A cable tunnel digital twin system monitoring method based on the Internet of Things

Abstract

The present application relates to the technical field of cable tunnel intelligent monitoring, Internet of Things, digital twin, and more particularly to a cable tunnel digital twin system monitoring method based on Internet of Things. Relying on micro-power Internet of Things to realize low-power networking of multi-dimensional sensing devices in the tunnel, combining with a three-dimensional digital base to build a full-factor digital twin model of the cable tunnel, real-time sensing of the environment, devices, structure and cable state, three-dimensional visual display, intelligent early warning, fault tracing and linkage control are realized, thereby reducing operation and maintenance costs and improving the operation safety and intelligent level of monitoring of the cable tunnel.

Description

Technical Field

[0001] This invention relates to the fields of intelligent monitoring of cable tunnels, Internet of Things (IoT), and digital twin technology, and more specifically to a monitoring method for a digital twin system of cable tunnels based on the Internet of Things. Background Technology

[0002] With the acceleration of urbanization, the number of cable tunnels is also increasing. Due to the confined space of cable tunnels, traditional cable tunnel monitoring and maintenance methods, which mainly rely on fiber optic communication and station-end centralized control stations, suffer from problems such as cumbersome deployment of wired sensors, inconsistent data access standards, incomplete network coverage, difficulty in personnel positioning, severe data fragmentation and silos, and insufficient data analysis and visualization management methods. These problems make it difficult to support efficient inspection and maintenance of cable tunnels. To address these issues, some cable tunnel IoT monitoring methods and cable digital twin system monitoring technologies have been proposed in China.

[0003] Chinese patents CN115395641A and CN116720242A, titled "A Comprehensive Monitoring System for Cable Tunnels Based on the Internet of Things," disclose a method for transmitting cable tunnel environmental monitoring, real-time online cable monitoring information, and security monitoring information via Wi-Fi and 4G / 5G networks. CN115395641A discloses a method and system for cable tunnel safety monitoring based on the Internet of Things, which includes a cable tunnel monitoring platform construction module, a monitoring feature set analysis module, a safety assessment index analysis module, a monitoring sequence generation module, and a dynamic early warning module. CN115395641A discloses a cable tunnel monitoring system that includes an acquisition module and a simulation module. While existing technologies have offered solutions for cable tunnel broadband and narrowband signal coverage, data analysis, and visualization applications to some extent, problems remain, such as complex wiring within tunnels, difficulties in personnel positioning and communication, and long and costly tunnel modeling cycles. The system's monitoring signal coverage, positioning accuracy, ease of construction and maintenance, visualization modeling efficiency, and visualization application functions still need improvement.

[0004] Therefore, how to provide a monitoring method for a digital twin system of cable tunnels based on the Internet of Things is a problem that urgently needs to be solved by those skilled in the art. Summary of the Invention

[0005] In view of this, the present invention provides a monitoring method for a digital twin system of cable tunnels based on the Internet of Things, aiming to solve the above-mentioned technical problems.

[0006] To achieve the above objectives, the present invention adopts the following technical solution: A monitoring method for a digital twin system of cable tunnels based on the Internet of Things includes the following steps: S1. Complete the construction of a full-area 3D model of the cable tunnel, spatial coordinate registration, and monitoring point marking through a 3D digital twin base, and establish a one-to-one correspondence between physical equipment and virtual model points; S2. Complete the on-site deployment and network pairing of the low-power IoT sensing terminals in the sensing layer, the low-power IoT gateways in the network layer, and the relay nodes to build a low-power IoT communication network inside the tunnel. S3. Each low-power IoT sensor terminal collects environmental, cable, structural and image data of the cable tunnel according to the preset collection cycle and sleep strategy, and uploads it to the low-power IoT gateway through the low-power IoT network. S4. The low-power IoT gateway performs edge preprocessing and encryption on the received data, and uploads it to the platform layer after completing signal transmission through the relay node. After the S5 platform layer data processing unit completes the data parsing, it will load various monitoring data into the corresponding model points of the three-dimensional digital twin base in real time, so as to realize the dynamic updating of the digital twin model. S6. The intelligent analysis unit compares and analyzes real-time data with preset thresholds and historical data to determine the tunnel's operating status, identify potential abnormalities, and predict the development trend of faults. S7. If an abnormal state is detected, the platform layer sends an alarm message to the application layer, the application layer executes audio and visual prompts and pushes messages, and automatically generates an operation and maintenance work order according to the abnormality level. S8. Based on the operation and maintenance results and tunnel renovation information, regularly revise and iterate the model parameters and point information of the three-dimensional digital twin base.

[0007] Furthermore, the digital twin system includes a perception layer, a network layer, a platform layer, and an application layer; The perception layer includes several sets of low-power IoT sensor terminals deployed inside the cable tunnel, used to collect environmental data, cable operation data, tunnel structure data and on-site image data of the cable tunnel. The low-power IoT sensor terminals operate in a low-power sleep-wake mode. The network layer includes a low-power IoT gateway and a relay node. The relay node is used for signal relay transmission within the tunnel. The low-power IoT gateway is communicatively connected to each low-power IoT sensing terminal and is used to receive and aggregate data collected by the sensing layer, and to encrypt and preprocess the data at the edge. The platform layer is communicatively connected to the network layer and the application layer, and includes a 3D digital twin base, a data processing unit, an intelligent analysis unit, and a data storage unit. The 3D digital twin base stores and loads a full-domain 3D model of the cable tunnel, realizing real-time data mapping between the physical tunnel and the virtual model. The data processing unit is used to clean, parse, and convert the data uploaded by the gateway. The intelligent analysis unit is used to perform data analysis, anomaly identification, and fault prediction by combining preset rules; the data storage unit is used to classify and store full monitoring data, model data, alarm data, and operation and maintenance logs. The application layer is used to receive information from the platform layer to realize 3D twin screen display, anomaly alarm, operation and maintenance scheduling, and remote equipment control.

[0008] Furthermore, the low-power IoT sensing terminals of the sensing layer include low-power temperature and humidity sensors, low-power combustible gas sensors, low-power water level sensors, low-power cable temperature sensors, low-power tunnel structure deformation sensors, and low-power camera terminals; each sensing terminal has a built-in low-power IoT communication module, and the device's transmission power does not exceed 10mW.

[0009] Furthermore, the low-power IoT gateway has a built-in edge computing module, a data encryption module, and a signal amplification module. The edge computing module is used to perform data filtering and simple anomaly detection locally, and only uploads valid data and abnormal data to the platform layer.

[0010] Furthermore, the three-dimensional digital twin base includes a three-dimensional modeling submodule, a coordinate registration submodule, a data mounting submodule, and a view roaming submodule; the view roaming submodule supports automatic roaming of the entire tunnel, fixed-point zoom-in viewing, and segmented switching display functions.

[0011] Furthermore, the intelligent analysis unit is equipped with a threshold comparison algorithm, a multi-dimensional data correlation analysis algorithm, and a time-series trend prediction algorithm to identify single-point anomalies, linked anomalies, and long-term operational risks.

[0012] Furthermore, the application layer includes a large-screen visualization terminal, a mobile operation and maintenance terminal, an audible and visual alarm, and a remote control unit; the mobile operation and maintenance terminal receives alarm messages and operation and maintenance work orders to realize on-site dispatching from the mobile terminal.

[0013] Furthermore, in S3, the sleep-wake strategy of the low-power IoT sensor terminal is that it is in a sleep state under normal conditions, wakes up at preset time intervals to complete data collection and uploading, and immediately returns to the sleep state after the collection is completed.

[0014] Furthermore, in S7, the abnormal state is divided into three levels: general warning, intermediate alarm, and emergency fault. Different levels correspond to different alarm methods, push targets, and handling time limits.

[0015] Furthermore, in S8, the model iterative update includes correction of tunnel structure modification points, input of newly added sensor terminal points, association and annotation of historical fault data, and dynamic optimization of monitoring thresholds.

[0016] Compared to existing technologies, this invention employs a low-power communication node combined with a power supply and sensing integrated smart line, enabling plug-and-play functionality for various low-power sensors such as partial discharge, temperature and humidity, water level, and gas sensors. This provides high flexibility for new deployments and device maintenance. When adding new deployments or handling faults, sensors integrating low-power modules for temperature, humidity, gas, partial discharge, and water level can be easily fixed for use. This avoids redundant construction of multiple systems in terms of power supply, sensing, and communication, reducing the workload and safety risks associated with tunnel construction, installation, wiring, and debugging. It not only possesses traditional cable equipment and tunnel monitoring information transmission functions but also enables personnel positioning and communication, significantly improving the safety and emergency response capabilities of cable tunnel operations. It effectively connects all aspects of cable tunnel wireless and wired data, broadband and narrowband data integrated transmission, containerized management, and visualization applications, comprehensively improving the efficiency and level of cable tunnel perception, monitoring, and visualization management. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0018] Figure 1 This is a schematic diagram of the architecture of a digital twin monitoring system for cable tunnels based on a low-power Internet of Things and a three-dimensional base, according to the present invention.

[0019] Figure 2 This is a schematic diagram of a narrowband sensing network structure for the Internet of Things according to the present invention.

[0020] Figure 3 This is a schematic diagram of a micropower module structure according to the present invention.

[0021] Figure 4 This is a block diagram of a LoRa 2.4G module architecture according to the present invention.

[0022] Figure 5 This is a schematic diagram of a service channel access process according to the present invention.

[0023] Figure 6 This is a schematic diagram illustrating the normal process of control channel request and response according to the present invention.

[0024] Figure 7 This is a schematic diagram of a smart line integrating power supply, sensing, and communication according to the present invention.

[0025] Figure 8 This is a schematic diagram of an integrated smart line that incorporates a low-power protocol node, according to the present invention.

[0026] Figure 9 This is a schematic diagram of the connection between a node device and a smart line T-connector according to the present invention.

[0027] Figure 10 This is a schematic diagram of a cable tunnel wide and narrow band communication network according to the present invention.

[0028] Figure 11 This is a schematic diagram of an edge IoT agent device according to the present invention.

[0029] Figure 12 This is a schematic diagram of an edge IoT agent hardware framework according to the present invention.

[0030] Figure 13 This is a schematic diagram of an edge computing framework for an edge IoT agent device according to the present invention.

[0031] Figure 14 This is a schematic diagram of a Docker container and APP for a cable edge IoT agent device according to the present invention.

[0032] Figure 15 This is a logical design diagram for constructing a three-dimensional skeletal semantic scene of a power grid according to the present invention.

[0033] Figure 16 This is a schematic diagram illustrating the construction of a semantic library for substations and cable tunnels according to the present invention.

[0034] Figure 17 This is a design diagram of a skeletal semantic construction module for a cable tunnel scene according to the present invention.

[0035] Figure 18 This is a schematic diagram of the application function interface of a cable digital twin system according to the present invention. Detailed Implementation

[0036] The technical solutions in the embodiments of the present invention will be clearly and completely described below. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0037] To overcome the shortcomings of existing technologies, this invention proposes a cable tunnel digital twin monitoring system and method that combines low-power IoT, integrated smart line, edge computing framework, and digital twin 3D base. The system is divided into four parts: application layer, platform layer, network layer, and perception layer. It mainly consists of low-power IoT, monitoring devices and aggregation nodes, integrated smart line, cable edge IoT agent device, cable digital twin base, and digital twin subsystem of high-voltage cable lean management integrated system.

[0038] like Figure 1 As shown. Narrowband data such as grounding circulation current, partial discharge, and water level from the built-in low-power module are connected to the integrated sensor-communication smart line via a aggregation node device or a regional control unit that integrates an aggregation node. The smart line then connects the data to a narrowband edge IoT agent device (also known as an access node) located at the substation at the end of the cable tunnel. After being transmitted from the edge device to the IoT management platform and enterprise middleware, it provides data services to the digital twin subsystem. Simultaneously, the digital twin base provides modeling, loading, and rendering services to the digital twin subsystem, constructing various cable digital twin functions such as monitoring, inspection, and positioning. For broadband data such as video and voice, the data is directly connected to the cable digital twin subsystem via the smart line, broadband edge IoT agent device, and enterprise middleware. The detailed technical solution is as follows.

[0039] The perception layer consists of various IoT sensors, sensor networks, and smart cables, enabling comprehensive perception and linkage of information on cable body monitoring, environmental monitoring, security monitoring, fire monitoring, and disaster monitoring. It is divided into two parts: the sensor layer and the data aggregation layer.

[0040] Sensor Layer: The sensor layer consists of various IoT sensors and sensor networks, used to collect different types of state parameters and upload the data to the aggregation node or access node through the sensor network. IoT sensors are divided into low-power wireless sensors (μW level) and low-power wireless sensors (mW level). Various sensing terminals and sensors include cable body sensing and cable corridor environment sensing sensors, independent transmission system temperature measurement fiber optics, voice communication and positioning terminals, video surveillance cameras, etc.

[0041] The transmission protocol between low-power wireless sensors and aggregation or access nodes conforms to the Q / GDW 12020 Internet of Things Low-Power Wireless Network Communication Protocol for Transmission and Transformation Equipment, and supports LoRa communication. The interface between wired sensors and aggregation or access nodes conforms to the RS485 bus communication protocol.

[0042] Data aggregation layer: An intelligent transmission line that integrates power supply, communication, sensing, and low-power wireless data access, realizing the integration of multiple functions such as wide / narrowband data aggregation, voice communication, personnel positioning, and intrusion detection in the perception layer.

[0043] The smart line aggregation node receives data from sensors and transmits it to access nodes via LoRa, Ethernet, etc. Access nodes receive data from sensors or aggregation nodes, perform edge computing, and then transmit it to a secure access gateway via power fiber optic, Wi-Fi, and 5G network layers. The secure access gateway then forwards the data to the IoT platform layer management system. The communication protocols between aggregation nodes and between aggregation nodes and access nodes conform to the Q / GDW 12021 wireless networking protocol for IoT nodes in power transmission and transformation equipment.

[0044] The network layer mainly includes a dedicated power fiber optic network composed of optical fibers, routers, and secure access devices.

[0045] The platform layer mainly includes an IoT management platform, an enterprise middleware platform, and a digital twin foundation. The IoT management platform, based on IoT protocols, enables the access of all cable monitoring data, the issuance of commands, and the management of IoT models. The enterprise middleware platform provides unified management of all enterprise data covering cable monitoring data and offers services to external parties. The digital twin foundation provides functions such as cable 3D model management, monitoring data binding, and cable twin construction, supporting the underlying development of the cable digital twin system.

[0046] The application layer is deployed in a micro-application manner to build a group of high-voltage cable business applications, supporting operation and maintenance personnel in carrying out defect management, fault repair and other tasks, and assisting management personnel in professional control and analysis decision-making. Among them, the cable digital twin system, as a subsystem of the high-voltage cable lean management integrated system, is deployed in a micro-application manner, and realizes the monitoring, positioning and emergency response of cable tunnel operation status in a three-dimensional visualization manner.

[0047] Low-power wireless communication technology is primarily used for data access from low-power sensors operating at high frequencies and with small data volumes (below the K-byte level), mainly including sensors for temperature and humidity, water level, smoke detection, and hazardous gases in tunnels. Its implementation principle mainly involves the following aspects: First, asynchronous communication, where the sensor does not need to listen to messages from the receiving node, but periodically initiates random communication, reducing the power consumption of the sensor while waiting for communication; second, one-way reporting, where routine monitoring data is reported on the service channel, and the sensor goes into sleep mode after sending, reducing sensor communication power consumption. However, the service channel may experience congestion due to large amounts of data transmission, so for important alarm information, it is uploaded to the control channel, and a confirmation and retransmission mechanism ensures reliable communication. Through these mechanisms, in the 2.4GHz band, the sensor communication distance can ultimately exceed 100 meters, with the average power consumption of various sensors in the microwatt range.

[0048] This project first constructs a narrowband IoT sensing network using low-power modules, aggregation nodes, and access nodes (also known as cable edge IoT agents) to achieve real-time monitoring and intelligent analysis of cable body and environmental parameters within cable tunnels, such as... Figure 2 As shown.

[0049] micropower modules such as Figure 3 As shown, the 2.4G micro-power module proposed in this embodiment has a size of only 28.0x22.0mm, which can be easily embedded in various small and micro-sized sensing devices such as grounding current, water level, temperature and humidity sensors. Moreover, the average power consumption is only in the microwatt range, which can increase the average service life of battery-powered sensors to 5-7 years. The main design achievements are as follows.

[0050] Low-power module design requirements: 1) Minimize the communication power consumption (shorten the time) of sensors such as grounding loop current, temperature, and water level in cable tunnels; 2) Primarily unidirectional transmission, but supports bidirectional configuration; 3) Business data is allowed a certain packet loss rate, supporting massive access; 4) Maintain sufficient communication coverage distance within the tunnel.

[0051] Design concept of low-power wireless network communication protocol for power transmission and transformation Internet of Things: 1) Physical Layer Design. In terms of communication system selection, based on the work of mainstream sensor manufacturers, the LoRa physical layer method is selected. On the one hand, it can meet the needs of narrowband IoT services, and on the other hand, it is compatible with the hardware configurations of current mainstream sensor manufacturers. In terms of frequency band legality, in order to meet the regulations of the State Radio Regulatory Commission on the use of low-power frequency bands, the 2.4G frequency band is selected.

[0052] 2) The link layer design primarily uses unidirectional data reporting, employing a unidirectional reporting mode in the message channel to minimize the actual current draw of the sensor. It adopts limited bidirectional configuration, implementing bidirectional configuration in the control channel to support configuration of sensor periods, thresholds, etc. It employs an energy-saving mechanism using asynchronous communication, where the sensor initiates random communication and does not need to listen to node messages, allowing it to remain in a dormant state most of the time. In terms of alarm mechanisms, an alarm retransmission mechanism is implemented in the control channel to improve service reliability.

[0053] Hardware design of low-power communication module: The hardware structure diagram of the LoRa 2.4G module is as follows: Figure 4 As shown, it consists of a microcontroller, a 2.4G Lora wireless communication chip, an external AD interface, a serial port, etc.

[0054] Low-power communication module software design: 1) The service channel access process is as follows: Figure 5 As shown, the sensor module nodes interact with the aggregation node according to a certain business cycle length.

[0055] 2) Control channel request and response process as follows Figure 6 As shown, the information exchange process also follows the IoT low-power wireless network communication protocol for power transmission and transformation equipment.

[0056] Low-power networking protocol nodes are divided into two categories: aggregation nodes and networking nodes. Aggregation nodes are cable monitoring information transceivers integrating low-power modules, and their communication protocol follows the Q / GDW 12020 transmission and transformation equipment IoT low-power wireless network communication protocol. Networking nodes are cable monitoring information transceivers and networking devices integrating low-power networking modules, and their communication protocol follows the Q / GDW 12021 transmission and transformation equipment IoT node device wireless networking protocol. The smart line is a single cable with a built-in power supply line, a 485 communication line, and a wireless chip, integrating power supply, sensing, and communication functions. The built-in wireless chip uses adaptive microwave array sensing technology to monitor the phase and amplitude changes of wireless signals caused by personnel, small animals, etc., in real time, achieving high-precision detection, positioning, and tracking. The intelligent detection line uses a coaxial cable to synchronously transmit DC power (10V~48V) and 10Mbps high-speed data, realizing the integrated integration of power supply and information transmission. Figure 7 As shown.

[0057] This invention integrates wireless broadband access and Internet of Things (IoT) communication technologies to construct a multifunctional integrated IoT sensing layer communication network based on intelligent detection lines.

[0058] like Figure 9 As shown, the low-power IoT aggregation or networking nodes are connected to the smart line T-connector via the 485 interface to transmit cable monitoring data to the substation edge IoT agent device through the smart line.

[0059] A broadband and narrowband communication network for cable tunnels was constructed based on smart detection lines. For narrowband data communication, low-power sensors connect to the aggregation node via the LoRa protocol, low-power sensors connect to the network layer via narrowband access nodes, and wired sensors connect to the network via T-connectors on the smart detection lines. For broadband data communication such as images and videos, the network is accessed via Wi-Fi or Ethernet cables. Compared to traditional discrete communication systems, this approach avoids redundant construction of multiple systems, reduces construction and maintenance costs, and significantly improves the data transmission efficiency and system stability of sensors within the cable tunnel.

[0060] The cable edge IoT agent device adopts lightweight computing framework technology and lightweight algorithms to realize all data aggregation, edge computing and linkage control at the end of the cable tunnel, realize tunnel self-manufacturing, significantly reduce data transmission latency and improve the speed of fault detection and response.

[0061] Edge IoT agent devices follow the hardware and software architecture of IoT standards, consisting of a hardware layer and a software layer. Examples of edge IoT agent devices include... Figure 11 As shown.

[0062] Edge IoT agent hardware framework such as Figure 12As shown, the northbound direction of the edge IoT agent implements 4G and Ethernet interfaces, with reserved interfaces for 4G / 5G, encryption module, BeiDou module, and 5.8G Wi-Fi module; the southbound direction implements RS485, RS232, USB, LoRa 2.4G, LoRa 470M, ZigBee, Wi-Fi, Bluetooth, and Ethernet interfaces, with reserved interfaces for the smart chip encryption chip, DI / DO interface, and power line communication (PLC) module.

[0063] After being aggregated through the smart cable, the cable tunnel status data is uploaded to the edge IoT unit for data analysis. To widely adapt to different algorithm models from different manufacturers, lightweight computing framework technology and lightweight algorithms are required to realize all data aggregation, edge computing and linkage control at the cable tunnel end side, achieve tunnel autonomy, significantly reduce data transmission latency, and improve the speed of fault detection and response. The edge computing framework of the edge IoT agent device is as Figure 13 shown.

[0064] The edge computing framework consists of 5 layers including device access. Relying on various collection APPs in the device access layer, various early warning and diagnosis APPs in the edge application layer, and various service interaction APPs in the external interaction layer of the edge computing framework, an edge computing system for containerized data aggregation, edge computing and linkage control of cable tunnels is constructed. Equipped with the multi-level intelligent recognition algorithm app of YOLOv3+ASM+SURF, an edge-side alarm closed loop of "recognition-authentication-alarm-recording" with millisecond-level linkage is established, which significantly improves the real-time security capability of cable tunnels and reduces cloud dependence and data transmission latency.

[0065] The edge computing framework provides a series of functions such as edge configuration, APP management, and container management. All kinds of our data analysis models can be deployed in DOCK containers, which can be easily downloaded, installed and applied from the edge IoT management platform, as Figure 14 shown.

[0066] According to the APP management function provided by the edge computing framework, artificial intelligence apps can be deployed in a containerized manner, and the specific implementation methods are as follows.

[0067] Install intelligent video monitoring devices in the cable tunnel, deploy edge computing boxes, and identify personnel safety and main equipment defects based on machine learning. Video signals are transmitted in real time through the integrated communication system. 1 edge intelligent defect identification device is configured for every 16 cameras to automatically identify equipment defects and personnel violations, and push the video and detection results to the cable digital twin micro-application module of the high-voltage cable lean integrated management platform, realizing 12 types of AI intelligent recognition such as flame recognition and face recognition, with the specific categories shown in Table 1.

[0068] Table 1 Types of AI intelligent recognition for cable tunnels The detailed software function list of the edge IoT agent device based on the lightweight edge computing framework is as follows.

[0069] Table 2 Detailed Software Functions of Edge IoT Agent

[0070] To support the development of a cable digital twin system and realize 3D modeling, digital twin construction, model loading, rendering, and twin function applications, this embodiment develops a 3D foundation software for power digital twins, and develops a cable digital twin system based on this software. The digital twin foundation consists of modules such as a model editor and a scene editor, as well as a cable digital twin model body. Through the model editor and scene editor, and with the help of a rich model library, digital 3D models of cable equipment, facilities, and structures can be quickly constructed.

[0071] The model editor is a fundamental feature of this project. Once the model is fully edited, it lays the foundation for scene construction and digital twin rigging. The model itself is not the smallest editable unit; rather, it consists of several components, which are the smallest editable units.

[0072] There are two ways to build components: parametric construction and importing structured components. Parametric construction includes the construction of the following four types of components: basic primitives for substation engineering, steel profiles for substation engineering, overhead transmission line components, and cable line components. Importing structured components supports GIM, FBX, IFC, STL files, and point cloud files. This function opens up a channel for importing models edited by mainstream modeling tools into the digital twin platform.

[0073] This involves unifying the coordinate systems of different components, and using the component adjustment interface to scale and move the components to achieve connection matching between them.

[0074] The material interface allows you to add materials to the entire model, add materials to specific locations on the model, or add materials to specific surfaces of the model, making the model visually closer to a real device.

[0075] Adding actions to the equipment means adding animation effects. An animation effect interface is provided to add various animations to the model, including position animations, color and material animations, and animations corresponding to changes in operating parameters. The animations corresponding to changes in operating parameters are tightly coupled to the actual operating parameters of the equipment. If operating parameters exceed limits, the animation will visually display an alarm effect, helping managers to intuitively grasp the equipment's operating status and promptly identify potential safety hazards.

[0076] Model database: The system utilizes a self-developed database, GimDB, to store 3D geometric data, material data, texture maps, and a directory for storing 3D model files. GimDB supports extended storage of runtime data, design parameters, physical parameters, and topological relationships. The model library embeds graphics calculation functions, including basic graphics functions such as 3D models, 2D images, and QR codes, as well as multiple UID generation methods to meet model ID definitions. Simultaneously, the model database has the ability to hierarchically link data, allowing for flexible expansion of model additional functions, such as model actions, material changes, special effects, dynamic characteristics of the model mechanism, twin association and construction, and attribute association.

[0077] Model file management: All models are stored in the Model directory, with model cache files stored in .model format and model icon files stored in .image format. Component models referenced by the models are stored in the Component directory, with entity cache files stored in .entity format, reference lines and rulers for the calculation model stored in .reference format, and component icons stored in .image format.

[0078] Model position editing: It provides a model position editing interface by acquiring mouse and keyboard signals, supporting changes in device position, rotation angle, and model scaling ratio.

[0079] Parameterized model building: Based on the model's geometric parameters, the model data is filled into the model table, the model generation interface is called to generate the model file and save it to the specified directory, and the model file storage directory field information in the model data record is updated.

[0080] The scene editor consists of a system management area, a scene editing area, a scene management area, and a model resource area. The scene editing area occupies the majority of the interface, facilitating detailed manipulation during scene editing. The system management area primarily handles the editor's entry point, assigning permissions to logged-in users, system exit, and other functions, ensuring system security and user access control. In the scene editing area, model resources can be combined to quickly construct the desired scene. The scene management area mainly includes scene creation, saving, restoring, and deleting. All 3D scenes begin with scene creation. After scene editing is complete, the 3D scene information is saved to the database. If the scene needs to be re-edited, the original scene can be restored by reading the scene data. If the scene is no longer needed, the scene data can be deleted from the database. Saving and restoring scenes involves processing object information and handling the relationships between objects, making it more complex than scene creation and deletion.

[0081] System management is mainly responsible for the editor's entry point, assigning permissions to logged-in users, and providing security and user permission settings for system exit.

[0082] The scene editing area allows for the manipulation and management of 3D objects, providing users with interactive ways to build 3D scenes. 3D object manipulation refers to editing 3D objects to create a 3D scene. Specific functions include creation, deletion, movement, copying, rotation, rotating at a fixed angle, grouping, ungrouping, batch processing, and attributes.

[0083] The scene management area is located in the upper right corner of the interface. It includes menus for creating, saving, restoring, and deleting scenes, enabling functions such as scene creation, saving, restoring, and deleting.

[0084] The main function of the power grid 3D cloud rendering service is to render 3D applications on a remote server. The user terminal sends rendering commands to the server, which then executes the corresponding rendering tasks and transmits the rendered result to the user terminal via the network for display.

[0085] Establish cable equipment models and reusable skeleton libraries to generate three-dimensional twin models that intelligently bind physical attributes and operating states, providing a structured digital twin foundation for cable condition monitoring and simulation calculations, and achieving deep integration of geometric models and condition analysis.

[0086] Model semantic construction is the foundation for realizing a digital twin of a 3D power grid scene. Based on the model's individual components, semantic technology is used to form a quantifiable and indexable individual power grid information model. Scene semantics should cover four basic scenarios: equipment inspection, equipment monitoring, equipment operation, and equipment maintenance, with semantic definitions for business attributes, material textures, and geometric structures respectively. The equipment animation skeleton library constructs animation skeletons based on the polymorphic equipment within the substation, defining the actual polymorphism of the equipment to ensure its association with real-time cable operating status data.

[0087] 3D skeleton semantic scene construction of power grid, such as Figure 15 As shown, the cable tunnel scene is modeled at the component level using a model editor. Component models are grouped to obtain part models, which are then further grouped to form equipment-level models. Semantic information corresponding to the target independent equipment or part is obtained, and a twin binding tool is used to bind the semantic information to the target independent equipment or part, forming a target device or part with semantic information. Simultaneously, animation information corresponding to the target independent equipment or part is obtained, and a twin binding tool is used to bind the skeleton to the target independent equipment or part, forming a target device or part with skeletal animation. A 3D scene model corresponding to the cable tunnel environment is built using a scene editor. The target equipment or part is then integrated into the 3D scene model to form a 3D semantic skeletal scene model.

[0088] After forming a 3D skeletal semantic scene model, a voice database corresponding to the 3D skeletal semantic scene model is constructed using the voice command information of the operation and maintenance personnel responsible for operation and management in the cable tunnel. The construction of the semantic database for substations and cable tunnels is as follows: Figure 16 As shown.

[0089] First, voice commands are recorded for the operation and maintenance personnel responsible for the operation and management of the cable tunnel, forming a personal voice database for each manager. Then, a correspondence is established between each voice command in each manager's personal voice database and a voice control command. Next, each voice command in the personal voice database is associated with a voice control command according to the correspondence between each voice command and a voice control command. After the association is completed, the voice control commands are used to form a semantic library corresponding to the three-dimensional visualization of the cable tunnel, thereby enabling operation and maintenance personnel to control the equipment and facility model through voice commands.

[0090] Cable tunnel scene skeletal semantic construction system such as Figure 17 As shown, the point cloud module is used to load point cloud data of the cable tunnel environment collected by LiDAR as the base map for size and location during modeling; the modeling module is used in the model editor to perform component-level modeling of the cable tunnel scene using parametric primitives, component libraries, and equipment libraries, combined with the point cloud data, to group the component models to obtain component models, and further group the components to form equipment-level models; the semantic acquisition and binding module includes a semantic extraction module and a semantic binding module. The semantic extraction module is used to acquire the semantic information corresponding to the target independent device or component, and the semantic binding module binds the semantic information to the target independent device or component to form a target device or component with semantic information; the skeleton construction and binding module includes skeleton construction. The system includes a module and a skeleton binding module. The skeleton construction module is used to construct the skeleton corresponding to the target independent device or component, and the skeleton binding module binds the skeleton to the target independent device or component to form a target device or component with a skeleton. The 3D scene model building module is used to construct a 3D scene model corresponding to the cable tunnel environment in the model editor by combining point cloud data, and to integrate the target device or component with semantic information and skeleton into the 3D scene model to form a 3D skeleton semantic scene model. The voice semantic building module is used to construct a voice database corresponding to the 3D skeleton semantic scene model after the 3D skeleton semantic scene model is formed by using the voice command information of the operation and maintenance personnel responsible for operation and management in the cable tunnel.

[0091] The semantic construction system and method for cable tunnel scenarios proposed in this embodiment can effectively improve the semantic representation accuracy of the 3D skeletal semantic scene model of cable tunnel scenarios. Simultaneously, the construction of a speech semantic library can effectively cooperate with the 3D skeletal semantic scene model to enable more accurate control of maintenance robots operating in cable tunnel scenarios. It can also effectively recognize any speech commands from workers with regional accents, significantly improving the accuracy of speech command recognition. Furthermore, unlike traditional dialect-to-Mandarin speech recognition algorithms, this method uses a numbered correspondence between worker-issued speech commands and voice control commands to recognize the correspondence between accented or dialectal speech and voice control commands. This effectively reduces the complexity and difficulty of speech recognition algorithms, significantly improves speech recognition processing speed and response speed, and thus greatly enhances speech recognition efficiency, making it more suitable for harsh environments like substations and cable tunnels where timely operational responses are crucial.

[0092] Based on the power grid digital twin foundation, a cable digital twin system is constructed, which integrates virtual simulation of reality, virtual mapping of reality, virtual control of reality, and virtual pre-realization (Level 3). Its main functions are as follows: A 3D model library was constructed, an innovative multi-mode combined modeling strategy was proposed, and different mode combination modeling was improved to construct a high-precision 3D model to achieve accurate reconstruction of cable tunnels. By using sensors to perceive the overall status information of the equipment, the system can achieve efficient interaction and mapping between the virtual computer world and the real cable tunnel, and collect and process data such as the environment and equipment operating status in the cable tunnel in real time. The system performs intelligent analysis and processing on the data collected by the Internet of Things, perceives the rapid synchronous update of the "virtual-real" space in real time, and uses artificial intelligence to predict the data, so as to achieve "predicting the real from the virtual". Based on the three-dimensional visualization scene model and data, intelligent remote inspection of cable tunnels is realized through the collection and intelligent analysis of data such as video, grounding current circulation, and partial discharge. Remote control technology enables remote operation, maintenance, and control of equipment within cable tunnels.

[0093] Compared with the prior art, the present invention has the following advantages: 1) By adopting a smart line that integrates low-power communication nodes and power supply induction, various low-power sensors such as partial discharge, temperature and humidity, water level, and gas can be plugged and played, and the access is highly flexible when adding new deployment points and maintaining the device. 2) When adding new installations or handling faults, sensors such as temperature, humidity, gas, partial discharge, and water level sensors that integrate micropower modules can be used simply by fixing them. This avoids the duplication of multiple systems in terms of power supply, sensing, and communication, and reduces the workload of construction, installation, wiring, and debugging in tunnels, as well as safety risks. 3) It not only has the traditional cable equipment and tunnel monitoring information transmission functions, but also enables personnel positioning and communication, which greatly improves the safety of cable tunnel operations and emergency response capabilities. 4) It effectively connects all aspects of wireless and wired data, broadband and narrowband data integrated transmission, containerized management and visualization applications in cable tunnels, comprehensively improving the efficiency and level of cable tunnel perception, monitoring and visualization management.

[0094] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. The same or similar parts between the various embodiments can be referred to each other.

[0095] The above description of the disclosed embodiments enables those skilled in the art to make or use the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A monitoring method for a digital twin system of cable tunnels based on the Internet of Things, characterized in that, Includes the following steps: S1. Complete the construction of a full-area 3D model of the cable tunnel, spatial coordinate registration, and monitoring point marking through a 3D digital twin base, and establish a one-to-one correspondence between physical equipment and virtual model points; S2. Complete the on-site deployment and network pairing of the low-power IoT sensing terminals in the sensing layer, the low-power IoT gateways in the network layer, and the relay nodes to build a low-power IoT communication network inside the tunnel. S3. Each low-power IoT sensor terminal collects environmental, cable, structural and image data of the cable tunnel according to the preset collection cycle and sleep strategy, and uploads it to the low-power IoT gateway through the low-power IoT network. S4. The low-power IoT gateway performs edge preprocessing and encryption on the received data, and uploads it to the platform layer after completing signal transmission through the relay node. After the S5 platform layer data processing unit completes the data parsing, it will load various monitoring data into the corresponding model points of the three-dimensional digital twin base in real time, so as to realize the dynamic updating of the digital twin model. S6. The intelligent analysis unit compares and analyzes real-time data with preset thresholds and historical data to determine the tunnel's operating status, identify potential abnormalities, and predict the development trend of faults. S7. If an abnormal state is detected, the platform layer sends an alarm message to the application layer, the application layer executes audio and visual prompts and pushes messages, and automatically generates an operation and maintenance work order according to the abnormality level. S8. Based on the operation and maintenance results and tunnel renovation information, regularly revise and iterate the model parameters and point information of the three-dimensional digital twin base.

2. The monitoring method for a cable tunnel digital twin system based on the Internet of Things according to claim 1, characterized in that, The digital twin system includes a perception layer, a network layer, a platform layer, and an application layer; The perception layer includes several sets of low-power IoT sensor terminals deployed inside the cable tunnel, used to collect environmental data, cable operation data, tunnel structure data and on-site image data of the cable tunnel. The low-power IoT sensor terminals operate in a low-power sleep-wake mode. The network layer includes a low-power IoT gateway and a relay node. The relay node is used for signal relay transmission within the tunnel. The low-power IoT gateway is communicatively connected to each low-power IoT sensing terminal and is used to receive and aggregate data collected by the sensing layer, and to encrypt and preprocess the data at the edge. The platform layer is communicatively connected to the network layer and the application layer, and includes a 3D digital twin base, a data processing unit, an intelligent analysis unit, and a data storage unit. The 3D digital twin base stores and loads a full-domain 3D model of the cable tunnel, realizing real-time data mapping between the physical tunnel and the virtual model. The data processing unit is used to clean, parse, and convert the data uploaded by the gateway. The intelligent analysis unit is used to perform data analysis, anomaly identification, and fault prediction by combining preset rules; the data storage unit is used to classify and store full monitoring data, model data, alarm data, and operation and maintenance logs. The application layer is used to receive information from the platform layer to realize 3D twin screen display, anomaly alarm, operation and maintenance scheduling, and remote equipment control.

3. The monitoring method for a cable tunnel digital twin system based on the Internet of Things according to claim 2, characterized in that, The low-power IoT sensing terminals of the sensing layer include low-power temperature and humidity sensors, low-power combustible gas sensors, low-power water level sensors, low-power cable temperature sensors, low-power tunnel structure deformation sensors, and low-power camera terminals; each sensing terminal has a built-in low-power IoT communication module, and the device's transmission power does not exceed 10mW.

4. A monitoring method for a cable tunnel digital twin system based on the Internet of Things according to claim 2, characterized in that, The low-power IoT gateway has a built-in edge computing module, a data encryption module, and a signal amplification module. The edge computing module is used to perform data filtering and simple anomaly detection locally, and only uploads valid data and abnormal data to the platform layer.

5. A monitoring method for a cable tunnel digital twin system based on the Internet of Things according to claim 2, characterized in that, The three-dimensional digital twin base includes a three-dimensional modeling submodule, a coordinate registration submodule, a data mounting submodule, and a view roaming submodule; the view roaming submodule supports automatic roaming of the entire tunnel, fixed-point zoom-in viewing, and segmented switching display functions.

6. A monitoring method for a cable tunnel digital twin system based on the Internet of Things according to claim 2, characterized in that, The intelligent analysis unit is equipped with a threshold comparison algorithm, a multi-dimensional data correlation analysis algorithm, and a time-series trend prediction algorithm, which are used to identify single-point anomalies, linked anomalies, and long-term operational risks.

7. A monitoring method for a cable tunnel digital twin system based on the Internet of Things according to claim 2, characterized in that, The application layer includes a large-screen visualization terminal, a mobile operation and maintenance terminal, an audible and visual alarm, and a remote control unit; the mobile operation and maintenance terminal receives alarm messages and operation and maintenance work orders, enabling on-site dispatching from a mobile terminal.

8. A monitoring method for a cable tunnel digital twin system based on the Internet of Things according to claim 1, characterized in that, In S3, the sleep-wake strategy of the low-power IoT sensing terminal is that it is in sleep mode under normal conditions, wakes up at preset time intervals to complete data collection and uploading, and immediately returns to sleep mode after the collection is completed.

9. A monitoring method for a cable tunnel digital twin system based on the Internet of Things according to claim 1, characterized in that, In S7, abnormal states are divided into three levels: general warning, intermediate alarm, and emergency fault. Different levels correspond to different alarm methods, push targets, and handling time limits.

10. A monitoring method for a cable tunnel digital twin system based on the Internet of Things according to claim 1, characterized in that, In S8, the model iterative update includes correction of tunnel structure modification points, input of new sensor terminal points, association and annotation of historical fault data, and dynamic optimization of monitoring thresholds.

Images