Real-time monitoring and early warning system for running state of digital twin-driven casing pipe

Abstract

The invention discloses a digital twin-driven bushing operation monitoring and early warning system, which relates to the technical field of digital twin bushing operation monitoring and early warning, and comprises a multi-source acquisition, transmission, analysis, early warning and interaction module, an acquisition module adopts an ad hoc network, a self-powered sensor acquires data in a multi-mode manner, and the sampling frequency is dynamically adjusted. The transmission module guarantees data transmission through LPWAN, satellite communication and quantum encryption, the analysis module builds a multi-scale coupling model to predict the state and the service life, the model can be adaptively adjusted, the early warning module determines the early warning level in combination with a fuzzy decision and a Bayesian network and gives decision suggestions, and the interaction module provides immersive experience and natural interaction based on VR / AR / MR. According to the invention, comprehensive monitoring, stable and safe data transmission, accurate analysis and prediction, scientific and reliable early warning, convenient man-machine interaction, rapid fault diagnosis, cooperation of all modules, real-time and accurate monitoring and early warning, fault reduction, service life prolonging of a sleeve, safety guarantee and benefit and competitiveness improvement are realized through multi-source acquisition.

Description

Technical Field

[0001] The present invention relates to the technical field of digital twin-driven casing operation monitoring and early warning, and particularly to a real-time monitoring and early warning system for casing operation status driven by digital twin. Background Art

[0002] In many fields such as petroleum, chemical industry, and construction, the casing, as a key component, its operation status is directly related to the safety and stability of the system. Traditional casing monitoring methods rely on manual inspections, which are not only inefficient but also greatly affected by subjective factors, making it difficult to detect early subtle hidden dangers. Under complex geological conditions, such as deep oil extraction, the casing is subjected to high temperature, high pressure, and formation stress. It is impossible to grasp its state changes in real time only by manual means, resulting in frequent casing failures, causing huge economic losses and safety risks.

[0003] Although existing monitoring systems use sensors, most of them are of a single type and can only obtain partial operation parameters of the casing, unable to comprehensively reflect its working conditions. For example, only monitoring temperature or pressure cannot comprehensively judge whether the casing has microcracks due to stress concentration. At the same time, data transmission is mostly through wired connections, with complex wiring, easy to be damaged, poor stability in harsh environments, and signals are easily interrupted by interference, resulting in missing monitoring data and affecting the accurate assessment of the casing operation status.

[0004] In terms of data analysis and early warning, traditional methods rely on empirical threshold judgment, which is difficult to adapt to the complex changes in casing operation under different working conditions. Facing a large amount of monitoring data, there is a lack of effective processing and analysis technologies, unable to deeply explore the data value and achieve accurate fault prediction and early warning. With the digital transformation of industry, there is an urgent need for an advanced casing operation status monitoring and early warning system to improve the monitoring accuracy, reliability, and early warning timeliness using cutting-edge technologies to ensure the safe and stable operation of the casing. Summary of the Invention

[0005] The real-time monitoring and early warning system for casing operation status driven by digital twin proposed by the present invention aims to solve the problems mentioned in the above prior art.

[0006] To achieve the above object, the present invention adopts the following technical solutions:

[0007] A real-time monitoring and early warning system for casing operation status driven by digital twin, comprising the following modules:

[0008] Multi-source data acquisition module: Adopting a distributed, miniaturized and self-organizing sensor network, including stress, temperature, displacement, and micro-nano sensors based on the quantum tunneling effect, used to detect the microscopic physical changes of the casing;

[0009] Data Transmission Module: Using low-power wide-area network technology, introducing satellite communication as a backup link, and adopting adaptive coding and modulation technology. The transmission rate R is adjusted according to the channel quality Q and interference intensity I. The formula is R = R0×(1 + k3×Q - k 14 ×I), where R0 is the initial transmission rate, and k3, k 14 are coefficients. Quantum encryption technology is adopted, and error correction codes and retransmission mechanisms are combined to ensure data security; this module has the ability of intelligent routing learning and can adapt to the dynamically changing network environment;

[0010] Data Analysis Module: Construct a multi-scale coupling model based on digital twin, and use finite element analysis and machine learning algorithms to simulate and analyze the digital twin model. The remaining life of the casing where L0 is the initial remaining life, k4, k 15 are coefficients, w i is the weight, x i is the influencing factor, and ΔS is the increment of microstructure damage. This module also has the function of model adaptive adjustment;

[0011] Early Warning Decision Module: According to the results of the data analysis module, set different levels of early warning thresholds, and use the fuzzy decision algorithm combined with the Bayesian network to determine the early warning level Level. The formula is where u i is the membership degree, x i is the factor value, P fault is the probability of failure occurrence calculated based on the Bayesian network, k 16 is the coefficient. At the same time, provide intelligent decision-making suggestions based on the knowledge graph, and recommend maintenance plans and emergency measures according to different early warning levels and casing conditions;

[0012] Human-Machine Interaction Module: Provide a visualization interface based on VR / AR / MR to display casing information, and users can interact through gestures and voices; it has the function of intelligent auxiliary decision-making, and analyzes users' operation habits and needs through machine learning algorithms.

[0013] Furthermore, it also includes a fault diagnosis module. This module uses a fault feature extraction algorithm based on deep learning, combines convolutional neural network and recurrent neural network to extract fault features from data, and through transfer learning technology, pre-trains the model using existing fault data of similar equipment. According to the fault severity S, influence range A and fault development trend T trend calculate the fault risk R f , the formula is R f = k5×S×A×(1 + k 17 ×T trend ), where k5, k 17is a coefficient. At the same time, fault repair suggestions and maintenance plans based on fault tree analysis and case-based reasoning are provided.

[0014] Furthermore, it also includes a data fusion module. This module uses data fusion technology to process the data collected by sensors, filters and optimizes the data using the Kalman filtering algorithm based on tensor decomposition, and synthesizes stress Synthetic damage factor where w i and v j are weights, σ i is the stress value of each sensor, D j is each damage index. This module also has a data quality assessment function to monitor and correct data in real time.

[0015] Furthermore, the sensors of the multi-source data acquisition module use multi-modal perception technology to collect physical quantities, and the sampling frequency is calculated by the formula where f0 is the initial frequency, k1, k2, k 13 are coefficients, dσ / dt, dT / dt, are the change rates of stress, temperature, and microphysical quantities respectively; the sensors have self-calibration functions and fault self-diagnosis functions, and the calibration period T cal is determined according to the usage time t, environmental conditions C, and measurement error E err The formula is T cal = T0×(1 + k6×t + k7×C + k 18 ×E err ), where T0 is the initial calibration period, k6, k7, k 18 are coefficients. When the sensor detects its own fault, it automatically switches to the backup sensor or uses data fusion to perform data compensation.

[0016] Furthermore, the data transmission module has an adaptive routing function and a network congestion control function. According to the network topology structure, node status, channel quality, and service priority, it automatically selects the data transmission path. Through the routing optimization algorithm, the path cost Cost is calculated based on the energy consumption E, communication delay D, and bandwidth utilization B of the node. The formula is Cost = k8×E + k9×D + k 19 ×B, where k8, k9, k 19 are coefficients. When network congestion occurs, a congestion control algorithm based on queue management and traffic shaping is used to adjust the data transmission rate.

[0017] Furthermore, the digital twin model of the data analysis module has a real-time update function and a multi-physical field coupling analysis function. The update frequency f update is determined according to the data change rate r and model error E model The formula is fupdate = f base × (1 + k 10 × r + k 20 × E model ), where f base is the base update frequency, k 10 , k 20 are coefficients. At the same time, through multi-physics finite element analysis and multi-scale modeling methods, the model accuracy is improved.

[0018] Furthermore, the warning signal of the warning decision module has a hierarchical push function and an intelligent interaction function. According to the warning level, the warning information is pushed to the management personnel. The push priority P is determined according to the warning level Level and the importance I of the management personnel's responsibilities role as follows. The formula is P = k 11 × Level × I role , where k 11 is a coefficient. When the management personnel receive the warning information, they communicate with the system in real time through the intelligent interaction interface, and the system dynamically adjusts according to the feedback of the management personnel.

[0019] Furthermore, the human-computer interaction module supports multi-user concurrent operations and multi-modal interaction functions. It adopts a distributed architecture, load balancing technology, and cloud computing technology to ensure the stability of the system when users access. At the same time, it has a user permission management function, and assigns operation permissions according to the roles and responsibilities of users. Users interact with the system in various ways, and the system automatically identifies the interaction methods of users and optimizes them.

[0020] Furthermore, it also includes a historical data management module, which stores and manages historical data. A data warehouse is established using a distributed file system and database technology to classify, index, and back up the data, support data query and statistical analysis. At the same time, data mining technology is used to discover rules and trends from historical data to provide references for system optimization.

[0021] Furthermore, it also includes a system self-maintenance module, which monitors the running status of the system in real time. When system anomalies are found, it automatically performs fault diagnosis and repair, optimizes and adjusts the system through system performance evaluation indicators. At the same time, this module has a software automatic update function to obtain system patches and function upgrades in a timely manner.

[0022] Compared with the existing technologies, the beneficial effects of the present invention are:

[0023] With the distributed, self-organizing network and self-powered sensors of the multi-source data acquisition module, it not only covers the monitoring of multiple physical quantities, but also can sense microscopic changes, greatly improving the comprehensiveness and accuracy of data acquisition, and making the operating conditions of the casing clearly visible.

[0024] The data transmission module adopts LPWAN combined with satellite communication, and cooperates with quantum encryption and intelligent routing learning. No matter how harsh the environment is, it can ensure the secure, stable and efficient transmission of data, providing a solid data foundation for subsequent analysis.

[0025] The data analysis module based on the multi-scale coupling model accurately predicts the remaining life and operating status of the casing through FEA and the deep reinforcement learning network. The model can also adaptively adjust to continuously improve the prediction accuracy.

[0026] The early warning decision-making module uses fuzzy decision-making combined with Bayesian networks to comprehensively determine the early warning level considering multiple factors, and provides intelligent decision-making suggestions with the help of the knowledge graph, making the early warning more accurate and the decision-making more scientific.

[0027] The human-computer interaction module with VR / AR / MR technology provides an immersive experience, natural interaction methods and multi-user collaboration functions, greatly improving the operation convenience and collaboration efficiency. The fault diagnosis module relies on deep learning and transfer learning to quickly and accurately diagnose faults and give repair solutions. The data fusion module improves the data quality. Each module works together to achieve real-time and accurate monitoring and early warning of the casing operating status, effectively reducing the failure rate, extending the service life of the casing, ensuring production safety, and enhancing the economic benefits and competitiveness of the enterprise. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] Figure 1 It is a schematic block diagram of the digital twin-driven real-time monitoring and early warning system for the casing operating status proposed by the present invention;

[0029] Figure 2 It is a bar chart comparing the comprehensiveness of data collection of the digital twin-driven real-time monitoring and early warning system for the casing operating status proposed by the present invention;

[0030] Figure 3 It is a line chart comparing the accuracy of fault prediction of the digital twin-driven real-time monitoring and early warning system for the casing operating status proposed by the present invention;

[0031] Figure 4 It is a radar chart comparing the system reliability of the digital twin-driven real-time monitoring and early warning system for the casing operating status proposed by the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0032] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0033] In the description of the present invention, it should be understood that the orientation or positional relationship indicated by terms such as "center", "longitudinal", "transverse", "length", "width", "thickness", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", "clockwise", "counterclockwise", etc. is based on the orientation or positional relationship shown in the drawings. It is only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be construed as a limitation to the present invention.

[0034] In addition, the terms "first" and "second" are only used for descriptive purposes and cannot be understood as indicating or implying relative importance or implicitly specifying the quantity of the indicated technical features. Thus, the features defined with "first" and "second" may explicitly or implicitly include one or more of the said features. In the description of the present invention, the meaning of "a plurality" is two or more unless otherwise specifically defined. In addition, the terms "mounted", "connected" and "coupled" should be understood in a broad sense. For example, it may be a fixed connection, a detachable connection or an integral connection; it may be a mechanical connection or an electrical connection; it may be directly connected or indirectly connected through an intermediate medium, and it may be the communication inside two elements. For those of ordinary skill in the art, the specific meanings of the above terms in the present invention can be understood according to specific situations. The present invention will be further described in detail below with reference to the drawings.

[0035] Refer to Figures 1 to 4 : A digital twin-driven real-time monitoring and early warning system for the operation status of casing pipes, comprising the following modules:

[0036] Multi-source data acquisition module: Adopting a distributed, miniaturized and self-organizing sensor network, the sensors include not only stress sensors, temperature sensors, displacement sensors, but also integrated high-sensitivity micro-nano sensors based on the quantum tunneling effect, which are used to detect weak physical changes at the microscopic level of the casing pipe, such as the tiny stress fluctuations generated by the propagation of microscopic cracks. The sensors are of high precision and high reliability. They adopt multi-modal sensing technology, can simultaneously collect multiple physical quantities, and perform preliminary data fusion processing. The sampling frequency is dynamically adjusted according to the operating environment and status of the casing pipe, and is calculated by the formula where f0 is the initial sampling frequency, k1, k2, k 13 are coefficients, dσ / dt is the stress change rate, dT / dt is the temperature change rate, is the change rate of microscopic physical quantities. The sensor nodes adopt energy harvesting technology, such as using the vibration, thermal energy, etc. around the casing pipe for self-power supply, so as to extend the service life of the sensors and reduce the maintenance cost.

[0037] Data Transmission Module: Utilize Low-Power Wide-Area Network (LPWAN) technologies such as LoRa and NB-IoT. Such technologies can achieve stable data transmission with relatively low energy consumption due to characteristics like low power consumption, wide coverage, and strong penetration ability. Meanwhile, to cope with extreme situations such as complex environments or ground network failures, satellite communication is introduced as a backup link to ensure the reliability of data transmission. Adaptive coding and modulation technology is adopted to adjust the transmission rate R according to the channel quality Q and interference intensity I, with the formula R = R0×(1 + k3×Q - k 14 ×I), where R0 is the initial transmission rate, and k3, k 14 are coefficients. At the same time, advanced quantum encryption technology is used to encrypt the data to ensure data confidentiality at the physical level. Combining error correction codes and retransmission mechanisms can prevent data errors or losses during transmission and guarantee data integrity. In addition, this module has the ability of intelligent routing learning. Through reinforcement learning algorithms, it continuously optimizes the routing strategy based on the real-time network status, enabling it to flexibly adapt to the dynamically changing network environment.

[0038] Data Analysis Module: Build a multi-scale coupling model based on digital twins. This model breaks through traditional limitations and comprehensively considers not only the macroscopic mechanical properties of the casing, such as force deformation and bearing capacity, but also deeply incorporates microscopic structures and material characteristics, such as crystal structures and interatomic forces. Use the powerful tool of finite element analysis (FEA) and combine advanced machine learning algorithms, such as deep reinforcement learning networks, to conduct real-time simulation and in-depth analysis of the digital twin model. Calculate and predict the operating state and remaining life L of the casing through the formula where L0 is the initial remaining life, k4, k 15 are coefficients, w i is the weight, x i are influencing factors, and ΔS is the increment of microscopic structure damage. In addition, this module has a key function of model adaptive adjustment. It will intelligently and automatically adjust the model parameters according to the deviation between the actual operating data and the model prediction results, thereby continuously improving the prediction accuracy and providing strong support for the assessment of the casing operating state.

[0039] Early Warning and Decision-Making Module: Set different levels of early warning thresholds according to the results of the data analysis module. When the operating state of the casing exceeds the early warning threshold, an early warning signal is automatically triggered. Use a fuzzy decision-making algorithm combined with a Bayesian network to comprehensively consider multiple factors (such as stress, temperature, displacement, microscopic structure changes, etc.) to determine the early warning level Level, with the formula where u i is the membership degree, x i is the factor value, P fault is the probability of failure occurrence calculated based on the Bayesian network, and k 16is a coefficient. At the same time, intelligent decision-making suggestions based on the knowledge graph are provided, and the optimal maintenance plan, emergency measures, etc. are recommended according to different warning levels and the specific conditions of the casing.

[0040] Human-computer interaction module: It is committed to creating an advanced and highly immersive interaction experience, providing an immersive visualization interface based on virtual reality (VR) / augmented reality (AR) / mixed reality (MR). In this interface, the real-time operating status of the casing is presented in an intuitive and vivid form, the simulation results of the digital twin model can also be clearly shown, and the warning information is even more obvious at a glance. Users can get rid of the shackles of traditional input devices and conveniently perform operations such as parameter setting, historical data query, and remote control by means of natural interaction methods such as gestures and voices. This module not only stops there, but also has a powerful intelligent auxiliary decision-making function. It deeply analyzes the user's operation habits and actual needs through machine learning algorithms, and then tailors personalized operation suggestions and prompts for users. In addition, fully considering the needs of team collaboration, it supports multi-user collaborative interaction. This means that users in different locations can easily synchronize through the network and jointly participate in the monitoring and decision-making process of the casing, greatly improving work efficiency and collaboration.

[0041] In the present invention, a fault diagnosis module is also included. This module uses a fault feature extraction algorithm based on deep learning, skillfully combines the powerful image feature extraction ability of the convolutional neural network (CNN) and the advantage of the recurrent neural network (RNN) in processing sequence data, and accurately mines deep-level fault features from the collected data. Through transfer learning technology, the model is pre-trained by making full use of the existing fault data of similar devices, greatly improving the accuracy and efficiency of fault diagnosis. According to the severity S, influence range A and fault development trend T of the fault trend Calculate the fault risk R f , the formula is R f = k5 × S × A × (1 + k 17 × T trend ), where k5, k 17 is a coefficient. Moreover, fault repair suggestions and maintenance plans based on fault tree analysis and case reasoning are provided, which can quickly and accurately match the best repair strategy according to different fault scenarios, providing comprehensive support for casing fault handling.

[0042] In the present invention, a data fusion module is also included. This module adopts advanced multi-scale and multi-modal data fusion technology, and can perform in-depth fusion processing on the data collected by different types and scales of sensors. Using the Kalman filtering algorithm based on tensor decomposition, the data is finely filtered and optimized, effectively improving the accuracy and reliability of the data. Through data fusion, more comprehensive casing operating status information can be obtained, such as the comprehensive stress σ total , comprehensive damage factor Dtotal The formulas are and where w i 、v j is the weight, σ i is the stress value measured by each sensor, D j This module has the function of data quality assessment, real-time monitoring of data accuracy, completeness and consistency, timely correction or elimination of low-quality data, and provides a reliable data basis for subsequent analysis.

[0043] In the present invention, the sensors of the multi-source data acquisition module have advanced self-calibration and fault self-diagnosis functions. Through the built-in precision calibration program, combined with high-precision reference sensors, the sensors can be calibrated regularly to ensure the reliability of measurement accuracy. Calibration cycle T cal According to the sensor's usage time t, environmental conditions C and measurement error E err OK, the formula is T cal =T0×(1+k6×t+k7×C+k 18 ×E err ), where T0 is the initial calibration period, k6, k7, k 18 This dynamic calibration mechanism fully considers the influence of various factors in actual use, making the calibration more scientific and reasonable. In addition, when the sensor detects a fault, its intelligent mechanism can respond quickly, automatically switch to a backup sensor, or use advanced data fusion methods for data compensation. In this way, even if a sensor fails, data collection can still be ensured to be uninterrupted, ensuring the continuity of data collection and providing a solid guarantee for the stable operation of the entire monitoring system.

[0044] In the present invention, the data transmission module has powerful adaptive routing function and network congestion control function. In terms of adaptive routing, it will comprehensively consider multiple factors such as network topology, node status, channel quality and service priority. Through real-time monitoring and analysis of these factors, the optimal data transmission path can be automatically screened out. Specifically, with the help of routing optimization algorithm, the path cost Cost is calculated according to the node's energy consumption E, communication delay D, and bandwidth utilization B. The formula is Cost = k8×E+k9×D+k 19 ×B, where k8, k9, k 19is a coefficient. This scientific calculation method can ensure that data always transmits along the most cost-effective path in a complex network environment. When network congestion occurs, this module will immediately start a congestion control algorithm based on queue management and traffic shaping. This algorithm can dynamically and precisely adjust the data transmission rate according to the degree of network congestion, avoiding data loss or excessive delay due to congestion, thus ensuring reliable data transmission under various network conditions and escorting the smooth data transmission of the entire system.

[0045] In the present invention, the digital twin model of the data analysis module has a real-time update function and a multi-physical field coupling analysis function. According to the newly collected data, the parameters and states of the digital twin model are updated in a timely manner to ensure the consistency between the model and the actual casing. The update frequency f update is determined according to the data change rate r and the model error E model , and the formula is f update = f base ×(1 + k 10 ×r + k 20 ×E model ), where f base is the basic update frequency, and k 10 , k 20 are coefficients. At the same time, considering the operating state of the casing under the coupling action of multiple physical fields such as mechanics, thermotics, and electricity, the accuracy and reliability of the model are improved through multi-physical field finite element analysis and multi-scale modeling methods.

[0046] In the present invention, the warning signal of the warning decision module has a hierarchical push function and an intelligent interaction function. In terms of hierarchical push, the system will accurately transmit the warning information to the management personnel at the corresponding level according to the strict warning level. Considering the differences in the responsibilities and processing capabilities of different management personnel, various push methods such as text messages, emails, APP messages, and voice calls are supported. The push priority P is determined according to the warning level Level and the importance I role of the management personnel, and the formula is P = k 11 ×Level×I role , where k 11 is a coefficient. When the management personnel receive the warning information, they can communicate with the system in real time and smoothly through a specially designed intelligent interaction interface. During this process, the management personnel can not only obtain more detailed information about the warning, such as the fault location and severity analysis, but also get professional decision-making suggestions given by the system. At the same time, the system has a good feedback mechanism, which can quickly make dynamic adjustments according to the feedback of the management personnel and optimize the subsequent warning strategies and processing procedures.

[0047] In the present invention, the human-computer interaction module supports multi-user concurrent operations and multi-modal interaction functions. To handle complex scenarios where multiple users access simultaneously, a distributed architecture, load balancing technology, and cloud computing technology are adopted. The distributed architecture disperses task processing, the load balancing technology rationally allocates system resources, and the cloud computing technology provides powerful computing power support, jointly ensuring the stability and response speed of the system during multi-user access, and avoiding lags and delays. At the same time, this module has a rigorous user permission management function. According to the roles and responsibilities of users, different operation permissions are carefully assigned to ensure the security and standardization of system operations. In terms of interaction methods, it greatly meets the diverse needs of users. Users can either immerse themselves in virtual scenarios and perform intuitive operations with the help of VR / AR / MR devices; or achieve convenient touch interaction through touchscreens, or use traditional mouse and keyboard for precise control. The system has intelligent recognition capabilities, can automatically identify the interaction methods of users, and quickly make corresponding responses and optimizations, creating a personalized and efficient interaction experience for users.

[0048] In the present invention, it also includes a historical data management module, which stores and manages the collected historical data. A distributed file system (such as Hadoop Distributed File System) and database technology (such as NoSQL database) are used to establish a data warehouse, classify, index, and back up the data. This module also supports the rapid query and statistical analysis of data. When it is necessary to evaluate the operation status of the casing, relevant data can be quickly extracted, providing solid data support for evaluation and optimization work. More advantageously, data mining technology is used to deeply analyze historical data, keenly discover potential laws and trends from it, providing extremely valuable reference bases for the further optimization and scientific decision-making of the system, and helping the casing operation monitoring and warning system to continuously improve and upgrade.

[0049] In the present invention, it also includes a system self-maintenance module, which monitors the running status of the system in real time, covering the working status of hardware devices, such as the temperature of the server and the read / write situation of the hard disk; the running situation of software programs, including the running logs of each module and whether there are error reports; and the network connection status, such as network bandwidth occupancy and signal stability. Once a system failure or abnormality is detected, this module will immediately activate the emergency mechanism, automatically carry out fault diagnosis, accurately locate the problem source with the built-in diagnostic algorithm, and then quickly repair it. In addition, it will comprehensively optimize and adjust the system according to system performance evaluation indicators, such as whether the response time is too long, whether the throughput can meet the requirements, and whether the resource utilization rate is reasonable, ensuring that the system is always in a reliable and stable running state. It is worth mentioning that this module has a software automatic update function, which can timely capture the latest system patches and function upgrades and seamlessly integrate them into the system, further enhancing the security and performance of the system and keeping the system always at the best working level.

[0050] The above are only the preferred specific embodiments of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present invention, according to the technical solution and inventive concept of the present invention, making equivalent substitutions or changes, should be covered within the protection scope of the present invention.

Claims

1. A digital twin-driven real-time monitoring and early warning system for the operating status of casing, characterized in that, It includes the following modules: Multi-source data acquisition module: It adopts a distributed, miniaturized and self-organizing sensor network, including stress, temperature, displacement, and micro-nano sensors based on the quantum tunneling effect, which are used to detect the microscopic physical changes of the casing; Data transmission module: Utilize low-power wide-area network technology, introduce satellite communication as a backup link, adopt adaptive coding and modulation technology, and adjust the transmission rate R according to the channel quality Q and interference intensity I. The formula is R = R0 × (1 + k3 × Q - k 14 × I), where R0 is the initial transmission rate, and k3 and k 14 are coefficients. Adopt quantum encryption technology, and combine error correction codes and retransmission mechanisms to ensure data security; this module has the ability of intelligent routing learning and can adapt to the dynamically changing network environment; Data analysis module: Construct a multi-scale coupling model based on digital twins, and use finite element analysis and machine learning algorithms to simulate and analyze the digital twin model, and the remaining life of the casing where L0 is the initial remaining life, k4, k 15 are coefficients, w i is the weight, x i is the influencing factor, ΔS is the increment of microstructural damage, and this module also has the function of model adaptive adjustment; Early warning decision-making module: According to the results of the data analysis module, set early warning thresholds at different levels, and use the fuzzy decision-making algorithm combined with the Bayesian network to determine the early warning level Level. The formula is where u i is the membership degree, x i is the factor value, P fault is the probability of fault occurrence calculated based on the Bayesian network, k 16 is the coefficient. At the same time, provide intelligent decision-making suggestions based on the knowledge graph, and recommend maintenance plans and emergency measures according to different early warning levels and casing conditions; Human-computer interaction module: It provides a visualization interface based on VR / AR / MR to display casing information, and users interact through gestures and voices; it has an intelligent auxiliary decision-making function, and analyzes the operation habits and needs of users through machine learning algorithms.

2. The digital twin-driven real-time monitoring and early warning system for the operating status of casing pipes according to claim 1, characterized in that, It also includes a fault diagnosis module, which uses a fault feature extraction algorithm based on deep learning to extract fault features from data by combining a convolutional neural network and a recurrent neural network. Through transfer learning technology, the model is pre-trained using existing fault data of similar devices. According to the fault severity S, the influence range A, and the fault development trend T trend calculate the fault risk R f , and the formula is R f = k5 × S × A × (1 + k 17 × T trend ), where k5 and k 17 are coefficients. At the same time, fault repair suggestions and maintenance plans based on fault tree analysis and case reasoning are provided.

3. The real-time monitoring and early warning system for the operating status of the casing driven by digital twin according to claim 1, characterized in that, It also includes a data fusion module which uses data fusion technology to process the data collected by sensors. The Kalman filtering algorithm based on tensor decomposition is used to filter and optimize the data, and the comprehensive stress Comprehensive damage factor where w i and v j are weights, σ i is the stress value of each sensor, D j is each damage index. This module also has a data quality assessment function to monitor and correct the data in real time.

4. The digital twin-driven real-time monitoring and early warning system for the running state of the casing according to claim 1, characterized in that The sensors of the multi-source data acquisition module use multi-modal perception technology to collect physical quantities, and the sampling frequency is calculated by the formula where \(f_0\) is the initial frequency, \(k_1\), \(k_2\), \(k\) 13 are coefficients, \(d\sigma / dt\), \(dT / dt\), are the change rates of stress, temperature, and microscopic physical quantities respectively; the sensors have self-calibration and self-fault diagnosis functions, and the calibration period \(T\) cal is determined according to the usage time \(t\), environmental conditions \(C\), and measurement error \(E\) err The formula is \(T\) cal = \(T_0\times(1 + k_6\times t + k_7\times C + k\) 18 \(\times E\) err ), where \(T_0\) is the initial calibration period, \(k_6\), \(k_7\), \(k\) 18 are coefficients. When the sensor detects its own fault, it automatically switches to a backup sensor or uses data fusion to perform data compensation.

5. The digital twin-driven real-time monitoring and early warning system for the running state of casing according to claim 1, characterized in that The data transmission module has an adaptive routing function and a network congestion control function. It automatically selects a data transmission path according to the network topology, node status, channel quality, and service priority. Through a routing optimization algorithm, it calculates the path cost Cost based on the energy consumption E, communication delay D, and bandwidth utilization B of the node. The formula is Cost = k8×E + k9×D + k 19 ×B, where k8, k9, k 19 are coefficients. When network congestion occurs, a congestion control algorithm based on queue management and traffic shaping is adopted to adjust the data transmission rate.

6. The digital twin-driven real-time monitoring and early warning system for the operating status of casing strings according to claim 1, wherein, The digital twin model of the data analysis module has a real-time update function and a multi-physical field coupling analysis function, and the update frequency f update is determined according to the data change rate r and the model error E model by the formula f update = f base ×(1 + k 10 ×r + k 20 ×E model ), where f base is the basic update frequency, and k 10 , k 20 are coefficients. At the same time, the model accuracy is improved through multi-physical field finite element analysis and multi-scale modeling methods.

7. The digital twin-driven real-time monitoring and early warning system for the running state of casing according to claim 1, characterized in that The warning signals of the warning decision-making module have a hierarchical push function and an intelligent interaction function. According to the warning level, the warning information is pushed to the management personnel, and the push priority P is determined according to the warning level Level and the importance of the responsibilities of the management personnel I role as P = k 11 ×Level×I role , where k 11 is a coefficient. When the management personnel receive the warning information, they communicate with the system in real time through the intelligent interaction interface, and the system adjusts dynamically according to the feedback of the management personnel.

8. The real-time monitoring and early warning system for the operating status of the casing driven by digital twins according to claim 1, characterized in that The human-computer interaction module supports multi-user concurrent operations and multi-modal interaction functions. It adopts a distributed architecture, load balancing technology, and cloud computing technology to ensure the stability of the system when users access. At the same time, it has a user permission management function, which assigns operation permissions according to the roles and responsibilities of users. Users interact with the system in various ways, and the system automatically identifies the interaction methods of users and optimizes them.

9. The real-time monitoring and early warning system for the running state of casing driven by digital twin according to claim 1, characterized in that, It also includes a historical data management module, which stores and manages historical data. It uses a distributed file system and database technology to establish a data warehouse, classifies, indexes, and backs up data, supports data query and statistical analysis. At the same time, it uses data mining technology to discover laws and trends from historical data to provide references for system optimization.

10. The digital twin-driven real-time monitoring and early warning system for the running state of the casing according to claim 1, characterized in that It also includes a system self-maintenance module, which monitors the running state of the system in real time. When system anomalies are found, it automatically performs fault diagnosis and repair, optimizes and adjusts the system through system performance evaluation indicators. At the same time, this module has a software automatic update function to obtain system patches and function upgrades in a timely manner.

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