Point inspection and maintenance method and system based on digital twin technology
Patent Information
- Application Number
- CN202611178138.X
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2026-08-05
- Publication Date
- 2026-10-30
AI Technical Summary
第一，设备图纸、管线图纸、电气图纸和机械图纸往往形成于不同时期，部分图纸经过多次改造后已经缺失、失真或与现场实际情况不一致，导致新入职人员或外协人员难以快速掌握设备布置、管线走向以及故障位置
（1）本发明通过获取目标厂区的多源运维数据，并构建包括三维几何模型、设备节点以及测点映射关系的数字孪生模型，使得目标厂区中的物理设备能够在数字空间中以设备节点的形式进行展示，同时通过测点映射关系建立监测数据源与设备节点之间的关联关系。这样，即使现场存在图纸缺失、设备改造频繁、管线布置复杂等情况，运维人员也可以基于数字孪生模型直观查看设备位置、设备关联关系以及监测数据归属，从而减少因图纸缺失或信息不一致导致的排查困难，提高设备管理水平。
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Figure CN123013436A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of intelligent operation and maintenance technology for industrial equipment, and in particular to an inspection and maintenance method and system based on digital twin technology. Background Technology
[0002] In industrial settings such as steel metallurgy, power, and data centers, the number of equipment in the plant area is large, the pipeline distribution is complex, and the operating environment is harsh. Equipment inspection and maintenance work usually relies on manual inspection, paper drawings, and the personal experience of maintenance personnel.
[0003] The relevant technologies have at least the following problems: First, equipment drawings, pipeline drawings, electrical drawings, and mechanical drawings are often created at different times. Some drawings have been modified multiple times and are now missing, distorted, or inconsistent with the actual site conditions, making it difficult for new employees or outsourced personnel to quickly grasp the equipment layout, pipeline routing, and fault location.
[0004] Second, field equipment operation data, inspection data, environmental data, and historical fault data are usually scattered across different systems, such as PDAs, PLCs, SCADA systems, inspection terminals, and air conditioning control systems. There is a lack of unified mapping between the data, making it difficult to perform correlation analysis and status judgment around specific equipment.
[0005] Third, after a fault occurs, communication between electricians and fitters is often poor due to information asymmetry, resulting in low efficiency in determining the cause of the fault, assigning responsibility, and confirming the troubleshooting path, leading to a longer average repair time.
[0006] Fourth, for areas sensitive to ambient temperature, such as electrical rooms and power distribution rooms, air conditioning control often relies on manual experience or simple threshold control, making it difficult to make predictive adjustments based on equipment heat generation, outdoor ambient temperature, weather changes, and equipment allowable temperature, which can easily lead to equipment overheating or overcooling. Summary of the Invention
[0007] The technical problem to be solved by the present invention is to overcome the shortcomings of the prior art and provide an inspection and maintenance method and system based on digital twin technology.
[0008] To solve the above technical problems, the technical solution of the present invention is as follows: On the one hand, the present invention provides an inspection and maintenance method based on digital twin technology, including: Acquire multi-source operation and maintenance data of the target plant area, and construct a digital twin model corresponding to the target plant area based on the multi-source operation and maintenance data; the digital twin model includes a three-dimensional geometric model, equipment nodes, and measurement point mapping relationships. The equipment nodes correspond to the physical equipment in the target plant area, and the measurement point mapping relationships are used to characterize the relationship between the monitoring data source and the equipment nodes. Acquire real-time operation and maintenance data, and map the real-time operation and maintenance data to the target device node of the digital twin model according to the measurement point mapping relationship to generate device status monitoring data; When the equipment status monitoring data meets the preset abnormal triggering conditions, the target fault case is determined based on the equipment status monitoring data and the historical fault database, and a fault investigation strategy is generated based on the target fault case and the digital twin model. Based on the fault diagnosis strategy, inspection and maintenance execution instructions are generated, and the execution feedback data corresponding to the inspection and maintenance execution instructions is updated to the digital twin model and historical fault database.
[0009] On the other hand, the present invention provides an inspection and maintenance system based on digital twin technology, comprising: The model building module is used to acquire multi-source operation and maintenance data of the target plant area and build a digital twin model corresponding to the target plant area based on the multi-source operation and maintenance data. The digital twin model includes a three-dimensional geometric model, equipment nodes and measurement point mapping relationships. The equipment nodes correspond to the physical equipment in the target plant area, and the measurement point mapping relationships are used to characterize the relationship between the monitoring data source and the equipment nodes. The status monitoring module is used to acquire real-time operation and maintenance data, and map the real-time operation and maintenance data to the target device node of the digital twin model according to the measurement point mapping relationship, so as to generate device status monitoring data. The fault diagnosis module is used to determine the target fault case based on the equipment status monitoring results and historical fault database when the equipment status monitoring data meets the preset abnormal triggering conditions, and to generate a fault diagnosis strategy based on the target fault case and digital twin model. The inspection and maintenance module is used to generate inspection and maintenance execution instructions based on the fault diagnosis strategy, and update the execution feedback data corresponding to the inspection and maintenance execution instructions to the digital twin model and the historical fault database.
[0010] The beneficial effects of this invention are: (1) This invention acquires multi-source operation and maintenance data of the target plant area and constructs a digital twin model including a three-dimensional geometric model, equipment nodes, and measurement point mapping relationships. This enables the physical equipment in the target plant area to be displayed in the form of equipment nodes in the digital space. At the same time, the association between the monitoring data source and the equipment nodes is established through the measurement point mapping relationship. In this way, even if there are situations such as missing drawings, frequent equipment modifications, and complex pipeline layouts on site, operation and maintenance personnel can intuitively view the equipment location, equipment association relationships, and monitoring data ownership based on the digital twin model, thereby reducing the difficulty of troubleshooting caused by missing drawings or inconsistent information and improving the level of equipment management.
[0011] (2) This invention acquires real-time operation and maintenance data and maps the real-time operation and maintenance data to the target device node in the digital twin model according to the measurement point mapping relationship, generating equipment status monitoring data. This enables data from different systems and different acquisition terminals to be uniformly aggregated and expressed around the same device node. On this basis, combined with data fusion processing and Kalman filtering and other status estimation methods, the health status of the equipment can be inverted from sparse measurement values, reducing the dependence on the deployment of new sensors, reducing the cost of on-site modification and the intensity of manual inspection, while improving the continuity and accuracy of equipment status judgment.
[0012] (3) This invention determines target fault cases based on equipment status monitoring data and historical fault database when the equipment status monitoring data meets preset abnormal triggering conditions, and generates fault investigation strategies based on target fault cases and digital twin models, enabling historical fault experience to be correlated and matched with the current real-time equipment status. The fault investigation strategy can be generated based on fault phenomena, alarm codes, handling steps, and maintenance records in historical fault cases, thereby reducing reliance on personal experience, improving fault location efficiency, shortening the average fault repair time, and helping to alleviate communication problems caused by information asymmetry among operators. Attached Figure Description
[0013] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. 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.
[0014] Figure 1 A flowchart illustrating the inspection and maintenance method based on digital twin technology provided by this invention; Figure 2 This is a schematic diagram of the inspection and maintenance system based on digital twin technology provided by the present invention. Detailed Implementation
[0015] To make the content of this invention easier to understand, the invention will be further described in detail below with reference to specific embodiments and accompanying drawings.
[0016] Example 1: This example provides an inspection and maintenance method based on digital twin technology. See [link to example]. Figure 1 The method specifically includes the following steps: Step S101: Obtain multi-source operation and maintenance data of the target plant area, and construct a digital twin model corresponding to the target plant area based on the multi-source operation and maintenance data.
[0017] Specifically, the target plant area can refer to an industrial area that requires inspection and maintenance management. For example, the target plant area can be a medium plate mill, a steel rolling workshop, an electrical room of a roughing mill, a power distribution room, a pumping station, a data center computer room, or other areas containing equipment, pipelines, lines, and environmental facilities.
[0018] The aforementioned multi-source operation and maintenance data includes data from different systems or carriers. Specifically, the multi-source operation and maintenance data includes equipment operation data, inspection data, environmental data, and equipment drawing data. Among them, equipment operation data is used to characterize the operating status of physical equipment in the target plant area. Inspection data is used to characterize equipment status information discovered during manual inspections. Environmental data is used to characterize the state of the environment in which the target plant area is located. Equipment drawing data is used to characterize the static layout information of equipment, pipelines, lines, plant structures, etc., in the target plant area.
[0019] In this embodiment, equipment operation data includes current data, voltage data, power data, load rate data, pressure data, flow rate data, temperature data, vibration data, rotational speed data, oil analysis data, and partial discharge monitoring data. Inspection data includes inspection images, videos, voice recordings, or text records entered by inspection personnel via handheld terminals. Environmental data includes indoor temperature, outdoor ambient temperature, humidity, dust concentration, smoke alarm status, air conditioning operation status, air conditioning return air temperature, air conditioning supply air temperature, and weather forecast data. Equipment drawing data includes one or more of the following: electrical drawings, fitter's drawings, pipeline drawings, equipment layout diagrams, plant structure diagrams, modification drawings, as-built drawings, equipment ledger information, equipment nameplate information, and equipment manual information.
[0020] After acquiring multi-source operation and maintenance data, a digital twin model corresponding to the target plant area is constructed. The digital twin model includes a three-dimensional geometric model, equipment nodes, and measurement point mapping relationships.
[0021] The 3D geometric model is used to represent the spatial structure of the target plant area in digital space. The 3D geometric model can include the plant outline, equipment shape, equipment spatial location, pipeline routing, wiring routing, void areas, junctions between new and old plants, maintenance access roads, fire escape routes, etc. The 3D geometric model can be generated based on CAD drawings, BIM models, laser point cloud data, oblique photogrammetry data, on-site measurement data, or manually modeled data.
[0022] Equipment nodes are used to represent the object-oriented representation of physical equipment in the target plant area within the digital twin model. Each equipment node corresponds to a physical device in the target plant area. For example, one equipment node can correspond to a motor, a pump, a fan, a transformer, an air conditioner, a distribution cabinet, a valve, a lubrication system, a rolling mill unit, or other physical equipment. Measurement point mapping relationships are used to represent the association between monitoring data sources and the equipment nodes. Monitoring data sources can refer to acquisition terminals, control systems, or data platforms that provide real-time or historical operation and maintenance data. For example, monitoring data sources may include production PDA systems, PLC control systems, SCADA systems, online monitoring devices, wireless temperature and vibration sensors, acoustic imagers, handheld inspection terminals, air conditioning controllers, smart meters, etc.
[0023] Measurement point mapping relationships are implemented through measurement point mapping tables, device code association tables, tag mapping rules, or unified namespaces. These relationships include information such as measurement point identifier, data source identifier, device node identifier, data type, acquisition frequency, unit, measurement range, threshold, installation location, and measurement point status. Through these mapping relationships, the system can accurately assign data uploaded by a sensor or control system to its corresponding device node, rather than remaining at the level of isolated measurement points.
[0024] In this embodiment, the multi-source operation and maintenance data includes pipeline drawing data from equipment drawing data. Accordingly, the process of constructing a digital twin model includes: A 3D pipeline model of the target plant area is generated based on the pipeline drawing data and 3D geometric model in the equipment drawing data, and the pipeline nodes in the 3D pipeline model are associated with the equipment nodes.
[0025] A 3D pipeline model provides a clear visual representation of the routing of pipes, cable trays, air ducts, water pipes, gas pipes, hydraulic lines, and other components within the target plant area. Pipeline nodes can be associated with equipment nodes; for example, a cooling water pipeline node can be linked to a corresponding motor, pump, or heat exchanger. When an equipment node malfunctions, the system can simultaneously display its associated pipeline nodes, allowing maintenance personnel to quickly determine whether the fault is caused by pipeline blockage, leakage, valve malfunction, or line fault.
[0026] Step S102: Obtain real-time operation and maintenance data, and map the real-time operation and maintenance data to the target device node of the digital twin model according to the measurement point mapping relationship to generate device status monitoring data.
[0027] Specifically, after the digital twin model is built, real-time operation and maintenance data can be continuously or periodically acquired. This real-time operation and maintenance data can come from existing systems, newly added sensors, or manual input from inspection terminals. Specifically, real-time operation and maintenance data can include at least one of the following: pressure data, flow data, current data, vibration data, oil analysis data, partial discharge monitoring data, and visual inspection data.
[0028] After acquiring real-time operation and maintenance data, the system determines the target device node to which the data belongs based on the measurement point mapping relationship. The target device node refers to the device node corresponding to the current real-time operation and maintenance data. For example, when data from a certain current measurement point is uploaded, the system can query the measurement point mapping relationship based on the measurement point identifier to determine which motor the current measurement point belongs to, and then designate that motor as the target device node.
[0029] During the mapping process, the system also preprocesses the real-time operation and maintenance data. Preprocessing includes data deduplication, outlier removal, timestamp alignment, unit standardization, range verification, missing value imputation, and data sampling frequency standardization. After preprocessing, the real-time operation and maintenance data undergoes data fusion processing to obtain fused measurement data. Data fusion processing can combine data from different data sources, different sampling frequencies, and different measurement dimensions.
[0030] In this embodiment, Kalman filtering is used to estimate the state of the fused measurement data to obtain equipment health status data. Kalman filtering recursively estimates the equipment state based on the equipment state equation and observation equation. In cases where some measurement points are missing, measurement noise is high, or sampling frequencies are inconsistent, Kalman filtering can estimate the equipment operating state from sparse measurement values, thereby improving the stability of equipment health status assessment.
[0031] Equipment health status data is used to characterize the health level of target equipment nodes. Equipment health status data may include equipment health index, equipment degradation trend, anomaly probability, remaining life estimate, vibration anomaly level, temperature anomaly level, insulation anomaly level, lubrication anomaly level, etc.
[0032] In another embodiment, the equipment condition monitoring data also includes environmental condition data. Accordingly, the process of generating the equipment condition monitoring data includes: Acquire real-time heat generation of indoor electrical equipment, outdoor ambient temperature and weather data, as well as the maximum allowable ambient temperature for the equipment; Based on real-time heat generation, outdoor ambient temperature, weather data, and the maximum allowable ambient temperature of the equipment, the temperature change trend within a preset time period is predicted to obtain environmental status data.
[0033] Taking the electrical room of a roughing mill as an example, the real-time heat generation of the equipment inside the electrical room can be calculated based on PDA data such as current and load rate. Outdoor ambient temperature and weather data can come from meteorological interfaces, outdoor temperature sensors, or weather forecast systems. The maximum allowable ambient temperature of the equipment can be obtained from equipment manuals, equipment nameplates, equipment ledgers, or historical operating data, and stored digitally in a digital twin model. Through environmental status data, the system can not only reflect the current environmental status but also predict future environmental change trends, providing a basis for subsequent intelligent control of air conditioning.
[0034] Step S103: When the equipment status monitoring data meets the preset abnormal triggering conditions, determine the target fault case based on the equipment status monitoring data and the historical fault database, and generate a fault investigation strategy based on the target fault case and the digital twin model.
[0035] Specifically, the abnormal triggering conditions can be configured based on equipment type, equipment importance, operating environment, historical fault patterns, etc. In this embodiment, the abnormal triggering conditions may include one or more of the following: equipment health status data is lower than a preset health threshold; equipment temperature exceeds a preset temperature threshold; equipment vibration exceeds a preset vibration threshold; abnormal fluctuation of equipment current exceeds a preset fluctuation range; predicted temperature exceeds the equipment's maximum allowable ambient temperature; alarm code or alarm signal is received.
[0036] When equipment status monitoring data meets preset anomaly triggering conditions, the system can determine the target fault case based on the equipment status monitoring data and the historical fault database. The historical fault database includes multiple historical fault case records. Each historical fault case record can be associated with a device node in the digital twin model.
[0037] In this embodiment, the process of constructing the historical fault database includes: Acquire historical fault data, which includes fault symptoms, alarm codes, handling steps, and maintenance records; A historical fault database was established based on historical fault data.
[0038] When establishing a historical fault database, the historical fault data can be structured. For example, fields can be extracted from paper maintenance records, Excel maintenance ledgers, ERP work order systems, and MES systems to form fault case records in a unified format. For unstructured content such as fault symptoms, alarm codes, and handling steps, structuring can be achieved through keyword extraction, rule matching, or manual annotation.
[0039] Simultaneously, the digital twin model allows for the simulation of fault propagation paths for equipment nodes. Fault propagation paths characterize how a fault in one equipment node might affect other equipment nodes or the overall system state. For example, a "high main motor bearing temperature" could lead to a "lubrication pump overload," which in turn triggers a "rolling mill speed reduction." Fault propagation path simulations can generate a fault contingency plan library. This library can include preventative measures and emergency operating procedures.
[0040] When identifying a target fault case, the system can match current equipment status monitoring data with historical fault case records. For example, the system can extract current abnormal features, such as abnormal equipment nodes, abnormal measuring points, alarm codes, temperature change trends, vibration change trends, etc., and then search for historical fault cases with similar abnormal features in the historical fault database. The historical fault case with the highest similarity or meeting a preset similarity threshold is identified as the target fault case. After identifying the target fault case, a fault investigation strategy can be generated based on the target fault case and the digital twin model. The fault investigation strategy includes one or more of the following: fault cause ranking, checkpoint location information, investigation steps, required tools, required spare parts, safety precautions, personnel division of labor, isolation operation suggestions, and recovery operation suggestions.
[0041] Step S104: Generate inspection and maintenance execution instructions based on the fault diagnosis strategy, and update the execution feedback data corresponding to the inspection and maintenance execution instructions to the digital twin model and historical fault database.
[0042] Specifically, after generating a fault diagnosis strategy, the system generates inspection and maintenance execution instructions based on the strategy. These instructions are a set of commands used to guide on-site personnel in performing inspection, maintenance, retesting, isolation, or recovery operations.
[0043] After executing the inspection and maintenance instructions, the system receives execution feedback data. The system can update the digital twin model with this feedback data. Simultaneously, the system can also update the historical fault database with the execution feedback data.
[0044] Example 2: This example provides an inspection and maintenance system based on digital twin technology. See [link to example]. Figure 2 The system includes: a model building module, a status monitoring module, a fault diagnosis module, and an inspection and maintenance module.
[0045] Specifically, the model building module is used to acquire multi-source operation and maintenance data of the target plant area and construct a digital twin model corresponding to the target plant area based on the multi-source operation and maintenance data. The digital twin model includes a three-dimensional geometric model, equipment nodes, and measurement point mapping relationships. The equipment nodes correspond to the physical equipment in the target plant area, and the measurement point mapping relationships are used to characterize the association between the monitoring data source and the equipment nodes.
[0046] The status monitoring module is used to acquire real-time operation and maintenance data, and map the real-time operation and maintenance data to the target device nodes of the digital twin model according to the measurement point mapping relationship, thereby generating device status monitoring data.
[0047] Specifically, the condition monitoring module includes a data receiving unit, a mapping and positioning unit, a data fusion unit, a condition estimation unit, and an environment prediction unit. The data receiving unit receives real-time operation and maintenance data. The mapping and positioning unit determines the target device node to which the real-time operation and maintenance data belongs based on the measurement point mapping relationship. The data fusion unit performs data fusion processing on the real-time operation and maintenance data to obtain fused measurement data. The condition estimation unit uses Kalman filtering to perform condition estimation on the fused measurement data to obtain device health status data. The environment prediction unit generates environmental status data.
[0048] The fault diagnosis module is used to determine the target fault case based on the equipment status monitoring results and historical fault database when the equipment status monitoring data meets the preset abnormal triggering conditions, and to generate a fault diagnosis strategy based on the target fault case and digital twin model.
[0049] Specifically, the fault diagnosis module includes an anomaly determination unit, a case matching unit, a contingency plan invocation unit, and a strategy generation unit. The anomaly determination unit determines whether the equipment status monitoring data meets preset anomaly triggering conditions. The case matching unit identifies target fault cases based on equipment status monitoring data and a historical fault database. The contingency plan invocation unit invokes the corresponding contingency plan for the target fault case. The strategy generation unit generates a fault diagnosis strategy based on the target fault case and a digital twin model.
[0050] The inspection and maintenance module is used to generate inspection and maintenance execution instructions based on the fault diagnosis strategy, and update the execution feedback data corresponding to the inspection and maintenance execution instructions to the digital twin model and historical fault database.
[0051] Specifically, the inspection and maintenance module includes an instruction generation unit, a task issuance unit, a feedback receiving unit, a model update unit, and a database update unit. The instruction generation unit generates inspection and maintenance execution instructions based on the fault diagnosis strategy. The task issuance unit issues the inspection and maintenance execution instructions to the fault diagnosis terminal. The feedback receiving unit receives the execution feedback data corresponding to the inspection and maintenance execution instructions. The model update unit updates the digital twin model based on the execution feedback data. The database update unit updates the historical fault database based on the execution feedback data.
[0052] Therefore, the technical solution of this application solves the technical problems in related technologies such as missing equipment drawings, disconnect between real-time data and equipment objects, reliance on experience for fault diagnosis, and lack of closed-loop feedback in the maintenance process.
[0053] In addition to the above embodiments, the present invention may have other implementation methods; all technical solutions formed by equivalent substitution or equivalent transformation fall within the protection scope claimed by the present invention.
Claims
1. A method for inspection and maintenance based on digital twin technology, characterized in that, include: Acquire multi-source operation and maintenance data of the target plant area, and construct a digital twin model corresponding to the target plant area based on the multi-source operation and maintenance data; wherein, the digital twin model includes a three-dimensional geometric model, equipment nodes and measurement point mapping relationships, the equipment nodes correspond to physical equipment in the target plant area, and the measurement point mapping relationships are used to characterize the association relationship between the monitoring data source and the equipment nodes; Acquire real-time operation and maintenance data, and map the real-time operation and maintenance data to the target device node of the digital twin model according to the measurement point mapping relationship to generate device status monitoring data; When the equipment status monitoring data meets the preset abnormal triggering conditions, a target fault case is determined based on the equipment status monitoring data and the historical fault database, and a fault investigation strategy is generated based on the target fault case and the digital twin model. Based on the fault diagnosis strategy, an inspection and maintenance execution instruction is generated, and the execution feedback data corresponding to the inspection and maintenance execution instruction is updated to the digital twin model and the historical fault database.
2. The inspection and maintenance method based on digital twin technology according to claim 1, characterized in that, The multi-source operation and maintenance data includes equipment operation data, inspection data, environmental data, and equipment drawing data.
3. The inspection and maintenance method based on digital twin technology according to claim 2, characterized in that, The step of constructing a digital twin model corresponding to the target plant area based on the multi-source operation and maintenance data includes: A three-dimensional pipeline model of the target plant area is generated based on the pipeline drawing data in the equipment drawing data and the three-dimensional geometric model, and the pipeline nodes in the three-dimensional pipeline model are associated with the equipment nodes.
4. The inspection and maintenance method based on digital twin technology according to claim 1, characterized in that, The equipment status monitoring data includes equipment health status data; The step of mapping the real-time operation and maintenance data to the target device node of the digital twin model according to the measurement point mapping relationship, and generating device status monitoring data, includes: The real-time operation and maintenance data is fused to obtain fused measurement data; Kalman filtering is used to perform state estimation on the fused measurement data to obtain the equipment health status data; The real-time operation and maintenance data includes at least one of the following: pressure data, flow data, current data, vibration data, oil analysis data, partial discharge monitoring data, and inspection visual inspection data.
5. The inspection and maintenance method based on digital twin technology according to claim 1, characterized in that, The equipment status monitoring data includes environmental status data; The step of mapping the real-time operation and maintenance data to the target device node in the digital twin model according to the measurement point mapping relationship, and generating device status monitoring data, includes: Acquire real-time heat generation of indoor electrical equipment, outdoor ambient temperature and weather data, as well as the maximum allowable ambient temperature for the equipment; Based on the real-time heat generation, the outdoor ambient temperature, the weather data, and the maximum allowable ambient temperature of the device, the temperature change trend within a preset time period is predicted to obtain the environmental status data.
6. The inspection and maintenance method based on digital twin technology according to claim 5, characterized in that, Also includes: When the predicted temperature in the environmental state data, which characterizes the temperature change trend, exceeds the maximum allowable ambient temperature of the device, an air conditioning control command is generated. The air conditioning control commands include air conditioning compressor frequency control commands and fan speed control commands.
7. The inspection and maintenance method based on digital twin technology according to claim 1, characterized in that, The method for constructing the historical fault database includes: Acquire historical fault data, which includes fault symptoms, alarm codes, handling steps, and maintenance records; The historical fault database is established based on the historical fault data.
8. An inspection and maintenance system based on digital twin technology, characterized in that, include: The model building module is used to acquire multi-source operation and maintenance data of the target plant area and construct a digital twin model corresponding to the target plant area based on the multi-source operation and maintenance data. The digital twin model includes a three-dimensional geometric model, equipment nodes and measurement point mapping relationships. The equipment nodes correspond to the physical equipment in the target plant area, and the measurement point mapping relationships are used to characterize the association between the monitoring data source and the equipment nodes. The status monitoring module is used to acquire real-time operation and maintenance data, and map the real-time operation and maintenance data to the target device node of the digital twin model according to the measurement point mapping relationship, so as to generate device status monitoring data. The fault diagnosis module is used to determine the target fault case based on the equipment status monitoring results and the historical fault database when the equipment status monitoring data meets the preset abnormal triggering conditions, and to generate a fault diagnosis strategy based on the target fault case and the digital twin model. The inspection and maintenance module is used to generate inspection and maintenance execution instructions based on the fault diagnosis strategy, and update the execution feedback data corresponding to the inspection and maintenance execution instructions to the digital twin model and the historical fault database.