A low-voltage power distribution network transparent fusion perception visualization scene construction method and system

Abstract

The application relates to the technical field of smart power grids, and provides a low-voltage power distribution network transparent fusion perception visualization scene construction method and system, which comprises the following steps: collecting voltage, current and device state data by using power line carrier communication, smart power meters and power distribution terminals synchronously, and establishing a time scale aligned ring queue; constructing a tree-shaped topological structure based on three-phase attributes and generating a connection matrix, dividing users into different transparency visualization levels through electrical distance calculation, and realizing gradual coloration and special effect warning of overload lines by combining current saturation mapping; fusing physical positions and electrical parameters through polar coordinate system conversion, generating dynamic particle flow to intuitively display current distribution; when voltage sag occurs, backtracking voltage distortion propagation path through waveform similarity, locating the disturbance source and triggering pulse diffusion animation. The method realizes transparent fusion perception of the operation state of the low-voltage power distribution network, real-time early warning of line overload and second-level positioning of the disturbance source, and improves the intelligent operation and maintenance efficiency.

Description

Technical Field

[0001] This application relates to the field of smart grid technology, specifically to the field of low-voltage distribution network simulation technology, and particularly to a method and system for constructing a transparent, integrated, and visualized low-voltage distribution network scenario. Background Technology

[0002] The content in this section provides only background information related to this application and may not constitute prior art.

[0003] With the development of power systems, smart grids are gradually entering all aspects of human life, and the intelligent and visualized management of low-voltage distribution networks is becoming increasingly important. Although existing power monitoring systems can achieve decentralized monitoring and centralized management of power distribution equipment, they still have shortcomings in data fusion, topology construction, visualization, and fault diagnosis.

[0004] For example, Chinese Patent Publication No. CN113050523A discloses a power monitoring system based on a big data fusion model. This system achieves decentralized monitoring and centralized management of high- and low-voltage electrical equipment in the power distribution system through a hierarchical distributed structure, making the power distribution system "transparent" and improving the unmanned operation capability of the distribution room. However, in terms of data acquisition and processing, this system has limited comprehensive processing capabilities for voltage waveform data, load current curves, and equipment status signals in the low-voltage distribution network. It cannot achieve time-scale alignment and dynamic updating of these data, making it difficult to meet the high-precision requirements of real-time monitoring and fault diagnosis.

[0005] Furthermore, in terms of topology construction, existing systems largely rely on pre-defined network topology models, which are insufficiently adaptable to the complex phase connections and dynamically changing equipment states in actual distribution networks, failing to accurately reflect the real operating conditions of the distribution network. Regarding visualization, there is a lack of differentiated displays at the user level and intuitive color mapping for current data, making it difficult to achieve intuitive perception and rapid response to the distribution network's operating status. Simultaneously, in terms of fault diagnosis, existing systems employ relatively simple methods for locating and diagnosing faults such as voltage sags, failing to accurately identify the disturbance source branches, thus affecting the efficiency and accuracy of fault handling.

[0006] Therefore, there is an urgent need for a method to realize the construction of transparent, integrated, and visualized scenarios for low-voltage distribution networks, in order to overcome the shortcomings of existing technologies and improve the intelligent management level of low-voltage distribution networks. Summary of the Invention

[0007] To address the aforementioned technical issues, this application aims to provide a method and system for constructing a transparent and integrated perception visualization scenario for low-voltage distribution networks. Through multi-source data time-stamp alignment, dynamic topology matrix updating, three-dimensional hierarchical visualization rendering, and voltage waveform distortion timing analysis technology, it achieves transparent and integrated perception of the operating status of low-voltage distribution networks, real-time early warning of line overload, and second-level location of disturbance sources, thereby improving the efficiency of intelligent operation and maintenance of distribution networks.

[0008] The objective of this application is achieved through the following technical solution:

[0009] In a first aspect, the present invention provides a method for constructing a transparent, integrated sensing and visualization scenario for a low-voltage distribution network, comprising:

[0010] Voltage waveform data of low-voltage distribution network is collected in real time by power line carrier communication concentrator, load current curve of user end is obtained by smart meter, and equipment status signal is obtained by distribution automation terminal. Time-aligned ring buffer queues are established for voltage waveform data, load current curve and equipment status signal respectively.

[0011] Based on the phase connection relationship of transformer outgoing switches in low-voltage distribution networks, the three-phase attribution attributes recorded in smart meter files are analyzed; based on the three-phase attribution attributes, a tree topology structure is constructed with transformers as root nodes, circuit breakers as intermediate nodes, and terminal smart meters as leaf nodes; a topology connection matrix is ​​generated based on the node connection relationship of the tree topology structure and the distance between nodes, and the topology connection matrix is ​​updated in real time by subscribing to circuit breaker opening and closing status change events;

[0012] Based on the electrical distance between nodes and transformers in the topology connection matrix, and taking the low-voltage bus voltage of the transformer as the benchmark, user levels are divided according to the voltage drop amplitude propagating along the distribution line, and a differentiated background transparency parameter is assigned to each level. Based on the background transparency parameter and the dynamically updated line current data in the circular buffer queue, the color saturation mapping value is calculated. When the line current value exceeds the rated current carrying capacity, the preset rendering engine is triggered to generate a preset first color and special effects alarm based on the color saturation mapping value.

[0013] A 3D visualization scene is constructed using a tree-like topology. A polar coordinate system is established with the physical location of any transformer as the origin. The latitude and longitude data of the smart meter installation location are converted into polar radius and azimuth parameters. A dynamic particle flow pointing in the direction of current is generated between adjacent electrical nodes. The particle motion rate is linearly proportional to the real-time current sampling value.

[0014] When a voltage sag is detected to exceed a preset threshold, voltage waveform data within a set time window before and after the event is extracted. A waveform similarity algorithm is used to compare the voltage distortion start time of each branch node to determine the earliest disturbance source branch where distortion occurs. A pulse propagation animation is generated for the disturbance source branch in a 3D visualization scene, and an audible and visual alarm sign is superimposed at the corresponding position. The pulse propagation speed is matched with the electrical parameters of the power distribution line.

[0015] Furthermore, the steps for establishing a time-stamped circular buffer queue include:

[0016] To address the difference in sampling frequency between voltage waveform data and load current curves, a sliding window mean algorithm is used to perform dimensionality reduction matching on high-frequency sampling data.

[0017] Furthermore, the steps for constructing a tree-like topology with transformers as root nodes, circuit breakers as intermediate nodes, and smart meters as leaf nodes specifically include:

[0018] Using the distribution transformer as the root node, circuit breakers and terminal smart meters are sequentially associated along the feeder branch direction to construct a tree-like topology. The circuit breaker node stores the list of its downstream connected child nodes, and the smart meter node is bound to the user identifier and location coordinates. A graph database is used to store the hierarchical relationship of the topology and generate a topology connection matrix. The rows and columns of the topology connection matrix correspond to the topology nodes, and the element values ​​represent the electrical connection status between the nodes.

[0019] Furthermore, the step of updating the topology connection matrix in real time by subscribing to circuit breaker opening and closing status change events specifically includes:

[0020] When a circuit breaker opening / closing status change event is received, the set of downstream nodes controlled by the circuit breaker is traversed, and the connection status values ​​of the corresponding rows and columns in the topology connection matrix are updated.

[0021] Furthermore, the step of assigning differentiated background transparency parameters to each level specifically includes:

[0022] Based on temperature data obtained from ambient temperature sensors and weather warnings issued by meteorological stations, a mapping relationship between temperature gradient and hierarchical transparency attenuation coefficient is established.

[0023] When the instantaneous rate of change of ambient temperature of any target transformer exceeds a preset threshold, a pulsed gradient of the background transparency of the corresponding level is triggered; at the same time, in the state of thunderstorm weather warning, a second color is generated for the two user levels that are farthest from the target transformer in electrical distance.

[0024] Furthermore, the formula for calculating the color saturation mapping value is as follows:

[0025]

[0026] α=α0· -λd ·(1+ηΔT)

[0027] Where S is the final color saturation mapping value, α is the layer transparency attenuation coefficient, and l c For real-time updated line current sampling values, l r S is the rated current carrying capacity of the line. a α0 is the fixed saturation value under the over-limit alarm state, λ is the basic transparency parameter, λ is the electrical distance attenuation factor, d is the electrical distance between the node and the transformer, η is the temperature change sensitivity factor, and ΔT is the instantaneous change rate of ambient temperature.

[0028] Furthermore, the method also includes:

[0029] In the 3D visualization scene, a draggable virtual measurement probe is set up. When the probe moves to any device icon, the voltage and current change curves of the device within a preset time period are displayed, along with a list of upstream and downstream devices electrically associated with it.

[0030] Secondly, the present invention provides a low-voltage distribution network transparent fusion perception and visualization scene construction system, comprising:

[0031] The data acquisition module is used to collect voltage waveform data of the low-voltage distribution network in real time through the power line carrier communication concentrator, obtain the load current curve of the user end through the smart meter, and obtain the equipment status signal through the distribution automation terminal. The voltage waveform data, load current curve and equipment status signal are respectively established into time-aligned ring buffer queues.

[0032] The topology update module is used to parse the three-phase attribution attributes recorded in the smart meter files based on the phase connection relationship of the transformer outgoing switches in the low-voltage distribution network; based on the three-phase attribution attributes, it constructs a tree-like topology with the transformer as the root node, the circuit breaker as the intermediate node, and the terminal smart meters as the leaf nodes; it generates a topology connection matrix based on the node connection relationship of the tree-like topology and the distance between the nodes, and updates the topology connection matrix in real time by subscribing to circuit breaker opening and closing status change events;

[0033] The hierarchical visualization module, based on the electrical distance between nodes and transformers in the topology connection matrix, uses the low-voltage bus voltage of the transformer as the benchmark value and divides user levels according to the voltage drop amplitude propagating along the distribution line. It assigns differentiated background transparency parameters to each level. Based on the background transparency parameters and dynamically updated line current data in the circular buffer queue, it calculates color saturation mapping values. When the line current value exceeds the rated current carrying capacity, it triggers the preset rendering engine to generate a preset first color and special effects alarm based on the color saturation mapping values.

[0034] The dynamic flow field module is used to construct a 3D visualization scene with a tree-like topology. It establishes a polar coordinate system with the physical location of any transformer as the origin and converts the latitude and longitude data of the smart meter installation location into polar radius and azimuth parameters. It generates a dynamic particle flow pointing in the direction of current between adjacent electrical nodes, and the particle motion speed is linearly proportional to the real-time current sampling value.

[0035] The fault tracing module is used to extract voltage waveform data within a set time window before and after the event when a voltage sag is detected to exceed a preset threshold. It then uses a waveform similarity algorithm to compare the voltage distortion start time of each branch node to determine the earliest disturbance source branch where distortion occurs. In a 3D visualization scene, it generates a pulse propagation animation for the disturbance source branch and overlays an audible and visual alarm sign at the corresponding position. The pulse propagation speed is matched with the electrical parameters of the power distribution line.

[0036] Thirdly, the present invention provides an electronic device, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps corresponding to the method in the first aspect.

[0037] Fourthly, the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps corresponding to the method in the first aspect.

[0038] In summary, the technical solutions of this application have at least the following advantages and beneficial effects:

[0039] This invention utilizes power line carrier communication, smart meters, and distribution terminals to synchronously collect voltage, current, and equipment status data, establishing a time-aligned ring queue to ensure timing consistency. Then, based on three-phase attributes, a tree-like topology is constructed and a connection matrix is ​​generated. Through electrical distance calculation, users are divided into visualization levels with varying transparency. Combined with current saturation mapping, gradient coloring and special effects warnings for overloaded lines are achieved. Simultaneously, physical location and electrical parameters are fused through polar coordinate system transformation, generating a dynamic particle flow to visually represent current distribution. When a voltage sag is detected, waveform similarity backtracking analysis of the voltage distortion propagation path accurately locates the disturbance source and triggers a pulse propagation animation. Its effect lies in achieving a multi-dimensional fusion presentation of the power grid status, reducing visual interference through layered transparency processing, enhancing the identification of key information through dynamic particles and color mapping, and improving fault location efficiency through spatiotemporal correlation analysis. It realizes transparent fusion perception of the low-voltage distribution network operating status, real-time early warning of line overloads, and second-level location of disturbance sources, improving the efficiency of intelligent operation and maintenance of the distribution network. Attached Figure Description

[0040] Figure 1 A flowchart of a method for constructing a transparent fusion perception and visualization scene for a low-voltage distribution network provided by the present invention;

[0041] Figure 2 A schematic diagram of the structure of a low-voltage distribution network transparent fusion perception and visualization scene construction system provided by the present invention;

[0042] Figure 3 This is a schematic diagram of the structure of an electronic device provided by the present invention. Detailed Implementation

[0043] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. The components of the embodiments of this application described and shown in the accompanying drawings can generally be arranged and designed in various different configurations.

[0044] like Figure 1 As shown in the embodiments of this application, a method for constructing a transparent fusion perception and visualization scene for a low-voltage distribution network includes:

[0045] S1 collects voltage waveform data of the low-voltage distribution network in real time through a power line carrier communication concentrator, obtains the load current curve of the user end through a smart meter, and obtains equipment status signals through a distribution automation terminal. It then establishes time-aligned ring buffer queues for the voltage waveform data, load current curve, and equipment status signals.

[0046] Specifically, a power line carrier communication concentrator is used to collect voltage waveform data of the low-voltage distribution network in real time. This utilizes the inherent physical channels of the power line to achieve lossless transmission of voltage signals, avoiding the complexity and cost of deploying additional communication lines. For example, in a single-phase 220V power supply line, the concentrator captures the voltage waveform at a 10kHz sampling rate, accurately detecting millisecond-level voltage sag events. Simultaneously, smart meters collect user-end load current curves at minute intervals. For instance, a residential user's smart meter uploads a sequence of effective current values ​​every 5 minutes, forming a time-series characteristic reflecting load fluctuations. Furthermore, the distribution automation terminal acquires equipment status signals such as circuit breaker opening and closing via event triggering. For example, when a branch circuit breaker trips due to overload, a status change event with a precise timestamp is immediately generated. After the above three types of data are transmitted to the data processing center through their respective communication protocols, they are respectively established as circular buffer queues with independent storage space. The circular queue structure adopts a first-in-first-out (FIFO) circular overlay mechanism to ensure that the latest monitoring data for the current period is dynamically retained within the limited memory space. For example, the capacity of the circular queue is set to 30 minutes of data. When new data arrives, it automatically overwrites the oldest data, thereby effectively avoiding processing delays caused by data accumulation.

[0047] Furthermore, to address the challenge of time-scale matching between high-frequency voltage waveform sampling data and low-frequency load current sampling data, a sliding window averaging algorithm is employed to reduce the dimensionality of the high-frequency voltage data. Specifically, within the circular queue of voltage waveform data, the sampling interval of the load current curve is used as the time window length. The arithmetic mean of all voltage sampling points within the window is calculated to generate an equivalent voltage feature value aligned with the current data timestamp. For example, when the load current is sampled at 5-minute intervals, the mean is calculated for 30,000 voltage sampling points (calculated at a 10kHz sampling rate) within the same time period, resulting in a sequence of effective voltage values ​​matching the time resolution of the current data. This processing method preserves the macroscopic trend of voltage waveform changes while significantly reducing data dimensionality, thereby improving the efficiency of voltage-current correlation calculations in subsequent topology analysis.

[0048] S2. Based on the phase connection relationship of the transformer outgoing switches in the low-voltage distribution network, the three-phase attribution attributes recorded in the smart meter archive are analyzed; based on the three-phase attribution attributes, a tree-like topology structure is constructed with the transformer as the root node, the circuit breaker as the intermediate node, and the terminal smart meter as the leaf node; a topology connection matrix is ​​generated according to the node connection relationship of the tree-like topology structure and the distance between nodes, and the topology connection matrix is ​​updated in real time by subscribing to the circuit breaker opening and closing status change events.

[0049] Specifically, by analyzing the three-phase attribution attributes recorded in the smart meter files, the phase correspondence between user equipment and transformer outgoing switches is clarified. The three-phase attribution attribute refers to the identification information of the specific phase (A-phase, B-phase, C-phase, or neutral line) connected to the user-side smart meter or equipment in the low-voltage distribution network, used to describe the phase distribution relationship of the electrical load in the three-phase balance system of the distribution network. For example, if a smart meter file indicates its phase as "A-phase," it is associated with the A-phase busbar on the low-voltage side of the transformer. This step solves the problem of errors easily caused by traditional manual phase verification and avoids topology analysis deviations due to phase mismatches. For example, if a single-phase user in a community is incorrectly associated with phase B, its electrical load will be incorrectly superimposed on the phase B statistics; this method can eliminate such errors through automatic file analysis.

[0050] Secondly, based on the three-phase attribution attributes, a tree-like topology is constructed with transformers as root nodes, circuit breakers as intermediate nodes, and smart meters as leaf nodes. This structure accurately reflects the actual physical connections of the low-voltage distribution network through hierarchical modeling. For example, a transformer is connected to multiple primary circuit breakers via outgoing switches, and each primary circuit breaker further branches to secondary circuit breakers or directly connects to smart meters, forming a multi-level tree structure. This structure makes the network hierarchy visible, facilitating maintenance personnel to quickly locate the scope of fault impact. For example, when a short circuit occurs downstream of a secondary circuit breaker, the tree structure can immediately identify all meter users controlled by that circuit breaker without needing to investigate each level step by step.

[0051] Furthermore, a graph database is used to store the hierarchical relationships of the topology and generate a topology connection matrix. The rows and columns of the matrix correspond to topology nodes (such as transformers, circuit breakers, and meters), and the element values ​​represent the electrical connection status between nodes (e.g., "1" indicates connectivity, and "0" indicates disconnection). Matrix operations allow for the rapid identification of connectivity paths and electrical distances between nodes. For example, when determining whether a smart meter is controlled by a specific circuit breaker, only the values ​​of the corresponding rows and columns in the matrix need to be queried, greatly improving the efficiency of topology queries. In addition, the introduction of the graph database supports rapid traversal and updates of complex network relationships; for example, tracing the complete path from a meter to the root node of a transformer requires only milliseconds of response time.

[0052] Finally, the topology connection matrix is ​​updated in real time by subscribing to circuit breaker opening and closing status change events. When a circuit breaker status changes (such as tripping or manual closing), the system traverses its downstream node set and updates the connection status values ​​in the matrix. For example, if a branch circuit breaker trips due to overload, the row and column elements corresponding to that circuit breaker in the matrix are immediately set to "0", and the connection status of all downstream meter nodes and transformers is synchronously invalidated. This mechanism ensures the consistency between the topology status and the physical network, providing real-time data support for subsequent fault isolation and power restoration. For example, in a scenario where lightning strikes cause multi-level circuit breaker cascading trips, the step-by-step update of the matrix can accurately represent the fault propagation path and assist in generating the optimal recovery strategy.

[0053] S3, based on the electrical distance between nodes and transformers in the topology connection matrix, takes the low-voltage bus voltage of the transformer as the benchmark value, divides user levels according to the voltage drop amplitude propagating along the distribution line, and assigns differentiated background transparency parameters to each level; based on the background transparency parameters and dynamically updated line current data in the circular buffer queue, calculates the color saturation mapping value, and when the line current value exceeds the rated current carrying capacity, triggers the preset rendering engine to generate a preset first color and special effects alarm based on the color saturation mapping value;

[0054] Specifically, through dynamic hierarchical division and visualized parameter mapping, the system enables intuitive perception of the distribution network's operational status and provides anomaly alarms. First, based on the electrical distance between each node and the transformer in the topology connection matrix, and using the low-voltage bus voltage of the transformer as a benchmark, user levels are divided according to the attenuation magnitude of voltage propagation along the distribution lines. For example, if a transformer has three circuit breaker branches downstream, each branch experiences different voltage drops due to variations in line impedance. The system classifies each branch into different levels based on the measured voltage drop values. This division method accurately reflects the impact of network topology on voltage distribution, allowing maintenance personnel to quickly identify areas of abnormal voltage. For instance, when a branch experiences an abnormal voltage drop due to poor contact, its level will be reclassified because the voltage deviates from the benchmark value, triggering dynamic adjustments to the visualized parameters.

[0055] Furthermore, differentiated background transparency parameters are assigned to each level. The principle behind this is to use transparency gradients to highlight changes in the status of key levels. In practice, the system combines real-time data from ambient temperature sensors with meteorological warning information to establish a mapping relationship between temperature gradients and transparency attenuation coefficients. For example, when the ambient temperature exceeds 35°C, the system automatically increases the transparency attenuation coefficient near the transformer level, making its background more transparent and thus highlighting the operating status of equipment in high-temperature areas. Simultaneously, when an abnormal situation is detected where the instantaneous rate of change in ambient temperature exceeds 5°C / min, a pulsed gradient effect on the background transparency of the corresponding level is triggered. For example, if a power distribution cabinet experiences a sudden temperature rise due to a heat dissipation failure, the background of that level will cycle between 50% and 80% transparency at a frequency of 0.5Hz, creating a dynamic warning effect. This mechanism effectively improves the visual perception priority in scenarios of sudden temperature changes, avoiding potential alarm omissions that might occur with traditional static display methods.

[0056] During thunderstorm warnings, the system generates a secondary color (e.g., purple) for the two user levels furthest from the transformer in electrical distance. For example, when a meteorological station issues a yellow thunderstorm warning, the level located at the end of the line automatically switches to a purple background, indicating its susceptibility to lightning-induced overvoltages due to its higher line impedance. This design combines meteorological information with electrical parameters to achieve a visualized early warning of the correlation between external environmental risks and grid vulnerability, providing support for proactive lightning protection decisions.

[0057] Furthermore, the calculation principle of color saturation mapping values ​​lies in dynamically comparing the real-time sampled value of the line current with the rated current carrying capacity, and intuitively reflecting the load level through changes in color depth. For example, if a line has a rated current carrying capacity of 200A, when the real-time current reaches 180A, the system generates a corresponding saturation value according to a preset algorithm, making the line appear orange in a 3D scene; if the current exceeds the limit to 220A, the saturation increases to a preset threshold, triggering a red flashing alarm effect. This mechanism transforms abstract current data into intuitive color signals, significantly improving the efficiency of overload identification. For example, if multiple branch currents in a residential area exceed the limit simultaneously during the evening peak, maintenance personnel can locate the overloaded areas by observing the color distribution, without having to check data reports one by one.

[0058] The formula for calculating the color saturation mapping value is as follows:

[0059]

[0060] α=α0·e -λd· (1+ηΔT) (2)

[0061] In the formula, S is the final color saturation mapping value, α is the layer transparency attenuation coefficient, and l c For real-time updated line current sampling values, lr S is the rated current carrying capacity of the line. a α0 is the fixed saturation value under the over-limit alarm state, λ is the basic transparency parameter, λ is the electrical distance attenuation factor, d is the electrical distance between the node and the transformer, η is the temperature change sensitivity factor, and ΔT is the instantaneous change rate of ambient temperature.

[0062] S4 constructs a 3D visualization scene with a tree-like topology, establishes a polar coordinate system with the physical location of any transformer as the origin, and converts the latitude and longitude data of the smart meter installation location into polar radius and azimuth parameters; it generates a dynamic particle flow pointing in the direction of current between adjacent electrical nodes, and the particle motion speed is linearly proportional to the real-time current sampling value.

[0063] Specifically, a 3D visualization scene is constructed based on a tree-like topology, and the operating status of the low-voltage distribution network is intuitively presented through polar coordinate transformation and dynamic particle flow technology. Its core principle lies in combining abstract electrical parameters with physical spatial locations, and using geometric transformation and dynamic rendering technology to construct a visualization model that maps virtual and real elements. In practical implementation, a polar coordinate system is first established with the actual physical location of the transformer as the origin. For example, the GPS coordinates of a transformer in a community's power distribution room are 31.23 degrees North latitude and 121.47 degrees East longitude; the system sets these coordinates as the origin of the polar coordinate system. Then, the latitude and longitude data of the smart meter's installation location are converted into polar radius and azimuth parameters. For example, the GPS coordinates of a smart meter in a residential building are 31.2305 degrees North latitude and 121.471 degrees East longitude; using a spherical coordinate transformation algorithm, its polar radius relative to the origin is calculated to be 150 meters and its azimuth to be 45 degrees. This polar coordinate transformation process effectively solves the scene distortion problem caused by the mapping of geographically dispersed device locations under the traditional rectangular coordinate system. For example, when multiple meters are distributed in a ring-shaped area centered on a transformer, the polar coordinate system can naturally present its radial distribution characteristics, significantly improving the recognizability of the spatial layout.

[0064] Furthermore, a dynamic particle flow pointing in the direction of the current is generated between adjacent electrical nodes, with the particle velocity linearly proportional to the real-time current sampling value. For example, in a branch line, when the real-time current is 50A, the particle flow moves along the line at a rate of 10 particles per second; when the current increases to 100A, the particle velocity increases to 20 particles per second. This dynamic mapping mechanism makes changes in current intensity intuitively reflected as increases or decreases in particle velocity, allowing maintenance personnel to quickly determine the line load status by observing the intensity of the particle flow. Simultaneously, particle colors are differentiated based on current phase attributes; for example, phase A corresponds to a red particle flow, phase B to green, and phase C to blue. When the three-phase load is unbalanced, the density difference of the different colored particle flows can immediately expose the phase imbalance problem. For instance, in a commercial area, the concentrated connection of single-phase high-power equipment resulted in a significantly higher particle flow density in phase A than in the other two phases, allowing maintenance personnel to locate the phase load anomaly without consulting data reports.

[0065] S5. When the voltage sag exceeds the preset threshold, extract the voltage waveform data of the set time window before and after the event, use the waveform similarity algorithm to compare the voltage distortion start time of each branch node, and determine the disturbance source branch that caused the earliest distortion. Generate a pulse diffusion animation for the disturbance source branch in the three-dimensional visualization scene, and overlay an audible and visual alarm sign at the corresponding position. The pulse diffusion speed matches the electrical parameters of the power distribution line.

[0066] Specifically, during the operation of a low-voltage distribution network, when a voltage sag is detected to exceed a preset threshold, the system first extracts voltage waveform data within a set time window before and after the event. For example, if a voltage sag in a commercial area's distribution line reaches 15% and lasts for more than 100ms due to the simultaneous startup of a group of high-power air conditioners, the system automatically captures the full waveform data from 5 seconds before the sag to 5 seconds after its recovery. This mechanism provides high-precision timing data for accurate location of subsequent disturbance sources by fully recording the waveform characteristics of the entire lifecycle of the sag event. Compared to traditional monitoring methods that only record effective values, this method can capture the instantaneous distortion details of the voltage waveform, such as the voltage waveform dip caused by the instant a motor starts, thereby improving the accuracy of disturbance type identification.

[0067] Furthermore, a waveform similarity algorithm is used to compare the voltage distortion start time of each branch node. In specific implementation, the system uses the voltage waveform of the low-voltage side busbar of the transformer as a benchmark and performs point-by-point similarity matching with the waveforms of each branch node. For example, when a voltage dip occurs in a branch due to poor contact, its voltage waveform distortion start time appears 2ms earlier than the benchmark waveform of the busbar, while the distortion time of other branches is relatively delayed. By calculating the similarity difference between the waveforms of each node and the benchmark waveform, the system can determine the branch from which the earliest distortion occurred. For example, in the power distribution line of a restaurant district, the system identified the branch node numbered B32 as having the earliest waveform distortion start time, 3ms earlier than the adjacent branch, thus determining that this branch is the source of the voltage dip. This technology overcomes the limitations of traditional reliance on manual inspection to locate faults, achieving millisecond-level automatic identification of disturbance sources and significantly shortening fault diagnosis time.

[0068] In a 3D visualization scenario, the system generates pulse propagation animations for defined disturbance source branches. Centered on the disturbance source node, the animation generates periodically spreading ring-shaped pulse effects along the power distribution line topology. For example, when a voltage dip occurs in a power distribution line in an industrial park due to an arc-ground fault, the system generates a red pulse ring on the corresponding faulty branch, propagating upstream and downstream along the line at a frequency of 5 times per second. The pulse propagation speed is dynamically adjusted according to the line's electrical parameters. For instance, for older lines with higher impedance, the pulse propagation speed is set to 100 meters per second, while for newly built low-impedance lines, it is adjusted to 150 meters per second. This visualization mechanism transforms abstract electrical parameters into intuitive physical motion effects, allowing maintenance personnel to quickly assess line electrical performance by observing differences in pulse propagation speed. For example, comparing the pulse propagation speed of the same line before and after a renovation in a residential area can directly reflect the improvement in line impedance, providing a visual basis for evaluating the effectiveness of power grid upgrades.

[0069] Simultaneously, the system overlays audible and visual alarm markers at the location of the disturbance source. The audible and visual alarms employ a multimodal interactive design. For example, in a 3D scene, the disturbance source node continuously flashes a bright yellow halo, simultaneously triggering a buzzer to emit a frequency-controlled alarm sound. For instance, when a hospital's power distribution system detects a voltage dip in the operating room's power supply line, the corresponding branch in the 3D scene immediately displays a high-frequency flashing yellow warning light, while the control room's audio equipment plays a voice prompt stating "Operating room line voltage abnormality." This multi-channel alarm mechanism, by integrating visual and auditory stimuli, significantly improves the perception priority of abnormal states, avoiding the risk of overlooking a single alarm method.

[0070] Furthermore, the above method also includes:

[0071] In the 3D visualization scene, a draggable virtual measurement probe is set up. When the probe moves to any device icon, the voltage and current change curves of the device within a preset time period are displayed, along with a list of upstream and downstream devices electrically associated with it.

[0072] Specifically, the virtual measurement probes are draggable floating icons. The event listening module of the graphics engine captures user dragging commands and updates the probe's spatial coordinates in the 3D scene in real time. For example, when maintenance personnel drag the probe icon over a 3D model of a distribution cabinet, the system immediately identifies the node number of the device in the topology using a coordinate mapping algorithm and triggers a preset data query command. At this time, the system extracts the voltage waveform data and load current curve of the corresponding device for the past 30 minutes from the circular buffer queue, generates continuous and smooth voltage-time and current-time curves using a time series interpolation algorithm, and overlays them as floating charts next to the device icon. For example, when a circuit breaker node is hovered over by the probe, a line graph pops up on its right showing the dynamic process of the effective voltage value fluctuating from 220V to 215V and then recovering within the past 30 minutes. Simultaneously, a trend line showing the current gradually increasing from 50A to 180A is displayed below, intuitively reflecting the overload development process of the line.

[0073] Furthermore, while displaying voltage and current change curves, the system simultaneously displays topology information related to the electrical connections of the device. In practice, based on the topology connection matrix, the system analyzes the upstream and downstream nodes of the current device in real time, displaying a tree-structured list of its upstream power supply nodes and downstream load nodes in a floating window. For example, when a probe hovers over a smart meter icon, the floating window not only displays the meter's current curve but also lists its directly connected circuit breaker nodes, its associated transformer nodes, and other meter nodes on the same branch, forming a local topology view. This design allows maintenance personnel to grasp the device's location and associated impact range in the network without switching screens. For instance, when a meter's current is abnormal, the topology list can immediately pinpoint whether its upstream circuit breaker is in a tripped state or whether other meters on the same branch also exhibit similar anomalies, thus quickly distinguishing between device-level faults and network-level problems.

[0074] Furthermore, the probe's visual design employs a gradual reveal animation and focus highlighting technology to enhance interactive perception. When the probe approaches a device icon, the target icon automatically enlarges by 10% and adds a glowing outline, while the floating window smoothly pops up with a gradual change in transparency, avoiding visual abruptness. For example, when the probe moves close to a tripped circuit breaker, the circuit breaker model instantly highlights with a red halo, accompanied by an alarm sound, and then the curved window gradually appears from a semi-transparent state, prompting maintenance personnel to pay close attention to this abnormal node. This interactive design effectively reduces information overload in complex scenarios, helping users quickly focus on key equipment through a visual guidance mechanism.

[0075] Based on the same inventive concept, such as Figure 2 As shown, this invention provides a low-voltage distribution network transparent fusion perception and visualization scene construction system, including:

[0076] The data acquisition module is used to collect voltage waveform data of the low-voltage distribution network in real time through the power line carrier communication concentrator, obtain the load current curve of the user end through the smart meter, and obtain the equipment status signal through the distribution automation terminal. The voltage waveform data, load current curve and equipment status signal are respectively established into time-aligned ring buffer queues.

[0077] The topology update module is used to parse the three-phase attribution attributes recorded in the smart meter files based on the phase connection relationship of the transformer outgoing switches in the low-voltage distribution network; based on the three-phase attribution attributes, it constructs a tree-like topology with the transformer as the root node, the circuit breaker as the intermediate node, and the terminal smart meters as the leaf nodes; it generates a topology connection matrix based on the node connection relationship of the tree-like topology and the distance between the nodes, and updates the topology connection matrix in real time by subscribing to circuit breaker opening and closing status change events;

[0078] The hierarchical visualization module, based on the electrical distance between nodes and transformers in the topology connection matrix, uses the low-voltage bus voltage of the transformer as the benchmark value and divides user levels according to the voltage drop amplitude propagating along the distribution line. It assigns differentiated background transparency parameters to each level. Based on the background transparency parameters and dynamically updated line current data in the circular buffer queue, it calculates color saturation mapping values. When the line current value exceeds the rated current carrying capacity, it triggers the preset rendering engine to generate a preset first color and special effects alarm based on the color saturation mapping values.

[0079] The dynamic flow field module is used to construct a 3D visualization scene with a tree-like topology. It establishes a polar coordinate system with the physical location of any transformer as the origin and converts the latitude and longitude data of the smart meter installation location into polar radius and azimuth parameters. It generates a dynamic particle flow pointing in the direction of current between adjacent electrical nodes, and the particle motion speed is linearly proportional to the real-time current sampling value.

[0080] The fault tracing module is used to extract voltage waveform data within a set time window before and after the event when a voltage sag is detected to exceed a preset threshold. It then uses a waveform similarity algorithm to compare the voltage distortion start time of each branch node to determine the earliest disturbance source branch where distortion occurs. In a 3D visualization scene, it generates a pulse propagation animation for the disturbance source branch and overlays an audible and visual alarm sign at the corresponding position. The pulse propagation speed is matched with the electrical parameters of the power distribution line.

[0081] Based on the same inventive concept, such as Figure 3As shown, the present invention provides an electronic device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements a method for constructing a transparent fusion perception and visualization scene of a low-voltage distribution network.

[0082] Based on the same inventive concept, the present invention provides a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements a method for constructing a transparent fusion perception and visualization scene of a low-voltage distribution network.

[0083] The above are merely preferred embodiments of this application and are not intended to limit this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.

Claims

1. A method for constructing a transparent, integrated sensing and visualization scenario for a low-voltage distribution network, characterized in that, include: Voltage waveform data of the low-voltage distribution network is collected in real time by a power line carrier communication concentrator, load current curves of the user end are obtained by smart meters, and equipment status signals are obtained by a distribution automation terminal. Time-aligned ring buffer queues are established for the voltage waveform data, the load current curves and the equipment status signals respectively. Based on the phase connection relationship of the transformer outgoing switches in the low-voltage distribution network, the three-phase attribution attributes recorded in the smart meter archives are analyzed; based on the three-phase attribution attributes, a tree-like topology structure is constructed with the transformer as the root node, the circuit breaker as the intermediate node, and the terminal smart meter as the leaf node; a topology connection matrix is ​​generated according to the node connection relationship of the tree-like topology structure and the distance between the nodes, and the topology connection matrix is ​​updated in real time by subscribing to circuit breaker opening and closing status change events; Based on the electrical distance between the nodes and the transformer in the topology connection matrix, and taking the low-voltage bus voltage of the transformer as the reference value, user levels are divided according to the voltage drop amplitude propagating along the distribution line, and a differentiated background transparency parameter is assigned to each level; based on the background transparency parameter and the dynamically updated line current data in the circular buffer queue, a color saturation mapping value is calculated; when the line current value exceeds the rated current carrying capacity, the preset rendering engine is triggered to generate a preset first color and special effects alarm based on the color saturation mapping value; A three-dimensional visualization scene is constructed using the tree-like topology. A polar coordinate system is established with the physical location of any transformer as the origin. The latitude and longitude data of the smart meter installation location are converted into polar radius and azimuth parameters. A dynamic particle flow pointing in the direction of current is generated between adjacent electrical nodes. The particle motion rate is linearly proportional to the real-time current sampling value. When the voltage sag exceeds the preset threshold, the voltage waveform data of the set time window before and after the event is extracted, and the voltage distortion start time of each branch node is compared using a waveform similarity algorithm to determine the disturbance source branch where the distortion occurred earliest. In the three-dimensional visualization scene, a pulse diffusion animation is generated for the disturbance source branch, and an audible and visual alarm sign is superimposed at the corresponding position. The speed of the pulse diffusion is matched with the electrical parameters of the power distribution line.

2. The method for constructing a transparent, fusion-sensing, and visual scene for a low-voltage distribution network according to claim 1, characterized in that, The step of establishing a time-stamped circular buffer queue includes: To address the difference in sampling frequency between voltage waveform data and load current curves, a sliding window mean algorithm is used to perform dimensionality reduction matching on high-frequency sampling data.

3. The method for constructing a transparent, fusion-sensing, and visual scene for a low-voltage distribution network according to claim 1, characterized in that, The steps for constructing a tree-like topology with transformers as root nodes, circuit breakers as intermediate nodes, and smart meters as leaf nodes specifically include: Using the distribution transformer as the root node, circuit breakers and terminal smart meters are sequentially associated along the feeder branch direction to construct a tree-like topology. The circuit breaker node stores the list of its downstream connected child nodes, and the smart meter node is bound to the user identifier and location coordinates. A graph database is used to store the hierarchical relationship of the topology and generate a topology connection matrix. The rows and columns of the topology connection matrix correspond to the topology nodes, and the element values ​​represent the electrical connection status between the nodes.

4. The method for constructing a transparent, fusion-sensing, and visual scene for a low-voltage distribution network according to claim 3, characterized in that, The step of updating the topology connection matrix in real time by subscribing to circuit breaker opening and closing status change events specifically includes: When a circuit breaker opening / closing status change event is received, the set of downstream nodes controlled by the circuit breaker is traversed, and the connection status values ​​of the corresponding rows and columns in the topology connection matrix are updated.

5. The method for constructing a transparent, fusion-sensing, and visual scene for a low-voltage distribution network according to claim 1, characterized in that, The step of assigning differentiated background transparency parameters to each level specifically includes: Based on temperature data obtained from ambient temperature sensors and weather warnings issued by meteorological stations, a mapping relationship between temperature gradient and hierarchical transparency attenuation coefficient is established. When the instantaneous rate of change of ambient temperature of any target transformer exceeds a preset threshold, a pulsed gradient of the background transparency of the corresponding level is triggered; at the same time, in the state of thunderstorm weather warning, a second color is generated for the two user levels that are farthest from the target transformer in electrical distance.

6. The method for constructing a transparent, fusion-sensing, and visual scene for a low-voltage distribution network according to claim 1, characterized in that, The formula for calculating the color saturation mapping value is: α=α0·e -λd ·(1+ηΔT) Where S is the final color saturation mapping value, α is the layer transparency attenuation coefficient, and l c For real-time updated line current sampling values, l r S is the rated current carrying capacity of the line. a α0 is the fixed saturation value under the over-limit alarm state, λ is the basic transparency parameter, λ is the electrical distance attenuation factor, d is the electrical distance between the node and the transformer, η is the temperature change sensitivity factor, and ΔT is the instantaneous change rate of ambient temperature.

7. The method for constructing a transparent, fusion-sensing, and visual scene for a low-voltage distribution network according to claim 1, characterized in that, The method further includes: In the 3D visualization scene, a draggable virtual measurement probe is set up. When the probe moves to any device icon, the voltage and current change curves of the device within a preset time period are displayed, and a list of upstream and downstream devices electrically associated with it is shown.

8. A transparent, integrated sensing and visualization scene construction system for low-voltage distribution networks, characterized in that, include: The data acquisition module is used to collect voltage waveform data of the low-voltage distribution network in real time through a power line carrier communication concentrator, obtain the load current curve of the user end through a smart meter, and obtain equipment status signals through a distribution automation terminal. The voltage waveform data, the load current curve and the equipment status signals are respectively established into time-aligned ring buffer queues. The topology update module is used to parse the three-phase attribution attributes recorded in the smart meter archives based on the phase connection relationship of the transformer outgoing switches in the low-voltage distribution network; based on the three-phase attribution attributes, a tree-like topology structure is constructed with the transformer as the root node, the circuit breaker as the intermediate node, and the terminal smart meters as the leaf nodes; a topology connection matrix is ​​generated according to the node connection relationship of the tree-like topology structure and the distance between the nodes, and the topology connection matrix is ​​updated in real time by subscribing to circuit breaker opening and closing status change events; The hierarchical visualization module, based on the electrical distance between nodes and transformers in the topology connection matrix, uses the low-voltage bus voltage of the transformer as a reference value and divides user levels according to the voltage drop amplitude propagating along the distribution line. It assigns differentiated background transparency parameters to each level. Based on the background transparency parameters and dynamically updated line current data in the circular buffer queue, it calculates color saturation mapping values. When the line current value exceeds the rated current carrying capacity, it triggers the preset rendering engine to generate a preset first color and special effects alarm based on the color saturation mapping values. The dynamic flow field module is used to construct a three-dimensional visualization scene based on the tree-like topology, establish a polar coordinate system with the physical location of any transformer as the origin, convert the latitude and longitude data of the smart meter installation location into polar radius and azimuth parameters, and generate a dynamic particle flow pointing in the direction of current between adjacent electrical nodes, with the particle motion speed being linearly proportional to the real-time current sampling value. The fault tracing module is used to extract voltage waveform data within a set time window before and after the event when the detected voltage sag exceeds a preset threshold. It then uses a waveform similarity algorithm to compare the voltage distortion start time of each branch node and determine the earliest disturbance source branch where distortion occurs. In the three-dimensional visualization scene, a pulse diffusion animation is generated for the disturbance source branch, and an audible and visual alarm sign is superimposed at the corresponding position. The speed of the pulse diffusion is matched with the electrical parameters of the power distribution line.

9. An electronic device, characterized in that, The electronic device includes: a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements a method for constructing a transparent fusion perception and visualization scene of a low-voltage distribution network as described in any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements a method for constructing a transparent fusion perception and visualization scene for a low-voltage distribution network as described in any one of claims 1 to 7.

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