Modulus-driven high-speed rail overhead line system full-life-cycle management method

Abstract

The invention discloses a modulus-driven high-speed rail overhead line system full life cycle management method, and relates to the technical field of railway overhead line systems, and the method comprises the steps: analyzing the full life cycle of an overhead line system, and constructing a life stage block; collecting life stage data, and storing the life stage data into the corresponding life stage blocks; respectively constructing a representation model library, a data lake and a mechanism model library for the collected data according to a subject domain; establishing a collaborative periodic relationship based on the axial data relationship; building a digital-analog period integrated atlas relation; and performing operation processing to generate stage management information. The technical problems of unreasonable resource allocation, slow fault response and low management efficiency caused by the fact that information is difficult to share in real time and the intelligent level of full-life-cycle management is not high due to neglecting of contact and mutual influence of all stages of the full-life-cycle of the overhead line system in the prior art are solved. Concentrated storage and efficient management of data in all stages are achieved, and the technical effect of improving operation and maintenance management efficiency and power supply stability is achieved.

Description

Technical Field

[0001] The present application relates to the technical field related to railway contact networks, and in particular to a full life cycle management method for analog-to-digital driven high-speed railway contact networks. Background Art

[0002] With the rapid development of high-speed railways (HSR), as a key component of the power supply system of HSR, the stability and reliability of the performance of the contact network is crucial to ensure the safe operation of HSR, and also directly affects the transportation efficiency of the railway network. The design of the HSR contact network is complex and widely distributed, covering all stages from design, manufacturing to construction, operation and maintenance. However, the traditional HSR contact network management often focuses on the independent processing of a single stage or link, such as the design stage focuses on structural design and material selection, the construction stage focuses on installation technology and quality control, and the operation and maintenance stage emphasizes fault monitoring and timely maintenance. It is unable to take into account the complex information flow between the planning and design of the HSR contact network, construction, and operation and maintenance. This management method ignores the inherent connection and mutual influence of each stage in the life cycle of the HSR contact network, resulting in serious information island phenomenon, difficulty in data sharing, low management efficiency, lack of scientific basis for decision-making, unreasonable overall resource allocation, slow response to faults, and difficulty in meeting the needs of modern HSR for efficient management and accurate decision-making.

[0003] At present, there are technical problems in the high-speed railway contact network management related technologies that ignore the connection and mutual influence of various stages in the entire life cycle of the contact network, which makes it difficult to share information in real time and the level of intelligent management of the entire life cycle is not high, resulting in unreasonable resource allocation, slow fault response and low management efficiency. Summary of the invention

[0004] This application provides an analog-to-digital driven high-speed railway contact network full life cycle management method to solve the technical problems existing in the prior art of ignoring the connection and mutual influence of various stages in the full life cycle of the contact network, resulting in difficulty in real-time information sharing and low level of intelligence in the full life cycle management, which in turn leads to unreasonable resource allocation, slow fault response and low management efficiency. It realizes centralized storage and efficient management of data at each stage, and achieves the technical effect of improving operation and maintenance management efficiency and power supply stability.

[0005] The present application provides an analog-to-digital driven high-speed railway contact network full life cycle management method, including: analyzing the full life cycle of the contact network, including at least design, manufacturing, construction, operation and maintenance, constructing a life stage block based on each stage of the full life cycle, and the life stage block has a life tag; collecting data of each life stage, and storing it in the corresponding life stage block based on the life tag; based on the life stage block, constructing a representation model library, a data lake, and a mechanism model library according to the subject domain of the collected data, the representation model library is used to store models, the data lake is used to store data, and the mechanism model library is used to store algorithms; obtaining the axial data relationship of each life stage, and establishing a collaborative periodic relationship based on the axial data relationship; according to the collaborative periodic relationship, the representation model library, the data lake, and the mechanism model library are associated and mapped to build a digital-analog cycle integrated graph relationship; according to the digital-analog cycle integrated graph relationship, the data of the data lake is used to perform calculations and processing through the models of the representation model library and the mechanism model library to generate stage management information.

[0006] In a possible implementation, based on the life stage block, the collected data is respectively constructed into a representation model library, a data lake, and a mechanism model library according to the subject domain, and the following processing is also performed: configuration design delivery requirements and high-speed rail contact network delivery requirements, the design delivery requirements include component granularity and data accuracy requirements, and the high-speed rail contact network delivery requirements include geometric parameter requirements and contact network equipment status requirements; based on the design delivery requirements and high-speed rail contact network delivery requirements, a BIM model is constructed, and the BIM model is used as a representation model for the design, manufacturing, and construction life stages; based on the BIM model combined with the monitoring equipment deployed for the high-speed rail contact network, an operation and maintenance monitoring model for the operation and maintenance life stage is constructed as a representation model for the operation and maintenance life stage; all data involved in each life stage are collected, including dynamic data and static data, the dynamic data is the physical parameters of the contact network components, and the dynamic data is the operating parameters of each life stage, and the data lake is constructed using the dynamic data and static data; based on the analysis requirements of each life stage, a mechanism model is constructed using the data lake, and the analysis requirements include verification, evaluation, monitoring, detection or early warning calculation analysis.

[0007] In a possible implementation, the axial data relationship of each life stage is obtained, and the following processing is also performed: abnormal cases of each life stage are collected to determine the top event; based on the top event, sub-events are identified through abnormal cases, and accident factors are decomposed according to the sub-events; according to the relationship path of the accident factor-sub-event-top event, the logical relationship is analyzed, and the accident factor and sub-event are linked to the top event in sequence using AND gate and OR gate logic to construct a stage logic tree; according to the logic tree of each stage, based on the time axial relationship of each life stage, the correlation between the top events and sub-events between each life stage is analyzed, and the accident factor data flow is fitted based on the correlation to obtain the axial data relationship of each life stage.

[0008] In a possible implementation, according to the logic tree of each stage and based on the time axial relationship of each life stage, the correlation between the top events and sub-events between each life stage is analyzed, and the following processing is also performed: according to the time axial relationship of the life stage, the stage logic trees of each life stage are axially connected; based on the time axial relationship, the axial correlation analysis of each stage is performed with the top event as the starting point to determine the correlation between the top events, and the axial connection is established according to the correlation; after completing the axial correlation analysis of the top events of all life stages, the axial correlation analysis of each sub-event is performed based on the order of the time axial relationship, and the sub-events are axially connected according to the correlation calculation results; the connection weight is configured according to the correlation of the axial connection, the logic trees of each stage are integrated and spliced, and the logic tree of the whole life cycle is established.

[0009] In a possible implementation, the accident factor data flow is fitted based on the correlation to obtain the axial data relationship of each life stage, and the following processing is also performed: according to the correlation of the accident factor to the sub-event, the accident factor is fitted into the full life cycle logic tree; according to the logical relationship between the sub-event and the accident factor, and the correlation of the sub-event between the life stages, the relationship is aggregated to obtain the first-order data flow relationship; according to the logical relationship between the top event and the accident factor, and the correlation of the top event between the life stages, the relationship is aggregated to obtain the second-order data flow relationship; according to the first-order data flow relationship and the second-order data flow relationship, the axial data relationship of each life stage is obtained.

[0010] In a possible implementation, a collaborative periodic relationship is established based on the axial data relationship, and the following processing is also performed: a stage data interface is established according to the axial data relationship; a data collaborative connection between periodic stages is established using the stage data interface, and a forward and reverse data sharing mechanism is configured for the stage data interface for forward data driving and reverse data tracing.

[0011] In a possible implementation, the stage data interface is used to establish a data collaborative connection between cycle stages, and the following processing is also performed: through the stage data interface, one-pole-one-file data of the collaborative cycle relationship is obtained; the operation and maintenance data of the operation and maintenance stage is obtained; the one-pole-one-file data is merged with the operation and maintenance data to identify abnormal contact network data; the abnormal contact network data is used to update the operation and maintenance model, or it is fed back to the collaborative cycle through the stage data interface for optimization search.

[0012] In a possible implementation, the representation model library, data lake, and mechanism model library are associated and mapped according to the collaborative cycle relationship to build an integrated digital-analog cycle graph relationship, and the following processing is performed: a data correspondence between the data lake and the representation model library and the mechanism model library is established to construct a mapping matrix, wherein the row elements of the mapping matrix are the data / model library and the column elements are each collaborative cycle; according to the full life cycle logic tree, the graph structure of the collaborative cycle is constructed with life stages, top events, sub-events, and accident factors as graph nodes and the relationships between graph nodes as edges; according to the mapping matrix, the data / model library is fitted into the graph structure of the collaborative cycle to build the integrated digital-analog cycle graph relationship.

[0013] In a possible implementation, according to the mapping matrix, the data / model library is fitted into the graph structure of the collaborative cycle, and the following processing is further performed: based on the graph structure of the collaborative cycle, the weight value of the graph node associated data is configured; according to the mapping matrix, the collaborative cycle weight associated with the graph node is identified, the weight in the data / model library is assigned to the corresponding graph node, and the data / model library is fitted into the graph structure of the collaborative cycle; wherein the assignment expression is: Among them, W n is the total weight of the node, N is the total number of cooperative cycles in the mapping matrix, D m,i is the weight of data / model m in the mapping matrix in the i-th collaborative cycle, C i is the weight of the i-th cooperative cycle in the mapping matrix.

[0014] The proposed method for the full life cycle management of high-speed rail contact network driven by analog and digital technology in this application is intended to analyze the full life cycle of the contact network and construct life stage blocks; collect data of each life stage and store them in the corresponding life stage blocks; construct the representation model library, data lake, and mechanism model library according to the subject domain of the collected data; establish a collaborative cycle relationship based on the axial data relationship; build a digital-analog cycle integrated graph relationship; perform calculations and processing to generate stage management information. This solves the technical problems existing in the prior art of ignoring the connection and mutual influence of each stage of the contact network's full life cycle, which makes it difficult to share information in real time and the level of intelligent management of the full life cycle is not high, resulting in unreasonable resource allocation, slow fault response, and low management efficiency. It realizes the centralized storage and efficient management of data at each stage, and achieves the technical effect of improving operation and maintenance management efficiency and power supply stability. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to more clearly illustrate the technical solution of the embodiment of the present invention, the accompanying drawings of the embodiment of the present invention will be briefly introduced below. A flow chart is used in the present application to illustrate the operations performed by the system according to the embodiment of the present application. It should be understood that the preceding or following operations are not necessarily performed accurately in order. On the contrary, various steps can be processed in reverse order or simultaneously as needed. At the same time, other operations can also be added to these processes, or one or more operations can be removed from these processes.

[0016] Figure 1 A schematic diagram of a flow chart of a method for managing the entire life cycle of a high-speed railway overhead contact network driven by an analog-digital method according to an embodiment of the present application;

[0017] Figure 2 A schematic diagram of the process of constructing a model library in a modular-driven high-speed railway contact network full life cycle management method provided in an embodiment of the present application. DETAILED DESCRIPTION

[0018] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below.

[0019] In order to make the purpose, technical solutions and advantages of the present application clearer, the present application will be further described in detail below in conjunction with the accompanying drawings. The described embodiments should not be regarded as limiting the present application. All other embodiments obtained by ordinary technicians in the field without making creative work are within the scope of protection of this application.

[0020] In the following description, reference is made to "some embodiments", which describe a subset of all possible embodiments, but it is understood that "some embodiments" may be the same subset or different subsets of all possible embodiments, and may be combined with each other without conflict, and the terms "first\second" involved are merely to distinguish similar objects and do not represent a specific ordering of objects. The terms "including" and "having" and any variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product, or server that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or modules that are not clearly listed or inherent to these processes, methods, products, or devices. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those generally understood by technicians in the technical field of this application. The terms used herein are for the purpose of describing the embodiments of the present application only.

[0021] The present application embodiment provides a method for managing the entire life cycle of a high-speed railway overhead contact network driven by an analog-digital system. Figure 1 As shown, the method includes:

[0022] Step S100, analyzing the entire life cycle of the contact network, including at least design, manufacturing, construction, operation and maintenance, and constructing a life stage block based on each stage of the entire life cycle, wherein the life stage block has a life label.

[0023] Preferably, the whole life cycle of the high-speed rail contact network mainly includes multiple stages such as design, manufacturing, construction, operation and maintenance. Each stage carries different tasks and responsibilities, which together constitute the complete process of the contact network from planning to retirement. By constructing life stage blocks based on each stage of the whole life cycle and giving each block a life label, the life cycle of the contact network can be understood and managed more clearly. Specifically, the design stage is used to complete the structural design of the contact network, including determining its layout, parameters and performance requirements, such as basic project information, geometric parameter information of the contact network construction, and early project drawing data and models. Its life label is planning and design; the manufacturing stage is used to manufacture various components and assemblies of the contact network according to the design drawings and specification requirements, including the pre-configuration of key components (such as arms, suspension strings, etc.), production control data, etc., and quality control is carried out during the production process to ensure that each component and assembly meets the design requirements and quality standards. Its life label is production and quality control.

[0024] Preferably, the construction phase is used to transport the manufactured contact network components and assemblies to the construction site and install and debug them, including the operation data of the arm intelligent installation, wire laying control, and H-type pillar assembly process, and to carry out progress management and material management during the construction process to ensure that the construction quality and progress meet the requirements. Its life label is installation and construction; the operation and maintenance phase is used for the whole process of the contact network equipment management unit maintaining its technical status and safely and reliably supplying power to the train after the contact network is fully completed and accepted, including the detection and monitoring data, skylight plan, operation plan and abnormal alarm operation and maintenance data during the operation and maintenance process, and its life label is operation and maintenance, such as the management of the basic information, technical data, operating status, maintenance quality and cost of high-speed rail contact network equipment; monitoring, detection, inspection and status evaluation of the contact network system and equipment status, accurately grasp the technical performance, characteristics, operating laws and safety status of the equipment, and promptly repair or replace the contact network equipment that does not meet the operating requirements to ensure the safe operation of the contact network; repair and maintain the equipment of the high-speed rail contact network that is in its service life to keep it in normal condition, replace the equipment that is close to or exceeds its service life to ensure the stable operation of the contact network. By constructing life stage blocks and assigning life tags to each block, the life cycle of the contact network can be more clearly understood and managed to ensure its safe and reliable operation.

[0025] Step S200, collecting data of each life stage and storing it in a corresponding life stage block based on the life tag.

[0026] Preferably, relevant data is collected from various stages of the contact network's entire life cycle, including information parameters of contact network components in the design stage, material parameters and process parameters in the manufacturing stage, installation information parameters in the construction stage, and detection and monitoring data, operation and maintenance logs, fault reports, etc. in the operation and maintenance stage. Specifically, life tags are tags used to identify and classify data at various stages in the contact network's entire life cycle. Each life stage has its own specific tag. For example, the "Planning and Design" tag may be used in the design stage, and the "Production and Quality Control" tag may be used in the manufacturing stage. This not only helps with data classification and storage, but also facilitates subsequent data retrieval and analysis. Then, based on the life tags, the collected data is stored in the corresponding life stage blocks. These life stage blocks are databases or data storage structures designed to store and manage data at specific stages. For example, data in the design stage will be stored in the "Planning and Design" block, and data in the manufacturing stage will be stored in the "Production and Quality Control" block. By analogy, all collected high-speed rail contact network-related data will be stored in the corresponding life stage blocks, and the reliability and traceability of the data will be ensured.

[0027] Step S300, based on the life stage block, the collected data is respectively constructed into a representation model library, a data lake, and a mechanism model library according to the subject domain, the representation model library is used to store models, the data lake is used to store data, and the mechanism model library is used to store algorithms.

[0028] Preferably, according to the life stage blocks, a representation model library, a data lake and a mechanism model library are respectively constructed to classify and store the corresponding collected data. Specifically, the representation model library is a database for storing various models. In the full life cycle management of the contact network, the representation model library mainly stores various models related to the contact network, such as the BIM model initially constructed according to the drawing data and geometric parameters in the design stage, the BIM deepening model after the inspection, conflict verification, calculation and design verification and other processes, the BIM design delivery model after review, the BIM deepening model of the intelligent production related components, the construction BIM deepening model, and the contact network BI for integrated display. M lightweight model and GIS model of project lines, BIM completion delivery model of contact network, etc.; data lake is used to store basic parameter information, production materials, progress management data, installation control, operation and maintenance data of contact network at each stage; mechanism model library is used to store algorithms at each stage, including calculation methods, simulation algorithms, prediction algorithms, etc., such as contact network component calculation model and project budget evaluation model in the design stage, arm suspension string pre-assembly model and automatic identification model in the manufacturing stage, intelligent equipment control model in the installation stage, and intelligent detection and monitoring model in the operation and maintenance stage, mining model based on a large amount of operation and maintenance data, prediction analysis model and early warning model, etc. In general, the representation model library, data lake and mechanism model library together constitute the data infrastructure for the full life cycle management of contact network.

[0029] In one possible implementation, Figure 2As shown, step S300 further includes step S310, configuring design delivery requirements and high-speed rail contact network delivery requirements, the design delivery requirements include component granularity and data accuracy requirements, and the high-speed rail contact network delivery requirements include geometric parameter requirements and contact network equipment status requirements; step S320, based on the design delivery requirements and high-speed rail contact network delivery requirements, constructing a BIM model, and using the BIM model as a characterization model for the design, manufacturing, and construction life stages; step S330, based on the BIM model combined with the monitoring equipment deployed for the high-speed rail contact network, constructing an operation and maintenance monitoring model for the operation and maintenance life stage as a characterization model for the operation and maintenance life stage; step S340, collecting all data involved in each life stage, including dynamic data and static data, the dynamic data is the physical parameters of the contact network components, and the dynamic data is the operating parameters of each life stage, and the data lake is constructed using the dynamic data and static data; step S350, based on the analysis requirements of each life stage, a mechanism model is constructed using the data lake, and the analysis requirements include verification, evaluation, monitoring, detection or early warning calculation analysis.

[0030] Preferably, the design delivery requirements and high-speed rail contact network delivery requirements are configured, wherein the design delivery requirements include component granularity and data accuracy requirements, and the high-speed rail contact network delivery requirements include geometric parameter requirements and contact network equipment status requirements. Specifically, component granularity refers to the level of detail and hierarchy of each contact network component in the BIM model. For example, whether each component of the contact network (such as pillars, arms, contact wires, etc.) needs to be modeled separately, or whether some parts should be merged into larger components. The choice of component granularity will affect the complexity and accuracy of the model; data accuracy requirements refer to the accuracy requirements of the size, position, attributes and other data of each component in the BIM model. The higher the data accuracy, the more the model can truly reflect the actual status of the contact network; geometric Parameter requirements refer to the geometric parameter requirements that need to be met when the contact network is laid, such as the height, pull-out value, span, etc. of the contact wire, which are used to ensure good contact and power supply quality between the electric locomotive and the contact network; the contact network equipment status requirements refer to the status requirements that the contact network equipment needs to maintain during operation, such as the insulation performance, mechanical strength, electrical connection, etc. of the equipment, to ensure the safe operation of the contact network and extend the life of the equipment; then based on the design delivery requirements and the high-speed rail contact network delivery requirements, a BIM model is constructed as a representation model for the design, manufacturing, and construction life stages. The BIM model can integrate all relevant information of the contact network, including geometric parameters, equipment status, material properties, etc., to provide comprehensive and accurate data support for design, manufacturing, and construction.

[0031] Preferably, in combination with the monitoring equipment (such as vibration sensors, temperature sensors, current sensors, etc.) deployed in the high-speed rail contact network, an operation and maintenance monitoring model for the operation and maintenance life stage is constructed. The operation and maintenance monitoring model can monitor the operation status of the contact network in real time, including physical parameters (such as vibration, temperature, current, etc.) and operation parameters (such as power supply quality, equipment failure, etc.), and combine this information with the BIM model to achieve comprehensive monitoring and early warning of the contact network, and then collect all data in each life stage, including dynamic data and static data. The dynamic data is the physical parameters (such as vibration, temperature, current, etc.) of the contact network components and the various life The static data are the basic information such as the geometric parameters and equipment status of the contact network. These data are stored in the data lake to form a huge data set. Finally, based on the analysis needs of each life stage (such as verification, evaluation, monitoring, detection or early warning calculation and analysis, etc.), the mechanism model is constructed using the data in the data lake. The mechanism model can simulate the physical behavior and operating laws of the contact network based on physical principles and mathematical formulas, and realize the performance evaluation, fault diagnosis, prediction and early warning of the contact network, which helps to ensure the safe operation of the contact network and extend the life of the equipment.

[0032] Step S400, obtaining the axial data relationship of each life stage, and establishing a collaborative periodic relationship based on the axial data relationship.

[0033] Preferably, obtaining the axial data relationship of each life stage refers to obtaining the data association and change law of each key field variable along the time axis in different life stages such as design, manufacturing, construction and operation and maintenance of the high-speed rail contact network. Specifically, the data relationship of the life stage may be manifested as periodic changes, trend changes or random fluctuations over time. For example, the design stage includes the structural design, material selection, performance prediction, etc. of the contact network; the manufacturing and construction stages include quality control, material use, construction progress, etc. during the construction process; the operation and maintenance stage involves the operation status monitoring, fault detection and maintenance, cost management, etc. of the contact network, with large data volume, strong real-time performance, and the need to interact with other systems (such as train control systems) for data; based on these axial data relationships, a collaborative periodic relationship is established. The collaborative periodic relationship refers to the data changes of the high-speed rail contact network in different life stages. It shows a certain periodicity or synergy, which helps to better understand and predict the behavior of the contact network, thereby optimizing its full life cycle management. Specifically, by analyzing the data changes of the contact network at different life stages, the periodic laws therein can be identified. For example, in the operation and maintenance stage, the failure rate of the contact network can be analyzed over time to identify the periodic laws of failure occurrence. The synergistic relationship between data at different life stages, that is, their mutual influence and dependence, is studied. For example, the structural design and material selection in the design stage will directly affect the failure rate and maintenance cost in the operation and maintenance stage. Based on the results of periodic analysis and synergy analysis, the synergistic periodic relationship of the high-speed rail contact network is established, and the data changes of the contact network at different life stages and their mutual influence are described, which helps to better understand and predict the behavior of the contact network, optimize its full life cycle management, and improve the safety and operational efficiency of high-speed rail.

[0034] In a possible implementation, step S400 further includes step S410, collecting abnormal cases of each life stage to determine the top event; step S420, based on the top event, identifying sub-events through abnormal cases, and decomposing accident factors according to the sub-events; step S430, analyzing the logical relationship according to the relationship path of the accident factor-sub-event-top event, using AND gate and OR gate logic to link the accident factor and sub-event to the top event in sequence, and constructing a stage logic tree; step S440, according to the logic tree of each stage, based on the time axial relationship of each life stage, analyzing the correlation between the top events and sub-events between each life stage, and fitting the accident factor data flow based on the correlation to obtain the axial data relationship of each life stage.

[0035] Preferably, all abnormal cases occurring in all stages of the high-speed rail contact network life cycle are collected, and the most critical and influential cases are identified as top events, which are usually the starting point of the entire analysis framework and represent the target of the analysis or the most serious accident that needs to be prevented. Then, by analyzing the abnormal cases, the direct causes or sub-events that lead to the top events are identified, and each sub-event is further analyzed and decomposed into more specific and easier to manage accident factors, where the accident factors are factors that may cause sub-events, such as design defects, operational errors, etc.; then, a stage logic tree is constructed based on the relationship between accident factors, sub-events and top events. Specifically, the causal relationship and logical relationship between accident factors, sub-events and top events are determined, and the AND gate and The stage logic tree is constructed by OR gate logic, where the AND gate indicates that all related accident factors or sub-events must occur at the same time to cause the sub-event or top event of the previous level; the OR gate indicates that as long as one related accident factor or sub-event occurs, it may cause the sub-event or top event of the previous level. Finally, by analyzing the logic tree of each life stage, the correlation between the top events, sub-events and accident factors between different life stages is identified. Based on these correlations, the flow and evolution process of accident factors in the life cycle, that is, the data flow, is simulated, and then how the accident factors are transmitted and influenced between different life stages is understood. Finally, the relationship between the top events, sub-events and accident factors between each life stage, that is, the axial data relationship, is obtained.

[0036] In a possible implementation, step S440 further includes step S441, axially connecting the stage logic trees of each life stage according to the time axial relationship of the life stages; step S442, based on the time axial relationship, taking the top event as the starting point, performing axial correlation analysis of each stage, determining the correlation between the top events, and establishing axial connection according to the correlation; step S443, after completing the axial correlation analysis of the top events of all life stages, performing axial correlation analysis of each sub-event based on the order of the time axial relationship, and axially connecting the sub-events according to the correlation calculation results; step S444, configuring the connection weight according to the correlation of the axial connection, integrating and splicing the logic trees of each stage, and establishing a full life cycle logic tree.

[0037] Preferably, the logic trees of each life stage of the high-speed rail contact network are connected in chronological order, that is, the output of one life stage (which may be a sub-event or accident factor) is used as the input of the next stage, thereby forming a continuous timeline, and then starting from the starting point of the entire life cycle (usually one or some top events), the correlation between these top events in different life stages is analyzed, including identifying which top events will evolve into other types of top events in subsequent stages, or which top events have common influencing factors in multiple stages, and establishing connections based on these correlations to correlate the top events of each life stage, such as using the Pearson correlation coefficient to calculate the correlation between top events; after determining the correlation between top events, further analyze the sub-events. The correlation between different life stages includes identifying which sub-events are cross-stage, which sub-events will evolve into other types of sub-events in subsequent stages, and which sub-events have common influencing factors in multiple stages, so as to establish connections on the timeline based on these correlation calculation results to link sub-events in different stages; finally, a weight is assigned to each axial connection to reflect the strength and importance of the correlation between different life stages, and the logic trees of all stages are integrated together to form a comprehensive full life cycle logic tree. This logic tree will show the correlation and flow path between all top events, sub-events and accident factors from the starting point to the end point of the full life cycle, which will help to formulate more effective prevention and response measures to reduce the probability and impact of accidents.

[0038] In a possible implementation, step S440 further includes step S445, fitting the accident factor into the full life cycle logic tree according to the correlation between the accident factor and the sub-event; step S446, performing relationship aggregation according to the logical relationship between the sub-event and the accident factor, and the correlation between the sub-events between the life stages, to obtain the first-order data flow relationship; step S447, performing relationship aggregation according to the logical relationship between the top event and the accident factor, and the correlation between the top events between the life stages, to obtain the second-order data flow relationship; step S448, obtaining the axial data relationship of each life stage according to the first-order data flow relationship and the second-order data flow relationship.

[0039] Preferably, according to the correlation between accident factors and sub-events, they are accurately placed in the corresponding positions in the full life cycle logic tree, that is, the accident factors are classified and coded in detail to ensure that they can accurately reflect the causes and mechanisms of sub-events. By fitting the accident factors into the logic tree, it is possible to see more clearly which factors have an impact on sub-events at different life stages, and how these impacts change over time. Then, the logical relationship between sub-events and accident factors, as well as the correlation between sub-events at different life stages, are analyzed. For example, sub-events and accident factors in the logic tree are clustered to identify their common features and association patterns. Through relationship aggregation, the first-order data flow relationship is obtained, that is, how accident factors affect sub-events, and how sub-events change at different life stages. Transmission and evolution between different life stages; then analyze the logical relationship between top events and accident factors, as well as the correlation of top events between different life stages, that is, conduct a higher-level analysis and clustering of top events and accident factors in the logic tree, and obtain the second-order data flow relationship through relationship aggregation, that is, how accident factors affect top events, and how top events interact and evolve between different life stages; finally, combine the first-order data flow relationship with the second-order data flow relationship to obtain a comprehensive perspective to understand the correlation and flow path between accident factors, sub-events and top events throughout the life cycle, that is, obtain the axial data relationship of each life stage, that is, in different life stages, which factors lead to the occurrence of which events, and how these events are related and influence each other.

[0040] In a possible implementation, step S400 further includes step S450, establishing a stage data interface based on the axial data relationship; step S460, using the stage data interface to establish a data collaborative connection between cycle stages, and configuring a forward and reverse data sharing mechanism for the stage data interface for forward data driving and reverse data tracing.

[0041] Preferably, based on the established axial data relationship, determine the data types and formats that need to be exchanged and shared between different life stages, and design corresponding data interfaces for each life stage. These interfaces will serve as a bridge for data exchange between different stages. The design of the data interface should ensure the accuracy, completeness and timeliness of the data to meet the needs of data collaboration. Then, use technical means (such as API, database connection, etc.) to connect the data interfaces of different life stages to achieve real-time or regular data exchange, ensuring that relevant data can be transmitted and shared in a timely and accurate manner at each stage of the life cycle. The establishment of data collaborative connection should take into account the flow direction of data, synchronization frequency, data format conversion, etc. factors such as exchange to ensure the seamless flow of data between different stages; finally, configure the forward and reverse data sharing mechanisms for the stage data interface. The forward data sharing mechanism refers to the transmission and sharing of data from the upstream stage to the downstream stage, which is usually used to support the analysis, decision-making and action of the downstream stage. The reverse data traceability mechanism refers to the backtracking and query of data from the downstream stage to the upstream stage, which helps to discover the root cause of the problem, trace the responsibility and formulate improvement measures; the configuration of the forward and reverse data sharing mechanisms should ensure the traceability, security and privacy protection of the data, and may require the use of technical means such as data encryption and access control to ensure the secure transmission and storage of data, thereby improving the accuracy, efficiency and response speed of decision-making.

[0042] In a possible implementation, step S450 further includes step S451, obtaining one-pole-one-file data of a collaborative cycle relationship through the stage data interface; step S452, obtaining operation and maintenance data of the operation and maintenance stage; step S453, using the one-pole-one-file data to merge with the operation and maintenance data to identify abnormal contact network data; step S454, using the abnormal contact network data to update the operation and maintenance model, or feeding back to the collaborative cycle through the stage data interface for optimization search.

[0043] Preferably, the one-pole-one-file data related to the overhead contact network is obtained from different stages of the entire life cycle through the stage data interface, including the design parameters, manufacturing information, installation data, etc. of each stage, and then the operation and maintenance data is obtained from the operation and maintenance stage, including the operating status of the facilities, fault records, maintenance records, detection data, etc., and then the one-pole-one-file data is fused with the operation and maintenance data, and abnormal overhead contact network data is identified through comparative analysis, such as pre-processing including data cleaning, format conversion, time alignment, etc., to ensure the accuracy and comparability of the data, and then based on statistical methods (such as mean, variance analysis) or machine learning algorithms (such as The abnormal data can be identified by classification and clustering, that is, the abnormal contact network data can be obtained. Finally, the identified abnormal contact network data can be processed. One is to use these data to update the operation and maintenance model to improve the accuracy and prediction ability of the model, where the operation and maintenance model includes fault prediction model, maintenance optimization model, etc.; the other is to feed back the abnormal data to other stages in the collaborative cycle through the stage data interface to trigger optimization search or improvement measures, such as re-evaluating design parameters, improving manufacturing processes, strengthening operation and maintenance measures, etc., so as to realize closed-loop management of data throughout the life cycle and continuously optimize the performance and operation and maintenance strategies of facilities.

[0044] Step S500, according to the collaborative cycle relationship, the representation model library, the data lake, and the mechanism model library are associated and mapped to build an integrated digital-analog cycle graph relationship.

[0045] Preferably, based on the collaborative cycle relationship, the data (data lake), model (characterization model library) and algorithm (mechanism model library) of each stage in the whole life cycle of the high-speed rail contact network are mutually correlated and mapped to build an overall graph relationship, that is, to form a digital-analog cycle integrated graph relationship, so as to better understand, predict and optimize the behavior and performance of the high-speed rail contact network. Specifically, the characterization model library and the data lake are associated and mapped. The models in the characterization model library are built based on the data in the data lake. The data lake stores a large amount of raw data and processed data at various stages in the whole life cycle of the high-speed rail contact network. Through data analysis and mining, key features and information can be extracted from the data lake for constructing various models in the characterization model library. At the same time, the models in the characterization model library can also predict the data in the data lake. For example, the machine learning model is used to train the fault data in the data lake and establish a fault prediction model, thereby realizing the prediction and early warning of future faults.

[0046] Preferably, the mechanism model library is associated with the characterization model library and mapped, and the algorithms and calculation methods in the mechanism model library provide an in-depth understanding and description of the physical behavior and operating laws of the high-speed rail contact network. Combined with the models in the characterization model library, a more accurate and reliable prediction and analysis tool is formed. For example, the simulation algorithm in the mechanism model library is used to simulate and analyze the current distribution, mechanical stress, etc. of the high-speed rail contact network, and then the simulation results are compared and verified with the model in the characterization model library to optimize the accuracy and reliability of the model; the data lake is associated with the mechanism model library and mapped, and the data in the data lake can also provide input and verification for the algorithms in the mechanism model library. For example, the mechanism model is verified and calibrated using real-time monitoring data in the data lake to ensure the accuracy and reliability of the model. On the basis of associative mapping, an integrated digital-analog cycle graph relationship is constructed to interconnect the data, models and algorithms of each stage in the whole life cycle of the high-speed rail contact network. For example, in the different life stages of the contact network's whole life cycle management, the contact network's various characterization models, mechanism models, dynamic and static data are integrated to achieve integrated transmission, and the analog-digital forward drive and reverse iterative optimization of the contact network from design, manufacturing, construction to operation are realized, which will help to better understand, predict and optimize the behavior and performance of the high-speed rail contact network and improve the safety and operational efficiency of the high-speed rail.

[0047] In a possible implementation, step S500 further includes step S510, establishing a data correspondence between the data lake and the representation model library and the mechanism model library, and constructing a mapping matrix, wherein the row elements of the mapping matrix are the data / model library and the column elements are each collaborative cycle; step S520, based on the full life cycle logic tree, with life stages, top events, sub-events, and accident factors as graph nodes, and the relationships between graph nodes as edges, to construct a graph structure of the collaborative cycle; step S530, according to the mapping matrix, fitting the data / model library to the graph structure of the collaborative cycle, and building the integrated graph relationship of the digital-analog cycle.

[0048] Preferably, the corresponding relationship between the data stored in the data lake and the models in the representation model library and the mechanism model library is determined, and a mapping matrix is ​​constructed. The mapping matrix is ​​a two-dimensional table, whose row elements represent data / model libraries (such as data lake, representation model library, mechanism model library), and column elements represent each collaborative cycle (such as design, manufacturing, installation, operation and maintenance, etc.). Each element in the matrix represents the relevance of a certain data / model in a certain collaborative cycle. Then, according to the established full life cycle logic tree, life stages (such as design, manufacturing, installation, operation and maintenance, etc.), top events (such as facility failures), sub-events (such as component damage), accident factors (such as material aging), etc. are used as map nodes, and map nodes are used as the nodes. The relationships between points (such as causal relationships, time sequence relationships, etc.) are used as edges to construct the graph structure of the collaborative cycle, which helps to intuitively understand the relationships between various events and factors in the entire life cycle of facilities or products, as well as their distribution and evolution in different collaborative cycles; finally, according to the constructed mapping matrix, the elements in the data / model library (such as specific data in the data set, representation models, mechanism models, etc.) are fitted into the graph structure of the collaborative cycle, that is, to find the appropriate position in the graph structure for each data / model, and establish the association between them and the graph nodes (such as life stages, top events, sub-events, accident factors, etc.), that is, to build an integrated graph relationship of digital and model cycles.

[0049] In a possible implementation, step S530 further includes step S531, configuring the weight value of the graph node associated data based on the graph structure of the collaborative cycle; step S532, identifying the collaborative cycle weight associated with the graph node according to the mapping matrix, assigning the weight in the data / model library to the corresponding graph node, and fitting the data / model library to the graph structure of the collaborative cycle; step S533, wherein the assignment expression is: Among them, W n is the total weight of the node, N is the total number of cooperative cycles in the mapping matrix, D m,i is the weight of data / model m in the mapping matrix in the i-th collaborative cycle, C i is the weight of the i-th cooperative cycle in the mapping matrix.

[0050] Preferably, the weight of the corresponding data / model in the mapping matrix is ​​multiplied by the weight of the collaboration period and the sum is obtained to obtain the weight value of the graph node associated data. Specifically, for each node, each column in the mapping matrix (representing each collaboration period) is traversed, the weight of the node in the data / model library (obtained from the mapping matrix) is multiplied by the weight of the collaboration period (obtained from the mapping matrix), and then all these products are added to obtain the total weight of the node. The weight calculation expression is as follows: Among them, W nis the total weight of the node, N is the total number of cooperative cycles in the mapping matrix, D m,i is the weight of data / model m in the mapping matrix in the i-th collaborative cycle, C i is the weight of the i-th cooperative cycle in the mapping matrix.

[0051] Step S600, based on the integrated graph relationship of the digital-analog cycle, using the data of the data lake, and performing calculations through the models of the characterization model library and the mechanism model library, to generate stage management information.

[0052] Preferably, by integrating and coordinating different types of models and algorithms, valuable information is extracted from the data lake, and stage management information that is instructive for the full life cycle management of the high-speed rail contact network is generated, including characterization model management, mechanism model management, dynamic and static data management, modular integrated management, etc., to achieve management information of massive data, characterization models and mechanism models at various life stages of the high-speed rail contact network. For example, in the design stage, a BIM collaborative design platform can be built to compare various links such as preliminary design reports and construction drawing designs to achieve dynamic optimization of design schemes; the design delivery model is used to deepen the processing of components (such as arms, suspension strings, etc.) that are intelligently produced in the construction stage, and the model data is applied to the intelligent production system to realize remote automatic production control, including automatic identification of pre-configured data, automatic feeding, automatic coding and marking, and finished product unloading. Materials, palletizing, etc.; During the construction phase, the BIM model delivered by manufacturing is used to deepen the data on quality, safety, control and environmental protection, study the collaborative association of multi-dimensional data, automatically generate one-pole-one-file data for the contact network, and realize the synchronous operation of BIM model data and intelligent installation equipment, so as to improve the effective installation of key components such as contact network pillar assembly and wrist arm; During the operation and maintenance phase, the one-pole-one-file data of the contact network is combined with the operation and maintenance data, that is, the one-pole-one-file data is integrated with the operation and maintenance data, the operation and maintenance model is updated, and data sharing of each life stage of design-construction-operation and maintenance is realized; thereby realizing the data connection and efficient management of the whole life cycle of the high-speed railway contact network, as well as the real-time monitoring, fault warning and intelligent operation and maintenance of the high-speed railway contact network status, so as to achieve the goal of improving management efficiency and optimizing resource allocation, and promote the intelligent and information management of high-speed railway.

[0053] The above specific implementation manner does not constitute a limitation to the protection scope of the present application. It should be understood by those skilled in the art that various modifications, combinations and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present application should be included in the protection scope of the present application. In some cases, the actions or steps recorded in the present application can be performed in an order different from that in the embodiment and can still achieve the desired results. In addition, the process depicted in the accompanying drawings does not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

Claims

1. A modular driven high-speed railway overhead contact network full life cycle management method, characterized in that: The method comprises: Analyze the whole life cycle of the contact network, including at least design, manufacturing, construction, operation and maintenance, and construct a life stage block based on each stage of the whole life cycle, wherein the life stage block has a life label; Collect data of each life stage and store it in the corresponding life stage block based on the life tag; Based on the life stage block, the collected data is respectively constructed into a representation model library, a data lake, and a mechanism model library according to the subject domain. The representation model library is used to store models, the data lake is used to store data, and the mechanism model library is used to store algorithms; Acquire the axial data relationship of each life stage, and establish a collaborative periodic relationship based on the axial data relationship; According to the collaborative cycle relationship, the representation model library, data lake, and mechanism model library are associated and mapped to build an integrated digital-analog cycle graph relationship; According to the integrated graph relationship of the digital-analog cycle, the data in the data lake is used to perform calculations and processing through the models of the characterization model library and the mechanism model library to generate stage management information.

2. The full life cycle management method of the modular-driven high-speed railway overhead contact network according to claim 1 is characterized in that: Based on the life stage blocks, the collected data is used to construct a representation model library, a data lake, and a mechanism model library according to the subject domain, including: Configuration design delivery requirements and high-speed rail contact network delivery requirements. The design delivery requirements include component granularity and data accuracy requirements, and the high-speed rail contact network delivery requirements include geometric parameter requirements and contact network equipment status requirements; Based on the design delivery requirements and high-speed rail contact network delivery requirements, a BIM model is constructed, and the BIM model is used as a representation model for the design, manufacturing, and construction lifecycle stages; Based on the BIM model and the monitoring equipment deployed in the high-speed rail contact network, an operation and maintenance monitoring model of the operation and maintenance life stage is constructed as a representation model of the operation and maintenance life stage; Collect all data involved in each life stage, including dynamic data and static data, wherein the dynamic data is the physical parameters of the contact network components and the dynamic data is the operating parameters of each life stage, and construct the data lake using the dynamic data and static data; The data lake is used to construct a mechanism model based on the analysis needs of each life stage, and the analysis needs include verification, evaluation, monitoring, detection or early warning calculation analysis.

3. The full life cycle management method of the modular-driven high-speed railway overhead contact network according to claim 2 is characterized in that: Obtain axial data relationships for each life stage, including: Collect abnormal cases at each life stage and identify top events; Based on the top event, sub-event identification is performed through abnormal cases, and accident factors are decomposed according to the sub-events; According to the relationship path of the accident factor-sub-event-top event, the logical relationship is analyzed, and the accident factor and sub-event are sequentially linked to the top event using AND gate and OR gate logic to construct a phase logic tree; According to the logic tree of each stage and based on the time axial relationship of each life stage, the correlation between the top events and sub-events between each life stage is analyzed, and the accident factor data flow is fitted based on the correlation to obtain the axial data relationship of each life stage.

4. The full life cycle management method of the modular-driven high-speed railway overhead contact network according to claim 3 is characterized in that: According to the logic tree of each stage and based on the time axis relationship of each life stage, the correlation between the top events and sub-events between each life stage is analyzed, including: According to the time axis relationship of the life stages, the stage logic trees of each life stage are axially connected; Based on the time axial relationship, taking the top event as the starting point, the axial correlation analysis of each stage is performed to determine the correlation between the top events, and the axial connection is established according to the correlation; After completing the axial correlation analysis of the top events of all life stages, the axial correlation analysis of each sub-event is performed based on the time axial relationship sequence, and the sub-events are axially connected according to the correlation calculation results; The connection weights are configured according to the relevance of the axial connections, and the logic trees of each stage are integrated and spliced ​​to establish a full life cycle logic tree.

5. The full life cycle management method of the modular-driven high-speed railway overhead contact network according to claim 4 is characterized in that: The accident factor data flow fitting based on correlation to obtain the axial data relationship of each life stage includes: According to the correlation between the accident factor and the sub-event, the accident factor is fitted into the full life cycle logic tree; According to the logical relationship between sub-events and accident factors, as well as the correlation between sub-events in life stages, the relationship is aggregated to obtain the first-order data flow relationship; Relation aggregation is performed based on the logical relationship between the top events and the accident factors, as well as the correlation between the top events between life stages, to obtain the second-order data flow relationship; According to the first-order data flow relationship and the second-order data flow relationship, the axial data relationship of each life stage is obtained.

6. The full life cycle management method of the modular-driven high-speed railway overhead contact network according to claim 1, characterized in that: Establishing a collaborative periodic relationship based on the axial data relationship includes: According to the axial data relationship, a phase data interface is established; The stage data interface is used to establish a data collaborative connection between the cycle stages, and a forward and reverse data sharing mechanism is configured for the stage data interface for forward data driving and reverse data tracing.

7. The method for managing the entire life cycle of a high-speed railway overhead line driven by analog and digital means as claimed in claim 6, characterized in that: The method further comprises: Obtaining one-bar-one-file data of the coordinated periodic relationship through the phase data interface; Obtain operation and maintenance data during the operation and maintenance phase; The one-pole-one-file data is integrated with the operation and maintenance data to identify abnormal contact network data; The abnormal contact network data is used to update the operation and maintenance model, or fed back to the collaborative cycle through the stage data interface for optimization search.

8. The method for managing the entire life cycle of a high-speed railway overhead contact network driven by analog and digital means as claimed in claim 4, characterized in that: According to the collaborative cycle relationship, the representation model library, data lake, and mechanism model library are associated and mapped to build a digital-analog cycle integrated graph relationship, including: Establishing a data correspondence between the data lake and the representation model library and the mechanism model library, and constructing a mapping matrix, wherein the row elements of the mapping matrix are the data / model library and the column elements are the collaborative cycles; According to the full life cycle logic tree, life stages, top events, sub-events, and accident factors are used as graph nodes, and the relationships between graph nodes are used as edges to construct a graph structure of the collaborative cycle; According to the mapping matrix, the data / model library is fitted into the graph structure of the collaborative cycle to build the integrated graph relationship of the digital-analog cycle.

9. The method for managing the entire life cycle of a high-speed railway overhead line driven by analog and digital means as claimed in claim 8, characterized in that: According to the mapping matrix, the data / model library is fitted into the graph structure of the synergy cycle, including: Based on the graph structure of the collaborative cycle, configure the weight values ​​of the graph node associated data; According to the mapping matrix, identify the collaborative cycle weights associated with the graph nodes, assign the weights in the data / model library to the corresponding graph nodes, and fit the data / model library to the graph structure of the collaborative cycle; The assignment expression is: Among them, W n is the total weight of the node, N is the total number of cooperative cycles in the mapping matrix, D m,i is the weight of data / model m in the mapping matrix in the i-th collaborative cycle, C i is the weight of the i-th cooperative cycle in the mapping matrix.

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