Equipment full-life-cycle intelligent operation and maintenance management method and system based on AI deep learning

Abstract

The invention discloses an equipment full-life-cycle intelligent operation and maintenance management method and system based on AI deep learning. The method comprises the following steps: collecting multi-source heterogeneous data generated in each stage in a full life cycle of equipment, deeply exploring a knowledge network for the heterogeneous data through semantics, converging and fusing associated equipment entities, constructing a triple knowledge network, and obtaining an equipment knowledge graph network; constructing an equipment digital twinborn body corresponding to the equipment entity in a high-fidelity manner through the digital twinborn virtual mapping and the equipment experiment sand table; carrying out equipment operation simulation deduction according to the real-time monitoring data to obtain digital twin simulation deduction data; machine learning deep intelligent analysis is carried out according to the digital twinborn simulation deduction data, the equipment health state is evaluated and predicted, and equipment degradation track life prediction information is obtained; and querying equipment knowledge graph information according to an equipment knowledge graph network, calibrating digital twin simulation deduction data, and reasoning and generating an equipment dynamic maintenance decision by referring to equipment degradation track life prediction information.

Description

Technical Field

[0001] This invention relates to the field of artificial intelligence equipment operation and maintenance simulation precision control and communication technology, and in particular to an intelligent operation and maintenance management method and system for the entire life cycle of equipment based on AI deep learning. Background Technology

[0002] With the deepening of equipment intelligence, the complexity and integration of high-end equipment are increasing, posing unprecedented challenges to its operation and maintenance management. Existing operation and maintenance models suffer from the following significant drawbacks: Over-maintenance and under-maintenance: Planned maintenance cannot accurately reflect the actual health status of equipment, potentially leading to unnecessary disassembly and replacement while the equipment is still in good condition (over-maintenance), or failure to intervene in time before potential failures occur (under-maintenance); Severe information silos: Data from each stage of the equipment's lifecycle (design, manufacturing, deployment, operation and maintenance, and scrapping) is scattered across different systems and formats, such as CAD models, simulation data, MES production data, SCADA operation data, and maintenance records. The data lacks correlation, making it impossible to form a unified decision-making view; Decision-making relies heavily on expert experience: Fault diagnosis and maintenance decisions heavily depend on the personal experience of senior engineers. Knowledge is difficult to accumulate, replicate, and pass on, and manual analysis is inefficient when dealing with massive amounts of data, easily overlooking deep-seated correlations; Lack of predictive and simulation capabilities: Existing methods struggle to accurately predict the future state of equipment and cannot perform virtual verification before implementing high-cost or high-risk maintenance operations, resulting in high decision-making risks. Predictive health management (PHM) and digital twin technologies have seen rapid development; however, existing technologies often focus on single aspects, such as fault prediction or simply building 3D visualization models, failing to deeply integrate equipment lifecycle data, knowledge systems, and dynamic simulations, thus failing to form a closed-loop, self-learning intelligent decision-making system. Knowledge graph technology provides a powerful means of integrating multi-source heterogeneous data, but its fusion with digital twins and deep learning to drive dynamic maintenance decisions remains an area requiring further exploration. This invention aims to overcome the shortcomings of the existing technologies mentioned above and provide a systematic solution, at least partially addressing the problems of the existing technologies. Summary of the Invention

[0003] To address the aforementioned technical problems, this invention provides an intelligent operation and maintenance management method for the entire lifecycle of equipment based on AI deep learning, comprising: Collect multi-source heterogeneous data generated at each stage of the equipment's entire life cycle, and then use semantic deep exploration knowledge networks to aggregate and fuse related equipment entities and construct a triplet knowledge network to obtain the equipment knowledge graph network. By using digital twin virtual mapping and equipment experimental sand table, a high-fidelity digital twin of the equipment is constructed, corresponding to the actual equipment; and equipment operation simulation is performed based on real-time monitoring data to obtain digital twin simulation data. Based on digital twin simulation data, deep intelligent analysis using machine learning is performed to assess and predict equipment health status and obtain equipment degradation trajectory life prediction information. Based on the equipment knowledge graph network query, the equipment knowledge graph information is calibrated, the digital twin simulation data is calibrated, and the equipment degradation trajectory life prediction information is referenced to infer and generate dynamic maintenance decisions for the equipment.

[0004] Preferably, multi-source heterogeneous data generated at each stage of the equipment's entire lifecycle are collected, and the heterogeneous data are then used to explore a semantic deep learning knowledge network. This involves aggregating and integrating related equipment entities to construct a triplet knowledge network, thereby obtaining an equipment knowledge graph network. Specifically, this includes: Collect multi-source heterogeneous data sources of equipment generated at each stage of the entire life cycle of historical equipment. By using extraction, transformation, and loading tools, unstructured semantics are extracted from multi-source heterogeneous data sources of equipment to obtain unstructured semantic data; natural language parsing tools are used to parse the unstructured semantic data, and data feature extraction and automatic feature recognition and classification are performed to extract equipment entity information and information relationships; time series data processing tools are used to process equipment monitoring sensor data and extract equipment monitoring feature events. Based on graph databases, a triplet knowledge network consisting of equipment entity information, information relationships, and equipment virtual entities is constructed to form an equipment knowledge graph network. The equipment's multi-source heterogeneous data sources include: equipment bill of materials, structural simulation data, production and manufacturing data, quality inspection data, sensor time-series data, operation and maintenance logs, fault records, maintenance manuals, expert experience documents, and component data sources.

[0005] Preferably, a high-fidelity digital twin of the equipment is constructed by using digital twin virtual mapping and an equipment experimental sandbox; and equipment operation simulation is performed based on real-time monitoring data to obtain digital twin simulation data, specifically including: By combining equipment knowledge graph networks, digital twin virtual mapping, and equipment experimental sandboxes, and by integrating digital twin modeling tools, a high-fidelity digital twin of the equipment is constructed that corresponds to the actual equipment. Establish a real-time data connection with the equipment entity, map the monitoring data of the equipment monitoring sensors to the virtual sensors corresponding to the digital twin, and drive the digital twin to keep synchronized with the equipment entity. Upon receiving a simulation command or abnormal triggering condition, a high-fidelity simulation is initiated; when an abnormal stress is detected in a certain part of the equipment, a transient dynamic simulation is initiated to simulate the crack propagation process in that part under the current working conditions and predict potential failures; before making a maintenance decision, the impact of different maintenance operations on the overall system performance is simulated. The equipment digital twin is presented in a three-dimensional visualization format, displaying the equipment status, alarm information and simulation results in a three-dimensional way; immersive equipment status monitoring is carried out. The equipment digital twin includes: equipment mechanical model, information transmission and control model, and equipment operation rule model; the equipment mechanical model includes CAD design model; the information transmission and control model includes CAE simulation model of mechanics, thermodynamics and fluid dynamics; the equipment operation rule model includes equipment control logic and dynamic virtual simulation of equipment operation rules.

[0006] Preferably, deep intelligent analysis using machine learning is performed based on digital twin simulation data to assess and predict equipment health status and obtain equipment degradation trajectory life prediction information, specifically including: Based on digital twin simulation data, a deep neural network classification and regression model is used to calculate the evolution of equipment health status and analyze equipment health degradation, thereby obtaining information on equipment health evolution and degradation analysis. Based on the equipment health evolution and degradation analysis information and referring to the equipment's historical state sequence, the future performance degradation trajectory and remaining useful life of the equipment are predicted, and the uncertainty range of the prediction is given to obtain the equipment degradation trajectory life prediction information. By training a large amount of historical normal data and fault data, a deep neural network classification and regression model is formed, which can output the real-time health status score or degradation stage label of the equipment based on the extracted equipment monitoring feature events.

[0007] Preferably, the system queries equipment knowledge graph information based on the equipment knowledge graph network, calibrates digital twin simulation data, and refers to equipment degradation trajectory life prediction information to infer and generate dynamic maintenance decisions for the equipment. Specifically, this includes: It receives and automatically aligns equipment monitoring status information, digital twin simulation data, and equipment-related knowledge and equipment degradation trajectory life prediction information retrieved from the equipment knowledge graph network. Employing a deep reinforcement learning framework, the system continuously learns and interacts with the virtual environment built from digital twins and knowledge graphs to optimize operational decisions and generate operational decision recommendations. It outputs operation and maintenance decision suggestions, provides analysis of decision basis, cites similar historical cases, displays comparison charts of different decision consequences derived from simulation, and provides intelligent guidance for operation and maintenance execution.

[0008] This invention relates to an AI deep learning-based intelligent operation and maintenance management system for the entire lifecycle of equipment, comprising: The equipment knowledge graph module collects multi-source heterogeneous data generated at each stage of the equipment's entire life cycle, and then uses semantic deep exploration of the knowledge network to aggregate and fuse related equipment entities and construct a triplet knowledge network to obtain the equipment knowledge graph network. The equipment digital twin simulation module constructs a high-fidelity digital twin of the equipment through digital twin virtual mapping and equipment experimental sandbox; and performs equipment operation simulation and deduction based on real-time monitoring data to obtain digital twin simulation and deduction data. The equipment status analysis and prediction module performs deep intelligent analysis using machine learning based on digital twin simulation data to assess and predict the health status of the equipment and obtain information on the predicted lifespan of the equipment's degradation trajectory. The equipment dynamic maintenance decision generation module queries equipment knowledge graph information based on the equipment knowledge graph network, calibrates digital twin simulation data, and refers to equipment degradation trajectory life prediction information to infer and generate equipment dynamic maintenance decisions.

[0009] Preferably, the equipment knowledge graph module includes: The data source acquisition submodule collects multi-source heterogeneous data sources generated by the equipment at each stage of the equipment's entire life cycle. The data processing submodule extracts unstructured semantics from the equipment's multi-source heterogeneous data sources using an extraction, transformation, and loading tool, and obtains unstructured semantic data. It then uses a natural language parsing tool to parse the unstructured semantic data, performs data feature extraction and automatic feature recognition and classification, and extracts equipment entity information and information relationships. Finally, it uses a time-series data processing tool to process equipment monitoring sensor data and extract equipment monitoring feature events. The knowledge construction submodule constructs a triplet knowledge network based on a graph database, consisting of equipment entity information, information relationships, and equipment virtual entities, thus forming an equipment knowledge graph network. The equipment's multi-source heterogeneous data sources include: equipment bill of materials, structural simulation data, production and manufacturing data, quality inspection data, sensor time-series data, operation and maintenance logs, fault records, maintenance manuals, expert experience documents, and component data sources.

[0010] Preferably, the equipment digital twin simulation module includes: The digital twin model submodule, combined with the equipment knowledge graph network, constructs a high-fidelity digital twin of the equipment entity through digital twin virtual mapping and equipment experimental sandbox, and by integrating digital twin modeling tools. The data-driven and synchronization submodule establishes a real-time data connection with the equipment entity, maps the monitoring data of the equipment monitoring sensors to the virtual sensors corresponding to the digital twin, and drives the digital twin to keep synchronized with the equipment entity. The simulation engine submodule initiates high-fidelity simulation upon receiving a simulation command or abnormal triggering condition; when abnormal stress is detected in a certain part of the equipment, it initiates transient dynamic simulation to simulate the crack propagation process of that part under the current working conditions and predict potential failures; before maintenance decisions are made, it simulates the impact of different maintenance operations on the overall performance of the system. The visualization submodule presents the equipment digital twin in a 3D visualization format, showcasing the equipment status, alarm information, and simulation results in a three-dimensional manner; enabling immersive equipment status monitoring. The equipment digital twin includes: equipment mechanical model, information transmission and control model, and equipment operation rule model; the equipment mechanical model includes CAD design model; the information transmission and control model includes CAE simulation model of mechanics, thermodynamics and fluid dynamics; the equipment operation rule model includes equipment control logic and dynamic virtual simulation of equipment operation rules.

[0011] Preferably, the equipment status analysis and prediction module includes: The equipment health analysis submodule uses a deep neural network classification and regression model based on digital twin simulation data to calculate the evolution of equipment health status and analyze equipment health degradation, thereby obtaining information on equipment health evolution and degradation analysis. The status prediction submodule, based on the equipment health evolution and degradation analysis information and referring to the equipment's historical status sequence, predicts the future performance degradation trajectory and remaining useful life of the equipment, and provides the uncertainty range of the prediction, thereby obtaining the equipment degradation trajectory life prediction information. By training a large amount of historical normal data and fault data, a deep neural network classification and regression model is formed, which can output the real-time health status score or degradation stage label of the equipment based on the extracted equipment monitoring feature events.

[0012] Preferably, the equipment dynamic maintenance decision generation module includes: The multi-source information alignment submodule receives and automatically aligns equipment monitoring status information, digital twin simulation data, and equipment-related knowledge and equipment degradation trajectory life prediction information queried from the equipment knowledge graph network. The decision model submodule adopts a deep reinforcement learning framework. Through a virtual environment built from digital twins and knowledge graphs, it continuously learns and optimizes operation and maintenance decisions to form operation and maintenance decision recommendations. The decision output and analysis submodule outputs operation and maintenance decision suggestions, provides analysis of decision basis, cites similar historical cases, displays comparison charts of different decision consequences derived from simulation, and provides intelligent guidance for operation and maintenance execution.

[0013] The beneficial effects of this invention include: the intelligent operation and maintenance management method and system for the entire life cycle of equipment based on AI deep learning provided by this invention addresses problems such as information silos, delayed decision-making, and reliance on experience in operation and maintenance; by integrating multi-stage data from the entire equipment life cycle to construct a knowledge graph, establishing a high-fidelity digital twin simulation platform for equipment, utilizing deep learning models for health status monitoring and prediction, intelligently generating optimized dynamic maintenance decisions, transforming preventative maintenance into predictive intelligent operation and maintenance, significantly reducing the cost of equipment operation and maintenance throughout its entire life cycle, and shortening the iteration cycle; linking historical information and equipment knowledge with the equipment knowledge graph; real-time mapping and simulation of equipment physical data using the virtual image of the equipment digital twin; using deep learning models to construct an intelligent engine for analysis, prediction, and large-scale decision-making and execution guidance; and achieving efficient intelligent operation and maintenance management of equipment through perception, analysis, decision-making, and execution. This invention enables deep fusion and value mining of equipment lifecycle data, constructing a data-related knowledge network for each stage of design, manufacturing, and operation and maintenance through knowledge graphs; integrating fragmented information into a semantically related knowledge network; utilizing deep learning models to automatically learn fault characteristics and degradation patterns from massive amounts of data, solving the technical problem of excessive reliance on prior knowledge and manual features, significantly improving the accuracy of early fault warnings, enhancing equipment lifespan prediction accuracy, and improving condition monitoring and prediction accuracy; the digital twin model promotes more comprehensive and accurate maintenance decisions, no longer limited to existing data and experience, and can be implemented in virtual space. By simulating and extrapolating the consequences of different decision-making schemes, a decision-making scheme with better overall benefits and lower risks is formed, significantly reducing equipment operation and maintenance risks. This invention adjusts decisions based on the real-time status of equipment, historical knowledge, resource conditions, and production dynamics, rather than being static and fixed. It provides dynamic and personalized intelligent maintenance decisions, offering tailored optimized operation and maintenance strategies for each piece of equipment, enabling precise operation and maintenance. The execution results of each decision are used as new learning samples to update the knowledge graph, digital twin model, and optimize the deep learning model. As the operational data grows, the decision-making ability continuously improves and optimizes, enabling intelligent cyclical self-evolution and continuous optimization.

[0014] Other features and advantages of the invention will be set forth in the description which follows, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of the invention may be realized and obtained by means of the structures particularly pointed out in this patent application.

[0015] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0016] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used in conjunction with embodiments of the invention to explain the invention and do not constitute a limitation thereof. In the drawings: Figure 1 This is a flowchart of the intelligent operation and maintenance management method for the entire life cycle of equipment based on AI deep learning in an embodiment of the present invention; Figure 2 This is a diagram of an embodiment of the AI-based deep learning-based intelligent operation and maintenance management system for the entire life cycle of equipment according to the present invention. Figure 3 This is another embodiment of the AI-based deep learning-based intelligent operation and maintenance management system for the entire life cycle of equipment, as presented in this invention. Detailed Implementation

[0017] The preferred embodiments of the present invention will be described below with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are for illustration and explanation only and are not intended to limit the present invention.

[0018] like Figure 1 As shown, this embodiment of the invention provides an intelligent operation and maintenance management method for the entire life cycle of equipment based on AI deep learning, including: S10 collects multi-source heterogeneous data generated at each stage of the equipment's entire life cycle, and then uses semantic deep exploration knowledge network to aggregate and fuse related equipment entities and construct a triplet knowledge network to obtain the equipment knowledge graph network. S20 constructs a high-fidelity digital twin of the equipment through digital twin virtual mapping and equipment experimental sandbox; and performs equipment operation simulation based on real-time monitoring data to obtain digital twin simulation data. S30 uses deep intelligent machine learning analysis based on digital twin simulation data to assess and predict equipment health status and obtain equipment degradation trajectory life prediction information. S40 queries equipment knowledge graph information based on the equipment knowledge graph network, calibrates digital twin simulation data, and infers and generates dynamic maintenance decisions for equipment by referring to equipment degradation trajectory life prediction information.

[0019] One embodiment involves collecting multi-source heterogeneous data generated at various stages throughout the equipment's lifecycle, and then using a semantic deep exploration knowledge network to aggregate and fuse related equipment entities and construct a triplet knowledge network to obtain an equipment knowledge graph network. Specifically, this includes: Collect multi-source heterogeneous data sources of equipment generated at each stage of the entire life cycle of historical equipment. By using extraction, transformation, and loading tools, unstructured semantics are extracted from multi-source heterogeneous data sources of equipment to obtain unstructured semantic data; natural language parsing tools are used to parse the unstructured semantic data, and data feature extraction and automatic feature recognition and classification are performed to extract equipment entity information and information relationships; time series data processing tools are used to process equipment monitoring sensor data and extract equipment monitoring feature events. Based on graph databases, a triplet knowledge network consisting of equipment entity information, information relationships, and equipment virtual entities is constructed to form an equipment knowledge graph network. The equipment's multi-source heterogeneous data sources include: equipment bill of materials, structural simulation data, production and manufacturing data, quality inspection data, sensor time-series data, operation and maintenance logs, fault records, maintenance manuals, expert experience documents, and component data sources.

[0020] In one embodiment, a high-fidelity digital twin of the equipment is constructed by using digital twin virtual mapping and an equipment experimental sandbox; and equipment operation simulation is performed based on real-time monitoring data to obtain digital twin simulation data, specifically including: By combining equipment knowledge graph networks, digital twin virtual mapping, and equipment experimental sandboxes, and by integrating digital twin modeling tools, a high-fidelity digital twin of the equipment is constructed that corresponds to the actual equipment. Establish a real-time data connection with the equipment entity, map the monitoring data of the equipment monitoring sensors to the virtual sensors corresponding to the digital twin, and drive the digital twin to keep synchronized with the equipment entity. Upon receiving a simulation command or abnormal triggering condition, a high-fidelity simulation is initiated; when an abnormal stress is detected in a certain part of the equipment, a transient dynamic simulation is initiated to simulate the crack propagation process of that part under the current working conditions and predict potential failures; before maintenance decisions are made, the impact of different maintenance operations on the overall system performance is simulated; the equipment digital twin is presented in a three-dimensional visualization form, displaying the equipment status, alarm information, and simulation results in a three-dimensional manner; immersive equipment status monitoring is conducted; the equipment digital twin also includes: an equipment mechanical model, an information transmission and control model, and an equipment operation rule model; the equipment mechanical model includes a CAD design model; the information transmission and control model includes a mechanical, thermal, and fluid dynamics CAE simulation model; the equipment operation rule model includes equipment control logic and dynamic virtual simulation of equipment operation rules.

[0021] One embodiment involves performing deep intelligent machine learning analysis based on digital twin simulation data to assess and predict equipment health status and obtain equipment degradation trajectory life prediction information, specifically including: Based on digital twin simulation data, a deep neural network classification and regression model is used to calculate the evolution of equipment health status and analyze equipment health degradation, thereby obtaining information on equipment health evolution and degradation analysis. Based on the equipment health evolution and degradation analysis information and referring to the equipment's historical state sequence, the future performance degradation trajectory and remaining useful life of the equipment are predicted, and the uncertainty range of the prediction is given to obtain the equipment degradation trajectory life prediction information. By training a large amount of historical normal data and fault data, a deep neural network classification and regression model is formed, which can output the real-time health status score or degradation stage label of the equipment based on the extracted equipment monitoring feature events.

[0022] One embodiment involves querying equipment knowledge graph information based on the equipment knowledge graph network, calibrating digital twin simulation data, and referring to equipment degradation trajectory life prediction information to infer and generate dynamic maintenance decisions for the equipment. Specifically, this includes: It receives and automatically aligns equipment monitoring status information, digital twin simulation data, and equipment-related knowledge and equipment degradation trajectory life prediction information retrieved from the equipment knowledge graph network. Employing a deep reinforcement learning framework, the system continuously learns and interacts with the virtual environment built from digital twins and knowledge graphs to optimize operational decisions and generate operational decision recommendations. It outputs operation and maintenance decision suggestions, provides analysis of decision basis, cites similar historical cases, displays comparison charts of different decision consequences derived from simulation, and provides intelligent guidance for operation and maintenance execution. The intelligent guidance for operation and maintenance includes: in-depth analysis based on real-time monitoring data and historical case parameter benchmarks, providing detailed decision-making basis analysis, listing the core indicators used for analysis and their current values ​​compared with thresholds; root cause reasoning to explain the inferred root causes, and cost-benefit analysis to assess the resource input and risks required to implement the decision; such as avoiding the risk of a potential 30-minute equipment outage; automatically matching and pushing similar historical failure cases from the knowledge base, after anonymization, to provide a basis for current decisions and avoid repeating mistakes; simulating and deducing the consequences of different decisions, and generating intuitive comparison charts of different decision consequences; using a multi-indicator dashboard to compare multiple strategy dimensions, clearly displaying the simulated trends of key indicators over a period of time under different decision paths, making abstract decision consequences visual and quantifiable; providing end-to-end intelligent execution guidance operation steps, command information, and backtracking schemes after the decision is determined; and providing risk warnings and risk avoidance intelligent guidance to alert potential risk points and avoidance measures during operation. By tracking and monitoring the status of multiple targets, the system optimizes operation and maintenance decisions and statistically calculates multiple optimization objectives, including maintenance costs, downtime losses, safety risks, and performance recovery. Through continuous interaction and learning with knowledge graphs and digital twin virtual environments, the system selects the operation and maintenance actions that yield the greatest long-term benefits under monitored conditions, i.e., setting the optimal maintenance decision for the cycle. The system outputs decision suggestions and provides analysis of the decision basis, cites similar historical cases, and displays comparison charts of different decision consequences derived from simulations to provide intelligent guidance for operation and maintenance execution, significantly increasing equipment operation and maintenance safety and significantly improving operation and maintenance efficiency.

[0023] like Figure 2As shown; the present invention is an intelligent operation and maintenance management system for the entire life cycle of equipment based on AI deep learning, comprising: The equipment knowledge graph module collects multi-source heterogeneous data generated at each stage of the equipment's entire life cycle, and then uses semantic deep exploration of the knowledge network to aggregate and fuse related equipment entities and construct a triplet knowledge network to obtain the equipment knowledge graph network. The equipment digital twin simulation module constructs a high-fidelity digital twin of the equipment through digital twin virtual mapping and equipment experimental sandbox; and performs equipment operation simulation and deduction based on real-time monitoring data to obtain digital twin simulation and deduction data. The equipment status analysis and prediction module performs deep intelligent analysis using machine learning based on digital twin simulation data to assess and predict the health status of the equipment and obtain information on the predicted lifespan of the equipment's degradation trajectory. The equipment dynamic maintenance decision generation module queries equipment knowledge graph information based on the equipment knowledge graph network, calibrates digital twin simulation data, and refers to equipment degradation trajectory life prediction information to infer and generate equipment dynamic maintenance decisions.

[0024] In one embodiment, the equipment knowledge graph module includes: The data source acquisition submodule collects multi-source heterogeneous data sources generated by the equipment at each stage of the equipment's entire life cycle. The data processing submodule extracts unstructured semantics from the equipment's multi-source heterogeneous data sources using an extraction, transformation, and loading tool, and obtains unstructured semantic data. It then uses a natural language parsing tool to parse the unstructured semantic data, performs data feature extraction and automatic feature recognition and classification, and extracts equipment entity information and information relationships. Finally, it uses a time-series data processing tool to process equipment monitoring sensor data and extract equipment monitoring feature events. The knowledge construction submodule constructs a triplet knowledge network based on a graph database, consisting of equipment entity information, information relationships, and equipment virtual entities, thus forming an equipment knowledge graph network. The equipment's multi-source heterogeneous data sources include: equipment bill of materials, structural simulation data, production and manufacturing data, quality inspection data, sensor time-series data, operation and maintenance logs, fault records, maintenance manuals, expert experience documents, and component data sources.

[0025] In one embodiment, the digital twin simulation module includes: The digital twin model submodule, combined with the equipment knowledge graph network, constructs a high-fidelity digital twin of the equipment entity through digital twin virtual mapping and equipment experimental sandbox, and by integrating digital twin modeling tools. The data-driven and synchronization submodule establishes a real-time data connection with the equipment entity, maps the monitoring data of the equipment monitoring sensors to the virtual sensors corresponding to the digital twin, and drives the digital twin to keep synchronized with the equipment entity. The simulation engine submodule initiates high-fidelity simulation upon receiving a simulation command or abnormal triggering condition; when abnormal stress is detected in a certain part of the equipment, it initiates transient dynamic simulation to simulate the crack propagation process of that part under the current working conditions and predict potential failures; before maintenance decisions are made, it simulates the impact of different maintenance operations on the overall system performance; it presents the equipment digital twin in a three-dimensional visualization, displaying the equipment status, alarm information, and simulation results in a three-dimensional manner; and it provides immersive equipment status monitoring; the equipment digital twin also includes: an equipment mechanical model, an information transmission and control model, and an equipment operation rule model; the equipment mechanical model includes a CAD design model; the information transmission and control model includes a mechanical, thermal, and fluid dynamics CAE simulation model; and the equipment operation rule model includes dynamic virtual simulation of equipment control logic and equipment operation rules.

[0026] In one embodiment, the equipment status analysis and prediction module includes: The equipment health analysis submodule uses a deep neural network classification and regression model based on digital twin simulation data to calculate the evolution of equipment health status and analyze equipment health degradation, thereby obtaining information on equipment health evolution and degradation analysis. The status prediction submodule, based on the equipment health evolution and degradation analysis information and referring to the equipment's historical status sequence, predicts the future performance degradation trajectory and remaining useful life of the equipment, and provides the uncertainty range of the prediction, thereby obtaining the equipment degradation trajectory life prediction information. By training a large amount of historical normal data and fault data, a deep neural network classification and regression model is formed, which can output the real-time health status score or degradation stage label of the equipment based on the extracted equipment monitoring feature events.

[0027] In one embodiment, the equipment dynamic maintenance decision generation module includes: The multi-source information alignment submodule receives and automatically aligns equipment monitoring status information, digital twin simulation data, and equipment-related knowledge and equipment degradation trajectory life prediction information queried from the equipment knowledge graph network. The decision model submodule adopts a deep reinforcement learning framework. Through a virtual environment built from digital twins and knowledge graphs, it continuously learns and optimizes operation and maintenance decisions to form operation and maintenance decision recommendations. The decision output and analysis submodule outputs operation and maintenance decision suggestions, provides analysis of decision basis, cites similar historical cases, displays comparison charts of different decision consequences derived from simulation, and provides intelligent guidance for operation and maintenance execution. The intelligent guidance for operation and maintenance includes: in-depth analysis based on real-time monitoring data and historical case parameter benchmarks, providing detailed decision-making basis analysis, listing the core indicators used for analysis and their current values ​​compared with thresholds; root cause reasoning to explain the inferred root causes, and cost-benefit analysis to assess the resource input and risks required to implement the decision; such as avoiding the risk of a potential 30-minute equipment outage; automatically matching and pushing similar historical failure cases from the knowledge base, after anonymization, to provide a basis for current decisions and avoid repeating mistakes; simulating and deducing the consequences of different decisions, and generating intuitive comparison charts of different decision consequences; using a multi-indicator dashboard to compare multiple strategy dimensions, clearly displaying the simulated trends of key indicators over a period of time under different decision paths, making abstract decision consequences visual and quantifiable; providing end-to-end intelligent execution guidance operation steps, command information, and backtracking schemes after the decision is determined; and providing risk warnings and risk avoidance intelligent guidance to alert potential risk points and avoidance measures during operation. The monitoring status includes: equipment physical monitoring status, equipment operation simulation results, equipment available resources and spare parts, and equipment update production plan; the operation and maintenance actions include: multiple maintenance options, continued operation after maintenance, adjustment of process parameters, planned minor maintenance, immediate shutdown for major maintenance, and replacement of parts. By tracking and monitoring the status of multiple targets, the system optimizes operation and maintenance decisions and statistically calculates multiple optimization objectives, including maintenance costs, downtime losses, safety risks, and performance recovery. Through continuous interaction and learning with knowledge graphs and digital twin virtual environments, the system selects the operation and maintenance actions that yield the greatest long-term benefits under monitored conditions, i.e., setting the optimal maintenance decision for the cycle. The system outputs decision suggestions and provides analysis of the decision basis, cites similar historical cases, and displays comparison charts of different decision consequences derived from simulations to provide intelligent guidance for operation and maintenance execution, significantly increasing equipment operation and maintenance safety and significantly improving operation and maintenance efficiency.

[0028] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

Claims

1. An AI deep learning-based equipment whole life cycle intelligent operation and maintenance management method, characterized in that, Comprise: Collect multi-source heterogeneous data generated in each stage of the whole life cycle of the equipment, and explore the knowledge network through semantic depth, gather and fuse related equipment entities, and build a triple knowledge network to obtain an equipment knowledge graph network; Through digital twin virtual mapping and equipment experiment sand table, build an equipment digital twin corresponding to the equipment entity with high fidelity; and perform equipment operation simulation and deduction according to real-time monitoring data to obtain digital twin simulation and deduction data; According to the digital twin simulation and deduction data, perform machine learning deep intelligent analysis, evaluate and predict the equipment health status, and obtain equipment degradation trajectory life prediction information; According to the equipment knowledge graph network, query the equipment knowledge graph information, calibrate the digital twin simulation and deduction data, refer to the equipment degradation trajectory life prediction information, and reason to generate equipment dynamic maintenance decision.

2. The AI deep learning-based equipment full-life-cycle intelligent operation and maintenance management method of claim 1, wherein Collect multi-source heterogeneous data generated in each stage of the whole life cycle of the equipment, and explore the knowledge network through semantic depth, gather and fuse related equipment entities, and build a triple knowledge network to obtain an equipment knowledge graph network, specifically comprising: Collecting equipment multi-source heterogeneous data sources generated in each stage of the whole life cycle of the historical equipment; Through the extraction, conversion and loading tool, extract unstructured semantics from the equipment multi-source heterogeneous data sources to obtain unstructured semantic data; use natural language parsing tool to parse unstructured semantic data, and perform data feature extraction and feature recognition automatic classification to extract equipment entity information and information relationship; use time series data processing tool to process equipment monitoring sensor data and extract equipment monitoring feature events; Based on the graph database, a triple knowledge network composed of equipment entity information-information relationship-equipment virtual body is built to form an equipment knowledge graph network. 3.The AI deep learning based equipment whole life cycle intelligent operation and maintenance management method of claim 1, wherein, Through digital twin virtual mapping and equipment experiment sand table, build an equipment digital twin corresponding to the equipment entity with high fidelity; and perform equipment operation simulation and deduction according to real-time monitoring data to obtain digital twin simulation and deduction data, specifically comprising: Integrating the equipment knowledge graph network, through digital twin virtual mapping and equipment experiment sand table, through the integrated digital twin modeling tool, build an equipment digital twin corresponding to the equipment entity with high fidelity; Establish real-time data connection with the equipment entity, map the monitoring data of the equipment monitoring sensor to the virtual sensor corresponding to the digital twin, and drive the digital twin and the equipment entity to keep synchronization; Upon receiving simulation instructions or abnormal triggering conditions, start high-fidelity simulation; when monitoring the stress anomaly of a part of the equipment, start transient dynamics simulation to simulate the crack propagation process of the part under the current working condition and predict potential failure; before maintenance decision, simulate the impact of different maintenance operations on the overall performance of the system; Present the equipment digital twin in three-dimensional visual form, stereoscopically display the equipment state, alarm information and simulation results, and perform immersive equipment state monitoring.

4. The AI deep learning-based equipment full-life-cycle intelligent operation and maintenance management method of claim 1, wherein, According to the digital twin simulation and deduction data, perform machine learning deep intelligent analysis, evaluate and predict the equipment health status, and obtain equipment degradation trajectory life prediction information, specifically comprising: According to the digital twin simulation deduction data, the equipment health state evolution is calculated and the equipment health degradation analysis is performed through a deep neural network classification regression model, and equipment health evolution and degradation analysis information is obtained; According to the equipment health evolution and degradation analysis information, the future performance degradation trajectory and the remaining useful life of the equipment are predicted by referring to the historical state sequence of the equipment, and the uncertainty range of the prediction is given, and equipment degradation trajectory life prediction information is obtained.

5. The AI deep learning-based equipment full-life-cycle intelligent operation and maintenance management method of claim 1, wherein, According to the equipment knowledge graph network, the equipment knowledge graph information is queried, the digital twin simulation deduction data is calibrated, the equipment degradation trajectory life prediction information is referred to, and the equipment dynamic maintenance decision is reasoned and generated, specifically including: Receiving and automatically aligning the equipment monitoring state information, the digital twin simulation deduction data, and the equipment related knowledge and equipment degradation trajectory life prediction information queried from the equipment knowledge graph network; Using a deep reinforcement learning framework, continuously interacting and learning in a virtual environment constructed by a digital twin and a knowledge graph, optimizing operation and maintenance decisions, and forming operation and maintenance decision suggestions; Output operation and maintenance decision suggestions, provide decision basis analysis, quote similar historical cases, show simulation deduction different decision consequences comparison chart, and perform operation and maintenance intelligent guidance.

6. An AI deep learning-based equipment whole life cycle intelligent operation and maintenance management system, characterized in that, Including: The equipment knowledge graph module collects multi-source heterogeneous data generated in each stage of the equipment life cycle, and through semantic deep exploration of the knowledge network, the heterogeneous data is gathered and fused to correlate the equipment entities and construct a triple knowledge network, and the equipment knowledge graph network is obtained; The equipment digital twin simulation module constructs an equipment digital twin body corresponding to the equipment entity with high fidelity through digital twin virtual mapping and equipment experiment sand table; and performs equipment operation simulation deduction according to real-time monitoring data to obtain digital twin simulation deduction data; The equipment state analysis and prediction module performs machine learning deep intelligent analysis according to the digital twin simulation deduction data, evaluates and predicts the equipment health state, and obtains equipment degradation trajectory life prediction information; The equipment dynamic maintenance decision generation module queries the equipment knowledge graph information according to the equipment knowledge graph network, calibrates the digital twin simulation deduction data, refers to the equipment degradation trajectory life prediction information, and reasons and generates the equipment dynamic maintenance decision.

7. The AI deep learning-based equipment whole life cycle intelligent operation and maintenance management system according to claim 6, characterized in that, The equipment knowledge graph module includes: The data source acquisition sub-module acquires multi-source heterogeneous data sources of equipment generated in each stage of the historical equipment life cycle; The data processing sub-module extracts unstructured semantics from the equipment multi-source heterogeneous data sources through an extraction, transformation, and loading tool to obtain unstructured semantic data; uses a natural language analysis tool to analyze the unstructured semantic data, and performs data feature extraction and feature recognition automatic classification to extract equipment entity information and information relationships; uses a time series data processing tool to process equipment monitoring sensor data and extract equipment monitoring feature events; The knowledge construction sub-module constructs a triple knowledge network composed of equipment entity information-information relationship-equipment virtual body based on a graph database to form an equipment knowledge graph network. 8.The AI deep learning based equipment whole life cycle intelligent operation and maintenance management system according to claim 6, characterized in that, The equipment digital twin simulation module includes: A digital twin model submodule, in combination with an equipment knowledge graph network, constructs an equipment digital twin corresponding to the equipment entity in high fidelity through digital twin virtual mapping and an equipment experiment sand table, and constructs the digital twin through an integrated digital twin modeling tool; A data driving and synchronization submodule establishes a real-time data connection with the equipment entity, maps the monitoring data of the equipment monitoring sensor to the virtual sensor corresponding to the digital twin, and drives the digital twin to keep synchronization with the equipment entity; A simulation engine submodule starts high-fidelity simulation upon receiving simulation instructions or abnormal triggering conditions; when an abnormal stress of a part of the equipment is monitored, transient dynamics simulation is started to simulate the crack propagation process of the part under the current working condition and predict potential failures; before maintenance decision-making, the influence of different maintenance operations on the overall performance of the system is simulated; A visualization submodule presents the equipment digital twin in a three-dimensional visual form, stereoscopically displays the equipment state, alarm information, and simulation results, and performs immersive equipment state monitoring. 9.The AI deep learning based equipment whole life cycle intelligent operation and maintenance management system according to claim 6, characterized in that, The equipment state analysis and prediction module includes: An equipment health analysis submodule performs equipment health state evolution calculation and equipment health degradation analysis through a deep neural network classification regression model based on digital twin simulation deduction data, and obtains equipment health evolution and degradation analysis information; A state prediction submodule predicts the future performance degradation trajectory and remaining useful life of the equipment based on the equipment health evolution and degradation analysis information, refers to the historical state sequence of the equipment, and gives the uncertainty range of the prediction, and obtains equipment degradation trajectory and life prediction information. 10.The AI deep learning based equipment whole life cycle intelligent operation and maintenance management system according to claim 6, characterized in that, The equipment dynamic maintenance decision-making module includes: A multi-source information alignment submodule receives and automatically aligns equipment monitoring state information, digital twin simulation deduction data, and equipment-related knowledge and equipment degradation trajectory and life prediction information queried from the equipment knowledge graph network; A decision model submodule adopts a deep reinforcement learning framework, continuously interacts and learns in a virtual environment constructed by the digital twin and the knowledge graph, optimizes operation and maintenance decision-making, and forms operation and maintenance decision-making suggestions; A decision output and analysis submodule outputs operation and maintenance decision-making suggestions, provides decision basis analysis, references similar historical cases, displays a comparison chart of different decision consequences simulated and deduced, and performs operation and maintenance execution intelligent guidance.

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