Operation and maintenance management system based on digital twinborn technology

Abstract

The invention discloses an operation and maintenance management system based on a digital twinning technology, and relates to the technical field of digital twinning operation and maintenance, and the system comprises the steps: collecting multi-dimensional real-time operation and maintenance data through a sensor network disposed on a physical entity; bidirectional data interaction with a physical entity is completed through a standardized interface; performing multi-source data collaborative governance on the multi-dimensional operation and maintenance data derived from the multi-dimensional attribute features of the physical entities; mapping the running state of the physical entity in the virtual space; a full-period intelligent operation and maintenance decision is promoted to be converted from passive response to active prevention; and operation and maintenance management of the digital twinning technology is completed. According to the method, high-fidelity, low-delay and two-way closed-loop linkage of the physical entity and the virtual space is realized, the accuracy and timeliness of fault prediction, root cause positioning and maintenance response are improved, and the digital twin is ensured to be reliable, credible and available in the whole life cycle, so that the intelligentization, automation and refinement levels of operation and maintenance management of a complex system are improved.

Description

Technical Field

[0001] This invention relates to the field of digital twin operation and maintenance technology, and in particular to an operation and maintenance management system based on digital twin technology. Background Technology

[0002] With the deepening of digital transformation, society's demand for intelligent operation and maintenance of infrastructure, industrial equipment, and complex systems is growing rapidly. Various critical facilities, such as power systems, rail transit, and intelligent manufacturing production lines, are rapidly developing towards high integration and automation, significantly increasing the complexity and real-time requirements of operation and maintenance management. At the same time, users have increasingly higher expectations for system reliability, security, and predictive maintenance capabilities. Traditional methods relying on manual inspections and static monitoring are no longer sufficient to meet the urgent needs of modern operation and maintenance scenarios for efficiency, accuracy, and foresight.

[0003] In existing technologies, most operation and maintenance management systems still rely on historical data statistics or simple threshold alarm mechanisms, lacking the ability to map and dynamically simulate the operating status of physical entities in real time. Furthermore, information silos exist severely between subsystems, hindering global collaborative analysis and intelligent decision-making. In addition, traditional methods generally lag behind in fault prediction, root cause analysis, and emergency response, failing to effectively support the full lifecycle management of complex systems. These shortcomings lead to low operation and maintenance efficiency, frequent false alarms and missed alarms, and make it difficult to shift from passive response to proactive prevention. Summary of the Invention

[0004] In view of the problems existing in the operation and maintenance management system based on digital twin technology, this invention is proposed.

[0005] Therefore, the problem that this invention aims to solve is that existing operation and maintenance management systems lack the ability to map the operating status of physical entities in real time, conduct global collaborative analysis, and proactively predict the status, making it difficult to achieve efficient, accurate, and closed-loop intelligent operation and maintenance.

[0006] To solve the above-mentioned technical problems, the present invention provides the following technical solution:

[0007] In a first aspect, embodiments of the present invention provide an operation and maintenance management system based on digital twin technology, which includes a physical entity perception module for collecting multi-dimensional real-time operation and maintenance data through a sensor network deployed on a physical entity;

[0008] The digital twin modeling module is used to construct a one-to-one virtual digital representation based on the multi-dimensional attribute characteristics of physical entities, and to complete bidirectional data interaction with physical entities through standardized interfaces.

[0009] The multi-source data fusion module is used for collaborative governance of multi-source data on multi-dimensional operation and maintenance data derived from the multi-dimensional attribute characteristics of physical entities.

[0010] The dynamic simulation state mapping module is used to drive digital twin modeling with multi-source fused data and map the operating state of physical entities in virtual space.

[0011] The intelligent analysis and decision support module is used to promote the transformation of full-cycle intelligent operation and maintenance decision-making from passive response to proactive prevention, based on the mapped physical entity's operating status and combined with full-cycle intelligent operation and maintenance decision-making.

[0012] The collaborative operation and maintenance execution module is used to transform intelligent analysis results into executable operation and maintenance instructions. Through integration with the operation and maintenance execution carrier terminal, it completes the operation and maintenance management of digital twin technology.

[0013] As a preferred embodiment of the operation and maintenance management system based on digital twin technology described in this invention, the physical entity perception module includes a sensor node deployment submodule, a multimodal data acquisition submodule, an edge preprocessing submodule, and a real-time communication transmission submodule.

[0014] The sensor node deployment submodule is used to deploy a covered sensor network node array on or inside the surface of a physical entity, based on the sensor network of the physical entity.

[0015] The multimodal data acquisition submodule is used to synchronously acquire heterogeneous signals reflecting physical entities through the sensor network node array to form a raw multidimensional real-time operation and maintenance data stream;

[0016] The edge preprocessing submodule is used to perform edge-side data normalization processing on the original multidimensional real-time operation and maintenance data stream in the edge computing unit close to the sensing node.

[0017] The real-time communication transmission submodule is used to upload the multi-dimensional real-time operation and maintenance data stream, which has been preprocessed at the edge, to the upper-layer module of the system through the industrial communication protocol, so as to complete the delivery of the data stream within the time window.

[0018] As a preferred embodiment of the operation and maintenance management system based on digital twin technology described in this invention, the digital twin modeling module includes a multi-dimensional attribute parsing submodule, a virtual representation construction submodule, an interface protocol adaptation submodule, and a two-way interactive control submodule.

[0019] The multidimensional attribute parsing submodule is used to parse the multidimensional attribute features of physical entities, extract the three-dimensional attribute system of physical entities, and transform the three-dimensional attribute system of physical entities into standardized attribute metadata that can be recognized and processed by virtual space.

[0020] The virtual representation construction submodule is used to generate virtual digital representations that correspond one-to-one with physical entities in the virtual space based on standardized attribute metadata.

[0021] The interface protocol adaptation submodule is used to configure and encapsulate standardized data interfaces that support multiple industry standards according to the physical side integrated interface environment to which the physical entity is connected.

[0022] The bidirectional interactive control submodule is used to receive operational data from physical entities through a standardized data interface to drive the state update of the virtual digital representation, and to send the virtual end control output set in the virtual space back to the physical entity, thus completing the closed-loop bidirectional data interaction between the virtual and the real.

[0023] As a preferred embodiment of the operation and maintenance management system based on digital twin technology described in this invention, the multi-source data fusion module includes a heterogeneous data access submodule, a spatiotemporal alignment submodule, a semantic mapping submodule, and a fused data output submodule.

[0024] The heterogeneous data access submodule is used to receive multi-dimensional real-time operation and maintenance data streams from the physical entity perception module, identify the data source feature metadata of the multi-dimensional real-time operation and maintenance data streams, and establish a unified data access channel.

[0025] The spatiotemporal alignment submodule is used to synchronize the multi-source data in the time dimension and to integrate the spatial reference based on the topological relationship in the three-dimensional attribute system of physical entities in the spatial dimension.

[0026] The semantic mapping submodule is used to map the original fields in various multi-source data to standard concept nodes in a consistent semantic space according to the unified semantic specification of the operation and maintenance domain, thereby eliminating semantic barriers.

[0027] The fusion data output submodule is used to integrate multi-source data after spatiotemporal alignment and semantic mapping to generate a fusion dataset, which is then output to the dynamic simulation state mapping module in a standardized format.

[0028] As a preferred embodiment of the operation and maintenance management system based on digital twin technology described in this invention, the dynamic simulation state mapping module includes a fusion data parsing submodule, a virtual state driving submodule, a real-time behavior simulation submodule, and a mapping consistency verification submodule.

[0029] The fusion data parsing submodule is used to receive the fusion dataset output from the multi-source data fusion module, parse the timestamps, spatial benchmark integration results and semantic standardization fields contained in the fusion dataset, and structure the fusion dataset into a state input vector of virtual digital representation.

[0030] The virtual state driving submodule is used to inject the state input vector into the constructed virtual digital representation and update the virtual state element set of the virtual digital representation according to the operation behavior rule system in the three-dimensional attribute system of the physical entity.

[0031] The real-time behavior simulation submodule is used to deduce the short-term evolution trend of the virtual state element set in the virtual space based on the updated virtual state element set and combined with the dynamic behavior rules of physical entities, and to simulate the possible evolution of the operating situation under the current working conditions.

[0032] The mapping consistency verification submodule is used to continuously compare the status output of the virtual digital representation with the latest multi-dimensional real-time operation and maintenance data returned by the physical entity perception module. It evaluates the mapping fidelity through a deviation detection algorithm and triggers a virtual representation parameter self-correction mechanism when the value exceeds a preset threshold.

[0033] As a preferred embodiment of the operation and maintenance management system based on digital twin technology described in this invention, the collaborative operation and maintenance execution module includes an instruction generation submodule, a carrier adaptation and scheduling submodule, a closed-loop feedback acquisition submodule, and an execution status synchronization submodule.

[0034] The instruction generation submodule is used to receive the full-cycle intelligent operation and maintenance decision results output by the intelligent analysis and decision support module, and parse the full-cycle intelligent operation and maintenance decision results and convert them into executable operation and maintenance instructions according to the preset operation and maintenance rule base.

[0035] The carrier adaptation and scheduling submodule is used to identify the operation and maintenance execution carrier terminal and adapt and deliver executable operation and maintenance instructions according to the target carrier type.

[0036] The closed-loop feedback acquisition submodule is used to monitor the execution process feedback data returned by the operation and maintenance execution carrier terminal, collect the response data during the instruction execution process, and form an execution feedback stream;

[0037] The execution status synchronization submodule is used to synchronize the status updates in the execution feedback stream back to the virtual digital representation, drive the virtual digital representation to dynamically correct the current operation and maintenance status of the physical entity, and associate the execution results with the original intelligent analysis and decision records to complete the full-link digital twin technology operation and maintenance management from decision-making to execution to status update.

[0038] As a preferred embodiment of the operation and maintenance management system based on digital twin technology described in this invention, the calculation formula of the deviation detection algorithm is as follows:

[0039]

[0040] in, Indicates the current timestamp. This indicates the number of dimensions in the set of virtual state elements participating in the comparison. This indicates that the physical entity perception module is at time [time]. The first collection Multidimensional real-time operation and maintenance data, Representing virtual digital representation at time The corresponding number Virtual state element value, Indicates the first The historical standard deviation of the data is used to normalize the dimensions. Indicates the first The weight coefficient of each element. This indicates a self-calibration mechanism that triggers the virtual representation parameters.

[0041] Secondly, embodiments of the present invention provide an operation and maintenance management method based on digital twin technology, which includes: collecting multi-dimensional real-time operation and maintenance data through a sensor network deployed on a physical entity;

[0042] Based on the multidimensional attribute characteristics of physical entities, a one-to-one virtual digital representation is constructed, and bidirectional data interaction with physical entities is completed through standardized interfaces;

[0043] Multi-source collaborative governance of multi-dimensional operation and maintenance data derived from the multi-dimensional attribute characteristics of physical entities;

[0044] The data from multiple sources is integrated to drive digital twin modeling, which maps the operating state of physical entities in virtual space.

[0045] Based on the mapped physical entity operating status, combined with full-cycle intelligent operation and maintenance decision-making, we promote the transformation of full-cycle intelligent operation and maintenance decision-making from passive response to proactive prevention.

[0046] The intelligent analysis results are transformed into executable operation and maintenance instructions, and through integration with the operation and maintenance execution carrier terminal, the operation and maintenance management of digital twin technology is completed.

[0047] Thirdly, embodiments of the present invention provide a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement any of the steps of the above-described operation and maintenance management system based on digital twin technology.

[0048] Fourthly, embodiments of the present invention provide a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program is executed by a processor, it implements any of the steps of the above-described operation and maintenance management system based on digital twin technology.

[0049] The beneficial effects of this invention are as follows: By constructing a digital twin operation and maintenance architecture that integrates perception, modeling, fusion, mapping, decision-making, and execution, this invention achieves high-fidelity, low-latency, and bidirectional closed-loop linkage between physical entities and virtual spaces. On the one hand, relying on multi-dimensional real-time operation and maintenance data collection and multi-source data collaborative governance, it effectively breaks down information silos and improves data consistency and timeliness. On the other hand, through dynamic simulation state mapping and operational status evolution deduction, the system possesses the ability to accurately mirror and predict equipment status in a short time. Based on this, combined with a full-cycle intelligent operation and maintenance decision-making and cross-carrier collaborative execution mechanism, it not only significantly improves the accuracy and timeliness of fault prediction, root cause localization, and maintenance response, but also promotes the transformation of operation and maintenance mode from passive response to proactive prevention. Simultaneously, through deviation detection algorithms and self-correction mechanisms, it continuously ensures the consistency between the virtual digital representation and the physical entity, ensuring that the digital twin is reliable, trustworthy, and usable throughout its entire lifecycle, thereby comprehensively improving the intelligence, automation, and refinement level of operation and maintenance management for complex systems. Attached Figure Description

[0050] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort. Wherein:

[0051] Figure 1 A flowchart of an operation and maintenance management system based on digital twin technology provided for an embodiment of the present invention.

[0052] Figure 2 A flowchart of a method for an operation and maintenance management system based on digital twin technology is provided for an embodiment of the present invention. Detailed Implementation

[0053] To make the above-mentioned objects, features, and advantages of the present invention more apparent and understandable, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the protection scope of the present invention.

[0054] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0055] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in different places in this specification does not necessarily refer to the same embodiment, nor is it a single or selective embodiment that is mutually exclusive with other embodiments.

[0056] This invention is described in detail with reference to the schematic diagrams. When detailing the embodiments of this invention, for ease of explanation, the cross-sectional views illustrating the device structure may be partially enlarged, not adhering to the usual scale. Furthermore, the schematic diagrams are merely examples and should not be construed as limiting the scope of protection of this invention. In actual fabrication, the three-dimensional spatial dimensions of length, width, and depth should be included.

[0057] Furthermore, in the description of this invention, it should be noted that the terms "upper," "lower," "inner," and "outer," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. These terms are used solely for the convenience of describing the invention and for simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on the invention. In addition, the terms "first," "second," or "third" are used for descriptive purposes only and should not be construed as indicating or implying relative importance.

[0058] Unless otherwise explicitly specified and limited, the terms "installation," "connection," and "joining" in this invention should be interpreted broadly. For example, they can refer to fixed connections, detachable connections, or integral connections; similarly, they can refer to mechanical connections, electrical connections, or direct connections, or indirect connections through an intermediate medium, or internal connections between two components. Those skilled in the art can understand the specific meaning of the above terms in this invention based on the specific circumstances.

[0059] Example

[0060] Reference Figure 1 and Figure 2 This is the first embodiment of the present invention, which provides an operation and maintenance management system based on digital twin technology, including:

[0061] S1: Physical entity perception module, used to collect multi-dimensional real-time operation and maintenance data through a sensor network deployed on physical entities.

[0062] The physical entity perception module includes a sensor node deployment submodule, a multimodal data acquisition submodule, an edge preprocessing submodule, and a real-time communication transmission submodule.

[0063] The sensor node deployment submodule is used to deploy a covered sensor network node array on or inside the surface of a physical entity, based on the sensor network of the physical entity.

[0064] The multimodal data acquisition submodule is used to synchronously acquire heterogeneous signals reflecting physical entities through the sensor network node array, forming a raw multidimensional real-time operation and maintenance data stream;

[0065] The edge preprocessing submodule is used to perform edge-side data normalization processing on the raw multi-dimensional real-time operation and maintenance data stream in the edge computing unit close to the sensing node;

[0066] The real-time communication transmission submodule is used to upload the multi-dimensional real-time operation and maintenance data stream, which has been preprocessed at the edge, to the upper-level module of the system through the industrial communication protocol, so as to complete the delivery of the data stream within the time window.

[0067] Furthermore, the physical entity perception module systematically completes the entire process of data acquisition and preprocessing from the physical world to the digital space through four collaborative sub-modules. First, the sensor node deployment submodule scientifically deploys a comprehensive and complementary array of sensor network nodes on or inside the physical entity, based on its structural characteristics, key component distribution, and operating environment, ensuring the observability of the device's full-dimensional status. Next, the multimodal data acquisition submodule synchronously triggers various sensors to acquire heterogeneous signals, including vibration, temperature, current, pressure, acoustics, and images, in real time, integrating them into a raw, multi-dimensional, real-time operational data stream with a unified time reference. Then, the edge preprocessing submodule, located in an edge computing unit near the data source, performs edge-side data normalization processing on the raw data stream, including filtering, noise reduction, format standardization, and timestamp alignment, effectively improving data quality and reducing transmission load. Finally, the real-time communication transmission submodule uses highly reliable, low-latency industrial communication protocols (such as TSN, 5G URLLC, or MQTT-SN) to upload the normalized data stream completely and orderly to the upper-layer modules of the system within a strict timeframe, providing a high-quality, timely data input foundation for subsequent digital twin modeling and state mapping.

[0068] Furthermore, the physical entity sensing module constructs a complete link from physical signal acquisition to digital data delivery in an end-to-end, layered, and progressive manner. During the deployment phase, the sensor node deployment submodule does not simply install sensors, but rather optimizes the configuration of sensor types (such as accelerometers, infrared thermometers, current transformers, acoustic emission probes, etc.), quantities, locations, and sampling frequencies based on the physical entity's geometry, functional zoning, and typical fault modes, forming a three-dimensional sensing network covering key monitoring points while balancing redundancy and cost. In the operation phase, the multimodal data acquisition submodule ensures that different physical quantities (such as mechanical vibration and electrical parameters) are synchronously captured within the same time window through hardware synchronization or software timestamp alignment mechanisms, thereby generating a raw, multi-dimensional, real-time operation and maintenance data stream with spatiotemporal consistency. Following this, the edge preprocessing submodule, located near the sensor... Lightweight yet efficient preprocessing operations are performed on the local edge nodes of the device, including removing abnormal pulses, suppressing environmental noise, unifying data encoding formats (such as converting analog quantities to standard floating-point numbers), and adding precise and aligned time tags to data from different sensors, thus completing edge-side data normalization. Finally, the real-time communication transmission submodule dynamically selects appropriate transmission protocols and QoS strategies based on the network conditions and data priorities of the industrial site, and reliably uploads the normalized high-quality data stream to the upper layer of the system in a deterministic and low-latency manner. This ensures that the digital twin can accurately model and map its state based on real, complete, and timely physical world inputs, thereby laying a solid data foundation for the entire intelligent operation and maintenance system.

[0069] S2: Digital Twin Modeling Module, used to construct a one-to-one virtual digital representation based on the multi-dimensional attribute characteristics of physical entities, and to complete bidirectional data interaction with physical entities through standardized interfaces.

[0070] The digital twin modeling module includes a multi-dimensional attribute parsing submodule, a virtual representation construction submodule, an interface protocol adaptation submodule, and a two-way interactive control submodule.

[0071] The multidimensional attribute parsing submodule is used to parse the multidimensional attribute features of physical entities, extract the three-dimensional attribute system of physical entities, and transform the three-dimensional attribute system of physical entities into standardized attribute metadata that can be recognized and processed by virtual space.

[0072] The virtual representation construction submodule is used to generate virtual digital representations that correspond one-to-one with physical entities in the virtual space based on standardized attribute metadata.

[0073] The interface protocol adaptation submodule is used to configure and encapsulate standardized data interfaces that support multiple industry standards based on the physical side integrated interface environment to which the physical entity is connected.

[0074] The bidirectional interactive control submodule is used to receive operational data from physical entities through a standardized data interface to drive the state update of the virtual digital representation, and to send the virtual end control output set in the virtual space back to the physical entity, thus completing the closed-loop bidirectional data interaction between the virtual and the real.

[0075] Furthermore, the digital twin modeling module systematically constructs a digital mapping foundation for structural consistency, semantic alignment, and smooth interaction between physical entities and virtual space through four closely cooperating sub-modules. First, the multi-dimensional attribute analysis sub-module deeply analyzes the inherent characteristics of physical entities in three dimensions: structure, function, and behavior. This includes their geometric topological relationships, logical dependencies between components, and dynamic operational rules, forming a complete three-dimensional attribute system for the physical entity. This system is then transformed into a set of standardized attribute metadata that is structurally clear, semantically explicit, and can be directly invoked by the computing engine. Based on this metadata, the virtual representation construction sub-module accurately reproduces the static configuration and dynamic mechanisms of the physical entity in the virtual space, generating a virtual digital representation that strictly corresponds one-to-one with the physical entity in terms of form, logic, and response characteristics, ensuring that it possesses the capabilities of state-driven, behavior-simulated, and evolution-deducible. To achieve virtual-physical interoperability, the interface protocol adaptation sub-module automatically identifies and encapsulates OPC adaptations for the actual industrial environment (such as the physical-side integrated interface environment composed of PLC control systems, SCADA platforms, or IoT gateways) connected to the physical entity. Standardized data interfaces for various mainstream industrial communication protocols such as UA, Modbus, Profinet, and MQTT eliminate access barriers caused by protocol heterogeneity. Finally, relying on the above-mentioned standardized interfaces, the bidirectional interactive control submodule receives multi-dimensional operational data from physical entities in real time, continuously driving the synchronous update of the virtual digital representation's state. On the other hand, it securely and reliably sends back control commands, parameter adjustments, or intervention strategies (i.e., virtual control output sets) generated in the virtual space to physical entities, thereby establishing a closed-loop bidirectional data interaction channel for perception, mapping, decision-making, and feedback, providing solid support for subsequent dynamic simulation, intelligent analysis, and collaborative execution.

[0076] S3: Multi-source data fusion module, used for multi-source collaborative governance of multi-dimensional operation and maintenance data derived from the multi-dimensional attribute characteristics of physical entities.

[0077] The multi-source data fusion module includes a heterogeneous data access submodule, a spatiotemporal alignment submodule, a semantic mapping submodule, and a fused data output submodule.

[0078] The heterogeneous data access submodule is used to receive multi-dimensional real-time operation and maintenance data streams from the physical entity perception module, identify the data source feature metadata of the multi-dimensional real-time operation and maintenance data streams, and establish a unified data access channel.

[0079] The spatiotemporal alignment submodule is used to synchronize the multi-source data in the time dimension and to integrate the spatial reference based on the topological relationship in the three-dimensional attribute system of physical entities in the spatial dimension.

[0080] The semantic mapping submodule is used to map the original fields in various multi-source data to standard concept nodes in a consistent semantic space based on the unified semantic specification of the operation and maintenance domain, thereby eliminating semantic barriers.

[0081] The fusion data output submodule is used to integrate multi-source data after spatiotemporal alignment and semantic mapping to generate a fusion dataset, which is then output to the dynamic simulation state mapping module in a standardized format.

[0082] Furthermore, the multi-source data fusion module, through four interconnected sub-modules, constructs a complete processing pipeline from heterogeneous data access to high-quality fusion output, effectively solving the information silo problem caused by diverse data sources, inconsistent formats, and semantic fragmentation in traditional operation and maintenance systems. First, the heterogeneous data access sub-module uniformly receives multi-dimensional real-time operation and maintenance data streams from the physical entity sensing module and automatically identifies the data source characteristic metadata attached to each data stream, including its source device, data type, sampling frequency, and communication protocol. Based on this, it dynamically establishes a highly compatible and scalable unified access channel, ensuring that heterogeneous data generated by various sensors, control systems, and edge nodes can be reliably aggregated. Subsequently, the spatiotemporal alignment sub-module performs refined processing on these multi-source data: in the time dimension, through high-precision clock synchronization or interpolation alignment mechanisms, it unifies data with different sampling rates or transmission delays to a consistent time base; in the spatial dimension, based on the component topology and coordinate system defined in the physical entity's three-dimensional attribute system, it maps sensor data scattered in different locations to a unified spatial reference frame, completing the spatial alignment. Quasi-integration; on this basis, the semantic mapping submodule, based on pre-built semantic specifications in the operation and maintenance domain (such as equipment status terminology library, fault code ontology, unit standard system, etc.), uniformly maps the original fields (such as temp_val, temperature value, TEMP) in each source data to standard concept nodes (such as bearing temperature) in a consistent semantic space, completely eliminating semantic ambiguity caused by naming habits, unit systems, or contextual differences; finally, the fusion data output submodule integrates the multi-source data that has undergone spatiotemporal alignment and semantic standardization according to logical correlation, generating a fusion dataset that is complete in content, standardized in format, and semantically consistent, and outputs it to the dynamic simulation status mapping module through a standardized interface (such as JSONSchema or industrial data bus format), providing a solid data foundation for building a high-fidelity, computable digital twin status.

[0083] Furthermore, the multi-source data fusion module, with access, alignment, understanding, and output as its logical framework, constructs a highly robust and consistent data fusion engine for complex industrial scenarios. During the access phase, the heterogeneous data access submodule not only passively receives multi-dimensional real-time operation and maintenance data streams from the physical entity sensing module, but also actively parses the data source characteristic metadata attached to each data stream, including unique device identifiers, signal types (such as analog quantities, digital quantities, and image frames), sampling periods, and transmission protocols (such as Modbus TCP, CAN bus, and OPC UA). PubSub and other similar libraries are used to dynamically load corresponding parsing drivers and buffering strategies, enabling lossless and low-latency aggregation of multimodal data such as vibration, temperature, current, video, and logs. In the alignment phase, the spatiotemporal alignment submodule first uses hardware timestamps or network time protocols (such as PTP) to synchronize each data stream at the nanosecond level. For asynchronously sampled data, sliding window interpolation or event-triggered alignment algorithms are used to ensure that all variables are comparable at the same logical moment. Simultaneously, combining the predefined component hierarchy and spatial coordinate relationships in the physical entity's three-dimensional attribute system (such as the assembly topology of motor-bearing-end cover), the originally isolated sensor readings (such as #3 bearing temperature and #5 vibration sensor) are associated with a unified device digital skeleton, completing spatial semantic binding. Subsequently, in the semantic mapping submodule, the system uses a set of data covering equipment, faults, operating conditions, and units. The semantic specification for the dimensional operation and maintenance domain performs deep normalization processing on the original fields. For example, regardless of whether the upstream system inputs TEMP_BEARING_A, shaft temperature A, or Temp_Brg1, it is mapped to the standard concept node main drive motor front bearing temperature (unit: °C), and the unit conversion (e.g., °F → °C) and range verification are automatically completed. Finally, the fusion data output submodule organizes the above-processed data in a structured manner according to the dimensions of equipment object, time slice, and state, generating a fusion dataset with complete context, strong correlation, and high cohesion. This dataset is encapsulated with a unified data model and pushed to the dynamic simulation state mapping module through a standardized interface, thereby providing high-quality input with clear semantics, spatiotemporal consistency, and logical integrity for subsequent high-fidelity state mapping and intelligent decision-making, truly realizing the leap from multi-source heterogeneous original data to usable and reliable fusion knowledge.

[0084] S4: Dynamic simulation state mapping module, used to drive digital twin modeling with multi-source fused data, and to map the operating state of physical entities in virtual space.

[0085] The dynamic simulation state mapping module includes a fusion data parsing submodule, a virtual state driving submodule, a real-time behavior simulation submodule, and a mapping consistency verification submodule.

[0086] The fusion data parsing submodule is used to receive the fusion dataset output from the multi-source data fusion module, parse the timestamps, spatial benchmark integration results and semantic standardization fields contained in the fusion dataset, and structure the fusion dataset into a state input vector of virtual digital representation.

[0087] The virtual state-driven submodule is used to inject the state input vector into the constructed virtual digital representation and update the virtual state element set of the virtual digital representation according to the operation behavior rule system in the three-dimensional attribute system of the physical entity.

[0088] The real-time behavior simulation submodule is used to deduce the short-term evolution trend of the virtual state element set in the virtual space based on the updated virtual state element set and combined with the dynamic behavior rules of physical entities, and to simulate the possible evolution of the operating situation under the current working conditions.

[0089] The mapping consistency verification submodule is used to continuously compare the status output of the virtual digital representation with the latest multi-dimensional real-time operation and maintenance data returned by the physical entity perception module. It evaluates the mapping fidelity through a deviation detection algorithm and triggers the virtual representation parameter self-correction mechanism when the deviation exceeds a preset threshold.

[0090] Furthermore, the dynamic simulation state mapping module constructs a high-fidelity, evolvable, and self-correcting virtual-real state synchronization mechanism through four tightly coupled, progressively layered sub-modules, ensuring that the virtual digital representation can reflect the full operational picture of the physical entity in real time, accurately, and proactively. First, the fusion data parsing sub-module receives the fusion dataset output from the multi-source data fusion module and deeply analyzes the embedded timestamp information, the topological location data with completed spatial benchmark integration, and the semantically standardized field content. It reorganizes this data into a clearly structured, dimensionally aligned, and temporally complete state input vector, serving as a unified data interface to drive the update of the virtual digital representation. Subsequently, the virtual state driving sub-module precisely injects this state input vector into the constructed virtual digital representation. Based on the operational behavior rules defined in the physical entity's three-dimensional attribute system (covering functional logic and dynamic response laws), it refreshes the virtual state element set, including internal variables, operational parameters, and state flags, in real time, ensuring that the virtual object remains synchronized with the physical entity in both static structure and dynamic behavior. Building upon this, the real-time behavior simulation sub-module further utilizes the updated virtual state element set, combined with the physical entity... The inherent dynamic behavior rules are used to conduct short-term simulations in virtual space, simulating the evolution of the equipment's operational status, such as performance drift, response delay, abnormal vibration, or operating condition switching, which may occur in the next few seconds to minutes, thus giving the system a certain predictive capability. Finally, the mapping consistency verification submodule continuously monitors the latest multi-dimensional real-time operation and maintenance data returned by the physical entity perception module and compares it item by item with the current output state of the virtual digital representation. The inconsistency between the two is quantified by the deviation detection algorithm. Once the assessed mapping fidelity deviation exceeds the preset safety threshold, the self-correction mechanism of the virtual representation parameters is automatically triggered (such as adjusting model coefficients, correcting initial conditions, or reloading local behavior rules), realizing the self-maintenance and accuracy guarantee of the digital twin in long-term operation. This forms a closed-loop mapping process of input, drive, simulation, verification, and correction, providing a reliable, trustworthy, and evolvable state foundation for upper-level intelligent analysis.

[0091] S5: Intelligent Analysis and Decision Support Module, which is used to promote the transformation of full-cycle intelligent operation and maintenance decision-making from passive response to proactive prevention based on the mapped physical entity operation status and combined with full-cycle intelligent operation and maintenance decision-making.

[0092] Among them, the intelligent analysis and decision support module is based on the high-fidelity virtual state output by the dynamic simulation state mapping module, and deeply integrates data-driven machine learning algorithms and knowledge-driven domain knowledge graphs to build an intelligent reasoning engine that integrates perception, understanding, prediction and decision-making. This module first performs multi-dimensional feature extraction and health assessment on the mapped physical entity's operating status, identifying the current operating stage, performance degradation trend, and potential risk level of the equipment. Based on this, combined with a full-cycle intelligent operation and maintenance decision-making framework, it systematically executes core tasks such as early fault prediction, anomaly root cause localization, remaining service life estimation, and maintenance strategy generation. It can not only provide early warning of latent faults that have not yet manifested, but also accurately trace the root cause in complex fault chains. Based on the equipment's health status, operation and maintenance resource constraints, and business priorities, it automatically generates optimal preventive or predictive maintenance recommendations (such as arranging bearing replacement or adjusting load distribution to alleviate overheating within 72 hours). The entire process abandons the traditional passive mode of relying on threshold alarms and post-event handling, and instead achieves a fundamental shift from responding after a fault occurs to intervening before a risk occurs through continuous learning and knowledge iteration, truly supporting the efficient, safe, and economical operation of complex systems throughout their entire lifecycle.

[0093] S6: Collaborative Operation and Maintenance Execution Module, which is used to transform intelligent analysis results into executable operation and maintenance instructions. Through integration with the operation and maintenance execution carrier terminal, it completes the operation and maintenance management of digital twin technology.

[0094] The collaborative operation and maintenance execution module includes an instruction generation submodule, a carrier adaptation and scheduling submodule, a closed-loop feedback collection submodule, and an execution status synchronization submodule.

[0095] The instruction generation submodule is used to receive the full-cycle intelligent operation and maintenance decision results output by the intelligent analysis and decision support module, and parse the full-cycle intelligent operation and maintenance decision results into executable operation and maintenance instructions according to the preset operation and maintenance rule base.

[0096] The carrier adaptation and scheduling submodule is used to identify the operation and maintenance execution carrier terminal and adapt and deliver executable operation and maintenance instructions according to the target carrier type.

[0097] The closed-loop feedback acquisition submodule is used to monitor the execution process feedback data returned by the operation and maintenance execution carrier terminal, collect the response data during the instruction execution process, and form an execution feedback stream;

[0098] The execution status synchronization submodule is used to synchronize the status updates in the execution feedback stream back to the virtual digital representation, drive the virtual digital representation to dynamically correct the current operation and maintenance status of the physical entity, and associate the execution results with the original intelligent analysis and decision records to complete the full-link digital twin technology operation and maintenance management from decision-making to execution to status update.

[0099] The calculation formula for the deviation detection algorithm is:

[0100]

[0101] in, Indicates the current timestamp. This indicates the number of dimensions in the set of virtual state elements participating in the comparison. This indicates that the physical entity perception module is at time [time]. The first collection Multidimensional real-time operation and maintenance data, Representing virtual digital representation at time The corresponding number Virtual state element value, Indicates the first The historical standard deviation of the data is used to normalize the dimensions. Indicates the first The weight coefficient of each element. This indicates a self-calibration mechanism that triggers the virtual representation parameters.

[0102] Furthermore, the collaborative operation and maintenance execution module constructs a complete operation and maintenance action chain through four highly collaborative sub-modules, from intelligent decision-making to physical execution and then back to digital space closed-loop feedback. First, the instruction generation sub-module receives the full-cycle intelligent operation and maintenance decision results output by the intelligent analysis and decision support module (e.g., predicting the main pump bearing will fail within 48 hours and recommending replacement). Based on a preset operation and maintenance rule base, including structured knowledge such as operating procedures, safety constraints, and resource scheduling strategies, it automatically parses the high-level decision intent into specific, operable, and traceable standardized operation and maintenance instructions, such as generating work orders, issuing equipment shutdown commands, or pushing inspection tasks to mobile devices. Subsequently, the carrier adaptation and scheduling sub-module identifies the capabilities and matches the protocols of the target execution terminal (such as the enterprise work order system, PLC automation control platform, AR inspection terminal, or mobile APP), dynamically converting general operation and maintenance instructions into formats that each carrier can understand (such as SOAP messages, Modbus write commands, or JSON task packages), and accurately routes them to the corresponding terminals through the instruction adaptation and delivery mechanism, ensuring seamless collaboration between people, machines, and systems. Incorrect; During instruction execution, the closed-loop feedback acquisition submodule continuously monitors the execution process feedback data returned by each operation and maintenance execution carrier terminal, including key information such as instruction confirmation, operation logs, and execution status changes (e.g., work order accepted, valve closed, maintenance completed), and aggregates them into a structured execution feedback stream. Finally, the execution status synchronization submodule injects the status updates in this feedback stream back into the virtual digital representation in real time, driving it to dynamically correct the current operation and maintenance status of the physical entity (e.g., equipment availability, maintenance progress, health recovery level). At the same time, it links and archives the execution results with the original intelligent analysis and decision records. This not only enables the digital twin to instantly perceive and synchronize the status of physical world operation and maintenance actions, but also forms a closed-loop chain of decision-making, execution, feedback, and calibration, giving the entire operation and maintenance management system the ability to self-perceive, self-execute, self-verify, and self-optimize, truly completing the closed-loop implementation of digital twin technology in operation and maintenance management.

[0103] In a preferred embodiment, an operation and maintenance management method based on digital twin technology includes: collecting multi-dimensional real-time operation and maintenance data through a sensor network deployed on a physical entity; constructing a one-to-one virtual digital representation based on the multi-dimensional attribute characteristics of the physical entity, and completing bidirectional data interaction with the physical entity through a standardized interface; performing multi-source data collaborative governance on the multi-dimensional operation and maintenance data derived from the multi-dimensional attribute characteristics of the physical entity; using the multi-source fused data to drive digital twin modeling and mapping the physical entity's operating status in virtual space; based on the mapped physical entity's operating status, combined with full-cycle intelligent operation and maintenance decision-making, promoting the transformation of full-cycle intelligent operation and maintenance decision-making from passive response to proactive prevention; and converting the intelligent analysis results into executable operation and maintenance instructions, and completing the operation and maintenance management based on digital twin technology through integration with the operation and maintenance execution carrier terminal.

[0104] The above-mentioned unit modules can be embedded in the processor of the computer device in hardware form or independent of it, or they can be stored in the memory of the computer device in software form, so that the processor can call and execute the corresponding operations of the above modules.

[0105] In one embodiment, a computer device is provided, which may be a terminal. The computer device includes a processor, memory, a communication interface, a display screen, and an input device connected via a system bus. The processor of the computer device provides computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores an operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage medium. The communication interface of the computer device is used for wired or wireless communication with external terminals. Wireless communication can be achieved through Wi-Fi, carrier networks, NFC (Near Field Communication), or other technologies. The display screen of the computer device may be an LCD screen or an e-ink display screen. The input device of the computer device may be a touch layer covering the display screen, or buttons, a trackball, or a touchpad located on the casing of the computer device, or an external keyboard, touchpad, or mouse, etc.

[0106] In summary, this invention achieves high-fidelity, low-latency, and bidirectional closed-loop linkage between physical entities and virtual spaces by constructing a digital twin operation and maintenance architecture that integrates perception, modeling, fusion, mapping, decision-making, and execution. On the one hand, relying on multi-dimensional real-time operation and maintenance data collection and multi-source data collaborative governance, it effectively breaks down information silos and improves data consistency and timeliness. On the other hand, through dynamic simulation state mapping and operational status evolution deduction, the system possesses the ability to accurately mirror and predict equipment status in a short time. Based on this, combined with a full-cycle intelligent operation and maintenance decision-making and cross-carrier collaborative execution mechanism, it not only significantly improves the accuracy and timeliness of fault prediction, root cause localization, and maintenance response, but also promotes the transformation of operation and maintenance mode from passive response to proactive prevention. Simultaneously, through deviation detection algorithms and self-correction mechanisms, it continuously ensures the consistency between the virtual digital representation and the physical entity, ensuring that the digital twin is reliable, trustworthy, and usable throughout its entire lifecycle, thereby comprehensively improving the intelligence, automation, and refinement level of operation and maintenance management for complex systems.

[0107] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. An operation and maintenance management system based on digital twin technology, characterized in that: include, The physical entity perception module is used to collect multi-dimensional real-time operation and maintenance data through a sensor network deployed on physical entities; The digital twin modeling module is used to construct a one-to-one virtual digital representation based on the multi-dimensional attribute characteristics of physical entities, and to complete bidirectional data interaction with physical entities through standardized interfaces. The multi-source data fusion module is used for collaborative governance of multi-source data on multi-dimensional operation and maintenance data derived from the multi-dimensional attribute characteristics of physical entities. The dynamic simulation state mapping module is used to drive digital twin modeling with multi-source fused data and map the operating state of physical entities in virtual space. The intelligent analysis and decision support module is used to promote the transformation of full-cycle intelligent operation and maintenance decision-making from passive response to proactive prevention, based on the mapped physical entity's operating status and combined with full-cycle intelligent operation and maintenance decision-making. The collaborative operation and maintenance execution module is used to transform intelligent analysis results into executable operation and maintenance instructions. Through integration with the operation and maintenance execution carrier terminal, it completes the operation and maintenance management of digital twin technology.

2. The operation and maintenance management system based on digital twin technology as described in claim 1, characterized in that: The physical entity perception module includes a sensor node deployment submodule, a multimodal data acquisition submodule, an edge preprocessing submodule, and a real-time communication transmission submodule. The sensor node deployment submodule is used to deploy a covered sensor network node array on or inside the surface of a physical entity, based on the sensor network of the physical entity. The multimodal data acquisition submodule is used to synchronously acquire heterogeneous signals reflecting physical entities through the sensor network node array to form a raw multidimensional real-time operation and maintenance data stream; The edge preprocessing submodule is used to perform edge-side data normalization processing on the original multidimensional real-time operation and maintenance data stream in the edge computing unit close to the sensing node. The real-time communication transmission submodule is used to upload the multi-dimensional real-time operation and maintenance data stream, which has been preprocessed at the edge, to the upper-layer module of the system through the industrial communication protocol, so as to complete the delivery of the data stream within the time window.

3. The operation and maintenance management system based on digital twin technology as described in claim 2, characterized in that: The digital twin modeling module includes a multi-dimensional attribute parsing submodule, a virtual representation construction submodule, an interface protocol adaptation submodule, and a two-way interactive control submodule. The multidimensional attribute parsing submodule is used to parse the multidimensional attribute features of physical entities, extract the three-dimensional attribute system of physical entities, and transform the three-dimensional attribute system of physical entities into standardized attribute metadata that can be recognized and processed by virtual space. The virtual representation construction submodule is used to generate virtual digital representations that correspond one-to-one with physical entities in the virtual space based on standardized attribute metadata. The interface protocol adaptation submodule is used to configure and encapsulate standardized data interfaces that support multiple industry standards according to the physical side integrated interface environment to which the physical entity is connected. The bidirectional interactive control submodule is used to receive operational data from physical entities through a standardized data interface to drive the state update of the virtual digital representation, and to send the virtual end control output set in the virtual space back to the physical entity, thus completing the closed-loop bidirectional data interaction between the virtual and the real.

4. The operation and maintenance management system based on digital twin technology as described in claim 3, characterized in that: The multi-source data fusion module includes a heterogeneous data access submodule, a spatiotemporal alignment submodule, a semantic mapping submodule, and a fused data output submodule. The heterogeneous data access submodule is used to receive multi-dimensional real-time operation and maintenance data streams from the physical entity perception module, identify the data source feature metadata of the multi-dimensional real-time operation and maintenance data streams, and establish a unified data access channel. The spatiotemporal alignment submodule is used to synchronize the multi-source data in the time dimension and to integrate the spatial reference based on the topological relationship in the three-dimensional attribute system of physical entities in the spatial dimension. The semantic mapping submodule is used to map the original fields in various multi-source data to standard concept nodes in a consistent semantic space according to the unified semantic specification of the operation and maintenance domain, thereby eliminating semantic barriers. The fusion data output submodule is used to integrate multi-source data after spatiotemporal alignment and semantic mapping to generate a fusion dataset, which is then output to the dynamic simulation state mapping module in a standardized format.

5. The operation and maintenance management system based on digital twin technology as described in claim 4, characterized in that: The dynamic simulation state mapping module includes a fusion data parsing submodule, a virtual state driving submodule, a real-time behavior simulation submodule, and a mapping consistency verification submodule. The fusion data parsing submodule is used to receive the fusion dataset output from the multi-source data fusion module, parse the timestamps, spatial benchmark integration results and semantic standardization fields contained in the fusion dataset, and structure the fusion dataset into a state input vector of virtual digital representation. The virtual state driving submodule is used to inject the state input vector into the constructed virtual digital representation and update the virtual state element set of the virtual digital representation according to the operation behavior rule system in the three-dimensional attribute system of the physical entity. The real-time behavior simulation submodule is used to deduce the short-term evolution trend of the virtual state element set in the virtual space based on the updated virtual state element set and combined with the dynamic behavior rules of physical entities, and to simulate the possible evolution of the operating situation under the current working conditions. The mapping consistency verification submodule is used to continuously compare the status output of the virtual digital representation with the latest multi-dimensional real-time operation and maintenance data returned by the physical entity perception module. It evaluates the mapping fidelity through a deviation detection algorithm and triggers a virtual representation parameter self-correction mechanism when the value exceeds a preset threshold.

6. The operation and maintenance management system based on digital twin technology as described in claim 5, characterized in that: The collaborative operation and maintenance execution module includes an instruction generation submodule, a carrier adaptation and scheduling submodule, a closed-loop feedback acquisition submodule, and an execution status synchronization submodule. The instruction generation submodule is used to receive the full-cycle intelligent operation and maintenance decision results output by the intelligent analysis and decision support module, and parse the full-cycle intelligent operation and maintenance decision results and convert them into executable operation and maintenance instructions according to the preset operation and maintenance rule base. The carrier adaptation and scheduling submodule is used to identify the operation and maintenance execution carrier terminal and adapt and deliver executable operation and maintenance instructions according to the target carrier type. The closed-loop feedback acquisition submodule is used to monitor the execution process feedback data returned by the operation and maintenance execution carrier terminal, collect the response data during the instruction execution process, and form an execution feedback stream; The execution status synchronization submodule is used to synchronize the status updates in the execution feedback stream back to the virtual digital representation, drive the virtual digital representation to dynamically correct the current operation and maintenance status of the physical entity, and associate the execution results with the original intelligent analysis and decision records to complete the full-link digital twin technology operation and maintenance management from decision-making to execution to status update.

7. The operation and maintenance management system based on digital twin technology as described in claim 6, characterized in that: The calculation formula for the deviation detection algorithm is as follows: in, Indicates the current timestamp. This indicates the number of dimensions in the set of virtual state elements participating in the comparison. This indicates that the physical entity perception module is at time [time]. The first collection Multidimensional real-time operation and maintenance data, Representing virtual digital representation at time The corresponding number Virtual state element value, Indicates the first The historical standard deviation of the data is used to normalize the dimensions. Indicates the first The weight coefficient of each element. This indicates a self-calibration mechanism that triggers the virtual representation parameters.

8. An operation and maintenance management method based on digital twin technology, based on the operation and maintenance management system based on digital twin technology as described in any one of claims 1 to 7, characterized in that: include, Collect multi-dimensional real-time operation and maintenance data through sensor networks deployed on physical entities; Based on the multidimensional attribute characteristics of physical entities, a one-to-one virtual digital representation is constructed, and bidirectional data interaction with physical entities is completed through standardized interfaces; Multi-source collaborative governance of multi-dimensional operation and maintenance data derived from the multi-dimensional attribute characteristics of physical entities; The data from multiple sources is integrated to drive digital twin modeling, which maps the operating state of physical entities in virtual space. Based on the mapped physical entity operating status, combined with full-cycle intelligent operation and maintenance decision-making, we promote the transformation of full-cycle intelligent operation and maintenance decision-making from passive response to proactive prevention. The intelligent analysis results are transformed into executable operation and maintenance instructions, and through integration with the operation and maintenance execution carrier terminal, the operation and maintenance management of digital twin technology is completed.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that: When the processor executes the computer program, it implements the steps of the operation and maintenance management system based on digital twin technology as described in any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by the processor, it implements the steps of the operation and maintenance management system based on digital twin technology as described in any one of claims 1 to 7.

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