One-key automatic replacement triggering method and system for offshore oilfield management and control platform

Abstract

The application relates to the technical field of control systems, and provides a one-key automatic replacement triggering method and system for an offshore oilfield management and control platform. Real-time data is acquired through a sensor, and backup data is generated by preprocessing the real-time data; the offshore oilfield management and control platform is evaluated based on the backup data, a dynamic evaluation index is generated, decision fusion processing is performed based on the dynamic evaluation index, and a triggering confidence is generated; a replacement instruction corresponding to the dynamic evaluation index and the triggering confidence is determined according to a preset corresponding relationship between the dynamic evaluation index and the replacement instruction; linear processing is performed on the backup data to determine a switching time corresponding to the replacement instruction; and the replacement instruction is executed based on the switching time. A complete and intelligent replacement triggering system is formed, production delay and yield loss caused by manual operation are reduced, and the efficiency and accuracy of one-key automatic replacement triggering of the offshore oilfield management and control platform are improved.

Description

Technical Field

[0001] This application relates to the field of control system technology, and more specifically, to a one-click automatic replacement triggering method and system for offshore oilfield management and control platforms. Background Technology

[0002] Offshore oil fields operate in extremely complex and harsh marine environments, with typhoons, waves, and currents constantly threatening the safe operation of oilfield facilities. In offshore oil and gas development, pipeline replacement on offshore platforms is a critical operation for ensuring production safety and continuity. In the face of extreme weather such as typhoons, timely pipeline replacement is necessary to guarantee production safety. However, traditional manual replacement operations rely on multiple steps of manual confirmation, which are cumbersome. In emergency situations, operators struggle to accurately complete all steps within a short timeframe, resulting in slow response times and potential delays in optimal operation. This leads to low efficiency in production control replacement operations, potentially reducing oilfield work efficiency and production capacity, and causing losses in output. Summary of the Invention

[0003] This application provides a one-click automatic replacement triggering system and method for offshore oilfield management and control platforms, which can at least partially solve the problem of low replacement operation efficiency in production control of offshore oilfield management and control platforms.

[0004] Other features and advantages of this application will become apparent from the following detailed description, or may be learned in part from practice of this application.

[0005] According to one aspect of this application, a one-click automatic replacement triggering method for an offshore oilfield management platform is provided, comprising: acquiring real-time data through sensors and preprocessing the real-time data to generate backup data; evaluating the offshore oilfield management platform based on the backup data to generate a dynamic evaluation index, and performing decision fusion processing based on the dynamic evaluation index to generate a trigger confidence level; determining the replacement command corresponding to the dynamic evaluation index and the trigger confidence level according to a preset correspondence between the dynamic evaluation index and the replacement command; performing linear processing on the backup data to determine the switching time corresponding to the replacement command; and executing the replacement command based on the switching time.

[0006] In this application, based on the aforementioned scheme, the step of acquiring real-time data through sensors and preprocessing the real-time data to generate backup data includes: acquiring pressure data of oilfield pipelines through pressure sensors, acquiring flow data of oilfield pipelines through flow sensors, and acquiring circuit data of the power system through circuit sensors; performing motion filtering on the pressure data, and normalizing the flow data and the circuit data to generate backup data.

[0007] In this application, based on the aforementioned scheme, the step of evaluating the offshore oilfield management platform based on the backup data, generating a dynamic evaluation index, and performing decision fusion processing based on the dynamic evaluation index to generate a trigger confidence level includes: performing nonlinear mapping on the backup data to generate a state space vector; evaluating the state of the offshore oilfield management platform based on the state space vector to generate a dynamic evaluation index; and performing decision fusion processing based on the dynamic evaluation index to generate a trigger confidence level.

[0008] In this application, based on the aforementioned scheme, the step of performing nonlinear mapping on the backup data to generate a state space vector includes: performing a nonlinear transformation on the electric power of each phase current in the backup data using Fourier transform to obtain a first component of the state space vector; performing an integral calculation on the second-order partial derivative of the pressure wave in the backup data to obtain a second component of the state space vector; performing joint modeling on the communication data in the backup data in the frequency domain and time domain to obtain a third component of the state space vector; and combining the first component, the second component, and the third component to generate a state space vector.

[0009] In this application, based on the aforementioned scheme, the state of the offshore oilfield management platform is evaluated based on the state space vector to generate a dynamic evaluation index. for:

[0010]

[0011] in, This represents a dynamic evaluation index, used to comprehensively evaluate the system's performance. t The state at any given moment; T Indicates the duration of the evaluation window; Indicates in t The first time of the state space vector i One component; Representing each component The mean; Representing each component Standard deviation; This indicates the preset memory decay factor; denoted by time component, and tanh denotes the hyperbolic tangent function.

[0012] In this application, based on the aforementioned scheme, determining the dynamic evaluation index and the replacement instruction corresponding to the trigger confidence level according to the preset correspondence between the dynamic evaluation index and the replacement instruction includes: retrieving the preset correspondence between the dynamic evaluation index and the replacement instruction from the database; and matching the dynamic evaluation index and the trigger confidence level in the correspondence to determine the replacement instruction corresponding to the dynamic evaluation index and the trigger confidence level.

[0013] In this application, based on the aforementioned scheme, the step of linearly processing the backup data to determine the switching time corresponding to the replacement command includes: calculating the pressure gradient at a preset location based on the fluid pressure of the subsea pipeline in the backup data; calculating the weighted sum between the pressure gradient and the fluid velocity in the backup data; integrating the reciprocal of the weighted sum to generate the switching time corresponding to the replacement command.

[0014] According to one aspect of this application, a one-click automatic replacement triggering system for an offshore oilfield management platform is provided, comprising:

[0015] The acquisition module is used to acquire real-time data through sensors and preprocess the real-time data to generate backup data;

[0016] The evaluation module is used to evaluate the offshore oilfield management platform based on the backup data, generate a dynamic evaluation index, and perform decision fusion processing based on the dynamic evaluation index to generate a trigger confidence level.

[0017] The replacement module is used to determine the dynamic evaluation index and the replacement instruction corresponding to the trigger confidence level based on the preset correspondence between the dynamic evaluation index and the replacement instruction.

[0018] The time module is used to perform linear processing on the backup data to determine the switching time corresponding to the replacement instruction;

[0019] An execution module is used to execute the replacement instruction based on the switching time.

[0020] According to one aspect of this application, a computer-readable medium is provided having a computer program stored thereon, which, when executed by a processor, implements the one-click automatic replacement triggering method for offshore oilfield management platforms as described in the above embodiments.

[0021] According to one aspect of this application, an electronic device is provided, comprising: one or more processors; and a storage device for storing one or more programs, which, when executed by the one or more processors, cause the one or more processors to implement the one-click automatic replacement triggering method for offshore oilfield management platforms as described in the above embodiments.

[0022] According to one aspect of this application, a computer program product or computer program is provided, comprising computer instructions stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the computer device to perform the one-click automatic replacement triggering method for offshore oilfield management platforms provided in the various optional implementations described above.

[0023] Compared with the prior art, the technical solution of this application has the following technical advantages:

[0024] On the one hand, most existing decision-making systems are based on simple threshold judgments or single models, lacking adaptability and intelligent decision-making capabilities for complex operating conditions. Especially when facing the multi-factor influence and highly uncertain offshore oilfield production environment, they struggle to make scientific and reasonable decisions, easily leading to decision errors or missing optimal decision-making opportunities. The technical solution in this application, based on the generation of a dynamic evaluation index, linearly processes the time average of the dynamic evaluation index to determine the complex parameters corresponding to the dynamic evaluation index. Then, it fuses the complex parameters with the output of a deep learning model through a decision fusion formula to generate a trigger confidence score. Compared to existing technologies, this can more accurately determine whether a replacement command is triggered, improving the scientific rigor and reliability of decision-making and adapting to the complex and ever-changing offshore oilfield production environment.

[0025] On the other hand, existing technologies often employ fixed control strategies for equipment control, failing to fully consider factors such as the actual operating state of the equipment and fluid characteristics. For example, in operations such as valve switching, the time delay caused by control and response leads to inaccurate control, impacting production efficiency. This technical solution, in optimizing the complex timing logic execution and equipment control of the platform's local control system, considers the non-constant flow characteristics of fluids within the subsea pipeline. A fluid flow model based on the method of characteristics is established to optimize equipment control parameters such as valve switching time. By integrating the effects of fluid velocity and pressure gradients at different locations within the subsea pipeline on valve switching time, more precise control is achieved. Compared to existing technologies, this solution can dynamically adjust equipment control parameters according to actual conditions, improving the accuracy of equipment control, ensuring efficient and stable operation of offshore oilfield production, and reducing production losses caused by inaccurate control.

[0026] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and do not limit this application. Attached Figure Description

[0027] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application. It is obvious that the drawings described below are merely some embodiments of this application, and those skilled in the art can obtain other drawings based on these drawings without any inventive effort.

[0028] Figure 1 The flowchart illustrating a one-click automatic replacement triggering method for an offshore oilfield management platform is shown in one embodiment of this application.

[0029] Figure 2 The flowchart illustrating the generation of trigger confidence is shown in one embodiment of this application.

[0030] Figure 3 The illustration shows a schematic diagram of a one-click automatic replacement triggering system for an offshore oilfield management platform in one embodiment of this application.

[0031] Figure 4 A schematic diagram of the structure of a computer system suitable for implementing the electronic device of the present application is shown. Detailed Implementation

[0032] Exemplary embodiments will now be described more fully with reference to the accompanying drawings. However, these exemplary embodiments can be implemented in many forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided to make this application more comprehensive and complete, and to fully convey the concept of the exemplary embodiments to those skilled in the art.

[0033] Furthermore, the described features, structures, or characteristics can be combined in any suitable manner in one or more embodiments. Numerous specific details are provided in the following description to give a thorough understanding of embodiments of this application. However, those skilled in the art will recognize that the technical solutions of this application can be practiced without one or more of the specific details, or other methods, components, apparatuses, steps, etc., can be employed. In other instances, well-known methods, apparatuses, implementations, or operations are not shown or described in detail to avoid obscuring various aspects of this application.

[0034] The block diagrams shown in the accompanying drawings are merely functional entities and do not necessarily correspond to physically independent entities. That is, these functional entities can be implemented in software, in one or more hardware modules or intelligent integrated circuits, or in different network and / or processor devices and / or microcontroller devices.

[0035] The flowcharts shown in the accompanying drawings are merely illustrative and do not necessarily include all content and operations / steps, nor do they necessarily have to be performed in the described order. For example, some operations / steps can be broken down, while others can be combined or partially combined; therefore, the actual execution order may change depending on the specific circumstances.

[0036] In practical applications, offshore oilfields are typically located in marine environments far from land, encompassing multiple production platforms, drilling platforms, and oil storage facilities. Offshore oilfield management platforms can centrally monitor these dispersed facilities, providing real-time insights into their operational status. Offshore operations face complex and ever-changing environments, including challenges from wind, waves, currents, and typhoons. Offshore oilfield management platforms must be capable of responding to these environmental changes to ensure the safety and stability of oilfield production. Furthermore, offshore oilfield production involves multiple stages, including drilling, oil production, oil and gas processing, storage, and transportation, requiring close coordination between these stages. Offshore oilfield management platforms enable information sharing and collaborative work across these stages, improving production efficiency.

[0037] In the process of operation and management through an offshore oilfield management platform, various sensors and monitoring equipment are used to collect real-time data on pressure, temperature, flow rate, and equipment operating status. This scattered data is then integrated into a unified data platform. Simultaneously, the production process of the offshore oilfield is monitored in real time to promptly detect anomalies. Through data analysis and model prediction, production parameters are optimized to improve oil and gas production and recovery rates. Furthermore, the offshore oilfield management platform is used for full lifecycle management of various equipment in the offshore oilfield, including monitoring equipment operating status, fault diagnosis, and the development of preventative maintenance plans to ensure reliable equipment operation and reduce equipment failure rates.

[0038] In addition to the aforementioned application functions, the offshore oilfield management platform can also monitor the safety status of offshore oilfields in real time, such as fires, leaks, and personnel safety. In the event of any anomalies, it can promptly issue alarms and provide emergency command and decision support to ensure personnel safety and minimize property damage. By utilizing big data collection, processing, and analysis, it can comprehensively analyze offshore oilfield production data and market information, providing management with decision-making support, such as production plan adjustments and investment decisions.

[0039] The implementation details of the technical solution of this application are described below:

[0040] Figure 1 A flowchart illustrating a one-click automatic replacement triggering method for an offshore oilfield management platform according to an embodiment of this application is shown. (Refer to...) Figure 1As shown, the one-click automatic replacement triggering method of the offshore oilfield control platform includes at least steps S110 to S150, which are described in detail below:

[0041] S110: Real-time data is acquired through sensors, and the real-time data is preprocessed to generate backup data.

[0042] In one embodiment of this application, various types of sensors are deployed in the offshore platform pipeline replacement system to acquire real-time data. Pressure sensors are installed at key locations within the pipeline to sense real-time pressure changes in the fluid within the pipeline and convert them into electrical signals. Flow sensors monitor the flow rate of the fluid within the pipeline, acquiring flow information through a specific sensing mechanism. Circuit sensors are installed on relevant lines of the power system of the offshore oilfield control platform to collect circuit data such as current and voltage. These sensors operate continuously, converting the collected physical quantity information into digital signals that can be processed by the system according to a preset sampling frequency, providing raw material for subsequent data processing.

[0043] In one embodiment of this application, real-time data is acquired through a sensor, and the real-time data is preprocessed to generate backup data, including:

[0044] Pressure data of oilfield pipelines is obtained through pressure sensors, flow data of oilfield pipelines is obtained through flow sensors, and circuit data of the power system is obtained through circuit sensors.

[0045] The pressure data is subjected to moving average filtering, and the flow data and circuit data are normalized to generate backup data.

[0046] In practical applications, the acquired pressure data is often affected by various noises, such as turbulence in the subsea pipeline and sensor noise. To remove these noises and make the pressure data smoother and more accurate, a moving filter method is used. A moving window is set, containing a certain number of continuous pressure data points. The average value of the data points within the window is calculated and used to represent the data value at the center of the window. As new data is continuously acquired, the window moves forward along the time series, and the averaging calculation is repeated to obtain the filtered pressure data. This processing method can effectively eliminate the influence of random noise and preserve the overall trend of pressure data variation.

[0047] Traffic flow data and circuit data differ in their units of measurement and numerical ranges, necessitating normalization for subsequent data analysis and processing. For traffic flow data, the maximum and minimum values ​​from historical data are identified, mapping the current flow rate to a specific numerical range, typically between 0 and 1. Circuit data undergoes a similar normalization process based on its own numerical range. This normalization ensures that different data types share the same scale, facilitating comprehensive analysis and comparison in subsequent algorithms and improving the efficiency and accuracy of data processing.

[0048] Pressure data processed by motion filtering, along with normalized flow and circuit data, constitute the backup data. This backup data is organized and tagged, recording information such as data acquisition time and sensor location. Then, the backup data is stored in a specific data storage area so that it can be readily accessed during subsequent subsea pipeline replacement processes, such as condition assessment and trigger decision-making. Simultaneously, to ensure data security and integrity, certain data backup measures are implemented to prevent data loss or corruption, ensuring the entire subsea pipeline replacement system operates normally based on accurate and reliable data.

[0049] The above process, through the acquisition of real-time data such as pressure, flow rate, and circuit data from multiple sensors, enables a comprehensive and timely perception of the operational status of the offshore oilfield control platform and related equipment. Moving average filtering of the pressure data effectively removes noise interference, making the pressure data smoother and more accurate. Normalization of the flow rate and circuit data eliminates differences in units and numerical ranges between different data points, facilitating subsequent data analysis and processing, and providing a high-quality data foundation for accurately assessing the platform's status.

[0050] S120, The offshore oilfield management platform is evaluated based on the backup data to generate a dynamic evaluation index, and a decision fusion process is performed based on the dynamic evaluation index to generate a trigger confidence level.

[0051] In one embodiment of this application, a nonlinear mapping operation is performed on the backup data, transforming various types of data into a state space vector that comprehensively characterizes the system state through preset rules. Optionally, the dimension of the state space vector can be determined based on historical data analysis and feature importance assessment. For example, the state space vector can be a 38-dimensional vector, where the first 8 components represent electrical power characteristics, corresponding to the harmonic components of the three-phase current; the middle 20 components represent pressure wave characteristics, covering the main modes of pressure propagation; and the last 10 components represent communication data characteristics, reflecting channel state indicators. In this embodiment, the correlation between each component of the state space vector and the system state is verified through covariance analysis. Electrical power characteristics reflect energy stability, pressure wave characteristics reflect pipeline integrity, and communication data characteristics reflect control link reliability. Comprehensive evaluation ensures comprehensiveness.

[0052] Based on the generated state space vector, an in-depth assessment of the current actual state of the offshore oilfield management platform is conducted, resulting in a dynamic assessment index that comprehensively reflects the platform system's operational status. Finally, based on the dynamic assessment index and combined with other relevant factors, decision fusion processing is performed to comprehensively consider the correlation and influence between various conditions and information, ultimately generating a trigger confidence level to provide crucial evidence for subsequent decision-making.

[0053] like Figure 2 As shown, in one embodiment of this application, the offshore oilfield management platform is evaluated based on the backup data to generate a dynamic evaluation index, and decision fusion processing is performed based on the dynamic evaluation index to generate a trigger confidence level, including:

[0054] S210, perform nonlinear mapping on the backup data to generate a state space vector;

[0055] S220, Evaluate the state of the offshore oilfield management platform based on the state space vector and generate a dynamic evaluation index;

[0056] S230, Decision fusion processing is performed based on the dynamic evaluation index to generate trigger confidence.

[0057] In one embodiment of this application, the backup data is nonlinearly mapped to generate a state space vector. A nonlinear reconstruction formula is used to map the collected multi-source data to a high-dimensional state space, generating a high-dimensional state space vector. In this embodiment, different methods are used to determine the component composition of the state space vector based on different data types.

[0058] Specifically, for the circuit data of the main power system, the power of each phase current is nonlinearly transformed by Fourier transform to obtain the first 8 components in the state space vector, namely the first component. This nonlinear transformation can better extract the potential information in the power data.

[0059] For the pressure data of the subsea pipeline, the second partial derivative of the pressure wave is integrated to obtain the middle 20 components (i=9 to i=28) in the state space vector, i.e., the second component. Optionally, an attenuation coefficient is introduced into the integral to account for the attenuation effect of the pressure wave over time, so as to capture the propagation and variation characteristics of the pressure wave in the subsea pipeline.

[0060] For communication data, by combining the channel noise power spectral density with the signal phase shift, joint modeling is performed in the frequency and time domains to obtain the last 10 components (i=29 to i=38) in the state space vector, i.e. the third component. This modeling method can comprehensively reflect the noise and signal phase changes of the communication system.

[0061] Then, the first component, the second component, and the third component are combined to generate a state space vector.

[0062] The above process involves using Fourier transform to nonlinearly transform the electrical power of each phase current in the backup data to obtain the first component, which allows for analysis of the power variation characteristics from a frequency domain perspective. Integrating the second-order partial derivative of the pressure wave yields the second component, highlighting the trend and characteristics of the pressure wave's variation. Joint modeling of the communication data in both the frequency and time domains yields the third component, comprehensively considering the characteristics of the communication data. Combining these three components to generate a state-space vector integrates information from electrical, pressure, and communication aspects, enabling the state-space vector to more comprehensively and accurately describe the platform's operating state.

[0063] In one embodiment of this application, an integral form is adopted, considering the evaluation window duration T. Each component is processed by the hyperbolic tangent function, multiplied, and then 1 is subtracted, with a memory decay factor introduced. The hyperbolic tangent function can map the components to a suitable range, the product form can capture the synergistic effect of state anomalies, and the integral and memory decay factors reflect the time accumulation effect. This allows the dynamic evaluation index to more accurately reflect the comprehensive state of the offshore oilfield management platform within a preset time period. Based on the state space vector, the state of the offshore oilfield management platform is evaluated, generating a dynamic evaluation index. for:

[0064]

[0065] in, This represents a dynamic evaluation index, used to comprehensively evaluate the system's performance. t The state at any given moment; T This indicates the duration of the evaluation window, which determines the time frame under consideration. Indicates in t The first time of the state space vector i One component; This represents the mean of all components; This represents the standard deviation of each component; This represents the memory decay factor, which reflects the greater weight given to recent data in the cumulative effect over time. It can be determined experimentally based on the system response time, and its value ranges from 0.1 to 0.5. denoted by time component, and tanh denotes the hyperbolic tangent function.

[0066] The process of generating a dynamic evaluation index based on state-space vector evaluation described above considers factors such as the evaluation window duration, the mean and standard deviation of each component, and the memory decay factor. The evaluation window duration ensures that the evaluation of the platform's state is based on a comprehensive situation over a period of time, rather than instantaneous data; the mean and standard deviation of each component reflect the central tendency and dispersion of the data; and the memory decay factor allows the evaluation results to take into account the influence of historical states while paying more attention to recent state changes, thus enabling the dynamic evaluation index to better reflect the dynamic changes in the platform's actual operating state.

[0067] In one embodiment of this application, the process of generating trigger confidence based on the dynamic evaluation index through decision fusion processing includes the following steps:

[0068] The time average value of the dynamic evaluation index is linearly processed to determine the complex parameters corresponding to the dynamic evaluation index.

[0069] By constructing a deep learning model, the dynamic evaluation index, the complex parameters, and the wind speed time series in the backup data are time-embedded to generate the trigger probability.

[0070] The dynamic evaluation index, the complex parameters, and the trigger probability are fused together to generate a trigger confidence score.

[0071] In this embodiment, the offshore oilfield management platform often exhibits fractal characteristics in its state changes under complex operating conditions. Complex parameters can characterize the system state from a new dimension, providing richer feature data for subsequent decision-making. By continuously narrowing the scale and observing the changes in the dynamic evaluation index, complex parameters are obtained. Specifically, the time average of the dynamic evaluation index is linearly processed to determine the complex parameters corresponding to the dynamic evaluation index. for:

[0072]

[0073] in, Represents complex parameters for quantization. t The complexity of the dynamic evaluation index at any given moment; The scale parameter is represented by continuously shrinking. To calculate the number of boxes at different scales;T Indicates the duration of the evaluation window; Indicates dynamic evaluation index The time average. The above calculation process, through quantization. The complexity of the original dynamic evaluation index is reduced, and it is transformed into more representative complex parameter features, providing more effective input to the deep learning model and richer feature information for subsequent decision-making.

[0074] In this embodiment, the deep learning model is built using the Transformer architecture, specifically including an input layer, a multi-head self-attention mechanism, and a fully connected layer. The deep learning model is trained based on historical operational data, which comes from historical operational data of offshore oil fields, including dynamic evaluation indices, complex parameters, and wind speed time series under normal and abnormal conditions. Specifically, the dynamic evaluation indices, complex parameters, and wind speed time series are used as inputs, and the trigger probability is output. During model training, whether a permutation is triggered is used as the true label, allowing the model to learn the mapping relationship between input data and trigger decisions. To enhance the model's robustness and generalization ability, a regularization term is introduced into the loss function design, using the mean squared error loss function and the Adam optimizer. The model is trained with a large amount of training data, and the model parameters are continuously adjusted so that the model can accurately capture patterns and regularities in the data, thereby outputting reasonable trigger probabilities based on the input feature data. This process utilizes the powerful feature learning and pattern recognition capabilities of deep learning to adapt to the complex and ever-changing operating conditions of offshore platforms.

[0075] In this embodiment, after obtaining the trigger probability output by the deep learning model, it needs to be fused with complex parameters and the cumulative impact of typhoon wind speed. Complex parameters reflect the complexity of the system state, the trigger probability is the deep learning model's prediction of the trigger likelihood, and the cumulative impact of typhoon wind speed considers the continuous effect of the typhoon throughout the process. By comprehensively considering these three aspects, the necessity of trigger replacement can be assessed more comprehensively. The fusion process employs specific decision rules to fuse the dynamic evaluation index, the complex parameters, and the trigger probability to generate a trigger confidence score. for:

[0076]

[0077] in, This indicates the trigger confidence level, with a value range of [0,1]. This represents the activation function. It can be the Sigmoid function, i.e., the S-shaped growth curve, used to map the fusion result to the [0,1] interval; Represents learnable weights, used to adjust complex parameters. Trigger probability output by deep learning model The importance of typhoon wind speed integral in decision fusion; Represents complex parameters; Indicates the time from the initial moment to the end of the typhoon mode. t Wind speed at any moment The integral reflects the cumulative impact of typhoon wind speed. Triggering decisions for offshore platforms are influenced by a combination of factors; information fusion can fully utilize information from various sources to improve the accuracy and reliability of decisions.

[0078] Specifically, in this embodiment, the wind speed timing... express t The instantaneous wind speed at any given moment is acquired in real time by a wind speed sensor installed on the platform. The sampling frequency is 1Hz. In this embodiment, integration is used... It reflects the cumulative effect of wind speed within the typhoon's duration window T.

[0079] It should be noted that in this embodiment, a dynamic evaluation index and complex parameters are first generated, and then used as input to the deep learning model to obtain the trigger probability. The dynamic evaluation index, complex parameters, and trigger probability are then fused to generate the trigger confidence score. In this embodiment, the dynamic evaluation index... Complex parameters Furthermore, the wind speed time series under typhoon mode is processed by time embedding to make the data correlated in the time dimension, forming a time embedding sequence suitable for model input, which is used as input to the deep learning model, and the output of the deep learning model is the trigger probability.

[0080] This embodiment quantifies the complexity of the system state by calculating complex parameters and combines this with a deep learning model to learn from multi-source data, enabling the system to adapt to the triggering decision requirements of offshore platforms under typhoon conditions. By learning latent patterns in the data through a deep learning model and combining this with a decision fusion formula to comprehensively consider the influence of complex parameters, triggering probability, and typhoon wind speed, intelligent and accurate triggering decisions are achieved, improving the scientific nature and reliability of the decisions.

[0081] The above process generates a state-space vector by nonlinearly mapping the backup data, comprehensively representing the platform's complex operational state from multiple dimensions and capturing deep-seated information hidden within the data. Based on the state-space vector, a dynamic evaluation index is generated, comprehensively considering multiple aspects of the platform's state and accurately reflecting its current overall operational status in real time. Further decision fusion processing generates a trigger confidence score, which is then combined with multiple factors for a comprehensive judgment, providing a scientific and reliable basis for deciding whether to trigger a replacement command.

[0082] S130, determine the dynamic evaluation index and the replacement instruction corresponding to the trigger confidence level according to the preset correspondence between the dynamic evaluation index and the replacement instruction.

[0083] In one embodiment of this application, after generating the dynamic evaluation index and trigger confidence level, a pre-defined parameter instruction mapping table is invoked. This table is established based on historical operational data of offshore platforms, simulations of various operating conditions, and expert experience summaries, and details the replacement instructions corresponding to different combinations of dynamic evaluation indices and trigger confidence levels. The actual dynamic evaluation index and trigger confidence level are precisely matched and compared with the contents of the table. Through this comparative lookup method, the specific replacement instruction corresponding to each value is quickly determined, thereby providing clear execution guidance for subsequent subsea pipeline replacement operations.

[0084] In one embodiment of this application, determining the dynamic evaluation index and the replacement instruction corresponding to the trigger confidence level based on a preset correspondence between the dynamic evaluation index and the replacement instruction includes:

[0085] Retrieve the preset correspondence between dynamic evaluation indices and replacement instructions from the database;

[0086] Based on the dynamic evaluation index and the trigger confidence level, a matching is performed in the correspondence to determine the replacement instruction corresponding to the dynamic evaluation index and the trigger confidence level.

[0087] In one embodiment of the offshore platform one-click automatic replacement triggering system of this application, a pre-defined correspondence between parameters and commands is established. This correspondence is based on a comprehensive analysis of the offshore platform's historical operating data, system performance under different operating conditions, and expert experience. Through extensive data research and simulation experiments, the type of replacement command to be executed under different combinations of dynamic assessment indices and trigger confidence levels is determined. For example, when the dynamic assessment index indicates that the system state is at a relatively dangerous level, and the trigger confidence level is high, the correspondence will point to a more urgent and forceful replacement command; while when the system state is relatively stable and the trigger confidence level is low, the correspondence will point to a more moderate command or a command to temporarily refrain from execution of the replacement. The construction of this correspondence fully considers the balance between system safety and operational efficiency.

[0088] After calculating the dynamic evaluation index and trigger confidence level, the data matching process begins. According to preset rules, the actual dynamic evaluation index and trigger confidence level are compared with a preset correspondence table, searching for the combination that best matches the current dynamic evaluation index and trigger confidence level. This matching process allows for a preliminary determination of the general direction of the replacement instruction corresponding to the current system state—for example, whether to immediately initiate the replacement process, perform some preprocessing operations, or continue monitoring system state changes. The key to this step is quickly and accurately locating the preset situation that best matches the current data.

[0089] Considering the complexity and uncertainty of offshore platform operation, even if the dynamic assessment index and trigger confidence level match the preset combination, there may still be special cases that require further consideration. For example, the current system may be in a transitional state, or there may be some external interference factors. Therefore, in this embodiment, a comprehensive analysis is conducted based on the preliminary judgment. Combining the overall operating environment of the current system, recent operation records, and other relevant factors, the replacement instructions based on the preliminary judgment are refined. If it is found that the current system is in a critical operation phase, the execution of the replacement instruction may be appropriately delayed even if the trigger conditions are partially met; conversely, if the risks faced by the system are constantly increasing, the replacement instruction may be executed earlier or the monitoring frequency may be increased even if the trigger confidence level does not reach a complete match.

[0090] After comprehensive analysis, the corresponding replacement instruction was finally determined. This replacement instruction is clear and actionable, detailing the specific operational steps, execution time, and involved equipment and system modules. For example, the instruction might specify which valves to close first, which pumps to start next, and how to adjust the relevant parameter settings. Once the replacement instruction is determined, execution preparations are immediately initiated, including sending pre-start signals to relevant equipment, checking whether the equipment's operating status meets the execution requirements, and alerting operators to the upcoming operation. This step ensures the smooth and accurate execution of the replacement instruction, guaranteeing the safe and stable operation of the offshore platform system.

[0091] The above process retrieves the pre-defined correspondence between dynamic evaluation indices and replacement instructions from the database. Based on this pre-defined correspondence, the dynamic evaluation index and the replacement instruction corresponding to the trigger confidence level are determined. This fully utilizes the rich information stored in the database, achieving rapid and accurate matching between evaluation results and actual operation instructions. The correspondence in the database has been accumulated and optimized over a long period, enabling it to quickly and accurately find the corresponding replacement instruction based on the current evaluation result. This pre-defined correspondence, based on a large amount of historical data and experience, ensures that the most suitable replacement instruction is selected under different platform conditions, guaranteeing the timeliness and accuracy of decision-making, improving the accuracy of decision-making and execution efficiency, and enabling the platform to react quickly according to the actual situation.

[0092] S140, perform linear processing on the backup data to determine the switching time corresponding to the replacement instruction.

[0093] In one embodiment of this application, based on the changing trends and interrelationships of various information such as pressure, flow rate, and circuitry in the backup data, and combined with pre-set linear analysis rules and logic, key information is extracted from the patterns presented by these data. By comprehensively analyzing the correlation between this information and the equipment switching in the replacement command, and considering the influence of various factors such as equipment characteristics and fluid dynamics on the switching time, the precise switching time of each device corresponding to the replacement command is finally determined, providing a time basis for the accurate execution of the replacement operation.

[0094] In one embodiment of this application, linear processing is performed on the backup data to determine the switching time corresponding to the replacement instruction, including:

[0095] Based on the fluid pressure in the subsea pipeline from the backup data, calculate the pressure gradient at the preset location;

[0096] Calculate the weighted sum between the pressure gradient and the fluid velocity in the backup data, and integrate the reciprocal of the weighted sum to generate the switching time corresponding to the replacement command.

[0097] In practical applications, control signals for relevant equipment on the subsea pipeline are generated according to the replacement command and a pre-set complex timing logic. Optionally, these equipment include shutting down production equipment, switching valves, and starting diesel fire pumps and booster pumps. The complex timing logic ensures the sequence and coordination of equipment operations, avoiding system failures or safety hazards caused by improper operation order.

[0098] During the generation of control signals, optimization control is achieved by fully considering factors such as equipment characteristics and fluid dynamics. Taking valve switching as an example, due to the non-constant flow characteristics of the fluid inside the subsea pipeline, the valve switching time has a significant impact on the replacement process. Therefore, based on the length of the subsea pipeline, the changes in fluid velocity and pressure are described, and the backup data is linearly processed. Specifically, based on the fluid pressure in the subsea pipeline in the backup data, the pressure gradient corresponding to a preset location is calculated. Then, the weighted sum between the pressure gradient and the fluid velocity in the backup data is calculated, and the reciprocal of the weighted sum is integrated to generate the switching time corresponding to the replacement command.

[0099] For example, the valve switching time is calculated in the manner described above. for:

[0100]

[0101] in, This indicates the valve switching time, which is the time required from the start of valve switching to the valve being fully switched into position. L This represents the length of the subsea pipeline, and the axial distance of the subsea pipeline. It is the upper limit of the integration and reflects the range of subsea pipelines that need to be considered. Indicates the location x and time t The fluid velocity within the subsea pipeline. Fluid velocity varies with location and time; this parameter reflects the non-steady flow characteristics of the fluid. This represents the preset pressure gradient correction coefficient, used to adjust the degree of influence of the pressure gradient on the valve switching time and compensate for the effect of fluid compressibility. Its value is determined based on the actual fluid characteristics and system requirements; Indicates the location x and time t Fluid pressure in the subsea pipeline The pressure gradient reflects the rate of change of pressure in space and has a significant impact on fluid flow during valve switching. This represents a preset valve response correction coefficient, used to adjust the weight of the valve's response time constant in the formula and compensate for the valve's mechanical delay; specifically, the pressure gradient correction coefficient. Valve response correction factor The results were determined through subsea pipeline fluid dynamics model simulation and field experiments. Related to fluid viscosity, The specific value is related to the valve type and is recorded in the system configuration database; n The response time constant of the valve itself represents the time required for the valve to start and complete its action from receiving a control signal. It is determined by the valve's physical structure and performance.

[0102] The above calculation process integrates the effects of fluid velocity and pressure gradient at different locations in the subsea pipeline on the valve switching time, while also incorporating the valve's own response time constant to achieve more precise control. After calculating the optimized valve switching time, it is applied to the valve control signal to ensure that the valve switches at the appropriate time.

[0103] For other equipment, such as diesel fire pumps and booster pumps, their startup and operating parameters are optimized based on the requirements of the displacement process and fluid dynamics principles. For example, the speed of the booster pump is adjusted according to the pressure and flow requirements within the subsea pipeline to ensure that the fluid has appropriate pressure and flow rate during the displacement process.

[0104] Optionally, detailed data of the replacement process can be monitored in real time throughout the entire equipment control process, including equipment operating status, fluid pressure, flow rate, and other parameters. If any abnormalities are detected, control signals can be adjusted promptly to ensure the safety and stability of the replacement process. This real-time monitoring and anomaly adjustment mechanism ensures the stability of the replacement process under various operating conditions. Even in the event of unexpected situations, a timely response can be made to ensure the smooth progress of the subsea pipeline replacement and reduce safety risks.

[0105] This embodiment achieves precise control of subsea pipeline equipment through timing logic and optimization formulas. In particular, the optimized calculation of valve switching time takes into account fluid characteristics and valve performance, resulting in more precise valve switching and reducing fluid fluctuations and pressure changes caused by improper switching. The optimized equipment control better adapts to fluid dynamics characteristics, ensuring that the fluid flows according to the expected parameters during the displacement process, thus improving the efficiency and smoothness of the displacement.

[0106] The above process, which uses linear processing based on backup data, can simulate the flow characteristics of fluids inside the subsea pipeline, providing a theoretical basis for calculating the switching time corresponding to the replacement command. In the process of calculating the switching time, the actual situation of fluid flow is fully considered, making the calculated switching time more in line with actual needs. This ensures that the equipment switching can match the fluid flow during the execution of the replacement command, thereby improving the stability and reliability of the replacement operation.

[0107] S150, execute the replacement instruction based on the switching time.

[0108] In one embodiment of this application, after obtaining the switching times of equipment such as valves based on complex calculations and optimizations, the replacement command and switching time are deeply integrated. The replacement command clarifies the operational requirements and process sequence of each piece of equipment during the entire subsea pipeline replacement process, while the switching time provides a precise time node reference for each key operational step. Following the logical sequence of the replacement command, the switching time corresponding to each equipment operation is precisely embedded within it. For example, for valves that need to be switched, based on their optimized switching time, the specific moment in the replacement process when the switching operation should be performed is determined, ensuring that the execution of the entire replacement command can fully utilize the precise switching time information, laying the foundation for subsequent accurate execution.

[0109] Based on the integrated replacement instructions, the platform control system begins to precisely control each device. For devices involving switching time, such as valves, a control signal is sent precisely at the predetermined switching time to initiate the one-button switching action of the valve.

[0110] Optionally, during valve switching, the valve's operating status is continuously monitored to ensure it switches at the expected speed and in the expected manner. For other equipment, such as diesel fire pumps and booster pumps, although the switching time may not be as precise as that of valves, their operating parameters, such as speed and power, are precisely adjusted according to the replacement instructions and the overall process progress to ensure that the equipment is put into operation at the appropriate time and in the appropriate condition, allowing the entire replacement process to proceed steadily according to the predetermined rhythm.

[0111] Optionally, during the execution of the replacement command, the operating status of the equipment and the fluid parameters within the subsea pipeline are monitored in real time. Key data such as pressure, flow rate, and temperature are acquired through sensors distributed throughout the pipeline. If abnormal fluctuations in the data are detected, or if the actual operating status of the equipment deviates from expectations, immediate analysis and judgment are performed. If the anomaly is due to deviations in equipment switching time or other controllable factors, the operating time and parameters of subsequent equipment are adjusted promptly to compensate for the earlier deviations, ensuring that the replacement process is not significantly affected. For example, if the switching time of a valve is slightly delayed, the start-up time or operating power of subsequent pumps is adjusted appropriately to maintain the stability of the entire replacement process.

[0112] Once all equipment has completed its operations according to the replacement instructions and switching time requirements, and the fluid parameters within the subsea pipeline have reached the expected replacement target, the entire replacement process is confirmed as complete. At this point, a final check is performed on the operating status of all equipment to ensure it is in a safe and stable condition. Simultaneously, key data and operational time points throughout the entire replacement process are recorded, generating a detailed operation report. This data is invaluable for subsequent system evaluation, maintenance, and reference for similar replacement operations. Finally, the equipment is switched to normal operating mode or standby mode, ending the execution of this replacement instruction.

[0113] The above process, based on a predetermined switching time, executes replacement commands, ensuring that equipment operation is carried out according to precise time nodes, thus guaranteeing the orderliness and coordination of the replacement process. This helps reduce problems such as fluid fluctuations and pressure changes caused by improper equipment operation timing, improving the quality and efficiency of subsea pipeline replacement and ensuring the continuity and safety of offshore oilfield production.

[0114] This application's technical solution acquires real-time data through sensors and preprocesses the real-time data to generate backup data. Based on the backup data, the offshore oilfield management platform is evaluated to generate a dynamic evaluation index. Decision fusion processing is then performed based on the dynamic evaluation index to generate a trigger confidence level. According to a preset correspondence between the dynamic evaluation index and the replacement command, the replacement command corresponding to the dynamic evaluation index and the trigger confidence level is determined. The backup data is linearly processed to determine the switching time corresponding to the replacement command. The replacement command is then executed based on the switching time. By acquiring and preprocessing multiple types of data in real time through sensors, evaluating and generating a dynamic evaluation index and trigger confidence level based on backup data to scientifically decide whether to replace, determining the replacement command and switching time according to a preset relationship, and finally executing the command, a complete and intelligent replacement triggering system is formed. This reduces production losses caused by manual intervention and improves the efficiency and accuracy of one-click automatic replacement triggering of the offshore oilfield management platform.

[0115] The following describes an embodiment of the one-click automatic replacement triggering system for offshore oilfield management platforms according to this application, which can be used to execute the one-click automatic replacement triggering method for offshore oilfield management platforms in the above embodiments of this application. It is understood that the one-click automatic replacement triggering system for offshore oilfield management platforms can be a computer program (including program code) running on a computer device; for example, the one-click automatic replacement triggering system for offshore oilfield management platforms is an application software. This one-click automatic replacement triggering system for offshore oilfield management platforms can be used to execute the corresponding steps in the method provided in the embodiments of this application. For details not disclosed in the embodiments of the one-click automatic replacement triggering system for offshore oilfield management platforms of this application, please refer to the embodiments of the one-click automatic replacement triggering method for offshore oilfield management platforms described above in this application.

[0116] Figure 3 A block diagram of a one-click automatic replacement triggering system for an offshore oilfield management platform according to an embodiment of this application is shown.

[0117] Reference Figure 3 As shown, a one-click automatic replacement triggering system for an offshore oilfield management platform according to an embodiment of this application includes:

[0118] The acquisition module 310 is used to acquire real-time data through sensors and preprocess the real-time data to generate backup data;

[0119] The evaluation module 320 is used to evaluate the offshore oilfield management platform based on the backup data, generate a dynamic evaluation index, and perform decision fusion processing based on the dynamic evaluation index to generate a trigger confidence level.

[0120] The replacement module 330 is used to determine the dynamic evaluation index and the replacement instruction corresponding to the trigger confidence level based on the preset correspondence between the dynamic evaluation index and the replacement instruction.

[0121] Time module 340 is used to perform linear processing on the backup data to determine the switching time corresponding to the replacement instruction;

[0122] The execution module 350 is used to execute the replacement instruction based on the switching time.

[0123] In this application, based on the aforementioned scheme, the step of acquiring real-time data through sensors and preprocessing the real-time data to generate backup data includes: acquiring pressure data of oilfield pipelines through pressure sensors, acquiring flow data of oilfield pipelines through flow sensors, and acquiring circuit data of the power system through circuit sensors; performing motion filtering on the pressure data, and normalizing the flow data and the circuit data to generate backup data.

[0124] In this application, based on the aforementioned scheme, the step of evaluating the offshore oilfield management platform based on the backup data, generating a dynamic evaluation index, and performing decision fusion processing based on the dynamic evaluation index to generate a trigger confidence level includes: performing nonlinear mapping on the backup data to generate a state space vector; evaluating the state of the offshore oilfield management platform based on the state space vector to generate a dynamic evaluation index; and performing decision fusion processing based on the dynamic evaluation index to generate a trigger confidence level.

[0125] In this application, based on the aforementioned scheme, the step of performing nonlinear mapping on the backup data to generate a state space vector includes: performing a nonlinear transformation on the electric power of each phase current in the backup data using Fourier transform to obtain a first component of the state space vector; performing an integral calculation on the second-order partial derivative of the pressure wave in the backup data to obtain a second component of the state space vector; performing joint modeling on the communication data in the backup data in the frequency domain and time domain to obtain a third component of the state space vector; and combining the first component, the second component, and the third component to generate a state space vector.

[0126] In this application, based on the aforementioned scheme, the state of the offshore oilfield management platform is evaluated based on the state space vector to generate a dynamic evaluation index. for:

[0127]

[0128] in, This represents a dynamic evaluation index, used to comprehensively evaluate the system's performance. t The state at any given moment; T Indicates the duration of the evaluation window; Indicates in t The first time of the state space vector i One component; Representing each component The mean; Representing each component Standard deviation; This indicates the preset memory decay factor; denoted by time component, and tanh denotes the hyperbolic tangent function.

[0129] In this application, based on the aforementioned scheme, determining the dynamic evaluation index and the replacement instruction corresponding to the trigger confidence level according to the preset correspondence between the dynamic evaluation index and the replacement instruction includes: retrieving the preset correspondence between the dynamic evaluation index and the replacement instruction from the database; and matching the dynamic evaluation index and the trigger confidence level in the correspondence to determine the replacement instruction corresponding to the dynamic evaluation index and the trigger confidence level.

[0130] In this application, based on the aforementioned scheme, the step of linearly processing the backup data to determine the switching time corresponding to the replacement command includes: calculating the pressure gradient at a preset location based on the fluid pressure of the subsea pipeline in the backup data; calculating the weighted sum between the pressure gradient and the fluid velocity in the backup data; integrating the reciprocal of the weighted sum to generate the switching time corresponding to the replacement command.

[0131] This application's technical solution acquires real-time data through sensors and preprocesses the real-time data to generate backup data. Based on the backup data, the offshore oilfield management platform is evaluated to generate a dynamic evaluation index. Decision fusion processing is then performed based on the dynamic evaluation index to generate a trigger confidence level. According to a preset correspondence between the dynamic evaluation index and the replacement command, the replacement command corresponding to the dynamic evaluation index and the trigger confidence level is determined. The backup data is linearly processed to determine the switching time corresponding to the replacement command. The replacement command is then executed based on the switching time. By acquiring and preprocessing multiple types of data in real time through sensors, evaluating and generating a dynamic evaluation index and trigger confidence level based on backup data to scientifically decide whether to replace, determining the replacement command and switching time according to a preset relationship, and finally executing the command, a complete and intelligent replacement triggering system is formed. This reduces production losses caused by manual intervention and improves the efficiency and accuracy of one-click automatic replacement triggering of the offshore oilfield management platform.

[0132] Figure 4 A schematic diagram of the structure of a computer system suitable for implementing the electronic device of the present application is shown.

[0133] It should be noted that the computer system of the electronic device in this embodiment is only an example and should not impose any limitations on the function and scope of use of the embodiments of this application.

[0134] In this embodiment, the computer system includes a central processing unit 401, which can perform various appropriate actions and processes based on programs stored in read-only memory 402 or programs loaded from storage section 408 into random access memory 403, such as executing the one-click automatic replacement triggering method for offshore oilfield management platforms described in the above embodiment. The random access memory 403 also stores various programs and data required for system operation. The central processing unit 401, read-only memory 402, and random access memory 403 are interconnected via bus 404. Input / output interface 405 is also connected to bus 404.

[0135] The following components are connected to the input / output interface 405: an input section 406 including a keyboard, mouse, etc.; an output section 407 including a cathode ray tube (CRT), liquid crystal display (LCD), etc., and speakers, etc.; a storage section 408 including a hard disk, etc.; and a communication section 409 including a network interface card such as a LAN (Local Area Network) card, modem, etc. The communication section 409 performs communication processing via a network such as the Internet. A drive 410 is also connected to the input / output interface 405 as needed. A removable medium 411, such as a disk, optical disk, magneto-optical disk, semiconductor memory, etc., is installed on the drive 410 as needed so that computer programs read from it can be installed into the storage section 408 as needed.

[0136] Specifically, according to embodiments of this application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of this application include a computer program product comprising a computer program carried on a computer-readable medium, the computer program including a computer program for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via communication section 409, and / or installed from removable medium 411. When the computer program is executed by central processing unit 401, it performs various functions defined in the system of this application.

[0137] It should be noted that the computer-readable medium shown in the embodiments of this application can be a computer-readable signal medium or a computer-readable storage medium, or any combination of the two. A computer-readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM), flash memory, optical fiber, portable compact disc read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this application, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. In this application, a computer-readable signal medium can include a data signal propagated in baseband or as part of a carrier wave, carrying a computer-readable computer program. The transmitted data signal can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. The computer-readable signal medium can also be any computer-readable medium other than a computer-readable storage medium, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The computer program contained on the computer-readable medium can be transmitted using any suitable medium, including but not limited to wireless, wired, etc., or any suitable combination thereof.

[0138] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this application. Each block in a flowchart or block diagram may represent a module, segment, or portion of code, which contains one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in a block diagram or flowchart, and combinations of blocks in a block diagram or flowchart, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0139] The units described in the embodiments of this application can be implemented in software or hardware, and the described units can also be located in a processor. The names of these units do not necessarily limit the specific unit itself.

[0140] According to one aspect of this application, a computer program product or computer program is provided, comprising computer instructions stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the computer device to perform the methods provided in the various alternative implementations described above.

[0141] In another aspect, this application also provides a computer-readable medium, which may be included in the electronic device described in the above embodiments; or it may exist independently and not assembled into the electronic device. The computer-readable medium carries one or more programs, which, when executed by the electronic device, cause the electronic device to implement the one-click automatic replacement triggering method for the offshore oilfield management platform described in the above embodiments.

[0142] It should be noted that although several modules or units for the device used to perform actions have been mentioned in the detailed description above, this division is not mandatory. In fact, according to the embodiments of this application, the features and functions of two or more modules or units described above can be embodied in one module or unit. Conversely, the features and functions of one module or unit described above can be further divided and embodied by multiple modules or units.

[0143] Through the above description of the embodiments, those skilled in the art will readily understand that the exemplary embodiments described herein can be implemented by software or by combining software with necessary hardware. Therefore, the technical solutions according to the embodiments of this application can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (such as a CD-ROM, USB flash drive, external hard drive, etc.) or on a network, including several instructions to cause a computing device (such as a personal computer, server, touch terminal, or network device, etc.) to execute the methods according to the embodiments of this application.

[0144] Other embodiments of this application will readily occur to those skilled in the art upon consideration of the specification and practice of the embodiments disclosed herein. This application is intended to cover any variations, uses, or adaptations of this application that follow the general principles of this application and include common knowledge or customary techniques in the art not disclosed herein.

[0145] It should be understood that this application is not limited to the precise structure described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of this application is limited only by the appended claims.

Claims

1. A one-touch automatic replacement triggering method for an offshore oilfield management and control platform, characterized in that, The method comprises the following steps: acquiring real-time data through a sensor and preprocessing the real-time data to generate backup data; evaluating an offshore oilfield management and control platform based on the backup data, generating a dynamic evaluation index, and performing decision fusion processing based on the dynamic evaluation index to generate a trigger confidence level; determining a replacement instruction corresponding to the dynamic evaluation index and the trigger confidence level according to a preset correspondence between the dynamic evaluation index and the replacement instruction; performing linear processing on the backup data to determine a switching time corresponding to the replacement instruction; executing the replacement instruction based on the switching time; wherein the evaluation of the offshore oilfield management and control platform based on the backup data, the generation of the dynamic evaluation index, and the decision fusion processing based on the dynamic evaluation index to generate the trigger confidence level comprise: performing nonlinear mapping on the backup data to generate a state space vector; evaluating the state of the offshore oilfield management and control platform based on the state space vector to generate the dynamic evaluation index; performing decision fusion processing based on the dynamic evaluation index to generate the trigger confidence level; wherein the decision fusion processing based on the dynamic evaluation index to generate the trigger confidence level comprises: performing linear processing on the time average of the dynamic evaluation index to determine a complex parameter corresponding to the dynamic evaluation index; performing time embedding processing on the dynamic evaluation index, the complex parameter, and the wind speed time sequence in the backup data through a deep learning model to generate a trigger probability; performing fusion processing on the dynamic evaluation index, the complex parameter, and the trigger probability to generate the trigger confidence level.

2. The offshore oilfield management and control platform one-touch automatic replacement triggering method according to claim 1, characterized in that, The method of acquiring real-time data through a sensor and preprocessing the real-time data to generate backup data comprises: acquiring pressure data of an oilfield pipeline through a pressure sensor, acquiring flow data of the oilfield pipeline through a flow sensor, and acquiring circuit data of a power supply system through a circuit sensor; performing moving filtering on the pressure data and normalizing the flow data and the circuit data to generate backup data.

3. The offshore oilfield management and control platform one-touch automatic replacement triggering method according to claim 1, characterized in that, The method of performing nonlinear mapping on the backup data to generate a state space vector comprises: performing nonlinear transformation on the electric power of each phase current in the backup data through Fourier transform to obtain a first component of the state space vector; integrating the second-order partial derivatives of the pressure wave in the backup data to obtain a second component of the state space vector; jointly modeling the communication data in the backup data in the frequency domain and the time domain to obtain a third component of the state space vector; combining the first component, the second component, and the third component to generate a state space vector.

4. The offshore oilfield management and control platform one-touch automatic replacement triggering method according to claim 1, characterized in that, The method of evaluating the state of the offshore oilfield management and control platform based on the state space vector to generate a dynamic evaluation index comprises: Based on the state space vector, the state of the offshore oilfield management and control platform is evaluated, and a dynamic evaluation index is generated To: wherein, denotes a dynamic assessment index for a comprehensive assessment of the state of the system at t a time instant; T denotes the assessment window length; denotes the t th component of the state space vector at i a time instant; denotes the mean of the components ; denotes the standard deviation of the components ; denotes a preset memory decay factor; denotes the time component, and tanh denotes the hyperbolic tangent function.

5. The offshore oilfield management and control platform one-touch automatic replacement triggering method according to claim 1, characterized in that, determining a replacement instruction corresponding to the dynamic evaluation index and the trigger confidence level according to a preset correspondence between the dynamic evaluation index and the replacement instruction comprises: calling the preset correspondence between the dynamic evaluation index and the replacement instruction from a database; Based on the dynamic evaluation index and the trigger confidence, matching is performed in the correspondence to determine the replacement instruction corresponding to the dynamic evaluation index and the trigger confidence.

6. The offshore oilfield management and control platform one-touch automatic replacement triggering method according to claim 1, characterized in that, The linear processing of the backup data is performed to determine the switching time corresponding to the replacement instruction, including: Based on the fluid pressure of the sea pipeline in the backup data, the pressure gradient corresponding to the preset position is calculated; The weighted sum between the pressure gradient and the fluid flow rate in the backup data is calculated, and the reciprocal of the weighted sum is integrated to generate the switching time corresponding to the replacement instruction.

7. An offshore oilfield management and control platform one-touch automatic replacement triggering system, characterized in that, Including: The acquisition module is configured to acquire real-time data through a sensor and generate backup data by preprocessing the real-time data; The evaluation module is configured to evaluate the offshore oilfield management and control platform based on the backup data, generate a dynamic evaluation index, and perform decision fusion processing based on the dynamic evaluation index to generate a trigger confidence; The replacement module is configured to determine the replacement instruction corresponding to the dynamic evaluation index and the trigger confidence according to a preset correspondence between the dynamic evaluation index and the replacement instruction; The time module is configured to perform linear processing on the backup data to determine the switching time corresponding to the replacement instruction; The execution module is configured to execute the replacement instruction based on the switching time; Wherein, based on the backup data, the offshore oilfield management and control platform is evaluated to generate a dynamic evaluation index, and decision fusion processing is performed based on the dynamic evaluation index to generate a trigger confidence, including: Nonlinear mapping is performed on the backup data to generate a state space vector; The state of the offshore oilfield management and control platform is evaluated based on the state space vector to generate a dynamic evaluation index; Decision fusion processing is performed based on the dynamic evaluation index to generate a trigger confidence; Wherein, based on the dynamic evaluation index, decision fusion processing is performed to generate a trigger confidence, including: The time average of the dynamic evaluation index is linearly processed to determine a complex parameter corresponding to the dynamic evaluation index; The dynamic evaluation index, the complex parameter, and the wind speed time sequence in the backup data are time embedded by constructing a deep learning model to generate a trigger probability; The dynamic evaluation index, the complex parameter, and the trigger probability are fused to generate a trigger confidence.

8. The offshore oilfield management and control platform one-touch automatic replacement triggering system according to claim 7, characterized in that, The real-time data is acquired through a sensor, and the backup data is generated by preprocessing the real-time data, including: The pressure data of the oilfield pipeline is acquired through a pressure sensor, the flow data of the oilfield pipeline is acquired through a flow sensor, and the circuit data of the power supply system is acquired through a circuit sensor; The pressure data is subjected to moving filtering, and the flow data and the circuit data are subjected to normalization processing to generate backup data.

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