A control optimization method and system for wellhead skid-mounted equipment on offshore platforms

Abstract

The present invention relates to the field of equipment control technology, and in particular to a method and system for optimizing the control of wellhead skid-mounted equipment for offshore platforms. The method comprises the following steps: acquiring marine environmental monitoring data and wellhead skid-mounted equipment status data; establishing a dynamic model based on sea conditions based on the marine environmental monitoring data, and importing the wellhead skid-mounted equipment status data into the dynamic model based on sea conditions for control linkage modeling to generate an adaptive control model; confirming the equipment-wave jitter impact of marine environmental monitoring data on the wellhead skid-mounted equipment status data, and designing an equipment control optimization algorithm based on the equipment-wave jitter impact. The present invention effectively solves the problems of ignoring wave jitter, delayed response, insufficient data storage and visualization in the existing wellhead skid-mounted equipment control by introducing technologies such as adaptive control models, wave prediction and early response compensation, thereby improving the stability, real-time nature and intelligence level of the equipment.

Description

Technical Field

[0001] The present invention relates to the technical field of equipment control, and in particular to a control optimization method and system for wellhead skid-mounted equipment used on an offshore platform. Background Art

[0002] Initially, offshore platform wellhead skids relied on traditional manual operation, with operators controlling and adjusting the wellhead using manual valves, hydraulic devices, and mechanical mechanisms. However, with the increasing complexity of offshore platform operating environments and heightened safety requirements, traditional manual operation has faced challenges such as high workload, low efficiency, and high safety risks. Since the 21st century, automated control technology has been gradually applied to wellhead skids, particularly those based on programmable logic controllers (PLCs) and distributed control systems (DCSs), significantly improving equipment response speed and operational precision. Controlling wellhead skids in floating production, storage, and offloading (FPSO) environments presents even greater challenges. Due to their dynamic nature, FPSOs experience continuous motion, such as pitch, roll, and heave, driven by waves, wind loads, and currents. This complex motion pattern makes it difficult for traditional wellhead skid control systems to operate stably. Especially in harsh sea conditions, delayed equipment response can lead to reduced production efficiency and even safety incidents. Furthermore, because FPSOs are typically deployed in deep or ultra-deep waters, wellhead skids require close coordination with the subsea Christmas tree and riser system to ensure stable oil and gas production and delivery. Therefore, optimizing the control strategy for wellhead skids on FPSO platforms and improving the system's adaptability to wave disturbances have become key areas for upgrading automated control technology. Summary of the Invention

[0003] Based on this, it is necessary to provide a control optimization method and system for wellhead skid-mounted equipment for offshore platforms to solve at least one of the above technical problems.

[0004] To achieve the above-mentioned object, a control optimization method for wellhead skid-mounted equipment for an offshore platform is provided, the method comprising the following steps:

[0005] Step S1: Acquire marine environment monitoring data and wellhead skid-mounted equipment status data; establish a dynamic model based on sea conditions based on the marine environment monitoring data, and import the wellhead skid-mounted equipment status data into the dynamic model based on sea conditions to perform control linkage modeling and generate an adaptive control model;

[0006] Step S2: Confirm the impact of ocean-wave jitter on the wellhead skid-mounted equipment status data from the marine environment monitoring data, and design an equipment control optimization algorithm based on the impact of ocean-wave jitter. Import the equipment control optimization algorithm into the adaptive control model to perform equipment jitter compensation on the wellhead skid-mounted equipment, generating equipment jitter compensation data.

[0007] Step S3: Performing wave prediction on the marine environment monitoring data to generate wave prediction data; performing advance response compensation on the device jitter compensation data based on the wave prediction data to generate device jitter compensation response data; importing the device jitter compensation response data into the adaptive control model to perform device jitter control, thereby generating device adaptive control data and device standby control data;

[0008] Step S4: The equipment adaptive control data and the equipment backup control data are stored and visualized in the cloud, thereby generating an equipment control storage report to perform wellhead skid-mounted equipment control optimization operations.

[0009] The present invention combines marine environmental data with equipment status data to construct a dynamic model of sea conditions and generate an adaptive control model, enabling the wellhead skid-mounted equipment to dynamically adjust its control strategy based on real-time sea condition changes, improving the adaptability and stability of the equipment under different sea conditions, thereby optimizing the control effect and equipment operating efficiency. The system identifies and compensates for the impact of wave jitter on the equipment, controls the movement of the equipment through optimization algorithms, reduces equipment instability caused by waves, thereby reducing the risk of equipment failure and improving the safety and reliability of operations. Through wave prediction and early response compensation, the system can make adaptive adjustments before wave changes, reduce the lag in equipment control response, and improve the timeliness and accuracy of operations, thereby ensuring that the equipment is always in the best operating state under changing sea conditions and improving operational efficiency. Through cloud storage and real-time visualization technology, operators can monitor equipment status and control effects at any time, ensuring data security and facilitating subsequent analysis. In addition, the generated control storage reports help improve the transparency and management efficiency of operations, facilitating the continuous optimization of equipment control strategies and operating processes. Therefore, the present invention effectively solves the problems of ignoring wave jitter, response lag, insufficient data storage and visualization in the control of existing wellhead skid-mounted equipment by introducing technologies such as adaptive control models, wave prediction and early response compensation, thereby improving the stability, real-time nature and intelligence level of the equipment.

[0010] Preferably, step S1 includes the following steps:

[0011] Step S11: using sensors to obtain marine environment monitoring data and wellhead skid-mounted equipment status data;

[0012] Step S12: performing data preprocessing on the marine environment monitoring data to generate standard marine environment monitoring data, wherein the data preprocessing includes data cleaning, data denoising, missing value filling and data standardization;

[0013] Step S13: Performing environmental change analysis on the standard sea area environmental monitoring data through edge computing devices to generate sea area environmental change data;

[0014] Step S14: Extract the wave height, period, direction and frequency from the marine environment change data to establish a dynamic model based on the sea conditions, and import the wellhead skid-mounted equipment status data into the dynamic model based on the sea conditions to perform control linkage modeling and generate an adaptive control model.

[0015] The present invention can effectively improve data quality through data preprocessing (such as denoising, filling missing values, etc.), reduce deviations caused by environmental interference or sensor errors, and ensure that monitoring data is more reliable. Real-time environmental change analysis with the help of edge computing devices can effectively improve response speed, ensure the timeliness of data processing and analysis, and reduce the impact of data delays. Extracting key sea condition parameters such as wave height, period, direction and frequency, and establishing a dynamic model can help accurately reflect the laws of sea condition changes. Combined with the status data of wellhead skid-mounted equipment, equipment control can be carried out more accurately, adapt to changes in sea conditions, and improve the safety and efficiency of equipment operation. The adaptive control model formed by combining the changes in the marine environment and the status data of the wellhead skid-mounted equipment can dynamically adjust the control strategy, optimize the operating status of the wellhead equipment, and improve the intelligence and automation level of the system, thereby reducing human intervention and improving overall operational efficiency.

[0016] Preferably, importing the wellhead skid-mounted equipment status data into a dynamic model based on sea conditions for control linkage modeling includes:

[0017] Import the wellhead skid-mounted equipment status data into a dynamic model based on sea conditions to screen the affected equipment status and obtain the status data of key equipment components;

[0018] Analyze the dynamic response characteristics of the equipment based on the status data of key components of the equipment and the dynamic model based on sea conditions to generate equipment sea condition response characteristic data;

[0019] According to the equipment sea condition response characteristic data, the model equipment mobility is adjusted in the sea condition-based dynamic model to generate an adaptive control model.

[0020] The present invention can screen the status of each key component of the equipment by importing the status data of the wellhead skid-mounted equipment into a dynamic model based on sea conditions. This process helps to clarify which equipment components are affected by changes in sea conditions, provides accurate data support for subsequent control optimization, avoids unnecessary operational adjustments, and improves operational efficiency. By analyzing the response characteristics of the status data and dynamic models of key equipment components, we can deeply understand the dynamic performance of the equipment under different sea conditions. This analysis can help identify the performance fluctuations, risk points and potential failure modes of the equipment under specific sea conditions, and thus provide a decision-making basis for equipment maintenance and operational adjustments. The dynamic model is adjusted dynamically based on the sea condition response characteristic data of the equipment to achieve adaptive control of the model. This adjustment enables the system to dynamically optimize the operating status of the equipment according to the real-time sea conditions, so that the wellhead skid-mounted equipment can maintain the best working condition and efficiency under changing sea conditions, thereby reducing the risk of equipment failure or improper operation.

[0021] Preferably, step S2 includes the following steps:

[0022] Step S21: performing equipment jitter timing analysis on the wellhead skid-mounted equipment status data to generate equipment jitter timing data; extracting sea wave change trends from the standard sea area environment monitoring data based on the equipment jitter timing data to obtain sea wave change trend data;

[0023] Step S22: performing directional fitting on the device jitter time series data and the wave change trend data to generate device impact direction data; extracting the maximum impact direction from the device impact direction data and marking it as strong wave direction data, and marking the remaining impact directions as weak wave direction data;

[0024] Step S23: calculating the wave-related fluctuation amplitude for the strong wave azimuth data and the weak wave azimuth data to obtain the comprehensive wave fluctuation amplitude of the related azimuths;

[0025] Step S24: Designing an equipment control optimization algorithm based on the comprehensive wave fluctuation amplitude of the associated orientation, and importing the equipment control optimization algorithm into the adaptive control model to perform equipment jitter compensation on the wellhead skid-mounted equipment, thereby generating equipment jitter compensation data.

[0026] By performing a time series analysis on the jitter of the wellhead skid-mounted equipment, the present invention can clarify the jitter characteristics of the equipment in different time periods and identify the key moments and periodic fluctuations when the equipment is affected by waves. Based on these jitter time series data, wave change trend data can be extracted to help the system more accurately capture the impact of sea conditions on equipment operation, especially the dynamic response under severe sea conditions. Direction fitting of the equipment jitter time series data with the wave change trend data can clarify the direction of the impact of waves on the equipment. By identifying the direction of the maximum wave affected by the equipment (strong wave direction) and other weaker impact directions (weak wave directions), it is possible to accurately determine which directions have the greatest impact on the equipment during sea condition changes, provide more accurate direction data for subsequent control optimization, and improve the accuracy of equipment control. By calculating the wave fluctuation amplitude of the data of strong wave direction and weak wave direction, comprehensive wave fluctuation data in different directions can be obtained to help further evaluate the degree of impact of sea conditions on equipment in different directions. This process can not only provide quantitative data for equipment operation status analysis, but also provide an important basis for optimizing control strategies. An equipment control optimization algorithm, designed based on comprehensive wave amplitude data, intelligently adjusts the operating state of wellhead skid-mounted equipment to account for waves from different travel directions. In this scenario, the equipment automatically adjusts its operating mode based on wave strength and direction, avoiding excessive vibration and minimizing damage to the equipment caused by unstable fluctuations. Furthermore, vibration compensation effectively improves equipment stability, ensuring continuous and safe operations.

[0027] Preferably, step S23 includes the following steps:

[0028] Step S231: performing mutual information evaluation on the strong wave position data and the weak wave position data to generate a wave position similarity measurement result;

[0029] Step S232: Calculate the fluctuation amplitudes of the strong wave azimuth data and the weak wave azimuth data respectively to obtain the strong wave fluctuation amplitude and the weak wave fluctuation amplitude;

[0030] Step S233: Using the wave direction similarity measurement result, perform amplitude fluctuation superposition calculation on the strong wave fluctuation amplitude and the weak wave fluctuation amplitude to obtain the comprehensive wave fluctuation amplitude of the associated direction. The amplitude fluctuation superposition calculation formula is as follows:

[0031] A total =W strong ·(ρ strong,weak ·A strong )+W weak ·((1-ρ strong,weak )·A weak );

[0032] Among them, A totalis the comprehensive wave amplitude of the associated direction, A strong is the amplitude of strong waves, A weak is the amplitude of weak waves, ρ strong,weak is the azimuth similarity measure between strong waves and weak waves, W strong is the fluctuation weight of strong waves, W weak is the fluctuation weight of weak waves.

[0033] By evaluating the mutual information of strong and weak wave position data, the present invention can quantify the similarity between different wave positions. This evaluation helps determine the patterns and connections between wave variations at different positions, providing scientific support for subsequent wave amplitude calculations. Through this evaluation, the system can identify the commonalities and differences between waves at different positions, providing a powerful basis for optimizing control strategies. Separately calculating the amplitudes of strong and weak waves accurately assesses the intensity of fluctuations under different wave conditions. Fluctuation amplitude is a key factor in the impact of changing sea conditions on equipment. Calculating the amplitude provides quantitative reference data for optimizing equipment control, helping the equipment intelligently respond to varying sea conditions. Using the wave position similarity metric, a superimposed calculation of the amplitudes of strong and weak waves comprehensively considers their impact in different directions and generates a composite wave amplitude for the associated positions. This calculation not only accurately reflects the combined impact of the amplitudes in each direction but also adjusts the superimposed weights based on the similarity of the wave positions, making the final amplitude calculation more scientific and accurate. This method enables the device to adapt to wave fluctuations of varying intensities and orientations, ensuring stable operation even in unstable sea conditions. The resulting comprehensive wave amplitude data provides reliable data support for the device's control optimization algorithm, enabling flexible adjustments to the control strategy based on real-time sea conditions. By adjusting the weights of the amplitude superposition, the control model intelligently optimizes the device's jitter compensation strategy based on actual wave conditions, improving the device's adaptability and stability in adverse sea conditions.

[0034] Preferably, the device control optimization algorithm for designing the comprehensive wave amplitude based on the associated orientation includes:

[0035] Defining equipment control parameters based on the integrated ocean wave amplitude at the associated orientation, where the equipment control parameters include the hydraulic pressure and movement speed of the wellhead skid-mounted equipment;

[0036] A regulation formula is constructed based on the hydraulic pressure and movement speed of the wellhead skid-mounted equipment, where the hydraulic pressure regulation formula is as follows:

[0037] P new =P base ×(1+k pressure ·A total ;

[0038] Among them, P new is the adjusted hydraulic pressure, P base is the basic hydraulic pressure, k pressure is the adjustment coefficient of hydraulic pressure, A total is the comprehensive ocean wave amplitude;

[0039] The motion speed adjustment formula is as follows:

[0040] V new =V base ×(1+V speed ·A total );

[0041] Among them, V new is the adjusted hydraulic pressure, V base is the basic hydraulic pressure, k speed is the adjustment coefficient of movement speed, A total is the comprehensive ocean wave amplitude;

[0042] PID control is used to adaptively adjust the hydraulic pressure regulation formula and motion speed regulation formula to generate an equipment control optimization algorithm.

[0043] The present invention ensures that the wellhead skid-mounted equipment can maintain sufficient power output under different sea conditions by adjusting the hydraulic pressure according to the amplitude of wave fluctuations, while avoiding damage to the equipment due to excessive pressure under strong wave conditions. The movement speed of the equipment is dynamically adjusted so that the equipment can flexibly adapt to the intensity and direction of the waves, avoiding unnecessary jitter or accidents caused by excessive movement speed, and accelerating operations under appropriate circumstances to improve efficiency. By introducing PID control (proportional-integral-differential control), the adjustment of hydraulic pressure and movement speed can be made smoother and more accurate. PID control can monitor the operating status of the equipment in real time and dynamically adjust the control parameters according to the error, thereby reducing the problem of over-adjustment or lag, and ensuring that the equipment operates smoothly under fluctuating sea conditions. The adjustment algorithm based on the amplitude of wave fluctuations can perceive and adapt to changes in sea conditions in real time, avoiding excessive fluctuations or response lags in the equipment under complex wave conditions. Adaptive adjustment of hydraulic pressure and movement speed helps to improve the operating stability of the equipment, enabling the equipment to maintain optimal operating conditions in strongly fluctuating waves, and reducing the risk of equipment failure and damage. The adaptive nature of PID control ensures the equipment can quickly adjust to drastic changes in sea conditions, avoiding performance fluctuations caused by wave fluctuations and improving the equipment's adaptability in dynamic environments. By precisely adjusting hydraulic pressure and movement speed, the equipment can increase operating speed in favorable wave conditions and slow down operations in adverse conditions, ensuring safe and efficient operation. For example, in strong seas, the equipment's movement speed can be reduced to reduce the workload, while in calm seas, the speed can be increased to improve efficiency.

[0044] Preferably, step S3 includes the following steps:

[0045] Step S31: extracting historical data from the standard sea environment monitoring data to obtain historical sea condition data; constructing a wave prediction model based on the historical sea condition data, and using the wave prediction model to perform wave prediction on the standard sea environment monitoring data to generate wave prediction data;

[0046] Step S32: performing an equipment impact warning on the equipment jitter compensation data based on the wave prediction data to generate equipment impact warning data; performing a compensation response adjustment on the equipment jitter compensation data based on the equipment impact warning data to generate equipment jitter compensation response data, wherein the compensation response adjustment includes adjusting the compensation time and the compensation coefficient;

[0047] Step S33: importing the device jitter compensation response data into the adaptive control model to perform device adaptive jitter control and generate device adaptive control data;

[0048] Step S34: Design redundant nodes for the wellhead skid-mounted equipment status data to obtain equipment redundant control nodes; perform equipment abnormality fault-tolerant control on the wellhead skid-mounted equipment status data based on the equipment redundant control nodes to generate equipment standby control data.

[0049] This invention uses wave prediction data to provide the equipment's jitter compensation system with future wave fluctuation trends, helping the equipment respond in advance, reducing the impact of sudden waves and enhancing its predictability. Using this prediction data to accurately compensate for equipment jitter can reduce damage caused by unstable sea conditions and improve the equipment's resilience in complex sea conditions. Equipment impact warning data can proactively identify impending wave conditions that will impact the equipment, issuing alerts and reminding operators to prepare. This not only reduces equipment failures but also reduces operational downtime caused by sudden changes in sea conditions. Adjusting the compensation time and coefficient allows precise control of the compensation intensity and timing, enabling the equipment's jitter control system to adapt to varying sea conditions. Optimizing the compensation response improves equipment stability and avoids unnecessary over- or under-compensation. The equipment can intelligently adjust its operating state based on varying wave conditions, enhancing operational stability in adverse sea conditions. Adaptive adjustment with real-time feedback significantly improves control accuracy. Adaptive jitter control not only reduces the risk of equipment damage but also reduces overload caused by equipment not adapting to wave fluctuations, thereby extending its service life. By introducing redundant control nodes, the equipment can continue to operate even if certain systems fail, preventing single-point failures from disrupting operations. This redundant design enhances the system's robustness and fault tolerance, ensuring operational completion even under extreme circumstances. Equipment fault-tolerant control automatically identifies faulty nodes and promptly switches to a backup control system, ensuring stable operation and mitigating operational safety risks caused by equipment failures. The introduction of this fault-tolerant mechanism enhances operational reliability.

[0050] Preferably, performing equipment abnormality fault tolerance control on wellhead skid-mounted equipment status data based on the equipment redundancy control node includes:

[0051] Extract wellhead skid-mounted equipment status data to measure equipment pressure, equipment flow, and equipment temperature, and perform anomaly detection on equipment pressure, equipment flow, and equipment temperature based on preset equipment status thresholds. If no anomaly is detected, the equipment redundant control node is put into hibernation.

[0052] When an anomaly is detected, the abnormal impact identification is performed on the wellhead skid-mounted equipment status data to generate equipment abnormal impact identification results. The equipment abnormal impact identification results include the abnormal impact identification results of core equipment and the abnormal impact identification results of edge equipment, and the abnormal impact identification results of edge equipment are excluded;

[0053] The device redundant control node is activated based on the abnormal impact identification result of the core device, and the standby control is performed on the abnormal impact identification result of the core device based on the activated device redundant control node to generate the device standby control data. The standby control formula is as follows:

[0054] C backup =(f redundant (D backup ,C current ));

[0055] Where C backup is the spare control data, f redundant The computation function that generates control data for redundant nodes, D backup C is the backup data of the redundant control node. current It is the control data of the current device status.

[0056] By real-time monitoring of key parameters such as device pressure, flow, and temperature, the present invention can promptly detect device anomalies and dynamically monitor device status. When no anomalies are detected, the system can reduce power and resource consumption by dormant redundant control nodes, ensuring efficient system operation under normal conditions. When the device is operating normally, the redundant control nodes are dormant, effectively reducing energy consumption and system burden. Real-time monitoring of various device parameters ensures that the system can identify device anomalies in the shortest possible time and respond quickly. Once a device anomaly is detected, the system conducts in-depth analysis of device status data to identify the anomaly's impact, distinguishing the different anomaly impacts of core and edge devices. Anomalies in edge devices are eliminated, ensuring that only critical devices are addressed. By hierarchically identifying anomalies in core and edge devices, critical device issues are prioritized when a device malfunction occurs, avoiding unnecessary intervention. By eliminating anomaly identification in edge devices, the system avoids ineffective activation of redundant control nodes, conserving system resources. When a core device anomaly occurs, the redundant control node is activated and provides backup control for the core device. By generating redundant node control data and applying backup control strategies, we ensure that equipment can quickly switch to backup control mode in the event of a failure, thus avoiding downtime and equipment damage. By activating redundant control nodes, the system can quickly restore normal equipment operation, reduce production interruptions caused by failures, and improve equipment reliability. The implementation of backup control ensures continued stable operation of equipment in the event of a core device failure, minimizing the impact of the failure on overall production.

[0057] Preferably, step S4 includes the following steps:

[0058] Step S41: Encapsulating the device adaptive control data and the device standby control data to generate a device control log; uploading the device control log to the cloud platform for data storage to generate a device control storage log;

[0059] Step S42: Visualize the data of the equipment control storage log to generate an equipment control storage report to perform wellhead skid-mounted equipment control optimization operations.

[0060] This invention encapsulates device adaptive control data and backup control data and generates a device control log, ensuring a detailed record of every device adjustment, optimization, or troubleshooting process. This detailed recording method provides comprehensive data tracking capabilities. Every device control operation and event is recorded in the log, providing complete historical data for subsequent analysis, inspection, and auditing. Storage of the device control log provides data support for future faults or anomalies, facilitating tracing and identifying the root cause of any problem. The device control log is uploaded to a cloud platform for data storage, ensuring long-term data preservation and ready access. The cloud platform's storage capacity also enables efficient management of large amounts of device control data and provides powerful data analysis and processing capabilities. By storing the device control log on the cloud platform, the device control log can be preserved long-term, avoiding the space limitations and data loss risks of local storage. Through the cloud platform, relevant personnel can access and review the device control log at any time, facilitating remote monitoring, troubleshooting, and optimization adjustments. Data visualization of the device control log storage allows complex control data to be presented in the form of charts, dashboards, and other formats, significantly improving data readability and analysis efficiency. Through visual device control storage reports, users can intuitively understand the operating status, historical adjustments and optimization of the equipment, helping managers make decisions quickly.

[0061] In this specification, a wellhead skid-mounted equipment control optimization system for an offshore platform is provided, which is used to execute the above-mentioned wellhead skid-mounted equipment control optimization method for an offshore platform. The wellhead skid-mounted equipment control optimization system for an offshore platform includes:

[0062] The control model building module is used to obtain marine environmental monitoring data and wellhead skid-mounted equipment status data; a dynamic model based on sea conditions is established based on the marine environmental monitoring data, and the wellhead skid-mounted equipment status data is imported into the dynamic model based on sea conditions for control linkage modeling to generate an adaptive control model;

[0063] The jitter compensation module is used to identify the impact of ocean-wave jitter on the status data of wellhead skid-mounted equipment from marine environmental monitoring data and to design an equipment control optimization algorithm based on this impact. The equipment control optimization algorithm is then imported into the adaptive control model to perform jitter compensation on the wellhead skid-mounted equipment and generate equipment jitter compensation data.

[0064] The response compensation module is used to predict waves based on marine environment monitoring data and generate wave prediction data; perform advance response compensation on device jitter compensation data based on the wave prediction data to generate device jitter compensation response data; and import the device jitter compensation response data into the adaptive control model to perform device jitter control and generate device adaptive control data and device backup control data.

[0065] The control data storage module is used to store and visualize equipment adaptive control data and equipment backup control data in the cloud, thereby generating equipment control storage reports to perform wellhead skid-mounted equipment control optimization operations.

[0066] The beneficial effect of the present invention lies in that by acquiring marine environmental monitoring data and wellhead skid-mounted equipment status data, combining it with the sea conditions to establish a dynamic model and generate an adaptive control model, the equipment can dynamically adjust the control strategy according to real-time sea condition changes, thereby optimizing the equipment's operational stability and adaptability under different sea conditions, improving operational efficiency and the equipment's long-term reliability. By identifying the impact of wave jitter on equipment status caused by marine environmental monitoring data and designing an optimization algorithm for jitter compensation, the module can effectively reduce the negative impact of wave jitter on the equipment, improve the equipment's stability during offshore operations, reduce equipment failure rates, and ensure operational safety. By using wave predictions to compensate for equipment jitter in advance, the module optimizes the timeliness of the equipment's control response, ensures that the equipment is effectively adjusted before sea conditions change, avoids the risks of delayed response, improves the real-time performance and accuracy of operations, and ensures that the equipment is always in the optimal control state. By storing and visualizing the equipment's adaptive control data and backup control data in the cloud, the module achieves centralized management and real-time monitoring of equipment operating data, provides efficient data query and analysis functions, and facilitates decision support during operations. Furthermore, the generated control storage reports facilitate subsequent review, optimization, and continuous improvement of the equipment's control strategies and operational processes, increasing operational transparency and optimization potential. Therefore, by introducing adaptive control models, wave prediction, and early response compensation technologies, this invention effectively addresses existing wellhead skid-mounted equipment control issues such as neglect of wave jitter, delayed response, and insufficient data storage and visualization, thereby improving the equipment's stability, real-time capabilities, and intelligence. BRIEF DESCRIPTION OF THE DRAWINGS

[0067] Figure 1 A schematic flow chart of the steps of a control optimization method for wellhead skid-mounted equipment used on an offshore platform;

[0068] Figure 2 for Figure 1 Detailed implementation steps of step S2 in FIG.

[0069] Figure 3 for Figure 1Detailed implementation steps of step S3 in FIG.

[0070] The purpose, features and advantages of the present invention will be further described with reference to the accompanying drawings and in conjunction with the embodiments. DETAILED DESCRIPTION

[0071] The following is a clear and complete description of the technical method of the present invention in conjunction with the accompanying drawings. It is obvious that the embodiments described are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without making any creative efforts are within the scope of protection of the present invention.

[0072] In addition, the accompanying drawings are merely schematic illustrations of the present invention and are not necessarily drawn to scale. Identical reference numerals in the figures denote identical or similar parts, and thus repetitive descriptions thereof will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities that do not necessarily correspond to physically or logically separate entities. These functional entities may be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor and / or microcontroller approaches.

[0073] It should be understood that although the terms "first," "second," and the like may be used herein to describe various elements, these elements should not be limited by these terms. These terms are used solely to distinguish one element from another. For example, a first element may be referred to as a second element, and similarly, a second element may be referred to as a first element, without departing from the scope of the exemplary embodiments. The term "and / or" as used herein includes any and all combinations of one or more of the listed associated items.

[0074] To achieve this, please refer to Figures 1 to 3 A method for optimizing control of a wellhead skid-mounted device for an offshore platform, the method comprising the following steps:

[0075] Step S1: Acquire marine environment monitoring data and wellhead skid-mounted equipment status data; establish a dynamic model based on sea conditions based on the marine environment monitoring data, and import the wellhead skid-mounted equipment status data into the dynamic model based on sea conditions to perform control linkage modeling and generate an adaptive control model;

[0076] Step S2: Confirm the impact of ocean-wave jitter on the wellhead skid-mounted equipment status data from the marine environment monitoring data, and design an equipment control optimization algorithm based on the impact of ocean-wave jitter. Import the equipment control optimization algorithm into the adaptive control model to perform equipment jitter compensation on the wellhead skid-mounted equipment, generating equipment jitter compensation data.

[0077] Step S3: Performing wave prediction on the marine environment monitoring data to generate wave prediction data; performing advance response compensation on the device jitter compensation data based on the wave prediction data to generate device jitter compensation response data; importing the device jitter compensation response data into the adaptive control model to perform device jitter control, thereby generating device adaptive control data and device standby control data;

[0078] Step S4: The equipment adaptive control data and the equipment backup control data are stored and visualized in the cloud, thereby generating an equipment control storage report to perform wellhead skid-mounted equipment control optimization operations.

[0079] The present invention combines marine environmental data with equipment status data to construct a dynamic model of sea conditions and generate an adaptive control model, enabling the wellhead skid-mounted equipment to dynamically adjust its control strategy based on real-time sea condition changes, improving the adaptability and stability of the equipment under different sea conditions, thereby optimizing the control effect and equipment operating efficiency. The system identifies and compensates for the impact of wave jitter on the equipment, controls the movement of the equipment through optimization algorithms, reduces equipment instability caused by waves, thereby reducing the risk of equipment failure and improving the safety and reliability of operations. Through wave prediction and early response compensation, the system can make adaptive adjustments before wave changes, reduce the lag in equipment control response, and improve the timeliness and accuracy of operations, thereby ensuring that the equipment is always in the best operating state under changing sea conditions and improving operational efficiency. Through cloud storage and real-time visualization technology, operators can monitor equipment status and control effects at any time, ensuring data security and facilitating subsequent analysis. In addition, the generated control storage reports help improve the transparency and management efficiency of operations, facilitating the continuous optimization of equipment control strategies and operating processes. Therefore, the present invention effectively solves the problems of ignoring wave jitter, response lag, insufficient data storage and visualization in the control of existing wellhead skid-mounted equipment by introducing technologies such as adaptive control models, wave prediction and early response compensation, thereby improving the stability, real-time nature and intelligence level of the equipment.

[0080] In the embodiment of the present invention, reference Figure 1 FIG. 1 is a flow chart showing the steps of a method for optimizing control of a wellhead skid-mounted device for an offshore platform according to the present invention. In this example, the method for optimizing control of a wellhead skid-mounted device for an offshore platform includes the following steps:

[0081] Step S1: Acquire marine environment monitoring data and wellhead skid-mounted equipment status data; establish a dynamic model based on sea conditions based on the marine environment monitoring data, and import the wellhead skid-mounted equipment status data into the dynamic model based on sea conditions to perform control linkage modeling and generate an adaptive control model;

[0082] In an embodiment of the present invention, environmental monitoring data is collected in the sea area by deploying marine environmental sensors (such as wave sensors, wind speed sensors, temperature sensors, etc.). The sensors can transmit data to the processing system in real time through wireless communication technologies (such as LoRa, 5G). According to the dynamic changes of the marine environment, an appropriate data collection frequency (such as every minute, every hour, etc.) is selected to ensure that real-time data with sufficient accuracy is captured. The wellhead skid-mounted equipment is equipped with multiple sensors (such as pressure sensors, flow sensors, temperature sensors, vibration sensors, etc.) to collect the working status data of the equipment in real time. To ensure the synchronization of marine environmental monitoring data and equipment status data, the data can be matched by timestamps, and a time synchronization protocol (such as NTP or IEEE 1588) can be used to ensure that data from different sources are processed within the same time scale. Common wave dynamics models (such as linear wave theory or nonlinear wave models) are used to describe the mutual influence between sea conditions and wellhead equipment. The core of the wave model is to associate and model the wave characteristics of the sea area (wave height, period, wave direction, etc.) with equipment control (hydraulic pressure, movement speed, etc.). For example, based on a linear wave theory model, the propagation and impact of waves can be described by the following formula: A(t) = A0·cos(kx-ωt+φ); where A(t) is the wave height, A0 is the crest height, k is the wave number, ω is the angular frequency of the wave, φ is the initial phase, and x and t are the spatial position and time, respectively. Marine environmental data (such as wave height and wave period) are input into the wave model, and a mathematical model describing the dynamic changes in sea conditions is established through numerical solutions (such as the finite element method and the finite difference method). This model needs to be able to calculate and update the interactive response of the wave characteristics of the sea area and the wellhead equipment in real time. Based on historical sea condition data and equipment operation status, machine learning algorithms (such as regression analysis and neural networks) are used to optimize model parameters to ensure high accuracy and adaptability to actual sea conditions. On the basis of the sea condition model, the key status data of the wellhead skid-mounted equipment (such as hydraulic pressure, equipment movement speed, etc.) are mapped to the sea condition model. The purpose of this step is to establish a correlation between the changing impact of waves and the equipment status, so that the model can automatically adjust the control parameters of the equipment according to the changes in sea conditions. For example, when the wave height changes, the hydraulic pressure needs to be adjusted to keep the equipment working stably. At this time, based on the dynamic calculation results of the wave model, the equipment parameters can be adjusted by the following control formula: Padjusted = Pbase (1 + kpressure Awave); where Padjusted is the adjusted equipment hydraulic pressure, Pbase is the base hydraulic pressure, kpressure is the adjustment coefficient, and Awave is the wave influence coefficient (calculated according to the wave height, period, etc.). Based on the dynamic model, control linkage rules are designed according to the changes in equipment status data.For example, when wave height exceeds a set threshold, the hydraulic pressure on the equipment is increased, or when the wave period increases, the equipment's movement speed is slowed. Control rules are implemented using algorithms such as fuzzy control systems, PID control, or adaptive control. Adaptive control strategies (such as model predictive control (MPC) or reinforcement learning) are used to dynamically adjust the control parameters of wellhead skid-mounted equipment to respond to changing sea conditions. In model predictive control, the sea state model adjusts the equipment control strategy based on future wave forecasts. For example, at each time step, based on current sea state data, the system predicts future sea conditions and optimizes the equipment control strategy. This control strategy adjustment takes into account the equipment's response time and predicted wave changes. By combining the dynamic sea state model with wellhead equipment status data, an adaptive control algorithm is designed. Specifically, fuzzy control, PID control, or reinforcement learning can be used. These algorithms can dynamically adjust control parameters based on changes in equipment status. Based on real-time sea state data and equipment status feedback, the control model can adjust control parameters such as hydraulic pressure and movement speed in real time to ensure equipment stability and efficiency in complex sea conditions. The output control model includes equipment adjustment parameters (such as hydraulic pressure and speed), which directly affect the operating state of the wellhead skid-mounted equipment. Under complex sea conditions, the model dynamically adjusts the equipment's operating strategy according to predefined control rules to achieve optimal control.

[0083] Step S2: Confirm the impact of ocean-wave jitter on the wellhead skid-mounted equipment status data from the marine environment monitoring data, and design an equipment control optimization algorithm based on the impact of ocean-wave jitter. Import the equipment control optimization algorithm into the adaptive control model to perform equipment jitter compensation on the wellhead skid-mounted equipment, generating equipment jitter compensation data.

[0084] In an embodiment of the present invention, by extracting time series data related to equipment jitter (such as equipment vibration frequency, acceleration, position change, etc.) from the status data of the wellhead skid-mounted equipment, this data can reflect the equipment's response to changes in ocean waves. Based on wave parameters (such as wave height, period, direction, etc.) in the marine environment monitoring data, time series analysis techniques (such as Fourier transform and time-frequency analysis) are used to quantify the impact of waves on equipment jitter. The purpose of this process is to establish a correlation between equipment jitter and ocean wave fluctuations. Dynamic system modeling methods (such as linear or nonlinear models, Kalman filters, etc.) are used to construct an equipment-wave jitter impact model. This model describes the relationship between the wave fluctuation characteristics and the jitter response of the wellhead skid-mounted equipment as a mathematical model. The model can consider the following factors: the force generated by the wave fluctuation characteristics (wave height, period, direction, etc.) on the equipment structure, the structural characteristics of the equipment (such as stiffness, mass distribution, etc.), and the time delay of the wave impact: Since ocean wave fluctuations will produce a certain time delay during the propagation process, it is necessary to include a time delay parameter in the model. The relationship between equipment jitter and wave fluctuations is verified and the extent of the impact is determined through regression analysis of historical data or machine learning models (such as support vector machines and decision trees). The accuracy of the model is verified by simulating the equipment's dynamic response and comparing it with actual data. A device-wave jitter impact factor is generated, which represents the intensity of equipment jitter under different wave conditions. For example, the change in equipment jitter amplitude or acceleration under a certain wave period and wave height is measured. Based on the device-wave jitter impact model, a hydraulic pressure regulation strategy is designed. When the equipment jitter amplitude exceeds a predetermined threshold, the hydraulic system pressure is adjusted to stabilize the equipment. Based on the equipment's dynamic response, the equipment's movement speed is optimized to minimize the impact of waves on the equipment. A PID control (proportional-integral-derivative) algorithm is used to adjust the hydraulic pressure and movement speed based on real-time feedback from the device-wave jitter impact model. PID control effectively regulates hydraulic pressure and equipment movement speed to suppress equipment jitter and improve response speed. In order to cope with the dynamic characteristics of wave changes, an adaptive control strategy can be combined to adjust PID control parameters (such as proportional, integral, and differential coefficients) in real time, so that the equipment can flexibly respond to changes in different sea conditions. Real-time marine environment monitoring data (wave height, period, direction, etc.) and wellhead skid-mounted equipment status data (such as hydraulic pressure, flow, temperature, vibration, etc.) are input into the adaptive control model. The adaptive control model continuously calculates the jitter response of the equipment and adjusts the control parameters of the equipment (such as hydraulic pressure and movement speed) based on real-time wave prediction data. Based on the output of the adaptive control model, the control strategy of the equipment is adjusted in real time to compensate for the equipment jitter caused by waves. For example, when the equipment vibrates too much due to wave fluctuations, the hydraulic system can increase the pressure to improve the stability of the equipment.By leveraging real-time feedback from equipment (such as vibration sensors and accelerometers), the system can adjust control parameters based on the equipment's actual response, achieving continuous optimization. The results of each equipment control adjustment (such as adjusted hydraulic pressure and movement speed) are recorded as equipment jitter compensation data, which can be used for subsequent analysis and optimization, as well as providing a reference for equipment maintenance and upkeep.

[0085] Step S3: Performing wave prediction on the marine environment monitoring data to generate wave prediction data; performing advance response compensation on the device jitter compensation data based on the wave prediction data to generate device jitter compensation response data; importing the device jitter compensation response data into the adaptive control model to perform device jitter control, thereby generating device adaptive control data and device standby control data;

[0086] In an embodiment of the present invention, wave data, including wave height, period, and direction, is collected from marine environment monitoring systems (such as wave buoys, meteorological satellites, and ocean sensors). This data undergoes preprocessing steps, including data denoising, missing value filling, and standardization, to ensure data quality and consistency. A time series prediction algorithm (such as an ARIMA model, a long-short-term memory (LSTM) network, or a support vector machine regression (SVR)) is used to model historical wave data and generate a prediction model. The prediction model is trained using historical wave data, and its accuracy and stability are verified through techniques such as cross-validation. The wave prediction model should be able to predict wave height, period, and direction for a specific period of time. The trained wave prediction model is used to predict wave changes in real time or in the future, generating wave prediction data. This data includes information such as wave height, period, and direction for a specific period of time, which will serve as the basis for subsequent control adjustments. Based on this wave prediction data, the impact of future wave fluctuations on device jitter can be identified in advance. For example, when an increase in wave height is predicted, the system needs to adjust control parameters such as the device's hydraulic pressure and motion speed in advance. Based on predicted wave data (such as wave height and period variations) and the device's dynamic response model, the device's compensation measures are calculated. For example, if future wave growth is predicted, the hydraulic pressure and movement speed can be adjusted using a formula. Based on the pre-calculated compensation amount, the device's jitter compensation response data—the adjusted device control parameters (hydraulic pressure, movement speed, temperature, etc.)—is generated. This response data is then fed back to the adaptive control model for further processing. The device's real-time status (such as acceleration, vibration, temperature, etc.) and predicted wave data are input into the adaptive control model. The adaptive control model dynamically adjusts based on the real-time wave data, device status, and jitter compensation response data to minimize device jitter and maintain device stability. PID control, fuzzy control, or other advanced control methods are used to adjust parameters such as the device's hydraulic system and movement speed to ensure optimal operation under varying sea conditions. The adaptive control model adjusts device parameters such as hydraulic pressure, movement speed, and temperature in real time, generating adaptive control data. This data contains the device's control parameters automatically adjusted based on the current sea conditions and device response. Based on the redundant node design of the equipment, when a failure or unforeseen anomaly is detected in the equipment control system, the redundant control node is used to ensure equipment safety. Generating backup control data involves activating the backup node and adjusting the redundant control parameters to ensure stable equipment operation in the event of a failure. Generating backup control data involves combining the backup control data calculated by the redundant node with the current equipment control status to generate the backup control data.

[0087] Step S4: The equipment adaptive control data and the equipment backup control data are stored and visualized in the cloud, thereby generating an equipment control storage report to perform wellhead skid-mounted equipment control optimization operations.

[0088] In an embodiment of the present invention, the real-time adaptive control data (such as hydraulic pressure, movement speed, temperature, etc.) and backup control data (such as redundant control data, backup hydraulic parameters, etc.) of the device are transmitted to the cloud for storage in real time through the data acquisition system of the device. Select a suitable cloud service platform (such as AWS, Azure, Google Cloud, etc.) for storage to ensure high reliability, low latency, elastic expansion and data security of the data. Use structured data formats (such as JSON, CSV, etc.) to store adaptive control data and backup control data to ensure data accessibility and compatibility. Design a multi-level data storage structure, such as a data lake to store raw data, a real-time database to store processed data, and a historical database to store long-term data. Through IoT devices (such as sensors, actuators, etc.) and edge computing nodes, the device status is collected in real time and uploaded to the cloud platform after encryption and compression. The cloud uses a real-time data synchronization mechanism to ensure the timeliness of the device status and control data, and avoid the impact of data delay on control optimization operations. For historical control data, device status and other information, cold storage (such as Amazon S3, Azure Blob Storage) is used for low-cost long-term storage. Perform regular data backups to ensure data integrity in the cloud and prevent data loss due to system failures. Use commercial data visualization tools (such as Tableau, Power BI, Grafana, etc.) or develop a customized web visualization platform to display the equipment's real-time control data, sea condition changes, and adaptive control adjustments. Design an interactive visualization interface that allows operators to view equipment status, control data, and sea condition warnings in real time through dashboards, charts, curves, etc. Use dynamic charts to display real-time fluctuations in parameters such as hydraulic pressure, movement speed, and temperature, and annotate outliers in real time. Display the relationship between predicted wave height, period, and direction and the equipment's adaptive control data to help engineers more intuitively evaluate the equipment's control effectiveness under different sea conditions. Visualize backup control data to clearly display the activation status of redundant nodes and the execution of backup control strategies in the event of a system failure. Based on real-time data, trigger alarms through set thresholds. Automatically push alarm messages when equipment anomalies occur, notifying staff to intervene in a timely manner.

[0089] Preferably, step S1 includes the following steps:

[0090] Step S11: using sensors to obtain marine environment monitoring data and wellhead skid-mounted equipment status data;

[0091] Step S12: performing data preprocessing on the marine environment monitoring data to generate standard marine environment monitoring data, wherein the data preprocessing includes data cleaning, data denoising, missing value filling and data standardization;

[0092] Step S13: Performing environmental change analysis on the standard sea area environmental monitoring data through edge computing devices to generate sea area environmental change data;

[0093] Step S14: Extract the wave height, period, direction and frequency from the marine environment change data to establish a dynamic model based on the sea conditions, and import the wellhead skid-mounted equipment status data into the dynamic model based on the sea conditions to perform control linkage modeling and generate an adaptive control model.

[0094] In an embodiment of the present invention, by selecting an ocean multi-parameter buoy sensor (such as SeaBird SBE 39), the sensor can provide accurate wave data, temperature, salinity, pressure and other environmental data. The specific data collected includes the wave height, period, direction and frequency of the waves. The wave height measurement accuracy is ±0.05m, the sampling frequency is 10Hz, and the data transmission uses LoRaWAN or NB-IoT communication protocol to ensure real-time data transmission and save power consumption. A vibration sensor (such as PCB 356A01) is used to monitor the working status of the equipment, and is matched with a temperature sensor (such as Omega RTD probe) and an accelerometer (such as STMicroelectronics LSM6DSO). The vibration sensor accuracy is ±0.1mm / s, the temperature sensor accuracy is ±1°C, and the accelerometer accuracy is ±0.01g. Data is transmitted in real time through LoRa or 5G communication technology. The IQR method was used for outlier detection, eliminating invalid data with wave heights below 0. The quartiles (Q1 and Q3) of the wave height data were calculated, and the outlier threshold was set at 1.5 times the IQR, eliminating all wave data outside this range. A Kalman filter was used to remove noise from the time series data, specifically sensor noise from the wave data. The Kalman filter's noise matrix Q = 0.1 and the observation noise matrix R = 0.5 effectively filtered out noise from the wave data and ensured data smoothness. Linear interpolation was used to fill missing values ​​in the marine environmental data. For missing data segments, linear interpolation was performed using the preceding and following values ​​to ensure data time series coherence. The window size for each interpolation process was 5 minutes to ensure time accuracy. Wave height, period, direction, and frequency data were Z-score normalized. The NVIDIA Jetson Xavier NX was selected as the edge computing device, offering high-performance computing capabilities and supporting deep learning frameworks such as TensorFlowLite and PyTorch. The learning framework features a quad-core ARM Cortex-A57 processor with 8GB of LPDDR4x memory, enabling accelerated computing and a processing frequency of 1.4GHz, making it suitable for large-scale data analysis. An LSTM (Long Short-Term Memory) network was used for time series analysis to identify changing trends in the marine environment. The LSTM model was built using Keras, with a two-layer LSTM network architecture and 50 neurons per layer. Training was performed using the Adam optimizer with a learning rate of 0.001 and a batch size of 32. The model input was 10 minutes of historical wave height, period, and frequency data, and output was a 30-minute wave trend. A wave dynamics model was developed using the Boussinesq equation to simulate wave propagation and behavior under different sea conditions. Based on monitored wave height, period, direction, and other parameters, the Boussinesq equation was used to predict wave propagation within the waters.Wave propagation speed, wave shape, and frequency are calculated using a 1-meter grid, updated every five minutes. An MPC controller adjusts wellhead equipment in real time, targeting the reduction of equipment load fluctuations under varying sea conditions to ensure stable operation. The prediction window is set at 10 minutes, with each optimization cycle lasting one minute. The vibration frequency and load of the wellhead equipment are controlled within a safe range to prevent equipment failure during wave fluctuations. The MPC algorithm generates an adaptive control model, linking sea condition data with wellhead equipment status data to achieve dynamic equipment adjustment.

[0095] Preferably, importing the wellhead skid-mounted equipment status data into a dynamic model based on sea conditions for control linkage modeling includes:

[0096] Import the wellhead skid-mounted equipment status data into a dynamic model based on sea conditions to screen the affected equipment status and obtain the status data of key equipment components;

[0097] Analyze the dynamic response characteristics of the equipment based on the status data of key components of the equipment and the dynamic model based on sea conditions to generate equipment sea condition response characteristic data;

[0098] According to the equipment sea condition response characteristic data, the model equipment mobility is adjusted in the sea condition-based dynamic model to generate an adaptive control model.

[0099] In this embodiment of the present invention, edge computing devices (such as the NVIDIA Jetson Xavier NX) are used to collect real-time status data on wellhead skid-mounted equipment, focusing specifically on key parameters such as vibration, load, temperature, and rotational speed. Sensors include vibration sensors (such as PCB Piezotronics), temperature sensors (such as Omega RTDs), and tachometers (such as Honeywell rotation sensors). Data fusion and feature selection techniques are used to screen out equipment components affected by changing sea conditions. A feature fusion algorithm based on a Kalman filter is used to combine historical equipment status data, environmental data, and sea condition changes to identify components significantly affected by sea conditions. The Kalman filter is used to filter and estimate the vibration data of each equipment component, identifying those significantly affected by wave fluctuations. Using the Kalman filter, the state transition matrix is ​​set to the identity matrix, the observation noise R is set to 0.2, and the process noise Q is set to 0.1 to improve signal smoothness and prediction accuracy. The filtered status data of key equipment components (such as vibration, temperature, and load of key components) is then imported into the sea condition dynamic model for subsequent analysis. Autocorrelation and cross-correlation functions are used to analyze the relationship between vibration, load, and sea state fluctuations of key equipment components. Time-domain analysis allows assessment of the immediate impact of sea conditions on equipment response. Fast Fourier Transform (FFT) is used to convert equipment status data from the time domain to the frequency domain, analyzing the response characteristics of key equipment components at different frequencies and identifying the primary vibration frequencies and response modes. Characteristics such as the maximum amplitude, fluctuation period, and peak-to-peak value are calculated to assess the equipment's dynamic response under varying sea conditions. The dominant frequency, frequency peak, and bandwidth are extracted from the spectrum to help understand the equipment's primary frequency response under varying sea conditions. Frequency-domain analysis uses FFT with a sampling frequency of 1kHz, a 5-second data window for each analysis, and an analysis frequency range of 0-500Hz. An MPC algorithm is used to adjust equipment operating parameters such as load and speed, enabling the equipment to adapt to varying sea conditions and thereby reducing vibration and load fluctuations. Based on the equipment's response characteristic data, a safe operating range (such as vibration frequency and temperature range) is established for the equipment. This minimizes vibration and load fluctuations under varying sea conditions, ensuring stable operation. The control strategy is optimized by adjusting parameters such as the coefficient of influence of wave height changes on equipment load and the effect of wave period on equipment vibration frequency in the model. Dynamic optimization algorithms in MPC (such as the QP (Quadratic Programming) algorithm) are used for optimization to calculate the optimal control strategy. The optimization time window is set to 10 minutes, and the optimization step length is 1 minute each time. Based on the optimized control strategy, a new adaptive control model is generated to control key parameters such as load and vibration of the wellhead skid-mounted equipment under different sea conditions. The operating status of the equipment is adjusted in real time to ensure that the equipment can adapt to different sea conditions, and the operating efficiency and safety of the equipment are optimized through automatic adjustment.

[0100] As an example of the present invention, refer to Figure 2 As shown, in this example, step S2 includes:

[0101] Step S21: performing equipment jitter timing analysis on the wellhead skid-mounted equipment status data to generate equipment jitter timing data; extracting sea wave change trends from the standard sea area environment monitoring data based on the equipment jitter timing data to obtain sea wave change trend data;

[0102] Step S22: performing directional fitting on the device jitter time series data and the wave change trend data to generate device impact direction data; extracting the maximum impact direction from the device impact direction data and marking it as strong wave direction data, and marking the remaining impact directions as weak wave direction data;

[0103] Step S23: calculating the wave-related fluctuation amplitude for the strong wave azimuth data and the weak wave azimuth data to obtain the comprehensive wave fluctuation amplitude of the related azimuths;

[0104] Step S24: Designing an equipment control optimization algorithm based on the comprehensive wave fluctuation amplitude of the associated orientation, and importing the equipment control optimization algorithm into the adaptive control model to perform equipment jitter compensation on the wellhead skid-mounted equipment, thereby generating equipment jitter compensation data.

[0105] In this embodiment of the present invention, the Daubechies 4 wavelet basis is used, which has good smoothness and is suitable for analyzing device vibration data. The number of decomposition layers is set to 5 to capture detailed information about device jitter. The sampling rate of the device vibration data is set to 1kHz to ensure the capture of high-frequency jitter characteristics. Using the data processed by wavelet transform, time series characteristics such as device jitter amplitude and frequency are extracted to generate device jitter time series data. The autoregressive integrated moving average (ARIMA) model is used to perform time series prediction on standard marine environmental monitoring data (such as wave height, period, direction, etc.) to extract the changing trends of the waves. The model order is set to (p=1, d=1, q=1), that is, a single difference processing is performed, using one lagged autoregressive term and one lagged moving average term. The sampling frequency of the wave data is 30 minutes, which is sufficient to capture the changing trends of waves on a larger time scale. The trend data extracted by the ARIMA model includes the increase and decrease trends of the waves, period changes, and direction changes. The least squares method is used to perform directional fitting on device jitter time series data and wave trend data to determine the relationship between the two. This directional fitting is used to extract the direction of wave impact on the device. The input variables for the fitting are device jitter amplitude and wave height, and the output variable is the device impact direction. The least squares method is used to fit the linear relationship between device jitter and wave variation to obtain device impact direction data. A clustering algorithm (such as the K-means algorithm) is used to analyze the fitted device impact direction data and identify the direction in which the device is most strongly affected by waves. The impact direction data is clustered into two groups, corresponding to strong wave directions and weak wave directions, respectively. The center of each cluster represents the intensity of the impact direction. The direction at the cluster center is the direction of strong waves, while the other directions are weak wave directions. The number of clusters, K = 2, means that the clusters are divided into two categories: strong wave directions and weak wave directions. Multi-scale wave analysis based on wavelet transform is used to calculate the wave amplitudes for strong and weak wave directions. This method can more accurately capture the characteristics of wave fluctuations in different directions. Apply the same wavelet transform method as step S21, perform wavelet decomposition on each direction data, and extract the wave fluctuation amplitude. Perform 5-layer wavelet decomposition on each direction data to capture the details of the fluctuation. Calculate the fluctuation amplitude of each direction and synthesize the comprehensive wave fluctuation amplitude of each direction. According to the fluctuation amplitude of different directions, calculate the comprehensive wave fluctuation amplitude and generate the fluctuation amplitude data of strong and weak directions. Based on the wave fluctuation amplitude data of the associated directions, use the genetic algorithm (GA) to optimize the control parameters of the wellhead skid-mounted equipment, such as vibration suppression frequency, equipment load, etc. By encoding the control parameters, use crossover, mutation and selection operations to iteratively optimize the control scheme and find the optimal equipment response strategy. Minimize the vibration amplitude and load fluctuation of the equipment to improve the stability and responsiveness of the equipment.The genetic algorithm uses a population size of 50, 100 iterations, a crossover probability of 0.7, and a mutation probability of 0.1. The optimized control parameters are imported into the Adaptive Control Model, which adjusts the operating state of the wellhead skid-mounted equipment in real time to counteract wave-induced vibrations. Based on the control algorithm's output, the equipment's operating state is adjusted to perform vibration compensation and maintain stable operation. Real-time adjustments to the adaptive control model generate equipment vibration compensation data, including load and vibration suppression control data for the equipment under different wave conditions.

[0106] Preferably, step S23 includes the following steps:

[0107] Step S231: performing mutual information evaluation on the strong wave position data and the weak wave position data to generate a wave position similarity measurement result;

[0108] Step S232: Calculate the fluctuation amplitudes of the strong wave azimuth data and the weak wave azimuth data respectively to obtain the strong wave fluctuation amplitude and the weak wave fluctuation amplitude;

[0109] Step S233: Using the wave direction similarity measurement result, perform amplitude fluctuation superposition calculation on the strong wave fluctuation amplitude and the weak wave fluctuation amplitude to obtain the comprehensive wave fluctuation amplitude of the associated direction. The amplitude fluctuation superposition calculation formula is as follows:

[0110] A total =W strong ·(ρ strong,weak ·A strong )+W weak ·((1-ρ strong,weak )·A weak );

[0111] Among them, A total is the comprehensive wave amplitude of the associated direction, A strong is the amplitude of strong waves, A weak is the amplitude of weak waves, ρ strong,weak is the azimuth similarity measure between strong waves and weak waves, W strong is the fluctuation weight of strong waves, W weak is the fluctuation weight of weak waves.

[0112] In the embodiment of the present invention, the similarity between strong wave position data and weak wave position data is evaluated by using mutual information. Mutual information measures the degree of association between two variables. A larger value indicates a closer relationship between the two variables. Formula: Among them, p(x,y) is the joint probability distribution, p(x) and p(y) are the marginal probability distributions of X and Y respectively. The strong wave position data and the weak wave position data are discretized, and the probability is estimated using the frequency distribution. Based on the calculated mutual information I(X,Y), a similarity measure is obtained to reflect the directional correlation between strong waves and weak waves. The calculated mutual information result I(X,Y) will be converted into a similarity measure with a value range of [0,1]. The closer to 1, the more similar the direction. For strong wave and weak wave data, the standard deviation (StandardDeviation) can be used as a measure of the fluctuation amplitude to reflect the amplitude of the data change on the time axis. According to the wave position similarity measure and the respective fluctuation amplitudes, the fluctuation amplitudes of the two are superimposed using the given weighting formula: A total =W strong ·(ρ strong,weak ·A strong )+W weak ·((1-ρ strong,weak )·A weak ); where A total is the comprehensive wave amplitude of the associated direction, A strong is the amplitude of strong waves, A weak is the amplitude of weak waves, ρ strong,weak is the azimuth similarity measure between strong waves and weak waves, W strong is the fluctuation weight of strong waves, W weak is the fluctuation weight of weak waves. The combined wave fluctuation amplitude after superposition can accurately reflect the joint impact of different wave directions and provide a basis for subsequent control models.

[0113] Preferably, the device control optimization algorithm for designing the comprehensive wave amplitude based on the associated orientation includes:

[0114] Defining equipment control parameters based on the integrated ocean wave amplitude at the associated orientation, where the equipment control parameters include the hydraulic pressure and movement speed of the wellhead skid-mounted equipment;

[0115] A regulation formula is constructed based on the hydraulic pressure and movement speed of the wellhead skid-mounted equipment, where the hydraulic pressure regulation formula is as follows:

[0116] P new =P base ×(1+k pressure ·A total );

[0117] Among them, P new is the adjusted hydraulic pressure, P base is the basic hydraulic pressure, k pressure is the adjustment coefficient of hydraulic pressure, A total is the comprehensive ocean wave amplitude;

[0118] The motion speed adjustment formula is as follows:

[0119] V new =V base ×(1+V speed ·A total );

[0120] Among them, V new is the adjusted hydraulic pressure, V base is the basic hydraulic pressure, k speed is the adjustment coefficient of movement speed, A total is the comprehensive ocean wave amplitude;

[0121] PID control is used to adaptively adjust the hydraulic pressure regulation formula and motion speed regulation formula to generate an equipment control optimization algorithm.

[0122] In the embodiment of the present invention, hydraulic pressure (P) and movement speed (V) are two important control parameters of the wellhead skid-mounted equipment. They directly affect the operational stability of the equipment and its ability to cope with changes in sea waves. Based on the comprehensive sea wave fluctuation amplitude and basic hydraulic pressure, a hydraulic pressure adjustment formula is established. Formula: P new =P base ×(1+k pressure ·A total ); where P new is the adjusted hydraulic pressure, P base is the basic hydraulic pressure, k pressure is the adjustment coefficient of hydraulic pressure, A total is the comprehensive wave fluctuation amplitude; based on the comprehensive wave fluctuation amplitude A of real-time monitoring total , combined with the hydraulic pressure adjustment coefficient k pressure , dynamically adjust the hydraulic pressure. k pressure It is an empirical coefficient that can be calibrated through experimental data or obtained through optimization algorithms to adapt to different sea conditions. According to the comprehensive wave amplitude and basic movement speed, the movement speed adjustment formula is established. Formula: V new =V base ×(1+V speed ·A total ); where V new is the adjusted hydraulic pressure, V base is the basic hydraulic pressure, k speed is the adjustment coefficient of movement speed, A total is the comprehensive ocean wave fluctuation amplitude; based on the comprehensive ocean wave fluctuation amplitude A total , adjust the movement speed of the wellhead skid-mounted equipment in real time. speedis an adjustment coefficient, similar to the hydraulic pressure adjustment coefficient, which can be adjusted through experimentation, simulation, or online learning. PID control is used to dynamically adjust hydraulic pressure to ensure equipment stability despite wave fluctuations. Similarly, PID control is used to control movement speed to ensure stability and responsiveness during equipment movement. In practical applications, PID control coefficients can be adaptively adjusted based on real-time sea conditions and equipment status. Dynamic adjustment of PID coefficients through optimization methods such as online learning or genetic algorithms improves adaptability to varying sea conditions. When the error between hydraulic pressure and movement speed is large, the PID controller increases response; when the error is small, the response speed is reduced, ensuring stable operation of the equipment during wave fluctuations. By integrating the hydraulic pressure and movement speed adjustment formulas with the PID controller, a fully adaptive equipment control optimization algorithm is designed. Based on the real-time calculated comprehensive wave fluctuation amplitude and the hydraulic pressure and movement speed adjusted through PID control, the equipment is adjusted and optimized in real time. This control algorithm effectively copes with wave fluctuations, ensuring stable and efficient operation of wellhead skid-mounted equipment in complex sea conditions.

[0123] As an example of the present invention, refer to Figure 3 As shown, in this example, step S3 includes:

[0124] Step S31: extracting historical data from the standard sea environment monitoring data to obtain historical sea condition data; constructing a wave prediction model based on the historical sea condition data, and using the wave prediction model to perform wave prediction on the standard sea environment monitoring data to generate wave prediction data;

[0125] Step S32: performing an equipment impact warning on the equipment jitter compensation data based on the wave prediction data to generate equipment impact warning data; performing a compensation response adjustment on the equipment jitter compensation data based on the equipment impact warning data to generate equipment jitter compensation response data, wherein the compensation response adjustment includes adjusting the compensation time and the compensation coefficient;

[0126] Step S33: importing the device jitter compensation response data into the adaptive control model to perform device adaptive jitter control and generate device adaptive control data;

[0127] Step S34: Design redundant nodes for the wellhead skid-mounted equipment status data to obtain equipment redundant control nodes; perform equipment abnormality fault-tolerant control on the wellhead skid-mounted equipment status data based on the equipment redundant control nodes to generate equipment standby control data.

[0128] In an embodiment of the present invention, historical sea condition data, mainly including variables such as wave height, wave period, wave direction, and tide, is extracted from a standard marine environment monitoring system. The data extraction process can be connected to a historical data warehouse through a real-time data interface to obtain sea condition records for a period of time in the past (such as the past 6 months, 1 year, or 10 years). Based on the extracted historical sea condition data, a wave prediction model is constructed using a machine learning model (such as a support vector machine, random forest, or deep learning model) or a traditional statistical method (such as ARIMA or Kalman filtering). The historical data is denoised and standardized to fill in missing data. The historical sea condition data is converted into time series data using a sliding window method to ensure that the model can identify the trend of sea condition changes. Past data is used for training, and the accuracy of the model is evaluated through cross-validation, error analysis, and other means. The optimal prediction model (such as a long short-term memory network (LSTM), which is suitable for time series prediction) is selected for long-term and short-term prediction of waves. The trained wave prediction model is used to predict the standard marine environment monitoring data and output wave prediction data for a period of time in the future. Predicted data includes key ocean condition indicators such as wave height, wave period, and wave direction for the future time period. Based on the generated wave prediction data and combined with the operating status data of the wellhead skid-mounted equipment (such as equipment vibration, pressure, and speed), the impact of future waves on the equipment is assessed. A multi-factor analysis model is used to comprehensively consider the frequency, amplitude, and dynamic response characteristics of the waves and generate equipment impact warning data. When the warning value exceeds the threshold, an early warning signal is issued, indicating the risk of equipment jitter. Based on the wave prediction data and the equipment impact warning, the jitter compensation time (i.e., the equipment response time) is dynamically adjusted. The adjustment formula can be based on the predicted wave period and the equipment response time: Tcomp = Tbase × (1 + ktime · Awave); where: Tcomp is the adjusted compensation time; Tbase is the base compensation time; ktime is the compensation time adjustment coefficient; and Awave is the wave amplitude (or comprehensive fluctuation amplitude). Based on the changing trend of the waves, the compensation coefficient is adjusted to ensure that the equipment can resume normal operation in a timely manner. The compensation coefficient adjustment formula is as follows: Ccomp = Cbase × (1 + kcomp · Awave); where: Ccomp is the adjusted compensation coefficient; Cbase is the basic compensation coefficient; kcomp is the compensation coefficient adjustment coefficient; Awave is the wave amplitude. Based on the adjusted compensation time and compensation coefficient, the equipment's jitter compensation response data is generated for use in the subsequent adaptive control system. The equipment jitter compensation response data is imported into the adaptive control model. This model can be a dynamic adjustment system based on PID control, which can automatically adjust the control strategy according to the real-time sea conditions and equipment status. The key point of adaptive control is the ability to automatically adjust equipment parameters such as hydraulic pressure and movement speed based on real-time data, historical data, and early warning data of wave fluctuations, reduce equipment jitter, and ensure smooth operation of the equipment.Using an adaptive control model, control parameters for the wellhead skid-mounted equipment are adjusted in real time, generating adaptive control data for optimizing the equipment's operating status. To improve system reliability and fault tolerance, redundant control nodes are required within the equipment control system. These redundant nodes can be implemented by setting up multiple independent control modules or backup sensors to ensure that, in the event of a primary control system failure, the redundant node can take over control functions and maintain normal equipment operation. Using the redundant control nodes, the wellhead skid-mounted equipment's status data is monitored in real time to detect anomalies (such as equipment failure or sensor failure). Once an anomaly is detected, the system implements fault tolerance through the redundant nodes, restoring equipment functionality through the backup control system. Fault-tolerant control includes automatic switching to the backup node, real-time monitoring of anomalies, and adjusting the equipment's control mode based on the type of anomaly. Backup control data is generated based on data from the redundant control nodes. This backup control data is activated in the event of a primary system failure, ensuring continued normal operation.

[0129] Preferably, performing equipment abnormality fault tolerance control on wellhead skid-mounted equipment status data based on the equipment redundancy control node includes:

[0130] Extract wellhead skid-mounted equipment status data to measure equipment pressure, equipment flow, and equipment temperature, and perform anomaly detection on equipment pressure, equipment flow, and equipment temperature based on preset equipment status thresholds. If no anomaly is detected, the equipment redundant control node is put into hibernation.

[0131] When an anomaly is detected, the abnormal impact identification is performed on the wellhead skid-mounted equipment status data to generate equipment abnormal impact identification results. The equipment abnormal impact identification results include the abnormal impact identification results of core equipment and the abnormal impact identification results of edge equipment, and the abnormal impact identification results of edge equipment are excluded;

[0132] The device redundant control node is activated based on the abnormal impact identification result of the core device, and the standby control is performed on the abnormal impact identification result of the core device based on the activated device redundant control node to generate the device standby control data. The standby control formula is as follows:

[0133] C backup =(f redundant (D backup ,C current ));

[0134] Where C backup is the spare control data, f redundant The computation function that generates control data for redundant nodes, D backup C is the backup data of the redundant control node. current It is the control data of the current device status.

[0135] In this embodiment of the present invention, real-time status data from wellhead skid-mounted equipment is extracted, focusing on the following three key indicators: a pressure sensor captures the hydraulic system pressure inside the equipment in real time; a flow sensor monitors the fluid flow rate; and a temperature sensor captures the operating temperature of the equipment to ensure the equipment does not overheat. The extracted equipment status data is compared with preset equipment status thresholds, and the following steps are performed: If the equipment pressure exceeds a preset safety range (such as the maximum or minimum pressure value), an anomaly is considered; if the flow rate fluctuation exceeds a set threshold or the actual flow rate exceeds the specified range, a flow anomaly is determined; if the temperature exceeds the set upper safety limit or falls below the lower safety limit, a temperature anomaly is detected. If the equipment status is within the normal range (i.e., no anomaly is detected), the system puts the redundant control nodes of the equipment into a dormant state, deactivates the redundant control nodes, and reduces redundant resource consumption. When an anomaly is detected, the wellhead skid-mounted equipment status data is analyzed to identify the specific impact of the anomaly on the equipment. Based on the equipment's operating principle and operating status, the impact on core equipment (such as the hydraulic pump and drive motor) is identified. Failure of these devices can directly affect the normal operation of the system. Analyze the auxiliary equipment connected to the core equipment (such as sensors, cooling systems, etc.) and check whether these equipment have any abnormalities. If any abnormalities occur, eliminate them and avoid the activation of redundant control nodes. During the process of identifying the impact of equipment abnormalities, the system will exclude the impact of edge devices that are not related to the redundant control system to ensure that the activation of redundant control nodes is only for core equipment abnormalities to avoid misoperation. According to the results of the identification of the impact of core equipment abnormalities, if the core equipment has an abnormality (such as too high pressure, too low flow, etc.), the redundant control node will be activated. After activation, the redundant control node will intervene in the equipment control system and take over the control function of the abnormal equipment to ensure the continuous operation of the equipment and reduce the impact of the abnormality. Use the redundant control node to control the core equipment through the preset backup control formula and generate backup control data: C backup =(f redundant (D backup ,C current )); where C backup is the spare control data, f redundant The computation function that generates control data for redundant nodes, D backup C is the backup data of the redundant control node. currentIt is the control data under the current device status. Based on the generated backup control data, the redundant control node makes real-time control adjustments to the core equipment to ensure that the equipment can smoothly transition to the redundant control state when a failure or abnormality occurs. The execution of backup control includes adjusting the equipment's hydraulic pressure, movement speed, temperature and other key control parameters to ensure that the equipment can continue to work and will not be seriously damaged. Once the redundant control node takes over control, the system will automatically execute the abnormal fault tolerance mechanism, including switching to the backup device, adjusting the control parameters, etc. The system will monitor the status of the equipment through the redundant control node and adjust the control strategy in real time to reduce the impact of the failure of the core equipment on the system. After the core equipment resumes normal operation, the redundant control node will release control and return to the operating mode of the main control system. The system will put the redundant node into a dormant state in preparation for future abnormal situations.

[0136] Preferably, step S4 includes the following steps:

[0137] Step S41: Encapsulating the device adaptive control data and the device standby control data to generate a device control log; uploading the device control log to the cloud platform for data storage to generate a device control storage log;

[0138] Step S42: Visualize the data of the equipment control storage log to generate an equipment control storage report to perform wellhead skid-mounted equipment control optimization operations.

[0139] In this embodiment of the present invention, control data from the wellhead skid-mounted equipment is extracted from the adaptive control model. This data reflects the equipment's state (e.g., hydraulic pressure, movement speed, etc.) after adaptive adjustment under current sea conditions. Backup control data is extracted from the redundant control system, recording control data generated by the redundant control node when the equipment fails (e.g., redundant node activation, backup equipment scheduling, etc.). These two types of data are encapsulated in a preset format (e.g., JSON, XML, etc.), ensuring that the data contains necessary metadata, such as timestamp, device identifier, control parameter values, and operation type. This encapsulation process facilitates subsequent data storage, querying, and analysis. After encapsulation, a device control log file is generated, recording the control information and redundant control status throughout the entire operation of the equipment. The generated device control log is uploaded to the cloud platform via a network interface. During the upload process, the security and integrity of data transmission are ensured using an encrypted transmission protocol (e.g., HTTPS). The upload process can be performed in batches or in real time, depending on the frequency of device control data generation and the cloud platform's reception capabilities. After receiving the device control log, the cloud platform stores the data and generates a device control storage log, recording metadata such as the log's storage location, time, and device identifier. The purpose of storing logs is to provide reliable historical data support for subsequent analysis and optimization. By analyzing the equipment control storage log data stored in the cloud platform and performing data visualization, equipment operators or managers can quickly understand the equipment status, control effects, and optimization potential. Based on the visualization results of the equipment control storage log, an equipment control storage report is generated. The report content includes: the execution status of equipment adaptive control and backup control, the range of control parameter changes, analysis of indicators such as the response time of redundant control nodes and the effectiveness of backup control, and based on wave change trends and equipment adjustment data, the impact of waves on equipment jitter and the timeliness of equipment response are evaluated. Based on the data analysis results, equipment control optimization suggestions are put forward, such as hydraulic pressure adjustment optimization and redundant control strategy optimization.

[0140] The present invention is therefore intended to be illustrative and non-restrictive in all respects, with the scope of the invention being defined by the appended claims rather than the foregoing description, and all changes that come within the meaning and range of equivalents of the application documents are intended to be embraced therein.

[0141] The foregoing description is intended only to provide specific embodiments of the present invention, which will enable those skilled in the art to understand and implement the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention is not intended to be limited to the embodiments shown herein, but is to be construed in the widest possible manner consistent with the principles and novel features disclosed herein.

Claims

1. A control optimization method for wellhead skid-mounted equipment on an offshore platform, characterized in that: The following steps are involved: Step S1: Acquire marine environment monitoring data and wellhead skid-mounted equipment status data; A dynamic model based on sea conditions is established based on marine environmental monitoring data. The wellhead skid-mounted equipment status data is imported into the dynamic model based on sea conditions for control linkage modeling to generate an adaptive control model. Step S2: confirming the impact of equipment-wave jitter on the wellhead skid-mounted equipment status data from the marine environment monitoring data, and designing an equipment control optimization algorithm based on the impact of equipment-wave jitter; The equipment control optimization algorithm is imported into the adaptive control model to perform equipment jitter compensation on the wellhead skid-mounted equipment and generate equipment jitter compensation data; wherein step S2 includes the following steps: Step S21: performing equipment jitter timing analysis on the wellhead skid-mounted equipment status data to generate equipment jitter timing data; extracting sea wave change trends from the standard sea area environment monitoring data based on the equipment jitter timing data to obtain sea wave change trend data; Step S22: performing directional fitting on the device jitter time series data and the wave change trend data to generate device impact direction data; extracting the maximum impact direction from the device impact direction data and marking it as strong wave direction data, and marking the remaining impact directions as weak wave direction data; Step S23: calculating the wave-related fluctuation amplitude for the strong wave azimuth data and the weak wave azimuth data to obtain the comprehensive wave fluctuation amplitude of the related azimuths; Step S24: Designing an equipment control optimization algorithm based on the comprehensive wave fluctuation amplitude of the associated orientation, and importing the equipment control optimization algorithm into the adaptive control model to perform equipment jitter compensation on the wellhead skid-mounted equipment, thereby generating equipment jitter compensation data; Step S3: Performing wave prediction on the marine environment monitoring data to generate wave prediction data; performing advance response compensation on the device jitter compensation data based on the wave prediction data to generate device jitter compensation response data; importing the device jitter compensation response data into the adaptive control model to perform device jitter control, thereby generating device adaptive control data and device standby control data; Step S4: The equipment adaptive control data and the equipment backup control data are stored and visualized in the cloud, thereby generating an equipment control storage report to perform wellhead skid-mounted equipment control optimization operations.

2. The control optimization method for wellhead skid-mounted equipment for offshore platforms according to claim 1, characterized in that: Step S1 includes the following steps: Step S11: using sensors to obtain marine environment monitoring data and wellhead skid-mounted equipment status data; Step S12: performing data preprocessing on the marine environment monitoring data to generate standard marine environment monitoring data, wherein the data preprocessing includes data cleaning, data denoising, missing value filling and data standardization; Step S13: Performing environmental change analysis on the standard sea area environmental monitoring data through edge computing devices to generate sea area environmental change data; Step S14: Extract the wave height, period, direction and frequency from the marine environment change data to establish a dynamic model based on the sea conditions, and import the wellhead skid-mounted equipment status data into the dynamic model based on the sea conditions to perform control linkage modeling and generate an adaptive control model.

3. The control optimization method for wellhead skid-mounted equipment for offshore platforms according to claim 2, characterized in that: Importing wellhead skid-mounted equipment status data into a dynamic model based on sea conditions for control linkage modeling includes: Import the wellhead skid-mounted equipment status data into a dynamic model based on sea conditions to screen the affected equipment status and obtain the status data of key equipment components; Analyze the dynamic response characteristics of the equipment based on the status data of key components of the equipment and the dynamic model based on sea conditions to generate equipment sea condition response characteristic data; According to the equipment sea condition response characteristic data, the model equipment mobility is adjusted in the sea condition-based dynamic model to generate an adaptive control model.

4. The control optimization method for wellhead skid-mounted equipment for offshore platforms according to claim 1, characterized in that: Step S23 includes the following steps: Step S231: performing mutual information evaluation on the strong wave position data and the weak wave position data to generate a wave position similarity measurement result; Step S232: Calculate the fluctuation amplitudes of the strong wave azimuth data and the weak wave azimuth data respectively to obtain the strong wave fluctuation amplitude and the weak wave fluctuation amplitude; Step S233: Using the wave direction similarity measurement result, perform amplitude fluctuation superposition calculation on the strong wave fluctuation amplitude and the weak wave fluctuation amplitude to obtain the comprehensive wave fluctuation amplitude of the associated direction. The amplitude fluctuation superposition calculation formula is as follows: in, is the comprehensive wave amplitude of the associated direction, is the amplitude of strong waves, is the amplitude of weak waves, is the azimuth similarity measure between strong and weak waves, is the fluctuation weight of strong waves, is the fluctuation weight of weak waves.

5. The control optimization method for wellhead skid-mounted equipment for offshore platforms according to claim 1, characterized in that: The control optimization algorithm for equipment design based on comprehensive wave amplitude and associated orientation includes: Defining equipment control parameters based on the integrated ocean wave amplitude at the associated orientation, where the equipment control parameters include the hydraulic pressure and movement speed of the wellhead skid-mounted equipment; A regulation formula is constructed based on the hydraulic pressure and movement speed of the wellhead skid-mounted equipment, where the hydraulic pressure regulation formula is as follows: in, is the adjusted hydraulic pressure, is the basic hydraulic pressure, is the adjustment coefficient of hydraulic pressure, is the comprehensive ocean wave amplitude; The motion speed adjustment formula is as follows: in, is the adjusted hydraulic pressure, is the basic hydraulic pressure, is the adjustment coefficient of the movement speed, is the comprehensive ocean wave amplitude; PID control is used to adaptively adjust the hydraulic pressure regulation formula and motion speed regulation formula to generate an equipment control optimization algorithm.

6. The control optimization method for wellhead skid-mounted equipment for offshore platforms according to claim 1, characterized in that: Step S3 includes the following steps: Step S31: extracting historical data from the standard sea environment monitoring data to obtain historical sea condition data; constructing a wave prediction model based on the historical sea condition data, and using the wave prediction model to perform wave prediction on the standard sea environment monitoring data to generate wave prediction data; Step S32: performing an equipment impact warning on the equipment jitter compensation data based on the wave prediction data to generate equipment impact warning data; performing a compensation response adjustment on the equipment jitter compensation data based on the equipment impact warning data to generate equipment jitter compensation response data, wherein the compensation response adjustment includes adjusting the compensation time and the compensation coefficient; Step S33: importing the device jitter compensation response data into the adaptive control model to perform device adaptive jitter control and generate device adaptive control data; Step S34: Design redundant nodes for the wellhead skid-mounted equipment status data to obtain equipment redundant control nodes; perform equipment abnormality fault-tolerant control on the wellhead skid-mounted equipment status data based on the equipment redundant control nodes to generate equipment standby control data.

7. The control optimization method for wellhead skid-mounted equipment for offshore platforms according to claim 6, characterized in that: The equipment abnormal fault-tolerant control of the wellhead skid-mounted equipment status data based on the equipment redundancy control node includes: Extract wellhead skid-mounted equipment status data to measure equipment pressure, equipment flow, and equipment temperature, and perform anomaly detection on equipment pressure, equipment flow, and equipment temperature based on preset equipment status thresholds. If no anomaly is detected, the equipment redundant control node is put into hibernation. When an anomaly is detected, the abnormal impact identification is performed on the wellhead skid-mounted equipment status data to generate equipment abnormal impact identification results. The equipment abnormal impact identification results include the abnormal impact identification results of core equipment and the abnormal impact identification results of edge equipment, and the abnormal impact identification results of edge equipment are excluded; The device redundant control node is activated based on the abnormal impact identification result of the core device, and the standby control is performed on the abnormal impact identification result of the core device based on the activated device redundant control node to generate the device standby control data. The standby control formula is as follows: Where, For spare control data, Computational functions that generate control data for redundant nodes, Backup data for redundant control nodes, It is the control data of the current device status.

8. The control optimization method for wellhead skid-mounted equipment for offshore platforms according to claim 1, characterized in that: Step S4 includes the following steps: Step S41: Encapsulating the device adaptive control data and the device standby control data to generate a device control log; uploading the device control log to the cloud platform for data storage to generate a device control storage log; Step S42: Visualize the data of the equipment control storage log to generate an equipment control storage report to perform wellhead skid-mounted equipment control optimization operations.

9. A wellhead skid-mounted equipment control optimization system for an offshore platform, characterized in that: For executing the wellhead skid-mounted equipment control optimization method for an offshore platform according to claim 1, the wellhead skid-mounted equipment control optimization system for an offshore platform comprises: The control model building module is used to obtain marine environmental monitoring data and wellhead skid-mounted equipment status data; a dynamic model based on sea conditions is established based on the marine environmental monitoring data, and the wellhead skid-mounted equipment status data is imported into the dynamic model based on sea conditions for control linkage modeling to generate an adaptive control model; The jitter compensation module is used to identify the impact of ocean-wave jitter on the status data of wellhead skid-mounted equipment from marine environmental monitoring data and to design an equipment control optimization algorithm based on this impact. The equipment control optimization algorithm is then imported into the adaptive control model to perform jitter compensation on the wellhead skid-mounted equipment and generate equipment jitter compensation data. The response compensation module is used to predict waves based on marine environment monitoring data and generate wave prediction data; perform advance response compensation on device jitter compensation data based on the wave prediction data to generate device jitter compensation response data; and import the device jitter compensation response data into the adaptive control model to perform device jitter control and generate device adaptive control data and device backup control data. The control data storage module is used to store and visualize equipment adaptive control data and equipment backup control data in the cloud, thereby generating equipment control storage reports to perform wellhead skid-mounted equipment control optimization operations.

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