Deepwater jacket basket structure hoisting construction method

Abstract

This invention discloses a method for hoisting a basket-shaped structure for deep-water jackets, relating to the technical field of hoisting devices. The method includes: reading a predetermined hoisting plan; dynamically monitoring the hoisting process to obtain first dynamic monitoring information; matching a first monitoring dataset with a first monitoring point; determining whether the first monitoring dataset meets the first predetermined constraint of the first-stage plan; if it does, determining whether the first monitoring dataset meets the predetermined overall assembly constraint; if it does, initiating a second-stage plan to perform assembly fine-tuning construction control on the target jacket, thereby achieving the hoisting construction of the target jacket. This method solves the technical problems of existing jacket construction methods, such as the difficulty in real-time and accurate monitoring of parameter changes during hoisting, leading to significant safety hazards and accuracy errors, resulting in poor hoisting efficiency and safety, and insufficient overall assembly accuracy of the jacket. It achieves the technical effect of improving hoisting efficiency, safety, and construction quality.

Description

Technical Field

[0001] This application relates to the technical field of hoisting devices, specifically to a method for hoisting and constructing a basket-shaped structure for deep-water jackets. Background Technology

[0002] In the field of marine engineering, as oil and gas resource exploration and development continue to extend into deep-sea areas, the safe and efficient installation technology of deep-water jackets, as key structures supporting subsea oil and gas production facilities, is particularly important. Deep-water jackets, especially basket-shaped structures, present extremely high technical requirements for hoisting operations due to their unique geometry and massive size. Traditional hoisting equipment often struggles to guarantee safety and precision when facing the challenges of complex deep-sea environments, strong currents, and high waves. Traditional hoisting operations rely heavily on experience and on-site command, making it difficult to monitor the hoisting process comprehensively and dynamically in real time, and to grasp changes in various parameters during the hoisting process. This results in significant safety hazards and accuracy errors, and the overall assembly accuracy of deep-water jacket basket structures is difficult to guarantee during hoisting.

[0003] Therefore, in the current technology related to the hoisting and construction of deep-water jacket basket structures, there are technical problems such as difficulty in real-time and accurate control of the changes in various parameters during the hoisting process, which poses significant safety hazards and accuracy errors, resulting in poor hoisting and construction efficiency and safety, and insufficient overall assembly accuracy of the jacket. Summary of the Invention

[0004] This application provides a method for hoisting and constructing basket-shaped structures for deep-water jackets, which solves the technical problems of existing deep-water jacket construction, such as the difficulty in real-time and accurate monitoring of various parameter changes during hoisting, resulting in significant safety hazards and precision errors, leading to poor hoisting efficiency and safety, and insufficient overall assembly precision of the jacket. This method achieves the technical effect of improving hoisting efficiency, safety, and construction quality.

[0005] This application provides a method for hoisting and constructing a basket-shaped structure for deep-water jackets. The method includes: reading a predetermined hoisting plan, which includes a first-stage plan and a second-stage plan for hoisting and constructing a target jacket using a target crane; dynamically monitoring the hoisting and construction process of the target jacket according to the first-stage plan to obtain first dynamic monitoring information, which includes multiple sets of monitoring datasets for multiple monitoring points; matching a first monitoring dataset for a first monitoring point in the multiple sets of monitoring datasets, where the first monitoring point refers to any one of the multiple monitoring points; determining whether the first monitoring dataset conforms to a first predetermined constraint of the first-stage plan; if it does, determining whether the first monitoring dataset meets a predetermined overall assembly constraint; if it does, initiating the second-stage plan to perform assembly fine-tuning construction control on the target jacket, thereby realizing the hoisting and construction of the target jacket by the target crane.

[0006] In a possible implementation, the deep-water jacket basket structure hoisting construction method further includes the following processing: a first monitoring component is deployed at the first monitoring point, the first monitoring component including a first center of gravity measuring instrument, a first distance sensor, a first image acquisition device, and a first velocity sensor; the first predetermined constraint includes a first predetermined attitude constraint, a first predetermined position constraint, and a first predetermined velocity constraint; the first center of gravity monitored by the first center of gravity measuring instrument and the first distance monitored by the first distance sensor are sequentially matched in the first monitoring dataset; the first attitude of the target jacket at the first monitoring point is obtained based on the first center of gravity and the first distance; the first position is obtained by analyzing the first jacket image of the first image acquisition device matched in the first monitoring dataset; the first velocity is matched in the first monitoring dataset; when the first attitude meets the first predetermined attitude constraint, the first position meets the first predetermined position constraint, and the first velocity meets the first predetermined velocity constraint, then the first monitoring dataset meets the first predetermined constraint.

[0007] In a possible implementation, the deep-water jacket basket structure hoisting construction method further performs the following processing: dividing the first jacket image into grids based on a predetermined unit grid to obtain a first image grid result; acquiring the first occupant grid block of the target jacket in the first image grid result, and collecting the first longest diameter grid number of the first occupant grid block; reading the pre-aiming grid number, and drawing a first target circle with the center of the first longest diameter grid number as the center and the pre-aiming grid number as the radius; taking the intersection of the first target circle and the first predetermined lifting and translation path in the first stage scheme as the pre-aiming point. The process involves: obtaining the first actual lifting and translation direction of the first occupant grid block, and using the angle between the first actual lifting and translation direction and the pre-aiming point as the first deviation angle; introducing a rotation angle calculation formula to calculate the first actual rotation angle of the target jacket based on the first actual rotation angle and the first position; and dynamically controlling the hoisting construction of the first monitoring point according to the first comparison result between the first actual lifting and translation path and the first predetermined lifting and translation path.

[0008] In a possible implementation, the deep-water jacket basket structure hoisting construction method further includes the following processing: the expression for the angle calculation formula is as follows:

[0009] δ = arctan(KL)

[0010]

[0011] Wherein, δ refers to the first actual turning angle, K refers to the rear travel curvature of the target guide frame, the rear travel curvature is the reciprocal of the rear turning radius, L refers to the first longest diameter grid number, l refers to the pre-aiming grid number, and θ refers to the first deviation angle.

[0012] In a possible implementation, the deep-water jacket basket structure hoisting construction method also performs the following processing: the predetermined assembly constraint refers to the position constraint of the predetermined assembly point of the target jacket.

[0013] In a possible implementation, the deep-water jacket basket structure hoisting construction method further includes the following steps: acquiring an environmental monitoring equipment group; obtaining first environmental information at a first time through dynamic monitoring by the environmental monitoring equipment group, and analyzing the first environmental information to obtain a first air density; performing spectral density prediction analysis on the air density time series constructed based on the first correspondence between the first time and the first air density to obtain the prediction time period corresponding to the spectral density valley value; and initiating the second stage scheme during the prediction time period.

[0014] In a possible implementation, the deep-water jacket basket structure hoisting construction method further performs the following processing: reading a predetermined stability factor; traversing and matching the predetermined stability factor in the first environmental information to obtain a first factor parameter; and weighting the first factor parameter to obtain the first air density.

[0015] In a possible implementation, the deep-water jacket basket structure hoisting construction method further performs the following processing: obtaining the air density spectral density of the air density time series based on Fourier transform; analyzing the air density spectral density to obtain the spectral density valley value; and back-matching the time range corresponding to the spectral density valley value, which is recorded as the prediction time period.

[0016] The proposed method for hoisting a deep-water jacket basket structure involves: reading a predetermined hoisting plan; dynamically monitoring the hoisting process to obtain first dynamic monitoring information; matching the first monitoring dataset with the first monitoring points; determining whether the first monitoring dataset meets the first predetermined constraints of the first-stage plan; if it does, determining whether the first monitoring dataset meets the predetermined overall assembly constraints; and if it does, initiating the second-stage plan to perform assembly fine-tuning construction control on the target jacket, thereby achieving the hoisting of the target jacket. This method solves the technical problems of existing jacket construction methods, which suffer from the difficulty in real-time and accurate monitoring of parameter changes during hoisting, resulting in significant safety hazards and accuracy errors, leading to poor hoisting efficiency and safety, and insufficient overall assembly accuracy of the jacket. The method achieves the technical effect of improving hoisting efficiency, safety, and construction quality. Attached Figure Description

[0017] To more clearly illustrate the technical solutions of the embodiments of this disclosure, the accompanying drawings of the embodiments of this disclosure will be briefly described below. Flowcharts are used in this application to illustrate the operations performed by the system according to the embodiments of this application. It should be understood that the preceding or following operations are not necessarily performed precisely in sequence. Instead, various steps can be processed in reverse order or simultaneously as needed. Furthermore, other operations can be added to these processes, or one or more steps can be removed from these processes.

[0018] Figure 1 A schematic diagram of the construction process for hoisting a deep-water jacket basket structure provided in this application embodiment;

[0019] Figure 2 This is a schematic diagram illustrating the process of determining whether a first predetermined constraint is met in the deep-water jacket basket structure hoisting construction method provided in this application embodiment. Detailed Implementation

[0020] The above description is only an overview of the technical solution of this application. In order to better understand the technical means of this application and to implement it in accordance with the contents of the specification, and to make the above and other objects, features and advantages of this application more obvious and understandable, the following are specific embodiments of this application.

[0021] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description of this application will be provided in conjunction with the accompanying drawings. The described embodiments should not be considered as limitations on this application. All other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0022] In the following description, references to "some embodiments" describe a subset of all possible embodiments. However, it is understood that "some embodiments" may be the same or different subsets of all possible embodiments and may be combined with each other without conflict. The terms "first" and "second" are used merely to distinguish similar objects and do not represent a specific ordering of objects. The terms "comprising" and "having," 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 is not necessarily limited to those steps or units explicitly listed, but may include other steps or modules not explicitly listed or inherent to these processes, methods, products, or devices. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein is for the purpose of describing embodiments of this application only.

[0023] This application provides a method for hoisting and constructing a basket-shaped structure for deep-water jackets, such as... Figure 1 As shown, the method includes:

[0024] Step S100: Read the predetermined hoisting plan, which includes a first-stage plan and a second-stage plan for hoisting the target jacket structure using a target crane. In the hoisting construction of deep-water jacket basket structures, the predetermined hoisting plan is a document that details the construction steps, methods, safety requirements, and expected results. This plan typically includes multiple stages to address different challenges and needs that may be encountered during the hoisting process. Specifically, it includes a first-stage plan and a second-stage plan for hoisting the target jacket structure using a target crane. The first stage involves roughly moving the jacket structure to the assembly position, i.e., the final assembly point. Specifically, this stage includes selecting a suitable target crane (considering its lifting capacity, operating radius, stability, etc.), determining the lifting point position (requiring structural analysis and calculation to ensure the lifting point strength meets requirements), and preparing necessary hoisting equipment and tools (such as wire ropes, lifting gear, pulley blocks, etc.); executing the hoisting operation, i.e., strictly controlling the crane's lifting speed, angle, and stability to ensure the jacket structure smoothly leaves the ground and begins its movement towards the predetermined position. The first stage involves moving the basket-shaped jacket structure to its intended location and making initial adjustments and securing it. The second stage involves fine-tuning the jacket structure at the assembly point. This stage includes using professional measuring and positioning equipment (such as total stations and laser rangefinders) to fine-tune the position, orientation, and height of the jacket structure to ensure it is precisely aligned with the intended installation position. After the jacket structure is precisely adjusted, assembly and securing work is carried out, including connecting and tightening the various parts of the jacket structure (such as legs and beams) according to design requirements, as well as installing necessary support and reinforcement structures. Finally, a full-process capability assessment (crane, slings, lifting points, and tilt angle adjustment), strength assessment (unhooking and welding ratio, strength of the finished product after hoisting), and interference checks (such as weld quality inspection and structural strength testing) are conducted to ensure the stability and safety of the entire structure.

[0025] Step S200: Dynamically monitor the hoisting process of the target jacket according to the first-stage plan to obtain first dynamic monitoring information. The first dynamic monitoring information includes multiple sets of monitoring data from multiple monitoring points. Specifically, the first dynamic monitoring information obtained by dynamically monitoring the hoisting process of the target jacket according to the first-stage plan refers to the data set collected in real time by sensors deployed at multiple key monitoring points during the hoisting process. These data sets reflect the state changes of the jacket at different time points, including but not limited to parameters such as stress, displacement, tilt angle, and vibration. Specifically, the monitoring points typically include the jacket's crane, slings, lifting points, key connection parts, areas prone to deformation, and predetermined assembly points. Each monitoring point represents a specific type or degree of change that may occur in the jacket during the hoisting process. Each monitoring point generates multiple sets of monitoring data during the hoisting process, containing monitoring data at different time points, such as stress values, displacement, and tilt angles. This monitoring data is usually presented in a time series format, intuitively reflecting the dynamic changes of the jacket during the hoisting process.

[0026] Step S300: Match the first monitoring dataset of the first monitoring point among the multiple monitoring datasets. The first monitoring point refers to any one of the multiple monitoring points. Specifically, each monitoring point generates a series of monitoring data that changes over time during the hoisting process. These data are organized into multiple monitoring datasets. Then, the monitoring dataset corresponding to the first monitoring point is found and matched from the multiple monitoring datasets. That is, the first monitoring dataset contains the monitoring data of the corresponding point at various time points during the hoisting process, such as stress value, displacement, tilt angle, etc. By matching and extracting the first monitoring dataset, a more in-depth and detailed analysis of the point can be performed, such as trend prediction and anomaly detection, so as to discover and deal with potential problems in a timely manner. At the same time, the first monitoring dataset can also be compared and analyzed with the datasets of other monitoring points to comprehensively evaluate the state changes and safety performance of the entire jacket during the hoisting process.

[0027] Step S400: Determine whether the first monitoring dataset meets the first predetermined constraint of the first stage scheme.

[0028] Furthermore, such as Figure 2As shown, step S400 further includes step S410, where a first monitoring component is deployed at the first monitoring point, the first monitoring component including a first center of gravity measuring instrument, a first distance sensor, a first image acquisition device, and a first speed sensor; step S420, where the first predetermined constraint includes a first predetermined attitude constraint, a first predetermined position constraint, and a first predetermined speed constraint; step S430, where the first center of gravity monitored by the first center of gravity measuring instrument and the first distance monitored by the first distance sensor are sequentially matched in the first monitoring dataset; step S440, where the first attitude of the target guide frame at the first monitoring point is obtained based on the first center of gravity and the first distance; step S450, where the first position is obtained by analyzing the first guide frame image of the first image acquisition device matched in the first monitoring dataset; step S460, where the first speed is matched in the first monitoring dataset by the first speed sensor; and step S470, where the first monitoring dataset conforms to the first predetermined constraint when the first attitude conforms to the first predetermined attitude constraint, the first position conforms to the first predetermined position constraint, and the first speed conforms to the first predetermined speed constraint.

[0029] Preferably, during the hoisting and installation of the target jacket, the first monitoring component includes a first center of gravity measuring instrument, a first distance sensor, a first image acquisition device, and a first speed sensor. These monitoring devices are respectively deployed at the first monitoring point to monitor the changes in the center of gravity, distance, image information, and speed information of the jacket at that point. The first predetermined constraint includes a first predetermined attitude constraint, a first predetermined position constraint, and a first predetermined speed constraint. Specifically, the first predetermined attitude constraint refers to the constraint on the specific attitude (such as angle, tilt, etc.) that the jacket should achieve or maintain at the first monitoring point, and the first predetermined position constraint refers to the constraint on the specific position that the jacket should achieve or maintain at the first monitoring point, which is usually defined by position parameters such as coordinates and distance. Constant speed constraint refers to the constraint on the speed at which the jacket structure moves or changes at the first monitoring point, which helps to ensure the stability and safety of the hoisting process. The data monitored by the first center of gravity measuring instrument and the first distance sensor are extracted from the first monitoring dataset, namely the first center of gravity and the first distance, which reflect the position and distance changes of the jacket structure's center of gravity at that point. The monitoring of the center of gravity takes into account the shape characteristics of the jacket structure and cannot simply locate the position change of a single point. The first distance refers to the distance between the two ends of the jacket structure and a predetermined object at the first monitoring point. This predetermined object is fixed. Based on the distance between the first endpoint and the predetermined object, it is determined whether the hoisting has deviated. In fact, it is to monitor the safe distance during the jacket structure hoisting process to avoid collision damage during the hoisting process.

[0030] Preferably, based on the data of the first center of gravity and the first distance, the first attitude of the target guide frame at the first monitoring point is obtained, that is, the first attitude of the guide frame at that point is calculated. For example, assuming the monitoring point coordinates (x p y p , z p ), first centroid coordinates (x c y c , z c ), calculate the attitude angle: pitch angle (θ), which describes the degree of inclination of the jacket structure relative to the horizontal plane, and the calculation formula is:

[0031]

[0032] Yaw angle (φ) describes the rotation angle of the jacket structure in the horizontal plane, and is calculated using the following formula:

[0033] φ=arctan2(y c -y p ,x c -x p );

[0034] in, It is the magnitude of the vector, that is: Using the image of the jacket structure acquired by the first image acquisition device, the first position information of the jacket structure at that point is extracted through image processing technology (such as image recognition, edge detection, etc.). The data monitored by the first velocity sensor is extracted from the first monitoring dataset, that is, the moving speed of the jacket structure at that point, i.e., the first velocity. The first velocity adopts a slow and steady hoisting speed to avoid structural vibration caused by sudden movements. The first monitoring dataset is judged to meet the first predetermined constraints if and only if the first attitude meets the first predetermined attitude constraint, the first position meets the first predetermined position constraint, and the first velocity meets the first predetermined velocity constraint. This means that the state of the jacket structure at the first monitoring point is safe, stable and as expected, which helps to ensure the quality and safety of the hoisting construction.

[0035] In one possible implementation, step S450 further includes step S451, dividing the first duct frame image into grids based on a predetermined unit grid to obtain a first image grid result; step S452, obtaining the first occupant grid block of the target duct frame in the first image grid result, and collecting the first longest diameter grid number of the first occupant grid block; step S453, reading the pre-aiming grid number, and drawing a first target circle with the center of the first longest diameter grid number as the center and the pre-aiming grid number as the radius; step S454, taking the intersection of the first target circle and the first predetermined lifting and translation path in the first stage scheme as the pre-aiming point; step S4 Step S455: Obtain the first actual lifting and translation direction of the first occupant grid block, and take the angle between the first actual lifting and translation direction and the pre-aiming point as the first deviation angle; Step S456: Introduce the angle calculation formula to calculate the first actual angle of the target jacket, the number of the first longest diameter grid, the number of the pre-aiming grid, and the first deviation angle to obtain the first actual angle of the target jacket; Step S457: Generate the first actual lifting and translation path of the target jacket based on the first actual angle and the first position; Step S458: Perform dynamic control of the hoisting construction of the first monitoring point according to the first comparison result between the first actual lifting and translation path and the first predetermined lifting and translation path.

[0036] Preferably, a predetermined unit grid is set as the basic unit for image analysis and processing. The image of the duct stent is then divided according to this predetermined unit grid to obtain a first image raster result. This rasterization process helps convert the duct stent in the image into multiple quantifiable data points. In the rasterized duct stent image, the grid block occupied by the target duct stent in the first image raster result is identified, i.e., the first occupying grid block. The first longest diameter grid number of the first occupying grid block is then collected, i.e., the duct stent's... The longest diameter (e.g., length, width, diagonal, etc.) is defined as the number of grid cells corresponding to this longest diameter, which is the first longest diameter grid cell count. A preset number of pre-aiming grid cells is used to determine the pre-aiming range and accuracy. Then, a circle is drawn on the rasterized image with the center of the first longest diameter grid cell count as the center and the number of pre-aiming grid cells as the radius. This target circle represents the pre-aiming range. The intersection of the first target circle and the predetermined lifting and translation path in the first stage scheme (i.e., the first predetermined lifting and translation path) is taken as the pre-aiming point. The pre-aiming point represents the target range of the jacket under the current control strategy. At a future point in time, the target jacket may intersect with a predetermined path at a location where the first predetermined lifting and translation path is the trajectory that the jacket should follow during hoisting. The actual lifting and translation direction of the first occupant grid block (the first actual lifting and translation direction) is obtained. The angle between this actual lifting and translation direction and the pre-aiming point is calculated, i.e., the first deviation angle. This deviation angle reflects the deviation between the actual path and the predetermined path. Then, a turning angle calculation formula is introduced, using the first longest diameter grid number, the pre-aiming grid number, and the first deviation angle as inputs to calculate the actual turning angle (the first actual turning angle) that the target jacket needs to adjust. Finally, based on the first actual turning angle and the first position (which may be the current or initial position of the jacket), the adjusted actual lifting and translation path (the first actual lifting and translation path) is generated. The first actual lifting and translation path is compared with the first predetermined lifting and translation path to obtain the first comparison result. Based on this comparison result, the hoisting operation is dynamically controlled. If there is a large deviation between the two, the operating parameters of the hoisting equipment (such as a crane) need to be adjusted to ensure that the jacket can move according to the actual lifting and translation path.

[0037] In one possible implementation, step S456 further includes the following expression for the angle calculation formula:

[0038] δ = arctan(KL)

[0039]

[0040] Wherein, δ refers to the first actual turning angle, K refers to the rear travel curvature of the target guide frame, the rear travel curvature is the reciprocal of the rear turning radius, L refers to the first longest diameter grid number, l refers to the pre-aiming grid number, and θ refers to the first deviation angle.

[0041] Step S500: If the condition is met, determine whether the first monitoring dataset satisfies the predetermined assembly constraints.

[0042] Furthermore, step S500 further includes the fact that the predetermined assembly constraint refers to the position constraint of the predetermined assembly point of the target guide frame.

[0043] Preferably, if the first monitoring dataset meets the first predetermined constraints, i.e., satisfies the first attitude constraint, the first position constraint, and the first velocity constraint, then it is further determined whether the first monitoring dataset meets the predetermined assembly constraints. The predetermined assembly constraints typically refer to the positional constraints set for the predetermined assembly point of the target jacket after the entire jacket hoisting construction is completed. These constraints ensure that the jacket can meet predetermined design requirements, safety standards, and functional performance during hoisting, installation, and final positioning. Examples include overall attitude (e.g., levelness, tilt, etc.) and position (e.g., coordinate position, relative positional relationship, etc.), structural stability (the structure should remain stable without abnormal deformation or damage, assessed by monitoring parameters such as stress, strain, and vibration), connection quality (the connections between various parts of the jacket, such as legs and beams, should be firm and reliable), and safety clearance (safety clearance requirements between the jacket and surrounding obstacles, such as other offshore facilities and submarine pipelines). Considering all these aspects, it is determined whether the first monitoring dataset meets the predetermined assembly constraints. If the jacket state reflected by the first monitoring dataset meets the requirements of the predetermined assembly constraints in all aspects, then the dataset can be considered to meet the predetermined assembly constraints.

[0044] Step S600: If the conditions are met, the second phase of the scheme is initiated to perform fine-tuning construction control on the target jacket, thereby enabling the target crane to perform hoisting construction on the target jacket. If the first monitoring dataset meets the predetermined assembly constraints, it signifies that the first stage of the target jacket installation has been successfully completed and the conditions for proceeding to the next stage of construction have been met. At this point, the second stage plan is initiated to control the assembly and fine-tuning of the target jacket, enabling the target crane to perform complete installation of the target jacket. Specifically, using lifting equipment and measuring instruments, the various parts of the target jacket are precisely aligned according to design requirements, with strict control over alignment accuracy. After alignment, temporary supports or clamps are used to temporarily fix the jacket to ensure its stability during subsequent fine-tuning. Then, by adjusting the lifting point position of the lifting equipment or using tools such as jacks, the attitude of the jacket is fine-tuned to meet predetermined attitude constraints. The position of the jacket is precisely adjusted to ensure it meets predetermined position constraints. The lifting and moving speed of the lifting equipment is strictly controlled to ensure smooth movement of the jacket and compliance with predetermined speed constraints. Ultimately, the target crane is used to perform installation of the target jacket, ensuring the safety, stability, and high-quality construction results of the jacket during the installation process.

[0045] In one possible implementation, step S600 further includes step S610, acquiring an environmental monitoring equipment group; step S620, dynamically monitoring the first environmental information at a first time through the environmental monitoring equipment group, and analyzing the first environmental information to obtain a first air density; step S630, performing spectral density prediction analysis on the air density time series constructed based on the first correspondence between the first time and the first air density to obtain the prediction time period corresponding to the spectral density valley; and step S640, activating the second stage scheme during the prediction time period.

[0046] Preferably, before initiating the second phase of the scheme for fine-tuning the assembly of the target jacket, the impact of wind and wave conditions must be considered. This includes predicting the minimum wind and wave conditions, selecting an appropriate assembly time, and choosing periods with favorable environmental conditions for hoisting, in order to minimize the impact of wind and wave conditions on the hoisting operation. First, an environmental monitoring equipment group, including but not limited to air quality monitoring equipment, thermometers, and barometers, is acquired. This group dynamically monitors the environment of the target jacket construction area. At a specific time (i.e., the first time), the equipment group collects multiple environmental data points, referred to as the first environmental information. This information may include temperature, humidity, wind direction, wind speed, air quality, and other aspects, which will directly affect the fine-tuning of the jacket assembly. After acquiring the first environmental information, meteorological and physical principles are used to analyze this data. The analysis and processing involve calculating the air density from the air quality data, thus obtaining the first air density. Air density data from multiple time points (including the first air density at the first time point) are arranged chronologically to construct an air density time series. Then, the air density time series is processed using spectral density prediction analysis, a prediction technique based on time series data. By analyzing factors such as periodicity, trend, and randomness in the data, it predicts the data value at a future time point. In this process, the prediction time period corresponding to the spectral density trough is identified. This prediction time period may represent a construction period with relatively low air density and favorable weather conditions. Finally, based on the needs of construction control, the second-stage plan is initiated within the prediction time period to perform fine-tuning of the target jacket assembly, ensuring construction quality and safety.

[0047] In one possible implementation, step S620 further includes step S621, reading a predetermined stability factor; step S622, traversing and matching the predetermined stability factor in the first environmental information to obtain a first factor parameter; and step S623, weighting the first factor parameter to obtain the first air density.

[0048] Preferably, the predetermined stability factor is a set of pre-set coefficients based on historical data, experimental results, etc., used to evaluate or adjust the impact of environmental information on a specific physical quantity (such as air density). It is used to adjust the calculation of air density, aiming to improve the accuracy and reliability of environmental information analysis. The predetermined stability factor is compared with each parameter in the first environmental information to find the specific value or state in the first environmental information corresponding to each stability factor. This is called the first factor parameter, which may include temperature, humidity, air pressure, wind speed, etc. For example, if wind speed is a stability factor, then the first factor parameter is the wind speed value monitored and recorded in the current environmental information. Different factors are assigned different weights according to their degree of influence on air density. For example, temperature may have a greater impact on air density than humidity, so temperature has a higher weight when calculating air density. Then, the first factor parameters are weighted and calculated to obtain the first air density.

[0049] In one possible implementation, step S630 further includes step S631, obtaining the air density spectral density of the air density time series based on Fourier transform; step S632, analyzing the air density spectral density to obtain the spectral density valley value; and step S633, back-matching the time range corresponding to the spectral density valley value, denoted as the prediction time period.

[0050] Preferably, the Fourier transform is a mathematical tool for converting signals from the time domain to the frequency domain. Specifically, the time-series data of air density (i.e., air density values ​​that change over time) is input into the Fourier transform to obtain a frequency spectrum of air density, i.e., the spectral density, which represents the intensity of different frequency components in the change of air density. Each point in the spectral density corresponds to a specific frequency and the amplitude of the change in air density at that frequency. After obtaining the spectral density of air density, this spectral density is analyzed to find the valleys. Valleys are relatively low or lowest points in the spectral density. They may correspond to relatively stable or non-volatile frequency components in the change of air density, reflecting the relatively stable characteristics of air density over certain time periods. The lower the valley, the weaker the energy of the signal in that frequency range. Then, the time range corresponding to the valley of the spectral density is back-matched. Usually, the inverse Fourier transform is used to map it to the time domain. That is, by back-mapping these frequency ranges back to the time domain, the time periods corresponding to these low-energy frequencies in the time series data are determined. Finally, the time range determined by back-matching is recorded as the prediction time period, where the prediction time period may correspond to relatively stable air density conditions.

[0051] The specific embodiments described above do not constitute a limitation on the scope of protection of this application. Those skilled in the art should understand that various modifications, combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this application should be included within the scope of protection of this application.

Claims

1. A method for hoisting and constructing a basket-shaped structure for deep-water jackets, characterized in that: include: Read the predetermined hoisting plan, which includes a first-stage plan and a second-stage plan for hoisting the target jacket structure using the target crane; The hoisting and construction process of the target jacket structure carried out according to the first phase plan is dynamically monitored to obtain first dynamic monitoring information, which includes multiple sets of monitoring data sets from multiple monitoring points. A first monitoring dataset that matches a first monitoring point in the plurality of monitoring datasets, wherein the first monitoring point refers to any one of the plurality of monitoring points; Determine whether the first monitoring dataset meets the first predetermined constraint of the first phase scheme; If it meets the requirements, determine whether the first monitoring dataset satisfies the predetermined assembly constraints; If the conditions are met, the second phase of the plan will be initiated to perform fine-tuning construction control on the target jacket, thereby enabling the target crane to perform the lifting and installation of the target jacket. The first monitoring point is equipped with a first monitoring component, which includes a first center of gravity measuring instrument, a first distance sensor, a first image acquisition device, and a first speed sensor. The first predetermined constraint includes a first predetermined attitude constraint, a first predetermined position constraint, and a first predetermined velocity constraint; In the first monitoring dataset, the first center of gravity detected by the first center of gravity measuring instrument and the first distance detected by the first distance sensor are matched sequentially; The first attitude of the target catheter at the first monitoring point is obtained based on the first center of gravity and the first distance; The first position is obtained by analyzing the first duct frame image of the first image acquisition device matched in the first monitoring dataset; Match the first speed detected by the first speed sensor in the first monitoring dataset; When the first posture conforms to the first predetermined posture constraint, the first position conforms to the first predetermined position constraint, and the first velocity conforms to the first predetermined velocity constraint, then the first monitoring dataset conforms to the first predetermined constraint.

2. The method for hoisting and constructing a deep-water jacket basket structure according to claim 1, characterized in that, Also includes: The first duct frame image is divided into raster units based on a predetermined unit raster to obtain the first image raster result; Obtain the first placeholder grid block of the target duct stent in the first image grid result, and collect the first longest diameter grid number of the first placeholder grid block; Read the number of pre-aiming grids, and draw a first target circle with the center of the first longest diameter grid as the center and the number of pre-aiming grids as the radius; The intersection of the first target circle and the first predetermined lifting and translation path in the first stage plan is taken as the aiming point; Obtain the first actual lifting and translation direction of the first occupant grid block, and take the angle between the first actual lifting and translation direction and the pre-aiming point as the first deviation angle; The first actual rotation angle of the target guide frame is obtained by introducing a rotation angle calculation formula to calculate the number of the first longest diameter grid, the number of the pre-aiming grid, and the first deviation angle. The first actual lifting and translation path of the target guide frame is generated based on the first actual turning angle and the first position; Dynamic control of the hoisting operation at the first monitoring point is performed based on the first comparison result between the first actual hoisting and translation path and the first predetermined hoisting and translation path.

3. The method for hoisting and constructing a deep-water jacket basket structure according to claim 2, characterized in that, The formula for calculating the angle is expressed as follows: ； in, This refers to the first actual turning angle. This refers to the rear-end travel curvature of the target guide frame, which is the reciprocal of the rear-end turning radius. This refers to the number of grid cells with the first longest diameter. This refers to the number of pre-aiming grids. This refers to the first deviation angle.

4. The method for hoisting and constructing a deep-water jacket basket structure according to claim 1, characterized in that, The predetermined assembly constraint refers to the positional constraint of the predetermined assembly point of the target guide frame.

5. The method for hoisting and constructing a deep-water jacket basket structure according to claim 1, characterized in that, Also includes: Acquire environmental monitoring equipment set; The environmental monitoring equipment group dynamically monitors and obtains the first environmental information at the first time, and analyzes the first environmental information to obtain the first air density; Spectral density prediction analysis is performed on the air density time series constructed based on the first correspondence between the first time and the first air density to obtain the predicted time period corresponding to the spectral density valley value. The second phase of the plan will be initiated during the predicted time period.

6. The method for hoisting and constructing a deep-water jacket basket structure according to claim 5, characterized in that, include: Read the predetermined stability factor; The predetermined stability factor is traversed and matched in the first environmental information to obtain the first factor parameter; The first air density is obtained by weighting the first factor parameters.

7. The method for hoisting and constructing a deep-water jacket basket structure according to claim 5, characterized in that, include: The air density spectral density of the air density time series is obtained based on Fourier transform; The valley value of the spectral density is obtained by analyzing the air density spectral density; The time range corresponding to the spectral density valley value is reverse-matched and denoted as the prediction time period.

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