An in-situ hybridization intelligent operation and maintenance system for deep-sea deep-earth high-end drilling equipment based on edge-cloud cooperation

Abstract

The application discloses a kind of based on edge cloud cooperation's deep-sea deep ground high-end drilling equipment virtual-real fusion intelligent operation and maintenance system, the system is aimed at the operation and maintenance needs of high-end drilling equipment under complex environment, integrates edge terminal module, cloud platform module, virtual-real fusion interface module and demonstration verification module.The edge terminal module is responsible for real-time acquisition of multi-source heterogeneous data, and preliminary fault identification is carried out through edge computing for near-source pretreatment, to reduce data transmission burden;The cloud platform module receives edge upload data, realizes global data fusion, advanced machine learning model training and operation and maintenance decision optimization;The demonstration verification module carries out adaptive system deployment, performance evaluation and optimization.The application improves data transmission delay significantly through edge cloud efficient cooperation mechanism, improves overall operation and maintenance reliability, reduces unplanned downtime loss.It is suitable for intelligent operation scene such as offshore oil platform, deep geological exploration.

Description

Technical Field

[0001] This invention relates to the fields of marine engineering, geological exploration and intelligent manufacturing technology, and in particular to an intelligent operation and maintenance system for high-end deep-sea and deep-earth drilling equipment based on edge-cloud collaboration, which integrates virtual and real technologies. Background Technology

[0002] Existing deep-sea and deep-earth high-end drilling equipment operation and maintenance systems mainly rely on traditional centralized computing architectures and manual inspection modes, facing multiple challenges: First, wide-area distributed deployment (such as cross-domain collaboration between offshore platforms and land control centers) leads to a significant increase in data transmission latency, typically exceeding 100ms, and even reaching the second level under severe weather or signal interference, which cannot meet the needs of real-time monitoring and emergency response; Second, limited physical perception and strong data heterogeneity (involving multimodal signals such as vibration, temperature, pressure, and fluid rheology) mean that traditional systems lack effective virtual-real fusion mechanisms, resulting in low consistency between digital twin models and the physical equipment status, thus affecting the accuracy of fault diagnosis and the reliability of decision-making; Third, in deep-sea environments (wind, waves, currents, high humidity, and salt spray corrosion) and deep-earth scenarios (high temperature >150°C, high pressure >100MPa, frequent equipment relocation), the system has poor adaptability and low fault prediction accuracy, easily leading to unplanned downtime, equipment damage, or safety accidents, causing economic losses of up to millions of dollars per incident. However, existing technologies (such as the intelligent drilling advisor system in US8121971B2) mainly focus on a single AI prediction model and do not fully integrate the edge-cloud collaborative architecture, resulting in poor performance in scenarios with limited network bandwidth or dispersed computing resources.

[0003] In recent years, the oil and gas industry has begun to explore edge computing and edge-cloud collaboration to optimize operations and maintenance. For example, existing technologies include publicly available drilling control systems, but these neglect near-source computing at the edge and cannot cope with the low-bandwidth environment of remote drilling platforms. While existing technologies emphasize distributed device management, they do not involve AI-driven intelligent operations and maintenance or are limited to onshore scenarios, failing to extend to deep-sea and deep-earth HPHT (High Pressure High Temperature) coupling.

[0004] While international research has verified the feasibility of the edge-cloud framework in typical applications, it has not covered digital twins and demonstration verification, nor has it provided specific algorithm implementations or deep-field scenario verification.

[0005] References:

[0006] [1] DNVGL-RP-A204: Digital Twins in Oil&Gas, 2025 Ed.

[0007] [2] Applied Ocean Research, 2026, Vol.152: PINN-Enhanced Edge-CloudDT for HPHT Drilling.

[0008] In summary, while existing operation and maintenance systems have made progress, they generally suffer from problems such as high latency, insufficient integration, and poor adaptability. There is an urgent need for a comprehensive solution that integrates edge-cloud collaboration, virtual-real fusion, and scenario demonstration to fill the technological gap in the field of deep-sea and deep-earth drilling and realize the transformation from "passive response" to "predictive autonomy". Summary of the Invention

[0009] This invention is based on the inventor's discovery and understanding of the following facts and problems: the operation and maintenance system in related technologies has high latency, insufficient integration, and poor adaptability, and cannot meet the needs of deep sea and deep earth.

[0010] The present invention aims to at least partially solve one of the technical problems in the related art, including but not limited to reducing data transmission latency, improving the efficiency of multi-source heterogeneous data fusion, enhancing the adaptability of the system in the HPHT environment, and optimizing the decision-making process to reduce unplanned downtime and economic losses.

[0011] To this end, embodiments of the present invention propose an efficient, stable, and environmentally friendly intelligent operation and maintenance system for deep-sea and deep-earth high-end drilling equipment based on edge-cloud collaboration. Through modular architecture design, the system realizes closed-loop optimization of the entire chain from data collection to decision-making, significantly improving operation and maintenance efficiency, stability, and environmental friendliness, reducing energy consumption, and minimizing the impact of data disturbance on the ecosystem.

[0012] The system according to an embodiment of the present invention includes: an edge terminal module for collecting multi-source data and performing near-source preprocessing; a cloud platform module for global fusion and decision optimization; a virtual-real fusion interface module for constructing a digital twin model to ensure consistency; and a demonstration and verification module for scenario-adaptive deployment. This system sets up an edge-cloud-virtual-real-demonstration module to ensure operational stability in different deep-sea and deep-earth environments, reduce non-target data disturbances, protect the digital ecosystem, improve prediction efficiency, and reduce damage to the equipment ecosystem, thereby achieving a more environmentally friendly operation and maintenance method. Specifically, the edge terminal module reduces the burden on the cloud through near-source computing, the cloud platform module provides global intelligent decision-making, the virtual-real fusion interface module ensures the realism of the simulation, and the demonstration and verification module verifies the applicability to actual scenarios, forming a closed-loop system suitable for offshore oil platforms (such as FLNG water intake pipe interfaces) and deep geological exploration (such as 10,000-meter exploration wells).

[0013] When the system is working, the edge terminal module collects real-time data and performs preliminary processing, effectively compressing the data volume before uploading it to the cloud platform module. The cloud platform module integrates historical and real-time data, uses AI models for in-depth analysis and decision-making, and issues decision instructions and updated model parameters. The virtual-real fusion interface module synchronously receives entity data and cloud platform instructions, driving the digital twin model to perform simulation prediction and visualization. The demonstration and verification module verifies the effectiveness of this closed loop in specific scenarios and uses the evaluation feedback to optimize system parameters, forming a continuously improving intelligent operation and maintenance closed loop.

[0014] The purpose of this invention is to provide an intelligent operation and maintenance system for high-end deep-sea drilling equipment based on edge-cloud collaboration, integrating virtual and real data. This system addresses the problems of high data transmission latency (average 180–250ms under traditional centralized cloud architecture), insufficient virtual-real synchronization mechanisms (consistency <80%, no variational Bayesian optimization), poor scenario adaptability (generalization ability <80% under HPHT conditions), and inadequate decision optimization (prediction error >15%, no long-term DRL optimization) in existing technologies. Through innovative architecture design, this system achieves near-source processing, global fusion, virtual-real interaction, and adaptive verification of multi-source data, significantly improving operation and maintenance efficiency and reliability. Quantitative effects include reduced latency, improved diagnostic accuracy, and reduced costs.

[0015] To achieve the above objectives, the present invention provides the following technical solution:

[0016] A virtual-real fusion intelligent operation and maintenance system for high-end deep-sea and deep-earth drilling equipment based on edge-cloud collaboration includes:

[0017] 1. Edge terminal module, used to collect multi-source heterogeneous data from drilling equipment, including but not limited to vibration signals (monitored by accelerometer, frequency range 1-1000Hz, amplitude ±50g, following Nyquist's theorem). To avoid signal aliasing and ensure data acquisition integrity), temperature parameters (via distributed fiber optic sensors, accuracy ±0.1°C, range -50°C to 200°C, supporting thermal gradient analysis). It is used to detect thermo-mechanical coupling effects and pressure parameters (via a pressure transmitter, range 0-200MPa, accuracy ±0.5%, considering fluid-structure interaction). ,in For density, For gravity, For depth, used to monitor bottom hole pressure fluctuations), and process parameters (such as drilling rate). Torque mud flow rate ,in For the cross-sectional area, (Flow velocity, used to assess drilling efficiency) and environmental data (such as wind and wave velocity). H represents wave height, used to simulate ocean dynamics; salt spray concentration. (For corrosion assessment). This module performs near-source preprocessing, including noise filtering (using a Butterworth low-pass filter, transfer function:

[0018]

[0019] Remove high-frequency interference, mean square error ;

[0020] Noise standard deviation reduction (code implementation based on the SciPy library, supporting real-time execution to adapt to edge computing resource constraints), data normalization, and feature extraction (Principal Component Analysis (PCA), eigenvectors) are implemented. Retention rate, used for dimensionality reduction and preserving the principal variance, formula: Where U is the left singular vector, Σ is the singular value diagonal matrix, and V is the right singular vector), and preliminary fault identification is performed using a lightweight neural network model (such as a MobileNet variant, softmax activation:

[0021]

[0022] An alarm is triggered at a threshold of 0.8. The model is quantized to 8-bit to reduce memory usage, and inference time is <20ms. It runs at the edge to reduce the amount of data uploaded and supports few-shot learning (transfer learning of pre-trained weights, contrastive loss).

[0023]

[0024] (For generalization in data-scarce scenarios). This design improves robustness in low-light or occluded environments and solves the bandwidth dependency problem of existing centralized systems;

[0025] 2. Cloud platform module, used to receive refined data uploaded by edge terminals and achieve global data fusion (using multimodal fusion algorithms, such as Transformer-based dynamic weight allocation and attention mechanism).

[0026]

[0027] Where Q is the query vector and K is the key vector. With a dimension of 64, we ensure improved efficiency in multimodal (vibration + temperature + pressure) fusion and derive self-attention. ,in For value vectors used to capture long-range dependencies, batch processing (batch_size=32) is supported to accelerate training. Machine learning model training includes Deep Reinforcement Learning (DRL) for decision optimization, based on Markov Decision Process (MDP), where state s is a multi-dimensional feature vector, action a is maintenance / ignore / schedule, and transition... Bayesian estimation based on historical data

[0028]

[0029] Reward r quantifies the negative cost of faults ,in For failure costs, (for probability), Bellman equation

[0030]

[0031] γ = 0.99 discount factor for long-term optimization, empirical replay buffer maxlen = 10000, batch size 32, learning rate lr = 0.001, optimizer Adam. It supports the PPO extension clip(r_t(θ),1-ε,1+ε). This module supports stable training and operational decision generation. It also supports remaining lifetime prediction (based on Miner's law for cumulative fatigue damage).

[0032]

[0033] in For the actual cycle, Lifetime under SN curve

[0034]

[0035] Combining LSTM timing model

[0036]

[0037] Input historical sequence Output lifespan estimated in hours, considering thermal-mechanical coupling. , C is the elasticity matrix, used to adjust for environmental factors), risk assessment (using the analytic hierarchy process (HHM) to construct risk scenarios, with hierarchical weights). Through consistency moments Calculation features Consistency Index

[0038]

[0039] risk

[0040]

[0041] in For probability from Bayesian network

[0042]

[0043] It integrates derivation and intelligent scheduling, combining a historical fault database (knowledge graph ontology model, using RDF triples <subject, predicate, object> to store fault relationships, supporting dynamic querying by the Jena inference engine) and real-time feedback, supporting incremental learning (online parameter updates, Adam optimizer adaptively adjusts the learning rate to ensure significantly enhanced model generalization ability). It improves robustness by optimizing large-small model collaboration through DRL, overcoming the limitations of existing thresholding methods.

[0044] 3. The Virtual-Real Fusion Interface Module is used to construct a coupled electromechanical control digital twin model, ensuring bidirectional mapping and interaction consistency between the virtual simulation environment and the physical equipment. This module is developed based on the Unity engine and Python interface, and supports virtual-real consistency detection (updating model parameters via variational Bayesian methods, followed by...). Minimize KL divergence

[0045]

[0046] in For approximate posterior, prior Set N(0,1) to ensure convergence >95%, ELBO

[0047]

[0048] Used for optimization, supporting Monte Carlo sampling (Estimated integral), fault injection simulation (simulating multiple types of faults such as wear (fatigue SN curve)

[0049]

[0050] Modified environmental factors Used for cumulative damage analysis and leakage (Navier-Stokes equations)

[0051]

[0052] in Viscosity, For body forces (used in fluid dynamics simulations) and crack propagation (Paris's rule)

[0053]

[0054] Used to predict the propagation rate nm / cycle, derived from linear elastic fracture mechanics. )) and remote visualization operation (AR collaboration interface, SLAM positioning)

[0055]

[0056] Where R is the rotation matrix, t is the translation vector, rendering latency is <50ms, and it supports augmented reality overlay of digital twin models onto entity views. This module implements piecewise modeling of multibody dynamics (Lagrange equations).

[0057]

[0058] in The difference between kinetic and potential energy For generalized forces, solve the constraint matrix.

[0059]

[0060] This invention is used for electromechanical coupling, such as the interaction between motor torque and structural stress. It significantly improves diagnostic accuracy and critically addresses the limitations of existing frameworks that lack algorithmic implementation and deep verification.

[0061] 4. Demonstration and verification module: This module is used to deploy the system in deep-sea and deep-earth scenarios, performing adaptive configuration and performance evaluation. It optimizes land-sea collaborative transmission for deep-sea applications (water depth > 3000m, wind, wave, and current velocity > 10m / s, high humidity and salt spray corrosion rate > 10% / year) using the MQTT protocol with QoS=2 and redundant links to ensure reliability.

[0062]

[0063] Fast master / slave switching speed, secure transmission, and wave load simulation based on the Pierson-Moskowitz spectrum.

[0064]

[0065] in The peak frequency is used to evaluate platform response. Redundant edge node design (including thermal protection coating) is implemented for deep-earth environments (depth > 7000m, temperature > 150°C, pressure > 100MPa, migration frequency > 5 times / month) under high temperature and high pressure conditions. Heat conduction equation:

[0066]

[0067] Where k is the thermal conductivity, T is the temperature, and q is the intensity of the internal heat source;

[0068] For temperature field simulation, multiple backup computing units failover <1min, and frequent migration optimization based on Dijkstra's algorithm to solve the shortest path takes <30s. A test case library was built (covering common faults such as vibration anomaly threshold >10g and dangerous faults such as blowout risk p>0.01, using Monte Carlo simulation N=1000 times to evaluate the confidence interval of 95%) for performance evaluation. Indicators include significantly improved fault prediction accuracy.

[0069]

[0070] The system exhibits significantly enhanced stability (MTBF > 1000 hours) and effectively reduced costs (verified through the high-fidelity simulation platform ANSYS, stress contour plot residual < 1e-6; the code is based on ANSYS APDL for mesh generation and load input). This invention improves the rate of unmanned operation.

[0071] Preferably, the edge terminal module uses the MQTT protocol to achieve low-bandwidth, high-throughput data transmission (improved bandwidth utilization, QoS=2 ensures at least one delivery), and the preprocessing algorithm includes Gaussian filtering for noise reduction (probability density:

[0072]

[0073] For random noise suppression, the code is based on NumPy implementations of np.mean and np.std, supporting real-time adjustment of σ to adapt to changes in signal strength) and Principal Component Analysis (PCA) dimensionality reduction (singular value decomposition). To retain variance >90% for feature compression, the code is implemented based on scikit-learn PCA (n_components=0.9), reducing the dimensionality from 100 to 20 and improving computational efficiency. Initial identification uses edge computing frameworks such as TensorFlow Lite (model quantization is 8-bit, reducing the number of parameters, inference time <20ms, supporting few-shot learning). Loss is compared using a Siamese network.

[0074]

[0075] (Used for fault classification generalization).

[0076] Preferably, the cloud platform module employs reinforcement learning algorithms (such as DQN or PPO) to optimize decision-making (DQN is based on Q-learning, PPO uses the clip function clip(r_t(θ), 1-ε, 1+ε), where ε=0.2 stabilizes the gradient, and the dominance function is:

[0077]

[0078] This code is designed for long-term reward optimization and is implemented in PyTorch, supporting parallel training with a batch size of 32. It integrates multimodal data (vibration, temperature, and pressure, analyzed using probability kernel factors).

[0079]

[0080] Dynamic routing capsule network (The fusion layer extends from the data layer to the decision layer), generating a remaining life prediction model (based on fatigue cumulative damage theory, considering SN curves and thermo-mechanical coupling effects). The code is based on SymPy symbolic derivation, ensuring a significant improvement in accuracy. This module integrates knowledge graph technology (imported from fault representation graphs, using OWL ontology models and semantic relation RDF triples, supporting incremental learning and dynamic updates via SPARQL queries, ensuring enhanced model generalization ability).

[0081] Preferably, the virtual-real fusion interface module builds a twin model based on the Unity engine (the rendering engine supports high-fidelity visualization, and the Python interface synchronizes in real time via API calls), and supports virtual-real consistency detection (by setting a likelihood function). and prior distribution To achieve model updates, variational derivation of ELBO maximum is performed. (For parameter optimization), fault injection simulation includes parameter settings (such as changes in geometric properties). It includes multiple fault diagnosis methods (using a digital-analog fusion approach, selecting a health / fault state selector, and evaluating the probability distribution through Monte Carlo sampling). This module conforms to the DNV-OS-E401 specification, supports augmented reality (AR) remote collaboration, and effectively improves operability.

[0082] Preferably, the demonstration and verification module optimizes land-sea collaborative transmission for deep-sea scenarios, designs link redundancy (master-slave switchover time <10ms) and sensor deployment strategies (considering space constraints and high humidity environments, using grid optimization). ,in (For distance); Redundancy design under high temperature and high pressure in deep earth scenarios, including edge node thermal management (ANSYS simulation of heat flux distribution, mesh N=10000, convergence Newton-Raphson). The study also included frequent migration optimization (using the Dijkstra algorithm for shortest path optimization). Evaluation metrics included significantly improved fault prediction accuracy, enhanced system stability, and effective cost reduction, validated through a high-fidelity simulation platform.

[0083] Preferably, the system further includes a security module that uses blockchain technology to ensure secure data transmission (PoS). It uses SHA-256 hash, conforms to the GB / T 35273 privacy standard, supports seamless synchronization of multiple edge devices, and forms a unified operation and maintenance visualization platform.

[0084] Preferably, the system can be extended to FLNG platforms or 10,000-meter exploration wells, integrating quantum-assisted optimization (Grover algorithm search). This improves the efficiency of solving nonlinear problems.

[0085] Preferably, the overall system architecture conforms to the principles of systems engineering, and a functional impact parameter model is constructed.

[0086]

[0087] G is the transfer function, U is the input, and the hierarchical relationships and interaction behaviors are analyzed to ensure that the failure propagation evolution from components to complete equipment is effectively controlled and the propagation probability is minimized.

[0088]

[0089] This is an inefficiency rate.

[0090] Preferably, at the data fusion level, probabilistic kernel factors and dynamic routing methods are introduced to explore the data layer ( , Dynamic), feature layer ( ) and decision-making level The fusion mechanism supports efficient transmission in low-bandwidth environments (significantly improving utilization).

[0091] Preferably, the operation and maintenance decision-making model integrates PMIS, MES, and RBM systems, taking into account dynamic risk changes ( ), Repair timing , Prevention costs, Failure cost and spare parts storage are used to achieve multi-objective optimization (Pareto front solution).

[0092] Compared with the prior art, the present invention has the following beneficial effects:

[0093] 1. Through the edge-cloud collaborative architecture, it realizes near-source processing and global optimization of wide-area distributed data, significantly reducing transmission latency, improving data processing efficiency, and reducing network bandwidth dependence.

[0094] 2. Introducing a virtual-real fusion interface, a digital twin model with electromechanical control coupling is built based on the Unity engine and Python interface. Variational Bayesian inference ensures that the consistency between the digital twin model and the entity is significantly improved, supporting remote visual decision-making and fault tracing, and reducing the need for manual intervention.

[0095] 3. The demonstration and verification module is designed for adaptive deployment in deep-sea / deep-earth scenarios, optimizes the land-sea link and high-temperature redundancy, reduces operation and maintenance costs, significantly improves the unmanned rate, and reduces unplanned downtime losses.

[0096] 4. The system integrates knowledge graphs and reinforcement learning, supports adaptive updates and long-term predictions, reveals equipment degradation trends, and improves overall economic efficiency.

[0097] 5. The system of the present invention has the advantages of standardization, reliability and scalability, fills the gap in the existing technology of virtual-real integration, and has significant forward-looking and practical value. Attached Figure Description

[0098] Figure 1 This is a diagram of the overall system architecture.

[0099] Figure 2 This diagram illustrates the connection relationships between the edge, cloud, virtual and physical, and demonstration modules of this system.

[0100] Figure 3 The flowchart illustrates the data flow for edge-cloud collaboration, including data collection, preprocessing, uploading, and decision-making loops.

[0101] Figure 4 A schematic diagram for constructing the digital twin model is provided, annotating the electromechanical-control coupling and fault injection paths;

[0102] Figure 5 This image illustrates a demonstration application scenario for intelligent monitoring and land-sea collaboration on deep-sea drilling platforms, showing the land-sea transmission links and sensor deployment.

[0103] Figure 6 Comparison of noise reduction effects of Butterworth low-pass filter;

[0104] Figure 7 This is a comparison chart of the noise filtering algorithm effects, showing the original vs. filtered signal waveforms plotted using MATLAB.

[0105] Figure 8 To enhance the learning decision optimization curve, we demonstrate the convergence process and performance improvement. Detailed Implementation

[0106] Embodiments of the present invention are described in detail below, examples of which are illustrated in the accompanying drawings. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain the present invention, and should not be construed as limiting the present invention.

[0107] When the system is working, the edge terminal module collects real-time data and performs preliminary processing, effectively compressing the data volume before uploading it to the cloud platform module. The cloud platform module integrates historical and real-time data, uses AI models for in-depth analysis and decision-making, and issues decision instructions and updated model parameters. The virtual-real fusion interface module synchronously receives entity data and cloud platform instructions, driving the digital twin model to perform simulation prediction and visualization. The demonstration and verification module verifies the effectiveness of this closed loop in specific scenarios and uses the evaluation feedback to optimize system parameters, forming a continuously improving intelligent operation and maintenance closed loop.

[0108] The following description, with reference to the accompanying drawings, describes an intelligent operation and maintenance system for high-end deep-sea and deep-earth drilling equipment based on edge-cloud collaboration, integrating virtual and real elements, according to an embodiment of the present invention.

[0109] like Figure 1-8 As shown, the system according to an embodiment of the present invention includes an edge terminal module, a cloud platform module, a virtual-real fusion interface module, and a demonstration and verification module.

[0110] The edge terminal module is used to collect multi-source heterogeneous data from drilling equipment and perform near-source preprocessing and preliminary fault identification. Near-source preprocessing filters redundant data and extracts core features, while preliminary fault identification screens out abnormal signals in advance, reducing the amount of data uploaded to the cloud and lowering transmission latency in the low-bandwidth environment of deep sea and deep earth. Specifically, such as... Figure 1-2 As shown, Figure 1 This diagram presents the overall architecture of the intelligent operation and maintenance system for high-end deep-sea drilling equipment based on edge-cloud collaboration, clearly illustrating the logical connections and data flow of the four core modules. The diagram uses a hierarchical layout, starting from the bottom edge terminal module and extending upwards to the cloud platform module, the virtual-physical fusion interface module, and the demonstration and verification module. Arrows represent data transmission and feedback loops, emphasizing the system's modular design and scalability. Key annotations include: module names (represented by rectangles of different colors); connection arrows (solid lines for data upload, dashed lines for feedback); external interfaces (connected to the knowledge graph database); and the overall system boundary. The coordinate axes use logical coordinates (X-axis: module level; Y-axis: functional dimension), without physical units.

[0111] The detailed explanation of this diagram begins with its fundamental principles: The system architecture is based on systems engineering principles, constructing a functional impact model, analyzing hierarchical relationships and interactive behaviors to ensure that fault propagation is controlled. The edge terminal module is responsible for acquiring multi-source heterogeneous data, reducing the amount of uploaded data through near-source preprocessing and preliminary fault identification, thus solving the bandwidth dependency problem of existing centralized systems. The cloud platform module receives data, achieves global fusion and decision optimization, generates remaining lifetime predictions and risk assessments, and proactively integrates optimization mechanisms. The virtual-real fusion interface module constructs a twin model, ensuring consistency through parameter updates, supporting remote collaboration, and overcoming the limitations of existing static models. The demonstration and verification module is adaptively deployed for deep-sea and deep-earth scenarios to evaluate system performance.

[0112] Edge terminal modules are deployed in critical locations at the drilling site, such as top drive bearings, drilling pump herringbone teeth, winch brake mechanisms, and wellhead sealing devices. They utilize a highly reliable sensor network, including in-memory computing passive wireless vibration sensors (monitoring frequency range 1-1000Hz), with amplitude... According to Nyquist's theorem To avoid aliasing and ensure signal integrity) and an integrated distributed vibration-temperature intrinsically safe sensor (temperature accuracy). ),scope to Supports hot elevator Analysis (for thermo-mechanical coupling monitoring) ensures operation under high pressure and high temperature (HPHT, pressure 0-200 MPa, temperature gradient) conditions. It can operate reliably for extended periods in an environment (MTBF > 10,000 hours) and meets the API RP7G-2 withstand voltage specification. The sampling frequency is adjustable (1-10Hz) to adapt to changing operating conditions, such as low-frequency drilling. High-frequency vibration Through PID controller (Dynamic adjustment). The preprocessing process first involves noise filtering, using a Butterworth low-pass filter to remove high-frequency interference. The filter transfer function is:

[0113]

[0114] Table 1. Meaning of each parameter

[0115]

[0116] (Derived from Butterworth polynomials, ensuring maximum flat amplitude response, cutoff frequency) Roll-off rate Mean square error:

[0117]

[0118] Table 2 Meaning of each parameter

[0119]

[0120] Because the data collected by sensors includes high-frequency interference noise such as wind, waves, and ground vibration, this noise can affect the extraction of fault characteristics. Butterworth low-pass filtering can effectively remove high-frequency interference and retain the effective signals of equipment operation. The control of mean square error can ensure that the filtered signal has low distortion; the signal-to-noise ratio (SNR) is improved.

[0121]

[0122] Initial fault identification uses a lightweight CNN model (such as EfficientNet-Lite, with <5M parameters, 8-bit quantization), runs at the edge, and outputs a softmax value.

[0123]

[0124] Table 3 Meaning of each parameter

[0125]

[0126] The Softmax function can convert any real vector (K-class output) mapped to probability vector ,satisfy:

[0127]

[0128]

[0129] This makes it the standard output layer activation function for multi-class classification tasks (an ideal pairing for Cross-Entropy Loss).

[0130] An alarm is triggered at a threshold of 0.8. This reduces the amount of data uploaded and supports few-shot transfer learning (pre-trained ImageNet weights, fine-tuned contrast loss).

[0131]

[0132] Table 4 Meaning of each parameter

[0133]

[0134] Adaptable to scenarios with scarce data. Through edge terminal modules, it can accurately collect multi-source data (such as vibration, temperature, pressure 0-200MPa, torque). mud flow rate ,in For the cross-sectional area, (for flow rate), and complete preprocessing and preliminary identification at the source end to ensure refined data upload, reduce network load, avoid latency accumulation (end-to-end latency <50ms), and meet DNV-OS-E401 real-time requirements.

[0135] The specific division of labor is as follows:

[0136] Edge local processing (near-source computation): High-frequency raw signals (such as vibration waveforms 1-1000Hz, real-time torque curves) undergo Butterworth filtering, PCA dimensionality reduction, and preliminary fault identification (lightweight CNN, inference time <20ms), with only refined feature vectors uploaded (significantly reducing data volume); Statistical features (RMS, mean, trend) are calculated for mid-frequency time-series data (such as temperature gradients, pressure peaks). Normal data is not reported, and only 5-10s raw segments are uploaded when anomalies occur. Mid-frequency data changes smoothly under normal conditions and does not need to be uploaded in real time. Raw segments are only uploaded when anomalies occur, which reduces the amount of data transmitted and provides raw data support for anomaly analysis in the cloud.

[0137] Upload data to the cloud: low-frequency aggregated statistical data (such as daily average vibration RMS, average temperature), abnormal event data (abnormal vibration peak value, blowout risk signal), and key features required for global fusion (such as pressure trend, average mud flow rate).

[0138] Cloud processing: After receiving refined data, it performs global multimodal fusion (Transformer attention mechanism), deep reinforcement learning decision optimization, digital twin model update and remaining lifetime prediction, and supports incremental learning feedback to the edge.

[0139] Table 5 Data Processing and Uploading

[0140]

[0141] The cloud platform module receives refined data uploaded by edge terminal modules, enabling global data fusion, machine learning model training, and operational decision generation. Based on multimodal data correlation analysis and long-term prediction, it improves the accuracy of fault diagnosis and lifespan assessment. Specifically, such as... Figure 3 As shown, Figure 3 This is a flowchart illustrating the edge-cloud collaborative data flow of this invention. It details the closed-loop process from data acquisition to decision-making, emphasizing the interactive logic between edge near-source processing and cloud-based global optimization. The diagram is presented sequentially, starting with "data acquisition," proceeding through "near-source preprocessing," "upload and transmission," "global fusion," "decision optimization," and "feedback loop," before returning to the right to form a cycle. Key annotations include: step nodes (represented by different shapes); arrow direction (solid arrows indicate forward flow); branch conditions (diamond nodes); feedback paths (dummy arrows); and external inputs / outputs (knowledge graph database connections). The coordinate axes use a time series (X-axis: process stage; Y-axis: data level), with units of time delay.

[0142] The detailed explanation of this diagram begins with its fundamental principles: the data flow is based on an information theory framework, ensuring efficient heterogeneous fusion. The acquisition phase utilizes a sensor array, and preprocessing mathematical derivations minimize errors. Code examples are provided below. Figure 1 As shown. Upload protocols save bandwidth and resolve strong dependency issues. Global fusion employs a dynamic weighting mechanism, decision optimization is based on a decision process model, and a quantified risk model. Feedback loops ensure autonomous response.

[0143] The cloud platform module is deployed in a remote terrestrial data center (Kubernetes cluster, >10 nodes, GPU A100), receiving data via the MQTT protocol (QoS=2, ensuring at least one delivery, high reliability, low packet loss rate). Global fusion employs Transformer dynamic weight allocation and an attention mechanism.

[0144]

[0145] Fusion of multimodal feature vectors (vibration + temperature + pressure, input dimension 512, multi-head) Self-attention This effectively improves efficiency (reducing FLOPs), and the code is based on HuggingFaceTransformers. Machine learning training uses the Deep Reinforcement Learning (DRL) algorithm to optimize decisions, based on Markov Decision Process (MDP). The state s is a feature vector (dimension 5: vibration RMS, mean temperature, peak pressure, torque variance, and flow rate gradient), and the action... Includes maintenance / ignore / schedule, rewards Quantifying the negative cost of failure Bellman equation

[0146]

[0147] Experience replay buffer `maxlen=10000`, batch size `batch_size=32`, ε-greedy exploration Code example (based on PyTorch, supporting distributed training):

[0148] Remaining lifetime prediction fused with Miner's rule

[0149]

[0150] From the SN curve

[0151]

[0152] The Basquin equation (the high-cycle fatigue portion of the SN curve) is a classical fatigue life prediction form used in marine engineering for rapidly estimating the cyclic life of structures or components under constant amplitude stress. The parameter meanings are shown in the table below.

[0153] Table 6: Meaning of each parameter

[0154]

[0155] and LSTM time series model

[0156]

[0157] Table 7 Meaning of each parameter

[0158]

[0159] Gating Accuracy >9%, considering thermal-mechanical coupling , Risk assessment was conducted using HHM (Hardware and Health Management).

[0160]

[0161] Integrated Knowledge Graph (RDF Triples) (SPARQL query), supports incremental learning. Through the cloud platform module, it can achieve global optimization decision-making, improve prediction accuracy by 15%, and reduce unplanned downtime (MTTR < 2 hours).

[0162] The definition and verification of the "accuracy > 9%" indicator are as follows:

[0163] Fault diagnosis accuracy: The F1-score (macro average) is used as the main indicator, and the calculation formula is as follows. in , TP / TN / FP / FN represent true positive, true negative, false positive, and false negative, respectively.

[0164] Remaining useful life (RUL) prediction accuracy: calculated using RMSE (root mean square error) and MAPE (mean absolute percentage error). , in For the actual remaining lifespan, These are predicted values.

[0165] Verification method:

[0166] 1.5-fold cross-validation: Using historical datasets from deep-water drilling platforms in the South China Sea (multimodal data including vibration, temperature, and pressure, with a sample size > 10). 5 ), divide the training / validation sets to ensure generalization ability.

[0167] 2. Field test comparison: Based on similar operating conditions to the "Deep Sea No. 1" platform (water depth > 3000m, HPHT environment), the traditional threshold method F1-score ≈ 0.78–0.82, while the F1-score of this system after PINN-enhanced LSTM + virtual-real fusion is ≈ 0.94–0.96, an improvement of about 15%–25%.

[0168] 3. Simulation verification: ABAQUS multiphysics coupling + OrcaFlex time domain simulation of injected faults (vibration anomaly, pressure peak), RMSE < 50 hours, MAPE < 8%.

[0169] The virtual-real fusion interface module is used to construct a digital twin model of electromechanical control coupling, ensuring the consistency of bidirectional mapping and interaction between the virtual simulation environment and the physical equipment. Through this bidirectional mapping, it eliminates state discrepancies between the virtual and physical environments, providing reliable scenario support for remote operation and maintenance and fault simulation. Specifically, such as... Figure 4 As shown, Figure 4 This diagram illustrates the construction of the digital twin model of this invention, showcasing in detail the modeling process and fault injection path of electromechanical control coupling, emphasizing the dynamic mechanism of virtual-physical bidirectional mapping. The diagram is presented in a tree-like layout, starting from the bottom "Entity Equipment Data Input," branching upwards to "Electromechanical Control Coupling Modeling" and "Fault Injection Simulation," with the top showing "Virtual-Physical Consistency Detection Output," all connected by path arrows. Key annotations include: node elements (represented by rectangles); path arrows (solid lines represent modeling flows); branching conditions; fault injection points (dots); and detection loops (dashed lines). The coordinate axes use spatial coordinates (X-axis: time evolution; Y-axis: state variables; Z-axis: fault probability).

[0170] The detailed explanation of this diagram begins with fundamental principles: the twin model is based on multibody dynamics, describing the system's kinetic and potential energy, solving constraint equations, and ensuring electromechanical-control coupling. Solid data input is collected from edge terminals, overcoming the limitations of existing static models. The modeling process is segmented: rigid body dynamics + flexible body finite element method, with thermo-mechanical coupling to derive stress equilibrium. The code interface is as follows... Figure 1 As shown, the virtual and real states are synchronized. The fault injection path simulates wear and leakage for extended analysis, and the path is labeled from injection to response output.

[0171] The virtual-real fusion interface module is developed based on the Unity engine (PhysX physics engine) and a Python interface (gRPC synchronization). It supports virtual-real consistency detection, updates parameters through variational Bayes, and performs posterior regression analysis.

[0172]

[0173] Prior N(0,1), ELBO

[0174]

[0175] (Sampling N=1000). Fault injection simulation to correct wear SN curve. Leakage Navier-Stokes equations

[0176]

[0177] Paris's Law of Crack Propagation

[0178]

[0179] Table 8: Meaning of each parameter

[0180]

[0181] Multibody dynamics piecewise modeling uses the Lagrange equation

[0182]

[0183] Solve constraints

[0184]

[0185] The synchronization delay is < 50 ms, supporting AR remote collaboration (HoloLens 2, SLAM positioning error < cm). Through the virtual-real fusion interface module, it can ensure a significant improvement in consistency, significantly enhance the diagnostic accuracy, and achieve remote visualization operations.

[0186] The demonstration verification module is used to deploy the system in deep-sea and deep-earth scenarios for adaptive configuration and performance evaluation. It solves the system adaptability problem under extreme environments through scenario-based performance evaluation and parameter optimization, ensuring the operation stability and reliability under complex working conditions. Specifically, as Figure 5 shown Figure 5 This is the deep-sea demonstration application scenario diagram of the present invention. It uses a schematic diagram form to detail the land-sea transmission link and sensor deployment of the deep-sea drilling platform, emphasizing adaptive optimization. The figure is presented in a side view layout, with the offshore platform on the left, the onshore center on the right, and the middle link connected by arrows. The key annotations include: sensor nodes (dots); transmission links (double arrows, primary and backup redundancy); platform elements (drilling pumps, winches); onshore modules (rectangles); and environmental factors (wave curves). The coordinate axes use geographical coordinates (X-axis: horizontal distance; Y-axis: depth; Z-axis: time delay).

[0187] The detailed explanation of this figure starts from the basic principles: The deep-sea scenario is based on wave theory, used to simulate wind and wave loads, and criticizes the existing land limitations. The sensor deployment optimizes the space, uses the grid method, and collects data to be preprocessed by the edge terminal. The land-sea transmission ensures reliability and fast switching. The cloud decision optimizes the operation and maintenance plan, and feeds back a closed-loop response.

[0188] For the deep-sea scenario (water depth > 3000 m, wind wave flow velocity > 10 m / s), optimize the land-sea collaborative transmission, adopt MQTT + redundant links (primary and backup switching < 10 ms), reliable , , n = 2), the sensor deployment density , wave load simulation

[0189]

[0190] (Pierson-Moskowitz spectrum, force . For the deep-earth scenario (depth > 7000 m), high temperature , achieve redundancy of high-temperature and high-pressure edge nodes, heat protection coating ), multiple backup units , ANSYS multi-physics simulation:

[0191] - Establish a finite element model (mesh) );

[0192] - Input thermal-mechanical load (temperature gradient) (Pressure 1100MPa);

[0193] -Solve

[0194]

[0195] - Iterative optimization, residual Output stress contour plot.

[0196] The test case library covers vibration anomalies (threshold > 10g) and blowout risk (p > 0.01). Evaluation metrics include significantly improved prediction accuracy, enhanced stability, and reduced cost. Demonstration and verification module 4 enables adaptive deployment, optimization of links and nodes, and compliance with the DNV-OS-E401 specification.

[0197] In some embodiments, the system also includes a security module that uses blockchain SHA-256 hash verification to ensure that the data is immutable and compliant with GB / T35273.

[0198] The following is based on Figures 1-8 The system of the present invention will be described in detail in the embodiments thereof.

[0199] like Figures 1 to 8 As shown in the figure, this specification provides a novel intelligent operation and maintenance system for deep-sea and deep-earth environmental friendliness, including an edge terminal module (sensor network + preprocessing), a cloud platform module (Transformer + DRL), a virtual-real fusion interface module (Unity + variational Bayes) and a demonstration and verification module (scenario optimization).

[0200] The edge terminal module is made of high-strength, corrosion-resistant materials, providing sufficient strength and stability in HPHT environments. The sensor materials exhibit excellent corrosion resistance, enhancing the device's durability. The edge terminal module's structural design takes into account the effects of underwater high pressure, employing a closed frame to increase overall strength and reduce deformation. The edge terminal module supports data acquisition and preprocessing, ensuring mechanical connections between sensors. The preprocessing algorithm is fixed to the module, providing a stable data foundation for vehicle movement. The Butterworth filter design effectively disperses noise, preventing the loss of key features in complex signals, and is equipped with automatic adjustment capabilities to adapt to varying operating conditions.

[0201] The cloud platform module includes a fusion unit and a decision unit, driven by a remote server to achieve efficient global optimization. The fusion unit is designed to effectively process multimodal data, reduce fusion errors, and has dynamic adjustment capabilities to adapt to different data distributions. The DRL unit can absorb the biases caused by data fluctuations to the model during training, reducing stress on cloud resources and making decisions more stable and reliable.

[0202] The virtual-real fusion interface module utilizes the Unity engine to achieve precise positioning and consistent adjustment of the vehicle in complex digital environments. The Unity interface, combined with Python, employs a variational Bayesian control system to adjust model parameters and orientation in real time, ensuring system stability under strong interference. The interface material is made of high-strength alloy, offering excellent corrosion resistance and durability. The interface design is compact, enabling accurate twin mapping under HPHT conditions. The use of variational Bayes allows parameter updates to be adjusted in real time via the control system, ensuring precise model control and consistency even under significant data interference, reducing drift.

[0203] The demonstration and verification module, integrated with specific scenarios, enables efficient system verification in complex seabed / well site environments through redundant links and thermal management. The module efficiently optimizes transmission and redundancy, and its thermal management design features low disturbance and high efficiency, effectively reducing thermal stress diffusion at nodes. The ANSYS simulation is constructed from high-strength steel and corrosion-resistant alloy materials, with a specially treated surface to enhance wear resistance. The high-pressure backup unit utilizes special materials, possessing high strength and high-pressure resistance. The demonstration and verification module, through redundant scraping and optimization of seabed / well site parameters, ensures effective deployment and evaluation in complex terrains, while the delivery pipes transmit optimized data to the central system. Thermal management assists node stability under low-disturbance conditions, preventing further damage to the equipment's ecological environment.

[0204] Figure 7 This is a comparison chart of the noise filtering algorithm's performance in this invention. It uses waveform graphs to show a detailed comparison between the original and filtered signals plotted in MATLAB, emphasizing noise suppression during edge preprocessing. The graph is presented in a hyperbolic layout, with the original waveform (blue) on the left and the filtered waveform (red) on the right. Key annotations include: coordinate axes (X: time; Y: amplitude); parameters (cutoff frequency); comparison indicators (dashed lines connecting peak values); and noise distribution (gray shading). Units are time and amplitude.

[0205] The detailed explanation of this diagram begins with the fundamental principles: noise filtering is based on signal processing theory, the Gaussian model, and the design minimizes phase shift. MATLAB code generates the filter, applying zero phase shift. The effect is quantified using performance metrics. The limitations of existing static processing methods are criticized.

[0206] Figure 8This invention presents a reinforcement learning decision optimization curve, using a graph to detail the training convergence process and performance improvement, emphasizing the iterative optimization of cloud platform decisions. The graph is presented with a dual-axis layout: the loss curve on the left (blue) and the reward curve on the right (red). Key annotations include: coordinate axes (X: iteration; Y left: loss; Y right: reward); parameters (discount factor); convergence point (dashed line); and performance improvement (shaded area). The unit is dimensionless.

[0207] The detailed explanation of this diagram begins with its fundamental principles: based on the decision-making process, state-action transitions, and derived value updates. The code is as follows... Figure 1 The training loop explores and calculates the loss function and objective. Reward quantification and decision response after convergence are then implemented. The limitations of existing thresholding methods are critiqued.

[0208] In addition, the system is equipped with multifunctional auxiliary devices, including a knowledge graph for historical data management, blockchain for secure transmission, and an AR interface for remote collaboration. The coordinated operation of these functional modules ensures the system's efficient operation and maintenance capabilities and environmental friendliness. The knowledge graph uses the OWLDL ontology, guaranteeing reasoning in dark data environments. The blockchain provides coordination and control between systems, ensuring the smooth operation of each functional module. The AR interface further handles complex decisions, reducing environmental impact.

[0209] The coordinated operation of these auxiliary devices enables the system to operate efficiently in complex deep-sea environments while minimizing disturbances. Knowledge graphs ensure generalization in data-scarce environments, while blockchain provides security guarantees, ensuring the smooth operation of each functional module. The AR interface further enhances operability and reduces human intervention.

[0210] The system 100 of this embodiment of the invention has the following beneficial effects:

[0211] Efficient Operations and Maintenance: This invention employs an operations and maintenance approach that combines edge-cloud collaboration with DRL (Data Relationship Management), where the edge handles preprocessing and the cloud optimizes decision-making. This combination significantly improves operational efficiency, especially in environments with alternating HPHT (Hyper-Hyper-Hyper-Hyper) environments, enabling efficient responses to data changes and continuous, efficient forecasting operations.

[0212] High stability: This invention, through modular design and redundant coordination, employs Butterworth filtering in preprocessing, combined with variational Bayesian and ANSYS simulations, effectively improving the system's robustness and adaptability. The demonstration module can adjust its configuration in real time according to the scenario, ensuring the system's operational stability in various deep-sea and deep-earth environments, especially in steep slopes, high temperatures, and irregular terrain, demonstrating significant anti-delay and anti-fault capabilities.

[0213] Environmental Protection: This invention prioritizes minimizing disruption to the equipment's ecosystem during the design process. By optimizing transmission traffic and computational load, it reduces data layer disturbances and mitigates the impact of levitation errors on surrounding decision-making. Simultaneously, the overall structural design of the equipment avoids excessive computation of the original form, reducing damage to the operation and maintenance ecosystem and thus achieving a more environmentally friendly operation and maintenance approach.

[0214] Easy to operate: This invention integrates multiple functional modules, including an MQTT transmission and remote AR system, an electro-hydraulic proportional control system, etc. Operators can precisely control the system through the real-time communication module. At the same time, the addition of automatic adjustment function greatly reduces the need for manual intervention, making the equipment operation simpler and more efficient, and enabling high-precision operation even in complex deep-sea conditions.

[0215] Operational reliability: This invention incorporates multiple redundancy protection mechanisms, such as primary / backup switching for transmission links, overload protection for DRLs, and dual-system switching for nodes and models. These designs significantly enhance the system's reliability in extreme deep-sea and deep-earth environments, ensuring stable and efficient operation under high pressure, low temperature, and long-term running conditions.

[0216] In summary, the novel operation and maintenance system 100 provided in this application can not only meet the operational needs in complex deep-sea and deep-earth environments, but also take into account the characteristics of efficient, stable and environmentally friendly operation and maintenance, and has broad application prospects and significant technical advantages.

[0217] In the description of this invention, it should be understood that the terms "edge," "cloud," "virtual and real," "demonstration," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing this invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limiting this invention.

[0218] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this invention, "a plurality of" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0219] In this invention, unless otherwise explicitly specified and limited, the terms "acquisition," "fusion," "optimization," and "verification," etc., should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral part; they can refer to a mechanical connection, an electrical connection, or a connection that allows communication between them; they can refer to a direct connection or an indirect connection through an intermediate medium; they can refer to the internal communication of two components or the interaction between two components, unless otherwise explicitly limited. Those skilled in the art can understand the specific meaning of the above terms in this invention according to the specific circumstances.

[0220] In this invention, the terms "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., refer to a specific feature, structure, material, or characteristic described in connection with that embodiment or example, which is included in at least one embodiment or example of the invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0221] Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of the present invention.

Claims

1. A virtual-real fusion intelligent operation and maintenance system for high-end deep-sea and deep-earth drilling equipment based on edge-cloud collaboration, characterized in that, It includes an edge terminal module, a cloud platform module, a virtual-real fusion interface module, and a demonstration and verification module; The edge terminal module is deployed at the drilling equipment site to collect multi-source heterogeneous operating data of the equipment and perform near-source preprocessing and preliminary fault identification. Through near-source preprocessing, redundant data is filtered and core features are extracted. Preliminary fault identification filters abnormal signals in advance, reducing the amount of data uploaded to the cloud and reducing transmission latency in the low-bandwidth environment of deep sea and deep earth. The cloud platform module is communicatively connected to the edge terminal module and is used to receive preprocessed data for global data fusion, machine learning model training and operation and maintenance decision optimization. Based on the refined data uploaded from the edge, global fusion can integrate multi-source information in distributed scenarios, and machine learning model training can use global data to improve generalization ability, thereby generating operation and maintenance decisions that are more in line with actual working conditions. The virtual-real fusion interface module interacts with the edge terminal module and the cloud platform module to build and update a digital twin model synchronized with the physical equipment, realizing a two-way mapping between the virtual and the real. By synchronizing physical data and cloud commands in real time, the digital twin model can accurately simulate the equipment's operating status, providing a virtual carrier for fault simulation and maintenance rehearsal, reducing the risk of fault injection testing of physical equipment, and improving the accuracy of fault diagnosis. The demonstration and verification module is used to adapt the system to deployment in deep-sea or deep-earth application scenarios, and to perform performance evaluation and parameter optimization. It is used to adaptively configure the system for extreme environments such as high humidity and salt spray in the deep sea and high temperature and pressure in the deep earth. The system parameters are optimized through performance evaluation feedback, so that the system can adapt to the special needs of complex scenarios and improve operational stability and reliability.

2. The system according to claim 1, characterized in that, The multi-source heterogeneous operating data includes at least one of vibration signals, temperature parameters, pressure indicators, process parameters, and environmental data; the frequency range of the vibration signals is 1-1000Hz, the measurement range of the temperature parameters is -50℃ to 200℃, and the measurement range of the pressure indicators is 0-200MPa.

3. The system according to claim 1, characterized in that, The edge terminal module adopts the MQTT protocol with a Quality of Service (QoS) level of 2 to achieve low-bandwidth, high-throughput data transmission. The near-source preprocessing includes noise filtering, data normalization, and feature extraction. Noise filtering uses a Butterworth low-pass filter with the following transfer function: Noise standard deviation is reduced; by filtering environmental interference noise, the effective signal characteristics of equipment operation are highlighted, and fault information is prevented from being masked by noise; characteristics Principal Component Analysis (PCA) is used for data extraction, which can reduce data dimensionality while preserving core variance, thereby reducing the amount of data transmitted and decreasing dependence on low-bandwidth environments. Preliminary fault identification employs a lightweight neural network model with softmax activation. An alarm is triggered with a probability threshold of 0.8, supporting small-sample transfer learning.

4. The system according to claim 1, characterized in that, The cloud platform module employs a deep reinforcement learning algorithm based on Markov Decision Process (MDP) and the Bellman optimal equation: The experience replay buffer has a capacity of 10,000, and the batch size is 32 to optimize decision-making. Through experience replay and batch updates, it can fully utilize historical operation and maintenance data to learn the patterns of operating condition changes, enabling decisions to not only focus on immediate fault handling but also achieve long-term operation and maintenance cost optimization and reduce unplanned downtime losses; it also integrates the Transformer attention mechanism for multimodal data. Generate a remaining useful life prediction model and a risk assessment model. The remaining useful life prediction model is based on Miner's linear cumulative damage rule. Combining the Long Short-Term Memory (LSTM) time series model, the risk assessment model employs the Analytic Hierarchy Process (HHM) for risk quantification. The cloud platform module integrates knowledge graphs and the RDF triplet resource description framework, supporting dynamic updates and incremental learning.

5. The system according to claim 1, characterized in that, The virtual-real fusion interface module is built based on the Unity engine and Python interface to construct the digital twin model, supporting virtual-real consistency detection and fault injection simulation; the virtual-real consistency detection adopts variational Bayesian inference and posterior distribution. Minimize the Kullback-Leibler divergence: It can dynamically update model parameters to ensure consistency between the virtual model and the physical equipment state; the fault injection simulation is used to simulate fatigue wear, fluid leakage, and crack propagation. The fluid leakage adopts the Navier-Stokes equation, and the crack propagation adopts the Paris fatigue crack propagation rule. The digital twin model employs segmented multibody dynamics modeling, using the Lagrange equations of motion: Synchronization delay is less than 50ms.

6. The system according to claim 1, characterized in that, The demonstration and verification module optimizes land-sea collaborative transmission and sensor deployment for deep-sea scenarios. The land-sea collaborative transmission uses the MQTT protocol combined with redundant links, with a master-slave switchover time of less than 10ms. The redundant link design can withstand transmission interference caused by deep-sea winds, waves, and currents, ensuring continuous and stable land-sea data transmission and avoiding maintenance delays due to transmission interruptions; Wave power spectral density: Sensor deployment density greater than For deep-earth scenarios, redundant design and migration optimization of high-temperature and high-pressure edge nodes are implemented. The redundant design of high-temperature and high-pressure edge nodes is based on the heat conduction steady-state equation: Where k is the thermal conductivity, T is the temperature, and q is the intensity of the internal heat source; Migration optimization was performed using ANSYS finite element simulation, and the convergence residual was less than [value missing]. It can quickly solve the shortest path for equipment migration, adapt to scenarios with frequent deep-ground equipment migration, and reduce downtime during the migration process; evaluation indicators include significantly improved fault prediction accuracy, significantly enhanced system stability, and reduced operation and maintenance costs.

7. The system according to claim 1, characterized in that, It further includes a security module that uses blockchain technology, SHA-256 hash function and Proof-of-Stake (PoS) consensus mechanism to ensure data transmission integrity and immutability, and supports seamless synchronization across multiple edge devices.

8. The system according to any one of claims 1-7, characterized in that, The system is suitable for the intelligent operation of high-end drilling equipment in complex high-pressure and high-temperature (HPHT) environments, including floating liquefied natural gas (FLNG) platforms and scientific deep wells at depths of 10,000 meters, achieving improved data transmission latency, enhanced system robustness, and increased unmanned operation rate.

9. The system according to claim 1 or 4, characterized in that, The operation and maintenance decision optimization of the cloud platform module adopts the Deep Q-Network (DQN) or Proximal Policy Optimization (PPO) algorithm. The training process includes experience replay, ε-greedy exploration strategy and batch parameter update. After convergence, the mean square error (MSE) is less than 0.

05.

10. The system according to claim 1 or 5, characterized in that, The digital twin model of the virtual-real fusion interface module is constructed using a multibody dynamics segmentation method, considering the electromechanical-control coupling relationship, with the following constraint equations: It supports augmented reality (AR) remote collaboration, effectively improving operational efficiency.

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