Low-voltage switch cabinet operation state visual monitoring method based on digital twinning

Abstract

The application discloses a low-voltage switch cabinet operation state visual monitoring method based on digital twinning, relates to the field of digital twinning, and comprises the following steps: deploying multiple types of sensors on a physical cabinet body, uploading data to an edge gateway through wired and wireless communication, and synchronously establishing a multi-dimensional digital twinning model based on geometry, electrical topology and historical data. The edge gateway pre-processes the data and maps it to the corresponding components of the model, assesses the health status of the components in real time through a preset rule library, and dynamically presents the results in a visual interface. Users can view real-time monitoring data, historical trends and alarm events through the interactive interface, support time axis rollback and fault deduction, and intuitively display the prediction impact range with highlighted paths. The application has the advantages that real-time data mapping is realized by combining a multi-sensor network and edge computing, accurate equipment operation state monitoring and hidden danger prediction are realized by relying on state assessment, visual presentation and fault deduction, and operation and maintenance efficiency and operation safety are greatly improved.

Description

Technical Field

[0001] This invention relates to the field of digital twins, and in particular to a method for visually monitoring the operating status of low-voltage switchgear based on digital twins. Background Technology

[0002] With the continuous advancement of industrialization, low-voltage switchgear, as the core equipment of power distribution systems, directly affects the safety and efficiency of the entire power supply network through its operational stability and reliability. Traditional inspection and fault diagnosis methods mainly rely on manual periodic inspections, instrument readings, and post-incident maintenance, which suffer from prominent problems such as poor real-time performance, delayed detection of potential hazards, high operation and maintenance costs, and inability to perform predictive maintenance. Especially in modern complex power environments, potential faults such as variable equipment loads, internal overheating, insulation aging, and loose connections are difficult to detect in a timely manner.

[0003] Most current methods on the market only monitor single parameters, lack multi-sensor network collaboration, have incomplete data collection, and do not incorporate edge computing for real-time preprocessing, resulting in data lag. Digital twin applications are superficial, often only constructing simple 3D geometric models without integrating logical and behavioral models, failing to accurately reflect the electrical connections and dynamic operating characteristics of components, and struggling to achieve precise physical-virtual linkage. Furthermore, status assessment rules are relatively simplistic, unable to comprehensively determine component health levels, and visualizations are mostly simple data listings, lacking dynamic mapping and intuitive warnings. They also generally lack historical backtracking and fault prediction capabilities, only enabling post-fault troubleshooting and failing to predict the scope of fault impact, leading to low maintenance efficiency, difficulty in proactively mitigating downtime risks, and an inability to meet the demands of intelligent industrial maintenance for precise and forward-looking monitoring. Summary of the Invention

[0004] To improve existing methods, a digital twin-based method for visual monitoring of the operating status of low-voltage switchgear is provided, which solves at least one of the technical problems mentioned in the background section.

[0005] To achieve the above objectives, the technical solution adopted by the present invention is as follows: A digital twin-based method for visually monitoring the operational status of low-voltage switchgear includes: On the physical entity of the target low-voltage switchgear, a sensor network consisting of temperature sensors, current sensors, voltage sensors, partial discharge sensors, arc light sensors and status indicators is deployed. The collected real-time physical data is uploaded to the edge computing gateway through wired industrial bus and wireless IoT communication protocols. Based on the three-dimensional dimensions, structural layout, and electrical connection topology of the physical switchgear, a three-dimensional geometric model is constructed. Based on the electrical connection topology and physical laws, a logical model reflecting the electrical relationships between the components of each three-dimensional model is constructed. Based on material properties, load characteristics, and historical operating data, a behavioral model reflecting the dynamic characteristics of the components is constructed. The geometric model, logical model, and behavioral model are integrated and correlated to form a multi-dimensional digital twin model of the low-voltage switchgear. The edge computing gateway receives real-time physical data streams, preprocesses the data, and maps the preprocessed real-time data streams to component instances with the same sensor identifiers in the digital twin model through industrial IoT protocols. In the digital twin development platform, a pre-defined status assessment rule library is set up for each monitoring parameter. The status data calculated or updated by the digital twin model after driving is matched and analyzed with the status assessment rule library in real time to determine the real-time health status level of each component, and the status assessment results are dynamically mapped with the visualization attributes of the digital twin model. A user-friendly visual monitoring interface is constructed. When a component is selected, a floating information panel pops up. The panel displays the component's real-time monitoring data, health status, historical trend curves of key parameters, and a list of related alarm events in layers. Through the timeline control, users can roll back to any point in history. By pre-setting fault points and fault types on the digital twin model, the state is extrapolated, the scope of the fault's impact is predicted, and the affected component paths are displayed in a visual chain highlight.

[0006] Preferably, the construction of the three-dimensional geometric model specifically includes: Acquire precise three-dimensional dimensional data, mechanical structure layout diagram and electrical connection topology diagram of the target low-voltage switchgear. The three-dimensional dimensional data is derived from equipment design drawings or three-dimensional laser scanning point cloud data. Based on the aforementioned three-dimensional dimensional data and mechanical structure layout diagram, structural component models of the cabinet, partitions, mounting beams, and door panels are constructed. Based on the component installation dimensions in the electrical connection topology diagram and equipment design drawings, core electrical component models of the bus system, circuit breaker unit, disconnector, current transformer, cable joint and secondary terminal block are constructed respectively. All the structural component models and core electrical component models are assembled in three-dimensional space according to the spatial positional relationships and connection interfaces defined by the mechanical structure layout diagram and electrical connection topology diagram to form a complete three-dimensional geometric model of the switch cabinet. The assembled 3D geometric model of the switchgear was compared and verified with the design drawings.

[0007] Preferably, the construction of the logical model reflecting the electrical relationships between the various 3D model components specifically includes: Based on the electrical connection topology graph, the physical connection relationships between the bus system, circuit breaker unit, disconnector, current transformer, cable joint and secondary terminal block are extracted. Each electrical component is abstracted as a node and the physical connection between components is abstracted as an edge, forming a node-edge topology graph that describes the circuit connectivity. The generated node-edge topology graph is structured, and the electrical properties of each edge are defined, including connection type, rated current and impedance. Based on the circuit connection relationship in the electrical connection topology diagram, and in accordance with Kirchhoff's current law and voltage law, combined with real-time collected current and voltage data, the energy flow path from the power input terminal to each load output terminal is calculated and dynamically generated. Based on the opening and closing status signals of circuit breaker units and disconnecting switches, as well as the secondary circuit control logic of protection relays and intelligent instruments, a set of logic rules with state machines as the core is established.

[0008] Preferably, the construction of the behavioral model reflecting the dynamic characteristics of the component specifically includes: Based on the material thermal conductivity, specific heat capacity and surface emissivity of busbars, circuit breaker contacts and cable joints, a finite element analysis mesh is established in combination with their three-dimensional geometry. The temperature field distribution of each component is calculated and dynamically updated to form a transient heat conduction model. For the busbar system and circuit breaker current-carrying conductors, an electromagnetic field calculation domain is established based on their spatial arrangement geometry. Based on real-time acquired current data, Ampere's law and Biot-Savart's law are applied to calculate the magnitude and direction of the electromagnetic forces between conductors and contacts under short-circuit or high-current conditions, forming an electrodynamic model. For the operating mechanism of circuit breakers and mechanical components of disconnect switches, based on their material fatigue characteristics, standard mechanical life curves, and real-time collected operation counts and load currents, combined with fault and maintenance records recorded in historical operating data, a remaining life assessment model characterizing their cumulative wear degree is established, forming a mechanical wear model. The constructed heat conduction model, electrodynamic model, and mechanical wear model are integrated to form a behavioral model that reflects the dynamic physical characteristics of the components.

[0009] Preferably, the process of integrating and associating the geometric model, logical model, and behavioral model to form a multi-dimensional digital twin model specifically includes: Each constructed 3D geometric model component instance is assigned a globally unique identifier. The nodes of the logical model, the computational units of the behavioral model, and the geometric model component instances are mapped to each other through the globally unique identifier, forming the basis for static association between the three models. Configure a data routing engine so that when real-time monitoring data or simulation input data from a physical entity is triggered, the data is synchronously distributed to the geometric model component corresponding to the identifier based on static association. The geometric, logical, and behavioral models that have completed mapping and linkage are encapsulated into a unified software object, providing standardized data access and function call interfaces to the outside world, forming a multi-dimensional digital twin model that unifies static attributes, dynamic relationships, and physical laws.

[0010] Preferably, mapping the preprocessed real-time data stream to component instances with the same sensor identifier in the digital twin model specifically includes: Based on the sensor identifiers in the preprocessed data packets, real-time data is mapped to corresponding component instances with the same identifiers in the digital twin model; For temperature data, the heat conduction model is driven to calculate and update the temperature field distribution of each part of the model components, which is then rendered on the surface of the three-dimensional geometric model in the form of a heat map. For current and voltage data, the drive circuit logic model calculates the loop power, power factor and three-phase unbalance, and visualizes them on the connecting bus in the form of dynamic vector arrows or flowing particles. For partial discharge and arc light data, as event signal drivers, high-frequency pulse or flash animation effects are triggered at the corresponding spatial location in the digital twin model; For the status indicator data, drive the three-dimensional model posture of the corresponding circuit breaker and disconnector components to synchronize the closing, opening, and energy storage status changes.

[0011] Preferably, determining the real-time health status level of each component specifically includes: A pre-defined rule base for status assessment of each monitoring parameter is provided. The rule base includes: temperature threshold rules based on the insulation level of component materials and historical temperature rise curves; overload classification rules based on the ratio of load current to rated current; insulation degradation diagnosis rules based on the amplitude-phase-frequency pattern of partial discharge signals; and wear warning rules based on the number of switching operations and mechanical life curves. It receives real-time state data synchronously updated from each component instance of the digital twin model, uses the state data of each component as input, and performs condition matching with the rules of the state evaluation rule base in parallel. Based on the matching results, a multi-rule comprehensive judgment logic is adopted. If none of the monitored parameters trigger any early warning or alarm rules, the status is judged as normal. If a parameter triggers the dynamic early warning threshold or reaches the mild overload level, the status is judged as caution. If a parameter triggers the static alarm threshold, reaches the overload level, or detects a definite insulation defect mode, the status is judged as abnormal. If a parameter reaches the severe overload threshold, its remaining life is about to be exhausted, or a critical insulation signal appears, the status is judged as dangerous.

[0012] Preferably, the dynamic mapping of the state assessment results to the visual attributes of the digital twin model specifically includes: In normal conditions, the component model is displayed with the preset base color and transparency; When in a state of attention, the component model begins to periodically flash a pale yellow halo; In abnormal states, the component model color gradually changes to orange, accompanied by a rotating warning indicator; In dangerous situations, the component model's color abruptly turns red, flashes violently, and generates 3D warning text and sound alarms; On the side of the digital twin model interface, a global status summary panel is generated, which summarizes and displays the real-time parameters and evaluation levels of all key components in the form of lists and dashboards.

[0013] Preferably, the step of pre-setting fault points and fault types on the digital twin model for state deduction specifically includes: Users can select at least one fault type from a predefined fault type library through a visual panel for configuration; Based on the configured fault parameters, in the data layer of the digital twin model, initial fault conditions are injected into the logical model nodes and behavioral model calculation units corresponding to the target components. Using the digital twin model after the injection of faults as the initial state, simulation calculations are performed based on the integrated behavioral and logical models; During the simulation, the propagation path of electrical anomalies caused by faults in the node-edge topology graph of the logic model is analyzed in real time, and all components whose states exceed the attention level threshold in the state evaluation rule base are recorded, forming an affected component chain. In the 3D geometric model, all components included in the affected component chain obtained from the analysis are highlighted in a chain with colors that are different from those in the normal state.

[0014] Compared with the prior art, the advantages of the present invention are: This method achieves comprehensive, precise, and visualized closed-loop management of low-voltage switchgear operation monitoring, effectively addressing the pain points of traditional monitoring methods, such as fragmented data, insufficient visualization, and weak fault prediction capabilities. By deploying multiple types of sensor networks combined with edge computing, it enables real-time acquisition and preprocessing of multiple parameters, including temperature and current, ensuring data timeliness and comprehensiveness. A multi-dimensional digital twin model integrates geometric, logical, and behavioral models to achieve precise mapping between physical entities and virtual models, dynamically reconstructing equipment operating status and component relationships. Based on a comprehensive status assessment rule base, it can accurately determine component health levels and dynamically present them through visualized attributes, making it intuitive and easy to understand. It also supports historical backtracking and fault simulation, accurately predicting the scope of fault impact and providing early warnings of potential hazards. This method significantly improves operation and maintenance efficiency, reduces labor costs, and minimizes downtime losses, providing reliable support for the safe and stable operation of low-voltage switchgear. It aligns with the development needs of intelligent industrial operation and maintenance. Reference materials indicate that similar applications can reduce maintenance costs and downtime losses, further highlighting its practical value. Attached Figure Description

[0015] Figure 1 This is a schematic diagram of the low-voltage switchgear operation status visualization monitoring method based on digital twin proposed in this invention. Detailed Implementation

[0016] The following description is intended to disclose the invention and enable those skilled in the art to implement it. The preferred embodiments described below are merely examples, and other obvious variations will occur to those skilled in the art.

[0017] See Figure 1 As shown, the low-voltage switchgear operation status visualization monitoring method based on digital twins includes: On the physical entity of the target low-voltage switchgear, a sensor network consisting of temperature sensors, current sensors, voltage sensors, partial discharge sensors, arc light sensors and status indicators is deployed. The collected real-time physical data is uploaded to the edge computing gateway through wired industrial bus and wireless IoT communication protocols. Based on the three-dimensional dimensions, structural layout, and electrical connection topology of the physical switchgear, a three-dimensional geometric model is constructed. Based on the electrical connection topology and physical laws, a logical model reflecting the electrical relationships between the components of each three-dimensional model is constructed. Based on material properties, load characteristics, and historical operating data, a behavioral model reflecting the dynamic characteristics of the components is constructed. The geometric model, logical model, and behavioral model are integrated and correlated to form a multi-dimensional digital twin model of the low-voltage switchgear. The edge computing gateway receives real-time physical data streams, preprocesses the data, and maps the preprocessed real-time data streams to component instances with the same sensor identifiers in the digital twin model through industrial IoT protocols. In the digital twin development platform, a pre-defined status assessment rule library is set up for each monitoring parameter. The status data calculated or updated by the digital twin model after driving is matched and analyzed with the status assessment rule library in real time to determine the real-time health status level of each component, and the status assessment results are dynamically mapped with the visualization attributes of the digital twin model. A user-friendly, visual monitoring interface is constructed. When a component is selected, a floating information panel pops up, displaying the component's real-time monitoring data, health status, historical trend curves of key parameters, and a list of associated alarm events in a layered manner. Users can use a timeline control to scroll back to any historical point in time. By pre-setting fault points and fault types on a digital twin model, state extrapolation is performed to predict the scope of fault impact and visually highlight the affected component paths in a chain-like manner. Specifically, each sensor uploads real-time data to the edge computing gateway via a wired industrial bus or wireless IoT protocol. The gateway's data preprocessing module performs timestamp alignment, filtering, and formatting of the data. The core objective is to establish a precise mapping relationship from physical sensors to digital model components. To this end, a mapping function is defined: ; in, For mapping functions, It is a collection of physical sensors. This is a collection of component instances in a digital twin model; it is implemented through the correspondence between sensor ID, model component ID, and spatial coordinates. For example, for a temperature sensor... The mapping relationship is as follows: ; here, It is the corresponding heat source component in the digital model. These are the coordinates of the component in model space. This allows the collected temperature data to accurately drive the simulation and visualization rendering of the thermal behavior of the model components.

[0018] Based on the completion of the physical data acquisition layer and the baseline geometric model, a digital twin model integrating geometry, logic, and behavior is established. The geometric model is its physical foundation, built using parametric 3D modeling software based on the acquired precise data. Structural component modeling involves inputting parameters such as cabinet dimensions, plate thickness, and mounting hole positions to generate solid models of cabinets, partitions, mounting beams, and door panels. Key dimensions are parameter-driven, facilitating adaptation to different models. Electrical component modeling includes: for the bus system, creating 3D bus routing based on the current path and phase sequence arrangement in the electrical topology diagram, with its cross-sectional shape and dimensions determined by rated current and dynamic / thermal stability parameters; for circuit breaker units and disconnectors, constructing component models including details such as the housing, contact system, and operating mechanism based on the precise dimensions and installation interface drawings provided by the manufacturer; and for cable joints, modeling the geometry of their crimped or bolted connections based on cable specifications and connection methods. Assembly and interference checks involve virtually assembling all component models according to the mechanical layout diagram and electrical connection topology, checking for spatial conflicts between components to ensure the physical rationality of the model.

[0019] In the digital space, based on the precise data of the physical switchgear, a high-fidelity three-dimensional geometric model, electrical logic model, and physical behavior model are constructed. These are then deeply integrated through a unified identifier and data-driven mechanism to form a multi-dimensional digital twin model that unifies static attributes, dynamic relationships, and physical laws.

[0020] The core of the electrical logic model is establishing a computable electrical network. Based on the electrical connection topology graph, each electrical component is abstracted as a node, and physical connections are abstracted as edges, forming a graph structure G(V,E), where V is the set of nodes and E is the set of edges. Each edge... Define electrical properties.

[0021] The energy flow path is calculated based on this topology and real-time data. For any node k, according to Kirchhoff's current law, the current relationship is as follows: ;

[0022] in, This represents the current flowing from node j to node k. Let k be the set of nodes connected to node k. By combining real-time acquired boundary node current data, the current distribution of each branch can be solved or estimated using the network topology, thereby dynamically generating and visualizing the energy flow path.

[0023] Define a discrete set of states for each circuit breaker unit, including closed, open, energy-stored, un-energy-stored, and fault-locked states. Define a discrete set of states for each disconnecting switch, including operating position, test position, isolation position, and grounding position. State transitions are triggered by hard-contact signals in the secondary circuit or digital outputs from intelligent devices. For example, the triggering event for a circuit breaker to transition from open to closed is: a valid closing command, and simultaneous fulfillment of interlocking conditions such as no protection trip signal, normal control power supply, and energy-stored spring mechanism.

[0024] The highest priority event is the trip signal received from the protection relay or smart meter. When this event occurs, regardless of the circuit breaker's process state, the logic model forces its target state to open and records the trip reason in the status information.

[0025] Physical behavior models describe the dynamic response of components using physical laws. The transient heat conduction model, for a heat-generating component, describes the transient changes in its three-dimensional temperature field using the heat conduction equation: ; in, For material density, ρ is the specific heat capacity, k is the thermal conductivity. The internal heat source generation rate is calculated using Joule's heat formula, where I is the real-time current and R is the component resistance. This equation is solved on the component's finite element mesh to obtain the dynamic temperature field distribution.

[0026] In the electrodynamic model, when a short-circuit current flows through parallel busbars, according to Ampere's law, the magnitude of the electrodynamic force per unit length of busbar can be estimated as follows: ; in, Let be the free permeability, and d be the distance between the centers of the two parallel generatrices. This is the short-circuit current. This electrodynamic force is used to assess the mechanical stress on the busbar support structure.

[0027] The mechanical wear model shows that the electrical wear life of circuit breaker contacts is related to the breaking current, and its cumulative wear is approximated based on an empirical model as follows: ; in, Let be the effective value of the current during the nth switching operation. The wear index is related to the material. This is a wear factor related to the current magnitude. The remaining electrical life can be assessed by combining it with the rated mechanical life cycles.

[0028] The key to multi-model fusion lies in establishing precise mapping and data routing. A globally unique identifier mapping function is defined, which associates physical entities, geometric component instances, logical model nodes, and behavioral model computation units. Based on this mapping, the data routing engine simultaneously distributes real-time data with physical entity IDs to drive the corresponding geometric component instances, logical model nodes, and behavioral model computation units. The calculation results then update the logical state and geometric visualization, achieving closed-loop linkage. Finally, the mapped and linked geometric, logical, and behavioral models are encapsulated into a unified software object, providing standardized data interfaces and simulation call interfaces, forming a multi-dimensional digital twin model that is data-driven, state-computable, and impact-inferable.

[0029] After the edge computing gateway preprocesses the raw data stream, it forms standardized data packets. The data access module of the digital twin platform, based on the identifier, finds the corresponding digital model component instance through a preset identifier mapping table. When the identifier corresponds to a temperature sensor, its measured value is used as a boundary condition or heat source input to drive the transient heat conduction model to perform a new round of calculations. The model uses the current temperature field as the initial condition, combined with the latest measured value and current, to solve for the temperature field at the next moment. The temperature field data calculated by the model is passed to the visualization engine. The renderer maps the temperature scalar field to color values, and the mapping function formula is: ; in, Here, `colormap` is the mapping function, and `colormap` is the preset heatmap color band. and This represents the currently displayed temperature range. This color value is rendered in real-time onto the surface mesh vertices of the corresponding component in the 3D geometric model, forming a dynamic heatmap.

[0030] For current and voltage data, the driving circuit logic model performs electrical calculations on circuit power and three-phase imbalance. The calculated parameters update the component state attributes in the logic model and drive the visualization.

[0031] For switching data such as opening / closing positions and energy storage status, the driving process involves direct control of the attitude of the three-dimensional geometric model components. For example, the change in the circuit breaker's state from open to closed corresponds to the rotational transformation of its moving contact's three-dimensional model around its axis. This transformation can be described by a rotation matrix, with the initial angle corresponding to opening and the target angle corresponding to closing. Upon receiving the state change signal, the rendering engine interpolates and updates the angle within a frame period to achieve smooth animation. ;

[0032] Where f(t) is the smooth interpolation function for time t. and These are the starting and ending angles of the animation, respectively. This achieves precise spatiotemporal synchronization between the digital model's posture and the physical switch state.

[0033] It incorporates a multi-dimensional, configurable status assessment rule base, which matches and analyzes collected or calculated data against the rule base in real time to determine a comprehensive health status level for each component. This determination is a multi-rule fusion decision-making process. Core Rule 1: Dynamic Temperature Threshold Rule – For components, their temperature status depends not only on the instantaneous temperature but also on their historical temperature rise baseline. Core Rule 2: Load Current Overload Grading Rule – For conductive circuits, their overload status is determined based on the current load rate. Core Rule 3: Insulation Deterioration Diagnosis Rule – For partial discharge signals, its statistical characteristics are extracted, such as the skewness and steepness in the pulse phase distribution diagram. Core Rule 4: Mechanical Wear Early Warning Rule – For circuit breaker operating mechanisms, their cumulative wear can be assessed using a linear cumulative damage model. ; in, To accumulate the number of operations, Let be the interrupting current for the kth time. and m are material constants.

[0034] Percentage of remaining lifespan for: ; in, This is the end-of-life threshold. When... When the value is below a preset threshold, the status is judged as "Caution"; when it is below an even lower threshold, it is judged as "Danger".

[0035] It receives real-time state data synchronously updated from each component instance of the digital twin model, uses the state data of each component as input, and performs condition matching with the rules of the state evaluation rule base in parallel. Based on the matching results, a multi-rule comprehensive judgment logic is adopted. If none of the monitored parameters trigger any early warning or alarm rules, the status is judged as normal. If a parameter triggers the dynamic early warning threshold or reaches the mild overload level, the status is judged as caution. If a parameter triggers the static alarm threshold, reaches the overload level, or detects a definite insulation defect mode, the status is judged as abnormal. If a parameter reaches the severe overload threshold, its remaining life is about to be exhausted, or a critical insulation signal appears, the status is judged as dangerous.

[0036] A global status summary panel is generated on the side of the interface. Its content is a real-time statistics and summary panel based on the status of all components, displayed in the form of a dashboard, and a list of all components whose evaluation results are abnormal or dangerous.

[0037] To enable state rewinding at any historical point in time, a complete state snapshot of the entire digital twin model is automatically stored at fixed intervals or when changes in key state variables exceed a threshold. This snapshot is a structured data package containing the geometric model pose, electrical parameters of all nodes and edges in the logical model, key field data of the behavioral model, and the evaluation state level. When the user selects a historical point in time, the state restoration function is executed, and the rendering engine redraws the 3D scene based on the pose matrix set and health state set of all geometric components.

[0038] Fault simulation allows users to inject preset faults into the model and observe their dynamic evolution. Users select components and fault types, and modify the parameters of the corresponding logical nodes or behavioral calculation units in the digital twin model according to the fault type. For example, for a contact resistance fault, the impedance of the corresponding edge in the logical model is modified as follows: ;

[0039] in, The multiplication factor is set according to the severity of the fault.

[0040] Starting with the fault-injected model as the initial state, an integrated behavioral model is invoked for ultra-real-time simulation. At each step, the system solves the coupled model equations. During the simulation, the system analyzes the propagation of the fault's impact in real time. Based on the updated logical model state, a graph traversal algorithm is used to analyze the scope of the fault's impact. Starting from the logical graph node corresponding to the fault source component, along the connecting edges, the system determines whether the impact has propagated to adjacent nodes based on whether the changes in electrical parameters exceed safety thresholds. To quantify the degree of impact, an impact index is defined for the affected components.

[0041] In the 3D geometric model, all components corresponding to the set are highlighted. The highlight color is mapped according to the intensity of the impact index. At the same time, the rendering engine draws dynamic, arrow-guided lines between the fault source and the affected components, forming a visual impact chain that intuitively shows the propagation path and scope of the fault.

[0042] When a user selects a component, an information query is triggered, retrieving multi-source data associated with that component in real time and presenting it as a floating panel. Historical trend curves for key parameters are generated by querying a historical snapshot database. For example, the temperature trend curve is plotted using a set of points. The list of associated alarm events is generated by filtering all abnormal records from historical status assessment results.

[0043] It should be noted that the order of the above embodiments of the present invention is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. Furthermore, the above description focuses on specific embodiments of this specification. Additionally, the processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired results. In some embodiments, multitasking and parallel processing are possible or may be advantageous.

[0044] The various embodiments in this specification are described in a progressive manner. The same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on describing the differences from other embodiments.

[0045] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for visually monitoring the operating status of low-voltage switchgear based on digital twins, characterized in that, include: On the physical entity of the target low-voltage switchgear, a sensor network consisting of temperature sensors, current sensors, voltage sensors, partial discharge sensors, arc light sensors and status indicators is deployed. The collected real-time physical data is uploaded to the edge computing gateway through wired industrial bus and wireless IoT communication protocols. Based on the three-dimensional dimensions, structural layout, and electrical connection topology of the physical switchgear, a three-dimensional geometric model is constructed. Based on the electrical connection topology and physical laws, a logical model reflecting the electrical relationships between the components of each three-dimensional model is constructed. Based on material properties, load characteristics, and historical operating data, a behavioral model reflecting the dynamic characteristics of the components is constructed. The geometric model, logical model, and behavioral model are integrated and correlated to form a multi-dimensional digital twin model of the low-voltage switchgear. The edge computing gateway receives real-time physical data streams, preprocesses the data, and maps the preprocessed real-time data streams to component instances with the same sensor identifiers in the digital twin model through industrial IoT protocols. In the digital twin development platform, a pre-defined status assessment rule library is set up for each monitoring parameter. The status data calculated or updated by the digital twin model after driving is matched and analyzed with the status assessment rule library in real time to determine the real-time health status level of each component, and the status assessment results are dynamically mapped with the visualization attributes of the digital twin model. A user-friendly visual monitoring interface is constructed. When a component is selected, a floating information panel pops up. The panel displays the component's real-time monitoring data, health status, historical trend curves of key parameters, and a list of related alarm events in layers. Through the timeline control, users can roll back to any point in history. By pre-setting fault points and fault types on the digital twin model, the state is extrapolated, the scope of the fault's impact is predicted, and the affected component paths are displayed in a visual chain highlight.

2. The method for visual monitoring of the operating status of low-voltage switchgear based on digital twins according to claim 1, characterized in that, The construction of the three-dimensional geometric model specifically includes: Acquire precise three-dimensional dimensional data, mechanical structure layout diagram and electrical connection topology diagram of the target low-voltage switchgear. The three-dimensional dimensional data is derived from equipment design drawings or three-dimensional laser scanning point cloud data. Based on the aforementioned three-dimensional dimensional data and mechanical structure layout diagram, structural component models of the cabinet, partitions, mounting beams, and door panels are constructed. Based on the component installation dimensions in the electrical connection topology diagram and equipment design drawings, core electrical component models of the bus system, circuit breaker unit, disconnector, current transformer, cable joint and secondary terminal block are constructed respectively. All the structural component models and core electrical component models are assembled in three-dimensional space according to the spatial positional relationships and connection interfaces defined by the mechanical structure layout diagram and electrical connection topology diagram to form a complete three-dimensional geometric model of the switch cabinet. The assembled 3D geometric model of the switchgear was compared and verified with the design drawings.

3. The method for visual monitoring of the operating status of low-voltage switchgear based on digital twins according to claim 1, characterized in that, The construction of the logical model reflecting the electrical relationships between the components of each 3D model specifically includes: Based on the electrical connection topology graph, the physical connection relationships between the bus system, circuit breaker unit, disconnector, current transformer, cable joint and secondary terminal block are extracted. Each electrical component is abstracted as a node and the physical connection between components is abstracted as an edge, forming a node-edge topology graph that describes the circuit connectivity. The generated node-edge topology graph is structured, and the electrical properties of each edge are defined, including connection type, rated current and impedance. Based on the circuit connection relationship in the electrical connection topology diagram, and in accordance with Kirchhoff's current law and voltage law, combined with real-time collected current and voltage data, the energy flow path from the power input terminal to each load output terminal is calculated and dynamically generated. Based on the opening and closing status signals of circuit breaker units and disconnecting switches, as well as the secondary circuit control logic of protection relays and intelligent instruments, a set of logic rules with state machines as the core is established.

4. The method for visual monitoring of the operating status of low-voltage switchgear based on digital twin as described in claim 1, characterized in that, The construction of a behavioral model that reflects the dynamic characteristics of components specifically includes: Based on the material thermal conductivity, specific heat capacity and surface emissivity of busbars, circuit breaker contacts and cable joints, a finite element analysis mesh is established in combination with their three-dimensional geometry. The temperature field distribution of each component is calculated and dynamically updated to form a transient heat conduction model. For the busbar system and circuit breaker current-carrying conductors, an electromagnetic field calculation domain is established based on their spatial arrangement geometry. Based on real-time acquired current data, Ampere's law and Biot-Savart's law are applied to calculate the magnitude and direction of the electromagnetic forces between conductors and contacts under short-circuit or high-current conditions, forming an electrodynamic model. For the operating mechanism of circuit breakers and mechanical components of disconnect switches, based on their material fatigue characteristics, standard mechanical life curves, and real-time collected operation counts and load currents, combined with fault and maintenance records recorded in historical operating data, a remaining life assessment model characterizing their cumulative wear degree is established, forming a mechanical wear model. The constructed heat conduction model, electrodynamic model, and mechanical wear model are integrated to form a behavioral model that reflects the dynamic physical characteristics of the components.

5. The method for visual monitoring of the operating status of low-voltage switchgear based on digital twins according to claim 1, characterized in that, The process of integrating and associating geometric models, logical models, and behavioral models to form a multi-dimensional digital twin model specifically includes: Each constructed 3D geometric model component instance is assigned a globally unique identifier. The nodes of the logical model, the computational units of the behavioral model, and the geometric model component instances are mapped to each other through the globally unique identifier, forming the basis for static association between the three models. Configure a data routing engine so that when real-time monitoring data or simulation input data from a physical entity is triggered, the data is synchronously distributed to the geometric model component corresponding to the identifier based on static association. The geometric, logical, and behavioral models that have completed mapping and linkage are encapsulated into a unified software object, providing standardized data access and function call interfaces to the outside world, forming a multi-dimensional digital twin model that unifies static attributes, dynamic relationships, and physical laws.

6. The method for visual monitoring of the operating status of low-voltage switchgear based on digital twin as described in claim 1, characterized in that, The process of mapping the preprocessed real-time data stream to component instances with the same sensor identifier in the digital twin model specifically includes: Based on the sensor identifiers in the preprocessed data packets, real-time data is mapped to corresponding component instances with the same identifiers in the digital twin model; For temperature data, the heat conduction model is driven to calculate and update the temperature field distribution of each part of the model components, which is then rendered on the surface of the three-dimensional geometric model in the form of a heat map. For current and voltage data, the drive circuit logic model calculates the loop power, power factor and three-phase unbalance, and visualizes them on the connecting bus in the form of dynamic vector arrows or flowing particles. For partial discharge and arc light data, as event signal drivers, high-frequency pulse or flash animation effects are triggered at the corresponding spatial location in the digital twin model; For the status indicator data, drive the three-dimensional model posture of the corresponding circuit breaker and disconnector components to synchronize the closing, opening, and energy storage status changes.

7. The method for visual monitoring of the operating status of low-voltage switchgear based on digital twin as described in claim 1, characterized in that, The determination of the real-time health status level of each component specifically includes: A pre-defined rule base for status assessment of each monitoring parameter is provided. The rule base includes: temperature threshold rules based on the insulation level of component materials and historical temperature rise curves; overload classification rules based on the ratio of load current to rated current; insulation degradation diagnosis rules based on the amplitude-phase-frequency pattern of partial discharge signals; and wear warning rules based on the number of switching operations and mechanical life curves. It receives real-time state data synchronously updated from each component instance of the digital twin model, uses the state data of each component as input, and performs condition matching with the rules of the state evaluation rule base in parallel. Based on the matching results, a multi-rule comprehensive judgment logic is adopted. If none of the monitored parameters trigger any early warning or alarm rules, the status is judged as normal. If a parameter triggers the dynamic early warning threshold or reaches the mild overload level, the status is judged as caution. If a parameter triggers the static alarm threshold, reaches the overload level, or detects a definite insulation defect mode, the status is judged as abnormal. If a parameter reaches the severe overload threshold, its remaining life is about to be exhausted, or a critical insulation signal appears, the status is judged as dangerous.

8. The method for visual monitoring of the operating status of low-voltage switchgear based on digital twins according to claim 1, characterized in that, The dynamic mapping of the state assessment results to the visual attributes of the digital twin model specifically includes: In normal conditions, the component model is displayed with the preset base color and transparency; When in a state of attention, the component model begins to periodically flash a pale yellow halo; In abnormal states, the component model color gradually changes to orange, accompanied by a rotating warning indicator; In dangerous situations, the component model's color abruptly turns red, flashes violently, and generates 3D warning text and sound alarms; On the side of the digital twin model interface, a global status summary panel is generated, which summarizes and displays the real-time parameters and evaluation levels of all key components in the form of lists and dashboards.

9. The method for visual monitoring of the operating status of low-voltage switchgear based on digital twins according to claim 1, characterized in that, The specific steps of pre-setting fault points and fault types on the digital twin model for state deduction include: Users can select at least one fault type from a predefined fault type library through a visual panel for configuration; Based on the configured fault parameters, in the data layer of the digital twin model, initial fault conditions are injected into the logical model nodes and behavioral model calculation units corresponding to the target components. Using the digital twin model after the injection of faults as the initial state, simulation calculations are performed based on the integrated behavioral and logical models; During the simulation, the propagation path of electrical anomalies caused by faults in the node-edge topology graph of the logic model is analyzed in real time, and all components whose states exceed the attention level threshold in the state evaluation rule base are recorded, forming an affected component chain. In the 3D geometric model, all components included in the affected component chain obtained from the analysis are highlighted in a chain with colors that are different from those in the normal state.

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