater jacket as claimed in claim 1, characterized in that: Performing multi-objective path optimization on the multiple hierarchical path sets based on the multi-dimensional performance adjustment function and the multi-stage performance indicators to generate an optimized hierarchical path set includes: Parsing the multi-stage performance indicators to obtain multiple lifting stage performance indicators, multiple horizontal transfer stage performance indicators, multiple positioning adjustment stage performance indicators, and multiple fixed installation stage performance indicators; Input the multiple lifting and hoisting stage performance indicators, multiple horizontal transfer stage performance indicators, multiple positioning adjustment stage performance indicators, and multiple fixed installation stage performance indicators into the multidimensional performance adjustment function to generate multiple lifting and hoisting stage adjustment coefficients, multiple horizontal transfer stage adjustment coefficients, multiple positioning adjustment stage adjustment coefficients, and multiple fixed installation stage adjustment coefficients; Based on the multiple lifting stage adjustment coefficients, the multiple horizontal transfer stage adjustment coefficients, the multiple positioning stage adjustment coefficients, and the multiple fixed installation stage adjustment coefficients, the multiple hierarchical path sets are parameter adjusted to obtain multiple adjusted hierarchical path sets; The path convergence judgment is performed based on the adjustment coefficient threshold to obtain the optimized hierarchical path set.

5. The method for constructing a deepwater jacket as claimed in claim 4, characterized in that: Path convergence judgment is performed based on the adjustment coefficient threshold to obtain an optimized hierarchical path set, including: Homologously matching the multiple lifting and hoisting stage adjustment coefficients, the multiple horizontal transfer stage adjustment coefficients, the multiple positioning stage adjustment coefficients, and the multiple fixed installation stage adjustment coefficients to obtain multiple stage adjustment coefficient combinations; Performing numerical judgment on the combination of the multiple stage adjustment coefficients and the adjustment coefficient threshold; For the first stage adjustment coefficient in the stage adjustment coefficient combination that is less than the adjustment coefficient threshold, a parameter freezing operation is performed to retain and solidify the adjustment hierarchical path of the first stage adjustment coefficient to generate a frozen hierarchical path, and for the second stage adjustment coefficient that is greater than or equal to the adjustment coefficient threshold, a parameter dynamic optimization adjustment is performed to obtain a dynamic hierarchical path; The dynamic hierarchical paths are used to perform multi-objective path optimization until all the adjusted hierarchical paths are frozen hierarchical paths, thereby obtaining an optimized hierarchical path set.

6. The method for constructing a deepwater jacket as claimed in claim 5, characterized in that: The multiple initial lifting paths are refined and layered to obtain multiple layered path sets, including: Performing phase-by-phase analysis on the multiple initial lifting paths, extracting path features and operation nodes, and generating multiple path phase identifiers; Based on the path stage identifier, the initial lifting path is decomposed into feature vectors to generate a plurality of lifting stages, a plurality of horizontal transfer stages, a plurality of positioning adjustment stages, and a plurality of fixed installation stages; The multiple lifting stages, the multiple horizontal transfer stages, the multiple positioning adjustment stages, and the multiple fixed installation stages are multi-dimensionally integrated to generate the multiple hierarchical path sets.

7. The method for constructing a deepwater jacket as claimed in claim 1, characterized in that: Constructing the path generation model includes: Obtain historical environmental data, historical deepwater jacket structure data, and historical lifting equipment performance data to build historical multi-dimensional fusion data, and combine historical lifting path data to build training data; Based on the training data, a path generation model is constructed through supervised learning and deep neural network.

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