A low-voltage area perception data-oriented visualization method and device

Abstract

This invention discloses a visualization method and device for low-voltage distribution area sensing data, belonging to the field of power systems. The method involves: acquiring multi-source data, a JSON tree structure, and an initial 3D model corresponding to the low-voltage distribution area; performing data synchronization and anomaly detection through collaborative constraint rules and a preset GNN model; constructing a branch-level model through data binding and establishing its mapping relationship with the 3D model; and finally achieving visualization display based on a dynamic visualization platform and 3D model components. Therefore, by implementing this invention, the problem of improving the real-time performance of the visualization process while ensuring the accuracy of data in the low-voltage distribution area visualization process can be solved in the prior art.

Description

Technical Field

[0001] This invention relates to the field of power systems, and in particular to a visualization method and apparatus for sensing data of low-voltage distribution areas. Background Technology

[0002] In modern power systems, the visualization of low-voltage distribution area sensing data is a key link in realizing lean management and real-time status awareness of the distribution network. Its goal is to integrate multi-source data such as dispersed distribution area physical topology, equipment operating status, and communication network quality into an intuitive and dynamic graphical interface to support the analysis and decision-making of operation and maintenance personnel.

[0003] Existing technologies commonly employ a siloed visualization approach: relying on separate topology drawing software, equipment monitoring systems, and communication network management systems to present data from different dimensions. Topology relationships are often statically drawn in drawing tools after manual on-site investigation, resulting in rigid electronic diagrams. Equipment status data and communication quality data are displayed in their respective monitoring windows as lists or two-dimensional charts. These systems are independent of each other, and data interaction relies on periodic file-based import / export or manual verification. Therefore, existing technologies suffer from two main problems: ① Severe data update lag: The transformer substation topology changes dynamically due to line modifications and equipment switching, but static topology diagrams cannot be automatically updated. Reliance on manual maintenance leads to data delays of several days or even weeks, causing a severe disconnect between the visualization interface and physical reality. ② Disconnect between physical topology and real-time monitoring data: When maintenance personnel observe an abnormal current in a meter, it is difficult to quickly and accurately locate the upstream faulty branch or associated communication interruption node on the static topology diagram. These problems make it impossible to locate and visualize abnormal line losses and hidden faults within minutes, thus hindering the improvement of intelligent operation and maintenance in low-voltage distribution areas. Summary of the Invention

[0004] This invention provides a visualization method and apparatus for low-voltage transformer area sensing data, which can solve the problem in the prior art of improving the real-time performance of the visualization process while ensuring the accuracy of the data.

[0005] In a first aspect, embodiments of the present invention provide a visualization method for sensing data in low-voltage distribution areas, comprising: Obtain the first physical topology data, first network topology data, first device status monitoring data, first communication link status data, JSON tree structure, and initial 3D model corresponding to the low-voltage distribution area; Based on the collaborative constraint rules and the preset GNN model, the first physical topology data, the first network topology data, the first device status monitoring data, and the first communication link status data are sequentially synchronized and anomaly detected to obtain the second physical topology data, the second network topology data, the second device status monitoring data, and the second communication link status data. The second physical topology data, the second network topology data, the second device status monitoring data, the second communication link status data, and the JSON tree structure are bound together to determine the first branch level model. Based on the first branch level model and the initial three-dimensional model, the first mapping relationship is obtained. Based on the preset dynamic visualization platform, the first branch hierarchical model, and the first mapping relationship, the 3D model components corresponding to each device in the low-voltage distribution area are obtained, and the visualization is performed based on the 3D model components corresponding to each device.

[0006] This application's embodiments comprehensively acquire multi-source data, including physical topology, network topology, equipment status monitoring, and communication link status of low-voltage distribution areas, along with a JSON tree structure and an initial 3D model. By leveraging collaborative constraint rules and a pre-set GNN model, data synchronization and anomaly detection are achieved, ensuring data accuracy and consistency. Furthermore, a branch-level model is constructed through data binding, establishing a mapping relationship between it and the 3D model. Finally, visualization is achieved using a dynamic visualization platform and 3D model components. This effectively solves the problems of data update lag and disconnect between physical topology and real-time monitoring data in existing technologies. It allows for an intuitive and dynamic presentation of various data and equipment statuses in low-voltage distribution areas, providing strong support for analysis and decision-making by maintenance personnel, thereby improving the intelligent operation and maintenance level of low-voltage distribution areas. Therefore, this application can solve the problem in existing technologies that it is difficult to improve the real-time performance of the visualization process while ensuring data accuracy.

[0007] As a preferred example of the first aspect, the step of sequentially synchronizing and detecting anomalies in the first physical topology data, the first network topology data, the first device status monitoring data, and the first communication link status data according to the collaborative constraint rules and the preset GNN model to obtain second physical topology data, second network topology data, second device status monitoring data, and second communication link status data specifically involves: According to the collaborative constraint rules, the first physical topology data, the first network topology data, the first device status monitoring data, and the first communication link status data are sequentially verified in terms of time dimension, space dimension, and logic dimension to obtain the third physical topology data, the third network topology data, the third device status monitoring data, and the third communication link status data. The third physical topology data and the third network topology data are input into a preset GNN model for correction to obtain the second physical topology data and the second network topology data; Anomaly detection is performed on the third device status monitoring data and the third communication link status data to obtain the second device status monitoring data and the second communication link status data.

[0008] In this preferred example, comprehensive verification of multiple types of initial data across time, space, and logical dimensions is performed using collaborative constraint rules. This allows for the screening and elimination of data that is out of sync in time, spatially mismatched, or logically contradictory from the data source, ensuring the consistency and compliance of the third type of data after initial processing across multiple dimensions and preventing data dimensional deviations from affecting subsequent analysis. The third type of topology data is then input into a preset GNN model for correction. Leveraging the GNN model's advantages in processing graph-structured data, the physical and network topology data can better reflect the actual topology of the low-voltage distribution area, reducing errors from manual topology data maintenance and improving the accuracy of the topology data. Finally, anomaly detection is performed on the third type of equipment status and communication link data, identifying and filtering out invalid data reflecting abnormal equipment operation or communication link failures in advance, ensuring that the final output of the second type of data accurately and reliably reflects the equipment operating status and communication link conditions. The overall process significantly improves the quality of low-voltage distribution area perception data through a combination of multi-dimensional verification, model correction, and anomaly detection. This provides an accurate and reliable data foundation for subsequent branch-level model construction, mapping relationship establishment, and visualization, effectively avoiding the problem of visualization being out of touch with the actual distribution area situation due to inaccurate or inconsistent data. It further supports maintenance personnel in conducting accurate analysis and decision-making based on high-quality data.

[0009] As a preferred example of the first aspect, the step of binding the second physical topology data, the second network topology data, the second device status monitoring data, the second communication link status data, and the JSON tree structure to determine the first branch hierarchy model specifically involves: The second physical topology data and the second network topology data are bound to the JSON tree structure to obtain the second branch hierarchical model; wherein, the first branch hierarchical model includes several hierarchical devices; The second device status monitoring data and the second communication link status data are associated with the corresponding hierarchical devices in the first branch hierarchical model to obtain the first branch hierarchical model.

[0010] In this preferred example, the processed second physical topology data and second network topology data are first bound to a JSON tree structure to obtain the second branch hierarchical model. By leveraging the hierarchical characteristics of the JSON tree structure, the potentially scattered topology data can be organized according to a clear hierarchical logic, clearly presenting the hierarchical relationship of devices within the low-voltage distribution area. This avoids the problem of disorganized topology data and lays an orderly structural foundation for subsequent data association. Then, the second device status monitoring data and second communication link status data are associated with the corresponding hierarchical devices in the first branch hierarchical model, ensuring that the device's operating status information and communication link information accurately correspond to the topology hierarchical position of the device, breaking the isolation between topology data and device status and communication data.

[0011] As a preferred example of the first aspect, the first mapping relationship obtained based on the first branch hierarchy model and the initial three-dimensional model is specifically as follows: Based on the devices at each level in the first branch hierarchical model and the corresponding device components in the initial 3D model, the identifier mapping corresponding to each level device is obtained; Based on the identifier mapping corresponding to each level of device, a responsive data binding mechanism is used to associate the first branch level model and the initial 3D model to obtain the first mapping relationship.

[0012] In this preferred example, the identifier mapping is first obtained based on the devices at each level in the first branch hierarchical model and the corresponding device components in the initial 3D model. This accurately establishes a one-to-one correspondence between devices in the hierarchical data model and device components in the 3D spatial model, avoiding misalignment and mismatch between data and 3D components. This provides a precise "bridge" for the subsequent association between data and the 3D model. Based on this identifier mapping, a responsive data binding mechanism is used to associate the first branch hierarchical model with the initial 3D model to obtain the first mapping relationship. With the help of the responsive binding feature, when the device data in the first branch hierarchical model changes, the corresponding device components in the initial 3D model can be updated synchronously without the need for manual adjustment of the association between the 3D model and the data.

[0013] As a preferred example of the first aspect, the visualization display based on the corresponding 3D model components of each device specifically includes: The 3D model components are rendered in real time using a preset WebGL engine to construct a complete three-dimensional scene corresponding to the low-voltage substation. The second device status monitoring data and the second communication link status data are dynamically bound to the corresponding 3D model components in the complete three-dimensional scene through the first mapping relationship, and the devices are visualized.

[0014] In this preferred example, a pre-defined WebGL engine is used to render 3D model components in real time to construct a complete 3D scene of the low-voltage distribution area. This allows for a three-dimensional and intuitive representation of the equipment distribution and physical layout of the low-voltage distribution area, breaking the limitations of traditional static drawings or 2D charts in presenting the distribution area structure. This enables maintenance personnel to quickly and clearly grasp the overall architecture of the distribution area without relying on abstract data. Furthermore, the second equipment status monitoring data and the second communication link status data are dynamically bound to the corresponding 3D model components of the complete 3D scene through a first mapping relationship and displayed visually. This enables deep integration of the real-time operating status of the equipment, the communication link status, and the 3D model. When the equipment status or communication link changes, the corresponding 3D model components can synchronously present the relevant changes, eliminating the need for maintenance personnel to switch between multiple independent systems to query data.

[0015] In a second aspect, the present invention provides a visualization device for sensing data of low-voltage distribution areas, comprising: a data acquisition module, a first processing module, a second processing module, and a visualization module; The data acquisition module is used to acquire the first physical topology data, the first network topology data, the first equipment status monitoring data, the first communication link status data, the JSON tree structure, and the initial three-dimensional model corresponding to the low-voltage distribution area. The first processing module is used to perform data synchronization and anomaly detection on the first physical topology data, the first network topology data, the first device status monitoring data and the first communication link status data in sequence according to the collaborative constraint rules and the preset GNN model, so as to obtain the second physical topology data, the second network topology data, the second device status monitoring data and the second communication link status data. The second processing module is used to bind the second physical topology data, the second network topology data, the second device status monitoring data, the second communication link status data and the JSON tree structure to determine the first branch level model, and obtain the first mapping relationship based on the first branch level model and the initial three-dimensional model; The visualization module is used to obtain the 3D model components corresponding to each device in the low-voltage distribution area according to the preset dynamic visualization platform, the first branch hierarchical model and the first mapping relationship, and to visualize and display the 3D model components corresponding to each device.

[0016] As a preferred example of the second aspect, the first processing module includes a first processing unit, a second processing unit, and a third processing unit; The first processing unit is configured to perform time-dimension verification, spatial-dimension verification, and logical-dimension verification on the first physical topology data, the first network topology data, the first device status monitoring data, and the first communication link status data in sequence according to the collaborative constraint rules, so as to obtain third physical topology data, third network topology data, third device status monitoring data, and third communication link status data. The second processing unit is used to input the third physical topology data and the third network topology data into a preset GNN model for correction, so as to obtain the second physical topology data and the second network topology data; The third processing unit is used to perform anomaly detection on the third device status monitoring data and the third communication link status data to obtain the second device status monitoring data and the second communication link status data.

[0017] As a preferred example of the second aspect, the second processing module includes a fourth processing unit and a fifth processing unit; The fourth processing unit is used to bind the second physical topology data and the second network topology data with the JSON tree structure to obtain a second branch hierarchical model; wherein, the first branch hierarchical model includes several hierarchical devices; The fifth processing unit is used to associate the second device status monitoring data and the second communication link status data with the corresponding hierarchical devices in the first branch hierarchical model to obtain the first branch hierarchical model.

[0018] As a preferred example of the second aspect, the second processing module further includes a sixth processing unit and a seventh processing unit; The sixth processing unit is used to obtain the identifier mapping corresponding to each level of device based on each level of device in the first branch hierarchical model and the corresponding device component in the three-dimensional model; The seventh processing unit is used to associate the first branch-level model and the three-dimensional model with the identifier mapping corresponding to each level of device using a responsive data binding mechanism to obtain the first mapping relationship.

[0019] As a preferred example of the second aspect, the visualization module includes a first visualization unit and a second visualization unit; The first visualization unit is used to dynamically bind the second device status monitoring data and the second communication link status data to the corresponding 3D model components in the complete three-dimensional scene through the first mapping relationship, and to visualize each device.

[0020] In summary, this application's embodiments comprehensively acquire multi-source data, including physical topology, network topology, equipment status monitoring, and communication link status of low-voltage distribution areas, along with a JSON tree structure and an initial 3D model. By leveraging collaborative constraint rules and a pre-set GNN model, data synchronization and anomaly detection are achieved, ensuring data accuracy and consistency. Furthermore, a branch-level model is constructed through data binding, establishing a mapping relationship between it and the 3D model. Finally, visualization is achieved using a dynamic visualization platform and 3D model components. This effectively solves the problems of data update lag and disconnect between physical topology and real-time monitoring data in existing technologies. It allows for an intuitive and dynamic presentation of various data and equipment statuses in low-voltage distribution areas, providing strong support for analysis and decision-making by maintenance personnel, thereby improving the intelligent operation and maintenance level of low-voltage distribution areas. Therefore, this application can solve the problem in existing technologies that it is difficult to improve the real-time performance of the visualization process while ensuring data accuracy.

[0021] Another embodiment of the present invention provides a terminal device, including: a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein when the processor executes the computer program, it implements the steps of the visualization method for low-voltage distribution area sensing data of the present invention.

[0022] Another embodiment of the present invention provides a computer-readable storage medium item, including: a stored computer program, which, when the computer program is running, controls the device where the computer-readable storage medium is located to perform the steps of the visualization method for low-voltage distribution area sensing data of the present invention. Attached Figure Description

[0023] To more clearly illustrate the technical solution of this application, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0024] Figure 1 A flowchart illustrating an embodiment of a visualization method for low-voltage distribution area sensing data provided by the present invention; Figure 2 This is a module structure diagram of one embodiment of a visualization device for sensing data in low-voltage distribution areas provided by the present invention. Detailed Implementation

[0025] To make the objectives, technical solutions, and advantages of this application clearer, the technical solutions of this application will be clearly and completely described below with reference to the accompanying drawings of the embodiments. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0026] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains; the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the application; the terms “comprising” and “having”, and any variations thereof, in the specification, claims, and foregoing description of the drawings are intended to cover non-exclusive inclusion.

[0027] In the description of the embodiments of this application, technical terms such as "first" and "second" are used only to distinguish different objects and should not be construed as indicating or implying relative importance or implicitly specifying the number, specific order, or primary and secondary relationship of the indicated technical features. In the description of the embodiments of this application, "multiple" means two or more, unless otherwise explicitly defined.

[0028] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0029] In the description of the embodiments in this application, the term "and / or" is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, and B existing alone. Additionally, the character " / " in this document generally indicates that the preceding and following related objects have an "or" relationship.

[0030] In the description of the embodiments of this application, the term "multiple" refers to two or more (including two), similarly, "multiple sets" refers to two or more (including two sets), and "multiple pieces" refers to two or more (including two pieces).

[0031] In the description of the embodiments of this application, unless otherwise expressly specified and limited, technical terms such as "installation," "connection," "joining," and "fixing" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral part; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; they can refer to the internal communication of two components or the interaction between two components. For those skilled in the art, the specific meaning of the above terms in the embodiments of this application can be understood according to the specific circumstances.

[0032] Example 1 See Figure 1 To address the challenge of simultaneously ensuring data accuracy and improving real-time performance during low-voltage transformer area visualization in existing technologies, an embodiment of the present invention provides a visualization method for low-voltage transformer area sensing data, comprising: S1. Obtain the first physical topology data, first network topology data, first device status monitoring data, first communication link status data, JSON tree structure, and initial three-dimensional model corresponding to the low-voltage distribution area; Specifically, the acquisition of the first physical topology data, first network topology data, first equipment status monitoring data, and first communication link status data corresponding to the low-voltage distribution area can be implemented through the following preferred methods: Data is exchanged through interface protocols to collect data related to the physical topology, network topology, equipment status monitoring, and communication link status of the transformer area.

[0033] The main equipment's physical topology data acquisition includes: channel feature inversion of line connection relationships; active detection triggering; simultaneous injection of linear frequency modulated signals (Chirp signals) into multiple HPLC modules; generation of node adjacency matrices based on signal propagation time delay (TDOA); and marking of physical connection relationships between devices. Impedance spectroscopy analysis is used to locate branch levels, signal reflection energy at each branch point is collected, and line impedance is calculated using a frequency domain impedance fitting algorithm to identify the bifurcation point locations.

[0034] Equipment status monitoring data acquisition for main equipment: Through carrier sensing multiplexing and HPLC channel characteristic inversion, the corresponding line aging degree is determined under the channel impulse response (CIR) attenuation slope; the corresponding loose connection terminal is determined under the condition of a sudden drop in signal-to-noise ratio (SNR) period; and the corresponding insulation degradation risk is determined under the condition of a continuous increase in bit error rate (BER).

[0035] The communication link status acquisition of the main equipment is a collaborative diagnosis of channel quality and load. Specifically, real-time channel parameter acquisition is performed periodically: bit error rate (BER), signal-to-noise ratio (SNR), frequency offset (CFO), and channel capacity. Service flow load correlation analysis identifies the root causes of network congestion by parsing HPLC communication packets, statistically analyzing the proportion of service types and queue delays. High-frequency acquisition is triggered by abnormal events: when the BER exceeds a preset threshold, the system automatically switches to a 1kHz sampling rate to record the channel impulse response and locate the instantaneous interference source.

[0036] S2. Based on the collaborative constraint rules and the preset GNN model, perform data synchronization and anomaly detection on the first physical topology data, the first network topology data, the first device status monitoring data and the first communication link status data in sequence to obtain the second physical topology data, the second network topology data, the second device status monitoring data and the second communication link status data. In some embodiments of this application, the step of sequentially synchronizing and detecting anomalies in the first physical topology data, the first network topology data, the first device status monitoring data, and the first communication link status data according to collaborative constraint rules and a preset GNN model to obtain second physical topology data, second network topology data, second device status monitoring data, and second communication link status data specifically involves: According to the collaborative constraint rules, the first physical topology data, the first network topology data, the first device status monitoring data, and the first communication link status data are sequentially verified in terms of time dimension, space dimension, and logic dimension to obtain the third physical topology data, the third network topology data, the third device status monitoring data, and the third communication link status data. The third physical topology data and the third network topology data are input into a preset GNN model for correction to obtain the second physical topology data and the second network topology data; Anomaly detection is performed on the third device status monitoring data and the third communication link status data to obtain the second device status monitoring data and the second communication link status data.

[0037] Specifically, to fully explain the above steps, the following scheme will be used as an example: The collected data is preprocessed based on collaborative constraint rules to achieve the synchronization of multi-source heterogeneous data. These collaborative constraint rules include time-dimension constraints, spatial-dimension constraints, and logical-dimension constraints.

[0038] ① Time dimension constraint rules: Use the adapter pattern to unify heterogeneous data formats, and label all data with a unified spatiotemporal stamp.

[0039] ② Spatial Dimension Constraint Rules: Device coordinates are bound to topology nodes, forming a "physical location-logical address" mapping table. This spatial mapping table addresses the problem of missing spatial location information for low-voltage distribution area data, ensuring that each piece of collected data is accurately linked to a specific device in the physical world. Without this mapping table, data would be fragmented, with "logical addresses unable to find physical locations, and physical devices unable to find corresponding data." When the edge computing module reconstructs the distribution area topology using the GNN model, it relies on the "spatial correlation of data" to determine the accuracy of the topology.

[0040] ③ Logical Dimension Constraint Rules: Communication link status is associated with device current data, forming a "Communication Link Status - Device Current Data" mapping table. This logical dimension mapping table addresses the lack of logical associations in low-voltage distribution area data, allowing different types of data to form "causal relationships" or "cooperative relationships," supporting in-depth business processes such as anomaly diagnosis and risk prediction. This mapping table provides a logical scale for anomaly association rules, helping edge computing modules quickly locate the root cause of faults and avoiding misjudgments caused by relying on single data points.

[0041] S3. Bind the second physical topology data, the second network topology data, the second device status monitoring data, the second communication link status data and the JSON tree structure to determine the first branch level model, and obtain the first mapping relationship based on the first branch level model and the initial three-dimensional model; In some embodiments of this application, the step of binding the second physical topology data, the second network topology data, the second device status monitoring data, the second communication link status data, and the JSON tree structure to determine the first branch hierarchy model specifically involves: The second physical topology data and the second network topology data are bound to the JSON tree structure to obtain the second branch hierarchical model; wherein, the first branch hierarchical model includes several hierarchical devices; The second device status monitoring data and the second communication link status data are associated with the corresponding hierarchical devices in the first branch hierarchical model to obtain the first branch hierarchical model.

[0042] In some embodiments of this application, obtaining the first mapping relationship based on the first branch hierarchy model and the initial 3D model specifically involves: Based on the devices at each level in the first branch hierarchical model and the corresponding device components in the initial 3D model, the identifier mapping corresponding to each level device is obtained; Based on the identifier mapping corresponding to each level of device, a responsive data binding mechanism is used to associate the first branch level model and the initial 3D model to obtain the first mapping relationship.

[0043] Specifically, to fully explain the above steps, the following scheme will be used as an example: Using a two-way data binding engine and a mechanism similar to Vue.js's v-model, the JSON data model is dynamically associated with 3D scene nodes. The specific implementation process is as follows: This two-way data binding process begins with the initialization phase: First, a JSON data model describing the branch hierarchy is defined, and corresponding 3D scene nodes are created in Three.js, binding a unique UUID to each object. Then, a proxy mechanism is used to intercept read and write operations of the JSON data, establishing a reactive binding engine. During the data-to-view synchronization process, when a JSON node property changes, the proxy triggers an update function, recursively traversing child nodes and dynamically adjusting the corresponding objects in the 3D scene. In the reverse synchronization from view to data, user interactions in the 3D scene are captured via an event bus, and the corresponding node data in the JSON model is located and updated using a UUID mapping table. This architecture adopts a Vue.js-like reactive design, forming a closed-loop mechanism that links the data layer, binding engine layer, and view layer.

[0044] S4. Based on the preset dynamic visualization platform, the first branch hierarchical model and the first mapping relationship, obtain the 3D model components corresponding to each device in the low-voltage distribution area, and visualize them according to the 3D model components corresponding to each device.

[0045] In some embodiments of this application, the visualization based on the corresponding 3D model components of each device specifically includes: The 3D model components are rendered in real time using a preset WebGL engine to construct a complete three-dimensional scene corresponding to the low-voltage substation. The second device status monitoring data and the second communication link status data are dynamically bound to the corresponding 3D model components in the complete three-dimensional scene through the first mapping relationship, so as to visualize each device.

[0046] Specifically, to fully explain the above steps, the following scheme will be used as an example: The 3D model components are rendered in real time using a preset WebGL engine to construct a complete 3D scene of the low-voltage distribution area, including transformers, branch cables, meter boxes, and meters. A hierarchical loading strategy is adopted to optimize performance. At the same time, the second equipment status monitoring data and the second communication link status data output by the edge intelligent computing module are dynamically bound to the corresponding 3D model components in the scene through the first mapping relationship between the branch hierarchy model and the 3D model determined in step two. This realizes an intuitive mapping from electrical parameters to deformation effects, temperature to heat maps, and communication quality to transparency and color warnings. It also supports interactive operations such as exploded splitting, cross-sectional perspective, and historical backtracking, thereby transforming abstract data into a visual scene, enabling maintenance personnel to quickly locate faults and diagnose hidden defects.

[0047] In summary, this application's embodiments comprehensively acquire multi-source data, including physical topology, network topology, equipment status monitoring, and communication link status of low-voltage distribution areas, along with a JSON tree structure and an initial 3D model. By leveraging collaborative constraint rules and a pre-set GNN model, data synchronization and anomaly detection are achieved, ensuring data accuracy and consistency. Furthermore, a branch-level model is constructed through data binding, establishing a mapping relationship between it and the 3D model. Finally, visualization is achieved using a dynamic visualization platform and 3D model components. This effectively solves the problems of data update lag and disconnect between physical topology and real-time monitoring data in existing technologies. It allows for an intuitive and dynamic presentation of various data and equipment statuses in low-voltage distribution areas, providing strong support for analysis and decision-making by maintenance personnel, thereby improving the intelligent operation and maintenance level of low-voltage distribution areas. Therefore, this application can solve the problem in existing technologies that it is difficult to improve the real-time performance of the visualization process while ensuring data accuracy.

[0048] Example 2 like Figure 2 As shown, based on the above method embodiments, corresponding device embodiments are provided; An embodiment of the present invention provides a visualization device for sensing data of low-voltage distribution areas, including: a data acquisition module 21, a first processing module 22, a second processing module 23, and a visualization module 24; Data acquisition module 21 is used to acquire the first physical topology data, first network topology data, first equipment status monitoring data, first communication link status data, JSON tree structure and initial three-dimensional model corresponding to the low-voltage distribution area; The first processing module 22 is used to perform data synchronization and anomaly detection on the first physical topology data, the first network topology data, the first device status monitoring data and the first communication link status data in sequence according to the collaborative constraint rules and the preset GNN model, so as to obtain the second physical topology data, the second network topology data, the second device status monitoring data and the second communication link status data. The second processing module 23 is used to bind the second physical topology data, the second network topology data, the second device status monitoring data, the second communication link status data and the JSON tree structure to determine the first branch level model, and obtain the first mapping relationship based on the first branch level model and the initial three-dimensional model. The visualization module 24 is used to obtain the 3D model components corresponding to each device in the low-voltage distribution area according to the preset dynamic visualization platform, the first branch hierarchical model and the first mapping relationship, and to visualize and display the 3D model components corresponding to each device.

[0049] In some embodiments of this application, the first processing module includes a first processing unit, a second processing unit, and a third processing unit; The first processing unit is configured to perform time-dimension verification, spatial-dimension verification, and logical-dimension verification on the first physical topology data, the first network topology data, the first device status monitoring data, and the first communication link status data in sequence according to the collaborative constraint rules, so as to obtain third physical topology data, third network topology data, third device status monitoring data, and third communication link status data. The second processing unit is used to input the third physical topology data and the third network topology data into a preset GNN model for correction, so as to obtain the second physical topology data and the second network topology data; The third processing unit is used to perform anomaly detection on the third device status monitoring data and the third communication link status data to obtain the second device status monitoring data and the second communication link status data.

[0050] In some embodiments of this application, the second processing module includes a fourth processing unit and a fifth processing unit; The fourth processing unit is used to bind the second physical topology data and the second network topology data with the JSON tree structure to obtain a second branch hierarchical model; wherein, the first branch hierarchical model includes several hierarchical devices; The fifth processing unit is used to associate the second device status monitoring data and the second communication link status data with the corresponding hierarchical devices in the first branch hierarchical model to obtain the first branch hierarchical model.

[0051] In some embodiments of this application, the second processing module further includes a sixth processing unit and a seventh processing unit; The sixth processing unit is used to obtain the identifier mapping corresponding to each level of device based on each level of device in the first branch hierarchical model and the corresponding device component in the three-dimensional model; The seventh processing unit is used to associate the first branch-level model and the three-dimensional model with the identifier mapping corresponding to each level of device using a responsive data binding mechanism to obtain the first mapping relationship.

[0052] In some embodiments of this application, the visualization module includes a first visualization unit and a second visualization unit; The first visualization unit is used to dynamically bind the second device status monitoring data and the second communication link status data to the corresponding 3D model components in the complete three-dimensional scene through the first mapping relationship, and to visualize each device.

[0053] For more detailed steps and working principles of this embodiment, please refer to the relevant description in Embodiment 1, but not limited to these descriptions.

[0054] In summary, this application's embodiments comprehensively acquire multi-source data, including physical topology, network topology, equipment status monitoring, and communication link status of low-voltage distribution areas, along with a JSON tree structure and an initial 3D model. By leveraging collaborative constraint rules and a pre-set GNN model, data synchronization and anomaly detection are achieved, ensuring data accuracy and consistency. Furthermore, a branch-level model is constructed through data binding, establishing a mapping relationship between it and the 3D model. Finally, visualization is achieved using a dynamic visualization platform and 3D model components. This effectively solves the problems of data update lag and disconnect between physical topology and real-time monitoring data in existing technologies. It allows for an intuitive and dynamic presentation of various data and equipment statuses in low-voltage distribution areas, providing strong support for analysis and decision-making by maintenance personnel, thereby improving the intelligent operation and maintenance level of low-voltage distribution areas. Therefore, this application can solve the problem in existing technologies that it is difficult to improve the real-time performance of the visualization process while ensuring data accuracy.

[0055] It is understood that the above-described device embodiments correspond to the method embodiments of the present invention, and can implement the visualization method for low-voltage distribution area sensing data provided by any of the above-described method embodiments of the present invention.

[0056] It should be noted that the device embodiments described above are merely illustrative, and some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Furthermore, in the accompanying drawings of the device embodiments provided by this invention, the connection relationships between modules indicate that they have communication connections, which can specifically be implemented as one or more communication buses or signal lines. Those skilled in the art can understand and implement this without any creative effort.

[0057] Example 3 Based on the above embodiments of the visualization method for low-voltage distribution area sensing data, another embodiment of the present invention provides a terminal device, which includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, it implements the visualization method for low-voltage distribution area sensing data of any embodiment of the present invention.

[0058] For example, in this embodiment, the computer program can be divided into one or more modules, which are stored in the memory and executed by the processor to complete the present invention. The one or more modules may be a series of computer program instruction segments capable of performing a specific function, which describe the execution process of the computer program in the terminal device.

[0059] The terminal device may be a desktop computer, laptop, handheld computer, or cloud server, etc. The terminal device may include, but is not limited to, a processor and a memory.

[0060] The processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor can be a microprocessor or any conventional processor. The processor is the control center of the terminal device, connecting all parts of the terminal device via various interfaces and lines.

[0061] Example 4 Based on the above-described method embodiments, another embodiment of the present invention provides a computer-readable storage medium including a stored computer program, wherein, when the computer program is executed, it controls the device where the computer-readable storage medium is located to execute the visualization method for low-voltage distribution area sensing data as described in any of the above-described method embodiments of the present invention.

[0062] The modules / units integrated in the device / terminal equipment, if implemented as software functional units and sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the above embodiments of the present invention can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include: any entity or device capable of carrying the computer program code, a recording medium, a USB flash drive, a portable hard drive, a magnetic disk, an optical disk, a computer memory, a read-only memory (ROM), a random access memory (RAM), an electrical carrier signal, a telecommunication signal, and a software distribution medium, etc.

[0063] The above description represents the preferred embodiments of the present invention. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principles of the present invention, and these improvements and modifications are also considered to be within the scope of protection of the present invention.

Claims

1. A visualization method for sensing data in low-voltage distribution areas, characterized in that, include: Obtain the first physical topology data, first network topology data, first device status monitoring data, first communication link status data, JSON tree structure, and initial 3D model corresponding to the low-voltage distribution area; Based on the collaborative constraint rules and the preset GNN model, the first physical topology data, the first network topology data, the first device status monitoring data, and the first communication link status data are sequentially synchronized and anomaly detected to obtain the second physical topology data, the second network topology data, the second device status monitoring data, and the second communication link status data. The second physical topology data, the second network topology data, the second device status monitoring data, the second communication link status data, and the JSON tree structure are bound together to determine the first branch level model. Based on the first branch level model and the initial three-dimensional model, the first mapping relationship is obtained. Based on the preset dynamic visualization platform, the first branch hierarchical model, and the first mapping relationship, the 3D model components corresponding to each device in the low-voltage distribution area are obtained, and the visualization is performed based on the 3D model components corresponding to each device.

2. The visualization method for low-voltage distribution area sensing data as described in claim 1, characterized in that, The process involves sequentially synchronizing and detecting anomalies in the first physical topology data, first network topology data, first device status monitoring data, and first communication link status data according to collaborative constraint rules and a preset GNN model, to obtain second physical topology data, second network topology data, second device status monitoring data, and second communication link status data. Specifically: According to the collaborative constraint rules, the first physical topology data, the first network topology data, the first device status monitoring data, and the first communication link status data are sequentially verified in terms of time dimension, space dimension, and logic dimension to obtain the third physical topology data, the third network topology data, the third device status monitoring data, and the third communication link status data. The third physical topology data and the third network topology data are input into a preset GNN model for correction to obtain the second physical topology data and the second network topology data; Anomaly detection is performed on the third device status monitoring data and the third communication link status data to obtain the second device status monitoring data and the second communication link status data.

3. The visualization method for low-voltage distribution area sensing data as described in claim 1, characterized in that, The step of binding the second physical topology data, the second network topology data, the second device status monitoring data, the second communication link status data, and the JSON tree structure to determine the first branch hierarchy model specifically involves: The second physical topology data and the second network topology data are bound to the JSON tree structure to obtain the second branch hierarchical model; wherein, the first branch hierarchical model includes several hierarchical devices; The second device status monitoring data and the second communication link status data are associated with the corresponding hierarchical devices in the first branch hierarchical model to obtain the first branch hierarchical model.

4. The visualization method for low-voltage distribution area sensing data as described in claim 1, characterized in that, The first mapping relationship is obtained based on the first branch hierarchy model and the initial 3D model, specifically as follows: Based on the devices at each level in the first branch hierarchical model and the corresponding device components in the initial 3D model, the identifier mapping corresponding to each level device is obtained; Based on the identifier mapping corresponding to each level of device, a responsive data binding mechanism is used to associate the first branch level model and the initial 3D model to obtain the first mapping relationship.

5. The visualization method for low-voltage distribution area sensing data as described in claim 1, characterized in that, The visualization based on the corresponding 3D model components of each device is as follows: The 3D model components are rendered in real time using a preset WebGL engine to construct a complete three-dimensional scene corresponding to the low-voltage substation. The second device status monitoring data and the second communication link status data are dynamically bound to the corresponding 3D model components in the complete three-dimensional scene through the first mapping relationship, so as to visualize each device.

6. A visualization device for sensing data in low-voltage distribution areas, characterized in that, include: The system comprises a data acquisition module, a first processing module, a second processing module, and a visualization module. The data acquisition module is used to acquire the first physical topology data, the first network topology data, the first equipment status monitoring data, the first communication link status data, the JSON tree structure, and the initial three-dimensional model corresponding to the low-voltage distribution area. The first processing module is used to perform data synchronization and anomaly detection on the first physical topology data, the first network topology data, the first device status monitoring data and the first communication link status data in sequence according to the collaborative constraint rules and the preset GNN model, so as to obtain the second physical topology data, the second network topology data, the second device status monitoring data and the second communication link status data. The second processing module is used to bind the second physical topology data, the second network topology data, the second device status monitoring data, the second communication link status data and the JSON tree structure to determine the first branch level model, and obtain the first mapping relationship based on the first branch level model and the initial three-dimensional model; The visualization module is used to obtain the 3D model components corresponding to each device in the low-voltage distribution area according to the preset dynamic visualization platform, the first branch hierarchical model and the first mapping relationship, and to visualize and display the 3D model components corresponding to each device.

7. A visualization device for low-voltage distribution area sensing data as described in claim 6, characterized in that, The first processing module includes a first processing unit, a second processing unit, and a third processing unit; The first processing unit is configured to perform time-dimension verification, spatial-dimension verification, and logical-dimension verification on the first physical topology data, the first network topology data, the first device status monitoring data, and the first communication link status data in sequence according to the collaborative constraint rules, so as to obtain third physical topology data, third network topology data, third device status monitoring data, and third communication link status data. The second processing unit is used to input the third physical topology data and the third network topology data into a preset GNN model for correction, so as to obtain the second physical topology data and the second network topology data; The third processing unit is used to perform anomaly detection on the third device status monitoring data and the third communication link status data to obtain the second device status monitoring data and the second communication link status data.

8. A visualization device for low-voltage distribution area sensing data as described in claim 6, characterized in that, The second processing module includes a fourth processing unit and a fifth processing unit; The fourth processing unit is used to bind the second physical topology data and the second network topology data with the JSON tree structure to obtain a second branch hierarchical model; wherein, the first branch hierarchical model includes several hierarchical devices; The fifth processing unit is used to associate the second device status monitoring data and the second communication link status data with the corresponding hierarchical devices in the first branch hierarchical model to obtain the first branch hierarchical model.

9. A visualization device for low-voltage distribution area sensing data as described in claim 6, characterized in that, The second processing module further includes a sixth processing unit and a seventh processing unit; The sixth processing unit is used to obtain the identifier mapping corresponding to each level of device based on each level of device in the first branch hierarchical model and the corresponding device component in the three-dimensional model; The seventh processing unit is used to associate the first branch-level model and the three-dimensional model with the identifier mapping corresponding to each level of device using a responsive data binding mechanism to obtain the first mapping relationship.

10. A visualization device for low-voltage distribution area sensing data as described in claim 6, characterized in that, The visualization module includes a first visualization unit and a second visualization unit; The first visualization unit is used to dynamically bind the second device status monitoring data and the second communication link status data to the corresponding 3D model components in the complete three-dimensional scene through the first mapping relationship, and to visualize each device.

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