Intelligent operation and maintenance system and method for turnout switch equipment

Abstract

The invention relates to an intelligent operation and maintenance system and method for turnout switch equipment, and relates to the technical field of intelligent operation and maintenance of rail transit. The system comprises a multi-source data acquisition module which is used for acquiring various data related to the operation state of the turnout switch equipment; the multi-source data fusion processing module is used for processing the acquired data and generating a unified analysis data set; the digital twin model building module is used for building a digital twin model by analyzing the data set and realizing real-time synchronization and dynamic updating of the digital twin model and the physical turnout switch equipment; the intelligent operation and maintenance decision module analyzes data in the digital twin model and generates operation and maintenance information; and the visualization and interaction module is used for visually displaying the operation and maintenance information, supporting remote interaction and operation of operation and maintenance personnel and mobile terminal application integration, and realizing intelligent management of turnout operation and maintenance. The method has the effects of improving the operation and maintenance precision and efficiency and reducing the maintenance cost and the safety risk.

Description

Technical Field

[0001] The present application relates to the technical field of intelligent operation and maintenance of rail transit, and in particular to an intelligent operation and maintenance system and method for turnout and switch equipment. Background Art

[0002] Railway turnouts, as key components of railway tracks, have a direct impact on the safety and efficiency of rail transportation. Traditional turnout operation and maintenance relies primarily on manual inspections and scheduled maintenance. This approach is not only inefficient, costly, and lacks real-time performance, but also makes it difficult to detect potential faults in a timely manner. The rapid development of the railway transportation industry has placed higher demands on intelligent and efficient turnout operation and maintenance.

[0003] Traditional turnout operation and maintenance has the following problems: 1. The collection of turnout data is seriously isolated: temperature, humidity, oil level, pressure, voltage, current, relay status, image and other multi-source data are collected independently, lacking in-depth fusion analysis.

[0004] 2. Insufficient virtual-real interaction between turnout equipment: The existing monitoring system cannot achieve real-time mapping and interactive verification between physical equipment and virtual models.

[0005] 3. Delayed decision-making in turnout equipment maintenance: Fault judgment relies on manual experience and cannot dynamically generate the optimal maintenance strategy.

[0006] With the development of technologies such as the Internet of Things, big data, and artificial intelligence, multi-source data fusion and digital twin technology have provided new solutions for the intelligent operation and maintenance of turnout equipment. Multi-source data fusion integrates data from various sensors and monitoring devices, enabling comprehensive awareness of the operating status of turnout equipment. Digital twin technology, by constructing a virtual model corresponding to the physical entity, enables real-time monitoring, fault diagnosis, and predictive maintenance of turnouts. However, the inability to dynamically generate optimal maintenance strategies still hinders the accuracy and efficiency of operation and maintenance. Summary of the Invention

[0007] In order to effectively improve the accuracy and efficiency of operation and maintenance and reduce maintenance costs and safety risks, the present application provides an intelligent operation and maintenance system and method for turnout and switch equipment.

[0008] On the one hand, the intelligent operation and maintenance system of a turnout switch equipment provided by this application adopts the following technical solutions: An intelligent operation and maintenance system for turnout equipment, comprising: Multi-source data acquisition module, used to obtain various data related to the operating status of turnout equipment; A multi-source data fusion processing module is used to clean, pre-process, convert, correlate, fuse and analyze the data acquired by the multi-source data acquisition module to generate a unified analysis data set; A digital twin model construction module, which constructs a digital twin model corresponding to the physical turnout device through the analysis data set, and drives the digital twin model and the physical turnout device to achieve real-time synchronization and dynamic update through the analysis data set; An intelligent operation and maintenance decision module analyzes the data in the digital twin model and generates operation and maintenance information based on the simulation results of the digital twin model; The visualization and interaction module is used to visualize the operation and maintenance information and support remote interaction and operation of the operation and maintenance personnel.

[0009] By implementing these technical solutions, a complete intelligent operation and maintenance system framework has been established. This system enables full lifecycle management of turnout equipment through multi-source data collection and fusion, digital twin modeling, and intelligent decision-making. The real-time, synchronized, and dynamically updated digital twin model improves condition monitoring accuracy, enables intelligent decision-making, and supports proactive operation and maintenance. The visualization module enhances human-computer interaction, effectively improving the accuracy and efficiency of operation and maintenance, while reducing maintenance costs and safety risks.

[0010] Preferably, the multi-source data acquisition module includes multiple sensors, and the multiple sensors are installed in key parts of the turnout equipment; the multiple sensors include some or all of mechanical status sensors, electrical parameter sensors, environmental sensors, visual acquisition devices, inspection and maintenance record data acquisition devices, and transportation plan and train operation data acquisition devices.

[0011] By adopting the above technical solutions, multi-dimensional data (mechanical, electrical, environmental, visual, etc.) on the operating status of the turnout can be comprehensively collected, providing complete and accurate input data for subsequent analysis and decision-making.

[0012] Preferably, the digital twin model includes one of a geometric model, a physical model, a behavioral model, a rule model, and any combination thereof.

[0013] By adopting the above technical solutions, the operating status of turnout equipment can be accurately simulated through multi-model fusion, supporting fault prediction and performance optimization.

[0014] Preferably, the geometric model is constructed based on a three-dimensional modeling tool, and the three-dimensional modeling tool includes at least one of CAD, BIM, 3ds Max, and SolidWorks.

[0015] By adopting the above technical solution, high-precision three-dimensional visualization of turnout equipment can be achieved, facilitating intuitive monitoring and interactive operation.

[0016] Preferably, the physical model is constructed based on a multi-body dynamics tool and / or a finite element analysis tool, and the multi-body dynamics tool includes at least one of ADAMS, RecurDyn, and Simpack.

[0017] By adopting the above technical solutions, the mechanical behavior and performance parameters (such as switching force and stress distribution) of the turnout equipment can be accurately simulated, providing a scientific basis for fault diagnosis.

[0018] Preferably, the intelligent operation and maintenance decision module uses a machine learning algorithm and / or an AR algorithm to analyze the data in the digital twin model, and the machine learning algorithm includes an LSTM neural network and a degradation model.

[0019] On the other hand, the present application provides an intelligent operation and maintenance method for turnout equipment, which adopts the following technical solutions: An intelligent operation and maintenance method for turnout equipment includes the following steps: Multi-source data collection to obtain various data related to the operating status of turnout equipment; Multi-source data fusion processing, correlating, fusing and analyzing the collected data, and generating a unified analysis data set; Building a digital twin model, building a digital twin model corresponding to the physical turnout device through the analysis data set; Intelligent operation and maintenance decision-making, analyzing the data in the digital twin model and generating operation and maintenance information; Visualization and interaction: Visualize the operation and maintenance information and support remote interactive operations.

[0020] By adopting the above technical solutions, remote monitoring and mobile management of turnout operation and maintenance can be realized, improving the convenience and response speed of operation and maintenance.

[0021] Preferably, the digital twin model construction step includes: Data fusion, integrating various data related to the operating status of turnout equipment with the digital twin model; Simulation and verification: performing simulation verification on the digital twin model; Actual system integration, integrating the digital twin model with the actual control system of the physical turnout equipment; Deployment and application: deploy the digital twin model to the monitoring system and / or maintenance management system.

[0022] By adopting the above technical solutions, the authenticity of the twin is ensured through model assembly and data fusion, simulation verification reduces the cost of trial and error, and system integration realizes virtual-reality linkage control.

[0023] Preferably, the operation and maintenance information includes at least one of fault cause location, maintenance decision suggestions, action plans and action instructions, and the action instructions are used to control and / or verify the physical turnout switching equipment.

[0024] By adopting the above technical solutions, comprehensive operation and maintenance support is provided to achieve intelligent and precise maintenance operations.

[0025] In summary, this application includes at least one of the following beneficial technical effects: 1. This system achieves precise monitoring and fault warnings for turnout equipment. By integrating multi-source data, including operational status data, environmental data, and video image data, it comprehensively and accurately monitors the operational status of turnouts, effectively avoiding the bias and errors inherent in monitoring using a single data source. Furthermore, by analyzing the fused data using intelligent algorithms, it can quickly and accurately identify potential faults, issuing early warnings and providing maintenance personnel with ample time to address them, significantly reducing the impact of faults on rail transportation.

[0026] 2. Enable intelligent decision-making and maintenance optimization. Based on the digital twin model, it can simulate the performance of turnouts under different operating conditions, providing a scientific basis for fault diagnosis and maintenance strategy formulation. Furthermore, by predicting future operating conditions and optimizing maintenance plans, it can rationally arrange maintenance time and content, achieve precise maintenance, avoid over- or under-maintenance, reduce maintenance costs, and extend the service life of turnouts.

[0027] 3. Enables visualization and remote management. A visual interface directly displays the turnout's operating status and maintenance information, enabling operators to quickly access critical data and improve decision-making efficiency. Furthermore, remote interaction allows operators to monitor and operate turnouts remotely, enhancing management flexibility and responsiveness. Furthermore, mobile application integration further facilitates anytime, anywhere information access for operators, enabling mobile and convenient operation and maintenance management.

[0028] 4. Improve rail transportation safety and efficiency. On the one hand, precise monitoring and intelligent maintenance will reduce the occurrence of turnout failures and ensure the safety of train operations. On the other hand, optimized maintenance plans and efficient operation and maintenance management will reduce the disruption of turnout maintenance to rail transportation, thereby improving the overall efficiency and reliability of rail transportation. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] Figure 1 This is a principle block diagram of Example 1 of the present application; Figure 2 This is a schematic diagram of the composition of the multi-source data acquisition module in Example 1 of the present application; Figure 3 This is a flow chart of Example 2 of the present application; Figure 4 This is a flow chart of the digital twin model construction module in Example 2 of the present application using the analysis data set to construct a digital twin model corresponding to the physical turnout switch equipment. DETAILED DESCRIPTION

[0030] The following combination Figures 1-4 This application is described in further detail.

[0031] Example 1: Example 1 of the present application discloses an intelligent operation and maintenance system for turnout equipment.

[0032] Reference Figure 1 An intelligent operation and maintenance system for turnout and switch equipment includes a multi-source data acquisition module, a multi-source data fusion processing module, a digital twin model component module, an intelligent operation and maintenance decision module, a communication module, and a visualization and interaction module. The communication module is used for data transmission and interaction and connects the multi-source data acquisition module, the multi-source data fusion processing module, the digital twin model component module, and the intelligent operation and maintenance decision module via a communication network. The intelligent operation and maintenance decision module is also signal-connected to the visualization and interaction module.

[0033] refer to Figure 2 ,The multi-source data acquisition module is used to obtain various ,data related to the operating status of turnout and switch ,equipment.

[0034] The multi-source data acquisition module includes multiple sensors installed in key locations on the turnout equipment. These sensors include some or all of the following: mechanical status sensors, electrical parameter sensors, environmental sensors, visual acquisition devices, inspection and maintenance record data acquisition devices, and transportation plan and train operation data acquisition devices. In other embodiments, other types of sensors may be added as needed.

[0035] Mechanical status sensors are used to obtain mechanical operating status data for turnout switching equipment. This data includes some or all of the following: switching force, contact force, frictional resistance, point rail displacement, rail temperature, humidity, switch machine gap, opening distance, locking amount, actuation time, the depth of the automatic switch's moving contact into the static contact, and relay status data. Switching force refers to the force required during the turnout switching process and can be used to assess smooth turnout switching. Point rail displacement refers to the displacement of the point rail relative to the stock rail and can be used to assess turnout contact. Contact force refers to the contact force between the point rail and the stock rail and can be used to assess train safety during passage. Rail temperature and humidity affect turnout performance and lifespan and can be used as predictors of turnout performance and lifespan.

[0036] Electrical parameter sensors are used to obtain the electrical characteristic parameters of turnout equipment. These parameters include some or all of the following: data transmission and communication status, phase protector output voltage, leakage current, cable insulation, operating current, operating power, operating voltage, indicated voltage, and indicated current. Operating current refers to the current value of the turnout during operation and can be used to monitor the turnout's operating status. Electrical parameter sensors include voltage sensors that obtain voltage data between the X2 and X3 channels of AC turnouts. This voltage data includes the voltage data between the X2 and X3 channels when the 1DQJ is lowered and / or when the 1DQJ is raised.

[0037] Environmental sensors are used to obtain meteorological and / or environmental data about the environment in which the turnout equipment operates. Meteorological data includes some or all of temperature, humidity, wind speed, rainfall, and snowfall. Environmental data includes some or all of ambient vibration, noise, and foreign object intrusion data. Meteorological data such as temperature, humidity, wind speed, rainfall, and snowfall can be used to assess the impact of the environment on turnout operation, while ambient vibration, noise, and foreign object intrusion data can be used to monitor the stability of the turnout's surrounding environment.

[0038] The visual acquisition device is used to acquire visual data from the switch equipment; this visual data includes video / image data, including high-definition video / images of the switch rail, video / images of the overall switch appearance, close-up video / images of switch components, video / images of the switch under different weather / lighting conditions, and dynamic video / images of the switch during a train passage. In some embodiments, industrial high-definition cameras installed on both sides of the switch rail capture video / images of the switch rail to monitor the contact and creep of the switch rail, as well as the presence of foreign objects in the surrounding area. In other embodiments, high-definition cameras capture video / images of the entire switch appearance, providing real-time video monitoring of the switch's overall appearance, allowing intuitive observation of the status of each switch component, its connections, and any anomalies in the surrounding environment. In other embodiments, high-definition cameras capture close-up images of key switch components, such as the switch machine and frog, to provide detailed observation of wear, deformation, looseness, and other conditions. Since turnouts are often located outdoors, varying weather and lighting conditions can affect the effectiveness of visual monitoring. In other embodiments, video / image data is collected under various weather conditions (sunny, rainy, foggy, etc.) and lighting conditions (daytime, nighttime, strong light, low light, etc.) to ensure the robustness and accuracy of the visual monitoring system. When a train passes through a turnout, the deformation and vibration of the turnout under dynamic loads (video / images of the turnout during train passage) are collected to analyze its performance and safety under actual operating conditions. Real-time video monitoring of the turnout's appearance and structure, along with regular image acquisition, allows for intuitive observation of the status of its components and surrounding environment.

[0039] The inspection and maintenance record data collection device is used to obtain part or all of the switch equipment status, fault information, maintenance operation records, and parts replacement information recorded by manual inspection personnel. Specifically, this data can be obtained by manual testing and inputting it into a computer or handheld intelligent terminal device.

[0040] The transport plan and train operation data acquisition device is used to obtain part or all of the train operation plan, train number information, the time and speed of train passing through switches, and the axle weight and wheelbase parameter data during the actual operation of the train from the railway transport dispatching department. The train operation plan, train number information, and the time and speed of train passing through switches from the railway transport dispatching department can be obtained by transmitting data on train operation control terminal equipment such as CSM, CBI, CTC, TDCS, RBC, and TCC. The axle weight and wheelbase parameter data during the actual operation of the train can be obtained by consulting train design documents and technical documents, analyzing and reviewing the on-board monitoring system, installing track monitoring and testing equipment, and consulting the operation management department.

[0041] In addition, in other embodiments, various data related to the operating status of the turnout and switch equipment can be obtained through other means. For example, data access from the monitoring system can be used to access terminal equipment such as the railway signal centralized monitoring system, the turnout gap monitoring system, CTC, TDCS, RBC, and TCC maintenance machines to obtain various data related to the operating status of the turnout and switch equipment. Alternatively, video / image data acquisition can be used to collect various data related to the operating status of the turnout and switch equipment using high-definition cameras installed at key locations of the turnout and switch equipment. Alternatively, various data related to the operating status of the turnout and switch equipment can be obtained through manual testing by operation and maintenance personnel, such as the switch gap, opening distance, locking amount, the depth of the automatic switch moving contact into the static contact, and the cable loop resistance. The data can then be input into a computer / handheld intelligent terminal device for acquisition by a multi-source data acquisition module.

[0042] The multi-source data fusion processing module cleans, preprocesses, converts, correlates, fuses, and analyzes the data acquired by the multi-source data acquisition module to generate a unified analytical dataset. The goal of this process is to transform raw, dispersed, and of varying quality multi-source data into a high-quality dataset suitable for analysis. This allows for more accurate simulation, emulation, and prediction of the operating status of turnout equipment, allowing for the development of effective operation and maintenance strategies.

[0043] The collected multi-source data may contain noise data (such as incorrect measurements, abnormal fluctuations), missing values, and duplicate values. Data cleaning is to identify and process these problem data to improve data quality.

[0044] For example, in some possible implementations of the present invention, a statistical analysis-based method, such as the 3B criterion, is used to identify and correct abnormal values ​​in sensor data and remove duplicate values ​​in monitoring system data.

[0045] Preprocessing is to further process the cleaned data to make it meet the requirements of subsequent analysis algorithms or models. For example, in some possible implementation methods of the embodiments of the present invention, data preprocessing is performed by data standardization, normalization, data discretization, etc.

[0046] The data after preprocessing may still not meet the purpose of data analysis. It is necessary to convert the format, structure or value of the data, standardize the data in different formats and units, unify the data format and dimension, and ensure the consistency and accuracy of the data. For example, in some possible implementation methods of the embodiments of the present invention, the temperature data collected by sensors of different brands are uniformly converted into Celsius units to ensure the consistency and comparability of the data. Or based on dimensions such as time and space, data from multiple independent data sources but describing the same entity or subject are linked together. In some possible implementation methods of the embodiments of the present invention, the turnout operation status data at the same time is associated with the corresponding environmental data and train operation data to construct a comprehensive data set of turnout operation; the video image data is matched with the component location, fault description and other information in the inspection and maintenance record data to further enrich the status feature data of the turnout components.

[0047] The digital twin model construction module uses the analysis data set to build a digital twin model corresponding to the physical turnout and switch equipment, and realizes real-time synchronization and dynamic update of the digital twin model and the physical turnout and switch equipment through the analysis data set drive.

[0048] The digital twin model includes one of the geometric model, physical model, behavioral model, rule model and any combination thereof; Geometric models are used to describe the geometric features of various components of the physical turnout equipment. This includes building detailed 3D models using 3D modeling and / or real-time rendering tools. 3D modeling tools include some or all of CAD, BIM, 3ds Max, SolidWorks, OpenRail Designer, Maya, and Rhino; real-time rendering tools include some or all of Unity, UnrealEngine, CryEngine, Enscape, Lumion, Twinmotion, KeyShot, Marmoset Toolbag, and Blender.

[0049] For example, in some possible implementations of the present invention, the functions of the building information modeling tool Revit, the 3D modeling tool 3ds Max, and the real-time rendering tool Unity3D are integrated to create a complete turnout equipment analysis data set database. The database not only contains the detailed parameters of the turnout equipment (such as model, specifications, operating status, etc.), but also includes the virtual model and operating status information of the turnout equipment, which can be used to monitor the operating status of the equipment in real time, perform fault diagnosis, and assist decision-making and support maintenance activities.

[0050] Specifically, a virtual equipment model (Building Information Modeling, BIM) of the turnout equipment is created in the building information modeling tool Revit. Various data related to the turnout equipment's operating status is added to the virtual equipment BIM model, including the turnout equipment's geometric shape and various attribute data, such as metal material, hardness, appearance color, and size. After the virtual equipment BIM model is created, it is imported into a 3D modeling tool (3D Studio Max, 3ds Max) for high-precision rendering to achieve more realistic visual effects and enhance the realism of the virtual equipment BIM model. After rendering, the virtual equipment BIM model is converted into an FBX format model file supported by Unity3D. The FBX format is a universal 3D file format that can easily transfer 3D models between different software platforms. Based on this, a virtual scene is built in Unity3D and the converted FBX format model file is imported. The turnout equipment is then programmed to construct a digital twin model corresponding to the physical turnout equipment.

[0051] The physical model is used to describe the physical characteristics and performance of each component of the physical turnout and switch equipment, including the integration of multi-body dynamics and / or finite element analysis tools to simulate the operating principles, various constraints, and performance parameters. Multi-body dynamics tools include some or all of ADAMS, RecurDyn, Simpack, Ansys Motion, COMSOL Multiphysics, Universal Mechanism, MotionSolve, and SIMULIA. Finite element analysis tools include some or all of ANSYS, ABAQUS, COMSOL Multiphysics, COMSOL Multiphysics, COMSOL Multiphysics, Altair HyperWorks, MSCNastran, CalculiX, CalculiX, Elmer FEM, Elmer FEM, and FreeFEM. The physical model is used to reflect its mechanical behavior, material properties, etc. during actual operation. For example, in some embodiments, as a possible implementation method, a performance simulation model of the turnout and switch equipment under different operating conditions is constructed based on the operating principle, kinematics, and dynamics of the turnout and switch equipment. For example, a mechanical model of the switch is constructed to simulate its conversion force and conversion time under different loads; a force analysis model of the point rail is constructed to analyze its stress distribution when a train passes.

[0052] The behavioral model is used to describe the behavioral characteristics of various parts of the physical turnout and switch equipment, including building and training LSTM neural networks and degradation models based on machine learning and artificial intelligence tools, learning historical action timing characteristics, and predicting the dynamic behavior of the turnout and switch equipment; machine learning and artificial intelligence tools include development frameworks, application platforms, and data mining and analysis tools; development frameworks include some or all of TensorFlow, PyTorch, Keras, Microsoft Cognitive Toolkit, caret, Deeplearning4j, Weka, Microsoft Cognitive Toolkit, Caffe, and Torch; application platforms include some or all of Google Vertex AI, Google Vertex AI, and Amazon Machine Learning; data mining and analysis include some or all of Weka and Apache Mahout.

[0053] In some embodiments, as a possible implementation method, various sensors installed on the turnout collect real-time operating status data of the turnout, such as switching force, operating current, point rail displacement, and contact force. Machine learning, data mining, and other technologies are then used to analyze the collected operating status data to identify the normal operating behavior patterns and various fault behavior patterns of the turnout. For example, cluster analysis can be used to group similar operating status data into different clusters of behavior patterns. A classification algorithm is then used to establish a mapping relationship between operating status data and behavior patterns. Based on the identified behavior patterns, a behavioral model of the turnout switching equipment is then constructed. This model can predict the current behavior of the turnout based on the input operating status data and issue timely warnings when abnormal behavior occurs.

[0054] The rule model is used to describe the operational processes and failure modes of physical turnout equipment, including building the operating rules and logic for the turnout equipment based on rule engine tools. Rule engine tools include some or all of Drools, Drools, Aviator, OpenL Tablets, RuleBook, ILOG JRules, Visual Rules, Blaze, NRules (.NET), CLIPS, and Jess. In some embodiments, as a possible implementation, the rule model is a series of rules and policies developed based on railway turnout operation and maintenance specifications, standards, and expert experience to guide turnout maintenance decisions and fault handling.

[0055] In some embodiments, as a possible implementation approach, key rules and strategies are extracted from data such as railway turnout operation and maintenance specifications, inspection standards, and troubleshooting manuals. These rules are then supplemented and refined by integrating expert experience and actual maintenance cases. The extracted rules are then represented in a computer-readable and processable format, such as production rules (If-Then rules), decision trees, and state transition diagrams. For example, "If the operating current of the turnout exceeds a threshold, this may indicate a switch machine overload" can be represented as a production rule. A rule engine or expert system is then used to reason and make decisions based on pre-set rules, based on the input turnout operating status data and fault diagnosis results, to generate corresponding maintenance recommendations and troubleshooting solutions. For example, when the system detects that the displacement of a turnout's point rail exceeds the allowable range, the rule model automatically recommends the steps and tools required to adjust or replace the point rail.

[0056] The intelligent operation and maintenance decision-making module uses machine learning algorithms / AR algorithms to analyze the data in the digital twin model and generates operation and maintenance information based on the twin model simulation results.

[0057] The visualization and interaction module is used to visualize operation and maintenance information, support remote interaction and operation of operation and maintenance personnel, and integrate mobile applications to achieve intelligent management of turnout operation and maintenance.

[0058] The implementation principle of the intelligent operation and maintenance system of a turnout device in Example 1 of the present application is as follows: multi-dimensional sensor data such as mechanical, electrical, and environmental data are acquired in real time through a multi-source data acquisition module, and transmitted to a multi-source data fusion processing module via a communication module for cleaning and correlation analysis to generate a unified data set. Based on the data set, the digital twin model construction module integrates BIM modeling, dynamic simulation, and machine learning technologies to construct a digital twin containing geometric, physical, behavioral, and rule models to achieve real-time synchronization and prediction of equipment status. The intelligent operation and maintenance decision module combines the twin model simulation results, uses LSTM networks and rule engines to perform fault diagnosis, and generates operation and maintenance decisions. Finally, status monitoring and remote control are achieved through the 3D interface of the visualization module and the mobile APP, forming an intelligent closed loop of "acquisition-analysis-decision-feedback", which significantly improves the operation and maintenance efficiency and reliability of the turnout equipment.

[0059] Example 2: Reference Figure 3 A method for intelligent operation and maintenance of turnout equipment is provided, based on the above-mentioned intelligent operation and maintenance system for turnout equipment, comprising the following steps: S1: Multi-source data acquisition, using the multi-source data acquisition module to obtain various data related to the operating status of turnout equipment; S2: Multi-source data fusion processing: Use the multi-source data fusion processing module to clean, pre-process, convert, correlate, fuse, and analyze various data related to the operating status of turnout equipment to generate a unified analysis data set; S3: Digital twin model construction, generating a unified analysis data set. The digital twin model construction module uses the analysis data set to build a digital twin model corresponding to the physical turnout equipment. The analysis data set drives the real-time synchronization and dynamic update of the digital twin model and the physical turnout equipment. Reference Figure 4 Specifically, using the analysis data set to build a digital twin model corresponding to the physical turnout equipment includes the following steps: S31: Model assembly: Assemble part or all of the geometric model, physical model, behavioral model, and rule model to form a complete digital twin model; based on the hierarchical structure of the turnout, gradually build the hierarchical relationship of the model from parts to components to the overall system, and add corresponding spatial constraints.

[0060] S32: Data fusion: Integrate the acquired sensor data, monitoring data, and historical data with the digital twin model to achieve real-time updating and dynamic adjustment of the model.

[0061] S33: Simulation and Verification: Use simulation tools to simulate the digital twin model to verify whether its behavior under different working conditions is consistent with the physical turnout equipment; adjust and optimize the model based on the simulation results.

[0062] S34: Integration with the actual system; Integrate the digital twin model with the actual control system of the physical turnout equipment to achieve real-time data interaction and two-way feedback.

[0063] S35: Deployment and application: Deploy the constructed digital twin model to the actual application environment, which includes the monitoring system and / or maintenance management system.

[0064] refer to Figure 3 ,S4: Intelligent operation and maintenance decision-making.

[0065] Build a digital twin model corresponding to the physical turnout equipment. The intelligent operation and maintenance decision module uses machine learning algorithms / AR algorithms to analyze the data in the digital twin model and generate operation and maintenance information based on the twin model simulation results. Specifically, the intelligent operation and maintenance decision module uses machine learning algorithms / AR algorithms to analyze the data in the digital twin model, and combines the twin model simulation results to generate operation and maintenance information; the operation and maintenance information includes fault cause location and / or maintenance decision recommendations and / or action plans and / or action instructions; the instructions include reacting to the physical turnout and switch equipment, verifying the principles and working processes of the physical turnout and switch equipment, and / or controlling the physical turnout and switch equipment.

[0066] S5: Visualization and interaction: Visualize the operation and maintenance information, support remote interaction and operation by operation and maintenance personnel, and integrate mobile applications to achieve intelligent management of turnout operation and maintenance.

[0067] The above are all preferred embodiments of the present application, and are not intended to limit the scope of protection of the present application. Therefore, any equivalent changes made based on the structure, shape, and principle of the present application should be included in the scope of protection of the present application.

Claims

1. An intelligent operation and maintenance system for turnout equipment, characterized by: include: Multi-source data acquisition module, used to obtain various data related to the operating status of turnout equipment; A multi-source data fusion processing module is used to clean, pre-process, convert, correlate, fuse and analyze the data acquired by the multi-source data acquisition module to generate a unified analysis data set; A digital twin model construction module, which constructs a digital twin model corresponding to the physical turnout device by analyzing the data set, and drives the digital twin model and the physical turnout device to achieve real-time synchronization and dynamic update through the analysis data set; An intelligent operation and maintenance decision module analyzes the data in the digital twin model and generates operation and maintenance information based on the simulation results of the digital twin model; The visualization and interaction module is used to visualize the operation and maintenance information and support remote interaction and operation of the operation and maintenance personnel.

2. The intelligent operation and maintenance system for turnout equipment according to claim 1, characterized in that: The multi-source data acquisition module includes multiple sensors, and the multiple sensors are installed in key parts of the turnout equipment; the multiple sensors include some or all of the mechanical status sensors, electrical parameter sensors, environmental sensors, visual acquisition devices, inspection and maintenance record data acquisition devices, and transportation plan and train operation data acquisition devices.

3. The intelligent operation and maintenance system for turnout equipment according to claim 1, characterized in that: The digital twin model includes one of a geometric model, a physical model, a behavioral model, a rule model, and any combination thereof.

4. The intelligent operation and maintenance system for turnout equipment according to claim 3, characterized in that: The geometric model is constructed based on a three-dimensional modeling tool, and the three-dimensional modeling tool includes at least one of CAD, BIM, 3ds Max, and SolidWorks.

5. The intelligent operation and maintenance system for turnout equipment according to claim 3 is characterized by: The physical model is constructed based on a multi-body dynamics tool and / or a finite element analysis tool, wherein the multi-body dynamics tool includes at least one of ADAMS, RecurDyn, and Simpack.

6. The intelligent operation and maintenance system for turnout equipment according to claim 1, characterized in that: The intelligent operation and maintenance decision module uses a machine learning algorithm and / or an AR algorithm to analyze the data in the digital twin model. The machine learning algorithm includes an LSTM neural network and a degradation model.

7. An intelligent operation and maintenance method for turnout equipment, based on the system according to any one of claims 1 to 6, characterized in that: The following steps are involved: Multi-source data collection to obtain various data related to the operating status of turnout equipment; Multi-source data fusion processing, correlating, fusing and analyzing the collected data, and generating a unified analysis data set; Building a digital twin model, building a digital twin model corresponding to the physical turnout device through the analysis data set; Intelligent operation and maintenance decision-making, analyzing the data in the digital twin model and generating operation and maintenance information; Visualization and interaction: Visualize the operation and maintenance information and support remote interactive operations.

8. The intelligent operation and maintenance method for turnout equipment according to claim 7, characterized in that: The digital twin model construction steps include: Data fusion, integrating various data related to the operating status of turnout equipment with the digital twin model; Simulation and verification: performing simulation verification on the digital twin model; Actual system integration, integrating the digital twin model with the actual control system of the physical turnout equipment; Deployment and application: deploy the digital twin model to the monitoring system and / or maintenance management system.

9. The intelligent operation and maintenance method for a turnout device according to claim 7, characterized in that: The operation and maintenance information includes at least one of fault cause location, maintenance decision suggestions, action plans and action instructions, and the action instructions are used to control and / or verify the physical turnout switching equipment.

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