Intelligent operation and maintenance method, system, equipment and medium for high-speed railway line infrastructure

Abstract

The invention provides a high-speed railway line infrastructure intelligent operation and maintenance method, system and device and a medium. The method comprises the steps of obtaining disease text data; extracting a disease entity in the disease text data; identifying a relationship between the disease entities based on a trigger word; constructing a disease knowledge graph based on the relationship between the disease entities; based on the disease knowledge graph and the incidence relation between the operation and maintenance information and the high-speed railway line infrastructure model entity, the disease and service state view of the high-speed railway line infrastructure entity is displayed, rapid response to disease information is achieved, and visualized display of infrastructure entity diseases is achieved.

Description

Technical Field

[0001] The present invention relates to the field of railway intelligent technology, and in particular to a method, system, equipment and medium for intelligent operation and maintenance of high-speed railway line infrastructure. Background Art

[0002] my country's high-speed railways, now operating for over 42,000 kilometers, are characterized by high transport demand, high operating speeds, complex service environments, and diverse road, bridge, tunnel, and track structures. This heavy traffic demand is leading to ever-shrinking maintenance windows; high speeds and high stability are driving increasingly stringent performance requirements. Traditional operations and maintenance platforms lack the ability to represent infrastructure service status, access and evaluate multi-source inspection data, or manage the entire maintenance process, making it difficult to achieve efficient, safe, and reliable infrastructure operations and maintenance.

[0003] With the development of technologies such as digital twins, large models, the Internet of Things, edge computing, and generative artificial intelligence, promising results have been achieved in some areas. Existing technologies typically utilize centralized signal monitoring systems, server platforms, and visualization terminals. These systems collect real-time status information from multiple physical signal devices and visualize them in a three-dimensional digital twin system. However, these systems primarily focus on centralized signal monitoring at single points, and since models must be configured in advance, they are unable to cover the multi-state intelligent operation and maintenance of high-speed railway infrastructure, long-distance lines, and roads, bridges, tunnels, and railroads.

[0004] Given the complexity and particularity of the operation and maintenance of high-speed railway line infrastructure, it is necessary to study an intelligent operation and maintenance platform architecture specifically for the cross-professional, cross-business, and cross-departmental operation and maintenance of line infrastructure, to achieve integrated and intelligent control of infrastructure status from detection and evaluation to maintenance and repair, and to improve the intelligence and efficiency of infrastructure operation and maintenance. Summary of the Invention

[0005] The present invention provides a method, system, equipment and medium for intelligent operation and maintenance of high-speed railway line infrastructure, so as to realize intelligent, efficient and visual operation and maintenance.

[0006] According to one aspect of the present invention, a method for intelligent operation and maintenance of high-speed railway line infrastructure is provided, comprising: Get disease text data; Extracting disease entities from the disease text data; Identifying relationships between the disease entities based on trigger words; Constructing a disease knowledge graph based on the relationship between the disease entities; Based on the disease knowledge graph and the association between operation and maintenance information and high-speed railway line infrastructure model entities, the disease and service status view of the high-speed railway line infrastructure entities is displayed. Optionally, before extracting the disease entities from the disease text data, the method further includes: Preprocessing the disease text data; The preprocessing includes at least word segmentation, part-of-speech tagging, removal of stop words and removal of special symbols.

[0007] Optionally, if the disease text data is semi-structured data, extracting disease entities from the disease text data includes: Performing importance evaluation on the terms in the disease text data, and taking terms whose importance is greater than a preset threshold as keywords; Sorting the keywords according to the order of the words in the disease text data; Input the sorted terms into the Bi-gram model, and set rules based on disease text data and disease knowledge in professional fields to constrain the combination of terms. The preset scoring mechanism is used to score the combinations of terms, and the term combination with the highest score is identified as the disease entity.

[0008] Optionally, if the disease text data is semi-structured data, identifying the relationship between the disease entities based on trigger words includes: The trigger word is a preset disease phrase or phrase pattern; Identify the relationships between disease entities based on trigger words.

[0009] Optionally, it also includes: Construct a high-speed railway line infrastructure model integrating BIM and GIS; Align the geodetic coordinates of the operation and maintenance information with the spatial rectangular coordinates of the BIM model; Establish an association relationship between the operation and maintenance information and the high-speed railway line infrastructure model entity.

[0010] Optionally, establishing an association relationship between the operation and maintenance information and the high-speed railway line infrastructure model entity includes: The association relationship between operation and maintenance information and high-speed railway line infrastructure model entities is determined based on the disease knowledge graph.

[0011] Optionally, the display of the damage and service status view of the high-speed railway line infrastructure entity based on the damage knowledge graph and the association between the operation and maintenance information and the high-speed railway line infrastructure model entity includes: Obtain the relationship between disease entities based on the disease knowledge graph; Based on the association between operation and maintenance information and high-speed railway line infrastructure model entities, the disease entities are visualized and the corresponding service status view is displayed; Trace the causes of diseases based on service status.

[0012] According to another aspect of the present invention, there is provided a high-speed railway line infrastructure intelligent operation and maintenance system, comprising: A data acquisition unit, used for acquiring disease text data; An entity extraction unit, configured to extract disease entities from the disease text data; a relationship identification unit, configured to identify the relationship between the disease entities based on trigger words; A graph construction unit, configured to construct a disease knowledge graph based on the relationship between the disease entities; Based on the disease knowledge graph and the association between operation and maintenance information and high-speed railway line infrastructure model entities, the disease and service status view of the high-speed railway line infrastructure entities is displayed.

[0013] According to another aspect of the present invention, an electronic device is provided, comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein, The memory stores a computer program that can be executed by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the intelligent operation and maintenance method of high-speed railway line infrastructure described in any embodiment of the present invention.

[0014] According to another aspect of the present invention, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the high-speed railway line infrastructure intelligent operation and maintenance method described in any embodiment of the present invention when executed.

[0015] The technical solution of the embodiment of the present invention obtains disease text data; extracts disease entities in the disease text data; identifies the relationship between the disease entities based on trigger words; constructs a disease knowledge graph based on the relationship between the disease entities; and displays the disease and service status view of the high-speed railway line infrastructure entity based on the disease knowledge graph and the association relationship between operation and maintenance information and the high-speed railway line infrastructure model entity, thereby realizing a rapid response to disease information and a visual display of infrastructure entity diseases. It should be understood that the content described in this section is not intended to identify the key or important features of the embodiments of the present invention, nor is it intended to limit the scope of the present invention. Other features of the present invention will become readily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0017] Figure 1 This is a flow chart of a method for intelligent operation and maintenance of high-speed railway line infrastructure provided according to the first embodiment of the present invention; Figure 2 This is a flowchart of the operation and maintenance of semi-structured data processing in one embodiment of the present invention; Figure 3 This is a flowchart of the collaboration between bigrams and rules in one embodiment of the present invention; Figure 4 A flowchart of geometric conversion of a BIM and GIS fusion model in one embodiment of the present invention; Figure 5 Schematic diagram of the process of converting operation and maintenance coordinates to BIM coordinates in one embodiment of the present invention; Figure 6 Schematic diagram of the association relationship between operation and maintenance information and high-speed railway line infrastructure model entities in one embodiment of the present invention Figure 7 This is a diagram of the overall system architecture of intelligent operation, maintenance and control of high-speed railways in one embodiment of the present invention; Figure 8 2 is a structural diagram of an intelligent operation and maintenance system for high-speed railway line infrastructure provided according to a second embodiment of the present invention; Figure 9 It is a structural diagram of an electronic device for implementing the intelligent operation and maintenance method of high-speed railway line infrastructure according to an embodiment of the present invention. DETAILED DESCRIPTION

[0018] In order to enable those skilled in the art to better understand the solutions of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the embodiments described are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts should fall within the scope of protection of the present invention.

[0019] It should be noted that the terms "first", "second", etc. in the description and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that the numbers used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device 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 units that are not clearly listed or inherent to these processes, methods, products or devices.

[0020] The present invention provides a method for constructing a digital-analog dual-drive extensible software framework for intelligent operation and maintenance of high-speed railway line infrastructure that integrates BIM+GIS models. Through the integration of GIS+BIM, the intelligent operation and maintenance of line infrastructure integrates the basic data of infrastructure such as roadbed (including slopes), bridges, tunnels (including portals), and tracks (including switches) with their BIM models. By locating geographic location information, three-dimensional scene representation interaction is achieved in various types of basic data. At the same time, the advantages of BIM+GIS are brought into play to form a technical route and method with the entire process as the main line, BIM as the contact point, and spatial location as the retrieval target. The safety, process, personnel, cost, material and other information in intelligent operation and maintenance are dynamically associated with BIM model components, GIS scenes, and spatial locations, realizing BIM model services based on spatial location information, thereby providing digital support for the process control of intelligent operation and maintenance.

[0021] Example 1 Figure 1 The first embodiment of the present invention provides an architecture diagram of a high-speed railway line infrastructure intelligent operation and maintenance system. Figure 1 As shown, the architecture includes: S101. Obtain disease text data.

[0022] In one embodiment, the method further comprises pre-processing the disease text data; The preprocessing includes at least word segmentation, part-of-speech tagging, removal of stop words and removal of special symbols.

[0023] Specifically, since structured text data can be directly extracted using data tables, semi-structured data records information such as disease phenomena, causes, and consequences in the form of natural language, making it difficult to directly extract entities. Therefore, preprocessing the disease text data of semi-structured data can remove content and non-text parts that are not related to the disease in the semi-structured data. Preprocessing includes word segmentation, removal of stop words, removal of special symbols, etc. Figure 2The preprocessed text section is shown.

[0024] S102: Extract disease entities from the disease text data.

[0025] Extract disease entities from disease text data. For structured text data, disease entities can be directly extracted using data tables. Semi-structured data can record disease phenomena, causes, and consequences in natural language. The methods for extracting disease entities include: S1021, evaluating the importance of terms in the disease text data, and using terms whose importance is greater than a preset threshold as keywords; S1022, sorting the keywords according to the order of the words in the disease text data; S1023, inputting the sorted terms into a Bi-gram model, and setting rules based on the disease text data and disease knowledge in professional fields to constrain the combination of terms; S1024. Use a preset scoring mechanism to score the combinations of terms, and identify the term combination with the highest score as the disease entity.

[0026] First, we can combine TF-IDF weight, word vector weight and context weight to comprehensively evaluate the importance of terms from the three dimensions of statistics, semantics and context and obtain term scores. By extracting multi-level semantic information, we can improve the accuracy of keyword extraction.

[0027] TF-IDF combines the frequency of a term in a single document with its inverse document frequency in the entire corpus to quantify the importance of a term:

[0028] in, It is a term In the documentation The number of occurrences in is the total number of documents, Contains terms The number of documents.

[0029] The unordered keywords are arranged in the order of the original text to obtain ordered keywords, ensuring that the keyword order is consistent with the original text, and the ordered keywords are input into the Bi-gram model.

[0030] Secondly, rules are set to constrain word combinations based on the disease text and specialized disease knowledge. These rules can be adjusted based on model feedback, and the two work together to assist in entity acquisition. For example, valid combinations are considered when keywords are adjacent in the sliding window or conform to common disease entity combination patterns such as "noun + verb" and "noun + noun + verb." In this embodiment, each word can be classified according to its part of speech. For example, "rail" is a noun, and "broken" is a verb. The combination of parts of speech can be used to determine whether a combination is valid.

[0031] Finally, these valid combinations are scored, and the combination with the highest score is identified as the disease entity. Figure 3 The big-gram and rule collaboration flowchart shown.

[0032] Specifically, in the scoring mechanism for disease entity recognition, the scoring process quantitatively evaluates candidate term combinations by combining bi-gram probabilities and domain rule weights, ultimately selecting the combination with the highest overall score as the entity. The following are the specific steps and mathematical logic of the scoring process: The final score for a term combination consists of two parts:

[0033] Where j to i represent the starting and ending positions of the merged terms (e.g., merging the second to fourth terms); λ is the rule weight coefficient, which is used to balance the contribution of the statistical model and domain rules (obtained through tuning).

[0034] Among them, the Bi-gram probability score is:

[0035] Based on the trained Bi-gram model, the co-occurrence probability P of adjacent terms w is calculated. To avoid numerical underflow caused by multiplying probabilities, the probabilities are converted to logarithms and added.

[0036] The rule score is: the legality of the word combination is constrained by domain knowledge, which is usually divided into two categories: hard rules and soft rules: Hard rules (directly filtering illegal combinations): Hard rules can directly exclude obviously illegal combinations from scoring. Common hard rules include illegal structures, such as verb + noun. Hard rules can also constrain part of speech. For example, since disease entities are typically noun phrases, combinations containing verbs or prepositions can be excluded.

[0037] Soft rules (adjusting scores by adding / subtracting points): Soft rules can add points to combinations that conform to domain knowledge, and subtract points otherwise.

[0038] In one embodiment, the scoring logic table of the soft rules is shown in Table 1: Table 1 Scoring logic table of soft rules

[0039] In this embodiment, the candidate word combinations are quantitatively evaluated by combining the Bi-gram probability and the domain rule weight, and the optimal path can be selected through the recursive formula:

[0040] Where, :Indicates that the processing has reached The highest rating for words.

[0041] In this example, the optimal merge path for each position is recorded and ultimately traced back to the entity. The scoring mechanism captures the statistical correlation between terms through bi-gram probabilities, incorporates domain rules to inject professional knowledge, and ultimately selects the optimal path through dynamic programming.

[0042] S103: Identify the relationship between the disease entities based on the trigger words.

[0043] In one embodiment, the trigger word is a preset disease phrase or phrase pattern; and the relationship between disease entities is identified based on the trigger word.

[0044] Relationships are the bridges connecting entities and can assist in analyzing the associated features between defects. First, for structured texts, entity relationships are constructed directly from existing structural information. For unstructured texts, predefined trigger words are used to identify and extract text relationships. Trigger words can identify potential relationships in the text by setting common defect phrases and phrase patterns. Combining contextual information and the semantics of words, the accuracy of relationship identification is further improved through a data-driven approach. Taking "repeated stress concentration on trains causes rail breakage" as an example, the trigger word "cause" indicates the relationship between cause and effect. In this sentence, "rail breakage" is a defect phenomenon, and "repeated stress concentration on trains" is the cause of the defect. By identifying the trigger word "cause", the relationship between the cause of the defect and the defect phenomenon can be effectively extracted from the text.

[0045] Through the above method, the railway line infrastructure knowledge graph node storage and disease visualization construction are realized.

[0046] S104: Construct a disease knowledge graph based on the relationship between the disease entities.

[0047] S105. Based on the disease knowledge graph and the association between the operation and maintenance information and the high-speed railway line infrastructure model entity, a disease and service status view of the high-speed railway line infrastructure entity is displayed.

[0048] It should be noted that based on the defect knowledge graph and the associations between operation and maintenance information and high-speed rail line infrastructure model entities, defect information can be displayed on the corresponding entities and formed into a 3D model. Service status refers to the comprehensive performance of the technical performance, safety performance, functional status, and degree of aging and wear of each component of the high-speed rail line infrastructure over time after it is put into operation. Therefore, service status can reflect the temporal changes in physical defects and the spatial connections between defects. For example, if defect A occurs on a track and defect B occurs one month later, it can be inferred that there is a certain correlation between defects A and B. The cause of the defect can then be determined by combining other factors in the service status.

[0049] In one embodiment, it further includes: S1051. Construct a high-speed railway line infrastructure model integrating BIM and GIS; The BIM+GIS fusion modeling technology in the existing solutions is relatively mature and can be implemented in the following steps: geometrically convert the BIM model format into a universal 3D file format; export the BIM model into an IFC file, and split and organize the components according to the semantic encapsulation logic; then, separate the digital and the model, separate the geometric information and attribute information of the components, and export and store them separately; convert the geometric components of the BIM model into OBJ-glTF-b3dm-3D Tiles in turn, which can be efficiently applied to GIS application scenarios to achieve BIM+GIS fusion. The specific steps to implement the BIM+GIS fusion model are as follows: Figure 4 shown.

[0050] S1052. Align the geodetic coordinates of the operation and maintenance information with the spatial rectangular coordinates of the BIM model; Specifically, there are three main situations in which coordinate conversion can be performed for BIM models: the BIM model has complete coordinate system information, the BIM model has incomplete coordinate system information, and the BIM model has no coordinate system information. Based on the above three situations, three coordinate conversion processes are proposed, such as Figure 5 The flowchart for aligning the geodetic coordinates of the operation and maintenance information with the spatial rectangular coordinates of the BIM model includes: The coordinate system of the high-speed railway BIM model is the local coordinate system (x, y, z). The conversion between it and the WGS84 coordinate system (B, L, H) is as follows: The conversion of spatial rectangular coordinates to geodetic coordinates is (x, y, z) -> (B, L, H) as shown in the formula. The inverse solution of spatial rectangular coordinates to geodetic coordinates is relatively complicated. Considering the complexity of the solution and the guarantee of accuracy, the Newton iteration method is used to solve the parameters.

[0051]

[0052] The relationship between the spatial rectangular coordinate system and the geodetic coordinate system is shown in the formula, which is also the conversion formula for converting geodetic coordinates to spatial rectangular coordinates, that is, (B, L, H) -> (x, y, z).

[0053]

[0054] Where N is the radius of curvature of the yoke , is the first eccentricity of the ellipsoid , a is the semi-major axis of the Earth ellipsoid, and b is the semi-minor axis of the Earth ellipsoid.

[0055] If the coordinate transformation is performed under different ellipsoid datums, it is often not rigorous. The original coordinates must first be converted into the spatial rectangular coordinates corresponding to the ellipsoid datum, and then converted into the spatial rectangular coordinates corresponding to the ellipsoid datum of the target coordinates through the "seven parameters". Finally, the coordinate transformation is performed within the same ellipsoid datum to obtain the result coordinates.

[0056] Considering the ellipsoid difference between coordinate systems, the Bursa seven-parameter model is used to realize the coordinate transformation from the spatial rectangular coordinate system A to the spatial rectangular coordinate system B, which includes three translation parameters: , three rotation parameters , a scale factor , as shown in the formula:

[0057] Where, is the translation of the origin coordinates of the two spatial coordinate systems A and B; The rotation angle of the X, Y, and Z axes between the two spatial coordinate systems; the scale factor is the scale ratio between the two spatial coordinate systems, The coordinate point in the spatial rectangular coordinate system A can be Convert the coordinate point to the rectangular coordinate system B .

[0058] S1053: Establish an association relationship between the operation and maintenance information and a high-speed railway line infrastructure model entity.

[0059] Specifically, the association relationship between operation and maintenance information and high-speed railway line infrastructure model entities can be determined based on the disease knowledge graph.

[0060] In this embodiment, the knowledge graph can be divided into an ontology layer and an entity layer in terms of architecture. The ontology layer mainly describes abstract semantic information and concept templates, and is highly structured and low-redundant. The entity layer is mainly composed of facts that exist in the real world and is an extension of the real entities under the ontology concept. By establishing connections and constraints between the ontology and the entity, the entity layer can be effectively organized, and semantic knowledge reasoning can be performed on this basis. In this embodiment, the knowledge graph of the entity layer reflects the association relationship between the various entities in the high-speed railway line infrastructure; and the ontology layer can reflect the association relationship between the disease entity and the disease entity, that is, the disease knowledge graph. In this embodiment, by combining the association relationship between the disease entity and the disease entity, and the association relationship between the various entities in the high-speed railway line infrastructure, a railway digital twin operation and maintenance ontology business classification set can be constructed, and then the association relationship between the operation and maintenance information and the high-speed railway line infrastructure model entity can be determined.

[0061] Integrate infrastructure operation and maintenance information into the model construction and subsequent operation and maintenance management of railway line infrastructure (roadbed, bridges, tracks, tunnels); define the railway digital twin operation and maintenance ontology business classification set N={T,Y,G,R,P}, such as Figure 6 As shown in the figure, T represents the railway infrastructure structural classification set, G represents the geometric information of the entity units in the line infrastructure twin model, Y represents the semantic information of the railway infrastructure (roadbed, bridges, tunnels, and tracks), R represents the entity relationship set consisting of the line infrastructure association relationships, and P represents the relationship set between railway operations and entities. Based on the railway digital twin operation and maintenance ontology business classification set, any operation and maintenance information can be associated with the corresponding railway line infrastructure entities.

[0062] In a specific embodiment, the intelligent operation and maintenance system architecture integrating GIS and BIM is constructed. Based on the evolution process of the occurrence of the disease, the railway line diseases can be classified and associated according to the cause drive, specific manifestation, and effect impact. An integrated expression model of the three domains of "cause domain-representation domain-effect domain" is established. The cause domain drives the change of the representation domain, the representation domain participates in the occurrence of the effect domain, and the change of the effect domain is indirectly affected by the cause domain. Through the BIM-GIS representation domain for visual expression, the cause and effect of the maintenance decision-making is based on the knowledge map of the line infrastructure operation and maintenance. Through this feedback mechanism, the system can adaptively respond to complex problems and finely manage the key links of the railway line disease operation and maintenance process. Figure 7 The diagram shows the high-speed railway intelligent operation and maintenance management architecture.

[0063] The technical solution of the embodiment of the present invention obtains disease text data; extracts disease entities in the disease text data; identifies the relationship between the disease entities based on trigger words; constructs a disease knowledge graph based on the relationship between the disease entities; and displays the disease and service status view of the high-speed railway line infrastructure entity based on the disease knowledge graph and the association relationship between operation and maintenance information and the high-speed railway line infrastructure model entity, thereby realizing a rapid response to disease information and a visual display of infrastructure entity diseases. Example 2 Figure 8 A high-speed railway line infrastructure intelligent operation and maintenance system is provided in the second embodiment of the present invention. Figure 8 Included are: The data acquisition unit 801 is used to acquire disease text data; An entity extraction unit 802 is used to extract disease entities from the disease text data; A relationship identification unit 803 is used to identify the relationship between the disease entities based on the trigger words; A graph construction unit 804 is configured to construct a disease knowledge graph based on the relationship between the disease entities; Based on the disease knowledge graph 805 and the association relationship between the operation and maintenance information and the high-speed railway line infrastructure model entity, the disease and service status view of the high-speed railway line infrastructure entity is displayed.

[0064] The high-speed railway line infrastructure intelligent operation and maintenance system provided by the embodiment of the present invention can execute the high-speed railway line infrastructure intelligent operation and maintenance method provided by any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the execution method.

[0065] Example 3 Figure 9 A schematic diagram of an electronic device 10 that can be used to implement an embodiment of the present invention is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital assistants, cellular phones, smartphones, wearable devices (such as helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present invention described and / or claimed herein.

[0066] like Figure 9As shown, electronic device 10 includes at least one processor 11 and memory, such as read-only memory (ROM) 12 and random access memory (RAM) 13, communicatively connected to at least one processor 11. The memory stores computer programs executable by the at least one processor. Processor 11 can perform various appropriate actions and processes based on the computer programs stored in ROM 12 or loaded from storage unit 18 into RAM 13. RAM 13 can also store various programs and data required for the operation of electronic device 10. Processor 11, ROM 12, and RAM 13 are interconnected via bus 14. An input / output (I / O) interface 15 is also connected to bus 14.

[0067] Multiple components in the electronic device 10 are connected to the I / O interface 15, including an input unit 16, such as a keyboard, a mouse, etc.; an output unit 17, such as various types of displays, speakers, etc.; a storage unit 18, such as a magnetic disk, an optical disk, etc.; and a communication unit 19, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information / data with other devices via a computer network such as the Internet and / or various telecommunication networks.

[0068] Processor 11 can be any general-purpose and / or specialized processing component with processing and computing capabilities. Examples of processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various specialized artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, digital signal processors (DSPs), and any other suitable processor, controller, microcontroller, etc. Processor 11 executes the various methods and processes described above, such as a method for intelligent operation and maintenance of high-speed rail line infrastructure.

[0069] In some embodiments, a method for intelligent operation and maintenance of high-speed railway line infrastructure can be implemented as a computer program tangibly embodied in a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program can be loaded and / or installed on electronic device 10 via ROM 12 and / or communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, one or more steps of the method for intelligent operation and maintenance of high-speed railway line infrastructure described above can be performed. Alternatively, in other embodiments, processor 11 can be configured to execute the method for intelligent operation and maintenance of high-speed railway line infrastructure via any other suitable means (e.g., via firmware).

[0070] Various embodiments of the systems and techniques described above can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), system-on-chip systems (SOCs), programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments can include being implemented in one or more computer programs that are executable and / or interpreted on a programmable system that includes at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0071] Computer programs for implementing the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when the computer program is executed by the processor, the functions / operations specified in the flowcharts and / or block diagrams are implemented. The computer program may be executed entirely on the machine, partially on the machine, as a stand-alone software package, partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

[0072] In the context of the present invention, a computer-readable storage medium may be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, device, or apparatus. A computer-readable storage medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or apparatus, or any suitable combination of the foregoing. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. More specific examples of machine-readable storage media may include an electrical connection based on one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0073] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device that has: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user can provide input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0074] The systems and techniques described herein can be implemented in a computing system that includes back-end components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes front-end components (e.g., a user computer with a graphical user interface or web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such back-end components, middleware components, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0075] A computing system may include clients and servers. The clients and servers are typically remote from each other and typically interact via a communication network. This client-server relationship arises through computer programs running on the respective computers, creating a client-server relationship. The server may be a cloud server, also known as a cloud computing server or cloud host. This server is a hosting product within the cloud computing service ecosystem that addresses the management difficulties and limited scalability of traditional physical hosting and VPS services.

[0076] It should be understood that the various forms of the processes shown above can be used to reorder, add, or delete steps. For example, the steps described in the present invention can be performed in parallel, sequentially, or in a different order, as long as the desired results of the technical solution of the present invention can be achieved. This is not limited herein.

[0077] The above specific embodiments do not limit the scope of protection of the present invention. Those skilled in the art will appreciate that various modifications, combinations, sub-combinations, and substitutions may be made based on design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention are intended to be included within the scope of protection of the present invention.

Claims

1. A method for intelligent operation and maintenance of high-speed railway line infrastructure, characterized in that: include: Get disease text data; Extracting disease entities from the disease text data; Identifying relationships between the disease entities based on trigger words; Constructing a disease knowledge graph based on the relationship between the disease entities; Based on the disease knowledge graph and the association between operation and maintenance information and high-speed railway line infrastructure model entities, the disease and service status view of the high-speed railway line infrastructure entities is displayed.

2. The intelligent operation and maintenance method for high-speed railway line infrastructure according to claim 1, characterized in that: Before extracting the disease entity from the disease text data, the method further includes: Preprocessing the disease text data; The preprocessing includes at least word segmentation, part-of-speech tagging, removal of stop words and removal of special symbols.

3. The intelligent operation and maintenance method for high-speed railway line infrastructure according to claim 2, characterized in that: If the disease text data is semi-structured data, extracting disease entities from the disease text data includes: Performing importance evaluation on the terms in the disease text data, and taking terms whose importance is greater than a preset threshold as keywords; Sorting the keywords according to the order of the words in the disease text data; Input the sorted terms into the Bi-gram model, and set rules based on disease text data and disease knowledge in professional fields to constrain the combination of terms. The preset scoring mechanism is used to score the combinations of terms, and the term combination with the highest score is identified as the disease entity.

4. The intelligent operation and maintenance method for high-speed railway line infrastructure according to claim 3 is characterized in that: If the disease text data is semi-structured data, identifying the relationship between the disease entities based on trigger words includes: The trigger word is a preset disease phrase or phrase pattern; Identify the relationships between disease entities based on trigger words.

5. The intelligent operation and maintenance method for high-speed railway line infrastructure according to claim 1, characterized in that: Also includes: Construct a high-speed railway line infrastructure model integrating BIM and GIS; Align the geodetic coordinates of the operation and maintenance information with the spatial rectangular coordinates of the BIM model; Establish an association relationship between the operation and maintenance information and the high-speed railway line infrastructure model entity.

6. The intelligent operation and maintenance method for high-speed railway line infrastructure according to claim 5, characterized in that: The establishment of an association relationship between the operation and maintenance information and the high-speed railway line infrastructure model entity includes: The association relationship between operation and maintenance information and high-speed railway line infrastructure model entities is determined based on the disease knowledge graph.

7. The intelligent operation and maintenance method for high-speed railway line infrastructure according to claim 1, characterized in that: The damage knowledge graph and the relationship between the operation and maintenance information and the high-speed railway line infrastructure model entity are used to display the damage and service status view of the high-speed railway line infrastructure entity, including: Obtain the relationship between disease entities based on the disease knowledge graph; Based on the association between operation and maintenance information and high-speed railway line infrastructure model entities, the disease entities are visualized and the corresponding service status view is displayed; Determine the cause of the disease based on the service status.

8. An intelligent operation and maintenance system for high-speed railway line infrastructure, characterized in that: include: A data acquisition unit, used for acquiring disease text data; An entity extraction unit, configured to extract disease entities from the disease text data; a relationship identification unit, configured to identify the relationship between the disease entities based on trigger words; A graph construction unit, configured to construct a disease knowledge graph based on the relationship between the disease entities; Based on the disease knowledge graph and the association between operation and maintenance information and high-speed railway line infrastructure model entities, the disease and service status view of the high-speed railway line infrastructure entities is displayed.

9. An electronic device, characterized in that: The electronic device comprises: at least one processor; and a memory communicatively connected to the at least one processor; wherein, The memory stores a computer program that can be executed by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the high-speed railway line infrastructure intelligent operation and maintenance method described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the high-speed railway line infrastructure intelligent operation and maintenance method according to any one of claims 1 to 7 when executed.

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