Intelligent station operation and maintenance management method and system, electronic equipment and storage medium

Abstract

The invention provides an intelligent station operation and maintenance management method and system, electronic equipment and a storage medium. The method comprises the steps that operation parameters and appearance image information of all equipment on an oil and gas station are collected in real time through a sensor and a camera; the pre-constructed three-dimensional model of the oil and gas station and the collected equipment operation parameters are displayed on an operation terminal of operation and maintenance personnel through a visualization technology, and interactive operation can be performed on the three-dimensional model; according to a data analysis algorithm and a machine learning model, the equipment operation parameters are analyzed in real time, and whether the equipment breaks down or not is judged; and when a fault is detected, positioning the cause of the fault in combination with an answer provided by a pre-constructed AI dialogue engine. According to the technical scheme, the three-dimensional model and the real-time operation data of the equipment are deeply fused, so that operation and maintenance personnel can quickly position a fault point by means of the three-dimensional model during troubleshooting; and meanwhile, an AI dialogue engine is trained, so that the system can give accurate diagnosis and maintenance suggestions for complex equipment faults, and the continuous production efficiency is improved.

Description

Technical Field

[0001] This invention relates to the field of oil and gas station management technology, specifically to an intelligent station operation and maintenance management method, system, electronic device, and storage medium. Technical Background

[0002] With the continuous development of the oil and gas industry, oil and gas stations, as the core hubs of the oil and gas industry chain, are becoming increasingly important in terms of operation and maintenance management. As the scale of oil and gas stations continues to expand, the number and types of equipment are increasing dramatically, and the processes are becoming more complex, placing extremely high demands on the efficiency, accuracy, and safety of station operation and maintenance. In the early stages of oil and gas station operation and maintenance, equipment inspection and fault handling relied entirely on manual experience. With technological advancements, simple sensors were applied to stations, forming monitoring systems based on two-dimensional data displays, which improved operation and maintenance efficiency to some extent. However, with industry development, this traditional operation and maintenance model has gradually exposed many drawbacks and is unable to meet the needs of modern station operation and maintenance. Currently, although 3D modeling technology has been applied to oil and gas stations, its application is significantly insufficient in key aspects of equipment operation and maintenance, such as equipment fault diagnosis and repair. When equipment malfunctions, existing 3D models cannot clearly and dynamically display information such as the internal structure of the equipment, fault-related components, and the fault development process. Maintenance personnel may find it difficult to quickly and comprehensively understand the fault situation using 3D models. They are prone to making mistakes in maintenance operations due to insufficient understanding of the equipment's spatial structure and incomplete fault information, which in turn affects the maintenance effect and efficiency.

[0003] Technical content

[0004] The present invention aims to solve at least one of the above-mentioned technical problems.

[0005] To address the aforementioned issues, this invention provides an intelligent station operation and maintenance management method, system, electronic device, and storage medium.

[0006] In a first aspect, the present invention provides an intelligent station operation and maintenance management method, comprising:

[0007] The system uses sensors and cameras to collect real-time operating parameters and appearance images of various equipment at the oil and gas station.

[0008] Visualization technology is used to display the pre-built 3D model of the oil and gas station and the collected equipment operating parameters on the operation terminal of the operation and maintenance personnel, and the 3D model can be interactively operated.

[0009] Based on data analysis algorithms and machine learning models, the operating parameters of the device are analyzed in real time to determine whether the device has malfunctioned. When a malfunction is detected, the malfunction characteristics are further analyzed, and the cause of the malfunction is located by combining the answers provided by the pre-built AI dialogue engine.

[0010] Optionally, the pre-construction of the three-dimensional model of the oil and gas station includes:

[0011] Using 3D laser scanning and UAV oblique photography technology, comprehensive data collection was carried out on oil and gas stations;

[0012] After preprocessing the collected point cloud data and image data, a 3D model is constructed, and the location, size, model and internal structure information of each device are labeled, and the connection relationship and process flow between the devices are established.

[0013] The completed 3D model of the oil and gas station is optimized and verified, including but not limited to: mesh optimization, texture and material optimization, geometry and structure optimization, repeating vertices or zero-area triangles, and using simpler shaders.

[0014] Optionally, the pre-built AI dialogue engine is based on natural language processing technology and deep learning algorithms, and is trained using a pre-defined knowledge base; the construction of the knowledge base includes:

[0015] Collect technical documents, operating procedures, common faults, troubleshooting methods, and maintenance cases related to the operation and maintenance of oil and gas station equipment, and store the multi-source data in the database after integration and processing.

[0016] Processing unstructured text, including word segmentation and part-of-speech tagging, entity recognition, and relation extraction;

[0017] Perform structured modeling, clarify entity types and relationship types, store entities with nodes, and record the relationships between entities with edges to form a visual knowledge network;

[0018] Entity linking technology maps entities with the same name to unique identifiers, and knowledge reasoning algorithms are used to uncover implicit relationships.

[0019] Optionally, when a fault is detected, the fault characteristics are further analyzed, and the cause of the fault is located by combining the answers provided by the pre-built AI dialogue engine, including:

[0020] Maintenance personnel ask questions to the AI ​​dialogue engine based on the detected faults;

[0021] The AI ​​dialogue engine retrieves historical fault cases and maintenance strategies for this model of equipment from the knowledge base, and simultaneously retrieves the implementation and operation parameters. It analyzes the abnormal threshold through deep learning algorithms to comprehensively determine the cause of the fault and the appropriate maintenance measures.

[0022] The AI ​​dialogue engine provides a step-by-step solution in natural language.

[0023] Optionally, the interactive operation includes the operation and maintenance personnel rotating, scaling, and sectioning the three-dimensional model of the oil and gas station on the operation terminal; the three-dimensional model of the oil and gas station presents the appearance of the station and equipment, the internal structure of the equipment, the connection relationship of each component, and the fault development process.

[0024] Secondly, the present invention provides an intelligent station operation and maintenance management system, comprising:

[0025] The data acquisition module is used to collect real-time operating parameters and appearance images of various equipment at the oil and gas station through sensors and cameras;

[0026] The 3D modeling and visualization module uses visualization technology to display the pre-built 3D model of the oil and gas station and the collected equipment operating parameters on the operation terminal of the operation and maintenance personnel, and allows them to interact with the 3D model.

[0027] The fault diagnosis and decision support module is used to perform real-time analysis of the equipment's operating parameters based on data analysis algorithms and machine learning models to determine whether the equipment has malfunctioned.

[0028] The AI ​​dialogue engine module is used to input questions and, based on the answers provided by the pre-built AI dialogue engine, to further analyze the detected faults and locate the causes of the faults.

[0029] Optionally, the system also includes a data storage and management module, which uses a distributed database to store and manage the collected equipment operating parameters, the 3D model data, AI dialogue records, and fault diagnosis results.

[0030] Thirdly, the present invention provides an electronic device, including a memory and a processor;

[0031] The memory is used to store computer programs;

[0032] The processor is configured to implement, when executing the computer program, an intelligent station operation and maintenance management method as described in the first aspect.

[0033] Fourthly, the present invention provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements an intelligent station operation and maintenance management method as described in the first aspect.

[0034] The technical solution of this invention deeply integrates 3D models with real-time equipment operation data, enabling maintenance personnel to quickly locate fault points and obtain key information during troubleshooting using the 3D model. Simultaneously, it trains the AI ​​dialogue engine, enriching its knowledge base, allowing the system to provide accurate diagnoses and repair suggestions for complex equipment faults, significantly shortening fault handling cycles, reducing equipment downtime, and ensuring the continuity of oil and gas production.

[0035] Instruction manual illustrations

[0036] Figure 1 This is a flowchart illustrating the intelligent station operation and maintenance management method according to an embodiment of the present invention;

[0037] Figure 2 This is a schematic diagram illustrating the process of constructing a three-dimensional model of an oil and gas station according to an embodiment of the present invention.

[0038] Figure 3 This is a schematic diagram illustrating the working principle of the intelligent station operation and maintenance management system according to an embodiment of the present invention. Detailed Implementation

[0039] To make the above-mentioned objects, features, and advantages of the present invention more apparent and understandable, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Although some embodiments of the present invention are shown in the drawings, it should be understood that the present invention can be implemented in various forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided to provide a more thorough and complete understanding of the present invention. It should be understood that the accompanying drawings and embodiments of the present invention are for illustrative purposes only and are not intended to limit the scope of protection of the present invention.

[0040] It should be understood that the various steps described in the method embodiments of the present invention may be performed in different orders and / or in parallel. Furthermore, the method embodiments may include additional steps and / or omit the steps shown. The scope of the present invention is not limited in this respect.

[0041] The term "comprising" and its variations as used herein are open-ended, meaning "including but not limited to"; the term "based on" means "at least partially based on"; the term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one additional embodiment"; the term "some embodiments" means "at least some embodiments"; and the term "optionally" means "optional embodiments". Definitions of other terms will be given in the following description. It should be noted that the concepts of "first," "second," etc., mentioned in this invention are used only to distinguish different devices, modules, or units, and are not intended to limit the order of functions performed by these devices, modules, or units or their interdependencies.

[0042] It should be noted that the terms "one" and "more" used in this invention are illustrative rather than restrictive. Those skilled in the art should understand that, unless otherwise expressly indicated in the context, they should be understood as "one or more".

[0043] The names of the messages or information exchanged between the multiple devices in the embodiments of the present invention are for illustrative purposes only and are not intended to limit the scope of these messages or information.

[0044] like Figure 1 As shown in the figure, an intelligent station operation and maintenance management method provided by an embodiment of the present invention includes the following steps:

[0045] S1. Real-time acquisition of operating parameters and appearance images of various equipment at the oil and gas station through sensors and cameras.

[0046] Specifically, various sensors, such as temperature sensors, pressure sensors, vibration sensors, and flow sensors, are widely deployed on the equipment at oil and gas stations to collect real-time operating parameters. Simultaneously, cameras are installed to capture images of the equipment's appearance. Taking the compressor equipment at a large oil and gas station as an example, temperature sensors are installed at key locations to monitor temperature changes in various compressor components; vibration sensors are deployed to capture the frequency and amplitude of vibrations during equipment operation. These sensors and cameras transmit the collected data to a data processing center via wired or wireless communication technologies, where the data undergoes preliminary processing and storage.

[0047] S2. Using visualization technology, the pre-built 3D model of the oil and gas station and the collected equipment operating parameters are displayed on the operation terminal of the operation and maintenance personnel, and the 3D model can be interactively operated.

[0048] Specifically, refer to Figure 2The pre-constructed 3D model method for oil and gas stations in this embodiment includes: using 3D laser scanning and UAV oblique photography technology to collect comprehensive data on the oil and gas station, obtaining detailed information on the station's topography, buildings, and equipment. Using professional 3D modeling software, a high-precision 3D model of the oil and gas station is constructed based on the collected data. During the modeling process, the location, size, model, and internal structure of each piece of equipment are detailed and labeled, and the connection relationships and process flows between the equipment are established. The detailed labeling of the high-precision 3D model, through a path of "structured data input - logical knowledge modeling - visual reasoning output," fundamentally improves the accuracy of fault diagnosis and the practicality of maintenance suggestions for the AI ​​dialogue engine. Its core value lies in transforming the spatial attributes and functional relationships of equipment into knowledge graph elements that AI can understand, enabling AI to move beyond the limitations of "simple data matching" and achieve deep reasoning based on "physical entity characteristics," ultimately achieving intelligent and precise operation and maintenance decisions. For example, when constructing a 3D model of an oil and gas storage tank, not only is its external shape and size accurately presented, but its internal layered structure and pipeline connection methods are also deeply displayed. Model optimization is mainly achieved through: (1) mesh optimization, simplifying the mesh by reducing the number of triangles while maintaining key geometric features of the model, such as edge and vertex curvature; (2) texture and material optimization, merging duplicate materials and reducing the number of material spheres; (3) using simpler shaders, such as eliminating unnecessary lighting calculations; (4) geometry and structure optimization, removing invisible faces, such as internal faces and back faces; and (5) repeating vertices or zero-area triangles. Model verification is mainly achieved through performance testing, including rendering performance testing, such as measuring frame rate (FPS), rendering time, memory usage, and interactivity testing; as well as verifying whether the model loading speed and interaction latency meet user experience requirements.

[0049] Visualization technology is used to display 3D models on the operation terminals of maintenance personnel, such as computer monitors, tablets, or smart wearable devices. Maintenance personnel can rotate, zoom, and section the 3D model on their terminals to observe the station and equipment from different angles. Simultaneously, real-time collected equipment operating data is displayed intuitively on the corresponding equipment in the 3D model. For example, different colors and flashing effects can be used to indicate the equipment's operating status, and real-time operating parameters can be presented numerically. With the help of this 3D model of the oil and gas station, maintenance personnel can quickly and comprehensively understand fault conditions from multiple dimensions, avoiding operational errors caused by insufficient spatial awareness and missing fault information, and significantly improving the accuracy and efficiency of maintenance.

[0050] S3. Based on data analysis algorithms and machine learning models, perform real-time analysis of the device's operating parameters to determine whether the device has malfunctioned; when a malfunction is detected, further analyze the malfunction characteristics and, in conjunction with the answers provided by the pre-built AI dialogue engine, locate the cause of the malfunction.

[0051] The AI ​​dialogue engine in this embodiment is built based on Natural Language Processing (NLP) technology and deep learning algorithms, and focuses on the operation and maintenance scenario of oil and gas station equipment, integrating multi-source data to construct a professional knowledge base. The specific implementation method is as follows:

[0052] Data Acquisition and Preprocessing. Technical documents, operating procedures, common faults and troubleshooting methods, and maintenance cases related to oil and gas station equipment operation and maintenance are collected to build a knowledge base. Multi-source fusion of data across all dimensions is achieved through diverse technical means. Specifically, OCR technologies such as Tesseract and PaddleOCR are used to intelligently extract text from paper documents and images, while PDF parsing libraries such as PyPDF2 are used to structure technical documents. Simultaneously, sensor data is collected in real time through IoT protocols such as MQTT and OPC UA, and time-series data is stored in the InfluxDB database.

[0053] Unstructured text is processed, including word segmentation and part-of-speech tagging. Tools such as spaCy or THULAC are used to accurately segment Chinese text, identify nouns and verbs such as "compressor" and "vibration", and construct basic semantic units.

[0054] Entity recognition, based on BERT / ERNIE pre-trained models with fine-tuning, enables intelligent identification of entities such as equipment models (e.g., "XX-200 compressor"), fault types (e.g., "abnormal vibration"), and maintenance procedures.

[0055] Relationship extraction involves mining the connections between entities using sequence labeling models (such as BiLSTM+CRF) or graph neural networks (GNN), for example, extracting the logical chain of "fault cause - excessive vibration frequency - solution - tightening bolts".

[0056] Define the ontology, clarifying the entity types, such as equipment, faults, indicators, maintenance measures, etc., as well as the relationship types, such as belonging to, causing, and applicable. For example, the subordinate relationship of "equipment-model", the causal relationship of "fault cause-fault phenomenon", and the adaptation relationship of "maintenance measure-fault type".

[0057] Graph database construction: Graph databases are built using Neo4j or Apache Jena, with nodes storing entities and edges recording the relationships between entities, forming a visual knowledge network.

[0058] To eliminate data ambiguity, knowledge fusion is implemented. Entity linking technology maps entities with the same name to unique identifiers; for example, "compressor" is uniformly associated with a specific model node, avoiding semantic confusion. Missing knowledge is supplemented: knowledge reasoning algorithms, such as TransE and ComplEx, are used to uncover implicit relationships. When "abnormal vibration" lacks a stated cause, potential factors, such as loose components, can be inferred from similar failure cases.

[0059] Based on a built-in knowledge base and real-time data, the AI ​​dialogue engine can achieve accurate problem responses. In a specific implementation scenario, data analysis algorithms and machine learning models are used to analyze the collected equipment operation data in real time. By comparing the normal operating parameter range of the equipment with the real-time data, it is determined whether the equipment has malfunctioned. If an anomaly is detected in the compressor, when maintenance personnel ask, "What should I do if a certain model of compressor is vibrating abnormally?", the AI ​​dialogue engine first retrieves historical fault cases and maintenance strategies for that model of equipment from the knowledge graph. At the same time, it retrieves real-time data such as vibration frequency and temperature collected by the Internet of Things. Through deep learning models, it analyzes the abnormal threshold, comprehensively judges the cause of the fault (such as loose bolts or worn bearings), and matches appropriate maintenance measures (such as tightening bolts or replacing bearings), providing a step-by-step solution in natural language. Furthermore, maintenance personnel can quickly locate the fault location through the display of a 3D model.

[0060] Through the aforementioned technological links, the AI ​​dialogue engine achieves a closed-loop process from data collection and knowledge modeling to intelligent response, providing intelligent and precise decision support for the operation and maintenance of oil and gas station equipment.

[0061] like Figure 3 As shown, this embodiment of the invention provides an intelligent station operation and maintenance management system for running the aforementioned intelligent station operation and maintenance management method, including:

[0062] The data acquisition module is used to collect real-time operating parameters and appearance images of various equipment at the oil and gas station through sensors and cameras;

[0063] The 3D modeling and visualization module uses visualization technology to display the pre-built 3D model of the oil and gas station and the collected equipment operating parameters on the operation terminal of the operation and maintenance personnel, and allows them to interact with the 3D model.

[0064] The fault diagnosis and decision support module is used to perform real-time analysis of the equipment's operating parameters based on data analysis algorithms and machine learning models to determine whether the equipment has malfunctioned.

[0065] The AI ​​dialogue engine module is used to input questions and, based on the answers provided by the pre-built AI dialogue engine, to further analyze the detected faults and locate the causes of the faults.

[0066] The data storage and management module employs a distributed database to store and manage collected equipment operating parameters, 3D model data, AI dialogue records, and fault diagnosis results. Simultaneously, a data backup and recovery mechanism is established to prevent data loss. Through the data management system, maintenance personnel can easily query historical data, perform data analysis and statistics, providing data support for long-term equipment maintenance and optimization.

[0067] An electronic device provided in this invention includes a memory and a processor; the memory is used to store a computer program; the processor is used to implement the intelligent station operation and maintenance management method as described above when the computer program is executed.

[0068] This invention provides a computer-readable storage medium storing a computer program. When the computer program is executed by a processor, it implements the intelligent station operation and maintenance management method described above.

[0069] Alternatively, a non-volatile computer-readable storage medium storing a computer program that, when executed by a processor, causes the processor to perform the following operations:

[0070] The pre-built scheduling model is configured with parameter script encoding and identification, and scheduling suggestions corresponding to various parameter conditions summarized from historical experience are preset. The types of parameters include positions, indicators and variables, and the variables include fixed values ​​and variable parameters.

[0071] Input the scheduling script and automatically parse and verify it to extract the corresponding scheduling parameters; when the scheduling parameter is a variable, the parameter value needs to be input through the front-end page and calculated; when the scheduling parameter is the position number, the value retrieval interface of the real-time database that obtains position number data in real time needs to be called to use the real-time data of the position number as the parameter value to participate in the calculation of the entire script;

[0072] After obtaining the scheduling parameters and calculated data through the interface, the system automatically selects and generates corresponding scheduling suggestions.

[0073] The present invention will now describe electronic devices that can serve as servers or clients of the present invention, which are examples of hardware devices that can be applied to various aspects of the present invention. Electronic devices are intended to represent various forms of digital electronic computer devices, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. Electronic devices can also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the invention described and / or claimed herein.

[0074] Electronic devices include a computing unit that can perform various appropriate actions and processes based on a computer program stored in read-only memory (ROM) or loaded from a storage unit into random access memory (RAM). The RAM can also store various programs and data required for device operation. The computing unit, ROM, and RAM are interconnected via a bus. Input / output (I / O) interfaces are also connected to the bus.

[0075] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. The storage medium can be a magnetic disk, optical disk, read-only memory (ROM), or random access memory (RAM), etc. In this application, the units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of the embodiments of the present invention according to actual needs. Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated units can be implemented in hardware or as software functional units.

[0076] While the present invention has been disclosed above, its scope of protection is not limited thereto. Those skilled in the art can make various changes and modifications without departing from the spirit and scope of the present invention, and all such changes and modifications will fall within the scope of protection of the present invention.

Claims

1. An intelligent station yard operation and maintenance management method, characterized in that The method comprises the following steps: Real-time acquisition of operation parameters and appearance image information of each device in the oil and gas station through sensors and cameras; The pre-constructed three-dimensional model of the oil and gas station and the acquired device operation parameters are displayed on the operation terminal of the operation and maintenance personnel through visualization technology, and the three-dimensional model can be interacted; According to the data analysis algorithm and the machine learning model, the device operation parameters are analyzed in real time to determine whether the device has failed; when a fault is detected, the fault characteristics are further analyzed, and the fault cause is located in combination with the answers provided by the pre-constructed AI dialogue engine. 2.The intelligent station yard operation and maintenance management method of claim 1, wherein The pre-construction of the three-dimensional model of the oil and gas station comprises: Using three-dimensional laser scanning and unmanned aerial vehicle oblique photography technology to collect data of the oil and gas station in all directions; After preprocessing the collected point cloud data and image data, a three-dimensional model is constructed, the position, size, model and internal structure information of each device are labeled, and the connection relationship between devices and the process flow are established; Optimization and verification of the constructed three-dimensional model of the oil and gas station, including but not limited to: mesh optimization, texture and material optimization, geometry and mechanism optimization, repeated vertex or zero-area triangle, use of simpler shader. 3.The intelligent station yard operation and maintenance management method of claim 1, wherein The pre-constructed AI dialogue engine is based on natural language technology and deep learning algorithm, and is trained through a pre-set knowledge base; the construction of the knowledge base comprises: Collecting technical documents, operation procedures, common faults, treatment methods and maintenance cases related to the operation and maintenance of oil and gas station equipment, and storing the processed multi-source data in a database; Processing unstructured text, including word segmentation and part-of-speech tagging, entity recognition and relationship extraction; Structured modeling to clearly define entity types and relationship types, store entities as nodes, and record entity relationships as edges to form a visual knowledge network; Mapping entities with the same name to unique identifiers through entity linking technology, and mining implicit relationships with the help of knowledge reasoning algorithm.

4. The intelligent station yard operation and maintenance management method of claim 3, wherein When a fault is detected, the fault characteristics are further analyzed, and the fault cause is located in combination with the answers provided by the pre-constructed AI dialogue engine, which comprises: The operation and maintenance personnel ask questions to the AI dialogue engine according to the detected fault; The AI dialogue engine retrieves historical fault cases and maintenance strategies of the device from the knowledge base, and retrieves the implementation operation parameters, analyzes the abnormal threshold value through the deep learning algorithm, and comprehensively judges the fault cause and the adaptive maintenance measures; The AI dialogue engine provides a step-by-step solution in natural language form. 5.The intelligent station yard operation and maintenance management method of claim 1 or 2, wherein The interactive operation comprises rotating, scaling and sectioning the three-dimensional model of the oil and gas station on the operation terminal by the operation and maintenance personnel; The three-dimensional model of the oil and gas station presents the appearance of the station and the devices, the internal structure of the devices, the connection relationship between parts, and the fault development process.

6. The intelligent station yard operation and maintenance management system of claim 1, wherein The method comprises the following steps: A data acquisition module is used to acquire operation parameters and appearance image information of each device in the oil and gas station in real time through sensors and cameras; A three-dimensional modeling and visualization module displays the pre-constructed three-dimensional model of the oil and gas station and the acquired device operation parameters on the operation terminal of the operation and maintenance personnel through visualization technology, and the three-dimensional model can be interacted; A fault diagnosis and decision support module is configured to analyze the equipment operation parameters in real time according to a data analysis algorithm and a machine learning model, and determine whether the equipment has a fault; An AI conversation engine module is configured to input a question, analyze and locate the fault cause according to an answer provided by a pre-built AI conversation engine.

7. The intelligent station yard operation and maintenance management system of claim 6, wherein Further comprising: A data storage and management module is configured to store and manage the collected equipment operation parameters, the three-dimensional model data, the AI conversation record and the fault diagnosis result by using a distributed database.

8. An electronic device, comprising: comprising a memory and a processor; The memory is configured to store a computer program; The processor is configured to implement the intelligent station operation and maintenance management method according to any one of claims 1 to 6 when executing the computer program.

9. A computer-readable storage medium, characterized in that, The storage medium has the computer program stored thereon, and the computer program, when executed by the processor, implements the intelligent station operation and maintenance management method according to any one of claims 1 to 6.

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